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Remote Sens., Volume 5, Issue 11 (November 2013), Pages 5424-6158

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Editorial

Jump to: Research, Other

Open AccessEditorial Remote Sensing for Landslide Investigations: From Research into Practice
Remote Sens. 2013, 5(11), 5488-5492; doi:10.3390/rs5115488
Received: 18 October 2013 / Accepted: 18 October 2013 / Published: 25 October 2013
Cited by 6 | PDF Full-text (172 KB) | HTML Full-text | XML Full-text
Abstract
The relevant impact [1] that landslide geo-hazards may have on society in terms of human lives and economic losses, has resulted in great efforts to develop sustainable solutions to deal with their prediction and mitigation. To date, several aspects have been investigated involving
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The relevant impact [1] that landslide geo-hazards may have on society in terms of human lives and economic losses, has resulted in great efforts to develop sustainable solutions to deal with their prediction and mitigation. To date, several aspects have been investigated involving geological and geo-statistical analysis, geotechnical modeling, design of effective mitigation and protection structures, and sensor development [2]. [...] Full article

Research

Jump to: Editorial, Other

Open AccessArticle A Practical Approach for Extracting Tree Models in Forest Environments Based on Equirectangular Projections of Terrestrial Laser Scans
Remote Sens. 2013, 5(11), 5424-5448; doi:10.3390/rs5115424
Received: 30 August 2013 / Revised: 19 October 2013 / Accepted: 21 October 2013 / Published: 24 October 2013
Cited by 20 | PDF Full-text (5581 KB) | HTML Full-text | XML Full-text
Abstract
Extracting 3D tree models based on terrestrial laser scanning (TLS) point clouds is a challenging task as trees are complex objects. Current TLS devices acquire high-density data that allow a detailed reconstruction of the tree topology. However, in dense forests a fully automatic
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Extracting 3D tree models based on terrestrial laser scanning (TLS) point clouds is a challenging task as trees are complex objects. Current TLS devices acquire high-density data that allow a detailed reconstruction of the tree topology. However, in dense forests a fully automatic reconstruction of trees is often limited by occlusion, wind influences and co-registration issues. In this paper, a semi-automatic method for extracting branching and stem structure based on equirectangular projections (range and intensity maps) is presented. The digitization of branches and stems is based on 2D maps, which enables simple navigation and raster processing. The modeling is performed for each viewpoint individually instead of using a registered point cloud. Previously reconstructed 2D-skeletons are transformed between the maps. Therefore, wind influences, orientation imperfections of scans and data gaps can be overcome. The method is applied to a TLS dataset acquired in a forest in Germany. In total 34 scans were carried out within a managed forest to measure approximately 90 spruce trees with minimal occlusions. The results demonstrate the feasibility of the presented approach to extract tree models with a high completeness and correctness and provide an excellent input for further modeling applications. Full article
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Open AccessArticle Detection of Forest Clear-Cuts with Shuttle Radar Topography Mission (SRTM) and Tandem-X InSAR Data
Remote Sens. 2013, 5(11), 5449-5462; doi:10.3390/rs5115449
Received: 3 September 2013 / Revised: 17 October 2013 / Accepted: 18 October 2013 / Published: 24 October 2013
Cited by 7 | PDF Full-text (440 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this study was to determine whether forest clear-cuts during 2000–2011 could be detected as a decrease in surface height by combining Digital Surface Models (DSMs) from the Shuttle Radar Topography Mission (SRTM) and Tandem-X, and to evaluate the performance of
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The aim of this study was to determine whether forest clear-cuts during 2000–2011 could be detected as a decrease in surface height by combining Digital Surface Models (DSMs) from the Shuttle Radar Topography Mission (SRTM) and Tandem-X, and to evaluate the performance of this method using SRTM X- and C-band data as references representing the heights before logging. The study area was located in a Norway spruce-dominated forest estate in southeastern Norway. We interpolated 11-year DSM changes into a 10 m × 10 m raster, and averaged these changes per forest stand. Based on threshold values for DSM decreases we classified the pixels and stands into the categories “clear-cut” and “not clear-cut”, and compared this to a complete record of logged stands during 2000–2011. The classification accuracy was moderate or fairly good. A correct detection was achieved for 59%–67% of the clear-cut stands. Omission errors were most common, occurring in 33%–42% of the stands. Commission errors were found in 13%–21% of the clear-cut stands. The results obtained for X-band SRTM were only marginally better than for C-band. In conclusion, the combination of SRTM and Tandem-X has the potential of providing near global data sets for the recent 12 years’ logging, which should be particularly valuable for deforestation mapping. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Open AccessArticle A Merging Algorithm for Regional Snow Mapping over Eastern Canada from AVHRR and SSM/I Data
Remote Sens. 2013, 5(11), 5463-5487; doi:10.3390/rs5115463
Received: 24 September 2013 / Revised: 18 October 2013 / Accepted: 18 October 2013 / Published: 24 October 2013
Cited by 3 | PDF Full-text (2690 KB) | HTML Full-text | XML Full-text
Abstract
We present an algorithm for regional snow mapping that combines snow maps derived from Advanced Very High Resolution Radiometer (AVHRR) and Special Sensor Microwave/Imager (SSM/I) data. This merging algorithm combines AVHRR’s moderate spatial resolution with SSM/I’s ability to penetrate clouds and, thus, benefits
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We present an algorithm for regional snow mapping that combines snow maps derived from Advanced Very High Resolution Radiometer (AVHRR) and Special Sensor Microwave/Imager (SSM/I) data. This merging algorithm combines AVHRR’s moderate spatial resolution with SSM/I’s ability to penetrate clouds and, thus, benefits from the advantages of the two sensors while minimizing their limitations. First, each of the two detection algorithms were upgraded before developing the methodology to merge the snow mapping results obtained using both algorithms. The merging methodology is based on a membership function calculated over a temporal running window of ±4 days from the actual date. The studied algorithms were developed and tested over Eastern Canada for the period from 1988 to 1999. The snow mapping algorithm focused on the spring melt season (1 April to 31 May). The snow maps were validated using snow depth observations from meteorological stations. The overall accuracy of the merging algorithm is about 86%, which is between that of the new versions of the two individual algorithms: AVHRR (90%) and SSM/I (83%). Furthermore, the algorithm was able to locate the end date of the snowmelt season with reasonable accuracy (bias = 0 days; SD = 11 days). Comparison of mapping results with high spatial resolution snow cover from Landsat imagery demonstrates the feasibility of clear-sky snow mapping with relatively good accuracy despite some underestimation of snow extent inherited from the AVHRR algorithm. It was found that the detection limit of the algorithm is 80% snow cover within a 1 × 1 km pixel. Full article
Open AccessArticle Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon
Remote Sens. 2013, 5(11), 5493-5513; doi:10.3390/rs5115493
Received: 22 July 2013 / Revised: 18 October 2013 / Accepted: 18 October 2013 / Published: 28 October 2013
Cited by 35 | PDF Full-text (4134 KB) | HTML Full-text | XML Full-text
Abstract
Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000–2010 using high-resolution
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Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000–2010 using high-resolution Landsat satellite imagery. Combining spectral mixture analysis, normalized difference fraction index, and knowledge-based decision tree classification, we mapped and assessed the accuracy to quantify forest (0.97), deforestation (0.85) and forest degradation (0.82) with an overall accuracy of 0.92. We show that 169,074 km2 of Amazonian forest was converted to human-dominated land uses, such as agriculture, from 2000 to 2010. In that same time frame, an additional 50,815 km2 of forest was directly altered by timber harvesting and/or fire, equivalent to 30% of the area converted by deforestation. While average annual outright deforestation declined by 46% between the first and second halves of the study period, annual forest degradation increased by 20%. Existing operational monitoring systems (PRODES: Monitoramento da Florestal Amazônica Brasileira por Satélite) report deforestation area to within 2% of our results, but do not account for the extensive forest degradation occurring throughout the region due to selective logging and forest fire. Annual monitoring of forest degradation across tropical forests is critical for developing land management policies as well as the monitoring of carbon stocks/emissions and protected areas. Full article
Open AccessArticle Evaluation of the Effects of Soil Layer Classification in the Common Land Model on Modeled Surface Variables and the Associated Land Surface Soil Moisture Retrieval Model
Remote Sens. 2013, 5(11), 5514-5529; doi:10.3390/rs5115514
Received: 23 August 2013 / Revised: 15 October 2013 / Accepted: 15 October 2013 / Published: 28 October 2013
Cited by 3 | PDF Full-text (2003 KB) | HTML Full-text | XML Full-text
Abstract
Land surface soil moisture (SSM) is crucial in research and applications in hydrology, ecology, and meteorology. A novel SSM retrieval model, based on the diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR), has recently been reported. It suggests
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Land surface soil moisture (SSM) is crucial in research and applications in hydrology, ecology, and meteorology. A novel SSM retrieval model, based on the diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR), has recently been reported. It suggests a promising avenue for the retrieval of regional SSM using LST and NSSR derived from geostationary satellites in a future development. As part of a further improvement of previous work, effects of soil layer classification in the Common Land Model (CoLM) on modeled LST, NSSR and the associated SSM retrieval model in particular, have been evaluated. To address this issue, the soil profile has been divided in to three layers, named upper layer (0–0.05 m), root layer (0.05–1.30 m) and bottom layer (1.30–2.50 m). By varying the number of soil layers with the three layer zones, nine different soil layer classifications have been performed in the CoLM to produce simulated data. Results indicate that (1) modeled SSM is less sensitive to soil layer classification while modeled LST and NSSR are sensitive, especially under wet conditions and (2) the simulated data based SSM retrieval model is stable for a fixed upper layer with varying classifications of root and bottom layers. It also concludes an optimal soil layer classification for the CoLM while producing simulated data to develop the SSM retrieval model. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Open AccessArticle A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI
Remote Sens. 2013, 5(11), 5530-5549; doi:10.3390/rs5115530
Received: 5 August 2013 / Revised: 15 October 2013 / Accepted: 16 October 2013 / Published: 28 October 2013
Cited by 39 | PDF Full-text (3268 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing has more advantages than the traditional methods of land surface water (LSW) mapping because it is a low-cost, reliable information source that is capable of making high-frequency and repeatable observations. The normalized difference water indexes (NDWIs), calculated from various band combinations
[...] Read more.
Remote sensing has more advantages than the traditional methods of land surface water (LSW) mapping because it is a low-cost, reliable information source that is capable of making high-frequency and repeatable observations. The normalized difference water indexes (NDWIs), calculated from various band combinations (green, near-infrared (NIR), or shortwave-infrared (SWIR)), have been successfully applied to LSW mapping. In fact, new NDWIs will become available when Advanced Land Imager (ALI) data are used as the ALI sensor provides one green band (Band 4), two NIR bands (Bands 6 and 7), and three SWIR bands (Bands 8, 9, and 10). Thus, selecting the optimal band or combination of bands is critical when ALI data are employed to map LSW using NDWI. The purpose of this paper is to find the best performing NDWI model of the ALI data in LSW map. In this study, eleven NDWI models based on ALI, Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data were compared to assess the performance of ALI data in LSW mapping, at three different study sites in the Yangtze River Basin, China. The contrast method, Otsu method, and confusion matrix were calculated to evaluate the accuracies of the LSW maps. The accuracies of LSW maps derived from eleven NDWI models showed that five NDWI models of the ALI sensor have more than an overall accuracy of 91% with a Kappa coefficient of 0.78 of LSW maps at three test sites. In addition, the NDWI model, calculated from the green (Band 4: 0.525–0.605 μm) and SWIR (Band 9: 1.550–1.750 μm) bands of the ALI sensor, namely NDWIA4,9, was shown to have the highest LSW mapping accuracy, more than the other NDWI models. Therefore, the NDWIA4,9 is the best indicator for LSW mapping of the ALI sensor. It can be used for mapping LSW with high accuracy. Full article
Open AccessArticle Time-Space Variability of Chlorophyll-a and Associated Physical Variables within the Region off Central-Southern Chile
Remote Sens. 2013, 5(11), 5550-5571; doi:10.3390/rs5115550
Received: 10 September 2013 / Revised: 12 October 2013 / Accepted: 17 October 2013 / Published: 28 October 2013
Cited by 3 | PDF Full-text (2611 KB) | HTML Full-text | XML Full-text
Abstract
Time-space fluctuations of chlorophyll-a (Chl-a) within the region off central-southern Chile (33–42°S), and their association with meteorological-oceanographic conditions, were analyzed using satellite time series data (2002–2012). The mean distribution of moderate values of Chl-a (~0.5 mg∙m−3) in the northern section (33–38°S)
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Time-space fluctuations of chlorophyll-a (Chl-a) within the region off central-southern Chile (33–42°S), and their association with meteorological-oceanographic conditions, were analyzed using satellite time series data (2002–2012). The mean distribution of moderate values of Chl-a (~0.5 mg∙m−3) in the northern section (33–38°S) extended out to ~200 km of the coast whereas they were restricted to a narrower band in the southern section (38–42°S). Mean wind stress and wind stress curl were upwelling favorable for most part of the year in the northern section whereas upwelling-downwelling periods were distinct in the southern section. The dominant frequency of Chl-a variability in the coastal zone and the coastal transition zone was annual, as it was for the rest of the variables, except in a transitional band between these zones and where a semi-permanent jet is located. At the annual frequency, the alongshore distribution of coastal Chl-a presented strong discontinuities, with minimum values around upwelling centers (~37 and 40°S) and higher values (> 2 mg∙m−3) in between. Also at the annual frequency, correlation analyses suggest that Ekman transport and Ekman pumping might act synchronously to extend the offshore distribution of the highest Chl-a values during the spring-summer period whereas mesoscale activity appears to contribute to Chl-a increases in the coastal transition zone. Sea surface temperature does not appear to be associated with the annual cycle of Chl-a in the coastal zone and in the coastal transition zone it might be linked to Chl-a variability through the effects of internal waves. Full article
(This article belongs to the Special Issue Remote Sensing of Phytoplankton)
Open AccessArticle Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions
Remote Sens. 2013, 5(11), 5574-5597; doi:10.3390/rs5115574
Received: 24 June 2013 / Revised: 22 October 2013 / Accepted: 23 October 2013 / Published: 29 October 2013
Cited by 22 | PDF Full-text (355 KB) | HTML Full-text | XML Full-text
Abstract
Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accuracy of biomass; in this respect, it is important to
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Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accuracy of biomass; in this respect, it is important to examine the potential offered by new sensors. To assess the contribution of the TanDEM-X mission, eighteen interferometric Synthetic Aperture Radar (SAR) image pairs acquired over the hemi-boreal test site of Remningstorp in Sweden were investigated. Three models were used for interpretation of TanDEM-X signatures and above-ground biomass retrieval: Interferometric Water Cloud Model (IWCM), Random Volume over Ground (RVoG) model, and a simple model based on penetration depth (PD). All use an allometric expression to relate above-ground biomass to forest height measured by TanDEM-X. The retrieval was assessed on 201 forest stands with a minimum size of 1 ha, and ranging from 6 to 267 Mg/ha (mean biomass of 105 Mg/ha) equally divided into a model training dataset and a validation test dataset. Biomass retrieved using the IWCM resulted in a Root Mean Square Error (RMSE) between 17% and 33%, depending on acquisition date and image acquisition geometry (angle of incidence, interferometric baseline, and orbit type). The RMSE in the case of the RVoG and the PD models were slightly higher. A multitemporal estimate of the above-ground biomass using all eighteen acquisitions resulted in an RMSE of 16% with R2 = 0.93. These results prove the capability of TanDEM-X interferometric data to estimate forest aboveground biomass in the boreal zone. Full article
Open AccessArticle A Multi-Scale Flood Monitoring System Based on Fully Automatic MODIS and TerraSAR-X Processing Chains
Remote Sens. 2013, 5(11), 5598-5619; doi:10.3390/rs5115598
Received: 15 September 2013 / Revised: 21 October 2013 / Accepted: 23 October 2013 / Published: 29 October 2013
Cited by 16 | PDF Full-text (3299 KB) | HTML Full-text | XML Full-text
Abstract
A two-component fully automated flood monitoring system is described and evaluated. This is a result of combining two individual flood services that are currently under development at DLR’s (German Aerospace Center) Center for Satellite based Crisis Information (ZKI) to rapidly support disaster management
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A two-component fully automated flood monitoring system is described and evaluated. This is a result of combining two individual flood services that are currently under development at DLR’s (German Aerospace Center) Center for Satellite based Crisis Information (ZKI) to rapidly support disaster management activities. A first-phase monitoring component of the system systematically detects potential flood events on a continental scale using daily-acquired medium spatial resolution optical data from the Moderate Resolution Imaging Spectroradiometer (MODIS). A threshold set controls the activation of the second-phase crisis component of the system, which derives flood information at higher spatial detail using a Synthetic Aperture Radar (SAR) based satellite mission (TerraSAR-X). The proposed activation procedure finds use in the identification of flood situations in different spatial resolutions and in the time-critical and on demand programming of SAR satellite acquisitions at an early stage of an evolving flood situation. The automated processing chains of the MODIS (MFS) and the TerraSAR-X Flood Service (TFS) include data pre-processing, the computation and adaptation of global auxiliary data, thematic classification, and the subsequent dissemination of flood maps using an interactive web-client. The system is operationally demonstrated and evaluated via the monitoring two recent flood events in Russia 2013 and Albania/Montenegro 2013. Full article
Open AccessArticle Bilateral Distance Based Filtering for Polarimetric SAR Data
Remote Sens. 2013, 5(11), 5620-5641; doi:10.3390/rs5115620
Received: 10 September 2013 / Accepted: 17 October 2013 / Published: 30 October 2013
Cited by 4 | PDF Full-text (14032 KB) | HTML Full-text | XML Full-text
Abstract
This paper introduces a non-linear Polarimetric SAR data filtering approach able to preserve the edges and small details of the data. It is based on exploiting the data locality in both, the spatial and the polarimetric domains, in order to avoid mixing heterogeneous
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This paper introduces a non-linear Polarimetric SAR data filtering approach able to preserve the edges and small details of the data. It is based on exploiting the data locality in both, the spatial and the polarimetric domains, in order to avoid mixing heterogeneous samples of the data. A weighted average is performed over a given window favoring pixel values that are close on both domains. The filtering technique is based on a modified bilateral filtering, which is defined in terms of spatial and polarimetric distances. These distances encapsulate all the knowledge in both domains for an adaptation to the data structure. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset. Full article
Open AccessArticle Spatio-Temporal Patterns of Cropland Conversion in Response to the “Grain for Green Project” in China’s Loess Hilly Region of Yanchuan County
Remote Sens. 2013, 5(11), 5642-5661; doi:10.3390/rs5115642
Received: 20 September 2013 / Revised: 22 October 2013 / Accepted: 24 October 2013 / Published: 30 October 2013
Cited by 8 | PDF Full-text (1098 KB) | HTML Full-text | XML Full-text
Abstract
To prevent environmental degradation, China’s central government launched the “Grain for Green Project” (GGP) in 1999. Since its beginning, the effects and influences of the GGP have been hotly debated among domestic and international scholars and policymakers. This paper is taking the County
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To prevent environmental degradation, China’s central government launched the “Grain for Green Project” (GGP) in 1999. Since its beginning, the effects and influences of the GGP have been hotly debated among domestic and international scholars and policymakers. This paper is taking the County of Yanchuan in the Loess Plateau as a case study, examines the spatio-temporal patterns of cropland conversion in response to the GGP. This research is methodologically based on remote sensing (RS) and geographic information systems (GIS), and also employs personal interviews with local government officials and farmers. The results show that land use/cover patterns in Yanchuan County have changed dramatically after the implementation of GGP. Cropland has decreased remarkably, while orchard land and sparse forest has increased significantly: 23.84% of cropland was converted to orchard, and 22.25% to sparse forest. Simultaneously, the landscape has become more fragmented but also more diversified, forestland has become more dominant. A total of 61.19% of the total converted cropland was on slopes greater than 15 degrees, 64.85% of which was lower-grade land. The converted cropland is mostly located in more accessible areas for convenient management. Partially affected by farmers’ self-willingness, sloping cropland was preferred to orchard (economic forest), and some gentle slope (less than 15 degrees) or higher-grade cropland were involved in the GGP. To maintain and reinforce the achievements of the GGP and further contribute to the GGP’s sustainability and rural development, the paper recommends that the Chinese government should build a continuous compensation mechanism for the households who lost cropland for the GGP while improving the productivity of flat cropland. Full article
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Open AccessArticle Assessing the Performance of a Northern Gulf of Mexico Tidal Model Using Satellite Imagery
Remote Sens. 2013, 5(11), 5662-5679; doi:10.3390/rs5115662
Received: 15 September 2013 / Revised: 29 September 2013 / Accepted: 28 October 2013 / Published: 1 November 2013
Cited by 2 | PDF Full-text (4962 KB) | HTML Full-text | XML Full-text
Abstract
Tidal harmonic analysis simulations along with simulations spanning four specific historical time periods in 2003 and 2004 were conducted to test the performance of a northern Gulf of Mexico tidal model. A recently developed method for detecting inundated areas based on integrated remotely
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Tidal harmonic analysis simulations along with simulations spanning four specific historical time periods in 2003 and 2004 were conducted to test the performance of a northern Gulf of Mexico tidal model. A recently developed method for detecting inundated areas based on integrated remotely sensed data (i.e., Radarsat-1, aerial imagery, LiDAR, Landsat 7 ETM+) was applied to assess the performance of the tidal model. The analysis demonstrates the applicability of the method and its agreement with traditional performance assessment techniques such as harmonic resynthesis and water level time series analysis. Based on the flooded/non-flooded coastal areas estimated by the integrated remotely sensed data, the model is able to adequately reproduce the extent of inundation within four sample areas from the coast along the Florida panhandle, correctly identifying areas as wet or dry over 85% of the time. Comparisons of the tidal model inundation to synoptic (point-in-time) inundation areas generated from the remotely sensed data generally agree with the results of the traditional performance assessment techniques. Moreover, this approach is able to illustrate the spatial distribution of model inundation accuracy allowing for targeted refinement of model parameters. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
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Open AccessArticle Evaluation of ALOS PALSAR Imagery for Burned Area Mapping in Greece Using Object-Based Classification
Remote Sens. 2013, 5(11), 5680-5701; doi:10.3390/rs5115680
Received: 30 September 2013 / Revised: 24 October 2013 / Accepted: 24 October 2013 / Published: 4 November 2013
Cited by 4 | PDF Full-text (7872 KB) | HTML Full-text | XML Full-text
Abstract
In this work, the potential of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) imagery to map burned areas was evaluated in two study areas in Greece. For this purpose, we developed an object-based classification scheme to map
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In this work, the potential of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) imagery to map burned areas was evaluated in two study areas in Greece. For this purpose, we developed an object-based classification scheme to map the fire-disturbed areas using the PALSAR imagery acquired before and shortly after fire events. The advantage of employing an object-based approach was not only the use of the temporal variation of the backscatter coefficient, but also the incorporation in the classification of topological features, such as neighbor objects, and class related features, such as objects classified as burned. The classification scheme resulted in mapping the burned areas with satisfactory results: 0.71 and 0.82 probabilities of detection for the two study areas. Our investigation revealed that the pre-fire vegetation conditions and fire severity should be taken in consideration when mapping burned areas using PALSAR in Mediterranean regions. Overall, findings suggest that the developed scheme could be applied for rapid burned area assessment, especially to areas where cloud cover and fire smoke inhibit accurate mapping of burned areas when optical data are used. Full article
Open AccessArticle Optimizing Satellite-Based Precipitation Estimation for Nowcasting of Rainfall and Flash Flood Events over the South African Domain
Remote Sens. 2013, 5(11), 5702-5724; doi:10.3390/rs5115702
Received: 16 August 2013 / Revised: 18 October 2013 / Accepted: 21 October 2013 / Published: 4 November 2013
Cited by 7 | PDF Full-text (1830 KB) | HTML Full-text | XML Full-text
Abstract
The South African Weather Service is mandated to issue warnings of hazardous weather events, including those related to heavy precipitation, in order to safeguard life and property. Flooding and flash flood events are common in South Africa. Frequent updates and real-time availability of
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The South African Weather Service is mandated to issue warnings of hazardous weather events, including those related to heavy precipitation, in order to safeguard life and property. Flooding and flash flood events are common in South Africa. Frequent updates and real-time availability of precipitation data are crucial to support hydrometeorological warning services. Satellite rainfall estimation provides a very important data source for flash flood guidance systems as well as nowcasting of precipitation events for the data sparse regions of the African continent. Although low earth orbiting satellites with microwave instruments provide good quality rainfall estimates, their temporal and spatial resolution are not adequate for time-critical services. Precipitation estimation using geostationary satellites is less accurate, but provides excellent spatial coverage, is updated frequently and is available in real-time. This study compares different ways to use and combine satellite precipitation estimates and numerical weather prediction model fields over the South African domain in order to determine the optimal estimate of precipitation, which can also be applied in real-time to support flash flood guidance. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
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Open AccessArticle Polarimetric Parameters for Growing Stock Volume Estimation Using ALOS PALSAR L-Band Data over Siberian Forests
Remote Sens. 2013, 5(11), 5725-5756; doi:10.3390/rs5115725
Received: 10 September 2013 / Revised: 19 October 2013 / Accepted: 24 October 2013 / Published: 4 November 2013
Cited by 2 | PDF Full-text (1835 KB) | HTML Full-text | XML Full-text
Abstract
In order to assess the potentiality of ALOS L-band fully polarimetric radar data for forestry applications, we investigated a four-component decomposition method to characterize the polarization response of Siberian forest. The decomposition powers of surface scattering, double-bounce and volume scattering, derived with and
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In order to assess the potentiality of ALOS L-band fully polarimetric radar data for forestry applications, we investigated a four-component decomposition method to characterize the polarization response of Siberian forest. The decomposition powers of surface scattering, double-bounce and volume scattering, derived with and without rotation of coherency matrix, were compared with Growing Stock Volume (GSV). To compensate for topographic effects an adaptive rotation of the coherency matrix was accomplished. After the rotation, the correlation between GSV and double-bounce increased significantly. Volume scattering remained same and the surface scattering power decreased slightly. The volume scattering power and double-bounce power increased as the GSV increased, whereas the surface scattering power decreased. In sparse forest, at unfrozen conditions the surface scattering was higher than volume scattering, while volume scattering was dominant in dense forest. The scenario was different at frozen conditions for dense forest where the surface scattering was higher than volume scattering. Moreover, a slight impact of tree species on polarimetric decomposition powers has been observed. Larch was differed from aspen, birch and pine by +2 dB surface scattering power and also by −1.5 dB and −1.2 dB volume scattering power and double-bounce scattering power respectively at unfrozen conditions. Full article
Open AccessArticle Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor
Remote Sens. 2013, 5(11), 5757-5782; doi:10.3390/rs5115757
Received: 10 September 2013 / Revised: 16 October 2013 / Accepted: 16 October 2013 / Published: 6 November 2013
PDF Full-text (3551 KB) | HTML Full-text | XML Full-text
Abstract
Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage
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Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage or a narrow field of view (FOV), is one of the problems preventing proper monitoring of the Earth. One of the possible solutions is predicting a detector or sensor’s missing data using another detector/sensor. In this paper, we propose a new method of predicting spectral emissivity in the long-wave infrared (LWIR) spectral region using the visible (VIS) spectral region. The proposed method is suitable for two main scenarios of missing data: sensor malfunctions and narrow FOV. We demonstrate the usefulness and limitations of this prediction scheme using the airborne hyperspectral scanner (AHS) sensor, which consists of both VIS and LWIR spectral regions, in a case study over the Sokolov area, Czech Republic. Full article
Open AccessArticle Large-Scale Water Productivity Assessments with MODIS Images in a Changing Semi-Arid Environment: A Brazilian Case Study
Remote Sens. 2013, 5(11), 5783-5804; doi:10.3390/rs5115783
Received: 3 September 2013 / Revised: 17 October 2013 / Accepted: 27 October 2013 / Published: 6 November 2013
Cited by 5 | PDF Full-text (1470 KB) | HTML Full-text | XML Full-text
Abstract
In the Brazilian semi-arid region, the intensification of agriculture results in a change of natural vegetation by irrigated crops. To quantify the contrast between these two ecosystems, the large-scale values of water productivity components were modelled in Petrolina (PE) and Juazeiro (BA) municipalities.
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In the Brazilian semi-arid region, the intensification of agriculture results in a change of natural vegetation by irrigated crops. To quantify the contrast between these two ecosystems, the large-scale values of water productivity components were modelled in Petrolina (PE) and Juazeiro (BA) municipalities. The SAFER (Simple Algorithm For Evapotranspiration Retrieving) algorithm was used to acquire evapotranspiration (ET), while the Monteith's radiation model was applied for estimating the biomass production (BIO). Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used together with agro-meteorological data. In Petrolina and Juazeiro, the mean monthly ET values for irrigated crops were 938 and 739 mm∙month−1, with the corresponding ones for natural vegetation of 385 and 194 mm∙month−1.Water productivity (WP) was analysed by the ratio of BIO to ET, defined here as the ratio of the net benefits from the mixed agricultural systems to the amount of water required for producing those benefits. The highest incremental WP values, as a result of the irrigated crops introduction, happened outside the rainy period. More spatial WP uniformity occurred in natural vegetation, when comparing with irrigated crops. The most frequent WP values in Petrolina were between 1.6 and 2.2 kg∙m−3 while in Juazeiro this range was from 1.0 to 1.6 kg∙m−3. The differences between the municipalities can be mainly explained by differences in precipitation and soil water storages conditions, promoting better rainfall use efficiency by the natural vegetation in the first one. The results of the current research are important for appraising the land use change impacts in situations of expanding irrigation areas. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Open AccessArticle Multi-Sensor Platform for Indoor Mobile Mapping: System Calibration and Using a Total Station for Indoor Applications
Remote Sens. 2013, 5(11), 5805-5824; doi:10.3390/rs5115805
Received: 10 September 2013 / Revised: 19 October 2013 / Accepted: 22 October 2013 / Published: 6 November 2013
Cited by 4 | PDF Full-text (19222 KB) | HTML Full-text | XML Full-text
Abstract
This paper addresses the calibration of mobile mapping systems and the feasibility of using a total station as a sensor for indoor mobile mapping systems. For this purpose, the measuring system of HafenCity University in Hamburg is presented and discussed. In the second
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This paper addresses the calibration of mobile mapping systems and the feasibility of using a total station as a sensor for indoor mobile mapping systems. For this purpose, the measuring system of HafenCity University in Hamburg is presented and discussed. In the second part of the calibration, the entire system will be described regarding the interaction of laser scanners and other parts of the system. Finally, the preliminary analysis of the use of a total station is presented in conjunction with the measurement system. The difficulty of time synchronization is also discussed. In multiple tests, a comparison was made versus a reference solution based on GNSS. Additionally, the suitability of the total station was also considered for indoor applications. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter
Remote Sens. 2013, 5(11), 5825-5850; doi:10.3390/rs5115825
Received: 29 August 2013 / Revised: 23 October 2013 / Accepted: 24 October 2013 / Published: 8 November 2013
Cited by 14 | PDF Full-text (1425 KB) | HTML Full-text | XML Full-text
Abstract
Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is
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Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Open AccessArticle GIS-Based Detection of Gullies in Terrestrial LiDAR Data of the Cerro Llamoca Peatland (Peru)
Remote Sens. 2013, 5(11), 5851-5870; doi:10.3390/rs5115851
Received: 19 August 2013 / Revised: 31 October 2013 / Accepted: 1 November 2013 / Published: 11 November 2013
Cited by 12 | PDF Full-text (7239 KB) | HTML Full-text | XML Full-text
Abstract
Cushion peatlands are typical features of the high altitude Andes in South America. Due to the adaptation to difficult environmental conditions, they are very fragile ecosystems and therefore vulnerable to environmental and climate changes. Peatland erosion has severe effects on their ecological functions,
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Cushion peatlands are typical features of the high altitude Andes in South America. Due to the adaptation to difficult environmental conditions, they are very fragile ecosystems and therefore vulnerable to environmental and climate changes. Peatland erosion has severe effects on their ecological functions, such as water storage capacity. Thus, erosion monitoring is highly advisable. Erosion quantification and monitoring can be supported by high-resolution terrestrial Light Detection and Ranging (LiDAR). In this study, a novel Geographic Information System (GIS)-based method for the automatic delineation and geomorphometric description of gullies in cushion peatlands is presented. The approach is a multi-step workflow based on a gully edge extraction and a sink filling algorithm applied to a conditioned digital terrain model. Our method enables the creation of GIS-ready polygons of the gullies and the derivation of geomorphometric parameters along the entire channel course. Automatically derived boundaries and gully area values correspond to a high degree (93%) with manually digitized reference polygons. The set of methods developed in this study offers a suitable tool for the monitoring and scientific analysis of fluvial morphology in cushion peatlands. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
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Open AccessArticle Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms
Remote Sens. 2013, 5(11), 5871-5906; doi:10.3390/rs5115871
Received: 15 August 2013 / Revised: 18 September 2013 / Accepted: 23 October 2013 / Published: 12 November 2013
Cited by 11 | PDF Full-text (13289 KB) | HTML Full-text | XML Full-text
Abstract
Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a
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Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets. Full article
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Open AccessArticle A Water Index for SPOT5 HRG Satellite Imagery, New South Wales, Australia, Determined by Linear Discriminant Analysis
Remote Sens. 2013, 5(11), 5907-5925; doi:10.3390/rs5115907
Received: 22 September 2013 / Revised: 7 November 2013 / Accepted: 7 November 2013 / Published: 13 November 2013
Cited by 9 | PDF Full-text (6151 KB) | HTML Full-text | XML Full-text
Abstract
A new water index for SPOT5 High Resolution Geometrical (HRG) imagery normalized to surface reflectance, called the linear discriminant analysis water index (LDAWI), was created using training data from New South Wales (NSW), Australia and the multivariate statistical method of linear discriminant analysis
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A new water index for SPOT5 High Resolution Geometrical (HRG) imagery normalized to surface reflectance, called the linear discriminant analysis water index (LDAWI), was created using training data from New South Wales (NSW), Australia and the multivariate statistical method of linear discriminant analysis classification. The index uses all four image bands, and is better at separating water and non-water pixels than the two commonly used variations of the normalized difference water index (NDWI), which each only use two image bands. Compared across 2,400 validation pixels, from six images spanning four years, the LDAWI attained an overall accuracy of 98%, a producer’s accuracy for water of 100%, and a user’s accuracy for water of 97%. These accuracy measures increase to 99%, 100% and 98% if cloud shadow and topographic shadow masks are applied to the imagery. The NDWI achieved consistently lower accuracies, with the NDWI calculated from the green and shortwave infrared (IR) bands performing slightly better (91% overall accuracy) than the NDWI calculated from the green and near IR bands (89% overall accuracy). The LDAWI is now being routinely used on an archive of over 2,000 images from across NSW, as part of an operational environmental monitoring program. Full article
Open AccessArticle A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US
Remote Sens. 2013, 5(11), 5926-5943; doi:10.3390/rs5115926
Received: 26 September 2013 / Revised: 6 November 2013 / Accepted: 7 November 2013 / Published: 14 November 2013
Cited by 16 | PDF Full-text (2337 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to
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Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the Midwestern US with low errors for both corn (R2 = 0.77; RMSE = 0.89 MT/ha) and soybeans (R2 = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas. Full article
Open AccessArticle Knowledge-Based Modeling of Buildings in Dense Urban Areas by Combining Airborne LiDAR Data and Aerial Images
Remote Sens. 2013, 5(11), 5944-5968; doi:10.3390/rs5115944
Received: 11 July 2013 / Revised: 28 October 2013 / Accepted: 5 November 2013 / Published: 14 November 2013
Cited by 8 | PDF Full-text (2863 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a knowledge-based algorithm is proposed for automatically generating three-dimensional (3D) building models in dense urban areas by using airborne light detection and ranging (LiDAR) data and aerial images. Automatic 3D building modeling using LiDAR is challenging in dense urban areas,
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In this paper, a knowledge-based algorithm is proposed for automatically generating three-dimensional (3D) building models in dense urban areas by using airborne light detection and ranging (LiDAR) data and aerial images. Automatic 3D building modeling using LiDAR is challenging in dense urban areas, in which houses are typically located close to each other and their heights are similar. This makes it difficult to separate point clouds into individual buildings. A combination of airborne LiDAR and aerial images can be an effective approach to resolve this issue. Information about individual building boundaries, derived by segmentation of images, can be utilized for modeling. However, shadows cast by adjacent buildings cause segmentation errors. The algorithm proposed in this paper uses an improved segmentation algorithm (Susaki, J. 2012.) that functions even for shadowed buildings. In addition, the proposed algorithm uses assumptions about the geometry of building arrangement to calculate normal vectors to candidate roof segments. By considering the segmented regions and the normals, models of four common roof types—gable-roof, hip-roof, flat-roof, and slant-roof buildings—are generated. The proposed algorithm was applied to two areas of Higashiyama ward, Kyoto, Japan, and the modeling was successful even in dense urban areas. Full article
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Open AccessArticle Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh
Remote Sens. 2013, 5(11), 5969-5998; doi:10.3390/rs5115969
Received: 7 September 2013 / Revised: 31 October 2013 / Accepted: 31 October 2013 / Published: 15 November 2013
Cited by 13 | PDF Full-text (5765 KB) | HTML Full-text | XML Full-text
Abstract
Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP)
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Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP) area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30 °C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of different scenarios (e.g., vegetation loss to accommodate urban growth) on LST and consequently to devise appropriate policy measures. Full article
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Open AccessArticle Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations
Remote Sens. 2013, 5(11), 5999-6025; doi:10.3390/rs5115999
Received: 4 September 2013 / Revised: 8 November 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
Cited by 3 | PDF Full-text (3259 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts’ knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case
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This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts’ knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet. Full article
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Open AccessArticle Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping
Remote Sens. 2013, 5(11), 6026-6042; doi:10.3390/rs5116026
Received: 9 September 2013 / Revised: 10 November 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
Cited by 19 | PDF Full-text (4632 KB) | HTML Full-text | XML Full-text
Abstract
Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study
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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. Full article
Open AccessArticle Recent Changes in Terrestrial Gross Primary Productivity in Asia from 1982 to 2011
Remote Sens. 2013, 5(11), 6043-6062; doi:10.3390/rs5116043
Received: 30 September 2013 / Revised: 25 October 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
Cited by 9 | PDF Full-text (1064 KB) | HTML Full-text | XML Full-text
Abstract
Past changes in gross primary productivity (GPP) were assessed using historical satellite observations based on the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series and four terrestrial biosphere
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Past changes in gross primary productivity (GPP) were assessed using historical satellite observations based on the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series and four terrestrial biosphere models to identify the trends and driving mechanisms related to GPP and NDVI in Asia. A satellite-based time-series data analysis showed that approximately 40% of the area has experienced a significant increase in the NDVI, while only a few areas have experienced a significant decreasing trend over the last 30 years. The increases in the NDVI are dominant in the sub-continental regions of Siberia, East Asia, and India. Simulations using the terrestrial biosphere models also showed significant increases in GPP, similar to the results for the NDVI, in boreal and temperate regions. A modeled sensitivity analysis showed that the increases in GPP are explained by increased temperature and precipitation in Siberia. Precipitation, solar radiation and CO2 fertilization are important factors in the tropical regions. However, the relative contributions of each factor to GPP changes are different among the models. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Autonomous Navigation Airborne Forward-Looking SAR High Precision Imaging with Combination of Pseudo-Polar Formatting and Overlapped Sub-Aperture Algorithm
Remote Sens. 2013, 5(11), 6063-6078; doi:10.3390/rs5116063
Received: 17 September 2013 / Revised: 25 October 2013 / Accepted: 4 November 2013 / Published: 15 November 2013
Cited by 1 | PDF Full-text (1708 KB) | HTML Full-text | XML Full-text
Abstract
Autonomous navigation airborne forward-looking synthetic aperture radar (SAR) observes the anterior inferior wide area with a short cross-track dimensional linear array as azimuth aperture. This is an application scenario that is drastically different from that of side-looking space-borne or air-borne SAR systems, which
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Autonomous navigation airborne forward-looking synthetic aperture radar (SAR) observes the anterior inferior wide area with a short cross-track dimensional linear array as azimuth aperture. This is an application scenario that is drastically different from that of side-looking space-borne or air-borne SAR systems, which acquires azimuth synthetic aperture with along-track dimension platform movement. High precision imaging with a combination of pseudo-polar formatting and overlapped sub-aperture algorithm for autonomous navigation airborne forward-looking SAR imaging is presented. With the suggested imaging method, range dimensional imaging is operated with wide band signal compression. Then, 2D pseudo-polar formatting is operated. In the following, azimuth synthetic aperture is divided into several overlapped sub-apertures. Intra sub-aperture IFFT (Inverse Fast Fourier Transform), wave front curvature phase error compensation, and inter sub-aperture IFFT are operated sequentially to finish azimuth high precision imaging. The main advantage of the proposed algorithm is its extremely high precision and low memory cost. The effectiveness and performance of the proposed algorithm are demonstrated with outdoor GBSAR (Ground Based Synthetic Aperture Radar) experiments, which possesses the same imaging geometry as the airborne forward-looking SAR (short azimuth aperture, wide azimuth swath). The profile response of the trihedral angle reflectors, placed in the imaging scene, reconstructed with the proposed imaging algorithm and back projection algorithm are compared and analyzed. Full article
Open AccessArticle Fraunhofer Lidar Prototype in the Green Spectral Region for Atmospheric Boundary Layer Observations
Remote Sens. 2013, 5(11), 6079-6095; doi:10.3390/rs5116079
Received: 8 October 2013 / Revised: 27 October 2013 / Accepted: 13 November 2013 / Published: 18 November 2013
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Abstract
A lidar detects atmospheric parameters by transmitting laser pulse to the atmosphere and receiving the backscattering signals from molecules and aerosol particles. Because of the small backscattering cross section, a lidar usually uses the high sensitive photomultiplier and avalanche photodiode as detector and
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A lidar detects atmospheric parameters by transmitting laser pulse to the atmosphere and receiving the backscattering signals from molecules and aerosol particles. Because of the small backscattering cross section, a lidar usually uses the high sensitive photomultiplier and avalanche photodiode as detector and uses photon counting technology for collection of weak backscatter signals. Photon Counting enables the capturing of extremely weak lidar return from long distance, throughout dark background, by a long time accumulation. Because of the strong solar background, the signal-to-noise ratio of lidar during daytime could be greatly restricted, especially for the lidar operating at visible wavelengths where solar background is prominent. Narrow band-pass filters must therefore be installed in order to isolate solar background noise at wavelengths close to that of the lidar receiving channel, whereas the background light in superposition with signal spectrum, limits an effective margin for signal-to-noise ratio (SNR) improvement. This work describes a lidar prototype operating at the Fraunhofer lines, the invisible band of solar spectrum, to achieve photon counting under intense solar background. The photon counting lidar prototype in Fraunhofer lines devised was used to observe the atmospheric boundary layer. The SNR was improved 2-3 times by operating the lidar at the wavelength in solar dark lines. The aerosol extinctions illustrate the vertical structures of aerosol in the atmospheric boundary over Qingdao suburban during summer 2011. Full article
(This article belongs to the Special Issue Optical Remote Sensing of the Atmosphere)
Open AccessArticle Spectral Properties of ENVISAT ASAR and QuikSCAT Surface Winds in the North Sea
Remote Sens. 2013, 5(11), 6096-6115; doi:10.3390/rs5116096
Received: 30 September 2013 / Revised: 25 October 2013 / Accepted: 8 November 2013 / Published: 18 November 2013
Cited by 2 | PDF Full-text (289 KB) | HTML Full-text | XML Full-text
Abstract
Spectra derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) and QuikSCAT near-surface ocean winds are investigated over the North Sea. The two sensors offer a wide range of spatial resolutions, from 600 m to 25 km, with different spatial coverage over the area
[...] Read more.
Spectra derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) and QuikSCAT near-surface ocean winds are investigated over the North Sea. The two sensors offer a wide range of spatial resolutions, from 600 m to 25 km, with different spatial coverage over the area of interest. This provides a unique opportunity to study the impact of the spatial resolution on the spectral properties of the wind over a wide range of length scales. Initially, a sub-domain in the North Sea is chosen, due to the overlap of 87 wind scenes from both sensors. The impact of the spatial resolution is manifested as an increase in spectral density over similar wavenumber ranges as the spatial resolution increases. The 600-m SAR wind product reveals a range of wavenumbers in which the exchange processes between micro- and meso-scales occur; this range is not captured by the wind products with a resolution of 1.5 km or lower. The lower power levels of coarser resolution wind products, particularly when comparing QuikSCAT to ENVISAT ASAR, strongly suggest that the effective resolution of the wind products should be high enough to resolve the spectral properties. Spectra computed from 87 wind maps are consistent with those obtained from several thousands of samples. Long-term spectra from QuikSCAT show that during the winter, slightly higher energy content is identified compared to the other seasons. Full article
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Open AccessArticle Live Coral Cover Index Testing and Application with Hyperspectral Airborne Image Data
Remote Sens. 2013, 5(11), 6116-6137; doi:10.3390/rs5116116
Received: 8 October 2013 / Revised: 13 November 2013 / Accepted: 18 November 2013 / Published: 20 November 2013
Cited by 4 | PDF Full-text (9338 KB) | HTML Full-text | XML Full-text
Abstract
Coral reefs are complex, heterogeneous environments where it is common for the features of interest to be smaller than the spatial dimensions of imaging sensors. While the coverage of live coral at any point in time is a critical environmental management issue, image
[...] Read more.
Coral reefs are complex, heterogeneous environments where it is common for the features of interest to be smaller than the spatial dimensions of imaging sensors. While the coverage of live coral at any point in time is a critical environmental management issue, image pixels may represent mixed proportions of coverage. In order to address this, we describe the development, application, and testing of a spectral index for mapping live coral cover using CASI-2 airborne hyperspectral high spatial resolution imagery of Heron Reef, Australia. Field surveys were conducted in areas of varying depth to quantify live coral cover. Image statistics were extracted from co-registered imagery in the form of reflectance, derivatives, and band ratios. Each of the spectral transforms was assessed for their correlation with live coral cover, determining that the second derivative around 564 nm was the most sensitive to live coral cover variations(r2 = 0.63). Extensive field survey was used to transform relative to absolute coral cover, which was then applied to produce a live coral cover map of Heron Reef. We present the live coral cover index as a simple and viable means to estimate the amount of live coral over potentially thousands of km2 and in clear-water reefs. Full article
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Open AccessArticle Quantitative Analysis of the Waterline Method for Topographical Mapping of Tidal Flats: A Case Study in the Dongsha Sandbank, China
Remote Sens. 2013, 5(11), 6138-6158; doi:10.3390/rs5116138
Received: 7 October 2013 / Revised: 6 November 2013 / Accepted: 13 November 2013 / Published: 21 November 2013
Cited by 4 | PDF Full-text (2744 KB) | HTML Full-text | XML Full-text
Abstract
Although the topography of tidal flats is important for understanding their evolution, the spatial and temporal sampling frequency of such data remains limited. The waterline method has the potential to retrieve past tidal flat topography by utilizing large archives of satellite images. This
[...] Read more.
Although the topography of tidal flats is important for understanding their evolution, the spatial and temporal sampling frequency of such data remains limited. The waterline method has the potential to retrieve past tidal flat topography by utilizing large archives of satellite images. This study performs a quantitative analysis of the relationship between the accuracy of tidal flat digital elevation models (DEMs) that are based on the waterline method and the factors that influence the DEMs. The three major conclusions of the study are as follows: (1) the coverage rate of the waterline points and the number of satellite images used to create the DEM are highly linearly correlated with the error of the resultant DEMs, and the former is more significant in indicating the accuracy of the resultant DEMs than the latter; (2) both the area and the slope of the tidal flats are linearly correlated with the error of the resultant DEMs; and (3) the availability analysis of the archived satellite images indicates that the waterline method can retrieve tidal flat terrains from the past forty years. The upper limit of the temporal resolution of the tidal flat DEM can be refined to within one year since 1993, to half a year since 2004 and to three months since 2009. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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Open AccessCorrection Correction: Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sens. 2013, 5, 1704-1733
Remote Sens. 2013, 5(11), 5572-5573; doi:10.3390/rs5115572
Received: 25 October 2013 / Accepted: 25 October 2013 / Published: 28 October 2013
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Abstract Due to an oversight by the authors, in the upper graph in Figure 4 [1] only the determination coefficients for Morocco are correct. Those for the other three countries are wrong. [...] Full article

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