Satellite Pour l'Observation De La Terre Research Articles

An analysis of oasis evolution based on land use and land cover change: A case study in the Sangong River Basin on the northern slope of the Tianshan Mountains

This study investigated oasis evolution and the changes of peripheral desert in the Sangong River Basin since the 1950s by rebuilding seven land cover maps derived from black-and-white aerial photographs (1958, 1968, and 1978), a color-infrared aerial photograph (1987), Landsat Thematic Mapper (TM) imagery (1998), Satellite Pour l’Observation de la Terre (SPOT) imagery (2004), and Landsat Operational Land Imager (OLI) imagery (2014). The results showed that: (1) Since 1950, the oasis consecutively expanded more than four times from an alluvial fan to an alluvial plain, causing the shrinkage of desert landscapes that were dominated by a Haloxylon ammodendron Bunge community (HBC) and a Tamarix chinensis Lour community (TLC). Furthermore, the primary (1958–1968) and final (2004–2014) stages were the most important periods, during which agricultural land experienced the most rapid expansion during the period 1958–1968, and the built-up area showed the most rapid expansion after the 2000s. (2) Two basic management modes, a “local mode” formed by the local governments and a “farm management mode” developed by Xinjiang Production and Construction Corps, together promoted oasis evolution under various land-use and landcover (LULC) stages. (3) The evolution of the modern oasis during the 1950s–2004 showed the general features of an arid oasis, while during the period of 2004–2014 it was characterized by a large-scale inter-basin water diversion or the import of new water sources. (4) The oasis expanded at the expense of desert vegetation, resulting in distinct variation in the structure of the desert plant community, which will make it more difficult to protect the desert ecosystem.

Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing

Sentinel-2A MSI is the Landsat-like spatial resolution (10–60 m) super-spectral instrument of the European Space Agency (ESA), aimed at additional data continuity for global land surface monitoring with Landsat and Satellite Pour l’Observation de la Terre (SPOT) missions. Several simulation studies have been conducted in the last several years to show the potential of Sentinel-2A MSI (MultiSpectral Instrument). Now that real data are available, the first confirmations of this potential and comparisons with other operational systems are being made. This paper aims at evaluating Sentinel-2A MSI band ratio products that are relevant for geological remote sensing. A Sentinel-2A MSI and a Landsat 8 OLI (Operational Land Imager) scene were processed from their respective levels L1C and L1T to level L2A (bottom of atmosphere reflectance). Then, three band ratios originally defined for Landsat TM (Thematic Mapper) were used to map mineralogy associated with a hydrothermal alteration system in southeast Spain. The results obtained with Sentinel-2A MSI were compared with those obtained with Landsat 8 OLI and a simulated Sentinel-2A MSI dataset that was used before actual data were released. Results show that the images appear similar to the human eye having a correlation of approximately 0.8 and higher, but that the associated data ranges differ significantly. The resulting products are also compared to a published geologic map of the study area, and it is shown that the resulting maps correspond with the conceptual geologic model of the epithermal deposit.

Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: a case study in Bangladesh

ABSTRACTThe environmental and societal impacts of tropical cyclones could be reduced using a range of management initiatives. Remote sensing can be a cost effective, accurate, and potential tool for mapping the multiple impacts caused by tropical cyclones using high-to-moderate spatial resolution (5–30 m) satellite imagery to provide data on the following essential parameters – evacuation, relief, and management of natural resources. This study developed and evaluated an approach for assessing the impacts of tropical cyclones through object-based image analysis and moderate spatial resolution imagery. Pre- and post-cyclone maps of artificial and natural features are required for assessing the overall impacts in the landscape that could be acquired by mapping specific land cover types. We used the object-based approach to map land-cover types in pre- and post-cyclone Satellite Pour l’Observation de la Terre (SPOT) 5 image data and the post-classification comparison technique to identify changes in the particular features in the landscape. Cyclone Sidr (2007) was used to test the applicability of this approach in Sarankhola Upazila in Bangladesh. The object-based approach provided accurate results for classifying features from pre- and post-cyclone satellite images with an overall accuracy of 95.43% and 93.27%, respectively. Mapped changes identified the extent, type, and form of cyclone induced impacts. Our results indicate that 63.15% of the study area was significantly affected by cyclone Sidr. The majority of mapped damage was found in vegetation, cropped lands, settlements, and infrastructure. The damage results were verified through the high spatial resolution satellite imagery, reports and pictures that were taken after the cyclone. The methods developed may be used in future to assess the multiple impacts caused by tropical cyclones in Bangladesh and other similar environments for the purposes of tropical cyclone disaster management.

A systematic investigation of geostatistical image fusion for the improvement of the spectral fidelity and spatial detail in Landsat MS imagery

ABSTRACTThis is the first systematic investigation into the assumptions of image fusion using regression Kriging (RK) – a geostatistical method – illustrated with Landsat MS (multispectral) and SPOT (Satellite Pour l’Observation de la Terre) panchromatic images. The efficiency of different linear regression and Kriging methods in the fusion process is examined by visual and quantitative indicators. Results indicate a trade-off between spectral fidelity and spatial detail preservation for the GLS (generalized least squares regression) and OLS (ordinary least squares regression) methods in the RK process: OLS methods preserve more spatial detail, while GLS methods retain more spectral information from the MS images but at a greater computational cost. Under either OK (ordinary Kriging) or UK (universal Kriging) with either OLS or GLS, the spherical variogram improves spatial details from the panchromatic image, while the exponential variogram maintains more spectral information from the MS image. Overall, RK-based fusion methods outperform conventional fusion approaches from both the spectral and spatial point of view.

Assessment of forest damage caused by an ice storm using multi-temporal remote-sensing images: a case study from Guangdong Province

ABSTRACTIn early 2008, forest ecosystems in southern China suffered damage due to a severe ice storm disaster. The area and degree of forest damage caused by the ice storm was assessed using Satellite Pour l’Observation de la Terre (SPOT)-Vegetation images for Guangdong Province acquired between 1999 and 2008. By using the maximum value composition method and image thresholding techniques, the forest vegetation loss, expressed as the change in net primary productivity (NPP) and two indicators (I1, I2), was estimated. The damage threshold was determined by comparing the standard deviation of pixels of the undamaged areas in 2008 and other years without any disaster, which was 10%. The area of damaged forest vegetation was 47,670 km2, with the northern Guangdong Province most seriously affected. The total loss of NPP for forest vegetation was 50,578,055 t (DW) year−1, with 52 counties (43.7%) suffering forest vegetation damage. Evergreen coniferous forest was most widely affected, but evergreen broad-leaved forest was the most severely damaged vegetation type. Terrain topography influenced the damage to forest vegetation, which was found to increase with increasing elevation and slope gradient. The range and degree of damaged forest determined by remote-sensing data is consistent with the extent of the ice storm, indicating that this study provides a new approach for rapid assessment of forest disasters at a regional scale.

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW), an area of over 800,000 km2. Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008–2011) for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC), a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (±0.51% standard error), while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r2 = 0.90) with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and is already widely used for natural resource management in the state.

Use of the DMSP-OLS Nighttime Light Data to Study Urbanization and Its Influence on NDVI in Taihu Basin, China

AbstractThis study investigates the urbanization of Taihu Basin and its influence on Satellite Pour l’Observation de la Terre (SPOT)-Normalized Difference Vegetation Index (NDVI). The correction model of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) of nighttime images was built to extract the dynamics of urban expansion. Tempo-spatial correlations between a vegetation index and nighttime light intensity are discussed to quantitatively assess the urbanization effect. The results show that Taihu Basin is in a period of rapid urbanization. Urban expansion is developing not only surrounding the city center circle but also along the main traffic arteries and rivers, which show a linear expansion resulting in an urban agglomeration. Under the influence of urbanization, the NDVI shows a slight decrease, and there is a significant negative tempo-spatial correlation in the urban areas of the eastern watershed between NDVI and nighttime light intensity, which are predominantly...

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Abstract From in situ measured woody cover we develop a phenology driven model to estimate the canopy cover of woody species in the Sahelian drylands at 1 km scale. The model estimates the total canopy cover of all woody phanerophytes and the concept is based on the significant difference in phenophases of dryland trees, shrubs and bushes as compared to that of the herbaceous plants. Whereas annual herbaceous plants are only green during the rainy season and senescence occurs shortly after flowering towards the last rains, most woody plants remain photosynthetically active over large parts of the year. We use Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l'Observation de la Terre (SPOT) — VEGETATION (VGT) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series and test 10 metrics representing the annual FAPAR dynamics for their ability to reproduce in situ woody cover at 43 sites (163 observations between 1993 and 2013) in the Sahel. Both multi-year field data and satellite metrics are averaged to produce a steady map. Multiple regression models using the integral of FAPAR from the onset of the dry season to the onset of the rainy season, the start date of the growing season and the rate of decrease of the FAPAR curve achieve a cross validated r 2 /RMSE (in % woody cover) of 0.73/3.0 (MODIS) and 0.70/3.2 (VGT). The extrapolation to Sahel scale shows agreement between VGT and MODIS at an almost nine times higher woody cover than in the global tree cover product MOD44B which only captures trees of a certain minimum size. The derived woody cover map of the Sahel is made publicly available and represents an improvement of existing products and a contribution for future studies of drylands quantifying carbon stocks, climate change assessment, as well as parametrization of vegetation dynamic models.

Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process‐oriented biosphere model

AbstractWe investigate the benefits of assimilating in situ and satellite data of the fraction of photosynthetically active radiation (FAPAR) relative to eddy covariance flux measurements for the optimization of parameters of the ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystem) biosphere model. We focus on model parameters related to carbon fixation, respiration, and phenology. The study relies on two sites—Fontainebleau (deciduous broadleaf forest) and Puechabon (Mediterranean broadleaf evergreen forest)—where measurements of net carbon exchange (NEE) and latent heat (LE) fluxes are available at the same time as FAPAR products derived from ground measurements or derived from spaceborne observations at high (SPOT (Satellite Pour l′Observation de la Terre)) and medium (MERIS (MEdium Resolution Imaging Spectrometer)) spatial resolutions. We compare the different FAPAR products, analyze their consistency with the in situ fluxes, and then evaluate the potential benefits of jointly assimilating flux and FAPAR data. The assimilation of FAPAR data leads to a degradation of the model‐data agreement with respect to NEE at the two sites. It is caused by the change in leaf area required to fit the magnitude of the various FAPAR products. Assimilating daily NEE and LE fluxes, however, has a marginal impact on the simulated FAPAR. The results suggest that the main advantage of including FAPAR data is the ability to constrain the timing of leaf onset and senescence for deciduous ecosystems, which is best achieved by normalizing FAPAR time series. The joint assimilation of flux and FAPAR data leads to a model‐data improvement across all variables similar to when each data stream is used independently, corresponding, however, to different and likely improved parameter values.

Detection of change in vegetation in the surrounding Desert areas of Northwest China and Mongolia with multi-temporal satellite images

Vegetation monitoring is an important step in developing a better understanding of land use and its changes, due to the sensitivity of surface vegetation to changes in the global climate and environment. In this study, normalized difference vegetation index (NDVI) of the area surrounding the Gobi Desert in North Asia was multi-temporally interpreted by analyzing time-series Satellite Pour l’Observation de la Terre (SPOT) Vegetation (VGT) data, over a roughly nine-year period from January 1999 to November 2007. The study area was classified into eight classes, and compared to classified Moderate resolution Imaging Spectrometer (MODIS) global land-cover data to select desertification-sensitive areas. The study focused on three classes (barren land, open shrubland, grassland) due to their high sensitivity to climate change. The results showed significant extension of the barren land class from 1992 to 1999, with 47.8% of the open shrubland transformed into barren land. Among five terms (1999–2003, 2003–2005, 2005–2007, 1999–2005, 1999–2007) which are carefully selected from variations of the annual NDVI mean for each class over nine years, significant changes were observed for barren land from 1999–2003, and for open shrubland and grassland from 2005–2007. An analysis of the positive change (the change from sparse vegetation to dense vegetation) and negative change (or desertification) was conducted over the study period; the number of pixels corresponding to a positive change for barren land was similar to the number of negative change pixels. Human activity and afforestation over the study area were also captured in multitemporal satellite imagery. For open shrubland and grassland, the negative change area was bigger than the positive change area. Precipitation data over the nine-year period exhibited a pattern similar to that for the vegetation data, as expected.

Paired-data fusion for improved estimation of pine plantation structure

Integration of optical, lidar, and radar data for estimating forest structural parameters has been extensively investigated in recent decades. While evidence in the literature reveals that a common fusion method, where all attributes derived from different types of remotely-sensed data are used directly in stepwise multiple-linear regression, is not able to increase the accuracy of the structural parameter estimations in all cases, it is possible to determine the ratio of the attributes (called the pseudo-attribute hereafter) derived from two different datasets for the modelling process. In this study, the performance of WorldView-2 (WV-2) and Satellite Pour l’Observation de la Terre-5 (SPOT-5) multispectral images, small foot-print lidar data, and multi-date dual-polarized Advanced Land Observing Satellite phased array type L-band synthetic aperture radar (ALOS–PALSAR) data in paired-data fusion have been assessed using the two methods of common and ratio fusion for estimating forest structural parameters over a Pinus radiata plantation. For this purpose, grey level co-occurrence matrix (GLCM) indices with different orientations and window sizes were calculated for the WV-2 and SPOT-5 multispectral data. The backscatter derivatives and statistical metrics were extracted from multi-date dual-polarized ALOS-PALSAR data and lidar-derived canopy height model (CHM), respectively. After applying stepwise multiple-linear regression, the results showed that the ratio fusion method provided more accurate models than the common fusion method. Finally, statistical analysis showed that there is no significant difference between results derived when SPOT-5 and WV-2 data were each fused with lidar data, resulting in estimation of structural parameters with less than 20% error, which is consistent with the quality of field inventories.

Understanding urban wetland dynamics: cross-scale detection and analysis of remote sensing

This study aimed to detect and understand remotely sensed urban wetland dynamics as a sensitive indicator of the combined effects of human disturbances and climate impacts in the course of global change. To address this objective, the study developed technical approaches to detect and interpret wetland changes across spatial scales in complex urban landscapes. Using a series of Satellite Pour l’Observation de la Terre (SPOT) images covering 1992–2010, the study was conducted in the Kansas City metropolitan area of the USA, which has experienced significant urban sprawl in recent decades. As a fine-tuning of the traditional supervised image classification, a knowledge-based classification algorithm was developed to identify fine-scale, hidden wetlands that cannot be appropriately detected based on their spectral differentiability. The analyses of wetland change were implemented at the metropolitan, watershed, and sub-watershed scales as well as being based on the size of surface water bodies in order to reveal real pictures of urban wetland change trends in relation to major driving factors. The results of the study indicated that the knowledge-based classification approach improved the detection capability and accuracy of urban wetlands by fine-tuning the traditional classification results. The cross-scale analysis of detected land covers revealed that wetland dynamics varied in trend and magnitude from metropolitan, watersheds, to sub-watershed scales. The study found that increased precipitation swelled wetlands, which inflated the findings of remotely sensed wetland cover and related trend interpretation. During an 18 year study period, human development activities in the study area resulted in a large increase in impervious surfaces, which was mainly at the expense of farmland/grassland areas and some small wetlands in all urban watersheds. In contrast, increased precipitation in the region swelled large wetlands in particular. This mixed picture of urban wetland dynamics, associated with the analysis of underlying driving factors, provides a new baseline for relevant urban planning, management, and research in a global change perspective.

Validation of a Simplified Model to Generate Multispectral Synthetic Images

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.

Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l’Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000km2 in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Additionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R2=0.01–0.28, while our algorithm performed showed a stronger correlation coefficient (R2=0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

The roles of textural images in improving land-cover classification in the Brazilian Amazon

Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l’Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving land-cover classification. The classification accuracy can be improved by 5.2–13.4% as the pixel size changes from 30 to 0.6 m.

Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

Abstract. Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the Earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour L'Observation de la Terre (SPOT) albedo. We compare results from offline simulations over the Australian continent. The control simulation has prescribed background snow-free and vegetation-free soil albedo derived from MODIS whilst the experiments use a simple parameterisation based on soil moisture and colour, originally from the Biosphere Atmosphere Transfer Scheme (BATS), and adopted in the Common Land Model (CLM). The control simulation, with prescribed soil albedo, shows that CABLE simulates overall albedo over Australia reasonably well, with differences compared to MODIS and SPOT albedos within ±0.1. Application of the original BATS scheme, which uses an eight-class soil classification, resulted in large differences of up to −0.25 for the near-infrared (NIR) albedo over large parts of the desert regions of central Australia. The use of a recalibrated 20-class soil colour classification from the CLM, which includes a higher range for saturated and VIS (visible) and NIR soil albedos, reduced the underestimation of the NIR albedo. However, this soil colour mapping is tuned to CLM soil moisture, a quantity which is not necessarily transferrable between land surface models. We therefore recalibrated the soil color map using CABLE's climatological soil moisture, which further reduced the underestimation of the NIR albedo to within ±0.15 over most of the continent as compared to MODIS and SPOT albedos. Small areas of larger differences of up to −0.25 remained within the central arid parts of the continent during summer; however, the spatial extent of these large differences is substantially reduced as compared to the simulation using the default eight-class uncalibrated soil colour map. It is now possible to use CABLE coupled to atmospheric models to investigate soil-moisture–albedo feedbacks, an important enhancement of the model.

Automatic change detection in high-resolution remote-sensing images by means of level set evolution and support vector machine classification

In this article, we propose a method for change detection in high-resolution remote-sensing images by means of level set evolution and support vector machine (SVM) classification, which combined both the pixel-level method and the object-level method. Both pixel-based change features and object-based ones are extracted to improve the discriminability between the changed class and the unchanged class. At the pixel level, the change detection problem is formulated as a segmentation issue using level set evolution in the difference images. At the object level, potential training samples are selected from the segmentation results without manual intervention into the SVM classifier. Thereafter, the final changes are obtained by combining the pixel-based changes and the object-based changes. A chief advantage of our approach is being able to select appropriate samples for SVM classifier training. Furthermore, our proposed method helps improve the accuracy and the degree of automation. We systematically evaluate it with various Satellite Pour l’Observation de la Terre (SPOT) 5 images and aerial images. Experimental results demonstrate the accuracy of our proposed method.

Marsh Dieback, loss, and recovery mapped with satellite optical, airborne polarimetric radar, and field data

Landsat Thematic Mapper and Satellite Pour l'Observation de la Terre (SPOT) satellite based optical sensors, NASA Uninhabited Aerial Vehicle synthetic aperture radar (UAVSAR) polarimetric SAR (PolSAR), and field data captured the occurrence and the recovery of an undetected dieback that occurred between the summers of 2010, 2011, and 2012 in the Spartina alterniflora marshes of coastal Louisiana. Field measurements recorded the dramatic biomass decrease from 2010 to 2011 and a biomass recovery in 2012 dominated by a decrease of live biomass, and the loss of marsh as part of the dieback event. Based on an established relationship, the near-infrared/red vegetation index (VI) and site-specific measurements delineated a contiguous expanse of marsh dieback encompassing 6649.9ha of 18,292.3ha of S. alterniflora marshes within the study region. PolSAR data were transformed to variables used in biophysical mapping, and of this variable suite, the cross-polarization HV (horizontal send and vertical receive) backscatter was the best single indicator of marsh dieback and recovery. HV backscatter exhibited substantial and significant changes over the dieback and recovery period, tracked measured biomass changes, and significantly correlated with the live/dead biomass ratio. Within the context of regional trends, both HV and VI indicators started higher in pre-dieback marshes and exhibited substantially and statistically higher variability from year to year than that exhibited in the non-dieback marshes. That distinct difference allowed the capturing of the S. alterniflora marsh dieback and recovery; however, these changes were incorporated in a regional trend exhibiting similar but more subtle biomass composition changes.

Efficiency of Orientation Parameters on Georeferencing Accuracy of SPOT-5 HRG Level-1A Stereoimages

The efficiency of interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) of the Satellite Pour l'Observation de la Terre 5 (SPOT-5) has been investigated with respect to georeferencing accuracy. A couple of SPOT-5 high-resolution geometric panchromatic level-1A stereoimages with 5-m ground sampling distance covering Zonguldak (Turkey) test field were used for this purpose using orientation parameters provided by metadata files. Ground control points are obtained by Global Positioning System observations. For the purpose of establishing geometric relationship between image and ground coordinate systems, an orbit-attitude parametric model has been used considering imaging geometry of sensor and ephemeris data of satellite. Adjustment consisting of the Gauss-Helmert model was performed in pre- and bundle-adjustment steps. Analyses show that the most efficient parameters are IOPs defined by look angles while EOPs are not significant on the accuracy. The validation of the EOPs and the correlation among IOPs and EOPs are also statistically analyzed. Different variations of point distribution and EOP configurations were preferred, achieving the georeferencing accuracies as ~ ±1 m and ~ ± 5 m at control and check points, respectively. All computations are performed via the developed program package called GeoSpot in Matlab platform.

Quantitative assessment of remotely sensed global surface models using various land classes produced from Landsat data in Istanbul

Digital elevation model (DEM) is the most popular product for three-dimensional (3D) digital representation of bare Earth surface and can be produced by many techniques with different characteristics and ground sampling distances (GSD). Space-borne optical and synthetic aperture radar (SAR) imaging are two of the most preferred and modern techniques for DEM generation. Using them, global DEMs that cover almost entire Earth are produced with low cost and time saving processing. In this study, we aimed to assess the Satellite pour l’observation de la Terre-5 (SPOT-5), High Resolution Stereoscopic (HRS), the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER), and the Shuttle Radar Topography Mission (SRTM) C-band global DEMs, produced with space-borne optical and SAR imaging. For the assessment, a reference DEM derived from 1:1000 scaled digital photogrammetric maps was used. The study is performed in 100 km2 study area in Istanbul including various land classes such as open land, forest, built-up land, scrub and rough terrain obtained from Landsat data. The analyses were realized considering three vertical accuracy types as fundamental, supplemental, and consolidated, defined by national digital elevation program (NDEP) of USA. The results showed that, vertical accuracy of SRTM C-band DEM is better than optical models in all three accuracy types despite having the largest grid spacing. The result of SPOT-5 HRS DEM is very close by SRTM and superior in comparison with ASTER models.