A Method for SRTM DEM Elevation Error Correction in Forested Areas Using ICESat-2 Data and Vegetation Classification Data

Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments

KOMPSAT-2와 같은 고해상 위성영상은 대상영역의 3차원 위치결정을 위하여 RPC(Rational Polynomial Coefficient)가 포함된 자료를 제공한다. 그러나 RPC로 계산된 영상기하는 일정량의 편이(systematic errors)를 지니고 있는 상태이며, 이를 보정하기 위해서는 수 개 이상의 지상기준점(ground control point)이 필요하다. 이에 본 논문에서는 지상기준점 없이 입체영상(stereo pair)과 SRTM(Shuttle Radar Topography Mission) DEM(Digital Elevation Model) 사이의 대응점(tie point)만을 이용하여 자동으로 영상 기하를 보정하는 효과적인 방법을 제안하였다. 이러한 방법은 4가지 단계를 포함 한다: 1) 대응점 추출, 2) 대응점에 대한 지상좌표 결정, 3) SRTM DEM을 이용한 지상좌표의 보정, 4) RPC 보정 모델의 파라미터 결정. 우리는 KOMPSAT-2 입체영상을 이용하여 제안된 방법의 성과를 입증하였다. 검사점(check point)을 통해 계산된 RMSE(Root Mean Square Error)는 X와 Y, Z방향으로 각각 약 3.55 m, 9.70 m, 3.58 m를 나타냈다. 이는 SRTM DEM을 이용하여 RPC가 지닌 편이를 X, Y 및 Z 모든방향에 대하여 10 m이내의 정확도로 자동보정할 수 있다는 것을 의미한다. A high resolution satellite imagery such as KOMPSAT-2 includes a material containing rational polynomial coefficient (RPC) for three-dimensional geopositioning. However, image geometries which are calculated from the RPC must have inevitable systematic errors. Thus, it is necessary to correct systematic errors of the RPC using several ground control points (GCPs). In this paper, we propose an efficient method for automatic correction of image geometries using tie points of a stereo pair and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) without GCPs. This method includes four steps: 1) tie points extraction, 2) determination of the ground coordinates of the tie points, 3) refinement of the ground coordinates using SRTM DEM, and 4) RPC adjustment model parameter estimation. We validates the performance of the proposed method using KOMPSAT-2 stereo pair. The root mean square errors (RMSE) achieved from check points (CPs) were about 3.55 m, 9.70 m and 3.58 m in X, Y;and Z directions. This means that we can automatically correct the systematic error of RPC using SRTM DEM.

The Shuttle Radar Topography Mission (SRTM) provided scientists with digital elevation data on a nearly global scale and with highly consistent accuracy. This paper compares elevation values of the C-band SRTM 30-m digital elevation model (DEM) with pointwise elevations from the Ice, Cloud, and land-Elevation Satellite (ICESat) laser altimetry for Otter Tail County, Minnesota. The accuracy of SRTM DEM is measured as a function of land covers and geomorphologic characteristics. The typical mean vertical difference between the SRTM DEM and ICESat elevations in this paper was determined in each classified land-use type and is approximately 1.5 m over bare ground, with the SRTM measuring lower elevations. Significant changes in the SRTM DEM uncertainties have been identified over different surface types classified from Landsat-7 imagery, e.g., bare ground, urban, and forested areas. Based on this result, the difference of the SRTM 30-m DEM and ICESat elevations has been removed from the DEM and made available for improved hydrological applications

Accurate measurements of glacier surface topography and their changes play an essential role in various glaciological studies related to glacier dynamics and mass balance. The focus of this study is on mapping glacier digital elevation model (DEM) and elevation changes in the western Qilian Mountains, northern Tibetan Plateau, by synergistically using the TanDEM-X (TDX) bistatic Interferometric Synthetic Aperture Radar (InSAR) data in 2013 and Shuttle Radar Topography Mission (SRTM) DEM in 2000. The first high-resolution and high-precision glacier DEM is derived in this region by a TDX InSAR procedure with a nonlocal (NL) filter. Validated against the Ice, Cloud, and land Elevation Satellite (ICESat) height references, the absolute height error of the TanDEM-X DEM derived with the NL filter and the Goldstein filter with the parameters investigated is, respectively, 1.493 ± 0.747 and 1.857 ± 1.709 m. Further, four combinations of differential phase method (DiffPha) and DEM differencing method (DiffDem) with Goldstein filter and NL filter are applied to estimate glacier elevation changes between 2000 and 2013. The synergistic use of the DiffPha method and the NL filter is superior to other three combinations in terms of uncertainty and noise reduction. Generally, a clear surface thinning can be found in most glacier tongue regions, the maximum value of elevation lowering up to approximately −40 m, whereas a slight thickening is detected in accumulation areas, which are in agreement with the height difference results between GPS measurements and SRTM DEM over Laohugou Glacier No.12. This study demonstrates the potential of the TanDEM-X bistatic InSAR in mapping surface topography and elevation changes of valley glaciers in the Tibetan Plateau.

Near-global Digital Elevation Model (DEM) products generated through space-based radar techniques have become a basic data source for a variety range of applications. However, these DEM products often contain typical errors such as vegetation bias and topography-related errors, which impede their practical utility. Despite the development of numerous correction methods based on mathematical fitting and artificial neural networks over recent decades, reliably correcting large-scale spaceborne radar-derived DEMs remains an open challenge due to issues like underfitting or overfitting. This paper introduces a novel framework called Feature-Reinforced Ensemble Learning (FREEL) designed specifically for correcting space-based radar-derived DEMs. Within this FREEL framework, a feature derivation module and a feature reinforcement module are integrated to enhance the original input features. Subsequently, an adaptive weighting variant of the DeepForest algorithm is proposed to emphasize critical features and improve training robustness, even with limited training data. The Shuttle Radar Topographic Mission (SRTM) DEMs of Hunan Province, China, characterized by diverse surface terrain and vegetation coverage, were selected to evaluate the FREEL framework. The results indicate that the accuracy of the SRTM DEM corrected using the FREEL framework improved by 40%, surpassing several mathematical fitting and machine learning baseline algorithms by an average of 45% and 23%, respectively. This method provides a more robust solution for correcting near-global space-based radar-derived DEM products.

Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.

Recently, there has been increasing attention to the digital elevation model (DEM) because of its ability to learn the Earth’s surface’s topography. Freely accessible DEMs such as the Cartosat-1, shuttle radar topography mission (SRTM), light detection and ranging, and advanced spaceborne thermal emission and reflection radiometer DEM, contain large vertical errors. The errors are aggravated over multifaceted geography and cannot rectify microgeographic deviations in the moderately flat landscape. As high-accuracy DEMs have limited availability, dated low-accuracy DEMs are still used in various models, specifically in the data-sparse areas. However, it is necessary to enhance the quality of these DEMs before their use in the geomorphometric analysis. We aim to investigate the effect of noise reduction filters on DEM’s accuracy and quality. The noise reduction filters such as weighted average filter, median filter, sharpen filter, Lee sigma filter, and local sigma filter are used for noise reduction in the stereo images. The quality and accuracy of the generated DEMs are further improved by selecting an optimum number of tie points in the image matching process. The effects are assessed by correlating the surface profiles for the final DEM obtained and SRTM DEM as a reference with a resolution of 1 arc sec.

Reconstruction of time-varying tidal flat topography using optical remote sensing imageries

DOI: 10.17014/ijog.6.3.291-309The researched area is situated at Mount Rendingan and its surrounding area, Lampung Province, the southern part of Sumatra Island. It has a big potential of geothermal resource in line with a unique graben like bowl landform and geothermal surface manifestations. This research was carried out using remote sensing and field observation methods. The remote sensing method used topography and Shuttle Radar Topography Mission -Digital Elevation Model (SRTM-DEM) maps to analyze morphotectonic attributes, and the result was validated by morphotectonic data analysis from field observation. The purpose of this research is to evaluate the relation between morphotectonic and the appearances of geothermal surface manifestations. The interpretation of SRTM-DEM and topographic maps are supported by morphotectonic analyses,which indicate that the geothermal surface manifestations in this area are controlled by tectonic activity. It has various levels of lift which are shown by the values of valley height weight ratio (Vf) from 0.16 to 3.31 and the values of mountain front sinuosity (Smf) from 1.05 to 2.09. The morphotectonic characteristics can describe the geological structure activity levels which are reflected in the landform and its rock. The volcanic areas consist of igneous rocks which have small primary permeabilities. However, the development of geological structure can cause fractures in the rock that step in as a medium for passing geothermal fluid from depth to surface. They are found on the fault intersection zone system trending NE-SW and NW-SE, NNE-SSW and NNW-SSE, and WE system directions that indicate the fault system is correlated with the appearances of geothermal manifestations.

A fast and robust bulk-loading algorithm for indexing very large digital elevation datasets: I. Algorithm

This study presents a remote sensing approach of using freely available Landsat 8 satellite Indicators ( Land Surface Temperature ( LST ), Soil Adjusted Vegetation Index ( SAVI )) and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) geospatial data to assess the impact of dumpsites on the environment in Benin City, Nigeria. The finding reveals that the average derived LST at the dumpsites were higher than the immediate surrounding, and the average SAVI values were lower than the immediate surrounding. The high values of LST at the dumpsites depict the effect of gases released because of decomposition activities, while low values of SAVI indicate vegetation response to soil and ground water contamination due to leachate infiltration. The average elevation within the dumpsite area derived from SRTM DEM was also applied as a proxy to estimate disposed waste quantity, and related closely with LST that depict biodegradation activities. The result presented here shows that bacterial and fungal counts correlate strongly with the LST and SAVI values at each of the dumpsites R 2 : LST vs Bacteria Count = 0.982, LST vs Fungi Count = 0.951; SAVI vs Bacteria Count = 0.745, SAVI vs Fungi Count = 0.664, thereby suggesting remote sensing can be applied to aid longterm dumpsite monitoring and management. http://dx.doi.org/10.4314/njt.v37i1.36

Bangladesh is the deltaic flood plain located in the lower ridge of the Ganges Brahmaputra and Meghna basins. The country is very flat having 40% of its landmass up to 10 m above the mean sea level. Shuttle Radar Topography Mission (SRTM) 90 m, i.e., 3-arc second digital elevation model (DEM) is being widely used to delineate river network and to extract catchment information using hydrology tool of ArcGIS. The hydrology tool uses the D8 method for extraction of drainage pattern. The study was carried out to find the limitation and suitability of 90 m SRTM DEM data in flat terrains especially in Bangladesh using ArcGIS. Twelve catchments of varying geomorphology were chosen from five hydrological zones of Bangladesh. Basin characteristics such as bifurcation ratio, drainage density, and channel slope of the catchments were estimated and analyzed to evaluate the suitability of 90 m SRTM DEM. The delineated catchments of slope 1:3,600 or more flat shows large deviation in river network alignment when compared with the digital river network developed by Bangladesh Water Development Board and with Google Earth’s images. The catchments having slope 1:2,850 and more steep were delineated correctly. The conclusion could not be established between slopes 1:2,850 and 1:3,600. The study also revealed that the catchment characteristics other than the slope have no effect on river network delineation.

This paper proposes a stereo‐image block‐adjustment algorithm without ground control points (GCPs) by adopting a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) as a height assistant. Using dense, evenly distributed tie points on images used in the block adjustment, the initial height values of tie points are derived directly from the SRTM DEM. In the subsequent block‐adjustment iterative computation, the height correction value for tie points is rigorously restricted by the SRTM DEM elevation. A total of 509 ZY‐3 satellite stereo‐images across 190 000 km2 of China's Hubei Province were used for this block‐adjustment experiment. The results show that the vertical root mean square errors (RMSEs) are improved from 7·2 to 2·3 m without GCPs, while the RMSEs in flat and mountainous terrain are improved to 1·5 and 2·8 m, respectively, fulfilling the vertical accuracy requirement of China's 1:25 000 scale mapping.

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.

In this study, we test the impact of using low-pass linear filters on the vertical accuracy of SRTM (Shuttle Radar Topography Mission) digital elevation model (DEM). We adopt three weight kernels to utilise low-pass filtering methods such as Distance Weighting (DW), Gaussian (G) and Inverse Distance (ID). The levelling data are used to validate the impact of the filtering methods on the DEM by adopting different values for the parameters of the weight kernels. As a result, we find that the DW and ID are more consistent with respect to the specified kernel power rather than G filter which shows a big range of bias variation. Another test is performed by generating diagonal transects over the filtered and unfiltered (SRTM) data, 90% of $\mathrm {D}\mathrm {W}/\mathrm {I}\mathrm {D}$ filtered datasets are consistent with SRTM data in a range of ±0.5 and 77% G data for the same range. Finally, the bias due to systematic errors is removed using stochastic 5- Parameter model. This shows an improvement $\mathrm {o}\mathrm {f}\sim 1 \mathrm {m}$ in the vertical accuracy with a standard deviation of about $0.~8m.$