Evaluating Shuttle radar and interpolated DEMs for slope gradient and soil erosion estimation in low relief terrain

The error in slope gradient estimates provided by digital elevation models propagates to spatial modelling of erosion and other environmental attributes, potentially impacting land management priorities. This study compared the slope estimates of Shuttle Radar Topographic Mission (SRTM) DEMs with those generated by interpolation of topographic contours, at two grid cell resolutions. The magnitude and spatial patterns of error in DEM slope, and derived erosion estimates using the Revised Universal Soil Loss Equation (RUSLE), were evaluated at three sites in eastern Australia. The sites have low-relief terrain and slope gradients less than 15%, characteristics which dominate the global land surface by area and are often highly utilised. Relative to a reference DEM resampled to the same resolution (a measure of DEM ‘quality’), the 90 m (3-s) SRTM DEM provided the best estimates of slopes, being within 20% for each 5% slope class outside alluvial floodplains where it over-predicted by up to 220%. Relative to a hillslope scale 10 m reference DEM, the 30 m (1-s) SRTM-derived DEM-S, provided slope gradient estimates slightly less biased towards under-prediction than the 90 m SRTM and significantly less biased on alluvial floodplains. In contrast, the 20 m vertical contour intervals underpinning the interpolated DEMs resulted in under-prediction of slope gradient by more than a factor of 5 over large contiguous areas (>1 km²). The 30 m DEM-S product provided the best estimate of hillslope erosion, being 3–4% better than the 90 m SRTM. The slope errors in the interpolated DEMs translated into generally poorer and less consistent erosion estimates than SRTM. From this study it is concluded that the SRTM DEM products, in particular the 30 m SRTM-derived DEM-S, provide estimates of slope gradient and erosion which are more accurate, and more consistent within and between low relief study sites, than interpolated DEMs.     

A robust estimator for the accuracy assessment of remote-sensing-derived DEMs

A robust estimator based on the M-estimation principle (REMP) has been developed for digital elevation model (DEM) accuracy assessment. Adaptive weights were employed to respond to a broad class of DEM error distributions, and an iterative procedure in terms of REMP starting from robust initial estimates with a high breakdown point was introduced. Original DEMs with the resolution of 2 m were obtained by means of light detection and Ranging (LiDAR) from two study sites. DEM errors in each study site were, respectively, calculated based on 100 checkpoints captured by real-time kinematic (RTK) in terms of stratified random sampling strategy. Each group of DEM errors was, respectively, contaminated by five groups of outliers from different distributions. Thus, ten groups of simulated DEM errors were employed to comparatively assess the estimation accuracies of REMP and the classical estimators. The results indicated that under the non-normal distribution of DEM errors, the classical non-robust estimators are seriously influenced by the non-normality. Some robust estimators such as 10%-trimmed or Winsorized mean and normalized median absolute deviation (MADN) are not very robust to resist the influence of outliers. REMP, slightly affected by the non-normal distribution of DEM errors, is more accurate than the classical estimators. The robust methodology can adapt to the DEMs, especially the ones derived from remote sensing such as LiDAR or digital photogrammetry in non-open terrain.     

Is high spatial resolution DEM data necessary for mapping palustrine wetlands?

Digital elevation models (DEMs) have been found to be an effective data source for automated mapping of wetlands. However, it is unclear whether high spatial resolution DEMs, which tend to be more expensive to acquire and process, are necessary for mapping wetlands such as those in the US National Wetland Inventory (NWI). Therefore, we compared predictions of the probability of palustrine wetland occurrence with a random forests (RF) algorithm using DEMs generated from light detection and ranging (LiDAR) at 1 m, 3 m, and 10 m raster cell sizes; and photogrammetrically-derived DEMs at 3 m and 10 m. For each classification, a wide range of terrain derivatives were generated and used as the input data for the classification. Comparisons between the wetland predictions were made using the receiver operating characteristic (ROC) area under the curve (AUC) measure, the Kappa statistic, overall accuracy, class user’s and producer’s accuracy, and the out of bag (OOB) error rate. For two different study sites, irrespective of the source of the digital terrain data, palustrine wetland occurrence was predicted with AUC values greater than 0.95, overall accuracies greater than 88%, Kappa greater than 0.77, and wetland user’s and producer’s accuracies above 0.85 when using a large training data set derived from the NWI or a small separate data set of non-NWI data derived from field samples. We therefore conclude that the source (LiDAR vs photogrammetric) and spatial scale (1 m, 3 m, or 10 m) of the DEM data does not have a large impact on the accuracy of the prediction of wetlands such as those in the NWI. However, for small wetlands, or more generally for wetlands unlike those in the NWI, finer scale data (e.g. 1 m) derived from LiDAR may be preferable.     

Assessment of Digital Elevation Model (DEM) aggregation methods for hydrological modeling: Lake Chad basin,Africa

Digital Elevation Models (DEMs) are used to compute the hydro-geomorphological variables required by distributed hydrological models. However, the resolution of the most precise DEMs is too fine to run these models over regional watersheds. DEMs therefore need to be aggregated to coarser resolutions, affecting both the representation of the land surface and the hydrological simulations. In the present paper, six algorithms (mean, median, mode, nearest neighbour, maximum and minimum) are used to aggregate the Shuttle Radar Topography Mission (SRTM) DEM from 3″ (90 m) to 5′ (10 km) in order to simulate the water balance of the Lake Chad basin (2.5 Mkm²). Each of these methods is assessed with respect to selected hydro-geomorphological properties that influence Terrestrial Hydrology Model with Biogeochemistry (THMB) simulations, namely the drainage network, the Lake Chad bottom topography and the floodplain extent.The results show that mean and median methods produce a smoother representation of the topography. This smoothing involves the removing of the depressions governing the floodplain dynamics (floodplain area<5000 km²) but it eliminates the spikes and wells responsible for deviations regarding the drainage network. By contrast, using other aggregation methods, a rougher relief representation enables the simulation of a higher floodplain area (>14,000 km² with the maximum or nearest neighbour) but results in anomalies concerning the drainage network. An aggregation procedure based on a variographic analysis of the SRTM data is therefore suggested. This consists of preliminary filtering of the 3″ DEM in order to smooth spikes and wells, then resampling to 5′ via the nearest neighbour method so as to preserve the representation of depressions. With the resulting DEM, the drainage network, the Lake Chad bathymetric curves and the simulated floodplain hydrology are consistent with the observations (3% underestimation for simulated evaporation volumes).     

NASA EOS Terra ASTER: Volcanic topographic mapping and capability

Up-to-date, accurate topographic data are a crucial resource for volcanic research and risk mitigation efforts, in particular, for modeling volcanic flow processes at a detailed spatial resolution. In this paper, we examine the utility of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument currently operating on the NASA Terra satellite, which provides near infrared (VNIR) stereo imaging from which topography can be derived. We wrote software to generate digital elevation models (DEMs) from the ASTER level 1A product, which employs an automated stereo matching technique to calculate the parallax offsets between the images acquired by the nadir- and aft-looking sensors. Comparison of ASTER DEMs with DEMs derived from other sources (digitized 1:50 K topographic maps and aerial interferometric radar) at Ruapehu volcano reveal an RMS error of about 10 m for the ASTER DEM, in the absence of significant atmospheric water vapor. A qualitative assessment of surface features showed that the ASTER DEM is superior to the interpolated 1:50 K map product but falls short of the detail provided by aerial interferometric radar, especially in terms of stream channel preservation. A second ASTER DEM was generated for Taranaki volcano, where previously only 1:50 K topographic map data were available. Although the 2000 Space Shuttle radar topography mission (SRTM) will largely remedy the previous global paucity of adequate topographic data at volcanoes, such as Taranaki, we anticipate the problem that at active volcanoes, the topography may change significantly following activity, rendering the SRTM data inaccurate. With the high temporal coverage of the dataset, ASTER not only provides a means to update significant (>10 m) topographic measurements at active volcanoes via a time-series of DEMs, but also provides a simultaneous means to map surface cover and localized land-use via the near infrared sensors. Thus we demonstrate the potential for up-to-date volcanic economic risk assessment using geographic information systems (GIS) analysis.     

Effect of SRTM resolution on morphometric feature identification using neural network—self organizing map

In this study, we present a semi-automatic procedure using Neural Networks—Self Organizing Map—and Shuttle Radar Topography Mission DEMs to characterize morphometric features of the landscape in the Man and Biosphere Reserve “Eastern Carpathians”. We investigate specially the effect of two resolutions, SIR-C with 3 arc seconds and X-SAR with 1 arc second for morphometric feature identification. Specifically we investigate how the SRTM/C band data with 30 m interpolated grid, corresponding to SRTM/X band 30 m, affect the morphometric characterization and topography derivatives. To reduce misregistration between the DEMs, spatial co-registration was performed and a RMSE of 0.48 pixel was achieved. Morphometric parameters such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived using a bivariate quadratic approximation on 90 m, 30 m and interpolated 30 m DEMs. Self Organizing Map (SOM) is used for the classification of morphometric parameters into ten exclusive and exhaustive classes. These classes were analyzed as morphometric features such as ridge, channel, crest line and planar for all data sets based on feature space (scatter plot), morphometric signatures and 3D inspection of the area. The map quality is analyzed by oblique views with contour lines overlaid. Using the X band DEM with 30 m grid as benchmark, a change detection technique was used to quantify differences in morphometric features and to assess the scale effect going from a 90 m (C-band) DEM to an interpolated 30 m DEM. The same procedure is used to study the effect of different resolutions on morphometric features. Morphometric parameters were computed by a moving window size 5 × 5 (corresponding to 450 m on the ground) over SRTM- 90 m. To cover the same ground area, a moving window size of 15 × 15 is used for the 30 m DEM. The change analysis showed the amount of resolution dependency of morphometric features. Overall, the results showed that the introduced method is very useful for identification of morphometric features based on SRTM resolution. Decreasing the grid size from 90 m to 30 m reveals considerably more detailed information emphasizing local conditions. Comparison between results from DEM-30 m as reference data set and interpolated 30 m, showed a rate of change of 31.5% which is negligible. About 17% of this rate correspond to classes with mean slope > 10°. Of the morphometric parameters, the cross sectional curvature is most sensitive to DEM resolution. Increasing spatial resolution reduces the main constrains for morphometric analysis with SRTM 90 m data, such as unrealistic features and isolated single elements in the output map. So in case of lack of high resolution data, the SRTM 90 m data could be interpolated and used for further geomorphic analysis.     

LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification

Orthophotos (or orthoimages if in digital form) have long been recognised as a supplement or alternative to standard maps. The increasing applications of orthoimages require efforts to ensure the accuracy of produced orthoimages. As digital photogrammetry technology has reached a stage of relative maturity and stability, the availability of high quality ground control points (GCPs) and digital elevation models (DEMs) becomes the central issue for successfully implementing an image orthorectification project. Concerns with the impacts of the quality of GCPs and DEMs on the quality of orthoimages inspire researchers to look for more reliable approaches to acquire high quality GCPs and DEMs for orthorectification. Light Detection and Ranging (LiDAR), an emerging technology, offers capability of capturing high density three dimensional points and generating high accuracy DEMs in a fast and cost-effective way. Nowadays, highly developed computer technologies enable rapid processing of huge volumes of LiDAR data. This leads to a great potential to use LiDAR data to get high quality GCPs and DEMs to improve the accuracy of orthoimages. This paper presents methods for utilizing LiDAR intensity images to collect high accuracy ground coordinates of GCPs and for utilizing LiDAR data to generate a high quality DEM for digital photogrammetry and orthorectification processes. A comparative analysis is also presented to assess the performance of proposed methods. The results demonstrated the feasibility of using LiDAR intensity image-based GCPs and the LiDAR-derived DEM to produce high quality orthoimages.     

Multi-point matching along vertical lines in SPOT images

Abstract

Digital elevation models(DEMs) are measured in SPOT images with a digital matching technique. A multi-point matching method developed by the author in earlier work is reformulated from epipolar geometry with the stereoparallaxes in one of the images used as unknowns, to SPOT geometry with the elevations as unknowns. Constraints minimizingthe curvature and the slope of the Computed DEM are investigated, as well as the use of data snooping, low-pass filtering and an alternative logarithmic grey level function. The investigatedimage pair was a stereo SPOT pair from Stockholm, with a rather poor base-to-height ratio of 0·29. All 2073 investigated points were compared with heights obtained from the official Swedish maps, scale 1:10000, with a 5 m contour interval. The results point towards a r.m.s. matching accuracy around 0·15-0·20 pixels in this image material.     

SRTM DEM accuracy assessment over vegetated areas in Norway

Results from the Shuttle Radar Topography Mission (SRTM) are presented. The SRTM C‐band and X‐band digital elevation models (DEMs) are evaluated with regard to elevation accuracies over agricultural fields, forest areas and man‐made features in Norway. High‐resolution digital maps and satellite images are used as background data. In general, many terrain details can be observed in the SRTM elevation datasets. The elevation accuracy (90% confidence level) of the two SRTM systems is estimated to less than 6.5 m for open agricultural fields and less than 11 m considering all land surface covers. This is better than specifications. Analysis of dense Norwegian forest stands shows that the SRTM system will produce elevation data that are as much as 15 m higher than the ground surface. The SRTM DEM products will therefore partly indicate canopy elevations in forested areas. We also show that SRTM data can be used to update older DEMs obtained from other sources, as well as estimating the volume of rock removed from large man‐made gravel pits.     

Sub-pixel water temperature estimation from thermal-infrared imagery using vectorized lake features

Water skin temperature derived from thermal infrared satellite data are used in a wide variety of studies. Many of these studies would benefit from frequent, high spatial resolution (100 m pixels) thermal imagery but currently, at any given location, such data are only available every few weeks from spaceborne sensors such as ASTER. Lower spatial resolution (1 km pixels) thermal imagery is available multiple times per day at any given location, from several sensors such as MODIS on board both the AQUA and TERRA satellite platforms. In order to fully exploit lower spatial resolution imagery, a sub-pixel unmixing technique has been developed and tested at Quesnel Lake, British Columbia, Canada. This approach produces accurate, frequent high spatial resolution water skin temperature maps by exploiting a priori knowledge of water boundaries derived from vectorized water features. The pixel water-fraction maps are then input to a gradient descent algorithm to solve the mixed pixel ground leaving radiance equation for sub-pixel water temperature. Ground-leaving radiance is estimated from standard temperature and emissivity data products for pure pixels and a simple regression technique to estimate atmospheric effects. In this test case, MODIS 1 km thermal imagery was used along with 1:50,000 water features to create a high-resolution (100 m) water skin temperature map. This map is compared to a concurrent ASTER temperature image and found to be within 1 °C of the ASTER skin temperature 99% of the time. This is a considerable improvement over the 2.55 °C difference between the original MODIS product and ASTER image due to land temperature contamination. The algorithm is simple, effective, and unlocks a largely untapped resource for limnological and hydrological studies.     

Large-scale mapping of the riverbanks,mud flats and salt marshes of the Scheldt basin,using airborne imaging spectroscopy and LiDAR

For maintaining the tidal waterways in the Scheldt basin, including the rivers Rupel and Durme and a large part of the Nete catchment, and for ecological monitoring of the mud flats, salt marshes and riverbank vegetation, the Flemish government needs detailed maps of these rivers and their bank structures. These maps indicate not only vegetation types, plant associations and sediment types but also hard structures, such as quays, locks, sluices and roads. Different remote sensing techniques were used to collect the data necessary to produce the required detailed maps. During the months of July and August 2007 an airborne flight campaign took place to collect hyperspectral and LiDAR data of the Scheldt basin and the Nete catchments. These rivers have a total length of about 240 km. The Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor acquired hyperspectral data in 32 spectral bands covering the visible/near-infrared (VIS/NIR) part of the electromagnetic spectrum with a ground resolution of 1 m. A multiple binary classification algorithm based on Fisher's linear discriminant analysis (LDA) was used to map the salt marshes and riverbank vegetation. Ground truth information, that is vegetation and sediment types, together with their geographical locations collected around the time of the flight campaign, was used to train the classifier in the later classification step. Laser scanning was performed using the Riegl LMS-Q560. The LiDAR dataset obtained had a resolution of at least 1 point per m² and was used to produce a digital elevation model (DEM) that contains all elements of the terrain. From this DEM a digital terrain model (DTM) was derived by applying appropriate filtering techniques. The elevation models were used primarily to derive information on the height, slope and aspect of the banks and dikes, but they also served as expert knowledge in the classification of the mud flats and bank vegetation.

Overall, this work illustrates how airborne hyperspectral and LiDAR data can be used to derive highly detailed maps of the sediments, vegetation and hard structures along tidal rivers in large river basins. It also shows how large datasets can be handled in an expert system, in combination with different classification techniques, to produce the required result and accuracy.     

Morphometric analysis in geographic information systems: applications of free software GRASS and R

Development and interpretation of morphometric maps are important tools in studies related to neotectonics and geomorphology; Geographic Information Systems (GIS) allows speed and precision to this process, but applied methodology will vary according to available tools and degree of knowledge of each researcher about involved software.A methodology to integrate GIS and statistics in morphometric analysis is presented for the most usual morphometric parameters—hypsometry, slope, aspect, swath profiles, lineaments and drainage density, surface roughness, isobase and hydraulic gradient.The GIS used was the Geographic Resources Analysis Support System (GRASS-GIS), an open-source project that offers an integrated environment for raster and vector analysis, image processing and maps/graphics creation. Statistical analysis of parameters can be carried out on R, a system for statistical computation and graphics, through an interface with GRASS that allows raster maps and points files to be treated as variables for analysis.The basic element for deriving morphometric maps is the digital elevation model (DEM). It can be interpolated from scattered points or contours, either in raster or vector format; it is also possible to use DEMs from NASA's Shuttle Radar Topographic Mission, with 30 m of ground resolution for the USA and 90 m for other countries.Proposed methodology can be adapted according to necessities and available tools. The use of free and open-source tools guarantees access to everyone, and its increasing popularization opens new development perspectives in this research field.     

Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau

The Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud and land Elevation Satellite (ICESat) provides elevation data with very high accuracy which can be used as ground data to evaluate the vertical accuracy of an existing Digital Elevation Model (DEM). In this article, we examine the differences between ICESat elevation data (from the 1064 nm channel) and Shuttle Radar Topography Mission (SRTM) DEM of 3 arcsec resolution (90 m) and map-based DEMs in the Qinghai-Tibet (or Tibetan) Plateau, China. Both DEMs are linearly correlated with ICESat elevation for different land covers and the SRTM DEM shows a stronger correlation with ICESat elevations than the map-based DEM on all land-cover types. The statistics indicate that land cover, surface slope and roughness influence the vertical accuracy of the two DEMs. The standard deviation of the elevation differences between the two DEMs and the ICESat elevation gradually increases as the vegetation stands, terrain slope or surface roughness increase. The SRTM DEM consistently shows a smaller vertical error than the map-based DEM. The overall means and standard deviations of the elevation differences between ICESat and SRTM DEM and between ICESat and the map-based DEM over the study area are 1.03 ± 15.20 and 4.58 ± 26.01 m, respectively. Our results suggest that the SRTM DEM has a higher accuracy than the map-based DEM of the region. It is found that ICESat elevation increases when snow is falling and decreases during snow or glacier melting, while the SRTM DEM gives a relative stable elevation of the snow/land interface or a glacier elevation where the C-band can penetrate through or reach it. Therefore, this makes the SRTM DEM a promising dataset (baseline) for monitoring glacier volume change since 2000.     

A semi-automated method to create a lidar-based hydro-flattened DEM

The response of water surfaces to light detection and ranging (lidar) pulses is unpredictable, which results in sparse lidar point density with varying intensity values. Due to the sparseness of the point cloud and lack of natural breaklines, lidar-derived digital elevation model (DEM) can produce unnatural surface over waterbodies. Such surfaces are not cartographically pleasing and can cause issues in the hydrologic and hydraulic modelling of a river. Hydro-flattening is the process of creating a lidar-derived DEM in which water surfaces appear and behave as they would in traditional topographic DEMs generated from photogrammetric digital terrain models. Hydro-flattened DEMs, created using lidar data, exclude the lidar points over waterbodies and include three-dimensional (3D) bank shorelines. In this article, a semi-automated method is presented for extracting bank shorelines for the purpose of creating lidar-derived hydro-flatten DEMs. Lidar point cloud and an approximate stream centreline are the primary data for this process. In the first step, a continuous bare ground surface (CBGS) is created by eliminating non-ground lidar points and by adding artificial underwater points. In the second step, the lowest elevation from the lidar point cloud within a radius distance from the river centreline is used to create a virtual water surface (VWS). This VWS is revised to consider water surface undulations such as ripples or waves, protruding underwater objects, etc. The revised VWS is then intersected with the CBGS to locate the two-dimensional (2D) bank shorelines. The 2D shorelines are assigned the elevations of the VWS and are used to produce a hydro-flattened DEM. The planimetric absolute mean separation of 0.94, 0.69, and 0.63 m for the three water surfaces is observed between the bank shoreline extracted using raw lidar points and a GPS (global positioning system) survey. The mean separation using vendor classified lidar points is 0.74, 0.67, and 0.64 m which are very similar to those using raw lidar.     

Effectiveness of high-resolution SAR for water resource management in low-income semi-arid countries

This article presents an efficient framework and a sustainable pilot project on the effective use of spaceborne synthetic aperture radar (SAR) in low-income countries and semi-arid climatic contexts. The technical efficiency was pursued by integrating SAR models and hydrological assessment methods; the socio-economical sustainability was guaranteed by the joint work of scientists, technicians, and volunteers. The pilot project was developed in the Yatenga region, a Sahelian area in northern Burkina Faso. In particular, an original development of SAR interferometry algorithms was tailored to the peculiar climate, the soil characteristics, and the land cover of the semi-arid regions. A digital elevation model (DEM) was derived, and an original approach based on the use of SAR amplitude images is proposed for its validation. The achieved resolution (9 m) is significantly better than that of the previously available DEMs in the study area (30 m). Based on the DEM, the soil sedimentation rate of small reservoirs was estimated together with the average soil loss in the contributing catchments due to the erosion process. A multi-temporal filter was implemented on the SAR images for monitoring of water intake volume in small reservoirs, and its seasonal evolution. The developed tools provide an innovative contribution for the improvement of water resource management in the study area. This approach is repeatable and scalable to suit situations with similar economic and climatic conditions.     

DEM generation using ASAR (ENVISAT) for addressing the lack of freshwater ecosystems management,Santa Cruz Island,Galapagos

Low relief oceanic islands often suffer from scarcity of freshwater resources. Remote sensing has proved to be an effective tool to generate valuable data for hydrological analysis and has improved the management of ecosystems and water. However, remotely sensed data are often tested over areas with existing validation databases and not always where the need is greatest. In this paper we address the need for topographical data to understand the hydrological system of Santa Cruz Island (Galapagos archipelago) so that management of freshwater ecosystems and resources can take place. No high resolution, high accuracy topographical data exist for Santa Cruz Island, and its growing population has created an urgent need for water resource management and protection of unique and pristine ecosystems.Inaccessible National Park land covers more than 97% of Galapagos territory, which makes the use of remote sensing methods indispensable. SRTM data was insufficient in terms of grid size (90 m) to carry out the needed data analysis. We used ASAR data (ENVISAT) in VV polarization image mode for Digital Elevation Model (DEM) generation, in order to extract drainage network, watersheds, and flow characteristics from a morpho-structural analysis.Results show the high potential of these data for both interferometric and radargrammetric generation methods. Although interferometry suffered from low coherence over highly vegetated areas, it showed high precision over the rest of the island. Radargrammetry gave consistent results over the entire island, and details were enhanced by integrating the 90 m SRTM data as an external DEM. Accuracy of the SRTM and the combined radargrammetric/SRTM DEM was similar, with the radargrammetric having a finer pixel-based resolution (20 m).Validation of the extracted drainage networks and watersheds was carried out using ground-based field observations and comparison to mapped river networks visually extracted from aerial photographs and high resolution (1 m) satellite imagery available on GoogleEarth^©. For the first time, watershed characteristics and flow paths were made available for an island of the Galapagos archipelago. Furthermore, the drainage network is shown to be strongly influenced by observed and extracted structural discontinuities. Having characterized freshwater flow, water balance calculations were carried out for Pelican Bay watershed, where urban areas, agricultural land and Galapagos National Park land are concomitant.     

SRTM vs ASTER elevation products. Comparison for two regions in Crete,Greece

The Shuttle Radar Topography Mission (SRTM) collected elevation data over 80% of earth's land area during an 11‐day Space Shuttle mission. With a horizontal resolution of 3 arc sec, SRTM represents the best quality, freely available digital elevation models (DEMs) worldwide. Since the SRTM elevation data are unedited, they contain occasional voids, or gaps, where the terrain lay in the radar beam's shadow or in areas of extremely low radar backscatter, such as sea, dams, lakes and virtually any water‐covered surface. In contrast to the short duration of the SRTM mission, the ongoing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is continuously collecting elevation information with a horizontal resolution of 15 m. In this paper we compared DEM products created from SRTM data with respective products created from ASTER stereo‐pairs. The study areas were located in Crete, Greece. Absolute DEMs produced photogrammetricaly from ASTER using differentially corrected GPS measurements provided the benchmark to infer vertical and planimetric accuracy of the 3 arc sec finished SRTM product. Spatial filters were used to detect and remove the voids, as well as to interpolate the missing values in DEMs. Comparison between SRTM‐ and ASTER‐derived DEMs allowed a qualitative assessment of the horizontal and vertical component of the error, while statistical measures were used to estimate their vertical accuracy. Elevation difference between SRTM and ASTER products was evaluated using the root mean square error (RMSE), which was found to be less than 50 m.     

Using MODIS images to examine the surface extents and variations derived from the DEM and laser altimeter data in the Danjiangkou Reservoir,China

We used publicly available digital spatial datasets to study the area extents and their horizontal variations of two water bodies within the Danjiangkou Reservoir, China. Between 2003 and 2005, the water levels varied from 140 to 149 m above mean sea level as measured by the Geoscience Laser Altimeter System (GLAS). Detailed procedures to derive the horizontal extents and variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) coupled with GLAS data and to verify the extents and variations spatially were provided. For the water bodies on the north and west, the surface water extents derived from four MODIS images varied between 174 and 218 km² and from 96 to 135 km², respectively. The extents by inundating the DEM using the GLAS data were 178–212 km² for the water body on the north and 104–118 km² for the water body on the west. The spatial verifications of surface water extents derived from the MODIS images versus DEM coupled with GLAS data agreed 83–93%. Within the ring areas between water/land boundaries at elevations of 140 and 147 m, and 140 and 149 m, the spatial agreement was 52–75%.     

Remote sensing estimates of glacier mass balances in the Himachal Pradesh (Western Himalaya, India)

Although they correspond to an important fraction of the total area of mountain glaciers (33,000 km² out of 546,000 km²), Himalayan glaciers and their mass balance are poorly sampled. For example, between 1977 and 1999, the average area surveyed each year on the field was 6.8 km² only. No direct mass balance measurement is available after 1999. To contribute to fill this gap, we use remote sensing data to monitor glacier elevation changes and mass balances in the Spiti/Lahaul region (32.2°N, 77.6°E, Himachal Pradesh, Western Himalaya, India). Our measurements are obtained by comparing a 2004 digital elevation model (DEM) to the 2000 SRTM (Shuttle Radar Topographic Mission) topography.The 2004 DEM is derived from two SPOT5 satellite optical images without any ground control points. This is achieved thanks to the good on-board geolocation of SPOT5 scenes and using SRTM elevations as a reference on the ice free zones. Before comparison on glaciers, the two DEMs are analyzed on the stable areas surrounding the glaciers where no elevation change is expected. Two different biases are detected. A long wavelength bias affects the SPOT5 DEM and is correlated to an anomaly in the roll of the SPOT5 satellite. A bias is also observed as a function of altitude and is attributed to the SRTM dataset. Both biases are modeled and removed to permit unbiased comparison of the two DEM on the 915 km² ice-covered area digitized from an ASTER image.On most glaciers, a clear thinning is measured at low elevations, even on debris-covered tongues. Between 1999 and 2004, we obtain an overall specific mass balance of − 0.7 to − 0.85 m/a (water equivalent) depending on the density we use for the lost (or gained) material in the accumulation zone. This rate of ice loss is twice higher than the long-term (1977 to 1999) mass balance record for Himalaya indicating an increase in the pace of glacier wastage. To assess whether these ice losses are size-dependant, all glaciers were classified into three samples according to their areal extent. All three samples show ice loss, the loss being higher for glaciers larger than 30 km². In the case of the benchmark Chhota Shigri glacier, a good agreement is found between our satellite observations and the mass balances measured on the field during hydrological years 2002-2003 and 2003-2004. Future studies using a similar methodology could determine whether similar ice losses have occurred in other parts of the Himalaya and may allow evaluation of the contribution of this mountain range to ongoing sea level rise.