Remote Sens., Volume 7, Issue 3 (March 2015) – 50 articles , Pages 2238-3425

Currently, thermal remote sensing-based evapotranspiration (ET) models can only calculate instantaneous ET at the time of satellite overpass. Five temporal upscaling methods, namely, constant evaporative fraction (ConEF), corrected ConEF (CorEF), diurnal evaporative fraction (DiEF), constant solar radiation ratio (SolRad), and constant reference evaporative fraction (ConET_rF), were selected to upscale the instantaneous ET to daily values. Moreover, five temporal reconstruction approaches, namely, data assimilation (ET_EnKF and ET_SCE_UA), surface resistance (ET_SR), reference evapotranspiration (ET_ET_rF), and harmonic analysis of time series (ET_HANTS), were used to produce continuous daily ET with discrete clear-sky daily ET values. For clear-sky daily ET generation, SolRad and ConET_rF produced the best estimates. In contrast, ConEF usually underestimated the daily ET. The optimum method, however, was found by combining SolRad and ConET_rF, which produced the lowest root-mean-square error (RMSE) values. For continuous daily ET production, ET_ET_rF and ET_SCE_UA performed the best, whereas the ET_SR and ET_HANTS methods had large errors. The annual ET distributions over the Beijing area were calculated with these methods. The spatial ET distributions from ET_ET_rF and ET_SCE_UA had the same trend as ETWatch products, and had a smaller RMSE when compared with ET observations derived from the water balance method. Full article

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way. Full article

Remote-sensing-derived elevation data sets often suffer from noise and outliers due to various reasons, such as the physical limitations of sensors, multiple reflectance, occlusions and low contrast of texture. Outliers generally have a seriously negative effect on DEM construction. Some interpolation methods like ordinary kriging (OK) are capable of smoothing noise inherent in sample points, but are sensitive to outliers. In this paper, a robust algorithm of multiquadric method (MQ) based on an Improved Huber loss function (MQ-IH) has been developed to decrease the impact of outliers on DEM construction. Theoretically, the improved Huber loss function is null for outliers, quadratic for small errors, and linear for others. Simulated data sets drawn from a mathematical surface with different error distributions were employed to analyze the robustness of MQ-IH. Results indicate that MQ-IH obtains a good balance between efficiency and robustness. Namely, the performance of MQ-IH is comparative to those of the classical MQ and MQ based on the Classical Huber loss function (MQ-CH) when sample points follow a normal distribution, and the former outperforms the latter two when sample points are subject to outliers. For example, for the Cauchy error distribution with the location parameter of 0 and scale parameter of 1, the root mean square errors (RMSEs) of MQ-CH and the classical MQ are 0.3916 and 1.4591, respectively, whereas that of MQ-IH is 0.3698. The performance of MQ-IH is further evaluated by qualitative and quantitative analysis through a real-world example of DEM construction with the stereo-images-derived elevation points. Results demonstrate that compared with the classical interpolation methods, including natural neighbor (NN), OK and ANUDEM (a program that calculates regular grid digital elevation models (DEMs) with sensible shape and drainage structure from arbitrarily large topographic data sets), and two versions of MQ, including the classical MQ and MQ-CH, MQ-IH has a better ability of maximally reducing the impact of outliers, while faithfully preserving terrain features. Theoretically, MQ-IH is not a promising interpolation method, and some side effects can be found from its simulation results. For example, the contours of MQ-IH are coarser than ANUDEM in some locations of the real-world study site, and its hill shade may not strictly agree with the real-world surface at rough terrain. Furthermore, the computing cost of MQ-IH is much bigger than that of the classical interpolation methods. However, compared with the classical interpolation methods, MQ-IH has significant potential for interpolating remote-sensing-derived elevation data sets. Full article

Despite the efforts for precise alignment of imaging sensors and attitude sensors before launch, the accuracy of pre-launch alignment is limited. The misalignment between attitude frame and camera frame is especially important as it is related to the localization error of the spacecraft, which is one of the essential factors of satellite image quality. In this paper, a framework for camera misalignment estimation is presented with its application to a high-resolution earth-observation satellite—Deimos-2. The framework intends to provide a solution for estimation and correction of the camera misalignment of a spacecraft, covering image acquisition planning to mathematical solution of camera misalignment. Considerations for effective image acquisition planning to obtain reliable results are discussed, followed by a detailed description on a practical method for extracting many GCPs automatically using reference ortho-photos. Patterns of localization errors that commonly occur due to the camera misalignment are also investigated. A mathematical model for camera misalignment estimation is described comprehensively. The results of simulation experiments showing the validity and accuracy of the misalignment estimation model are provided. The proposed framework was applied to Deimos-2. The real-world data and results from Deimos-2 are presented. Full article

The geometric accuracy of 2008 AVHRR orthoimages from Metop-A, NOAA-17 and NOAA-18 satellites over Switzerland have been investigated here. The methods employed in the study are fully automated, with an accuracy of 0.1–0.2 pixels, however, blunders do occur and this requests a careful blunder detection approach. The investigations include analysis of relative, absolute and band-to-band registration (BBR) accuracy. Regarding relative accuracy, thousands of points are matched between Metop-A, NOAA-17 and NOAA-18 images of the same day. The accuracy is quite high with mean shifts between 0.2 and 0.4 pixels. Systematic stripes have been observed when NOAA-18 images are involved in matching. In spite of many efforts to find the source of this error, no explanation could be found. In addition, large shifts up to 2.9 pixels on some days between September and December 2008 were observed. Regarding absolute accuracy, digitized lakes as reference polygons have been used and a subpixel lake matching method has been applied. The mean shifts generally fulfilled EUMETSAT and GCOS specifications, although some partial results exceed them, especially for Metop-A. Regarding BBR accuracy, six multispectral bands have been compared, also with point matching. The EUMETSAT specification is 0.1 km, however, this specification refers to original images, not orthoimages. Taking also into account the matching errors of 0.1 km, the EUMETSAT specifications are in principle fulfilled in all cases except matching of Metop-A and NOAA-17 Band-2 images with Bands 4 and 5. The overall work showed that although, in general, accuracies are high and fulfill specifications, errors exceeding the specifications can occur and vary depending on the satellite used, time and location. Such errors influence subsequent geometric or thematic processing; thus, an automated and permanent quality control of such images should be executed. Full article

Although shrublands, savannas and grasslands account for 37% of the world’s terrestrial area, not many studies have analysed the role of these ecosystems in the global carbon cycle at a regional scale. The MODIS Gross Primary Production (GPP) product is used here to help bridge this gap. In this study, the agreement between the MODIS GPP product (GPPm) and the GPP Eddy Covariance tower data (GPPec) was tested for six different sites in temperate and dry climatic regions (three grasslands, two shrublands and one evergreen forest). Results of this study show that for the non-forest sites in water-limited areas, GPPm is well correlated with GPPec at annual scales (r² = 0.77, n = 12; SEE = 149.26 g C∙m⁻²∙year⁻¹), although it tends to overestimate GPP and it is less accurate in the sites with permanent water restrictions. The use of biome-specific models based on precipitation measurements at a finer spatial resolution than the Data Assimilation Office (DAO) values can increase the accuracy of these estimations. The seasonal dynamics and the beginning and end of the growing season were well captured by GPPm for the sites where (i) the productivity was low throughout the year or (ii) the changes in the flux trend were abrupt, usually due to the restrictions in water availability. The agreement between GPPec and GPPm in non-forested sites was lower on a weekly basis than at an annual scale (0.44 ≤ r² ≤ 0.49), but these results were improved by including meteorological data at a finer spatial scale, and soil water content and temperature measurements in the model developed to predict GPPec (0.52 ≤ r² ≤ 0.65). Full article

This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs) were proportional to the viewing zenith angles (VZAs) and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm², provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K. Full article

The purpose of this paper was to investigate the early water stress in maize using leaf-level measurements of chlorophyll fluorescence and temperature. In this study, a series of diurnal measurements, such as leaf chlorophyll fluorescence (Fs), leaf spectrum, temperature and photosynthetically active radiation (PAR), were conducted for maize during gradient watering and filled watering experiments. Fraunhofer Line Discriminator methods (FLD and 3FLD) were used to obtain fluorescence from leaves spectrum. This simulated work using the SCOPE model demonstrated the variations in fluorescence and temperature in stress levels expressed by different stress factors. In the field measurement, the gradient experiment revealed that chlorophyll fluorescence decreased for plants with water stress relative to well-water plants and T_leaf-T_air increased; the filled watering experiment stated that chlorophyll fluorescence of maize under water stress were similar to those of maize under well-watering condition. In addition, the relationships between the Fs, retrieved fluorescence, T_leaf-T_air and water content were analyzed. The Fs determination resulted to the best coefficients of determination for the normalized retrieved fluorescence FLD/PAR (R² = 0.54), T_leaf-T_air (R² = 0.48) and water content (R² = 0.71). The normalized retrieved fluorescence yielded a good coefficient of determination for T_leaf-T_air (R² = 0.48). This study demonstrated that chlorophyll fluorescence could reflect variations in the physiological states of plants during early water stress, and leaf temperature confirmed the chlorophyll fluorescence analysis results and improved the accuracy of the water stress detection. Full article

Surface soil moisture is one of the operational products derived from Advanced Scatterometer (ASCAT) data. The reliability of its estimation depends on the detection of predominantly frozen conditions of the landscape (including soil and vegetation) and the presence of wet snow, which would otherwise impede the estimation. As the robust determination of the freeze/thaw (F/T) state using exclusively scatterometer measurements on a global basis is complicated due to the myriad of different climatic and land cover conditions; we propose to support the retrieval using ERA Interim temperature data. The approach is based on a probabilistic time series model, whereby backscatter and temperature data are combined to estimate the freeze/thaw state. The method is assessed with proxy F/T states derived from modeled and in situ air and soil temperature data on a global basis. These analyses show an improved consistency compared to a previously published ASCAT F/T algorithm, with typical agreements between the external data and the results of the algorithm exceeding 80%. The quantitative interpretation of these comparisons is, however, hampered by discrepancies between the F/T state derived from temperature data and the one pertinent to radar remote sensing, as the former does not account for, e.g., wet snow conditions. The inclusion of the ERA Interim temperature data can improve the accuracy of the algorithm by more than 10 percentage points in regions where freezing conditions are rare. Full article

The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R²) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOL_CC and NINSON_CC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² superior to 0.76 and Root mean square errors inferior to 8.3% m³∙m⁻³ in all cases) and using in-situ data, where WISOIL, NINSOL_CC and NINSON_CC criteria stand out among the NSMI and CH. Full article

Synthetic Aperture Radar (SAR) has been successfully used to map wetland’s inundation extents and types of vegetation based on the fact that the SAR backscatter signal from the wetland is mainly controlled by the wetland vegetation type and water level changes. This study describes the relation between L-band PALSAR and seasonal water level changes obtained from Envisat altimetry over the island of Île Mbamou in the Congo Basin where two distinctly different vegetation types are found. We found positive correlations between and water level changes over the forested southern Île Mbamou whereas both positive and negative correlations were observed over the non-forested northern Île Mbamou depending on the amount of water level increase. Based on the analysis of sensitivity, we found that denser vegetation canopy leads to less sensitive variation with respect to the water level changes regardless of forested or non-forested canopy. Furthermore, we attempted to estimate water level changes which were then compared with the Envisat altimetry and InSAR results. Our results demonstrated a potential to generate two-dimensional maps of water level changes over the wetlands, and thus may have substantial synergy with the planned Surface Water and Ocean Topography (SWOT) mission. Full article

Manta rays are classified as Vulnerable to Extinction on the IUCN Red List for Threatened Species. In Australia, a key aggregation site for reef manta rays is Lady Elliot Island (LEI) on the Great Barrier Reef, ~7 km from the shelf edge. Here, we investigate the environmental processes that triggered the largest manta ray feeding aggregation yet observed in Australia, in early 2013. We use MODIS sea surface temperature (SST), chlorophyll-a concentration and photic depth data, together with in situ data, to show that anomalous river discharges led to high chlorophyll (anomalies: 10–15 mg∙m⁻³) and turbid (photic depth anomalies: −15 m) river plumes extending out to LEI, and that these became entrained offshore around the periphery of an active cyclonic eddy. Eddy dynamics led to cold bottom intrusions along the shelf edge (6 °C temperature decrease), and at LEI (5 °C temperature decrease). Strongest SST gradients (>1 °C∙km⁻¹) were at the convergent frontal zone between the shelf and eddy-influenced waters, directly overlying LEI. Here, the front intensified on the spring ebb tide to attract and shape the aggregation pattern of foraging manta rays. Future research could focus on mapping the probability and persistence of these ecologically significant frontal zones via remote sensing to aid the management and conservation of marine species. Full article

High spatial resolution airborne data with little sub-pixel heterogeneity were used to evaluate the suitability of the temperature/vegetation (Ts/VI) space method developed from satellite observations, and were explored to improve the performance of the Ts/VI space method for estimating soil moisture (SM). An evaluation of the airborne ΔT_s/Fr space (incorporated with air temperature) revealed that normalized difference vegetation index (NDVI) saturation and disturbed pixels were hindering the appropriate construction of the space. The non-disturbed ΔT_s/Fr space, which was modified by adjusting the NDVI saturation and eliminating the disturbed pixels, was clearly correlated with the measured SM. The SM estimations of the non-disturbed ΔT_s/Fr space using the evaporative fraction (EF) and temperature vegetation dryness index (TVDI) were validated by using the SM measured at a depth of 4 cm, which was determined according to the land surface types. The validation results show that the EF approach provides superior estimates with a lower RMSE (0.023 m³·m⁻³) value and a higher correlation coefficient (0.68) than the TVDI. The application of the airborne ΔT_s/Fr space shows that the two modifications proposed in this study strengthen the link between the ΔT_s/Fr space and SM, which is important for improving the precision of the remote sensing Ts/VI space method for monitoring SM. Full article

Coal fires are a common and serious problem in most coal-bearing countries. Thus, it is very important to monitor changes in coal fires. Remote sensing provides a useful technique for investigating coal fields at a large scale and for detecting coal fires. In this study, the spreading direction of a coal fire in the Wuda Coal Field (WCF), northwest China, was analyzed using multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) thermal infrared (TIR) data. Using an automated method and based on the land surface temperatures (LST) that were retrieved from these thermal data, coal fires related to thermal anomalies were identified; the locations of these fires were validated using a coal fire map (CFM) that was developed via field surveys; and the cross-validation of the results was also carried out using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images. Based on the results from longtime series of satellite TIR data set, the spreading directions of the coal fires were determined and the coal fire development on the scale of the entire coal field was predicted. The study delineated the spreading direction using the results of the coal fire dynamics analysis, and a coal fire spreading direction map was generated. The results showed that the coal fires primarily spread north or northeast in the central part of the WCF and south or southwest in the southern part of the WCF. In the northern part of the WCF, some coal fires were spreading north, perhaps coinciding with the orientation of the coal belt. Certain coal fires scattered in the northern and southern parts of the WCF were extending in bilateral directions. A quantitative analysis of the coal fires was also performed; the results indicate that the area of the coal fires increased an average of approximately 0.101 km² per year. Full article

As a typical inland river basin, Heihe River basin has been experiencing severe water resource competition between different land cover types, especially in the middle stream and downstream areas. Terrestrial actual evapotranspiration (ETa), including evaporation from soil and water surfaces, evaporation of rainfall interception, transpiration of vegetation canopy and sublimation of snow and glaciers, is an important component of the water cycle in the Heihe River basin. We developed a hybrid remotely sensed ETa estimation model named ETMonitor to estimate the daily actual evapotranspiration of the Heihe River basin for the years 2009–2011 at a spatial resolution of 1 km. The model was forced by a variety of biophysical parameters derived from microwave and optical remote sensing observations. The estimated ETa was evaluated using eddy covariance (EC) flux observations at local scale and compared with the annual precipitation and the MODIS ETa product (MOD16) at regional scale. The spatial distribution and the seasonal variation of the estimated ETa were analyzed. The results indicate that the estimated ETa shows reasonable spatial and temporal patterns with respect to the diverse cold and arid landscapes in the upstream, middle stream and downstream regions, and is useful for various applications to improve the rational allocation of water resources in the Heihe River basin. Full article

Local-scale environmental heterogeneity can provide microhabitats that influence the spatial distribution of competing species. Microhabitats may influence the distribution of seagrasses along elevation gradients, but difficulty measuring intertidal microtopography has hindered quantification. Using a terrestrial laser scanner (TLS), we mapped and monitored a 1.84 ha study site for three years to understand spatial and temporal patterns of sediment microtopography. We performed high-accuracy GPS surveys and vegetation surveys of a native and an invasive seagrass. TLS provided sub-decimeter scale precision in digital elevation models (DEMs) of the tideflat. The location and shape of microtopographic features were stable from year to year, but the magnitude of local relief varied. A simple index of topographic context predicted the shoot density of the native seagrass, Zostera marina and the invasive seagrass, Zostera japonica, but the shoot density of the invasive seagrass was better predicted by the shoot density of Z. marina than by topographic context. Microtopographic relief at this site appears to exert a strong influence on the meter-scale distribution of seagrass. We demonstrate the potential for TLS mapping of habitat-relevant microtopography in a soft sediment intertidal environment where TLS faces substantial challenges but promises unique insights. Full article

The ability for satellite sensors to detect lit fishing boats has been known since the 1970s. However, the use of the observations has been limited by the lack of an automatic algorithm for reporting the location and brightness of offshore lighting features arising from boats. An examination of lit fishing boat features in Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) data indicates that the features are essentially spikes. We have developed a set of algorithms for automatic detection of spikes and characterization of the sharpness of spike features. A spike detection algorithm generates a list of candidate boat detections. A second algorithm measures the height of the spikes for the discard of ionospheric energetic particle detections and to rate boat detections as either strong or weak. A sharpness index is used to label boat detections that appear blurry due to the scattering of light by clouds. The candidate spikes are then filtered to remove features on land and gas flares. A validation study conducted using analyst selected boat detections found the automatic algorithm detected 99.3% of the reference pixel set. VIIRS boat detection data can provide fishery agencies with up-to-date information of fishing boat activity and changes in this activity in response to new regulations and enforcement regimes. The data can provide indications of illegal fishing activity in restricted areas and incursions across Exclusive Economic Zone (EEZ) boundaries. VIIRS boat detections occur widely offshore from East and Southeast Asia, South America and several other regions. Full article

Natura 2000 Habitat Conservation Status is currently evaluated based on fieldwork. However, this is proving to be unfeasible over large areas. The use of remote sensing is increasingly encouraged but covering the full range of ecological variables by such datasets and ensuring compatibility with the traditional assessment methodology has not been achieved yet. We aimed to test Airborne Laser Scanning (ALS) as a source for mapping all variables required by the local official conservation status assessment scheme and to develop an automated method that calculates Natura 2000 conservation status at 0.5 m raster resolution for 24 km² of Pannonic Salt Steppe habitat (code 1530). We used multi-temporal (summer and winter) ALS point clouds with full-waveform recording and a density of 10 pt/m². Some required variables were derived from ALS product rasters; others involved vegetation classification layers calculated by machine learning and fuzzy categorization. Thresholds separating favorable and unfavorable values of each variable required by the national assessment scheme were manually calibrated from 10 plots where field-based assessment was carried out. Rasters representing positive and negative scores for each input variable were integrated in a ruleset that exactly follows the Hungarian Natura 2000 assessment scheme for grasslands. Accuracy of each parameter and the final conservation status score and category was evaluated by 10 independent assessment plots. We conclude that ALS is a suitable data source for Natura 2000 assessments in grasslands, and that the national grassland assessment scheme can successfully be used as a GIS processing model for conservation status, ensuring that the output is directly comparable with traditional field based assessments. Full article

Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to low operational costs, high operational flexibility and high spatial resolution of imagery. In order to optimize the use of these technologies for precision viticulture, their technical, scientific and economic performances need to be assessed. The aim of this work is to compare NDVI surveys performed with UAV, aircraft and satellite, to assess the capability of each platform to represent the intra-vineyard vegetation spatial variability. NDVI images of two Italian vineyards were acquired simultaneously from different multi-spectral sensors onboard the three platforms, and a spatial statistical framework was used to assess their degree of similarity. Moreover, the pros and cons of each technique were also assessed performing a cost analysis as a function of the scale of application. Results indicate that the different platforms provide comparable results in vineyards characterized by coarse vegetation gradients and large vegetation clusters. On the contrary, in more heterogeneous vineyards, low-resolution images fail in representing part of the intra-vineyard variability. The cost analysis showed that the adoption of UAV platform is advantageous for small areas and that a break-even point exists above five hectares; above such threshold, airborne and then satellite have lower imagery cost. Full article

Future spaceborne synthetic aperture radar (SAR) missions require complete and frequent coverage of the earth with a high resolution. Terrain Observation by Progressive Scans (TOPS) is a novel wide swath mode but has impaired azimuth resolution. In this paper, an innovative extended TOPS mode named Alamouti Space-time Coding multiple-input multiple-output TOPS (ASTC-MIMO-TOPS) mode combined with digital beam-forming (DBF) in elevation and multi-aperture SAR signal reconstruction in azimuth is proposed. This innovative mode achieves wide-swath coverage with a high geometric resolution and also overcomes major drawbacks in conventional MIMO SAR systems. The data processing scheme of this imaging scheme is presented in detail. The designed system example of the proposed ASTC-MIMO-TOPS mode, which has the imaging capacity of a 400 km wide swath with an azimuth resolution of 3 m, is given. Its system performance analysis results and simulated imaging results on point targets demonstrate the potential of the proposed novel spaceborne SAR mode for high-resolution wide-swath (HRWS) imaging. Full article

A new procedure to assess the quality of topographic correction (TOC) algorithms applied to remote sensing imagery was previously proposed by the authors. This procedure was based on a model that simulated synthetic scenes, representing the radiance an optical sensor would receive from an area under some specific conditions. TOC algorithms were then applied to synthetic scenes and the resulting corrected scenes were compared with a horizontal synthetic scene free of topographic effect. This comparison enabled an objective and quantitative evaluation of TOC algorithms. This approach showed promising results but had some shortcomings that are addressed herein. First, the model, originally built to simulate only broadband panchromatic scenes, is extended to multispectral scenes in the visible, near infrared (NIR), and short wave infrared (SWIR) bands. Next, the model is validated by comparing synthetic scenes with four Satellite pour l'Observation de la Terre 5 (SPOT5) real scenes acquired on different dates and different test areas along the Pyrenees mountain range (Spain). The results obtained show a successful simulation of all the spectral bands. Therefore, the model is deemed accurate enough for its purpose of evaluating TOC algorithms. Full article

A large decrease in the land surface albedo of the Loess Plateau was observed from 2000 to 2010, as measured using satellite imagery. In particular, ecological restoration program regions experienced a decrease in peak season land surface albedo exceeding 0.05. In this study, we examined the spatial and temporal patterns of variation during the peak season albedo in the Loess Plateau and analyzed its relationships with changes of anthropogenic and natural factors at the pixel level. Our analysis revealed that increasing grassland coverage due to returning rangeland to grassland could lead to a maximum albedo decrease of 0.030 in peak season. This result highlighted the human-induced land use change in driving the decreasing albedo on an annual scale. There was no significant correlation between precipitation change and albedo reduction. Precipitation could influence the spatial pattern of albedo in drought years by influencing the natural vegetation water requirement. However, the role of precipitation was not obvious in the ecological restoration program regions. This article demonstrates the substantial role that land use change could play in regional-scale albedo change and climate. Finally, some implications for the radiative forcing of land use change are discussed. Full article

The main objective of this study is to assess the spatio-temporal dynamics of land cover/land use changes in the lower Mekong Delta over the last 40 years with the coastal Tran Van Thoi District of Ca Mau Province, Vietnam as a case study. Land cover/land use change dynamics are derived from moderate to high spatial resolution (Landsat and SPOT) satellite imagery in six time intervals ranging from 1973 to 2011. Multi-temporal satellite images were collected, georeferenced, classified using per-pixel method, validated, and compared in post classification for the land use/land cover change detection in decades. Seven major land cover/land use classes were obtained, including cultivated lands, aquaculture ponds, mangrove forest, melaleuca forest, built up areas, bare lands, and natural water bodies. The accuracies of the land cover/land use maps for 1973, 1979, 1989, 1995, 2004, and 2011 were 81%, 82%, 86%, 87%, 89%, and 89%, respectively. The results show that the area of cultivated lands reduced over the period 1973–2011, however, it still represents the dominant land use in the case study. Aquaculture ponds were almost absent in 1973 but greatly increased from 1995 to 2004, to represent 20% of the land surface in 2011. Overall, from 1973 to 2011, bare lands, cultivated lands, mangrove forest, and melaleuca forest decreased by 104 km², 77 km², 61 km², and 5 km², respectively. In contrast, aquaculture lands and built up areas increased by 123 km² and 120 km², respectively. Temporal analysis highlights that these changes took place mostly between 1995 and 2004. This study is a first step to identify the main drivers of land use changes in this delta region, which include economical policies as well as demographic, socio-economic, and environmental changes. Full article

The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species’ habitats. In our study we combine information from a species ordination with hyperspectral reflectance signatures to predict occurrence probabilities for Natura 2000 habitat types and their conservation status. We examine how accurate habitat types and habitat threat, expressed by pressure indicators, can be described in an ordination space using spatial correlation functions from the geostatistic approach. We modeled habitat quality assessment parameters using floristic gradients derived by non-metric multidimensional scaling on the basis of 58 field plots. In the resulting ordination space, the variance structure of habitat types and pressure indicators could be explained by 69% up to 95% with fitted variogram models with a correlation to terrestrial mapping of >0.8. Models could be used to predict habitat type probability, habitat transition, and pressure indicators continuously over the whole ordination space. Finally, partial least squares regression (PLSR) was used to relate spectral information from AISA DUAL imagery to floristic pattern and related habitat quality. In general, spectral transferability is supported by strong correlation to ordination axes scores (R² = 0.79–0.85), whereas second axis of dry heaths (R² = 0.13) and first axis for pioneer grasslands (R² = 0.49) are more difficult to describe. Full article

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing. Full article

Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Our case study is located in an area highly affected by coal mining activity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI and EVI2), chlorophyll vegetation index (CVI), and tasseled cap transformations were used as vegetation spectral indices to estimate canopy height. In turn, canopy height was used to predict a coniferous forest’s biomass using Jenkins allometric and Lambert and Ung allometric equations. The biophysical properties of 700 individual trees at eight different scan stations in the study area were obtained using high-resolution ground LiDAR. Nine models (H_i) were established to discover the best relationship between the canopy height model (CHM) from the airborne LiDAR and the vegetation spectral indices (VSIs) from Landsat images for the year 2005, and HB₉ (Jenkins allometric equation) and HY₉ (Lambert and Ung allometric equation) proved to be the best models (r² = 0.78; root mean square error (RMSE) = 44 Mg/H, r² = 0.67; RMSE = 58.01 Mg/H, respectively; p < 0.001) for estimating the canopy height and the biomass. This model accurately captured the most affected areas (deforested) and the reclaimed areas (forested) in the study area. Five years were chosen for studying the biomass change: 1988, 1990, 2001, 2005, and 2011. Additionally, four pixel-based image comparisons were analyzed (i.e., 1988–1990, 1990–2005, 2005–2009, and 2009–2011), and Mann-Kendall statistics for the subsets of years were obtained. The detected change showed that, in general, the environment in the study area was recovering and regaining its initial biomass after the dramatic decrease that occurred in 2005 as a result of intensive mining activities and disturbance. Full article

Leaf Area Index (LAI) is an important variable for numerous processes in various disciplines of bio- and geosciences. In situ measurements are the most accurate source of LAI among the LAI measuring methods, but the in situ measurements have the limitation of being labor intensive and site specific. For spatial-explicit applications (from regional to continental scales), satellite remote sensing is a promising source for obtaining LAI with different spatial resolutions. However, satellite-derived LAI measurements using empirical models require calibration and validation with the in situ measurements. In this study, we attempted to validate a direct LAI retrieval method from remotely sensed images (RapidEye) with in situ LAI (LAI_destr). Remote sensing LAI (LAI_rapideye) were derived using different vegetation indices, namely SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index). Additionally, applicability of the newly available red-edge band (RE) was also analyzed through Normalized Difference Red-Edge index (NDRE) and Soil Adjusted Red-Edge index (SARE). The LAI_rapideye obtained from vegetation indices with red-edge band showed better correlation with LAI_destr (r = 0.88 and Root Mean Square Devation, RMSD = 1.01 & 0.92). This study also investigated the need to apply radiometric/atmospheric correction methods to the time-series of RapidEye Level 3A data prior to LAI estimation. Analysis of the the RapidEye Level 3A data set showed that application of the radiometric/atmospheric correction did not improve correlation of the estimated LAI with in situ LAI. Full article

Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001–2007 and 2007–2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite’s comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today’s continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. Full article

We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field- to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support. Full article

Full-waveform airborne laser scanning systems provide fundamental observations for each echo, such as the echo width and amplitude. Geometric and physical information about illuminated surfaces are simultaneously provided by a single scanner. However, there are concerns about whether the physical meaning of observations is consistent among different scanning missions. Prior to the application of waveform features for multi-temporal data classification, such features must be normalized. This study investigates the transferability of normalized waveform features to different surveys. The backscatter coefficient is considered to be a normalized physical feature. A normalization process for the echo width, which is a geometric feature, is proposed. The process is based on the coefficient of variation of the echo widths in a defined neighborhood, for which the Fuzzy Small membership function is applied. The normalized features over various land cover types and flight missions are investigated. The effects of different feature combinations on the classification accuracy are analyzed. The overall accuracy of the combination of normalized features and height-based attributes achieves promising results (>93% overall accuracy for ground, roof, low vegetation, and tree canopy) when different flight missions and classifiers are used. Nevertheless, the combination of all possible features, including raw features, normalized features, and height-based features, performs less well and yields inconsistent results. Full article