http://www.mdpi.com/journal/remotesensing Latest open access articles published in Remote Sens. at http://www.mdpi.com/journal/remotesensing http://www.mdpi.com/2072-4292/5/5/2554 An inventory of landslides with an indication of the state of activity is necessary in order to establish hazard maps. We combine interpretation of aerial photographs and information on surface displacement from satellite Synthetic Aperture Radar (SAR) interferometry for mapping landslides and intensity classification. Sketch maps of landslides distinguished by typology and depth, including geomorphological features, are compiled by stereoscopic photo-interpretation. Results achieved with differential SAR interferometry (InSAR) and Persistent Scatterer Interferometry (PSI) are used to estimate the state of activity of landslides around villages and in sparsely vegetated areas with numerous exposed rocks. For validation and possible extension of the inventory around vegetated areas, where InSAR and PSI failed to retrieve displacement information, traditional monitoring data such as topographic measurements and GPS are considered. Our results, covering extensive areas, are a valuable contribution towards the analysis of landslide hazards in areas where traditional monitoring techniques are sparse or unavailable. In this contribution we discuss our methodology for a study area around the deep-seated landslide in Osco in southern Switzerland. Remote Sensing 2013-05-22 5 5 Article 10.3390/rs5052554 2554 2570 2072-4292 2013-05-22 doi: 10.3390/rs5052554 Tazio Strozzi Christian Ambrosi Hugo Raetzo http://www.mdpi.com/2072-4292/5/5/2534 Accurate assessments of spatio-temporal variations in gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) play a crucial role in the evaluation of carbon and water balance as well as have considerable effects on climate change. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to quantify the mean annual GPP and ET at Korean regional flux network site. We found that the seasonal mean values of WUE were 2.86 to 2.92 g∙C∙g∙H2O−1 in the dormant season and 1.81 to 1.88 g∙C∙g∙H2O−1 in the growing season during 2007 and 2008. The WUE was relatively stable during the growing season and tended to vary in the dormant season. Remote sensing data obtained by the MODIS satellite were appeared to be effective to improve our understanding of the spatio-temporal variation of ecohydrological parameters which have not yet been investigated in a number of previous articles. Based on the results of this study, we summarize the interactions between carbon and water circulation in terrestrial ecosystems and how their ecological procedures generated by the photosynthesis of vegetation influence in climatological perspectives. Remote Sensing 2013-05-21 5 5 Article 10.3390/rs5052534 2534 2553 2072-4292 2013-05-21 doi: 10.3390/rs5052534 Chanyang Sur Minha Choi http://www.mdpi.com/2072-4292/5/5/2513 This paper focuses on the use of the Land-based Mobile Mapping System (LMMS) for the unscheduled updates of a National Base Map, which has nationwide coverage and was made using aerial photogrammetry. The objectives of this research are to improve the weak points of LMMS surveying for its application to the updates of a National Base Map (NBM), which has rigorous accuracy and quality standards. For this, methods were suggested for the (1) improvement of the accuracy of the Global Positioning System/Inertial Navigation System (GPS/INS) in the long-term exposure of environments with poor GPS reception; (2) elimination of mutual deviations between LMMS data obtained in duplicate to meet resolution standards; (3) devising an effective way of mapping objects using LMMS data; and (4) analysis of updatable regions and map layers via LMMS. To verify the suggested methods, experiments and analyses were conducted using two LMMS devices in four target areas for unscheduled updates of the National Base Map. Remote Sensing 2013-05-17 5 5 Article 10.3390/rs5052513 2513 2533 2072-4292 2013-05-17 doi: 10.3390/rs5052513 Jinsang Hwang Hongsik Yun Taejun Jeong Yongcheol Suh He Huang http://www.mdpi.com/2072-4292/5/5/2492 Pasture ecosystems may be particularly vulnerable to land degradation due to the high risk of human disturbance (e.g., overgrazing, burning, etc.), especially when compared with natural ecosystems (non-pasture, non-cultivated) where direct human impacts are minimal. Using maximum annual leaf area index (LAImax) as a proxy for standing biomass and peak annual aboveground productivity, we analyze greening and browning trends in pasture areas from 1982–2008. Inter-annual variability in pasture productivity is strongly controlled by precipitation (positive correlation) and, to a lesser extent, temperature (negative correlation). Linear temporal trends are significant in 23% of pasture cells, with the vast majority of these areas showing positive LAImax trends. Spatially extensive productivity declines are only found in a few regions, most notably central Asia, southwest North America, and southeast Australia. Statistically removing the influence of precipitation reduces LAImax trends by only 13%, suggesting that precipitation trends are only a minor contributor to long-term greening and browning of pasture lands. No significant global relationship was found between LAImax and pasture intensity, although the magnitude of trends did vary between cells classified as natural versus pasture. In the tropics and Southern Hemisphere, the median rate of greening in pasture cells is significantly higher than for cells dominated by natural vegetation. In the Northern Hemisphere extra-tropics, conversely, greening of natural areas is 2–4 times the magnitude of greening in pasture areas. This analysis presents one of the first global assessments of greening and browning trends in global pasture lands, including a comparison with vegetation trends in regions dominated by natural ecosystems. Our results suggest that degradation of pasture lands is not a globally widespread phenomenon and, consistent with much of the terrestrial biosphere, there have been widespread increases in pasture productivity over the last 30 years. Remote Sensing 2013-05-17 5 5 Article 10.3390/rs5052492 2492 2512 2072-4292 2013-05-17 doi: 10.3390/rs5052492 Benjamin Cook Stephanie Pau http://www.mdpi.com/2072-4292/5/5/2475 GCOS (Global Climate Observing System) is a long-term program for monitoring the climate, detecting the changes, and assessing their impacts. Remote sensing techniques are being increasingly used for climate-related measurements. Imagery of the SEVIRI instrument on board of the European geostationary satellites Meteosat-8 and Meteosat-9 are often used for the estimation of essential climate variables. In a joint project between the Swiss GCOS Office and ETH Zurich, geometric accuracy and temporal stability of 1-km resolution HRV channel imagery of SEVIRI have been evaluated over Switzerland. A set of tools and algorithms has been developed for the investigations. Statistical analysis and blunder detection have been integrated in the process for robust evaluation. The relative accuracy is evaluated by tracking large numbers of feature points in consecutive HRV images taken at 15-minute intervals. For the absolute accuracy evaluation, lakes in Switzerland and surroundings are used as reference. 20 lakes digitized from Landsat orthophotos are transformed into HRV images and matched via 2D translation terms at sub-pixel level. The algorithms are tested using HRV images taken on 24 days in 2008 (2 days per month). The results show that 2D shifts that are up to 8 pixels are present both in relative and absolute terms. Remote Sensing 2013-05-17 5 5 Article 10.3390/rs5052475 2475 2491 2072-4292 2013-05-17 doi: 10.3390/rs5052475 Sultan Aksakal http://www.mdpi.com/2072-4292/5/5/2451 The structure of benthic macrophyte habitats is known to indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats enables us to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes and describe processes that are behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view. A big challenge in remote sensing mapping of benthic habitats is to define appropriate mapping classes that are also meaningful from the ecological point of view. In this study, the benthic habitat classification scheme was defined for the study areas in the relatively turbid north-eastern Baltic Sea coastal environment. Two different classification methods—image-based and the spectral library—method were used for image classification. The image-based classification method can provide benthic habitat maps from coastal areas, but requires extensive field studies. An alternative approach in image classification is to use measured and/or modelled spectral libraries. This method does not require fieldwork at the time of image collection if preliminary information about the potential benthic habitats and their spectral properties, as well as variability in optical water properties exists from earlier studies. A spectral library was generated through radiative transfer model HydroLight computations using measured reflectance spectra from representative benthic substrates and water quality measurements. Our previous results have shown that benthic habitat mapping should be done at high spatial resolution, owing to the small-scale heterogeneity of such habitats in the Estonian coastal waters. In this study, the capability of high spatial resolution hyperspectral airborne a Compact Airborne Spectrographic Imager (CASI) sensor and a high spatial resolution multispectral WorldView-2 satellite sensor were tested for mapping benthic habitats. Initial evaluations of habitat maps indicate that image-based classification provides higher quality benthic maps compared to the spectral library method. Remote Sensing 2013-05-16 5 5 Article 10.3390/rs5052451 2451 2474 2072-4292 2013-05-16 doi: 10.3390/rs5052451 Ele Vahtmäe Tiit Kutser http://www.mdpi.com/2072-4292/5/5/2436 Using remotely sensed satellite products is the most efficient way to monitor global land, water, and forest resource changes, which are believed to be the main factors for understanding global climate change and its impacts. A reliable remotely sensed product should be retrieved quantitatively through models or statistical methods. However, producing global products requires a complex computing system and massive volumes of multi-sensor and multi-temporal remotely sensed data. This manuscript describes the ground Global LAnd Surface Satellite (GLASS) product generation system that can be used to generate long-sequence time series of global land surface data products based on various remotely sensed data. To ensure stabilization and efficiency in running the system, we used the methods of task management, parallelization, and multi I/O channels. An array of GLASS remote sensing products related to global land surface parameters are currently being produced and distributed by the Center for Global Change Data Processing and Analysis at Beijing Normal University in Beijing, China. These products include Leaf Area Index (LAI), land surface albedo, and broadband emissivity (BBE) from the years 1981 to 2010, downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) from the years 2008 to 2010. Remote Sensing 2013-05-15 5 5 Article 10.3390/rs5052436 2436 2450 2072-4292 2013-05-15 doi: 10.3390/rs5052436 Xiang Zhao Shunlin Liang Suhong Liu Wenping Yuan Zhiqiang Xiao Qiang Liu Jie Cheng Xiaotong Zhang Hairong Tang Xin Zhang Qiang Liu Gongqi Zhou Shuai Xu Kai Yu http://www.mdpi.com/2072-4292/5/5/2411 The objective of this study was to analyze land use/land cover (LULC) changes in the landscape of Munessa-Shashemene area of the Ethiopian highlands over a period of 39 years (1973–2012). Satellite images of Landsat MSS (1973), TM (1986), ETM+ (2000), and RapidEye (2012) were used. All images were classified using object-based image classification technique. Accuracy assessments were conducted for each reference year. Change analysis was carried out using post classification comparison in GIS. Nine LULCs were successfully captured with overall accuracies ranging from 85.7% to 93.2% and Kappa statistic of 0.822 to 0.924. The classification result revealed that grasslands (42.3%), natural forests (21%), and woodlands (11.4%) were dominant LULC types in 1973. In 2012, croplands (48.5%) were the major LULC types followed by others. The change result shows that a rapid reduction in woodland cover of 81.8%, 52.3%, and 36.1% occurred between the first (1973–1986), second (1986–2000), and third (2000–2012) study periods, respectively. Similarly, natural forests cover decreased by 26.1% during the first, 21.1% during the second, and 24.4% during the third periods. Grasslands also declined by 11.9, 17.5, and 21.1% during the three periods, respectively. On the contrary, croplands increased in all three periods by 131, 31.5, and 22.7%, respectively. Analysis of the 39-year change matrix revealed that about 60% of the land showed changes in LULC. Changes were also common along the slope gradient and agro-ecological zones with varying proportions. Further study is suggested to investigate detailed drivers and consequences of changes. Remote Sensing 2013-05-15 5 5 Article 10.3390/rs5052411 2411 2435 2072-4292 2013-05-15 doi: 10.3390/rs5052411 Mengistie Kindu Thomas Schneider Demel Teketay Thomas Knoke http://www.mdpi.com/2072-4292/5/5/2389 This study aims to assess the localization and size distribution of landslides using automatic remote sensing techniques in (semi-) arid, non-vegetated, mountainous environments. The study area is located in the Kurdistan region (NE Iraq), within the Zagros orogenic belt, which is characterized by the High Folded Zone (HFZ), the Imbricated Zone and the Zagros Suture Zone (ZSZ). The available reference inventory includes 3,190 landslides mapped from sixty QuickBird scenes using manual delineation. The landslide types involve rock falls, translational slides and slumps, which occurred in different lithological units. Two hundred and ninety of these landslides lie within the ZSZ, representing a cumulated surface of 32 km2. The HFZ implicates 2,900 landslides with an overall coverage of about 26 km2. We first analyzed cumulative landslide number-size distributions using the inventory map. We then proposed a very simple and robust algorithm for automatic landslide extraction using specific band ratios selected upon the spectral signatures of bare surfaces as well as posteriori slope and the normalized difference vegetation index (NDVI) thresholds. The index is based on the contrast between landslides and their background, whereas the landslides have high reflections in the green and red bands. We applied the slope threshold map to remove low slope areas, which have high reflectance in red and green bands. The algorithm was able to detect ~96% of the recent landslides known from the reference inventory on a test site. The cumulative landslide number-size distribution of automatically extracted landslide is very similar to the one based on visual mapping. The automatic extraction is therefore adapted for the quantitative analysis of landslides and thus can contribute to the assessment of hazards in similar regions. Remote Sensing 2013-05-15 5 5 Article 10.3390/rs5052389 2389 2410 2072-4292 2013-05-15 doi: 10.3390/rs5052389 Arsalan Othman Richard Gloaguen http://www.mdpi.com/2072-4292/5/5/2368 Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height histograms for 2007 (r2 = 0.77, n = 79) and 2011 (r2 = 0.81, n = 53), taking the different point densities into account. Changes in peat swamp forests were identified by analyzing multispectral imagery. Unaffected forests accumulated on average 20 t/ha AGB with a canopy height increase of 2.3 m over the four year time period. Selective logged forests experienced an average AGB loss of 55 t/ha within 30 m and 42 t/ha within 50 m of detected logging trails, although the mean canopy height increased by 0.5 m and 1.0 m, respectively. Burned forests lost 92% of the initial biomass. These results demonstrate the great potential of repetitive airborne LiDAR surveys to precisely quantify even small scale AGB and canopy height dynamics in remote tropical forests, thereby featuring the needs of REDD+. Remote Sensing 2013-05-14 5 5 Article 10.3390/rs5052368 2368 2388 2072-4292 2013-05-14 doi: 10.3390/rs5052368 Sandra Englhart Juilson Jubanski Florian Siegert http://www.mdpi.com/2072-4292/5/5/2348 Satellite-based temperature measurements are an important indicator for global climate change studies over large areas. Records from Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR) and (Advanced) Along Track Scanning Radiometer ((A)ATSR) are providing long-term time series information. Assessing the quality of remote sensing-based temperature measurements provides feedback to the climate modeling community and other users by identifying agreements and discrepancies when compared to temperature records from meteorological stations. This paper presents a comparison of state-of-the-art remote sensing-based land surface temperature data with air temperature measurements from meteorological stations on a pan-arctic scale (north of 60° latitude). Within this study, we compared land surface temperature products from (A)ATSR, MODIS and AVHRR with an in situ air temperature (Tair) database provided by the National Climate Data Center (NCDC). Despite analyzing the whole acquisition time period of each land surface temperature product, we focused on the inter-annual variability comparing land surface temperature (LST) and air temperature for the overlapping time period of the remote sensing data (2000–2005). In addition, land cover information was included in the evaluation approach by using GLC2000. MODIS has been identified as having the highest agreement in comparison to air temperature records. The time series of (A)ATSR is highly variable, whereas inconsistencies in land surface temperature data from AVHRR have been found. Remote Sensing 2013-05-14 5 5 Article 10.3390/rs5052348 2348 2367 2072-4292 2013-05-14 doi: 10.3390/rs5052348 Marcel Urban Jonas Eberle Christian Hüttich Christiane Schmullius Martin Herold http://www.mdpi.com/2072-4292/5/5/2327 Optical and thermal remote sensing data were acquired at ground level over several turfgrass species under different soil and irrigation treatments in northern Colorado, USA. Three vegetation indices (VIs), estimated based on surface spectral reflectance, were sensitive to the effect of reduced water application on turfgrass quality. The temperature-based Grass Water Stress Index (GWSI) was also estimated by developing non-transpiring and non-water-stressed baselines. The VIs and the GWSI were all consistent in (i) having a non-linear relationship with the water application depth; and, (ii) revealing that the sensitivity of studied species to water availability increased in order from warm season mix to Poa pratensis L. and then Festuca spp.. Implemented soil preparation treatments had no significant effect on turfgrass quality and water stress. The differences between GWSI-based estimates of water use and the results of a complex surface energy balance model (METRIC) were not statistically significant, suggesting that the empirical GWSI method could provide similar results if the baselines are accurately developed under the local conditions of the study area. Remote Sensing 2013-05-14 5 5 Article 10.3390/rs5052327 2327 2347 2072-4292 2013-05-14 doi: 10.3390/rs5052327 Saleh Taghvaeian José Chávez Mary Hattendorf Mark Crookston http://www.mdpi.com/2072-4292/5/5/2308 Plot-based sampling with ground measurements or photography is typically used to establish and maintain National Forest Inventories (NFI). The re-measurement phase of the Canadian NFI is an opportunity to develop novel methods for the estimation of forest attributes such as stand height, crown closure, volume, and aboveground biomass (AGB) from satellite, rather than, airborne imagery. Based on panchromatic Very High Spatial Resolution (VHSR) images and Light Detection and Ranging (LiDAR) data acquired in the Yukon Territory, Canada, we propose an approach for boreal forest stand attribute characterization. Stand and tree objects are delineated, followed by modeling of stand height, volume, and AGB using metrics derived from the stand and tree crown objects. The calibration and validation of the models are based on co-located LiDAR-derived estimates. A k-nearest neighbor approach provided the best accuracy for stand height estimation (R2 = 0.76, RMSE = 1.95 m). Linear regression models were the most efficient for estimating stand volume (R2 = 0.94, RMSE = 9.6 m3/ha) and AGB (R2 = 0.92, RMSE = 22.2 t/ha). This study was implemented for one Canadian ecozone and demonstrated the capacity of a methodology to produce forest inventory attributes with acceptable accuracies offering potential to be applied to other boreal regions. Remote Sensing 2013-05-14 5 5 Article 10.3390/rs5052308 2308 2326 2072-4292 2013-05-14 doi: 10.3390/rs5052308 Brice Mora Michael Wulder Joanne White Geordie Hobart http://www.mdpi.com/2072-4292/5/5/2292 In several areas of the Alps, steep grassland is characterized by shallow erosions. These erosions represent a hazard through the increased availability of unconsolidated material in steep locations, loss of soil and impaired landscape aesthetics. Generally, the erosions concern only small areas but sometimes occur in large numbers. Remote sensing technologies have emerged as suitable tools to study the spatio-temporal changes of these eroded areas. The detection of such eroded areas is often done by manual digitalization of aerial photographs, which is labour-intensive and includes a certain risk of subjectivity. In this study we present a methodological tool that allows the automatic classification of shallow erosions on the basis of orthophoto series. The approach was carried out within a test site in the inner Schmirn Valley, Austria. The study covers both the detection of erosion areas and a multi-temporal analysis of the geomorphological changes. The presented approach is an appropriate tool for detecting shallow erosions and for analysing them in multi-temporal terms. The multi-temporal analysis revealed one period of higher increases in eroded areas compared to shrinking during the other periods. However, the analysis of the change of all single erosions indicates that in each study period there was both increase and decrease of erosion areas. The differences in the rates of increase between the observation years are most likely due to the irregular occurrence of events that encourage erosion. In contrast, the rates of decrease are almost constant and suggest a continuous rate of recovery. Remote Sensing 2013-05-13 5 5 Article 10.3390/rs5052292 2292 2307 2072-4292 2013-05-13 doi: 10.3390/rs5052292 Christoph Wiegand Martin Rutzinger Kati Heinrich Clemens Geitner http://www.mdpi.com/2072-4292/5/5/2275 This paper proposes a multi-level max-margin discriminative analysis (M3DA) framework, which takes both coarse and fine semantics into consideration, for the annotation of high-resolution satellite images. In order to generate more discriminative topic-level features, the M3DA uses the maximum entropy discrimination latent Dirichlet Allocation (MedLDA) model. Moreover, for improving the spatial coherence of visual words neglected by M3DA, conditional random field (CRF) is employed to optimize the soft label field composed of multiple label posteriors. The framework of M3DA enables one to combine word-level features (generated by support vector machines) and topic-level features (generated by MedLDA) via the bag-of-words representation. The experimental results on high-resolution satellite images have demonstrated that, using the proposed method can not only obtain suitable semantic interpretation, but also improve the annotation performance by taking into account the multi-level semantics and the contextual information. Remote Sensing 2013-05-13 5 5 Article 10.3390/rs5052275 2275 2291 2072-4292 2013-05-13 doi: 10.3390/rs5052275 Fan Hu Wen Yang Jiayu Chen Hong Sun http://www.mdpi.com/2072-4292/5/5/2257 Airborne scanning LiDAR is a promising technique for efficient and accuratebiomass mapping due to its capacity for direct measurement of the three-dimensionalstructure of vegetation. A combination of individual tree detection (ITD) and an area-basedapproach (ABA) introduced in Vastaranta et al. [1] to map forest aboveground biomass(AGB) and stem volume (VOL) was investigated. The main objective of this study was totest the usability and accuracy of LiDAR in biomass mapping. The nearest neighbourmethod was used in the ABA imputations and the accuracy of the biomass estimation wasevaluated in the Finland, where single tree-level biomass models are available. The relativeroot-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9%and 26.4% when field measurements were used in training the ABA. When ITDmeasurements were used in training, the respective accuracies ranged between 28.5%–34.9%and 29.2%–34.0%. Overall, the results show that accurate plot-level AGB estimates can beachieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL wasencouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that itis not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide acost-efficient application for acquiring training data for ABA in forest biomass mapping. Remote Sensing 2013-05-13 5 5 Article 10.3390/rs5052257 2257 2274 2072-4292 2013-05-13 doi: 10.3390/rs5052257 Ville Kankare Mikko Vastaranta Markus Holopainen Minna Räty Xiaowei Yu Juha Hyyppä Hannu Hyyppä Petteri Alho Risto Viitala http://www.mdpi.com/2072-4292/5/5/2238 Results of recent monitoring activities on potentially unstable areas of the NW volcano flank of La Fossa cone (Vulcano Island, Italy) are shown here. They are obtained by integration of data by aerial photogrammetry, terrestrial laser scanning (TLS) and GPS taken in the 1996–2011 time span. A comparison between multi-temporal models built from remote sensing data (photogrammetry and TLS) highlights areas characterized by ~7–10 cm/y positive differences (i.e., elevation increase) in the upper crown of the slope. The GPS measurements confirm these results. Areas characterized by negative differences, related to both mass collapses or small surface lowering, also exist. The higher differences, positive and negative, are always observed in zones affected by higher fumarolic activity. In the 2010–2012 time span, ground motions in the northern part of the crater rim, immediately above the upper part of observed area, are also observed. The results show different trends for both vertical and horizontal displacements of points distributed along the rim, with a magnitude of some centimeters, thus revealing a complex kinematics. A slope stability analysis shows that the safety factors estimated from these data do not indicate evidence of possible imminent failures. Nevertheless, new time series are needed to detect possible changes with the time of the stability conditions, and the monitoring has to go on. Remote Sensing 2013-05-13 5 5 Article 10.3390/rs5052238 2238 2256 2072-4292 2013-05-13 doi: 10.3390/rs5052238 Arianna Pesci Giordano Teza Giuseppe Casula Massimo Fabris Alessandro Bonforte http://www.mdpi.com/2072-4292/5/5/2219 Mass movements in Brazil are common phenomena, especially during strong rainfall events that occur frequently in the summer season. These phenomena cause losses of lives and serious damage to roads, bridges, and properties. Moreover, the illegal occupation by slums on the slopes around the cities intensifies the effect of the mass movement. This study aimed to develop a methodology that combines models of shallow landslides and debris-flows in order to create a map with landslides initiation and debris-flows volume and runout distance. The study area comprised of two catchments in Rio de Janeiro city: Quitite and Papagaio that drained side by side the west flank of the Maciço da Tijuca, with an area of 5 km2. The method included the following steps: (a) location of the susceptible areas to landslides using SHALSTAB model; (b) determination of rheological parameters of debris-flow from the back-analysis technique; and (c) combination of SHALSTAB and FLO-2D models to delineate the areas more susceptible to mass movements. These scenarios were compared with the landslide and debris-flow event of February 1996. Many FLO-2D simulations were exhaustively made to estimate the rheological parameters from the back-analysis technique. Those rheological coefficients of single simulation were back-calculated by adjusting with area and depth of the debris-flow obtained from field data. The initial material volume in the FLO-2D simulations was estimated from SHALSTAB model. The combination of these two mathematical models, SHALSTAB and FLO-2D, was able to predict both landslides and debris-flow events. Such procedures can reduce the casualties and property damage, delineating hazard areas, to estimate hazard intensities for input into risk studies providing information for public policy and planning. Remote Sensing 2013-05-10 5 5 Article 10.3390/rs5052219 2219 2237 2072-4292 2013-05-10 doi: 10.3390/rs5052219 Roberto Gomes Renato Guimarães Osmar de Carvalho Nelson Fernandes Eurípedes do Amaral http://www.mdpi.com/2072-4292/5/5/2200 Phenological metrics are of potential value as direct indicators of climate change. Usually they are obtained via either satellite imaging or ground based manual measurements; both are bespoke and therefore costly and have problems associated with scale and quality. An increase in the use of camera networks for monitoring infrastructure offers a means of obtaining images for use in phenological studies, where the only necessary outlay would be for data transfer, storage, processing and display. Here a pilot study is described that uses image data from a traffic monitoring network to demonstrate that it is possible to obtain usable information from the data captured. There are several challenges in using this network of cameras for automatic extraction of phenological metrics, not least, the low quality of the images and frequent camera motion. Although questions remain to be answered concerning the optimal employment of these cameras, this work illustrates that, in principle, image data from camera networks such as these could be used as a means of tracking environmental change in a low cost, highly automated and scalable manner that would require little human involvement. Remote Sensing 2013-05-10 5 5 Article 10.3390/rs5052200 2200 2218 2072-4292 2013-05-10 doi: 10.3390/rs5052200 David Morris Doreen Boyd John Crowe Caroline Johnson Karon Smith http://www.mdpi.com/2072-4292/5/5/2184 This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002–2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R2 = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R2 = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t·ha−1. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the size of MODIS 250 m pixel, and in the context of a 15-month crop cycle with no seasonal cropping calendar. Future satellite missions should permit monitoring of sugarcane yields using image resolutions that facilitate extraction of crop phenology from a group of individual plots. Remote Sensing 2013-05-10 5 5 Article 10.3390/rs5052184 2184 2199 2072-4292 2013-05-10 doi: 10.3390/rs5052184 Betty Mulianga Agnès Bégué Margareth Simoes Pierre Todoroff http://www.mdpi.com/2072-4292/5/5/2164 This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and oblique) were collected and used to create a SfM point cloud. All points were classified as ground or non-ground points. Non-ground points, presumably representing vegetation and other above ground objects, were used to create visualizations of the study vineyard blocks. Further, the relationship between non-ground points in close proximity to 67 sample vines and collected leaf area index (LAI) measurements for those same vines was also explored. Points near sampled vines were extracted from which several metrics were calculated and input into a stepwise regression model to attempt to predict LAI. This analysis resulted in a moderate R2 value of 0.567, accounting for 57 percent of the variation of LAISQRT using six predictor variables. These results provide further justification for SfM datasets to provide three-dimensional datasets necessary for vegetation structure visualization and biophysical modeling over areas of smaller extent. Additionally, SfM datasets can provide an increased temporal resolution compared to traditional three-dimensional datasets like those captured by light detection and ranging (lidar). Remote Sensing 2013-05-07 5 5 Article 10.3390/rs5052164 2164 2183 2072-4292 2013-05-07 doi: 10.3390/rs5052164 Adam Mathews Jennifer Jensen http://www.mdpi.com/2072-4292/5/5/2145 This paper proposes the mixture of Alpha-stable (MAS) distributions for modeling statistical property of Synthetic Aperture Radar (SAR) images in a supervised Markovian classification algorithm. Our work is motivated by the fact that natural scenes consist of various reflectors with different types that are typically concentrated within a small area, and SAR images generally exhibit sharp peaks, heavy tails, and even multimodal statistical property, especially at high resolution. Unimodal distributions do not fit such statistical property well, and thus a multimodal approach is necessary. Driven by the multimodality and impulsiveness of high resolution SAR images histogram, we utilize the mixture of Alpha-stable distributions to describe such characteristics. A pseudo-simulated annealing (PSA) estimator based on Markov chain Monte Carlo (MCMC) is present to efficiently estimate model parameters of the mixture of Alpha-stable distributions. To validate the proposed PSA estimator, we apply it to simulated data and compare its performance to that of a state-of-the-art estimator. Finally, we exploit the MAS distributions and a Markovian context for SAR images classification. The effectiveness of the proposed classifier is demonstrated by experiments on TerraSAR-X images, which verifies the validity of the MAS distributions for modeling and classification of SAR images. Remote Sensing 2013-05-03 5 5 Article 10.3390/rs5052145 2145 2163 2072-4292 2013-05-03 doi: 10.3390/rs5052145 Yijin Peng Jiayu Chen Xin Xu Fangling Pu http://www.mdpi.com/2072-4292/5/5/2113 Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy. Remote Sensing 2013-05-03 5 5 Article 10.3390/rs5052113 2113 2144 2072-4292 2013-05-03 doi: 10.3390/rs5052113 Matthias Forkel Nuno Carvalhais Jan Verbesselt Miguel Mahecha Christopher Neigh Markus Reichstein http://www.mdpi.com/2072-4292/5/5/2093 Arctic-Boreal region—mainly consisting of tundra, shrub lands, and boreal forests—has been experiencing an amplified warming over the past 30 years. As the main driving force of vegetation growth in the north, temperature exhibits tight coupling with the Normalized Difference Vegetation Index (NDVI)—a proxy to photosynthetic activity. However, the comparison between North America (NA) and northern Eurasia (EA) shows a weakened spatial dependency of vegetation growth on temperature changes in NA during the past decade. If this relationship holds over time, it suggests a 2/3 decrease in vegetation growth under the same rate of warming in NA, while the vegetation response in EA stays the same. This divergence accompanies a circumpolar widespread greening trend, but 20 times more browning in the Boreal NA compared to EA, and comparative greening and browning trends in the Arctic. These observed spatial patterns of NDVI are consistent with the temperature record, except in the Arctic NA, where vegetation exhibits a similar long-term trend of greening to EA under less warming. This unusual growth pattern in Arctic NA could be due to a lack of precipitation velocity compared to the temperature velocity, when taking velocity as a measure of northward migration of climatic conditions. Remote Sensing 2013-05-02 5 5 Article 10.3390/rs5052093 2093 2112 2072-4292 2013-05-02 doi: 10.3390/rs5052093 Jian Bi Liang Xu Arindam Samanta Zaichun Zhu Ranga Myneni http://www.mdpi.com/2072-4292/5/5/2072 During the CINDY/DYNAMO field campaign (fall/winter 2011), intensive measurements of the upper ocean, including an array of several surface moorings and ship observations for the area around 75°E–80°E, Equator-10°S, were conducted. In this study, large-scale upper ocean variations surrounding the intensive array during the field campaign are described based on the analysis of satellite-derived data. Surface currents, sea surface height (SSH), sea surface salinity (SSS), surface winds and sea surface temperature (SST) during the CINDY/DYNAMO field campaign derived from satellite observations are analyzed. During the intensive observation period, three active episodes of large-scale convection associated with the Madden-Julian Oscillation (MJO) propagated eastward across the tropical Indian Ocean. Surface westerly winds near the equator were particularly strong during the events in late November and late December, exceeding 10 m/s. These westerlies generated strong eastward jets (>1 m/s) on the equator. Significant remote ocean responses to the equatorial westerlies were observed in both Northern and Southern Hemispheres in the central and eastern Indian Oceans. The anomalous SSH associated with strong eastward jets propagated eastward as an equatorial Kelvin wave and generated intense downwelling near the eastern boundary. The anomalous positive SSH then partly propagated westward around 4°S as a reflected equatorial Rossby wave, and it significantly influenced the upper ocean structure in the Seychelles-Chagos thermocline ridge about two months after the last MJO event during the field campaign. For the first time, it is demonstrated that subseasonal SSS variations in the central Indian Ocean can be monitored by Aquarius measurements based on the comparison with in situ observations at three locations. Subseasonal SSS variability in the central Indian Ocean observed by RAMA buoys is explained by large-scale water exchanges between the Arabian Sea and Bay of Bengal through the zonal current variation near the equator. Remote Sensing 2013-04-29 5 5 Article 10.3390/rs5052072 2072 2092 2072-4292 2013-04-29 doi: 10.3390/rs5052072 Toshiaki Shinoda Tommy Jensen Maria Flatau Sue Chen Weiqing Han Chunzai Wang http://www.mdpi.com/2072-4292/5/5/2057 Airborne remote sensing has an important role to play in mapping and monitoring biodiversity over large spatial scales. Techniques for applying this technology to biodiversity mapping have focused on remote species identification of individual crowns; however, this requires collection of a large number of crowns to train a classifier, which may limit the usefulness of this approach in many study regions. Based on the premise that the spectral variation among sites is related to their ecological dissimilarity, we asked whether it is possible to estimate the beta diversity, or turnover in species composition, among sites without the use of training data. We evaluated alternative methods using simulated communities constructed from the spectra of field-identified tree and shrub crowns from an African savanna. A method based on the k-means clustering of crown spectra produced beta diversity estimates (measured as Bray-Curtis dissimilarity) among sites with an average pairwise correlation of ~0.5 with the true beta diversity, compared to an average correlation of ~0.8 obtained by a supervised species classification approach. When applied to savanna landscapes, the unsupervised clustering method produced beta diversity estimates similar to those obtained from supervised classification. The unsupervised method proposed here can be used to estimate the spatial structure of species turnover in a landscape when training data (e.g., tree crowns) are unavailable, providing top-down information for science, conservation and ecosystem management applications. Remote Sensing 2013-04-25 5 5 Article 10.3390/rs5052057 2057 2071 2072-4292 2013-04-25 doi: 10.3390/rs5052057 Claire Baldeck Gregory Asner http://www.mdpi.com/2072-4292/5/5/2037 The scale invariant feature transform (SIFT) is a widely used interest operator for supporting tasks such as 3D matching, 3D scene reconstruction, panorama stitching, image registration and motion tracking. Although SIFT is reported to be robust to disparate radiometric and geometric conditions in visible light imagery, using the default input parameters does not yield satisfactory results when matching imagery acquired at non-overlapping wavelengths. In this paper, optimization of the SIFT parameters for matching multi-wavelength image sets is documented. In order to integrate hyperspectral panoramic images with reference imagery and 3D data, corresponding points were required between visible light and short wave infrared images, each acquired from a slightly different position and with different resolutions and geometric projections. The default SIFT parameters resulted in too few points being found, requiring the influence of five key parameters on the number of matched points to be explored using statistical techniques. Results are discussed for two geological datasets. Using the SIFT operator with optimized parameters and an additional outlier elimination method, allowed between four and 22 times more homologous points to be found with improved image point distributions, than using the default parameter values recommended in the literature. Remote Sensing 2013-04-24 5 5 Article 10.3390/rs5052037 2037 2056 2072-4292 2013-04-24 doi: 10.3390/rs5052037 Aleksandra Sima Simon Buckley http://www.mdpi.com/2072-4292/5/4/2014 Canopy structure, the vertical distribution of canopy material, is an important element of forest ecosystem dynamics and habitat preference. Although vertical stratification, or “canopy layering,” is a basic characterization of canopy structure for research and forest management, it is difficult to quantify at landscape scales. In this paper we describe canopy structure and develop methodologies to map forest vertical stratification in a mixed temperate forest using full-waveform lidar. Two definitions—one categorical and one continuous—are used to map canopy layering over Hubbard Brook Experimental Forest, New Hampshire with lidar data collected in 2009 by NASA’s Laser Vegetation Imaging Sensor (LVIS). The two resulting canopy layering datasets describe variation of canopy layering throughout the forest and show that layering varies with terrain elevation and canopy height. This information should provide increased understanding of vertical structure variability and aid habitat characterization and other forest management activities. Remote Sensing 2013-04-22 5 4 Article 10.3390/rs5042014 2014 2036 2072-4292 2013-04-22 doi: 10.3390/rs5042014 Amanda Whitehurst Anu Swatantran J. Blair Michelle Hofton Ralph Dubayah http://www.mdpi.com/2072-4292/5/4/1998 This study aims to investigate the impact of the spatial size of the study domain on the performance of the triangle method using progressively smaller domains and Moderate Resolution Imaging Spectroradiometer (MODIS) observations in the Heihe River basin located in the arid region of northwestern China. Data from 10 clear-sky days during the growing season from April to September 2009 were used. Results show that different dry/wet edges in the surface temperature-vegetation index space directly led to the deviation of evapotranspiration (ET) estimates due to the variation of the spatial domain size. The slope and the intercept of the limiting edges are dependent on the range and the maximum of surface temperature over the spatial domain. The difference of the limiting edges between different domain sizes has little impact on the spatial pattern of ET estimates, with the Pearson correlation coefficient ranging from 0.94 to 1.0 for the 10 pairs of ET estimates at different domain scales. However, it has a larger impact on the degree of discrepancies in ET estimates between different domain sizes, with the maximum of 66 W∙m−2. The largest deviation of ET estimates between different domain sizes was found at the beginning of the growing season. Remote Sensing 2013-04-22 5 4 Article 10.3390/rs5041998 1998 2013 2072-4292 2013-04-22 doi: 10.3390/rs5041998 Jing Tian Hongbo Su Xiaomin Sun Shaohui Chen Honglin He Linjun Zhao http://www.mdpi.com/2072-4292/5/4/1974 This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps. Remote Sensing 2013-04-19 5 4 Article 10.3390/rs5041974 1974 1997 2072-4292 2013-04-19 doi: 10.3390/rs5041974 Matthew Stites Jacob Gunther Todd Moon Gustavious Williams http://www.mdpi.com/2072-4292/5/4/1956 This work discusses the accuracies of geophysical model functions (GMFs) for retrieval of sea surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images in Japanese coastal waters characterized by short fetches and variable atmospheric stability conditions. In situ observations from two validation sites, Hiratsuka and Shirahama, are used for comparison of the retrieved sea surface wind speeds using CMOD (C-band model)4, CMOD_IFR2, CMOD5 and CMOD5.N. Of all the geophysical model functions (GMFs), the latest C-band GMF, CMOD5.N, has the smallest bias and root mean square error at both sites. All of the GMFs exhibit a negative bias in the retrieved wind speed. In order to understand the reason for this bias, all SAR-retrieved wind speeds are separated into two categories: onshore wind (blowing from sea to land) and offshore wind (blowing from land to sea). Only offshore winds were found to exhibit the large negative bias, and short fetches from the coastline may be a possible reason for this. Moreover, it is clarified that in both the unstable and stable conditions, CMOD5.N has atmospheric stability effectiveness, and can keep the same accuracy with CMOD5 in the neutral condition. In short, at the moment, CMOD5.N is thought to be the most promising GMF for the SAR wind speed retrieval with the atmospheric stability correction in Japanese coastal waters, although there is ample room for future improvement for the effect from short fetch. Remote Sensing 2013-04-19 5 4 Article 10.3390/rs5041956 1956 1973 2072-4292 2013-04-19 doi: 10.3390/rs5041956 Yuko Takeyama Teruo Ohsawa Katsutoshi Kozai Charlotte Hasager Merete Badger http://www.mdpi.com/2072-4292/5/4/1932 Individual tree crowns may be delineated from airborne laser scanning (ALS) data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized for 3D analysis to any great extent. In this study, an existing surface segmentation method was used as a basis for a new tree model 3D clustering method applied to ALS returns in 104 circular field plots with 12 m radius in pine-dominated boreal forest (64°14'N, 19°50'E). For each cluster below the tallest canopy layer, a parabolic surface was fitted to model a tree crown. The tree model clustering identified more trees than segmentation of the surface model, especially smaller trees below the tallest canopy layer. Stem attributes were estimated with k-Most Similar Neighbours (k-MSN) imputation of the clusters based on field-measured trees. The accuracy at plot level from the k-MSN imputation (stem density root mean square error or RMSE 32.7%; stem volume RMSE 28.3%) was similar to the corresponding results from the surface model (stem density RMSE 33.6%; stem volume RMSE 26.1%) with leave-one-out cross-validation for one field plot at a time. Three-dimensional analysis of ALS data should also be evaluated in multi-layered forests since it identified a larger number of small trees below the tallest canopy layer. Remote Sensing 2013-04-19 5 4 Article 10.3390/rs5041932 1932 1955 2072-4292 2013-04-19 doi: 10.3390/rs5041932 Eva Lindberg Johan Holmgren Kenneth Olofsson Jörgen Wallerman Håkan Olsson http://www.mdpi.com/2072-4292/5/4/1912 Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management decisions to prevent larger damages. For this reason, a field-based bark beetle monitoring program is currently implemented in Germany. The combination of remote sensing and field data may help minimizing the reaction time and reducing costs of monitoring programs covering large forested areas. In this case study, RapidEye and TerraSAR-X data were analyzed separately and in combination to detect bark beetle green attack. The remote sensing data were acquired in May 2009 for a study site in south-west Germany. In order to distinguish healthy areas and areas affected by bark beetle green attack, three statistical approaches were compared: generalized linear models (GLM), maximum entropy (ME) and random forest (RF). The spatial scale (minimum mapping unit) was 78.5 m2. TerraSAR-X data resulted in fair classification accuracy with a cross-validated Cohen’s Kappa Coefficient (kappa) of 0.23. RapidEye data resulted in moderate classification accuracy with a kappa of 0.51. The highest classification accuracy was obtained by combining the TerraSAR-X and RapidEye data, resulting in a kappa of 0.74. The accuracy of ME models was considerably higher than the accuracy of GLM and RF models. Remote Sensing 2013-04-16 5 4 Article 10.3390/rs5041912 1912 1931 2072-4292 2013-04-16 doi: 10.3390/rs5041912 Sonia Ortiz Johannes Breidenbach Gerald Kändler http://www.mdpi.com/2072-4292/5/4/1894 Temperature and precipitation have been separately reported to be the main factors affecting the Normalized Difference Vegetation Index (NDVI) in the Tibetan Plateau. The effects of the main climatic factors on the yearly maximum NDVI (MNDVI) in the Tibetan Plateau were examined on different scales. The result underscored the observation that both precipitation and temperature affect MNDVI based on weather stations or physico-geographical regions. Precipitation is the main climatic factor that affects the vegetation cover in the entire Tibetan Plateau. Both annual mean precipitation and annual mean precipitation of the growing period are related with MNDVI, and the positive correlations are manifested in a linear manner. By comparison, the weakly correlated current between MNDVI and all the temperature indexes is observed in the study area. Remote Sensing 2013-04-16 5 4 Article 10.3390/rs5041894 1894 1911 2072-4292 2013-04-16 doi: 10.3390/rs5041894 Jian Sun Genwei Cheng Weipeng Li Yukun Sha Yunchuan Yang http://www.mdpi.com/2072-4292/5/4/1875 Image acquisition systems based on multi-head arrangement of digital cameras are attractive alternatives enabling a larger imaging area when compared to a single frame camera. The calibration of this kind of system can be performed in several steps or by using simultaneous bundle adjustment with relative orientation stability constraints. The paper will address the details of the steps of the proposed approach for system calibration, image rectification, registration and fusion. Experiments with terrestrial and aerial images acquired with two Fuji FinePix S3Pro cameras were performed. The experiments focused on the assessment of the results of self-calibrating bundle adjustment with and without relative orientation constraints and the effects to the registration and fusion when generating virtual images. The experiments have shown that the images can be accurately rectified and registered with the proposed approach, achieving residuals smaller than one pixel. Remote Sensing 2013-04-15 5 4 Article 10.3390/rs5041875 1875 1893 2072-4292 2013-04-15 doi: 10.3390/rs5041875 Antonio Tommaselli Mauricio Galo Marcus de Moraes José Marcato Carlos Caldeira Rodrigo Lopes http://www.mdpi.com/2072-4292/5/4/1856 The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical–conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. Remote Sensing 2013-04-15 5 4 Article 10.3390/rs5041856 1856 1874 2072-4292 2013-04-15 doi: 10.3390/rs5041856 Fernanda Watanabe Nilton Imai Enner Alcântara Luiz da Silva Rotta Alex Utsumi http://www.mdpi.com/2072-4292/5/4/1842 Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability sample of 18.5 km × 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000–2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (πpx) design was implemented for 1990–2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990–2000 and 0.64 Mha/yr (SE = 0.055) during 2000–2005. Our use of the πpx sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the πpx design relative to other sampling strategies is needed before general design recommendations can be put forth. Remote Sensing 2013-04-15 5 4 Article 10.3390/rs5041842 1842 1855 2072-4292 2013-04-15 doi: 10.3390/rs5041842 Namita Giree Stephen Stehman Peter Potapov Matthew Hansen http://www.mdpi.com/2072-4292/5/4/1809 This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos. Remote Sensing 2013-04-15 5 4 Article 10.3390/rs5041809 1809 1841 2072-4292 2013-04-15 doi: 10.3390/rs5041809 A.S.M. Shihavuddin Nuno Gracias Rafael Garcia Arthur Gleason Brooke Gintert http://www.mdpi.com/2072-4292/5/4/1787 Tree parameter determinations using airborne Light Detection and Ranging (LiDAR) have been conducted in many forest types, including coniferous, boreal, and deciduous. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction at an individual tree level. The main objective of this study was to investigate the potential of using LiDAR data to estimate the biophysical parameters of mangrove trees at an individual tree scale. The Variable Window Filtering (VWF) and Inverse Watershed Segmentation (IWS) methods were investigated by comparing their performance in individual tree detection and in deriving tree position, crown diameter, and tree height using the LiDAR-derived Canopy Height Model (CHM). The results demonstrated that each method performed well in mangrove forests with a low percentage of crown overlap conditions. The VWF method yielded a slightly higher accuracy for mangrove parameter extractions from LiDAR data compared with the IWS method. This is because the VWF method uses an adaptive circular filtering window size based on an allometric relationship. As a result of the VWF method, the position measurements of individual tree indicated a mean distance error value of 1.10 m. The individual tree detection showed a kappa coefficient of agreement (K) value of 0.78. The estimation of crown diameter produced a coefficient of determination (R2) value of 0.75, a Root Mean Square Error of the Estimate (RMSE) value of 1.65 m, and a Relative Error (RE) value of 19.7%. Tree height determination from LiDAR yielded an R2 value of 0.80, an RMSE value of 1.42 m, and an RE value of 19.2%. However, there are some limitations in the mangrove parameters derived from LiDAR. The results indicated that an increase in the percentage of crown overlap (COL) results in an accuracy decrease of the mangrove parameters extracted from the LiDAR-derived CHM, particularly for crown measurements. In this study, the accuracy of LiDAR-derived biophysical parameters in mangrove forests using the VWF and IWS methods is lower than in coniferous, boreal, pine, and deciduous forests. An adaptive allometric equation that is specific for the level of tree density and percentage of crown overlap is a solution for improving the predictive accuracy of the VWF method. Remote Sensing 2013-04-12 5 4 Article 10.3390/rs5041787 1787 1808 2072-4292 2013-04-12 doi: 10.3390/rs5041787 Wasinee Wannasiri Masahiko Nagai Kiyoshi Honda Phisan Santitamnont Poonsak Miphokasap http://www.mdpi.com/2072-4292/5/4/1774 Shanghai is a modern metropolis characterized by high urban density and anthropogenic ground motions. Although traditional deformation monitoring methods, such as GPS and spirit leveling, are reliable to millimeter accuracy, the sparse point subsidence information makes understanding large areas difficult. Multiple temporal space-borne synthetic aperture radar interferometry is a powerful high-accuracy (sub-millimeter) remote sensing tool for monitoring slow ground deformation for a large area with a high point density. In this paper, the Interferometric Point Target Time Series Analysis method is used to extract ground subsidence rates in Shanghai based on 31 C-Band and 35 X-Band synthetic aperture radar (SAR) images obtained by Envisat and COSMO SkyMed (CSK) satellites from 2007 to 2010. A significant subsidence funnel that was detected is located in the junction place between the Yangpu and the Hongkou Districts. A t-test is formulated to judge the agreements between the subsidence results obtained by SAR and by spirit leveling. In addition, four profile lines crossing the subsidence funnel area are chosen for a comparison of ground subsidence rates, which were obtained by the two different band SAR images, and show a good agreement. Remote Sensing 2013-04-11 5 4 Article 10.3390/rs5041774 1774 1786 2072-4292 2013-04-11 doi: 10.3390/rs5041774 Jie Chen Jicang Wu Lina Zhang Junping Zou Guoxiang Liu Rui Zhang Bing Yu http://www.mdpi.com/2072-4292/5/4/1754 We introduce and test the performance of two sampling methods that utilize distance distributions of laser point clouds in terrestrial and mobile laser scanning geometries. The methods are leveled histogram sampling and inversely weighted distance sampling. The methods aim to reduce a significant portion of the laser point cloud data while retaining most characteristics of the full point cloud. We test the methods in three case studies in which data were collected using a different terrestrial or mobile laser scanning system in each. Two reference methods, uniform sampling and linear point picking, were used for result comparison. The results demonstrate that correctly selected distance-sensitive sampling techniques allow higher point removal than the references in all the tested case studies. Remote Sensing 2013-04-09 5 4 Article 10.3390/rs5041754 1754 1773 2072-4292 2013-04-09 doi: 10.3390/rs5041754 Eetu Puttonen Matti Lehtomäki Harri Kaartinen Lingli Zhu Antero Kukko Anttoni Jaakkola http://www.mdpi.com/2072-4292/5/4/1734 Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI), fractal dimension, and Accumulated Growing Degree Days (AGDDs). In our case, these features are global variable, and measured in the state-level. Moreover, global feature in each Day of Year (DOY) would be impacted by multiple progress stages. Therefore, a mixture model is employed to model the observation probability distribution with all possible stage components. Then, a filtering based algorithm is utilized to estimate the proportion of each progress stage in the real-time. Experiments are conducted in the states of Iowa, Illinois and Nebraska in the USA, and our results are assessed and validated by the Crop Progress Reports (CPRs) of the National Agricultural Statistics Service (NASS). Finally, a quantitative comparison and analysis between our method and spectral pixel-wise based methods is presented. The results demonstrate the feasibility of the proposed method for the estimation of corn progress stages. The proposed method could be used as a supplementary tool in aid of field survey. Moreover, it also can be used to establish the progress stage estimation model for different types of crops. Remote Sensing 2013-04-08 5 4 Article 10.3390/rs5041734 1734 1753 2072-4292 2013-04-08 doi: 10.3390/rs5041734 Yonglin Shen Lixin Wu Liping Di Genong Yu Hong Tang Guoxian Yu Yuanzheng Shao http://www.mdpi.com/2072-4292/5/4/1704 Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today. Remote Sensing 2013-04-08 5 4 Review 10.3390/rs5041704 1704 1733 2072-4292 2013-04-08 doi: 10.3390/rs5041704 Felix Rembold Clement Atzberger Igor Savin Oscar Rojas http://www.mdpi.com/2072-4292/5/4/1681 High resolution Digital Surface Models (DSMs) produced from airborne laser-scanning or stereo satellite images provide a very useful source of information for automated 3D building reconstruction. In this paper an investigation is reported about extraction of 3D building models from high resolution DSMs and orthorectified images produced from Worldview-2 stereo satellite imagery. The focus is on the generation of 3D models of parametric building roofs, which is the basis for creating Level Of Detail 2 (LOD2) according to the CityGML standard. In particular the building blocks containing several connected buildings with tilted roofs are investigated and the potentials and limitations of the modeling approach are discussed. The edge information extracted from orthorectified image has been employed as additional source of information in 3D reconstruction algorithm. A model driven approach based on the analysis of the 3D points of DSMs in a 2D projection plane is proposed. Accordingly, a building block is divided into smaller parts according to the direction and number of existing ridge lines for parametric building reconstruction. The 3D model is derived for each building part, and finally, a complete parametric model is formed by merging the 3D models of the individual building parts and adjusting the nodes after the merging step. For the remaining building parts that do not contain ridge lines, a prismatic model using polygon approximation of the corresponding boundary pixels is derived and merged to the parametric models to shape the final model of the building. A qualitative and quantitative assessment of the proposed method for the automatic reconstruction of buildings with parametric roofs is then provided by comparing the final model with the existing surface model as well as some field measurements. Remote Sensing 2013-04-02 5 4 Article 10.3390/rs5041681 1681 1703 2072-4292 2013-04-02 doi: 10.3390/rs5041681 Hossein Arefi Peter Reinartz http://www.mdpi.com/2072-4292/5/4/1651 The purpose of this paper is to estimate the water balance in a semi-arid environment with limited in situ data using a remote sensing approach. We focus on the Lake Manyara catchment, located within the East African Rift of northern Tanzania. We use a distributed conceptual hydrological model driven by remote sensing data to study the spatial and temporal variability of water balance parameters within the catchment. Satellite gravimetry GRACE data is used to verify the trends of the inferred lake level changes. The results show that the lake undergoes high spatial and temporal variations, characteristic of a semi-arid climate with high evaporation and low rainfall. We observe that the Lake Manyara water balance and GRACE equivalent water depth show comparable trends; a decrease after 2002 followed by a sharp increase in 2006–2007. Our modeling confirms the importance of the 2006–2007 Indian Ocean Dipole fluctuation in replenishing the groundwater reservoirs of East Africa. We thus demonstrate that water balance modeling can be performed successfully using remote sensing data even in complex climatic settings. Despite the small size of Lake Manyara, GRACE data showed great potential for hydrological research on smaller un-gauged lakes and catchments in similar semi-arid environments worldwide. The water balance information can be used for further analysis of lake variations in relation to soil erosion, climate and land cover/land use change as well as different lake management and conservation scenarios. Remote Sensing 2013-04-02 5 4 Article 10.3390/rs5041651 1651 1680 2072-4292 2013-04-02 doi: 10.3390/rs5041651 Dorothea Deus Richard Gloaguen Peter Krause http://www.mdpi.com/2072-4292/5/4/1624 Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy. Remote Sensing 2013-03-28 5 4 Article 10.3390/rs5041624 1624 1650 2072-4292 2013-03-28 doi: 10.3390/rs5041624 Ahmad Aijazi Paul Checchin Laurent Trassoudaine http://www.mdpi.com/2072-4292/5/4/1603 The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications. In this study we evaluated the quality and spatiotemporal variability of the soil moisture product retrieved from C-band scatterometers data across the LMB sub-catchments. The soil moisture retrieval algorithm showed reasonable performance in most areas of the LMB with the exception of a few sub-catchments in the eastern parts of Laos, where the land cover is characterized by dense vegetation. The best performance of the retrieval algorithm was obtained in agricultural regions. Comparison of the available in situ evaporation data in the LMB and the Basin Water Index (BWI), an indicator of the basin soil moisture condition, showed significant negative correlations up to R = −0.85. The inter-annual variation of the calculated BWI was also found corresponding to the reported extreme hydro-meteorological events in the Mekong region. The retrieved soil moisture data show high correlation (up to R = 0.92) with monthly anomalies of precipitation in non-irrigated regions. In general, the seasonal variability of soil moisture in the LMB was well captured by the retrieval method. The results of analysis also showed significant correlation between El Niño events and the monthly BWI anomaly measurements particularly for the month May with the maximum correlation of R = 0.88. Remote Sensing 2013-03-27 5 4 Article 10.3390/rs5041603 1603 1623 2072-4292 2013-03-27 doi: 10.3390/rs5041603 Vahid Naeimi Patrick Leinenkugel Daniel Sabel Wolfgang Wagner Heiko Apel Claudia Kuenzer http://www.mdpi.com/2072-4292/5/4/1588 Crop coefficient (Kc)-based estimation of crop evapotranspiration is one of the most commonly used methods for irrigation water management. However, uncertainties of the generalized dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper No. 56 can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. Similarities between the crop coefficient curve and a satellite-derived vegetation index showed potential for modeling a crop coefficient as a function of the vegetation index. Therefore, the possibility of directly estimating the crop coefficient from satellite reflectance of a crop was investigated. The Kc data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop coefficients procedure using field data obtained during 2007 from representative US cropping systems in the High Plains from AmeriFlux sites. A simple linear regression model ( ) is developed to establish a general relationship between a normalized difference vegetation index (NDVI) from a moderate resolution satellite data (MODIS) and the crop coefficient (Kc) calculated from the flux data measured for different crops and cropping practices using AmeriFlux towers. There was a strong linear correlation between the NDVI-estimated Kc and the measured Kc with an r2 of 0.91 and 0.90, while the root-mean-square error (RMSE) for Kc in 2006 and 2007 were 0.16 and 0.19, respectively. The procedure for quantifying crop coefficients from NDVI data presented in this paper should be useful in other regions of the globe to understand regional irrigation water consumption. Remote Sensing 2013-03-26 5 4 Article 10.3390/rs5041588 1588 1602 2072-4292 2013-03-26 doi: 10.3390/rs5041588 Baburao Kamble Ayse Kilic Kenneth Hubbard http://www.mdpi.com/2072-4292/5/4/1568 The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images at 250 m resolution is validated using snow cover maps (SCA) based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA) MODIS snow products (MOD10 and MYD10). It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition. Remote Sensing 2013-03-26 5 4 Article 10.3390/rs5041568 1568 1587 2072-4292 2013-03-26 doi: 10.3390/rs5041568 Claudia Notarnicola Martial Duguay Nico Moelg Thomas Schellenberger Anke Tetzlaff Roberto Monsorno Armin Costa Christian Steurer Marc Zebisch http://www.mdpi.com/2072-4292/5/4/1549 The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past yield performance over different growing seasons can inform agricultural management decisions ranging from fertilizer applications at the sub-paddock scale to changes in land use at a landscape scale. However, an understanding of the magnitude of prediction errors is needed. In this study, we examine the predictive wheat yield relationships developed from Normalised Difference Vegetation Index (NDVI) acquired Landsat imagery and combine-mounted yield monitors for three Western Australian farms over different growing seasons. We further analysed their predictive capability when these relationships are used to extrapolate yield from one farm to another. Over all seasons, the best predictions were achieved with imagery acquired in September. Of the five seasons reviewed, three showed very reasonable prediction accuracies, with the low and high rainfall years providing good predictions. Medium rainfall years showed the greatest variation in prediction accuracy with marginal to poor predictions resulting from narrow ranges of measured wheat yield and NDVI values. These results demonstrate the potential benefit of fusing together two high resolution datasets to create robust wheat yield prediction models over different growing seasons, the outputs of which can be used to inform agricultural decision making. Remote Sensing 2013-03-26 5 4 Article 10.3390/rs5041549 1549 1567 2072-4292 2013-03-26 doi: 10.3390/rs5041549 Greg Lyle Megan Lewis Bertram Ostendorf http://www.mdpi.com/2072-4292/5/4/1524 The quantification of forest above-ground biomass (AGB) is important for such broader applications as decision making, forest management, carbon (C) stock change assessment and scientific applications, such as C cycle modeling. However, there is a great uncertainty related to the estimation of forest AGB, especially in the tropics. The main goal of this study was to test a combination of field data and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) backscatter intensity data to reduce the uncertainty in the estimation of forest AGB in the Miombo savanna woodlands of Mozambique (East Africa). A machine learning algorithm, based on bagging stochastic gradient boosting (BagSGB), was used to model forest AGB as a function of ALOS PALSAR Fine Beam Dual (FBD) backscatter intensity metrics. The application of this method resulted in a coefficient of correlation (R) between observed and predicted (10-fold cross-validation) forest AGB values of 0.95 and a root mean square error of 5.03 Mg·ha−1. However, as a consequence of using bootstrap samples in combination with a cross validation procedure, some bias may have been introduced, and the reported cross validation statistics could be overoptimistic. Therefore and as a consequence of the BagSGB model, a measure of prediction variability (coefficient of variation) on a pixel-by-pixel basis was also produced, with values ranging from 10 to 119% (mean = 25%) across the study area. It provides additional and complementary information regarding the spatial distribution of the error resulting from the application of the fitted model to new observations. Remote Sensing 2013-03-25 5 4 Article 10.3390/rs5041524 1524 1548 2072-4292 2013-03-25 doi: 10.3390/rs5041524 João Carreiras Joana Melo Maria Vasconcelos http://www.mdpi.com/2072-4292/5/4/1498 Water bodies are ubiquitous features in Arctic wetlands. Ponds, i.e., waters with a surface area smaller than 104 m2, have been recognized as hotspots of biological activity and greenhouse gas emissions but are not well inventoried. This study aimed to identify common characteristics of three Arctic wetlands including water body size and abundance for different spatial resolutions, and the potential of Landsat-5 TM satellite data to show the subpixel fraction of water cover (SWC) via the surface albedo. Water bodies were mapped using optical and radar satellite data with resolutions of 4mor better, Landsat-5 TM at 30mand the MODIS water mask (MOD44W) at 250m resolution. Study sites showed similar properties regarding water body distributions and scaling issues. Abundance-size distributions showed a curved pattern on a log-log scale with a flattened lower tail and an upper tail that appeared Paretian. Ponds represented 95% of the total water body number. Total number of water bodies decreased with coarser spatial resolutions. However, clusters of small water bodies were merged into single larger water bodies leading to local overestimation of water surface area. To assess the uncertainty of coarse-scale products, both surface water fraction and the water body size distribution should therefore be considered. Using Landsat surface albedo to estimate SWC across different terrain types including polygonal terrain and drained thermokarst basins proved to be a robust approach. However, the albedo–SWC relationship is site specific and needs to be tested in other Arctic regions. These findings present a baseline to better represent small water bodies of Arctic wet tundra environments in regional as well as global ecosystem and climate models. Remote Sensing 2013-03-25 5 4 Article 10.3390/rs5041498 1498 1523 2072-4292 2013-03-25 doi: 10.3390/rs5041498 Sina Muster Birgit Heim Anna Abnizova Julia Boike http://www.mdpi.com/2072-4292/5/3/1484 Using a recent Leaf Area Index (LAI) dataset and the Community Land Model version 4 (CLM4), we investigated percent changes and controlling factors of global vegetation growth for the period 1982 to 2009. Over that 28-year period, both the remote-sensing estimate and model simulation show a significant increasing trend in annual vegetation growth. Latitudinal asymmetry appeared in both products, with small increases in the Southern Hemisphere (SH) and larger increases at high latitudes in the Northern Hemisphere (NH). The south-to-north asymmetric land surface warming was assessed to be the principal driver of this latitudinal asymmetry of LAI trend. Heterogeneous precipitation functioned to decrease this latitudinal LAI gradient, and considerably regulated the local LAI change. A series of factorial experiments were specially-designed to isolate and quantify contributions to LAI trend from different external forcings such as climate variation, CO2, nitrogen deposition and land use and land cover change. The climate-only simulation confirms that climate change, particularly the asymmetry of land temperature variation, can explain the latitudinal pattern of LAI change. CO2 fertilization during the last three decades was simulated to be the dominant cause for the enhanced vegetation growth. Our study, though limited by observational and modeling uncertainties, adds further insight into vegetation growth trends and environmental correlations. These validation exercises also provide new quantitative and objective metrics for evaluation of land ecosystem process models at multiple spatio-temporal scales. Remote Sensing 2013-03-21 5 3 Article 10.3390/rs5031484 1484 1497 2072-4292 2013-03-21 doi: 10.3390/rs5031484 Jiafu Mao Xiaoying Shi Peter Thornton Forrest Hoffman Zaichun Zhu Ranga Myneni http://www.mdpi.com/2072-4292/5/3/1465 In recent decades, investigations of NPP (net primary production) or proxies here of (normalized difference vegetation index, NDVI) and land degradation in Sahelian West Africa have yielded inconsistent and sometimes contradicting results. Large-scale, long-term investigations using remote sensing have shown greening and an increase in NPP in locations and periods where specific, small scale field studies have documented environmental degradation. Our purpose is to cast some light on the reasons for this phenomenon. This investigation focuses on the south of Ouagadougou, Burkina Faso, a city undergoing rapid growth and urban sprawl. We combine long-term MODIS (moderate resolution imaging spectroradiometer) image analysis of NDVI between 2002 and 2009, and by using high resolution satellite images for the same area and a field study, we compare trends of NDVI to trends of change in different categories of land cover for a selected number of MODIS pixels. Our results indicate a strong, positive association between changes in tree cover vegetation and trends of NDVI and moderate association between man-made constructions and trends of NDVI. The observed changes are discussed in relation to the unique processes of urban sprawl characterizing Ouagadougou and relative to their spatiotemporal scale. Remote Sensing 2013-03-19 5 3 Article 10.3390/rs5031465 1465 1483 2072-4292 2013-03-19 doi: 10.3390/rs5031465 Yonatan Kelder Thomas Nielsen Rasmus Fensholt http://www.mdpi.com/2072-4292/5/3/1439 Empirical band ratio algorithms for the estimation of colored dissolved organic matter (CDOM) and dissolved organic carbon (DOC) for Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and MERIS ocean color sensors were assessed and developed for the northern Gulf of Mexico. Match-ups between in situ measurements of CDOM absorption coefficients at 412 nm (aCDOM(412)) with that derived from SeaWiFS were examined using two previously reported reflectance band ratio algorithms. Results indicate better performance using the Rrs(510)/Rrs(555) (Bias = −0.045; RMSE = 0.23; SI = 0.49, and R2 = 0.66) than the Rrs(490)/Rrs(555) reflectance band ratio algorithm. Further, a comparison of aCDOM(412) retrievals using the Rrs(488)/Rrs(555) for MODIS and Rrs(510)/Rrs(560) for MERIS reflectance band ratios revealed better CDOM retrievals with MERIS data. Since DOC cannot be measured directly by remote sensors, CDOM as the colored component of DOC is utilized as a proxy to estimate DOC remotely. A seasonal relationship between CDOM and DOC was established for the summer and spring-winter with high correlation for both periods (R2~0.9). Seasonal band ratio empirical algorithms to estimate DOC were thus developed using the relationships between CDOM-Rrs and seasonal CDOM-DOC for SeaWiFS, MODIS and MERIS. Results of match-up comparisons revealed DOC estimates by both MODIS and MERIS to be relatively more accurate during summer time, while both of them underestimated DOC during spring-winter time. A better DOC estimate from MERIS in comparison to MODIS in spring-winter could be attributed to its similarity with the SeaWiFS band ratio CDOM algorithm. Remote Sensing 2013-03-19 5 3 Article 10.3390/rs5031439 1439 1464 2072-4292 2013-03-19 doi: 10.3390/rs5031439 Nazanin Tehrani Eurico D'Sa Christopher Osburn Thomas Bianchi Blake Schaeffer http://www.mdpi.com/2072-4292/5/3/1425 The rainfall and runoff relationship becomes an intriguing issue as urbanization continues to evolve worldwide. In this paper, we developed a simulation model based on the soil conservation service curve number (SCS-CN) method to analyze the rainfall-runoff relationship in Guangzhou, a rapid growing metropolitan area in southern China. The SCS-CN method was initially developed by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA), and is one of the most enduring methods for estimating direct runoff volume in ungauged catchments. In this model, the curve number (CN) is a key variable which is usually obtained by the look-up table of TR-55. Due to the limitations of TR-55 in characterizing complex urban environments and in classifying land use/cover types, the SCS-CN model cannot provide more detailed runoff information. Thus, this paper develops a method to calculate CN by using remote sensing variables, including vegetation, impervious surface, and soil (V-I-S). The specific objectives of this paper are: (1) To extract the V-I-S fraction images using Linear Spectral Mixture Analysis; (2) To obtain composite CN by incorporating vegetation types, soil types, and V-I-S fraction images; and (3) To simulate direct runoff under the scenarios with precipitation of 57mm (occurred once every five years by average) and 81mm (occurred once every ten years). Our experiment shows that the proposed method is easy to use and can derive composite CN effectively. Remote Sensing 2013-03-18 5 3 Article 10.3390/rs5031425 1425 1438 2072-4292 2013-03-18 doi: 10.3390/rs5031425 Fenglei Fan Yingbin Deng Xuefei Hu Qihao Weng http://www.mdpi.com/2072-4292/5/3/1405 Climate change has increased the occurrence of heavy storms that cause damage to forests. After a storm, it is necessary to obtain knowledge about the injured trees quickly in order to detect and aid in collecting the fallen trees and estimate the total damage. The objective in this study was to develop an automatic method for storm damage detection based on comparisons of digital surface models (DSMs), where the after-storm DSM was derived by automatic image matching using high-altitude photogrammetric imagery. This DSM was compared to a before-storm DSM, which was computed using national airborne laser scanning (ALS) data. The developed method was tested using imagery collected in extreme illumination conditions after winter storms on 8 January 2012 in Finland. The image matching yielded a high-quality surface model of the forest areas, which were mainly coniferous and mixed forests. The entire set of major damage forest test areas was correctly classified using the method. Our results showed that airborne, high-altitude photogrammetry is a promising tool for automating the detection of forest storm damage. With modern photogrammetric cameras, large areas can be collected efficiently, and the imagery also provides visual, stereoscopic support for various forest storm damage management tasks. Developing methods that work in different seasons are becoming more important, due to the increase in the number of natural disasters. Remote Sensing 2013-03-18 5 3 Article 10.3390/rs5031405 1405 1424 2072-4292 2013-03-18 doi: 10.3390/rs5031405 Eija Honkavaara Paula Litkey Kimmo Nurminen http://www.mdpi.com/2072-4292/5/3/1389 Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture RADAR-SAR) opened new opportunities to develop agro-hydrological applications. Indeed, it represents a valuable source of data for operational use, due to the high spatial and temporal resolutions. Although X-band is not the most suitable to model agricultural and hydrological processes, an assessment of vegetation development can be achieved combing optical vegetation indices (VIs) and SAR backscattering data. In this paper, a correlation analysis has been performed between the crossed horizontal-vertical (HV) backscattering (s°HV) and optical VIs (VIopt) on several plots. The correlation analysis was based on incidence angle, spatial resolution and polarization mode. Results have shown that temporal changes of s°HV (Δs°HV) acquired with high angles (off nadir angle; θ > 40°) best correlates with variations of VIopt (ΔVI). The correlation between ΔVI and Δs°HV has been shown to be temporally robust. Based on this experimental evidence, a model to infer a VI from s° (VISAR) at the time, ti + 1, once known, the VIopt at a reference time, ti, and Δs°HV between times, ti + 1 and ti, was implemented and verified. This approach has led to the development and validation of an algorithm for coupling a VIopt derived from DEIMOS-1 images and s°HV. The study was carried out over the Sele plain (Campania, Italy), which is mainly characterized by herbaceous crops. In situ measurements included leaf area index (LAI), which were collected weekly between August and September 2011 in 25 sites, simultaneously to COSMO-SkyMed (CSK) and DEIMOS-1 imaging. Results confirm that VISAR obtained using the combined model is able to increase the feasibility of operational satellite-based products for supporting agricultural practices. This study is carried out in the framework of the COSMOLAND project (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI). Remote Sensing 2013-03-14 5 3 Article 10.3390/rs5031389 1389 1404 2072-4292 2013-03-14 doi: 10.3390/rs5031389 Fulvio Capodici Guido D'Urso Antonino Maltese http://www.mdpi.com/2072-4292/5/3/1355 Radiative transfer models predicting the bidirectional reflectance factor (BRF) of leaf canopies are powerful tools that relate biophysical parameters such as leaf area index (LAI), fractional vegetation cover fV and the fraction of photosynthetically active radiation absorbed by the green parts of the vegetation canopy (fAPAR) to remotely sensed reflectance data. One of the most successful approaches to biophysical parameter estimation is the inversion of detailed radiative transfer models through the construction of Look-Up Tables (LUTs). The solution of the inverse problem requires additional information on canopy structure, soil background and leaf properties, and the relationships between these parameters and the measured reflectance data are often nonlinear. The commonly used approach for optimization of a solution is based on minimization of the least squares estimate between model and observations (referred to as cost function or distance; here we will also use the terms “statistical distance” or “divergence” or “metric”, which are common in the statistical literature). This paper investigates how least-squares minimization and alternative distances affect the solution to the inverse problem. The paper provides a comprehensive list of different cost functions from the statistical literature, which can be divided into three major classes: information measures, M-estimates and minimum contrast methods. We found that, for the conditions investigated, Least Square Estimation (LSE) is not an optimal statistical distance for the estimation of biophysical parameters. Our results indicate that other statistical distances, such as the two power measures, Hellinger, Pearson chi-squared measure, Arimoto and Koenker–Basset distances result in better estimates of biophysical parameters than LSE; in some cases the parameter estimation was improved by 15%. Remote Sensing 2013-03-14 5 3 Article 10.3390/rs5031355 1355 1388 2072-4292 2013-03-14 doi: 10.3390/rs5031355 Ganna Leonenko Sietse Los Peter North http://www.mdpi.com/2072-4292/5/3/1335 For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which reference information was available, the root mean squared error (RMSE) of winter crop acreage at sub-pixel level was 10%. When combined with current and future sensors, such as MODIS and Sentinel-3, the unmixing of AVHRR data can help in the building of an extended time series of crop distributions and cropping patterns dating back to the 80s. Remote Sensing 2013-03-14 5 3 Article 10.3390/rs5031335 1335 1354 2072-4292 2013-03-14 doi: 10.3390/rs5031335 Clement Atzberger Felix Rembold http://www.mdpi.com/2072-4292/5/3/1311 In the face of increasing climate-related impacts on coral reefs, the integration of ecosystem resilience into marine conservation planning has become a priority. One strategy, including resilient areas in marine protected area (MPA) networks, relies on information on the spatial distribution of resilience. We assess the ability to model and map six indicators of coral reef resilience—stress-tolerant coral taxa, coral generic diversity, fish herbivore biomass, fish herbivore functional group richness, density of juvenile corals and the cover of live coral and crustose coralline algae. We use high spatial resolution satellite data to derive environmental predictors and use these in random forest models, with field observations, to predict resilience indicator values at unsampled locations. Predictions are compared with those obtained from universal kriging and from a baseline model. Prediction errors are estimated using cross-validation, and the ability to map each resilience indicator is quantified as the percentage reduction in prediction error compared to the baseline model. Results are most promising (percentage reduction = 18.3%) for mapping the cover of live coral and crustose coralline algae and least promising (percentage reduction = 0%) for coral diversity. Our study has demonstrated one approach to map indicators of coral reef resilience. In the context of MPA network planning, the potential to consider reef resilience in addition to habitat and feature representation in decision-support software now exists, allowing planners to integrate aspects of reef resilience in MPA network development. Remote Sensing 2013-03-14 5 3 Article 10.3390/rs5031311 1311 1334 2072-4292 2013-03-14 doi: 10.3390/rs5031311 Anders Knudby Stacy Jupiter Chris Roelfsema Mitchell Lyons Stuart Phinn http://www.mdpi.com/2072-4292/5/3/1292 High-altitude platforms (HAP) or near-space vehicle offers several advantages over current low earth orbit (LEO) satellite and airplane, because HAP is not constrained by orbital mechanics and fuel consumption. These advantages provide potential for some specific remote sensing applications that require persistent monitoring or fast-revisiting frequency. This paper investigates the azimuth-variant signal processing in HAP-borne bistatic synthetic aperture radar (BiSAR) with spaceborne or airborne transmitter for high-resolution remote sensing. The system configuration, azimuth-variant Doppler characteristics and two-dimensional echo spectrum are analyzed. Conceptual system simulation results are also provided. Since the azimuth-variant BiSAR geometry brings a challenge for developing high precision data processing algorithms, we propose an image formation algorithm using equivalent velocity and nonlinear chirp scaling (NCS) to address the azimuth-variant signal processing problem. The proposed algorithm is verified by numerical simulation results. Remote Sensing 2013-03-14 5 3 Article 10.3390/rs5031292 1292 1310 2072-4292 2013-03-14 doi: 10.3390/rs5031292 Wen-Qin Wang Huaizong Shao http://www.mdpi.com/2072-4292/5/3/1274 This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R2) between measured and predicted LAI, as well as corresponding confidence intervals. The most suitable approach, which at the same time had the minimum requirements for fieldwork, resulted in a RMSE of 0.407 and R2 of 0.88 for Italy and a RMSE of 0.86 and R2 of 0.64 for the Austrian test site. Considering this procedure, we also evaluated the transferability of the local CLAIR model parameters between the two test sites observing no significant decrease in estimation accuracies. Additionally, we investigated two other statistical methods to estimate LAI based on: (a) Support Vector Machine (SVM) and (b) Random Forest (RF) regressions. Though the accuracy was comparable to the CLAIR model for each test site, we observed severe limitations in the transferability of these statistical methods between test sites with an increase in RMSE up to 24.5% for RF and 38.9% for SVM. Remote Sensing 2013-03-12 5 3 Article 10.3390/rs5031274 1274 1291 2072-4292 2013-03-12 doi: 10.3390/rs5031274 Francesco Vuolo Nikolaus Neugebauer Salvatore Bolognesi Clement Atzberger Guido D'Urso http://www.mdpi.com/2072-4292/5/3/1258 Projected changes in the frequency and severity of droughts as a result of increase in greenhouse gases have a significant impact on the role of vegetation in regulating the global carbon cycle. Drought effect on vegetation Gross Primary Production (GPP) is usually modeled as a function of Vapor Pressure Deficit (VPD) and/or soil moisture. Climate projections suggest a strong likelihood of increasing trend in VPD, while regional changes in precipitation are less certain. This difference in projections between VPD and precipitation can cause considerable discrepancies in the predictions of vegetation behavior depending on how ecosystem models represent the drought effect. In this study, we scrutinized the model responses to drought using the 30-year record of Global Inventory Modeling and Mapping Studies (GIMMS) 3g Normalized Difference Vegetation Index (NDVI) dataset. A diagnostic ecosystem model, Terrestrial Observation and Prediction System (TOPS), was used to estimate global GPP from 1982 to 2009 under nine different experimental simulations. The control run of global GPP increased until 2000, but stayed constant after 2000. Among the simulations with single climate constraint (temperature, VPD, rainfall and solar radiation), only the VPD-driven simulation showed a decrease in 2000s, while the other scenarios simulated an increase in GPP. The diverging responses in 2000s can be attributed to the difference in the representation of the impact of water stress on vegetation in models, i.e., using VPD and/or precipitation. Spatial map of trend in simulated GPP using GIMMS 3g data is consistent with the GPP driven by soil moisture than the GPP driven by VPD, confirming the need for a soil moisture constraint in modeling global GPP. Remote Sensing 2013-03-12 5 3 Article 10.3390/rs5031258 1258 1273 2072-4292 2013-03-12 doi: 10.3390/rs5031258 Hirofumi Hashimoto Weile Wang Cristina Milesi Jun Xiong Sangram Ganguly Zaichun Zhu Ramakrishna Nemani http://www.mdpi.com/2072-4292/5/3/1235 The Leaf Area Index (LAI) is a key variable in many land surface and climate modeling studies. To date, a number of LAI datasets have been developed based on time series of medium resolution optical remote sensing observations. Global validation exercises show the high value of these datasets, but at the same time they point out shortcomings, particularly in the presence of persistent cloud coverage and dense vegetation. For regional modeling studies, the choice of an ideal LAI input dataset is not straightforward as global validation, and intercomparison studies do not necessarily allow conclusions on data quality at regional scale. This paper provides a comprehensive relative intercomparison of four freely available LAI products for a wide gradient of ecosystems in Africa. The region of investigation, West Africa, comprises typical African sub-humid to arid landscapes. The selected LAI time series are the Satellite Pour l’Observation de la Terre-VEGETATION (SPOT-VGT)-based Carbon Cycle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) LAI, the SPOT-VGT-based Bio-geophysical Parameters (BioPar) LAI product GEOV1, the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD15A2, and the Meteosat-SEVIRI-based Satellite Application Facility on Land Surface Analysis (LSA-SAF) LAI. The comparative analyses focus on data gap occurrence, on the consistency of temporal LAI profiles, on their ability to adequately reproduce the phenological cycle and on the plausibility of LAI magnitudes for major land cover types in West Africa. A detailed quantitative validation of the LAI datasets, however, was not possible due to insufficient ground LAI measurements in the study region. Remote Sensing 2013-03-11 5 3 Article 10.3390/rs5031235 1235 1257 2072-4292 2013-03-11 doi: 10.3390/rs5031235 Ursula Gessner Markus Niklaus Claudia Kuenzer Stefan Dech http://www.mdpi.com/2072-4292/5/3/1220 The mapping of changes in the distribution of insect-caused forest damage remains an important forest monitoring application and challenge. Efficient and accurate methods are required for mapping and monitoring changes in insect defoliation to inform forest management and reporting activities. In this research, we develop and evaluate a LiDAR-driven (Light Detection And Ranging) approach for mapping defoliation caused by the Common pine sawfly (Diprion pini L.). Our method requires plot-level training data and airborne scanning LiDAR data. The approach is predicated on a forest canopy mask created by detecting forest canopy cover using LiDAR. The LiDAR returns that are reflected from the canopy (that is, returns > half of maximum plot tree height) are used in the prediction of the defoliation. Predictions of defoliation are made at plot-level, which enables a direct integration of the method to operational forest management planning while also providing additional value-added from inventory-focused LiDAR datasets. In addition to the method development, we evaluated the prediction accuracy and investigated the required pulse density for operational LiDAR-based mapping of defoliation. Our method proved to be suitable for the mapping of defoliated stands, resulting in an overall mapping accuracy of 84.3% and a Cohen’s kappa coefficient of 0.68. Remote Sensing 2013-03-07 5 3 Article 10.3390/rs5031220 1220 1234 2072-4292 2013-03-07 doi: 10.3390/rs5031220 Mikko Vastaranta Tuula Kantola Päivi Lyytikäinen-Saarenmaa Markus Holopainen Ville Kankare Michael Wulder Juha Hyyppä Hannu Hyyppä http://www.mdpi.com/2072-4292/5/3/1204 We present the results of a project to develop a proof of concept for a novel hyperspectral imager based on the use of advanced micro-optics technology. The technology gives considerably more spatial elements than a classic pushbroom which translates into far more light being integrated per unit of time. This permits us to observe at higher spatial and/or spectral resolution, darker targets and under lower illumination, as in the early morning. Observations of faint glow at night should also be possible but need further studies. A full instrument for laboratory demonstration and field tests has now been built and tested. It has about 10,000 spatial elements and spectra 150 pixel long. It is made of a set of cylindrical fore-optics followed by a new innovative optical system called a microslice Integral Field Unit (IFU) which is itself followed by a standard spectrograph. The fore-optics plus microslice IFU split the field into a large number of small slit-like images that are dispersed in the spectrograph. Our goal is to build instruments with at least hundreds of thousands of spatial elements. Remote Sensing 2013-03-06 5 3 Article 10.3390/rs5031204 1204 1219 2072-4292 2013-03-06 doi: 10.3390/rs5031204 Robert Content Simon Blake Colin Dunlop David Nandi Ray Sharples Gordon Talbot Tom Shanks Danny Donoghue Nikolaos Galiatsatos Peter Luke http://www.mdpi.com/2072-4292/5/3/1177 Increasing water use and droughts, along with climate variability and land use change, have seriously altered vegetation growth patterns and ecosystem response in several regions alongside the Andes Mountains. Thirty years of the new generation biweekly normalized difference vegetation index (NDVI3g) time series data show significant land cover specific trends and variability in annual productivity and land surface phenological response. Productivity is represented by the growing season mean NDVI values (July to June). Arid and semi-arid and sub humid vegetation types (Atacama desert, Chaco and Patagonia) across Argentina, northern Chile, northwest Uruguay and southeast Bolivia show negative trends in productivity, while some temperate forest and agricultural areas in Chile and sub humid and humid areas in Brazil, Bolivia and Peru show positive trends in productivity. The start (SOS) and length (LOS) of the growing season results show large variability and regional hot spots where later SOS often coincides with reduced productivity. A longer growing season is generally found for some locations in the south of Chile (sub-antarctic forest) and Argentina (Patagonia steppe), while central Argentina (Pampa-mixed grasslands and agriculture) has a shorter LOS. Some of the areas have significant shifts in SOS and LOS of one to several months. The seasonal Multivariate ENSO Indicator (MEI) and the Antarctic Oscillation (AAO) index have a significant impact on vegetation productivity and phenology in southeastern and northeastern Argentina (Patagonia and Pampa), central and southern Chile (mixed shrubland, temperate and sub-antarctic forest), and Paraguay (Chaco). Remote Sensing 2013-03-06 5 3 Article 10.3390/rs5031177 1177 1203 2072-4292 2013-03-06 doi: 10.3390/rs5031177 Willem van Leeuwen Kyle Hartfield Marcelo Miranda Francisco Meza http://www.mdpi.com/2072-4292/5/3/1152 Coal fires have been found to be a serious problem worldwide in coal mining reserves. Coal fires burn valuable coal reserves and lead to severe environmental degradation of the region. Moreover, coal fires can result in massive surface displacements due to the reduction in volume of the burning coal and can cause thermal effects in the adjacent rock mass particularly cracks and fissures. The Wuda coalfield in Northern China is known for being an exclusive storehouse of prime coking coal as well as for being the site of occurrence of the maximum number of known coal fires among all the coalfields in China and worldwide, and is chosen as our study area. In this study, we have investigated the capabilities and limitations of ALOS PALSAR data for monitoring the land subsidence that accompanies coal fires by means of satellite differential interferometric synthetic aperture radar (DInSAR) observations. An approach to map the large and highly non-linear subsidence based on a small number of SAR images was applied to the Wuda coalfield to reveal the spatial and temporal signals of land subsidence in areas affected by coal fires. The DInSAR results agree well with coal fire data obtained from field investigations and thermal anomaly information, which demonstrates that the capability of ALOS PALSAR data and the proposed approach have remarkable potential to detect this land subsidence of interest. In addition, our results also provide a spatial extent and temporal evolution of the land subsidence behavior accompanying the coal fires, which indicated that several coal fire zones suffer accelerated ongoing land subsidence, whilst other coal fire zones are newly subsiding areas arising from coal fires in the period of development. Remote Sensing 2013-03-04 5 3 Article 10.3390/rs5031152 1152 1176 2072-4292 2013-03-04 doi: 10.3390/rs5031152 Lifan Zhou Dengrong Zhang Jie Wang Zhaoquan Huang Delu Pan http://www.mdpi.com/2072-4292/5/3/1134 Detecting land use or land cover changes is a challenging problem in analyzing images. Change-detection plays a fundamental role in most of land use or cover monitoring systems using remote-sensing techniques. The reliability of individual automatic change-detection algorithms is currently below operating requirements when considering the intrinsic uncertainty of a change-detection algorithm and the complexity of detecting changes in remote-sensing images. In particular, most of these algorithms are only suited for a specific image data source, study area and research purpose. Only a number of comprehensive change-detection methods that consider the reliability of the algorithm in different implementation situations have been reported. This study attempts to explore the advantages of combining several typical change-detection algorithms. This combination is specifically designed for a highly reliable change-detection task. Specifically, a fusion approach based on reliability is proposed for an exclusive land use or land cover change-detection. First, the reliability of each candidate algorithm is evaluated. Then, a fuzzy comprehensive evaluation is used to generate a reliable change-detection approach. This evaluation is a transformation between a one-way evaluation matrix and a weight vector computed using the reliability of each candidate algorithm. Experimental results reveal that the advantages of combining these distinct change-detection techniques are evident. Remote Sensing 2013-03-01 5 3 Article 10.3390/rs5031134 1134 1151 2072-4292 2013-03-01 doi: 10.3390/rs5031134 Penglin Zhang Wenzhong Shi Man Wong Jiangping Chen http://www.mdpi.com/2072-4292/5/3/1117 Vegetation belongs to the components of the Earth surface, which are most extensively studied using historic and present satellite records. Recently, these records exceeded a 30-year time span composed of preprocessed fortnightly observations (1981–2011). The existence of monotonic changes and trend shifts present in such records has previously been demonstrated. However, information on timing and type of such trend shifts was lacking at global scale. In this work, we detected major shifts in vegetation activity trends and their associated type (either interruptions or reversals) and timing. It appeared that the biospheric trend shifts have, over time, increased in frequency, confirming recent findings of increased turnover rates in vegetated areas. Signs of greening-to-browning reversals around the millennium transition were found in many regions (Patagonia, the Sahel, northern Kazakhstan, among others), as well as negative interruptions—“setbacks”—in greening trends (southern Africa, India, Asia Minor, among others). A minority (26%) of all significant trends appeared monotonic. Remote Sensing 2013-03-01 5 3 Article 10.3390/rs5031117 1117 1133 2072-4292 2013-03-01 doi: 10.3390/rs5031117 Rogier de Jong Jan Verbesselt Achim Zeileis Michael Schaepman http://www.mdpi.com/2072-4292/5/3/1091 A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing. Remote Sensing 2013-03-01 5 3 Article 10.3390/rs5031091 1091 1116 2072-4292 2013-03-01 doi: 10.3390/rs5031091 Grégory Duveiller Raúl López-Lozano Bettina Baruth http://www.mdpi.com/2072-4292/5/3/1066 The analysis of a series of five normalized difference vegetation index (NDVI) images produced information about a Labrador (Canada) portion of the tundra-taiga interface. The twenty-five year observation period ranges from 1983 to 2008. The series composed of Landsat, SPOT and ASTER images, provided insight into regional scale characteristics of the tundra-taiga interface that is usually monitored from coarse resolution images. The image set was analyzed by considering an ordinal classification of the NDVI to account for the cumulative effect of differences of near-infrared spectral resolutions, the temperature anomalies, and atmospheric conditions. An increasing trend of the median values in the low, intermediate and high NDVI classes is clearly marked while accounting for variations attributed to cross-sensor radiometry, phenology and atmospheric disturbances. An encroachment of the forest on the tundra for the whole study area was estimated at 0 to 60 m, depending on the period of observation, as calculated by the difference between the median retreat and advance of an estimated location of the tree line. In small sections, advances and retreats of up to 320 m are reported for the most recent four- and seven-year periods of observations. Remote Sensing 2013-03-01 5 3 Article 10.3390/rs5031066 1066 1090 2072-4292 2013-03-01 doi: 10.3390/rs5031066 Élizabeth Simms Heather Ward http://www.mdpi.com/2072-4292/5/3/1045 : The measurement of landslide superficial displacement often represents the most effective method for defining its behavior, allowing one to observe the relationship with triggering factors and to assess the effectiveness of the mitigation measures. Persistent Scatterer Interferometry (PSI) represents a powerful tool to measure landslide displacement, as it offers a synoptic view that can be repeated at different time intervals and at various scales. In many cases, PSI data are integrated with in situ monitoring instrumentation, since the joint use of satellite and ground-based data facilitates the geological interpretation of a landslide and allows a better understanding of landslide geometry and kinematics. In this work, PSI interferometry and conventional ground-based monitoring techniques have been used to characterize and to monitor the Santo Stefano d’Aveto landslide located in the Northern Apennines, Italy. This landslide can be defined as an earth rotational slide. PSI analysis has contributed to a more in-depth investigation of the phenomenon. In particular, PSI measurements have allowed better redefining of the boundaries of the landslide and the state of activity, while the time series analysis has permitted better understanding of the deformation pattern and its relation with the causes of the landslide itself. The integration of ground-based monitoring data and PSI data have provided sound results for landslide characterization. The punctual information deriving from inclinometers can help in defining the actual location of the sliding surface and the involved volumes, while the measuring of pore water pressure conditions or water table level can suggest a correlation between the deformation patterns and the triggering factors. Remote Sensing 2013-03-01 5 3 Article 10.3390/rs5031045 1045 1065 2072-4292 2013-03-01 doi: 10.3390/rs5031045 Veronica Tofani Federico Raspini Filippo Catani Nicola Casagli http://www.mdpi.com/2072-4292/5/3/1024 The objective of this study is to understand the effect of landslides on the drainage network within the area of interest. We thus test the potential of rivers to record the intensity of landslides that affected their courses. The study area is located within the Zagros orogenic belt along the border between Iraq and Iran. We identified 280 landslides through nine QuickBird scenes using visual photo-interpretation. The total landslide area of 40.05 km2 and their distribution follows a NW–SE trend due to the tectonic control of main thrust faults. We observe a strong control of the landslides on the river course. We quantify the relationship between riverbed displacement and mass wasting occurrences using landslide sizes versus river offset and hypsometric integrals. Many valleys and river channels are curved around the toe of landslides, thus producing an offset of the stream which increases with the landslide area. The river offsets were quantified using two geomorphic indices: the river with respect to the basin midline (Fb); and the offset from the main river direction (Fd). Hypsometry and stream offset seem to be correlated. In addition; the analysis of selected river courses may give some information on the sizes of the past landslide events and therefore contribute to the hazard assessment. Remote Sensing 2013-03-01 5 3 Article 10.3390/rs5031024 1024 1044 2072-4292 2013-03-01 doi: 10.3390/rs5031024 Arsalan Othman Richard Gloaguen http://www.mdpi.com/2072-4292/5/3/1001 Estimation of forest aboveground biomass (AGB) has become one of the main challenges of remote sensing science for global observation of carbon storage and changes in the past few decades. We examine the impact of plot size at different spatial resolutions, incidence angles, and polarizations on the forest biomass estimation using L-band polarimetric Synthetic Aperture Radar data acquired by NASA’s Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne system. Field inventory data from 32 1.0 ha plots (AGB < 200 Mg ha−1) in approximately even-aged forests in a temperate to boreal transitional region in the state of Maine were divided into subplots at four different spatial scales (0.0625 ha, 0.25 ha, 0.5 ha, and 1.0 ha) to quantify aboveground biomass variations. The results showed a large variability in aboveground biomass at smaller plot size (0.0625 ha). The variability decreased substantially at larger plot sizes (>0.5 ha), suggesting a stability of field-estimated biomass at scales of about 1.0 ha. UAVSAR backscatter was linked to the field estimates of aboveground biomass to develop parametric equations based on polarized returns to accurately map biomass over the entire radar image. Radar backscatter values at all three polarizations (HH, VV, HV) were positively correlated with field aboveground biomass at all four spatial scales, with the highest correlation at the 1.0 ha scale. Among polarizations, the cross-polarized HV had the highest sensitivity to field estimated aboveground biomass (R2 = 0.68). Algorithms were developed that combined three radar backscatter polarizations (HH, HV, and VV) to estimate aboveground biomass at the four spatial scales. The predicted aboveground biomass from these algorithms resulted in decreasing estimation error as the pixel size increased, with the best results at the 1 ha scale with an R2 of 0.67 (p < 0.0001), and an overall RMSE of 44 Mg·ha−1. For AGB < 150 Mg·ha−1, the error reduced to 23 Mg·ha−1 (±15%), suggesting an improved AGB prediction below the L-band sensitivity range to biomass. Results also showed larger bias in aboveground biomass estimation from radar at smaller scales that improved at larger spatial scales of 1.0 ha with underestimation of −3.62 Mg·ha−1 over the entire biomass range. Remote Sensing 2013-02-26 5 3 Article 10.3390/rs5031001 1001 1023 2072-4292 2013-02-26 doi: 10.3390/rs5031001 Chelsea Robinson Sassan Saatchi Maxim Neumann Thomas Gillespie http://www.mdpi.com/2072-4292/5/2/982 The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts of Africa—or coarse-resolution rainfall estimates derived from weather satellites. In this study, we analyzed LGP and its variability based on the 1981–2011 GIMMS NDVI3g dataset. We applied a variable threshold method in combination with a searching algorithm to determine start- and end-of-season. We obtained reliable LGP estimates for arid, semi-arid and sub-humid climates that are consistent in space and time. This approach effectively mapped bimodality for clearly separated wet seasons in the Horn of Africa. Due to cloud contamination, the identified bimodality along the Guinea coast was judged to be less certain. High LGP variability is dominant in arid and semi-arid areas, and is indicative of crop failure risk. Significant negative trends in LGP were found for the northern part of the Sahel, for parts of Tanzania and northern Mozambique, and for the short rains of eastern Kenya. Positive trends occurred across western Africa, in southern Africa, and in eastern Kenya for the long rains. Our LGP analysis provides useful information for the mapping of farming systems, and to study the effects of climate variability and other drivers of change on vegetation and crop suitability. Remote Sensing 2013-02-22 5 2 Article 10.3390/rs5020982 982 1000 2072-4292 2013-02-22 doi: 10.3390/rs5020982 Anton Vrieling Jan de Leeuw Mohammed Said http://www.mdpi.com/2072-4292/5/2/949 Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of Agriculture”. To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations. Remote Sensing 2013-02-22 5 2 Review 10.3390/rs5020949 949 981 2072-4292 2013-02-22 doi: 10.3390/rs5020949 Clement Atzberger http://www.mdpi.com/2072-4292/5/2/927 Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website. Remote Sensing 2013-02-22 5 2 Article 10.3390/rs5020927 927 948 2072-4292 2013-02-22 doi: 10.3390/rs5020927 Zaichun Zhu Jian Bi Yaozhong Pan Sangram Ganguly Alessandro Anav Liang Xu Arindam Samanta Shilong Piao Ramakrishna Nemani Ranga Myneni http://www.mdpi.com/2072-4292/5/2/909 Winter annual plants in southwestern North America influence fire regimes, provide forage, and help prevent erosion. Exotic annuals may also threaten native species. Monitoring winter annuals is difficult because of their ephemeral nature, making the development of a satellite monitoring tool valuable. We mapped winter annual aboveground biomass in the Desert Southwest from satellite observations, evaluating 18 algorithms using time-series vegetation indices (VI). Field-based biomass estimates were used to calibrate and evaluate each algorithm. Winter annual biomass was best estimated by calculating a base VI across the period of record and subtracting it from the peak VI for each winter season (R2 = 0.92). The normalized difference vegetation index (NDVI) derived from 8-day reflectance data provided the best estimate of winter annual biomass. It is important to account for the timing of peak vegetation when relating field-based estimates to satellite VI data, since post-peak field estimates may indicate senescent biomass which is inaccurately represented by VI-based estimates. Images generated from the best-performing algorithm show both spatial and temporal variation in winter annual biomass. Efforts to manage this variable resource would be enhanced by a tool that allows the monitoring of changes in winter annual resources over time. Remote Sensing 2013-02-22 5 2 Article 10.3390/rs5020909 909 926 2072-4292 2013-02-22 doi: 10.3390/rs5020909 Grant Casady Willem van Leeuwen Bradley Reed http://www.mdpi.com/2072-4292/5/2/891 The use of spectral response curves for estimating nitrogen (N) leaf concentrations generally has been found to be a challenging task for a variety of plant species. In this investigation, leaf N concentration and corresponding laboratory hyperspectral data were examined for two species of mangrove (Avicennia germinans, Rhizophora mangle) representing a variety of conditions (healthy, poor condition, dwarf) of a degraded mangrove forest located in the Mexican Pacific. This is the first time leaf nitrogen content has been examined using close range hyperspectral remote sensing of a degraded mangrove forest. Simple comparisons between individual wavebands and N concentrations were examined, as well as two models employed to predict N concentrations based on multiple wavebands. For one model, an Artificial Neural Network (ANN) was developed based on known N absorption bands. For comparative purposes, a second model, based on the well-known Stepwise Multiple Linear Regression (SMLR) approach, was employed using the entire dataset. For both models, the input data included continuum removed reflectance, band depth at the centre of the absorption feature (BNC), and log (1/BNC). Weak to moderate correlations were found between N concentration and single band spectral responses. The results also indicate that ANNs were more predictive for N concentration than was SMLR, and had consistently higher r2 values. The highest r2 value (0.91) was observed in the prediction of black mangrove (A. germinans) leaf N concentration using the BNC transformation. It is thus suggested that artificial neural networks could be used in a complementary manner with other techniques to assess mangrove health, thereby improving environmental monitoring in coastal wetlands, which is of prime importance to local communities. In addition, it is recommended that the BNC transformation be used on the input for such N concentration prediction models. Remote Sensing 2013-02-22 5 2 Article 10.3390/rs5020891 891 908 2072-4292 2013-02-22 doi: 10.3390/rs5020891 Chunhua Zhang John Kovacs Mark Wachowiak Francisco Flores-Verdugo http://www.mdpi.com/2072-4292/5/2/864 This paper presents a theoretical study of microwave remote sensing of vegetated surfaces. The purpose of this study is to find out if satellite bistatic radar systems can provide a performance, in terms of sensitivity to vegetation geophysical parameters, equal to or greater than the performance of monostatic systems. Up to now, no suitable bistatic data collected over land surfaces are available from satellite, so that the electromagnetic model developed at Tor Vergata University has been used to perform simulations of the scattering coefficient of corn, over a wide range of observation angles at L- and C-band. According to the electromagnetic model, the most promising configuration is the one which measures the VV or HH bistatic scattering coefficient on the plane that lies at the azimuth angle orthogonal with respect to the incidence plane. At this scattering angle, the soil contribution is minimized, and the effects of vegetation growth are highlighted. Remote Sensing 2013-02-20 5 2 Article 10.3390/rs5020864 864 890 2072-4292 2013-02-20 doi: 10.3390/rs5020864 Leila Guerriero Nazzareno Pierdicca Luca Pulvirenti Paolo Ferrazzoli http://www.mdpi.com/2072-4292/5/2/862 Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for 2013. Nominations were selected by the Editor-in-Chief and selected editorial board members from among all the papers published in 2009. Reviews and research papers were evaluated separately. Remote Sensing 2013-02-20 5 2 Editorial 10.3390/rs5020862 862 863 2072-4292 2013-02-20 doi: 10.3390/rs5020862 Prasad Thenkabail http://www.mdpi.com/2072-4292/5/2/845 Using estimated leaf unfolding data and two types of Normalized Difference Vegetation Index (NDVI and NDVI3g) data generated from the Advanced Very High Resolution Radiometer (AVHRR) in the deciduous broadleaf forest of northern China during 1986 to 2006, we analyzed spatial, temporal and spatiotemporal relationships and differences between ground-based growing season beginning (BGS) and NDVI (NDVI3g)-retrieved start of season (SOS and SOS3g), and compared effectiveness of NDVI and NDVI3g in monitoring BGS. Results show that the spatial series of SOS (SOS3g) correlates positively with the spatial series of BGS at all pixels in each year (P < 0.001). Meanwhile, the time series of SOS (SOS3g) correlates positively with the time series of BGS at more than 65% of all pixels during the study period (P < 0.05). Furthermore, when pooling SOS (SOS3g) time series and BGS time series from all pixels, a significant positive correlation (P < 0.001) was also detectable between the spatiotemporal series of SOS (SOS3g) and BGS. In addition, the spatial, temporal and spatiotemporal differences between SOS (SOS3g) and BGS are at acceptable levels overall. Generally speaking, SOS3g is more consistent and accurate than SOS in capturing BGS, which suggests that NDVI3g data might be more sensitive than NDVI data in monitoring vegetation leaf unfolding. Remote Sensing 2013-02-18 5 2 Article 10.3390/rs5020845 845 861 2072-4292 2013-02-18 doi: 10.3390/rs5020845 Xiangzhong Luo Xiaoqiu Chen Lin Xu Ranga Myneni Zaichun Zhu http://www.mdpi.com/2072-4292/5/2/830 Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and mixed pixels to evaluate the effects of biome mixture on LAI estimation. Misclassification between crops and shrubs does not generally translate into large LAI errors (<0.37 or 27.0%), partly due to their relatively lower LAI values. Biome misclassification generally leads to an LAI overestimation for savanna, but an underestimation for forests. The largest errors caused by misclassification are also found for savanna (0.51), followed by evergreen needleleaf forests (0.44) and broadleaf forests (~0.31). Comparison with MODIS uncertainty indicators show that biome misclassification is a major factor contributing to LAI uncertainties for savanna, while for forests, the main uncertainties may be introduced by algorithm deficits, especially in summer. The LAI climatologies for pure pixels are recommended for land surface modeling studies. Future studies should focus on improving the biome classification for savanna systems and refinement of the retrieval algorithms for forest biomes. Remote Sensing 2013-02-15 5 2 Article 10.3390/rs5020830 830 844 2072-4292 2013-02-15 doi: 10.3390/rs5020830 Hongliang Fang Wenjuan Li Ranga Myneni http://www.mdpi.com/2072-4292/5/2/810 In this paper, we estimate the trends and variability in Advanced Very High Resolution Radiometer (AVHRR)-derived terrestrial net primary productivity (NPP) over India for the period 1982–2006. We find an increasing trend of 3.9% per decade (r = 0.78, R2 = 0.61) during the analysis period. A multivariate linear regression of NPP with temperature, precipitation, atmospheric CO2 concentration, soil water and surface solar radiation (r = 0.80, R2 = 0.65) indicates that the increasing trend is partly driven by increasing atmospheric CO2 concentration and the consequent CO2 fertilization of the ecosystems. However, human interventions may have also played a key role in the NPP increase: non-forest NPP growth is largely driven by increases in irrigated area and fertilizer use, while forest NPP is influenced by plantation and forest conservation programs. A similar multivariate regression of interannual NPP anomalies with temperature, precipitation, soil water, solar radiation and CO2 anomalies suggests that the interannual variability in NPP is primarily driven by precipitation and temperature variability. Mean seasonal NPP is largest during post-monsoon and lowest during the pre-monsoon period, thereby indicating the importance of soil moisture for vegetation productivity. Remote Sensing 2013-02-15 5 2 Article 10.3390/rs5020810 810 829 2072-4292 2013-02-15 doi: 10.3390/rs5020810 Govindasamy Bala Jaideep Joshi Rajiv Chaturvedi Hosahalli Gangamani Hirofumi Hashimoto Rama Nemani http://www.mdpi.com/2072-4292/5/2/808 This practical resource provides a current and comprehensive treatment of GPS/GNSS antennas, taking into account modernized systems and new and developing applications. The book presents a number of key applications, describing corresponding receiver architectures and antenna details. You find important discussions on antenna characteristics, including theory of operation, gain, bandwidth, polarization, phase center, mutual coupling effects, and integration with active components. Remote Sensing 2013-02-05 5 2 New Book Received 10.3390/rs5020808 808 809 2072-4292 2013-02-05 doi: 10.3390/rs5020808 Shu-Kun Lin http://www.mdpi.com/2072-4292/5/2/716 The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin. Remote Sensing 2013-02-05 5 2 Review 10.3390/rs5020716 716 807 2072-4292 2013-02-05 doi: 10.3390/rs5020716 Kazuo Ouchi http://www.mdpi.com/2072-4292/5/2/687 Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007–2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people’s rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR-WSM based flood mapping are discussed. The results contribute to a comprehensive understanding of Mekong Delta flood dynamics in an environment where the flow regime is influenced by the Mekong River, overland water-flow, anthropogenic floodwater control, as well as the tides. Remote Sensing 2013-02-05 5 2 Article 10.3390/rs5020687 687 715 2072-4292 2013-02-05 doi: 10.3390/rs5020687 Claudia Kuenzer Huadong Guo Juliane Huth Patrick Leinenkugel Xinwu Li Stefan Dech http://www.mdpi.com/2072-4292/5/2/664 The ‘rain use efficiency’ (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to non-precipitation related land degradation. Consequently, RUE may be regarded as means of normalizing ANPP for the impact of annual precipitation, and as an indicator of non-precipitation related land degradation. Large scale and long term identification and monitoring of land degradation in drylands, such as the Sahel, can only be achieved by use of Earth Observation (EO) data. This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) (National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g)) over the year (ΣNDVI), and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of Precipitation, CMAP) results in RUE-estimates which are highly correlated with precipitation, rendering RUE useless as a means of normalizing for the impact of annual precipitation on ANPP. By replacing ΣNDVI by a ‘small NDVI integral’, covering only the rainy season and counting only the increase of NDVI relative to some reference level, this problem is solved. Using this approach, RUE is calculated for the period 1982–2010. The result is that positive RUE-trends dominate in most of the Sahel, indicating that non-precipitation related land degradation is not a widespread phenomenon. Furthermore, it is argued that two preconditions need to be fulfilled in order to obtain meaningful results from the RUE temporal trend analysis: First, there must be a significant positive linear correlation between annual precipitation and the ANPP proxy applied. Second, there must be a near-zero correlation between RUE and annual precipitation. Thirty-seven percent of the pixels in Sahel satisfy these requirements and the paper points to a range of different reasons why this may be the case. Remote Sensing 2013-02-04 5 2 Article 10.3390/rs5020664 664 686 2072-4292 2013-02-04 doi: 10.3390/rs5020664 Rasmus Fensholt Kjeld Rasmussen Per Kaspersen Silvia Huber Stephanie Horion Else Swinnen http://www.mdpi.com/2072-4292/5/2/648 Assessment of forest degradation has been emphasized as an important issue for emission calculations, but remote sensing based detecting of forest degradation is still in an early phase of development. The use of optical imagery for degradation assessment in the tropics is limited due to frequent cloud cover. Recent studies based on radar data often focus on classification approaches of 2D backscatter. In this study, we describe a method to detect areas affected by forest degradation from digital surface models derived from COSMO-SkyMed X-band Spotlight InSAR-Stereo Data. Two test sites with recent logging activities were chosen in Cameroon and in the Republic of Congo. Using the full resolution COSMO-SkyMed digital surface model and a 90-m resolution Shuttle Radar Topography Mission model or a mean filtered digital surface model we calculate difference models to detect canopy disturbances. The extracted disturbance gaps are aggregated to potential degradation areas and then evaluated with respect to reference areas extracted from RapidEye and Quickbird optical imagery. Results show overall accuracies above 75% for assessing degradation areas with the presented methods. Remote Sensing 2013-02-04 5 2 Article 10.3390/rs5020648 648 663 2072-4292 2013-02-04 doi: 10.3390/rs5020648 Janik Deutscher Roland Perko Karlheinz Gutjahr Manuela Hirschmugl Mathias Schardt http://www.mdpi.com/2072-4292/5/2/631 The frequency diverse multiple-input-multiple-output (FD-MIMO) radar synthesizes a wideband waveform by transmitting and receiving multiple frequency signals simultaneously. For FD-MIMO radar imaging, conventional imaging methods based on Matched Filter (MF) cannot enjoy good imaging performance owing to the few and incomplete wavenumber-domain coverage. Higher resolution and better imaging performance can be obtained by exploiting the sparsity of the target. However, good sparse recovery performance is based on the assumption that the scatterers of the target are positioned at the pre-discretized grid locations; otherwise, the performance would significantly degrade. Here, we propose a novel approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the general off-grid FD-MIMO radar imaging. SACR-iMAP contains three loop stages: sparse recovery, off-grid errors calibration and parameter update. The convergence and the initialization of the method are also discussed. Numerical simulations are carried out to verify the effectiveness of the proposed method. Remote Sensing 2013-02-04 5 2 Article 10.3390/rs5020631 631 647 2072-4292 2013-02-04 doi: 10.3390/rs5020631 Xuezhi He Changchang Liu Bo Liu Dongjin Wang http://www.mdpi.com/2072-4292/5/2/612 Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery. Remote Sensing 2013-01-29 5 2 Article 10.3390/rs5020612 612 630 2072-4292 2013-01-29 doi: 10.3390/rs5020612 Mustafa Mirik R. James Ansley Karl Steddom David Jones Charles Rush Gerald Michels Norman Elliott http://www.mdpi.com/2072-4292/5/2/584 As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data. Remote Sensing 2013-01-28 5 2 Article 10.3390/rs5020584 584 611 2072-4292 2013-01-28 doi: 10.3390/rs5020584 Bin Wu Bailang Yu Wenhui Yue Song Shu Wenqi Tan Chunling Hu Yan Huang Jianping Wu Hongxing Liu http://www.mdpi.com/2072-4292/5/2/558 Due to the abandonment of former agricultural management practices such as mowing and grazing, an increasing amount of grassland is no longer being managed. This has resulted in increasing shrub encroachment, which poses a threat to a number of species. Monitoring is an important means of acquiring information about the condition of the grasslands. Though the use of traditional remote sensing is an effective means of mapping and monitoring land cover, the mapping of small shrubs and trees based only on spectral information is challenged by the fact that shrubs and trees often spectrally resemble grassland and thus cannot be safely distinguished and classified. With the aid of LiDAR-derived information, such as elevation, the classification of spectrally similar objects can be improved. In this study, we applied high point density LiDAR data and colour-infrared orthoimages for the classification of shrubs and trees in a study area in Denmark. The classification result was compared to a classification based only on colour-infrared orthoimages. The overall accuracy increased significantly with the use of LiDAR and, for shrubs and trees specifically, producer’s accuracy increased from 81.2% to 93.7%, and user’s accuracy from 52.9% to 89.7%. Object-based image analysis was applied in combination with a CART classifier. The potential of using the applied approach for mapping and monitoring of large areas is discussed. Remote Sensing 2013-01-28 5 2 Article 10.3390/rs5020558 558 583 2072-4292 2013-01-28 doi: 10.3390/rs5020558 Thomas Hellesen Leena Matikainen http://www.mdpi.com/2072-4292/5/2/539 Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose, given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Performances in estimating the temporal and spatial variability of yield, and implications of data scarcity in both dimensions are investigated. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument. Best performances, up to 0.8 of R2, are achieved using the most physiologically sound remote sensing indicator, in conjunction with statistical specifications allowing for parsimonious spatial adjustment of the parameters. Remote Sensing 2013-01-28 5 2 Article 10.3390/rs5020539 539 557 2072-4292 2013-01-28 doi: 10.3390/rs5020539 Michele Meroni Eduardo Marinho Nabil Sghaier Michel Verstrate Olivier Leo http://www.mdpi.com/2072-4292/5/2/521 We describe a prototype compact mobile laser scanning system that may be operated from a backpack or unmanned aerial vehicle. The system is small, self-contained, relatively inexpensive, and easy to deploy. A description of system components is presented, along with the initial calibration of the multi-sensor platform. The first field tests of the system, both in backpack mode and mounted on a helium balloon for real-world applications are presented. For both field tests, the acquired kinematic LiDAR data are compared with highly accurate static terrestrial laser scanning point clouds. These initial results show that the vertical accuracy of the point cloud for the prototype system is approximately 4 cm (1σ) in balloon mode, and 3 cm (1σ) in backpack mode while horizontal accuracy was approximately 17 cm (1σ) for the balloon tests. Results from selected study areas on the Sacramento River Delta and San Andreas Fault in California demonstrate system performance, deployment agility and flexibility, and potential for operational production of high density and highly accurate point cloud data. Cost and production rate trade-offs place this system in the niche between existing airborne and tripod mounted LiDAR systems. Remote Sensing 2013-01-25 5 2 Article 10.3390/rs5020521 521 538 2072-4292 2013-01-25 doi: 10.3390/rs5020521 Craig Glennie Benjamin Brooks Todd Ericksen Darren Hauser Kenneth Hudnut James Foster Jon Avery http://www.mdpi.com/2072-4292/5/2/491 This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of the method is a point cloud of a single tree scanned from multiple positions. The surface of the visible parts of the tree is robustly reconstructed by making a flexible cylinder model of the tree. The thorough quantitative model records also the topological branching structure. In this paper, every major step of the whole model reconstruction process, from the input to the finished model, is presented in detail. The model is constructed by a local approach in which the point cloud is covered with small sets corresponding to connected surface patches in the tree surface. The neighbor-relations and geometrical properties of these cover sets are used to reconstruct the details of the tree and, step by step, the whole tree. The point cloud and the sets are segmented into branches, after which the branches are modeled as collections of cylinders. From the model, the branching structure and size properties, such as volume and branch size distributions, for the whole tree or some of its parts, can be approximated. The approach is validated using both measured and modeled terrestrial laser scanner data from real trees and detailed 3D models. The results show that the method allows an easy extraction of various tree attributes from terrestrial or mobile laser scanning point clouds. Remote Sensing 2013-01-25 5 2 Article 10.3390/rs5020491 491 520 2072-4292 2013-01-25 doi: 10.3390/rs5020491 Pasi Raumonen Mikko Kaasalainen Markku Åkerblom Sanna Kaasalainen Harri Kaartinen Mikko Vastaranta Markus Holopainen Mathias Disney Philip Lewis http://www.mdpi.com/2072-4292/5/2/473 Mesoscale regional climate models (RCMs), the primary tool for climate predictions, have recently increased in sophistication and are being run at increasingly higher resolutions to be also used in climate impact studies on ecosystems, particularly in agricultural crops. As satellite remote sensing observations of the earth terrestrial surface become available for assimilation in RCMs, it is possible to incorporate complex land surface processes, such as dynamics of state variables for hydrologic, agricultural and ecologic systems at the smaller scales. This study focuses on parameterization of vegetation characteristics specifically designed for high resolution RCM applications using various remote sensing products, such as Advanced Very High Resolution Radiometer (AVHRR), Système Pour l’Observation de la Terre-VEGETATION (SPOT-VGT) and Moderate Resolution Imaging Spectroradiometer (MODIS). The primary vegetative parameters, such as land surface characteristics (LCC), fractional vegetation cover (FVC), leaf area index (LAI) and surface albedo localization factors (SALF), are currently presented over the Nakdong River Watershed domain, Korea, based on 1-km remote sensing satellite data by using the Geographic Information System (GIS) software application tools. For future high resolution RCM modeling efforts on climate-crop interactions, this study has constructed the deriving parameters, such as FVC and SALF, following the existing methods and proposed the new interpolation methods to fill missing data with combining the regression equation and the time series trend function for time-variant parameters, such as LAI and NDVI data at 1-km scale. Remote Sensing 2013-01-24 5 2 Article 10.3390/rs5020473 473 490 2072-4292 2013-01-24 doi: 10.3390/rs5020473 Hyun Il Choi http://www.mdpi.com/2072-4292/5/2/454 In the Amazon, the development and paving of roads connects regions and peoples, and over time can form dense and recursive networks, which often serve as nodes for continued development. These developed areas exhibit robust fractal structures that could potentially link their spatial patterns with deforestation processes. Fractal dimension is commonly used to describe the growth trajectory of such fractal structures and their spatial-filling capacities. Focusing on a tri-national frontier region, we applied a box-counting method to calculate the fractal dimension of the developed areas in the Peruvian state of Madre de Dios, Acre in Brazil, and the department of Pando in Bolivia, from 1986 through 2010. The results indicate that development has expanded in all three regions with declining forest cover over time, but with different patterns and rates in each country. Such differences were summarized within a proposed framework to indicate deforestation progress/level, which can be used to understand and regulate deforestation and its evolution in time. In addition, the role and influence of scale was also assessed, and we found local fractal dimensions are not invariant at different spatial scales and thus concluded such scale-dependent features of fragmentation patterns are here mainly shaped by the road paving. Remote Sensing 2013-01-24 5 2 Article 10.3390/rs5020454 454 472 2072-4292 2013-01-24 doi: 10.3390/rs5020454 Jing Sun Jane Southworth