Accuracy assessment of automatically derived digital elevation models from aster data in mountainous terrain

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Glaciated regions are known to be particularly sensitive to climate change. Historical archives of glacier volume change are important, as they provide context for present-day changes. Although photogrammetric archives exist for many regions, their usefulness is often limited by a lack of contemporary ground control. High quality digital elevation models (DEMs) underpin a range of change analysis activities. This paper presents a cost-effective solution which utilizes Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs as control for the scaling and orientation of archival data sets. Instead of relying upon ground-control points, a robust surface matching algorithm is employed to automatically determine the transformation required to register two overlapping DEMs. Through application to the Slakbreen glacier system in Svalbard, Norway, the strategy is assessed by first matching an ASTER DEM to a fixed lidar reference surface. This demonstrates that ASTER DEMs are effectively correct in scale, supporting their use as a control surface. The second stage of the research implements this by matching an aerial photogrammetric DEM to an ASTER reference surface. Resultant volumetric and annual elevation change rates are compared to those derived from lidar data, which are considered in this paper as a truth data set. ASTER-based matching produced a mean annual elevation change rate of <formula formulatype="inline"><tex Notation="TeX">$-4.12\ \hbox{ma}^{-1}$ </tex></formula>, compared to a value of <formula formulatype="inline"><tex Notation="TeX">$-4.11\ \hbox{ma}^{-1}$</tex></formula> derived from the lidar data. In volumetric terms, this equates to a difference of 0.6%. A major advantage of this approach is the near-global coverage offered by ASTER data and the opportunity that this presents for remote glacial change analysis over regional extents. </para>

Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. &amp;lt;br&amp;gt;&amp;lt;br&amp;gt; As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. &amp;lt;br&amp;gt;&amp;lt;br&amp;gt; The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.

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.

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

Porphyry systems are critical sources of economically valuable metals, making their identification and exploration a key focus in mineral resource studies. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data have emerged as a powerful tool for mapping the geochemical and mineralogical characteristics of these systems, enabling more precise exploration efforts. This review examines the geochemical and mineralogical indicators of deep porphyry systems and evaluates the effectiveness of ASTER in their exploration. It begins by exploring the geological framework and alteration zones typical of porphyry deposits, emphasizing the unique advantages of ASTER in remote sensing applications. The methodology focuses on evaluating case studies where ASTER data were utilized to map alteration minerals and detect geochemical anomalies, alongside techniques for data processing and ground-truth validation. Results reveal that ASTER effectively identifies critical alteration zones and mineralogical features, though challenges remain in distinguishing overlapping mineral signatures in complex terrains. Recommendations include integrating ASTER with complementary geophysical and geochemical methods and advancing data processing techniques to enhance exploration accuracy. This study highlights ASTER’s significant potential in driving efficient and sustainable exploration of deep porphyry systems.

Accurate elevation data are essential for effective oil palm plantation planning, hydrological modeling, and environmental management particularly in tropical regions characterized by complex topography and dense vegetation. While LiDAR-derived Digital Elevation Models (DEMs) offer high vertical precision, their acquisition costs often hinder adoption in resource-limited settings. Open-source DEMs provide accessible alternatives but are frequently affected by vegetation interference and coarse resolution, leading to reduced vertical accuracy. This study proposes a hybrid correction framework that integrates a random forest (RF) machine learning algorithm and a geographically weighted regression (GWR) a spatially adaptive statistical method to enhance the vertical accuracy of open-source DEMs for terrain-sensitive applications. The study used 377 high-precision Ground Control Points (GCPs) and LiDAR data to evaluate and correct six global DEMs: Advanced Land Observing Satellite (ALOS), TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), Copernicus GLO-30, Shuttle Radar Topography Mission (SRTM), Forest And Buildings removed Copernicus DEM (FABDEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM, and a high-resolution drone-derived DEM. RF was used to identify key topographic predictors, including aspect, slope, curvature, Topographic Position Index (TPI), and Terrain Ruggedness Index (TRI), while GWR applied spatially adaptive corrections to the RF residuals. The integrated RF–GWR model significantly improved the vertical accuracy across all DEMs. The post-correction R² values reached 0.914 for TanDEM-X, 0.910 for ALOS, and increased from 0.608 to 0.914 for Copernicus, with the residual standard deviations reduced by up to 75% and near-zero mean bias. These results highlight the model’s ability to correct both systematic and spatially varying elevation errors. The framework presents a scalable and alternative to LiDAR for use in precision agriculture, flood risk modeling, and infrastructure planning. Future work should explore integration with deep learning to improve the temporal responsiveness and operational scalability.

Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Advanced Land Observing Satellite World-3D (ALOSW3D), and TerraSAR-X Digital Elevation Measurement (TanDEM-X) are open-source Digital Elevation Model (DEM) datasets for environmental modelling and studies. The spatial resolution and vertical accuracy of DEM data sources play a significant role, particularly in dealing with land inundation, periodic flooding, and coastal erosion. In this study, the comparison between orthometric height, H from Global Navigation Satellite System (GNSS) observation, and DEM is performed to evaluate the accuracy of each DEM in terms of their Root Mean Square Error (RMSE) and correlation coefficient, R2 for monitoring the coastline. The result has indicated that TanDEM-X shows the smallest RMSE of 2.574 m compared to SRTM30, SRTM90, ASTER, GMTED10, and ALOSW3D with RMSE of 2.968 m, 3.006 m, 3.217 m, 2.975 m, and 2.876 m respectively. Furthermore, TanDEM-X illustrates the largest correlation coefficients, R2 of 0.959 m, compared to SRTM30, SRTM90, ASTER, GMTED10, and ALOSW3D at 0.891 m, 0.899 m, 0.590 m, 0.888 m 0.913 m respectively. Hence, the result has indicated that TanDEM-X is the best option among all DEMs for any topographic applications.

This paper presents a simple but effective method to improve Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation models (DEMs). Digital elevation models are an important component of geomorphological modelling so their integrity is vital to achieve reliable results. For this investigation the relative ASTER DEM produced from an automated cross-correlation algorithm was not considered accurate enough, so 3 arcsecond (90 m) Shuttle Radar Topography Mission (SRTM) data were used to modulate the ASTER DEM. The method presented in this paper allows the SRTM to enforce vertical control on the relative ASTER DEM whilst attempting to maintain the ASTER DEM's 30 m spatial resolution. The process is fast and efficient, and can be applied to other DEMs. There is, however, a compromise since the fusion process, involving the averaging of the values, does potentially mean that some of the detail in the original 30 m ASTER DEM will be lost.

A waterbody detection technique is an essential part of a digital elevation model (DEM) generation to delineate land–water boundaries and set flattened elevations. This paper describes the technical methodology for improving the initial tile-based waterbody data that are created during production of the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) GDEM, because without improvement such tile-based waterbodies data are not suitable for incorporating into the new ASTER GDEM Version 3. Waterbodies are classified into three categories: sea, lake, and river. For sea-waterbodies, the effect of sea ice is removed to better delineate sea shorelines in high latitude areas: sea ice prevents accurate delineation of sea shorelines. For lake-waterbodies, the major part of the processing is to set the unique elevation value for each lake using a mosaic image that covers the entire lake area. Rivers present a unique challenge, because their elevations gradually step down from upstream to downstream. Initially, visual inspection is required to separate rivers from lakes. A stepwise elevation assignment, with a step of one meter, is carried out by manual or automated methods, depending on the situation. The ASTER global water database (GWBD) product consists of a global set of 1° latitude-by-1° longitude tiles containing water body attribute and elevation data files in geographic latitude and longitude coordinates and with one arc second posting. Each tile contains 3601-by-3601 data points. All improved waterbody elevation data are incorporated into the ASTER GDEM to reflect the improved results.

<strong class="journal-contentHeaderColor">Abstract.</strong> Digital elevation models (DEMs) are currently one of the most widely used data sources in glacier thickness change research, due to the high spatial resolution and continuous coverage. However, raw DEM data are often misaligned with each other, due to georeferencing errors, and a co-registration procedure is required before DEM differencing. In this paper, we present a comparative analysis of the two classical co-registration methods proposed by Nuth and K&auml;&auml;b (2011) and Rosenholm and Torlegard (1988) . The former is currently the most commonly used method in glacial studies, while the latter is a seminal work in the photogrammetric field that has not been extensively investigated by the cryosphere community. Furthermore, we also present a new residual correction method using a generalized additive model (GAM) to eliminate the remaining systematic errors in DEM co-registration results. The performance of the two DEM co-registration methods and three residual correction algorithms (the GAM-based method together with two parametric-model-based methods) was evaluated using 23 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM pairs from the western margin of the Greenland Ice Sheet. The experimental results confirm our theoretical analysis of the two co-registration methods. The method of Rosenholm and Torlegard has a greater ability to remove DEM misalignments (4.6 % on average and 15.3 % maximum) because it models the translation, scale, and rotation-induced biases, while the method of Nuth and K&auml;&auml;b considers translation only. The proposed GAM-based method performs statistically better than the two residual correction methods based on parametric regression models (high-order polynomials and the sum of the sinusoidal functions). A visual inspection reveals that the GAM-based method, as a non-parametric regression technique, can capture complex systematic errors in the DEM co-registration residuals.

Landsat Thematic Mapper and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data were used for lithological mapping and chromite prospecting in Neyriz ophiolitic zone in the south of Iran. Image transformation techniques, namely decorrelation stretch, band ratio, principal component analysis (PCA) were applied to the datasets. The RGB decorrelated image of Landsat TM bands 7, 5 and 4, and the principal components images of ASTER bands in the visible near infrared (VNIR) and shortwave infrared (SWIR) portions demonstrated the occurrence of major lithological units in the study area. The developed band ratios on the VNIR-SWIR spectral bands of ASTER, including 5/3, 5/1, 7/5 discriminated all rock units distinctly and delineated the host rock of chromite mineralized zone along the transition zone in the study area. Image processing results were verified by laboratory analysis and fieldwork. Result of this investigation indicate that the integration of Landsat TM and ASTER datasets is one of the most effective tool for lithological mapping and chromite prospecting in mountainous and inaccessible regions such Iranian ophiolitic zones.

The aim of this work is to develop an object-based methodology for semi-automatic geomorphological mapping. The study area is the municipality of São José dos Campos, located in the Paraíba do Sul Valley, São Paulo State, Brazil. A multilevel hierarchical semantic network driven by a set of multispectral, geomorphometric and texture variables is used. The geomorphometric and texture variables are extracted from a digital elevation model (DEM) generated from a stereo pair of advanced spaceborne thermal emission and reflection radiometer (ASTER)/Terra images. The DEM accuracy is evaluated and its root mean square error (RMSE) is found to be 9.38 m. The validation of the geomorphological map is accomplished by means of object-based statistical indices derived from an error matrix, obtained by a comparison between the classified scene and a reference map produced by visual interpretation. The object-based approach is found to be effective for geomorphological mapping.

The study area is located in the Kerman magmatic arc in southern Iran, which is known for its world-class porphyry-type deposits. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Land Imager (ALI) visible–near-infrared to shortwave-infrared bands were used to investigate the spectral discrimination of hydrothermally altered rocks based on their mineral assemblages. Band ratioing, directed principal component analysis (DPCA), and the Spectral Angle Mapper (SAM) were applied on ASTER and ALI data for separating ferric iron-poor from ferric iron-rich phyllic alteration zones. The individual principal component images through DPCA could detect specific alteration zones dominated by minerals such as iron oxides, sericite, kaolinite, chlorite, and epidote. The phyllic zone associated with copper mineralization is generally rich in iron oxide minerals at the surface, which can be especially detected by ALI. The altered areas were sampled and studied using X-ray diffraction analysis, spectral measurements, chemical analysis, and thin-section studies. The results of this analysis have shown that more than 90% of the known copper mineralization falls within the ASTER/ALI-mapped alteration areas. These data can be useful for mapping alteration minerals related to porphyry deposits in other regions with similar geological settings.

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image analysis is a proven method for mapping mineral and geochemical zonation associated with a variety of ore types, including orogenic Au, porphyry Cu-(Mo), porphyry-skarn, Pb-Zn-Au, and Mn systems. Only recently has this technique been applied, in a general sense, to mineral alteration mapping and exploration for Fe ore deposits hosted by banded iron formations (BIFs). For this reason, the Archean Weld Range greenstone belt that hosts the Beebyn and Madoonga Fe ore deposits has been chosen as a case study area to test the effectiveness of ASTER imaging techniques for the identification of Fe orebodies. Banded iron formations in the Weld Range district crop out as a series of parallel, 10- to 500-m-wide, 55 wt % Fe) iron ore deposits host Archean hypogene magnetite and specular hematite orebodies that are locally replaced by more recently formed, supergene goethite-hematite ore within several hundred meters of the present erosion surface. A common feature of all ore types hosted by BIFs is a high Fe content relative to SiO2. Consequently, all types of Fe ore in the Weld Range district are best identified by the ferric iron to silica index and the opaques to silica index, for the reason that these ASTER image products detect surfaces that are rich in (opaque) Fe oxide minerals and have a low silica abundance. Gabbro, dolerite, and basalt country rocks located within 20 m of high-grade Fe ore zones in BIFs are altered to hypogene Fe-rich chlorite and, more rarely, are altered by Fe-rich talc. These hypogene alteration zones are best detected by the ferrous iron content in MgOH minerals and carbonates and the FeOH group abundance products, which identify hypogene Fe chlorite and Fe talc. This study demonstrates that integrated remote spectral sensing techniques (ASTER, airborne hyperspectral, and radiometric) used in conjunction with geophysical surveys (aeromagnetic and gravity) are useful for district-scale exploration for Fe orebodies hosted by BIFs. The spectral sensing techniques are a rapid, cost-effective, and efficient means for generating and ranking exploration targets that are located in areas with restricted physical access.

Abstract. The objective of this work was to evaluate the potential of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), subsystems VNIR (Visible and Near Infrared) and SWIR (Short Wave Infrared) images, for discrimination and mapping of amethyst mining residues (basalt) in the Ametista do Sul Region, Rio Grande do Sul State, Brazil. This region provides the most part of amethyst mining of the World. The basalt is extracted during the mining process and deposited outside the mine. As a result, mounts of residues (basalt) rise up. These mounts are many times smaller than ASTER pixel size (VNIR – 15 meters and SWIR – 30 meters). Thus, the pixel composition becomes a mixing of various materials, hampering its identification and mapping. Trying to solve this problem, multispectral algorithm Maximum Likelihood (MaxVer) and the hyperspectral technique SAM (Spectral Angle Mapper) were used in this work. Images from ASTER subsystems VNIR and SWIR were used to perform the classifications. SAM technique produced better results than MaxVer algorithm. The main error found by the techniques was the mixing between "shadow" and "mining residues/basalt" classes. With the SAM technique the confusion decreased because it employed the basalt spectral curve as a reference, while the multispectral techniques employed pixels groups that could have spectral mixture with other targets. The results showed that in tropical terrains as the study area, ASTER data can be efficacious for the characterization of mining residues.