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Remote Sens., Volume 5, Issue 3 (March 2013), Pages 1001-1497

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Research

Open AccessArticle Impacts of Spatial Variability on Aboveground Biomass Estimation from L-Band Radar in a Temperate Forest
Remote Sens. 2013, 5(3), 1001-1023; doi:10.3390/rs5031001
Received: 20 December 2012 / Revised: 17 February 2013 / Accepted: 18 February 2013 / Published: 26 February 2013
Cited by 22 | PDF Full-text (2988 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Forestry)
Open AccessArticle River Courses Affected by Landslides and Implications for Hazard Assessment: A High Resolution Remote Sensing Case Study in NE Iraq–W Iran
Remote Sens. 2013, 5(3), 1024-1044; doi:10.3390/rs5031024
Received: 8 January 2013 / Revised: 14 February 2013 / Accepted: 26 February 2013 / Published: 1 March 2013
Cited by 9 | PDF Full-text (2477 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle Persistent Scatterer Interferometry (PSI) Technique for Landslide Characterization and Monitoring
Remote Sens. 2013, 5(3), 1045-1065; doi:10.3390/rs5031045
Received: 5 January 2013 / Revised: 26 February 2013 / Accepted: 26 February 2013 / Published: 1 March 2013
Cited by 50 | PDF Full-text (1863 KB) | HTML Full-text | XML Full-text
Abstract
: 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 [...] Read more.
: 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. Full article
Open AccessArticle Multisensor NDVI-Based Monitoring of the Tundra-Taiga Interface (Mealy Mountains, Labrador, Canada)
Remote Sens. 2013, 5(3), 1066-1090; doi:10.3390/rs5031066
Received: 4 January 2013 / Revised: 26 February 2013 / Accepted: 26 February 2013 / Published: 1 March 2013
Cited by 6 | PDF Full-text (912 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle Enhanced Processing of 1-km Spatial Resolution fAPAR Time Series for Sugarcane Yield Forecasting and Monitoring
Remote Sens. 2013, 5(3), 1091-1116; doi:10.3390/rs5031091
Received: 5 January 2013 / Revised: 26 February 2013 / Accepted: 27 February 2013 / Published: 1 March 2013
Cited by 12 | PDF Full-text (2580 KB) | HTML Full-text | XML Full-text
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Shifts in Global Vegetation Activity Trends
Remote Sens. 2013, 5(3), 1117-1133; doi:10.3390/rs5031117
Received: 20 December 2012 / Revised: 14 February 2013 / Accepted: 21 February 2013 / Published: 1 March 2013
Cited by 48 | PDF Full-text (17198 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle A Reliability-Based Multi-Algorithm Fusion Technique in Detecting Changes in Land Cover
Remote Sens. 2013, 5(3), 1134-1151; doi:10.3390/rs5031134
Received: 7 January 2013 / Revised: 22 February 2013 / Accepted: 26 February 2013 / Published: 1 March 2013
Cited by 5 | PDF Full-text (1039 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle Mapping Land Subsidence Related to Underground Coal Fires in the Wuda Coalfield (Northern China) Using a Small Stack of ALOS PALSAR Differential Interferograms
Remote Sens. 2013, 5(3), 1152-1176; doi:10.3390/rs5031152
Received: 12 January 2013 / Revised: 19 February 2013 / Accepted: 28 February 2013 / Published: 4 March 2013
Cited by 8 | PDF Full-text (2240 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
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Open AccessArticle Trends and ENSO/AAO Driven Variability in NDVI Derived Productivity and Phenology alongside the Andes Mountains
Remote Sens. 2013, 5(3), 1177-1203; doi:10.3390/rs5031177
Received: 31 December 2012 / Revised: 27 February 2013 / Accepted: 27 February 2013 / Published: 6 March 2013
Cited by 10 | PDF Full-text (4919 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
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 [...] Read more.
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). Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle New Microslice Technology for Hyperspectral Imaging
Remote Sens. 2013, 5(3), 1204-1219; doi:10.3390/rs5031204
Received: 30 October 2012 / Revised: 22 February 2013 / Accepted: 22 February 2013 / Published: 6 March 2013
Cited by 1 | PDF Full-text (938 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
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Open AccessArticle Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR
Remote Sens. 2013, 5(3), 1220-1234; doi:10.3390/rs5031220
Received: 20 December 2012 / Revised: 20 February 2013 / Accepted: 4 March 2013 / Published: 7 March 2013
Cited by 8 | PDF Full-text (247 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Forestry)
Open AccessArticle Intercomparison of Leaf Area Index Products for a Gradient of Sub-Humid to Arid Environments in West Africa
Remote Sens. 2013, 5(3), 1235-1257; doi:10.3390/rs5031235
Received: 17 January 2013 / Revised: 22 February 2013 / Accepted: 5 March 2013 / Published: 11 March 2013
Cited by 10 | PDF Full-text (29908 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production
Remote Sens. 2013, 5(3), 1258-1273; doi:10.3390/rs5031258
Received: 19 January 2013 / Revised: 28 January 2013 / Accepted: 5 March 2013 / Published: 12 March 2013
Cited by 11 | PDF Full-text (851 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas
Remote Sens. 2013, 5(3), 1274-1291; doi:10.3390/rs5031274
Received: 16 January 2013 / Revised: 6 March 2013 / Accepted: 6 March 2013 / Published: 12 March 2013
Cited by 16 | PDF Full-text (717 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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Open AccessArticle Azimuth-Variant Signal Processing in High-Altitude Platform Passive SAR with Spaceborne/Airborne Transmitter
Remote Sens. 2013, 5(3), 1292-1310; doi:10.3390/rs5031292
Received: 15 January 2013 / Revised: 19 February 2013 / Accepted: 22 February 2013 / Published: 14 March 2013
Cited by 3 | PDF Full-text (490 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data
Remote Sens. 2013, 5(3), 1311-1334; doi:10.3390/rs5031311
Received: 31 December 2012 / Revised: 4 March 2013 / Accepted: 5 March 2013 / Published: 14 March 2013
Cited by 13 | PDF Full-text (1042 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets
Remote Sens. 2013, 5(3), 1335-1354; doi:10.3390/rs5031335
Received: 31 December 2012 / Revised: 1 March 2013 / Accepted: 1 March 2013 / Published: 14 March 2013
Cited by 17 | PDF Full-text (1808 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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Open AccessArticle Statistical Distances and Their Applications to Biophysical Parameter Estimation: Information Measures, M-Estimates, and Minimum Contrast Methods
Remote Sens. 2013, 5(3), 1355-1388; doi:10.3390/rs5031355
Received: 16 January 2013 / Revised: 4 March 2013 / Accepted: 7 March 2013 / Published: 14 March 2013
Cited by 9 | PDF Full-text (1318 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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%. Full article
Open AccessArticle Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection
Remote Sens. 2013, 5(3), 1389-1404; doi:10.3390/rs5031389
Received: 25 January 2013 / Revised: 11 March 2013 / Accepted: 12 March 2013 / Published: 14 March 2013
Cited by 15 | PDF Full-text (1223 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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 (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 HV (Δs°HV) acquired with high angles (off nadir angle; θ > 40°) best correlates with variations of VIopt (ΔVI). The correlation between ΔVI and ΔHV has been shown to be temporally robust. Based on this experimental evidence, a model to infer a VI from (VISAR) at the time, ti + 1, once known, the VIopt at a reference time, ti, and ΔHV 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 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). Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Automatic Storm Damage Detection in Forests Using High‑Altitude Photogrammetric Imagery
Remote Sens. 2013, 5(3), 1405-1424; doi:10.3390/rs5031405
Received: 26 January 2013 / Revised: 11 March 2013 / Accepted: 12 March 2013 / Published: 18 March 2013
Cited by 14 | PDF Full-text (1447 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle Estimating Composite Curve Number Using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China
Remote Sens. 2013, 5(3), 1425-1438; doi:10.3390/rs5031425
Received: 26 January 2013 / Revised: 14 March 2013 / Accepted: 14 March 2013 / Published: 18 March 2013
Cited by 5 | PDF Full-text (921 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
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Open AccessArticle Chromophoric Dissolved Organic Matter and Dissolved Organic Carbon from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and MERIS Sensors: Case Study for the Northern Gulf of Mexico
Remote Sens. 2013, 5(3), 1439-1464; doi:10.3390/rs5031439
Received: 4 January 2013 / Revised: 6 March 2013 / Accepted: 12 March 2013 / Published: 19 March 2013
Cited by 17 | PDF Full-text (2019 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle The Role of Methodology and Spatiotemporal Scale in Understanding Environmental Change in Peri-Urban Ouagadougou, Burkina Faso
Remote Sens. 2013, 5(3), 1465-1483; doi:10.3390/rs5031465
Received: 18 January 2013 / Revised: 24 February 2013 / Accepted: 15 March 2013 / Published: 19 March 2013
Cited by 3 | PDF Full-text (1392 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
Open AccessArticle Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982–2009
Remote Sens. 2013, 5(3), 1484-1497; doi:10.3390/rs5031484
Received: 1 February 2013 / Revised: 13 March 2013 / Accepted: 18 March 2013 / Published: 21 March 2013
Cited by 31 | PDF Full-text (4309 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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