Digital Elevation Model Datasets 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.

The importance of digital elevation model accuracy in XCO2 retrievals: improving the Orbiting Carbon Observatory 2 Atmospheric Carbon Observations from Space version 11 retrieval product

Abstract. Knowledge of surface pressure is essential for calculating column-averaged dry-air mole fractions of trace gases, such as CO2 (XCO2). In the NASA Orbiting Carbon Observatory 2 (OCO-2) Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm, the retrieved surface pressures have been found to have unacceptable errors, warranting a parametric bias correction. This correction depends on the difference between retrieved and a priori surface pressures, which are derived from a meteorological model that is hypsometrically adjusted to the surface elevation using a digital elevation model (DEM). As a result, the effectiveness of the OCO-2 bias correction is contingent upon the accuracy of the referenced DEM. Here, we investigate several different DEM datasets for use in the OCO-2 ACOS retrieval algorithm: the OCODEM used in ACOS v10 and previous versions, the NASADEM+ (a composite of SRTMv4, ASTER GDEMv3, GIMP, and RAMPv2 DEMs) used in ACOS v11, the Copernicus GLO-90 DEM (GLO-90 DEM), and two polar regional DEMs (ArcticDEM and REMA). We find that the NASADEM+ (ASTER GDEMv3) has a persistent negative bias on the order of 10 to 20 m across most regions north of 60° N latitude, relative to all the other DEMs considered (OCODEM, ArcticDEM, and GLO-90 DEM). Variations of 10 m in DEM elevations lead to variations in XCO2 of approximately 0.4 ppm, meaning that the XCO2 from OCO-2 ACOS v11 retrievals tends to be 0.4 to 0.8 ppm lower across regions north of 60° N than XCO2 from OCO-2 ACOS v10. Our analysis also suggests that the GLO-90 DEM has superior global continuity and accuracy compared to the other DEMs, motivating a post-processing update from OCO-2 v11 Lite files (which used NASADEM+) to OCO-2 v11.1 by substituting the GLO-90 DEM globally. We find that OCO-2 v11.1 improves accuracy and spatial continuity in the bias-corrected XCO2 product relative to both v10 and v11 in high-latitude regions while resulting in marginal or no change in most regions within ± 60° latitude. In addition, OCO-2 v11.1 provides increased data throughput after quality control filtering in most regions, partly due to the change in DEM but mostly due to other corrections to quality control parameters. Given large-scale differences north of 60° N between the OCODEM and NASADEM+, we find that replacing the OCODEM with NASADEM+ yields a ∼ 100 TgC shift in inferred carbon uptake for the zones spanning 30 to 60° N and 60 to 90° N, which is on the order of 5 % to 7 % of the estimated pan-Arctic land sink. Changes in inferred fluxes from replacing the OCODEM with the GLO-90 DEM are smaller, and given the evidence for improved accuracies from this DEM, this suggests that large changes in inferred fluxes from the NASADEM+ are likely erroneous.

Evaluating the effects of DEM and soil data resolution on streamflow and sediment yield simulations in the Upper Blue Nile basin.

While the availability of "big data" on biophysical parameters through citizen science and/or from public/private sources is expected to help in addressing data scarcity issues, there is little understanding of whether and/or how such data will improve watershed simulations. This research aimed to evaluate whether improvements in resolutions of Digital Elevation Model (DEM) and soil data will enhance streamflow and sediment yield simulations and thereby improve soil and water management decisions. The study was conducted in two different-sized watersheds (Anjeni and Gilgel Abay with ~ 1 km2 and ~ 1655 km2 area, respectively) in the Upper Blue Nile basin in Ethiopia. Effects of DEM and soil data resolutions on streamflow and sediment yield were evaluated using the Soil and Water Assessment Tool (SWAT). The results showed that the effect of DEM and soil data resolution on streamflow and sediment yield simulation was scale dependent finer resolution DEM and soil datasets improved streamflow and sediment yield simulations in the smaller Anjeni watershed, whereasDEM resolution had no effect in the bigger Gilgel Abay watershed. Small watersheds are often used to understand watershed processes, and thus theuse of finer-resolution spatial data for watershed simulations could result in better results. Findings from the smaller Anjeni watershed suggested that the combined use of finer resolution DEM and soil data could potentially improve sediment yield simulations although the lack of observed sediment yield data did not allow verification of this at the larger Gilgel Abay watershed.

Uncertainty analysis of potential population exposure within the coastal lowlands of mainland China

With accelerating global sea level rise driven by climate change, accurate estimates of potential population exposure (PPE) within the low-elevation coastal zones (LECZ) are critical for coastal planning and assessing the benefits of climate mitigation. Multiple digital elevation models (DEM) and population grid datasets have been used for the PPE assessment of coastal lowlands. However, the uncertainty arising from differences in data sources and production methods results in poorly guided estimates. In this study, four global DEM and five population datasets were used to estimate the PPE in the LECZ of China and to assess the uncertainty of PPE estimation. Based on the DEM and population grid with the best accuracy, we found that more than 13.82% of China’s residents lived in the LECZ in 2010. Different DEM-population combinations yielded significantly different PPE estimates, ranging between 3.59–24.61 million and 31.56–112.24 million people in the LECZ below 1 m and 4 m elevation, respectively. The satellite Lidar-based DEM improves the estimates of the LECZ and obtains the PPE within LECZ below 4 m elevation that far exceeds those of other DEM datasets. The usage of WorldPop and LandScan population datasets leads to an underestimation of PPE within the LECZ of China. In contrast, integrating more geospatial big data helps generate better population grids, thus reducing the uncertainty of coastal PPE estimates. There is still a need to improve the availability and accuracy of coastal geospatial data and to deepen the understanding of coastal vulnerability.

3D Scene Modeling Method and Feasibility Analysis of River Water-Land Integration

Aiming at the problem of rapid construction of a river three-dimensional 3D scene, this article integrates remote sensing, 3D modeling, and CityEngine technology to construct a 3D scene model reconstruction method of river water-land integration. The method includes intelligent extraction of underwater topography, refined modeling of hydraulic structures, and construction of a water-land integrated real scene model. Based on this method, the high-fidelity land-underwater seamless digital terrain and the water-land 3D real scene models can be formed. Through experiments, the feasibility and limitations of this method are verified. It can effectively extract the shallow underwater terrain of inland rivers, and the overall accuracy of the study area is less than 2 m. The performance of the seamless fusion 3D terrain is better than the public digital elevation model data set. In the inland basin of Class I to II water quality, it can meet the needs of intelligent perception of a river- and lake-integrated 3D scene model.

Contour detection with bicubic spline surfaces

ABSTRACT Contour detection has a rich history in multiple fields such as geography, engineering, and earth science. The predominant approach is based on piecewise planar tessellation and now being challenged concerning the extraction of contour objects for non-linear elevation functions, particularly with respect to bicubic spline functions. A storage-efficient method was developed in previous research, but the detection of the complete set of contour objects is yet to be realized. Although intractable, theoretical underpinnings pertinent to curvature resulted in an approach to realize the complete detection of objects. Given a digital elevation model dataset, in this study, a bicubic spline surface function was first determined. Thereafter, candidate initial points on the edges across the region of interest were identified, and the recursive disaggregation of rectangles was repeated if the non-existence of a solution could not be assured. A developed tracking method was then applied. During advancement, other initial points on the same contour curve were identified and eliminated to circumvent duplicate detection. The completeness of the outlets provides analytical tools for elevation and other geographical assessments. Demonstrative experiments included the development of a three-dimensional contour-based network and slope assessments. The latter application transforms the slope analysis type from raster-based to vector-based. Highlights Detection of a complete set of contour objects amenable to bicubic spline surfaces. Small closure inside a single patch is detectable if size exceeds the standard. Curvature & tolerances central to step length adjustment and tangent angle determination. Redundant initial points are identified and eliminated during the tracking process. Various potential applications in addition to geographical elevations.

Enhancing the Accuracy of Boresight Calibration with Coplanarity Constraint and Relative Height from DEM

As the resolution of airborne hyperspectral imagers (AHIs) continues to improve, the demand for accurate boresight calibration also increases. However, the high cost of ground control points (GCPs) and the low horizontal resolution of open digital elevation model (DEM) datasets limit the accuracy of AHI’s boresight calibration. We propose a method to enhance the accuracy of DEM-based boresight calibration using coplanarity constraints to address this issue. Our approach utilizes the relative accuracy of DEM in low-resolution DEM datasets. To make better use of the DEM, we apply coplanarity constraints to identify image features that display similar displacement in overlapping areas, and extract their corresponding elevation values from the DEM. These features and their relative heights are then incorporated into an optimization problem for boresight calibration. In the case of low-resolution DEM datasets, our method fully utilizes the relative accuracy of the DEM to improve the boresight correction precision. We have proven that the relative accuracy of elevation is more reliable than absolute accuracy in this situation. Our approach has been tested on the dataset from AHI, and the results have shown that the proposed method has better accuracy on low-resolution DEM datasets. In summary, our method provides a novel approach to improving the accuracy of DEM-based boresight calibration for AHIs, which can benefit applications, such as remote sensing and environmental monitoring. This research highlights the importance of utilizing the relative accuracy of low-resolution DEM datasets for improving the accuracy of boresight calibration.

Sensitivity assessment of morphometric parameters of monogenetic volcanic landforms with global free DEMs

Improvements in the resolution of Digital Elevation Model (DEM) have greatly facilitated the research of small to moderate landforms such as dunes, landslides/rockfalls, moraines/drumlins and monogenetic volcanoes. Global free 30 m DEMs including data derived from Shuttle Radar Topography Mission (SRTM), Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM) are widely used. However, little is known of whether they can be used effectively in monogenetic volcanology. The focus of this study is to explore the accuracy and potential application of global free SRTM 30 m and AW3D30, as well as ASTER GDEM (previously studied) in monogenetic volcanic fields. We compared SRTM and AW3D30 with four reference higher-resolution DEM dataset, including National Elevation Dataset (NED) 10 m in Uinkaret Volcanic Field (UVF) (United States), WorldDEM 12 m in Longgang Volcanic Field (LVF) (China), Contour Line Based DEM (CLBD) 1 m in Jeju Island Volcanic Field (JIVF) (South Korea), and Light Detection And Ranging (LiDAR) 1 m in Lunar Crater Volcanic Field (LCVF) (United States). The results indicate that AW3D30 has higher accuracy than SRTM in all comparisons including elevation, slope angle and morphological parameters of scoria cones and associated lava flows. However, the accuracy of AW3D30 is almost the same as that of SRTM in that it is affected by slope angle, volume of volcanic edifices, and morphological features of scoria cones. In general, slope angle has a significant effect on the elevation and slope angle errors, which is consistent with previous studies. When the volume of the scoria cones is >5 × 106 m3 and the volume of the lava flows is above 10 × 106 m3, the average errors of their morphological parameters generated by SRTM and AW3D30 do not exceed 16.3 %. The morphological features are classified as crater (the central depression within a scoria cone), crater rim (the top 15 % of the highest elevations), lower flank (the lowest 15 % of the elevations), and middle flank (the remainder of a scoria cone). The area near the crater rim is often the area where larger errors occur, especially on the crater side. The middle and lower flank areas have milder errors. Additionally, the simulation of pre-eruptive surfaces should take into account the effects of certain factors such as roughness and flatness when calculating the volume. Overall, AW3D30 has the best potential in monogenetic volcano studies, followed by SRTM and ASTER GDEM.

Generation of the Forest Cover Map of the Qinghai–Tibet Plateau Based on the Multisource Dataset and Random Forest

The Qinghai–Tibet Plateau (QTP) plays a significant role in global climate change and biodiversity conservation. As the third pole of the Earth, it has a wide range and complex terrain. QTP has a vertical distribution of vegetation, and its forest ecosystems play a key role in the region. Forest extraction in this region is still a comprehensive problem because of the phenological periods of different forest types in distinct regions of the QTP and the characteristics of frequent rain and cloudy weather in the south. Taking these factors into consideration, multiple features, including reflectance, spectral indices, statistical backscattering coefficients, topographic slope, and aspect, derived from a multisource dataset incorporating optical remote sensing data, synthetic radar, and digital elevation models, were applied to extract forest in the QTP based on the random forest (RF) classification method. As more than 30 features were involved, the 5-folded cross-validation method was used to determine the optimal parameters and features for the RF model. Using 14,919 forest samples and a multifeature optimized RF classification model, a 10-m resolution forest cover map of QTP in 2021 was generated based on the Sentinel series of satellite datasets and digital elevation model datasets on the Google Earth Engine (GEE) platform. After verification, the overall accuracy of the forest cover map generated in this article is 98.3%, and the Kappa coefficient is 0.95, which is better than the European Space Agency (ESA) WorldCover forest layer.

A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area.

The purpose of this study is to compare the results of the frequency ratio (FR) model with the weight of evidence (WOE) and the logical regression (LR) methods when applied to the landslide susceptibility evaluation in coal mining subsidence areas. Key geological disaster prevention and control areas are taken as the research areas. Field investigation is carried out according to the recorded landslide disaster points in the past five years, and 86 landslide disaster points are determined from the remote sensing satellite images. Furthermore, 12 factors affecting the occurrence of landslide are selected as landslide sensitivity evaluation factors. Among them, slope degree, curvature, elevation, and slope aspect are derived using the digital elevation model (DEM) through 30 m × 30 m resolution. The DEM datasets are derived from the geospatial data cloud, lithology datasets are derived from the geological lithology maps, and land use type map is derived from the current situation of national land use. The distances between roads and coal mining subsidence areas are calculated according to field investigation and remote sensing image interpretation results. In addition, the evaluation model includes an annual rainfall distribution map. Finally, the accuracy of three models is compared by ROC curve analysis. The elevation results demonstrate that the frequency ratio-logic regression (FR-LR) model takes the maximum accurateness of 0.913, subsequent to the FR model and the frequency ratio-weight of evidence (FR-WOE) model, respectively. Thus, using LR method based on the FR model has guiding significance for predicting the landslide sensitivity in coal mining. This reduces probable risks and disasters that affect human health. Subsequently, this ensures higher safety from the healthcare perspective in the mining fields.

A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning

The study of digital elevation model (DEM) super-resolution reconstruction algorithms has solved the problem of the need for high-resolution DEMs. However, the DEM super-resolution reconstruction algorithm itself is an inverse problem, and making full use of the DEM a priori information is an effective way to solve this problem. In our work, a new DEM super-resolution reconstruction method is proposed based on the complementary relationship between internally learned super-resolution reconstruction methods and externally learned super-resolution reconstruction methods. The method is based on the presence of a large amount of repetitive information within the DEM. Using an internal learning approach to learn the internal prior of the DEM, a low-resolution dataset of the DEM rich in detailed features is generated, and based on this, the training of a constrained external learning network is constructed for the discrepancy data pair. Finally, it introduces residual learning based on the network model to accelerate the operation rate of the network and to solve the model degradation problem brought about by the deepening of the network. This enables the better transfer of learned detailed features in deeper network mappings, which in turn ensures accurate learning of the DEM prior information. The network utilizes the internal prior of the specific DEM as well as the external prior of the DEM dataset and achieves better super-resolution reconstruction results in the experimental results. The results of super-resolution reconstruction by the Bicubic method, Super-Resolution Convolutional Neural Networks (SRCNN), very deep convolutional networks (VDSR), ”Zero-Shot” Super-Resolution networks (ZSSR) and the new method in this paper were compared, and the average RMSE of the super-resolution reconstruction results of the five methods were 8.48 m, 8.30 m, 8.09 m, 7.02 m and 6.65 m, respectively. The mean elevation error at the same resolution is 21.6% better than that of the Bicubic method, 19.9% better than that of the SRCNN, 17.8% better than that of the VDSR method, and 5.3% better than that of the ZSSR method.

Evaluating Vertical Accuracies of Open-Source Digital Elevation Models over Multiple Sites in China Using GPS Control Points

Digital elevation models (DEMs) are widely used across a range of fields. Several open-source global DEMs have been released, including the advanced land observing satellite world 3D 30 m DEM (AW3D30DEM), advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTMDEM), and TerraSAR-X for digital elevation measurement (TanDEM-X). ASTER and SRTM are the most widely used DEMs, while the newer models AW3D30DEM and TanDEM-X are becoming increasingly popular. Many studies have evaluated the qualities of these DEMs; however, few multi-regional studies have been conducted in China. To comprehensively and systematically evaluate the qualities of these DEMs in China, the vertical accuracies of AW3D, ASTER, STRM (all 30 m), and TanDEM-X (90 m) were tested across 16 regions in China. Using high-precision global positioning system control points for reference, error values were determined by subtracting these reference values from corresponding global DEM elevation values. As the study only covered flat areas (slope < 5°), slope was treated as a controlled variable. After assessing the impacts of the slope aspect and land cover type, variations in vertical accuracy were examined with respect to longitude and latitude. Overall, TanDEM-X exhibited the highest stability and accuracy, AW3D30 and SRTM also performed well, while ASTER exhibited the worst accuracy. The DEMs showed relationships with the slope aspect and land cover type, assuming that slope had no influence on vertical accuracy. In general, vertical accuracy in high latitudes was slightly better than that in low latitudes, and no evident variations were observed with respect to longitude. This study is the first to conduct DEM analysis across many regions in China from open sources. Since most of the users rely on public domain DEM datasets, this work contributes to their analysis in academic and engineering fields.

Large-Window Curvature Computations for High-Resolution Digital Elevation Models

With the increasing availability of high-resolution digital elevation model (DEM) data, a need has emerged for new processing techniques. Topographic variables such as slope and curvature are relevant on length scales far larger than the pixel resolution of modern DEM data sets. We propose an approach for computing slope and curvature that uses standard regression coefficients over large windows while generating output on the full resolution of the original data, without adding substantially to the computation time. In our approach, to which we will refer as Iterative Aggregation of Regression Terms (IART), aggregates for fitting a quadratic function are computed iteratively from the DEM data in a process that scales logarithmically with the window size. We show that the IART algorithm produces results of much higher-quality than the two-step process of applying neighborhood operations like focal statistics followed by small-window topographic computations, at comparable computational cost.

Exploration of Glacial Landforms by Object-Based Image Analysis and Spectral Parameters of Digital Elevation Model

Glacial landforms are a significant element of landscape in many regions of Earth. The increasing availability of high-resolution digital elevation models (DEMs) provides an opportunity to develop automated methods of glacial landscape exploration and classification. In this study, we aimed to: 1) identify glacial landforms based on high-resolution DEM datasets; 2) determine relevant geomorphometric and spectral parameters and object-based features for the mapping of glacial landforms; and 3) develop an accurate workflow for glacial landform classification based on DEM. The developed methodology included the extraction of secondary features from DEM, feature selection with the Boruta algorithm, object-based image analysis, and random forest supervised classification. We applied the workflow for three study sites: one in Svalbard and two in Poland. It allowed the identification of six categories of glacial landforms: till plains, end moraines, hummocky moraines, outwash/glaciolacustrine plains, valleys, and kettle holes. The majority of relevant secondary features represented DEM spectral parameters calculated from 2-D Fourier analysis. The supervised classification models with the highest performance exhibited up to 96% overall accuracy with regard to a groundtruth dataset. This study showed that glacial landforms can be identified using novel image-processing methodology and spectral parameters of high-resolution DEM. The complete classification workflow developed herein provides a solution for the transparent generation of thematic maps of glacial landforms that may be reproducible and transferrable to various glacial regions worldwide.

The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai

Digital Elevation Models (DEMs) play a critical role in hydrologic and hydraulic modeling. Flood inundation mapping is highly dependent on the accuracy of DEMs. Various vertical differences exist among open access DEMs as they use various observation satellites and algorithms. The problem is particularly acute in small, flat coastal cities. Thus, it is necessary to assess the differences of the input of DEMs in flood simulation and to reduce anomalous errors of DEMs. In this study, we first conducted urban flood simulation in the Huangpu River Basin in Shanghai by using the LISFLOOD-FP hydrodynamic model and six open-access DEMs (SRTM, MERIT, CoastalDEM, GDEM, NASADEM, and AW3D30), and analyzed the differences in the results of the flood inundation simulations. Then, we processed the DEMs by using two statistically based methods and compared the results with those using the original DEMs. The results show that: (1) the flood inundation mappings using the six original DEMs are significantly different under the same simulation conditions—this indicates that only using a single DEM dataset may lead to bias of flood mapping and is not adequate for high confidence analysis of exposure and flood management; and (2) the accuracy of a DEM corrected by the Dixon criterion for predicting inundation extent is improved, in addition to reducing errors in extreme water depths—this indicates that the corrected datasets have some performance improvement in the accuracy of flood simulation. A freely available, accurate, high-resolution DEM is needed to support robust flood mapping. Flood-related researchers, practitioners, and other stakeholders should pay attention to the uncertainty caused by DEM quality.

A newly developed South American Mapping of Temperature with estimated lapse rate corrections

AbstractIn the present work a recently developed product, the South American Mapping of Temperature (SAMeT), is presented. SAMeT minimizes certain deficiencies associated with both the eventual lack of observations and incorporates temperature corrections due to elevation or lapse rate of temperature (LRT) considerations. SAMeT is based on combinations of observed 2‐m temperatures and ERA5 reanalysis, as well as LRTs values computed from maximum (Tmax) and minimum (Tmin) temperatures. The LRT were computed using a simple linear regression on a 40‐year ERA5 reanalysis dataset and a digital elevation model dataset from Global Digital Elevation (GTOPO30). It was obtained that the computed LRT is consistently smaller than the standard LRT for all the seasons and studied regions. The South American territory was divided into four subdomains and the evaluation performed via a cross validation. This methodology consists in removing 90% of the observations to form OBS90 and generate the SAMeT fields. The remaining 10% (OBS10) is used for validation purposes. An additional dataset, hereafter referred to as OBS90i, is obtained using Kriging interpolation of OBS90 along with GTOPO30 and temperature adjustment by a standard LRT (−6.5°C·km−1). The evaluation period was from January 2011 to December 2013. Thus by using the standard LRT, a systematic negative bias for the temperature is obtained. On the other hand, ERA5 displays underestimation for Tmax and overestimation for Tmin. Furthermore, in comparative terms ERA5 displays the larger errors; thus, it is suggested that a bias remotion is important for any application involving these data. The systematic application of computed values for LRT in addition to a combination between observations and ERA5 data allows SAMeT to generate fields with smaller errors compared to observations than ERA5 while keeping spatial correlations. Thus, the present product brings an innovation to the temperature space fields for the South American region.

Stream Network Modeling Using Remote Sensing Data in an Alpine Cold Catchment

The hydrological information derived from a digital elevation model is very important in distributed hydrological modeling. As part of alpine hydrological research on stream network modeling using remote sensing data in the northeast of the Tibetan Plateau, three digital elevation model (DEM) datasets were obtained for the purpose of hydrological features, mainly including channel network, watershed extent and terrain character. The data sources include the airborne light detection and ranging (LiDAR) with point spacing of 1 m, the High Mountain Asia (HMA) DEM and the Shuttle Radar Topography Mission (SRTM) DEM. Mapping of the watershed and stream network was conducted using each of the three DEM datasets. The modeled stream networks using the different DEMs were verified against the actual network mapped in the field. The results show that the stream network derived from the LiDAR DEM was the most accurate representation of the network mapped in the field. The SRTM DEM overestimated the basin hypsometry relative to the LiDAR watershed at the lowest elevation, while the HMA DEM underestimated the basin hypsometry relative to the LiDAR watershed at the highest elevation. This may be because, compared with the SRTM DEM and the HMA DEM, the LiDAR DEM has higher initial point density, accuracy and resolution. It can be seen that the LiDAR data have great potential for the application in hydrologic modeling and water resource management in small alpine catchments.

Vertical Accuracy of Digital Elevation Models Based on Differential Global Positioning System

The Digital Elevation Model (DEM) has been known as a quantitative description of the surface of the Earth, which provides essential information about the terrain. DEMs are significant information sources for a number of practical applications that need surface elevation data. The open-source DEM datasets, such as the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and the Advanced Land Observing Satellite (ALOS) usually have approximately low accuracy and coarser resolution. The errors in many datasets of DEMs have already been generally examined for their importance, where their quality could be affected within different aspects, including the types of sensors, algorithms, terrain types, and other features. Ground control points (GCPs) used in this study were observed through the utilization of differential global positioning system (DGPS) with dual frequencies. Statistical indices were used to compare, evaluate, and validate the DEMs data against DGPS data. Statistical analysis of DEMs pointed out that SRTM accuracy was higher, with Root Mean Square Error (RMSE) of ±6.276m as compared to the other DEMs. ASTER showed the biggest residual error with an RMSE of ±10.241m. Nevertheless, ALOS was noticeably improved by having an RMSE of ±6.988m.

Combined SBAS-InSAR and geostatistics to detect topographic change and fluid paths in geothermal areas

Topographic changes or ground surface deformation provide critical information regarding the fluid ascent paths that form in permeable fracture zones. This study combines surface displacement data using the small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technique, lineament density, topographic elevation, and water type in geothermally active areas to detect highly permeable zones to assess geothermal resource potential. The Bandung Basin (2000 km2) with several geothermal fields, including Kamojang (KGF), Wayang Windu (WWGF), and Tangkuban Parahu (TPGF), is selected as a case study. An ALOS PALSAR dataset composed of 20 descending and 24 ascending orbital modes acquired in 2007–2011 is used for SBAS-InSAR together with a digital elevation model dataset from the Shuttle Radar Topography Mission (SRTM) for lineament analysis. Although interferograms are available in only half of the study area owing to low coherence, ordinary kriging is successfully applied to clarify the vertical and horizontal E-W displacement over the entire area. Both displacements are in agreement with the GPS data within a 95% confidence level. The detected uplift and subsidence depend on the activity and location of production and injection wells in KGF and WWGF, whereas topographic change in TPGF is dominated by magmatic activity. An important finding is the geologic structural control over topographic change, and in particular, that displacement tends to abruptly change near faults, and the displacement pattern becomes complicated in closely distributed fault zones. A weakly positive correlation between vertical displacement and lineament density is observed in water types classified as sulfate, chloride, and bicarbonate. The largest vertical displacement occurs in the sulfate water type, which shows advanced hydrothermal alteration, weakened rocks, and large topographic changes owing to natural and anthropogenic factors.

Comparison of Watershed Delineation Accuracy using Open Source DEM Data in Large Area

The availability of watershed delineation that has been generated from DEM data is difficult to obtain and the accessibility of DEM data which are unrestricted and precise are hard to obtain in Malaysia. The aim of this study is to examine the accuracy of watershed delineation between Digital Elevation Model (DEM) from ASTER and SRTM in Johor State. In this study, free online data sources from USGS website are used to delineate watershed from ASTER and SRTM satellite imageries. The hydrological modelling tool namely ArcSWAT is utilized to delineate watersheds for both DEM datasets. Both DEM data that had been mosaiced using ERDAS imagine and their DEM is generated using ArcGIS. Watershed boundary for the whole Johor State is then being delineated by using ArcSWAT. ASTER and SRTM accuracies were verified using correlation analysis and mean center distance with data from Department of Irrigation and Drainage (DID). Study indicated that DID watershed area is correlated to ASTER and SRTM at 67.60% and 67.85%. In addition, total mean center distance for ASTER and SRTM are 148.485 and 200.200 where it shows the total mean center distance of ASTER is almost close with DID. The results from this study have successfully indicated that both ASTER and SRTM DEMs are suitable for watershed delineation for Johor State at free and reliable source.