Global Digital Elevation Model Version Research Articles

Detection of karst depression in Brazil comparing different semantic and instance segmentations and global digital elevation models

This research assessed the efficacy of deep segmentation in identifying and measuring natural karst depressions in the Bambuí Group's carbonate rocks in Western Bahia, Brazil. The investigation used five Global Digital Elevation Models (DEMs) with 30-m resolution: Advanced Spaceborne Thermal Emission and Reflection Radiometer - Global Digital Elevation Model version 3 (ASTER-GDEM v3), Advanced Land Observing Satellite World 3D – 30 m version 3.2 (AW3D30 v3.2), Copernicus 30 m global DEM (GLO-30), NASA Digital Elevation Model version 1 (NASADEM v1), and Shuttle Radar Topography Mission version 3 (SRTM v3). The karst feature detection analysis compared five semantic segmentation architectures: Feature Pyramid Networks (FPN), LinkNet, UNet, UNet++, DVL3+, using an EfficientNet-B7 backbone, one instance segmentation model (Mask-RCNN), and a semantic-to-instance conversion method. This comparison considers different datasets, including one, two, or eleven variables (DEM, DEM-based sink depth, and nine terrain attributes). Besides, we performed a combinatorial analysis of the DEMs to evaluate the improvement in karst feature detection. The methodology involved the generation of geomorphometric attributes, sample labeling from Sentinel-2 and Operational Land Imager-Landsat 8 datasets, training-validation-testing steps with 128 × 128 samples, reconstruction of large images for semantic and instance segmentation, and accuracy analysis. The findings revealed that GLO-30 and AW3D30 data were the most accurate, while ASTER GDEM performed poorly in both segmentation forms. Among the semantic models, FPN showed the highest accuracy. The 11-variable models preferentially outperformed those with fewer in both types of segmentation. The approach of semantic-to-instance conversion with Geographic Information System tools favored individualizing karst depressions and obtaining efficient quantification, consisting of an easy alternative to achieve instance segmentation. Reconstruction of large remote sensing images through sliding windows considering the specifics of instance and semantic segmentation demonstrated that smaller stride enhances object coverage and reduces the risk of losing crucial information, effectively improving the predictions. The combinatorial analysis for semantic and instance segmentation indicates that models incorporating more DEMs (4- and 5-DEM models) and variables achieve generally higher accuracy. The best result in semantic segmentation was combining the five DEM datasets using 11 variables, reaching an F-score of 85.06 and IoU of 74.00. In instance segmentation, the best result for the bounding box was the model that integrates AW3D30, GLO-30, NASADEM, and SRTM with eleven variables reaching Average Precision (AP) of 45.64, AP50 of 88.40 and AP75 of 39.38, while the best result for the segmentation mask was the model that integrates the five models with eleven variables with AP of 42.61, AP50 of 86.79 and AP75 of 35.43. These results demonstrate that integrating a wide range of DEM data, even the lowest performing ones, improves model generalization and accuracy in segmentation, leveraging strengths and mitigating individual weaknesses. Future work could explore high-resolution DEMs and the integration of various deep-learning methods.

Vertical accuracy evaluation free access digital elevation models (DEMs): case Fergana Valley in Uzbekistan

In this study, the vertical accuracy of the Shuttle Radar Topography Mission Digital Elevation Model Version 2.0 (SRTM30), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM Version 2.0 (ASTER GDEM2), and Advanced Land Observing Satellite World 3D Digital Surface Model Version 2.1 (ALOS AW3D30) was statistically assessed using GPS data. The Fergana Valley area was chosen as a study region, where the land surface can reflect tectonic processes. The values of ellipsoidal heights of 27 points of the regional GPS network were chosen as reference data. The geometric approach using GPS/leveling data and EGM96 global geopotential model-based geoid undulations was applied for geoid surface fitting. The geoid height corrections range ranged from –0.66 m to 0.87 m. Root-Mean-Square errors of ~10.0 m, ~16.4 m, and ~6.6 m was obtained for SRTM30, ASTER GDEM2, and ALOS AW3D30, respectively. It was found that compared with the reference model, all the global DEMs in mountainous areas generally overestimated elevation and the value of vertical accuracy at a 90% confidence level by 3-6 meters exceeded the declared by distributors. But ALOS AW3D30 proved to be the most accurate DEM that best represents the topography of the earth’s surface and could be used for some engineering applications in Fergana Valley.

Assessment of vertical accuracy of open source 30m resolution space-borne digital elevation models

Highly accurate air-borne Digital Elevation Models (DEMs) are not commonly available in most regions of the world, particularly developing countries like Nepal. Better topographic data are required to assess catchment hydrology, flood/inundation propagation, and sediment transport/deposition. Ground-based surveys (i.e., topographic and bathymetry) and Light Detection and Ranging (LIDAR) are expensive in terms of cost and time. We rely mostly on space-borne DEMs when the area of interest is quite large. And, for river cross-sections at specific regions, ground-based surveys are conducted at some regular intervals. The ALOS World 3 D − 30 m (AW3D30), ASTER Global DEM Version 3 (ASTER GDEM v3), SRTM-30, and Carto DEM v3 are some of the popular and latest space-borne DEMs that have been made available to the general public free of charge at a spatial resolution of 30 m. The availability of multiple space-borne DEMs may likely puzzle the practitioners on the selection of appropriate products. Here in this study, we evaluate the vertical accuracy of different space-borne DEMs with respect to the ground-based survey points (2891 points) at river cross-sections and ground-based indicative spot heights (8102 points) across the West Rapti River basin, Nepal. Performance metrics, like mean error and root mean square error (RMSE), were used to quantify deviation with respect to observed elevation. The AW3D30 showed the lowest RMSE value (i.e., 3.41 m) in the lower region of the study area. We numerically simulated the inundation propagation based on four selected DEMs using a calibrated two-dimensional runoff inundation model, and we find the AW3D30 outperforms other DEMs. All four DEMs showed a similar tendency on a catchment scale with respect to the information of indicative spot heights, suggesting interchangeable applications of all four DEMs in hydrologic analyses. However, precise ground-based observations across the catchment are needed for assuring the applicability.

Glacier mapping and change analysis in Chandra basin, Western Himalaya, India during 1971–2016

ABSTRACT For carrying out glacier change analysis in Chandra basin, Western Himalaya, India during 1971–2016, glacier inventory is generated for year 2002 using Landsat Enhanced Thematic Mapper+ (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM V2) data. Total 395 glaciers are mapped covering 703.3 ± 20.4 km2 area with minimum glacier size being >0.02 km2. Out of these, 59 glaciers have debris-covered ice covering an area of 67.2 ± 1.9 km2. In this study, a subset of 169 glaciers is used for glacier area change analysis using Corona KH-4B (1971), Landsat ETM+ (2002) and Sentinel-2 (2016) images due to the availability of cloud free data sources. In 1971, 639.4 ± 5.8 km2 glacier area is estimated which decreased to 620.2 ± 18.0 km2 (–3.0 ± 3.0%) in 2002 and 608.1 ± 10.3 km2 (–4.9 ± 1.9%) in 2016. The change in glacier length of Samudra Tapu and Gepang Gath glaciers is showing increasing trend since last two decades as compared to Hamtah, Chhota Shigri and Bara Shigri glaciers due to presence of pro-glacial lake at their snout. Clean-ice glaciers, glaciers with low mean elevation, glaciers having steep surface, glaciers with west to south and east to south orientation, small size glaciers and glaciers having pro-glacial lake at the snout are responsible to higher loss in glacier area. Statistical trend analysis using Mann-Kendall test and Sen’s slope estimator test show that Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) based annual mean temperature have rising trend at a rate of 0.02°C per year during 1961–2015, and can be ascribed as one of the factors for glacier shrinking.

An evaluation of available digital elevation models (DEMs) for geomorphological feature analysis

A digital elevation model (DEM) is an essential dataset for many land feature analyses, such as geomorphological feature analyses, which are very important for many types of studies. The present study involved the evaluation of five of the most common DEMs and their derived geomorphological features in the Humrat Assahn basin. A 10-m reference DEM was generated for the study area from a contour map produced at the Royal Jordanian Geographic Center (RJGC). In addition, an aerial photography DEM with a 10-m resolution was generated using PCI Geomatica software. Both the second version of the 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM version 2 (ASTER GDEM 2) and a DEM derived from 2000 Google earth (GE) elevation points were evaluated as well. The validation process included a comparison of elevation profiles, the calculation of DEM accuracies and statistics, and the evaluation of some delineated geomorphological features such as hill shade and watershed, which are derivatives of each DEM. The results revealed a strong relationship between the reference DEM and the aerial photo DEM, and it is found that there is a moderate relationship between the reference DEM and the Shuttle Radar Topography Mission (SRTM), GE, and ASTER GDEM 2 DEMs. The root mean square errors (RMSEs) of the aforementioned DEM values were ± 12.526 m, ± 40.411 m, ± 42.332 m, and ± 43.383 m, respectively.

Layered compressive sensing reconstruction for non-scanning three-dimensional laser imaging

ABSTRACTAs an active remote-sensing technique, three-dimensional (3D) laser radar enables accurate target detection and identification. However, the resolution of obtained image is limited by the integrated level of the sensor array. In this article, we propose a layered compressive sensing reconstruction method for high-resolution 3D laser imaging. The range profile is first reconstructed by applying a designed range observation matrix to the original sampling data. According to the distribution of range values in the reconstructed range profile, the original sampling data are divided into several layers and the corresponding intensity images are reconstructed by different algorithms. The global intensity image is generated by the reconstructed intensity images with different range compensation coefficients. In our simulations based on real data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 (ASTER GDEM V2) and Landsat-8 Operational Land Imager (OLI), the proposed method realised 3D laser imaging for complex landforms with a pixel resolution of , and reached a satisfactory performance for reconstructions of the range profile (peak signal-to-noise ratio (PSNR) = 33.08 dB, feature similarity (FSIM) = 0.9450) and the intensity image (PSNR = 20.63 dB, FSIM = 0.8893) with a subrate of 0.25.

Evaluasi Tingkat Akurasi Digital Elevation Model (DEM) SRTM dan ASTER GDEM dalam Pemodelan Daerah Aliran Sungai (DAS) Ciliwung

Availability of Digital Elevation Model (DEM) dataset and Geographic Information System (GIS), makes the watershed properties can be extracted automatically. There are two DEM providers which are freely accessible for research purposes and commonly use that is the Shuttle Radar Topographic Mission (SRTM) - DEM (30m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM V2). Based on the result of modeling conducted at Ciliwung River Basin with Qgis application, area generated from SRTM data is 5% smaller than Ciliwung River Basin which obtained from BPDAS Ciliwung-Citarum as a reference, while the result of ASTER-GDEM data is 87% larger than reference. Linear Regression Test and t-Test performed on three segments of the watershed shows that the upstream of both samples gives a good accuracy result that is R2 = 0,999; P = 0,499 (SRTM) and R2 = 0,999; P = 0,481 (ASTER-GDEM), while in the middle and downstream segments respectively for both samples are SRTM with R2 = 0,993; P = 0,413 and R2 = 0,734; P = 0,088; and then ASTER-GDEM with R2 = 0,784; P = 0,00038 and R2 = 0,376; P = 1,27209 x10-22.

A mutual assessment of the uncertainties of digital elevation models using the triple collocation technique

ABSTRACTThis study investigates the uncertainties of digital elevation models (DEMs) using the triple collocation (TC) method. DEMs from satellite missions are important for many geoscience disciplines and for economic benefits and are freely available. Validating DEMs is necessary to select an appropriate model for a given region and application. Provided certain assumptions about the error structure of any three data sets – measuring the same phenomenon – can be made, the TC approach can be used to provide an unbiased and scaled estimate of the error variances of the data sets, without requiring a reference data. We compared the TC approach to the traditional approach of using a reference data set using the Shuttle Radar Topography Mission version 4.1 (SRTM v4.1) DEM, ASTER (the Advanced Spaceborne Thermal Emission and Reflection Radiometer) GDEM (Global DEM) version 2, the 1 arc-minute global relief model (ETOPO1), a DEM compiled by the Survey and Mapping Division of Ghana (SMD DEM), and 545 ground control stations (GCSs). The error estimates for the DEMs via TC were considerably smaller than those obtained from the reference-based approach. As an example, the best performing DEM (SRTM v4.1) recorded a root-mean-square error (RMSE) of 15.601 m using the GCSs as reference, while its TC-derived accuracy was 6.517 m. We note that based on the results of the TC, the estimated error of the GCSs is approximately 14 m. Using a data set with an error of 14 m to validate other data sets is certainly bound to result in unfavorable results. Thus, we have demonstrated in this work that the TC approach is able to provide an unbiased error of DEMs. The approach is important even for regions where GCSs are highly accurate, but more so for regions with low-quality GCSs.

Geo-spatial Modeling for Automated Demarcation of Snow Avalanche Hazard Areas Using Landsat-8 Satellite Images and In Situ Data

The aim of this study is to generate a reliable dynamic snow avalanche hazard map using analytical hierarchy process method based on multisource geo-spatial data for the Chowkibal–Tangdhar (C–T) road axis in Jammu and Kashmir (J&K), India. Avalanche-prone areas of C–T axis have been demarcated using three causative parameters, i.e., terrain, ground cover and meteorological parameters. Terrain parameters, e.g., elevation, slope, aspect and curvature, have been estimated from Advanced Spaceborne Thermal Emission and Reflection Radiometer, Global Digital Elevation Model Version 2. Ground cover information has been extracted from Landsat-8 data. Meteorological parameters maps, i.e., snow depth, relative humidity and air temperature, have been generated using geo-spatial interpolation techniques of in situ data. All the parameters have been incorporated in Geographic Information System environment to generate the hazard map. Validation of hazard map was accomplished with the area under the curve method. The prediction rate was observed to be 93.2%. Further, 20% of the study area was estimated having no hazard, 55% as low hazard, 12% as moderate hazard and 13% as high hazard on April 13, 2015. Dynamic hazard map thus generated using remote sensing and in situ data will be useful for mitigation of snow avalanche hazard, regional planning for development of infrastructure, transportation, troops movement, army deployments and communication network.

Accuracy Assessment of Alos W3d30, Aster Gdem and Srtm30 Dem: A Case Study of Nigeria, West Africa

Digital Elevation Models (DEMs) depict the configuration of the earth surface and are being applied in many areas in earth and environmental sciences. In this study, the accuracy of the Advanced Land Observing Satellite World 3D Digital Surface Model version 2.1 (ALOS W3D30), the Shuttle Radar Topography Mission Digital Elevation Model version 3.0 (SRTM30) and the Advanced Space borne Thermal Emission and Reflection Radiometer Global DEM version 2.0 (ASTER GDEM2) was statistically assessed using high accuracy GPS survey data. Root-Mean-Square errors of ~5.40 m, ~7.47 m and ~20.03 m were obtained for ALOS W3D30, SRTM30 and ASTER GDEM2 respectively. In further analyses, we discovered that ALOS W3D30 and SRTM30 were much more accurate in regions where the height intervals were within 201 m - 400 m and >801 m. ALOS W3D30 proved to be the most accurate DEM that best represents the topography of the earth’s surface and could be used for some earth and environmental applications in Nigeria. We recommend that this study should serve as a guide in the use of any of these DEMs for earth and environmental applications in Nigeria.

Evaluating Glacier Volume Changes since the Little Ice Age Maximum and Consequences for Stream Flow by Integrating Models of Glacier Flow and Hydrology in the Cordillera Blanca, Peruvian Andes

Evaluating the historical contribution of the volume loss of ice to stream flow based on reconstructed volume changes through the Little Ice Age (LIA) can be directly related to the understanding of glacier-hydrology in the current epoch of rapid glacier loss that has disquieting implications for a water resource in the Cordillera Blanca in the Peruvian Andes. However, the accurate prediction of the future glacial meltwater availability for the developing regional Andean society needs more extensive quantitative estimation from long-term glacial meltwater of reconstructed glacial volume. Modeling the LIA paleoglaciers through the mid-19th century (with the most extensive recent period of mountain glacier expansion having occurred around 1850 AD) in different catchments of the Cordillera Blanca allows us to reconstruct glacier volume and its change from likely combinations of climatic control variables and time. We computed the rate and magnitude of centennial-scale glacier volume changes for glacier surfaces between the LIA and the modern era, as defined by 2011 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM V2) and 2008 Light Detection and Range (LiDAR) data. The model simulation showed good agreement with the observed geomorphic data and the volume and surface area (V-S) scaling remained within the 25% error range in the reconstructed simulation. Also, we employed a recently demonstrated approach (Baraër, M. et al.) to calculate meltwater contribution to glacierized catchment runoff. The results revealed multiple peaks of both mean annual and dry season discharge that have never been shown in previous research on the same mountain range.

Evaluation of the Newly Released Worldwide AW3D30 DEM Over Typical Landforms of China Using Two Global DEMs and ICESat/GLAS Data

This paper evaluates the accuracy of newly released global 1 arc-second advance land observing satellite (ALOS) world 3D–30 m (AW3D30) digital elevation model (DEM) over five validation samples of typical landforms across China using ICESat/GLAS (ice, cloud, and land elevation satellite/geoscience laser altimeter system) data. In comparison, shuttle radar topography mission (SRTM) 1-arc second (SRTM1) and global DEM version 2 (GDEM2) are also considered. The result reveals that the RMSEs of mean horizontal shifts compared with SRTM1 and GDEM2 are 5.30 and 10.07 m in x -direction, 5.64 and 11.75 m in y -direction, respectively. The vertical accuracy analysis shows that the AW3D30 offers the highest accuracy with RMSE of 4.81 m, followed by SRTM1 (5.86 m), and then by GDEM2 (14.14 m). Our result reveals that the vertical accuracy can be improved by horizontal alignment. The analysis suggests that terrain slopes and land cover types jointly affect the vertical accuracies of AW3D30 and the former is likely to have a greater impact than the latter. The results of hydrological potential assessment reveal that the drainage network derived from AW3D30 is more accurate than that from SRTM1 and GDEM2. However, all the DEMs exhibit river extraction limitations over the flat area due to the unresolved flow direction based on local elevation information alone. This study provides a comprehensive understanding of the AW3D30 and recommends that applications requiring accurate Earth surface depictions should utilize AW3D30 over SRTM1 and GDEM2, as it presents the best accuracy among the freely available DEMs.

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

Abstract. Knowledge about the coverage and characteristics of glaciers in High Mountain Asia (HMA) is still incomplete and heterogeneous. However, several applications, such as modelling of past or future glacier development, run-off, or glacier volume, rely on the existence and accessibility of complete datasets. In particular, precise outlines of glacier extent are required to spatially constrain glacier-specific calculations such as length, area, and volume changes or flow velocities. As a contribution to the Randolph Glacier Inventory (RGI) and the Global Land Ice Measurements from Space (GLIMS) glacier database, we have produced a homogeneous inventory of the Pamir and the Karakoram mountain ranges using 28 Landsat TM and ETM+ scenes acquired around the year 2000. We applied a standardized method of automated digital glacier mapping and manual correction using coherence images from the Advanced Land Observing Satellite 1 (ALOS-1) Phased Array type L-band Synthetic Aperture Radar 1 (PALSAR-1) as an additional source of information; we then (i) separated the glacier complexes into individual glaciers using drainage divides derived by watershed analysis from the ASTER global digital elevation model version 2 (GDEM2) and (ii) separately delineated all debris-covered areas. Assessment of uncertainties was performed for debris-covered and clean-ice glacier parts using the buffer method and independent multiple digitizing of three glaciers representing key challenges such as shadows and debris cover. Indeed, along with seasonal snow at high elevations, shadow and debris cover represent the largest uncertainties in our final dataset. In total, we mapped more than 27 800 glaciers >0.02 km2 covering an area of 35 520±1948 km2 and an elevation range from 2260 to 8600 m. Regional median glacier elevations vary from 4150 m (Pamir Alai) to almost 5400 m (Karakoram), which is largely due to differences in temperature and precipitation. Supraglacial debris covers an area of 3587±662 km2, i.e. 10 % of the total glacierized area. Larger glaciers have a higher share in debris-covered area (up to >20 %), making it an important factor to be considered in subsequent applications (https://doi.org/10.1594/PANGAEA.894707).

Geo-Localization of Target in 3-Dimensional Coordinate Using SRTM Data

This paper deals with Estimation of 3-Dimensional Geo-Location Coordinate (Latitude, Longitude and Elevation) of a given target location using the image of a Target captured from a Gimballed Camera mounted on an Unmanned Aerial Vehicle (UAV). The 2-Dimensional Geo-Location Coordinate (Latitude and Longitude) is estimated by the application of Geo-Location Algorithm based on the knowledge of target pixel location in the image. The Target Elevation is estimated by Elevation Estimation Algorithm using the 2-Dimensional Geo-Location Coordinate of target and Elevation Database such as NASA Shuttle Radar Topography Mission (SRTM) Database, National Geospatial-Intelligence Agency (NGA) SRTM Database and Advanced Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2. This paper illustrates the Target Geo-Location Coordinate Estimation for two different experimental setup: UAV Simulation Setup and a Real Time Setup. The Geo-Location Estimation result obtained from the Simulation Setup is found to have localization error around 0.3 m, while the estimates from the Real Time Setup are found to have a higher localization error of around 2.3 m. The elevation result obtained using Elevation Estimation Algorithm for SRTM 1-Arc Second database is proved to be more accurate but this comes with a downside of having longer processing time when compared with other databases. NGA 3-Arc Second database provides the result with the shortest computation period.

Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach

BackgroundIn low-income countries it is difficult to obtain complete data that show spatial heterogeneity in the risk of tuberculosis within-and-between smaller administrative units. This may contribute to the partial effectiveness of tuberculosis control programs. The aim of this study was to estimate the spatial risk of tuberculosis distribution in Gurage Zone, Southern Ethiopia using limited spatial datasets.MethodsA total of 1601 patient data that were retrieved from unit tuberculosis registers were included in the final analyses. The population and geo-location data were obtained from the Central Statistical Agency of Ethiopia. Altitude data were extracted from ASTER Global Digital Elevation Model Version 2. Aggregated datasets from sample of 169(40%), 254(60%) and 338(80%) kebeles were used to estimate the spatial risk of TB distribution in the Gurage Zone by using a geostatistical kriging approach. The best set of input parameters were decided based on the lowest prediction error criteria of the cross-validation technique. ArcGIS 10.2 was used for the spatial data analyses.ResultsThe best semivariogram models were the Pentaspherical, Rational Quadratic, and K-Bessel for the 40, 60 and 80% spatial datasets, respectively. The predictive accuracies of the models have improved with the true anisotropy, altitude and latitude covariates, the change in detrending pattern from local to global, and the increase in size of spatial dataset. The risk of tuberculosis was estimated to be higher at western, northwest, southwest and southeast parts of the study area, and crossed between high and low at west-central parts.ConclusionThis study has underlined that the geostatistical kriging approach can be applied to estimate the spatial risk of tuberculosis distribution in data limited settings. The estimation results may help local public health authorities measure burden of the disease at all locations, identify geographical areas that require more attention, and evaluate the impacts of intervention programs.

VERTICAL ACCURACY EVALUATION OF ASTER GDEM2 OVER A MOUNTAINOUS AREA BASED ON UAV PHOTOGRAMMETRY

Abstract. Global digital elevation models (GDEM) provide elementary information on heights of the Earth’s surface and objects on the ground. GDEMs have become an important data source for a range of applications. The vertical accuracy of a GDEM is critical for its applications. Nowadays UAVs has been widely used for large-scale surveying and mapping. Compared with traditional surveying techniques, UAV photogrammetry are more convenient and more cost-effective. UAV photogrammetry produces the DEM of the survey area with high accuracy and high spatial resolution. As a result, DEMs resulted from UAV photogrammetry can be used for a more detailed and accurate evaluation of the GDEM product. This study investigates the vertical accuracy (in terms of elevation accuracy and systematic errors) of the ASTER GDEM Version 2 dataset over a complex terrain based on UAV photogrammetry. Experimental results show that the elevation errors of ASTER GDEM2 are in normal distribution and the systematic error is quite small. The accuracy of the ASTER GDEM2 coincides well with that reported by the ASTER validation team. The accuracy in the research area is negatively correlated to both the slope of the terrain and the number of stereo observations. This study also evaluates the vertical accuracy of the up-sampled ASTER GDEM2. Experimental results show that the accuracy of the up-sampled ASTER GDEM2 data in the research area is not significantly reduced by the complexity of the terrain. The fine-grained accuracy evaluation of the ASTER GDEM2 is informative for the GDEM-supported UAV photogrammetric applications.

Validation of Global Digital Elevation Models in Lagos State, Nigeria

Satellite-derived Digital Elevation Models (DEM) are fast replacing the classical method of elevation data acquisition by ground survey methods. The availability of free and easily accessible DEMs is no doubt of great significance and importance, and a valuable resource in the quest to accurately model the earth's surface topography. However, the suitability of Digital Elevation Models in simulating the topography of the earth at micro, local and regional scales is still an active area of research. The accuracy of Digital Elevation Models vary from one location to another. As such, it is important to conduct local and regional assessments to inform the global user community on the relative performance of these DEMs. This study evaluates the accuracy of the 30-metre Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models version 2, the 1-kilometre GTOPO30, the 90-metre Shuttle Radar Topography Mission v4 and the 1-kilometre Shuttle Radar Topography Missionv2.1 Digital Elevation Models by validating with highly accurate GPS check-points over Lagos, Nigeria. With a Root Mean Square Error of 3.75m, the results show that Shuttle Radar Topography Mission v4 has the highest vertical accuracy followed by Shuttle Radar Topography Mission v2.1 (Root Mean Square Error: 5.73m), Advanced Spaceborne Thermal Emission and Reflection Radiometer (Root Mean Square Error: 21.70m), and GTOPO30 which shows the lowest vertical accuracy (Root Mean Square Error: 29.41m). By conducting the accuracy assessment of these products in Lagos, this study informs efforts directed at the exploitation of these Digital Elevation Models for topographic mapping and other scientific and environmental application.

Uncovering the ancient canal-based tuntian agricultural landscape at China's northwestern frontiers

The tuntian system was a state-promoted system of military–agriculture, which originated in the Western Han dynasty (206 BC–9 AD). All the imperial dynasties in Chinese history adopted the practice of tuntian to cultivate and guard frontier areas as an important state policy for developing border areas and consolidating frontier defense. This paper describes the use of satellite remote sensing data to uncover an ancient canal-based tuntian system located in an oasis agricultural landscape adjacent to the ancient Kingdom of Loulan at the southern margin of the Tarim Basin. The remote sensing data examined include Chinese Gaofen-1 (GF-1) VHR imagery, Landsat-8 (LS-8) OLI data and ASTER Global Digital Elevation Model Version 2 (ASTER GDEMV2) products. The effective irrigated tuntian area was estimated to be 2800 ha and the maximum irrigated tuntian area was found to be more than 8000 ha during the area's most prosperous period. The overall spatial structure of Milan's tuntian agricultural landscape was explored using the patch–corridor–matrix model. By detailed analysis of satellite remote sensing data, this study reconstructed a 3D view of Milan's tuntian agricultural landscape in a GIS.

Absolute and relative height-pixel accuracy of SRTM-GL1 over the South American Andean Plateau

Previously available only over the Continental United States (CONUS), the 1 arc-second mesh size (spatial resolution) SRTM-GL1 (Shuttle Radar Topographic Mission – Global 1) product has been freely available worldwide since November 2014. With a relatively small mesh size, this digital elevation model (DEM) provides valuable topographic information over remote regions. SRTM-GL1 is assessed for the first time over the South American Andean Plateau in terms of both the absolute and relative vertical point-to-point accuracies at the regional scale and for different slope classes. For comparison, SRTM-v4 and GDEM-v2 Global DEM version 2 (GDEM-v2) generated by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) are also considered. A total of approximately 160,000 ICESat/GLAS (Ice, Cloud and Land Elevation Satellite/Geoscience Laser Altimeter System) data are used as ground reference measurements. Relative error is often neglected in DEM assessments due to the lack of reference data. A new methodology is proposed to assess the relative accuracies of SRTM-GL1, SRTM-v4 and GDEM-v2 based on a comparison with ICESat/GLAS measurements. Slope values derived from DEMs and ICESat/GLAS measurements from approximately 265,000 ICESat/GLAS point pairs are compared using quantitative and categorical statistical analysis introducing a new index: the False Slope Ratio (FSR). Additionally, a reference hydrological network is derived from Google Earth and compared with river networks derived from the DEMs to assess each DEM’s potential for hydrological applications over the region. In terms of the absolute vertical accuracy on a global scale, GDEM-v2 is the most accurate DEM, while SRTM-GL1 is more accurate than SRTM-v4. However, a simple bias correction makes SRTM-GL1 the most accurate DEM over the region in terms of vertical accuracy. The relative accuracy results generally did not corroborate the absolute vertical accuracy. GDEM-v2 presents the lowest statistical results based on the relative accuracy, while SRTM-GL1 is the most accurate. Vertical accuracy and relative accuracy are two independent components that must be jointly considered when assessing a DEM’s potential. DEM accuracies increased with slope. In terms of hydrological potential, SRTM products are more accurate than GDEM-v2. However, the DEMs exhibit river extraction limitations over the region due to the low regional slope gradient.

VALIDATION OF THE ASTER GLOBAL DIGITAL ELEVATION MODEL VERSION 3 OVER THE CONTERMINOUS UNITED STATES

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of −1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from −2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.