Shuttle Radar Topography Mission Dataset Research Articles

Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland.

Tropical peatlands such as Ghana’s Greater Amanzule peatland are highly valuable ecosystems and under great pressure from anthropogenic land use activities. Accurate measurement of their occurrence and extent is required to facilitate sustainable management. A key challenge, however, is the high cloud cover in the tropics that limits optical remote sensing data acquisition. In this work we combine optical imagery with radar and elevation data to optimise land cover classification for the Greater Amanzule tropical peatland. Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission (SRTM) imagery were acquired and integrated to drive a machine learning land cover classification using a random forest classifier. Recursive feature elimination was used to optimize high-dimensional and correlated feature space and determine the optimal features for the classification. Six datasets were compared, comprising different combinations of optical, radar and elevation features. Results showed that the best overall accuracy (OA) was found for the integrated Sentinel-2, Sentinel-1 and SRTM dataset (S2+S1+DEM), significantly outperforming all the other classifications with an OA of 94%. Assessment of the sensitivity of land cover classes to image features indicated that elevation and the original Sentinel-1 bands contributed the most to separating tropical peatlands from other land cover types. The integration of more features and the removal of redundant features systematically increased classification accuracy. We estimate Ghana’s Greater Amanzule peatland covers 60,187 ha. Our proposed methodological framework contributes a robust workflow for accurate and detailed landscape-scale monitoring of tropical peatlands, while our findings provide timely information critical for the sustainable management of the Greater Amanzule peatland.

DEM-based morphometry of large-scale sand dune patterns in the Grand Erg Oriental (Northern Sahara Desert, Africa)

Formerly, sand dune patterns were investigated mostly by aerial and satellite images, but more recently, geomorphometric analysis based on digital elevation models (DEMs) has become an important approach. In this paper, sand dune patterns of the Grand Erg Oriental (Sahara) are studied using the De Ferranti (2014) DEM, which is a blending of SRTM (Shuttle Radar Topography Mission), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and other elevation datasets. In the Grand Erg Oriental, there are four large-scale dune pattern types with gradual transitions between them and with several subtypes, namely P1, consisting of large, branching linear dunes; P2, a complex pattern including small-size and widely spaced star and dome dunes; P3, a network type created mostly from crescentic dunes; and finally, P4, consisting of large and closely spaced star dunes. The largest dunes with 90–100-m mean height can be found in the southern parts of the Grand Erg Oriental, where P1 and P4 patterns dominate, and these areas are also characterised by the most intensive sand accumulation with 25–30-m equivalent sand thickness. In the present study, we use regression analysis to investigate the functional relationships between sand dune characteristics. Further on, we have elaborated a DEM-based method to delineate dunes and calculate sand volumes and dune orientations. Comparing wind rose data and sand dune axis rose diagrams, it is concluded that in some parts of the Grand Erg Oriental, the present dune types and patterns are in agreement with the actual wind regime, but in other cases, the present dune patterns are at least partially the results of former wind regimes.

Analysis of the accuracy of Shuttle Radar Topography Mission (SRTM) height models using International Global Navigation Satellite System Service (IGS) Network

The Shuttle Radar Topography Mission (SRTM) carried out in February 2000 has provided near global topographic data that has been widely used in many fields of earth sciences. The mission goal of an absolute vertical accuracy within 16 m (with 90% confidence)/RMSE ∼10 m was achieved based on ground validation of SRTM data through various studies using global positioning system (GPS). We present a new and independent assessment of the vertical accuracy of both the X- and C-band SRTM datasets using data from the International GNSS Service (IGS) network of high-precision static GPS stations. These stations exist worldwide, have better spatial distribution than previous studies, have a vertical accuracy of 6 mm and constitute the most accurate ground control points (GCPs) possible on earth; these stations are used as fiducial stations to define the International Terrestrial Reference Frame (ITRF). Globally, for outlier-filtered data (135 X-band stations and 290 C-band stations), the error or difference between IGS and SRTM heights exhibits a non-normal distribution with a mean and standard error of 8.2 ± 0.7 and 6.9 ± 0.5 m for X- and C-band data, respectively. Continent-wise, Africa, Australia and North America comply with the SRTM mission absolute vertical accuracy of 16 m (with 90% confidence)/RMSE ∼10 m. However, Asia, Europe and South America have vertical errors higher than the SRTM mission goal. At stations where both the X- and C-band SRTM data were present, the root mean square error (RMSE) of both the X- and C-bands was identical at 11.5 m, indicating similar quality of both the X- and C-band SRTM data.

Detecting and Quantifying Forest Change: The Potential of Existing C- and X-Band Radar Datasets

This paper evaluates the opportunity provided by global interferometric radar datasets for monitoring deforestation, degradation and forest regrowth in tropical and semi-arid environments. The paper describes an easy to implement method for detecting forest spatial changes and estimating their magnitude. The datasets were acquired within space-borne high spatial resolutions radar missions at near-global scales thus being significant for monitoring systems developed under the United Framework Convention on Climate Change (UNFCCC). The approach presented in this paper was tested in two areas located in Indonesia and Australia. Forest change estimation was based on differences between a reference dataset acquired in February 2000 by the Shuttle Radar Topography Mission (SRTM) and TanDEM-X mission (TDM) datasets acquired in 2011 and 2013. The synergy between SRTM and TDM datasets allowed not only identifying changes in forest extent but also estimating their magnitude with respect to the reference through variations in forest height.

Accuracy of land surface elevation from CALIPSO mission data

We assess the accuracy of land surface elevation retrieved from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission through comparisons with the U.S. Geological Survey National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and the altimetry product from the Geoscience Laser Altimeter System onboard the Ice, Cloud, and Land Elevation Satellite (ICESat). The vertical accuracy of the CALIPSO-derived land surface elevation was tested against these three datasets for about 16 million lidar shots over the continental United States. The results show that the CALIPSO-derived elevation was highly correlated with the elevation result from the NED, SRTM, and ICESat datasets. The overall absolute vertical accuracies of the CALIPSO-derived land surface elevation expressed as the root mean square error (RMSE) are 5.58 and 5.90 m when compared with the SRTM and NED results, respectively. Lower accuracy of the CALIPSO-derived land surface elevation was achieved by comparison with the ICESat results (8.35-m RMSE), primarily due to the several kilometers distance between the CALIPSO and ICESat ground footprints. The results show that the variability in terrain, vegetation, canopy, and footprint size can all influence comparisons between the CALIPSO-derived elevation and the results obtained from NED, SRTM, and ICESat datasets.

Model-Based Estimation of Forest Canopy Height in Red and Austrian Pine Stands Using Shuttle Radar Topography Mission and Ancillary Data: A Proof-of-Concept Study

In this paper, accurate tree stand height retrieval is demonstrated using C-band Shuttle Radar Topography Mission (SRTM) height and ancillary data. The tree height retrieval algorithm is based on modeling uniform tree stands with a single layer of randomly oriented vegetation particles. For such scattering media, the scattering phase center height, as measured by SRTM, is a function of tree height, incidence angle, and the extinction coefficient of the medium. The extinction coefficient for uniform tree stands is calculated as a function of tree height and density using allometric equations and a fractal tree model. The accuracy of the proposed algorithm is demonstrated using SRTM and TOPSAR data for 15 red pine and Austrian pine stands (TOPSAR is an airborne interferometric synthetic aperture radar). The algorithm yields root-mean-square (rms) errors of 2.5-3.6 m, which is a substantial improvement over the 6.8-8.3-m rms errors from the raw SRTM minus National Elevation Dataset Heights.

The Landcover Impact on the Aspect/Slope Accuracy Dependence of the SRTM-1 Elevation Data for the Humboldt Range.

The U.S. National Landcover Dataset (NLCD) and the U.S National Elevation Dataset (NED) (bare earth elevations) were used in an attempt to assess to what extent the directional and slope dependency of the Shuttle Radar Topography Mission (SRTM) finished digital elevation model is affected by landcover. Four landcover classes: forest, shrubs, grass and snow cover, were included in the study area (Humboldt Range in NW portion of Nevada, USA). Statistics, rose diagrams, and frequency distributions of the elevation differences (NED-SRTM) per landcover class per geographic direction were used. The decomposition of elevation differences on the basis of aspect and slope terrain classes identifies a) over-estimation of elevation by the SRTM instrument along E, NE and N directions (negative elevation difference that decreases linearly with slope) while b) underestimation is evident towards W, SW and S directions (positive elevation difference increasing with slope). The aspect/slope/landcover elevation differences modelling overcome the systematic errors evident in the SRTM dataset and revealed vegetation height information and the snow penetration capability of the SRTM instrument. The linear regression lines per landcover class might provide means of correcting the systematic error (aspect/slope dependency) evident in SRTM dataset.

New products from the shuttle radar topography mission

New data products with broad applicability to the Earth sciences are now available from the Shuttle Radar Topography Mission (SRTM). SRTM, a joint project of the National Geospatial‐Intelligence Agency (NGA) and NASA, flew aboard the Space Shuttle Endeavour on an 11‐day mission in February 2000 with the goal of collecting a near‐global data set of high‐resolution elevation data [Fan and Kobrick, 2000]. Data from the mission have been available to researchers for several years, but newly available products offer enhanced usability and applicability.Final products include elevation data resulting from a substantial editing effort by the NGA in which water bodies and coastlines were well defined and data artifacts known as spikes and wells (single pixel errors) were removed. This second version of the SRTM data set, also referred to as ‘finished’ data, represents a significant improvement over earlier versions that had nonflat water bodies, poorly defined coastlines, and numerous noise artifacts. The edited data are available at a one‐arc‐second resolution (approximately 30 meters) for the United States and its territories, and at a three‐arc‐second resolution (approximately 90 meters) for non‐U.S. areas.

Vertical accuracy of the SRTM DTED level 1 of Crete

A reference digital elevation model (DEM), produced from contour lines digitization, from topographic maps at scale 1:250.000 is used in order to assess the vertical accuracy of the SRTM DTED level 1 in Crete Island in Southern Greece. The error image interpretation revealed three types of systematic errors: (a) stripping, (b) large voids and (c) those errors resulted from the mis-registration of the Shuttle Radar Topography Mission (SRTM) imagery to the local datum. Terrain was segmented to plane regions and sloping regions. Sloping regions were segmented to aspect regions (aspect being standardized to the eight geographic directions defined in a raster/grid image). Error statistics was computed for the study area as well as the individual terrain classes. Vertical accuracy was found to be terrain class dependent. Sloping regions present greater mean error than the plane ones. Statistical tests verified that the difference in mean error between aspect regions that slope in opposite geographic directions is statistically significant. The greater mean error is observed for SW, W and NW aspect regions. The additional finishing steps applied to the SRTM dataset were not sufficient enough for the systematic errors and the terrain class dependency of the error to be corrected. The observed root-mean-square error (RMSE) for the SRTM DTED-1 of Crete do not fulfil the 16m RMSE specification for the SRTM mission while the USA national map accuracy standards for the scale 1:250.000 are satisfied.