Shuttle Radar Topography Mission Digital Elevation Model Research Articles

Water reservoir techniques using remote sensing and GIS: a literature survey

Abstract The issue of storing flood waters caused by rains and river floods is very important to address the problems of drought. This study reviewed the latest research to address the problems of water scarcity and conservation with advanced methods. these study methodologies range from employing Geographic Information Systems Programs to handle and analyses remote sensing data to incorporating contemporary techniques to enhance morphometric analysis, where researchers employed various analytical techniques. To determine if these watersheds are appropriate for storing water, some studies have preferred Multi-Criteria Decision-Making models (MCDM), more especially the fuzzy analytical hierarchy (FAHP) Fuzzy Analytical Hierarchy Process. Others have employed techniques for hydrological analysis, such as the ArcGIS program’s Arc hydrology tools. Researchers reached a consensus regarding the fundamental parameters required for morphometric analysis in the demarcation of watersheds, notwithstanding methodological variances. Additionally, many researchers have argued the importance of using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) mission and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER DEM) in morphometric and hydrologic studies.

On-land manifestation of along-strike transitioning of the Red Sea from continental rifting to sea floor spreading

This study uses Landsat Thematic Mapper (TM), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and aeromagnetic data to examine possible reactivation of the Ediacaran, NE-trending Ad Damm Fault Zone (AFZ) and the Fatima Fault Zone (FFZ) in the western Arabian Shield. Reactivation is by latest Paleogene to Neogene and Quaternary structures of the Red Sea. The AFZ is the tectonic boundary between the Tonian Jeddah terrane in the northwest and the Asir terrane to the southeast of the Arabian Shield. The AFZ and FFZ coincide with possible onshore continuation of a transform fault that separates the sea floor spreading segment of the Red Sea in the south from its transitional segment to the north. It has been suggested that the AFZ is a candidate for the formation of a future onshore transform fault that would result in an eastward jump of the Red Sea sea floor spreading center onto the Arabian Shield. The future spreading center will follow the latest Neogene to Quaternary volcanic rocks (Harrat) that are exposed on the eastern side of the Red Sea. The Landsat TM image and the SRTM DEM show that the AFZ dissects the Red Sea topographic escarpment, and both the AFZ and FFZ control paleo-drainage system (wadis). The aeromagnetic data show that there is a ∼100 km wide zone between the AFZ and FFZ dominated by NE-trending short wavelength magnetic anomalies, possibly related to regional tectonic fabric. Additionally, the aeromagnetic data show that both fault zones dissect NW-trending short wavelength magnetic anomalies, interpreted as latest Paleogene to earliest Neogene dike swarms associated with the opening of the Red Sea. This dissection also includes possible Red Sea related narrow marginal grabens and horsts that were formed onshore and offshore parallel to the Red Sea shoreline. Red Sea-related structure might have reactivated the AFZ and FFZ.

Prioritization of sub-watersheds based on quantitative morphometric analysis of the Narmada River, India, using SRTM-DEM and GIS techniques

Investigating the contribution of basin to the cycle of hydrological and the area's size, shape, and creation through quantitative analysis of these characteristics of the local scenery. Additionally, estimates of the denudation rate and hypsometric analysis were made in order to comprehend the sub-basin's quantitative geomorphological properties. The link between the morphometric parameters shows that the local geological and geomorphological features significantly influence the drainage system. On a less elevated surface with a moderate slope, stream geometry displays a pattern as semi-dendritic in greater stream order flow. On mountainous terrain surfaces, main-order streams display a dendritic drainage pattern combined with a coarse texture of drainage. The center zone of the Narmada River basin's sub-watersheds area (9461 km2) underwent morphometric investigation utilizing geographical information systems (GIS) with remote sensing methods. It highlights the usefulness of using Shuttle Radar Topographic Mission – Digital Elevation Model (SRTM-DEM) and satellite images to enhance basin management to evaluate and comprehend many geo-hydrological aspects, like topographic & drainage analyses. ArcGIS hydrological modeling has been used to identify and study basins utilizing SRTM-DEM having 10 m resolution. For the micro-level research of its physiographic characteristics and flow structural control along with runoff, using morphometric parameters like relief, aerial, and linear is also beneficial. This may assist in predicting floods, their extent, and their severity.

Exploring the Differences in Tree Species Classification between Typical Forest Regions in Northern and Southern China

Focusing on the trend of continuously seeking high-precision tree species classification results in small areas from the perspectives of sensors and classification algorithms. This study aimed to explore the effects of data sources, classifiers, and seasons on classification accuracy in regions with significant environmental variation, examining patterns of tree species classification to enhance the transferability of classification. Considering two typical forest distribution regions in the north and south of China, this study utilized the revisitation cycle and open-source advantages of Sentinel-2 and Landsat-8. Leveraging the Google Earth Engine (GEE) platform, this study captured spectral features, vegetation indices, and texture features for single seasonal and seasonal combination images. With the assistance of Sentinel-1A and SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), backscattering coefficient features and topographical features were extracted and input with features captured from Sentinel-2 and Landsat-8 into three types of classifiers: random forest (RF), support vector machine (SVM), and gradient tree boosting (GTB) for major tree species classification. In this research, we discovered that the best classification for single season in the northern study area was spring, whereas, for the southern study area, it was winter. Seasonal combination images effectively improved the classification accuracy of single seasonal images, with Sentinel-2 imagery displaying better classification performance compared to Landsat-8, and the optimal classifier differing between the north and the south. The inclusion of topographical or backscattering coefficient features in the four-season combination imagery contributed to improvements in classification accuracy, with topographical features significantly enhancing the classification performance in the topographically varied southern study area. The evaluation of feature importance indicated that elevation was the most critical feature for classification, while spectral features and vegetation indices were also significant. In the southern study area with large topographical discrepancies, subdividing into different terrain units led to improved tree species classification accuracy in medium-altitude, gentle slope areas. These findings provide insights into the regularity of enhancing tree species classification accuracy in environmentally diverse areas through the use of multi-source remote sensing data and multi-seasonal imagery. Consequently, the results offer a reference for the identification of tree species across large areas and the creation of spatial distribution maps.

New approach for satellite DEM accuracy enhancement by combing machine learning, fuzzy majority voting, and weighted interpolation techniques

The digital elevation model (DEM) is crucial in many global and regional scientific studies in civilian and military applications. The aim of this research is to develop and test a new DEM approach for correcting the various errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model. Firstly, the DEMs with the feature attributes from Sentinel-2 multispectral imagery are generated. Secondly, SRTM DEM with one band and attributes of a sentinel-2 image with eight bands are used as input data in supervised max-like hood, an artificial neural network (ANN), and support vector machine (SVM) classification models. Thirdly, ANN, supervised max-like hood, and SVM classification models, which have various properties, are fused by fuzzy majority voting (probability fusion). Finally, the fused probability is assigned for each pixel of the image, which has 12 fixed ground control points (GCPs), which is considered new input data for the inverse probability weighted interpolation (IPWI) approach to create the corrected SRTM elevations. The results were contrasted with a reference DEM (RD) created by image matching with Worldview-1 stereo satellite images, which had a 1-m vertical accuracy. The results of this study demonstrated that the RMSE of the original SRTM DEM was 5.95. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 1.98 compared with that of the MLR method (3.01). The study shows a series of significant improvements in the SRTM when assessed with the reference DEM, with an RMSE reduction of (66.72%) when compared to the widely utilized multiple linear regression (MLR) method. It can be concluded that the elevation error of the original SRTM DEM is clearly reduced by the suggested approach.

Sustainability Evaluation of the Al-Qasab Playa by Integrating Hydrogeochemical and Graphical, ArcGIS Watershed, and Thermodynamic Geochemical Modeling Approaches

Drawing development plans requires evaluating the available resources and assessing their sustainable and subsequent utilization-driven environmental impacts. The current work is concerned with evaluating the sustainability of the halite harvesting process from Al-Qasab Playa, Shaqra, Central Saudi Arabia. The authors integrated, conceptually and quantitatively, ArcGIS-processed SRTM-DEM (Shuttle Radar Topography Mission-Digital Elevation Model) data, hydrogeochemical and thermodynamic-based geochemical modelling, and graphical approaches to achieve the ultimate aims of the study. The watershed is identified as a nonmarine closed basin with a drainage area of 1290 km2, with the slope controlling recharge to the Playa. The Chadha plot including the rainwater exhibits linear regression, with an R2 value of 0.9947, confirming the rainwater origin of the Playa water. The hardness-forming ions are primarily removed in pond 3, eliminating the need for costly and power-consuming steps of softening with ion exchange resins or nanofiltration as it can be used directly as a readily available feed for the chlor-alkali process for producing NaOH, Cl2, and H2 gases through electrolysis. XRD (X-ray diffraction) analysis and the SEM-EDS (Scanning electron microscopy-energy dispersive X-ray spectroscopy) of the harvested halite confirmed its purity. An improved design of the current folkloric harvesting process has been proposed based on the saturation indices calculated thermodynamically to provide a readily available feed intake for the electrolysis chlor-alkali process with or without minimal pretreatment to produce higher value chemicals. The methodological aspects presented here are deemed robust and valid for applications in other study areas, including the assessment of the exploitation of the rejected brine from the desalination plants to achieve the zero liquid discharge approach, as well as other types of sabkhas, regardless of their geographical location.

Vertical Accuracy Assessment of the ASTER, SRTM, GLO-30, and ATLAS in a Forested Environment

Understory topography serves as a crucial data source, playing an instrumental role in numerous forest ecosystem applications. However, the use of synthetic aperture radar interferometry and optical stereo for the acquisition of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), SRTM (Shuttle Radar Topography Mission), and GLO-30 (Copernicus Digital Elevation Model) DEM presents unique challenges, particularly in forested environments. These challenges are primarily due to limitations in penetration capability and the effects of foreshortening. ICESat-2/ATLAS, with its higher spatial sampling rate and strong penetrability, presents a new opportunity for estimating forest height parameters and understory terrain. We assessed the vertical accuracy of ASTER, SRTM, GLO-30, and ATLAS in the forest study areas of the United States compared to the reference dataset DTM provided by G-LiHT and we will further discuss the influence of different ground altitudes, forest types, slopes, and aspects on vertical accuracy. The study reveals that in a forested environment, ICESat-2 ATL03 exhibits the highest accuracy at the footprint scale, with a correlation coefficient (R2) close to 1 and Root Mean Square Error (RMSE) = 1.96 m. SRTM exhibits the highest accuracy at the regional scale, with an R2 close to 0.99, RMSE = 11.09 m. A significant decrease in accuracy was observed with increasing slope, especially for slopes above 15°. With a sudden increase in altitude, such as in mountainous situations, the accuracy of vertical estimation will significantly decrease. Aspect and forest cover indeed influence the accuracy of the four DEM products, but this influence lacks a clear pattern. Our results show that ICESat-2 and SRTM data might show sufficient and stable vertical accuracy in a forested environment.

A statistical-based geospatial approach to prioritize the watersheds for soil erosion conservation in the Upper Awash Basin (Upstream Koka), Ethiopia

The current study focuses on the Upper Awash basin's morphometric parameters to understand the watershed's hydrologic characters with respect to soil erosion conservation. A correlation analysis based on a weighted sum approach was adopted to address the objectives of this research. The drainage patterns were extracted from SRTM-DEM (Shuttle Radar Topography Mission-digital elevation model), and ‘Stahler’s method’ was adopted to determine the stream order. The standardized formulas were employed to calculate about 23 different morphometric parameters. The Upper Awash basin (Koka) has been classified into six watersheds (WS-1 to WS-6). There are eleven selected variables, namely bifurcation ratio (Rb), drainage/stream frequency (Fs), drainage density (Dd), texture ratio (T), drainage texture (If), length of overland flow (Lo), form factor (Ff), shape factor (Bs), elongation ratio (Re), compact coefficient (CC) and circularity ratio (Rc) were used for prioritization ranking and categorization. The study has inferred four categories: very high, high, medium, and low, using the quartile statistical method and weighted compound value. Soil erosion is a major concern in watersheds composed of weak igneous rocks such as ignimbrite and holocene sediments such as limestone, conglomerate, sand, silt, and clay. Hence, three watersheds (WS-4, WS-5, and WS-6) with 53 % of the area have been identified as having strong soil erosion potential, representing very high and high categories requiring immediate conservation measures. Soil erosion conservation efforts and water resource management initiatives will benefit greatly from this effort. In addition, this study confirms that environmental studies utilizing a statistical approach based on GIS (geographical information system) are highly beneficial.

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas.

Locating Potential Run-of-River Hydropower Sites by Developing Novel Parsimonious Multi-Dimensional Moving Window (PMMW) Algorithm with Digital Elevation Models

We developed a Parsimonious Multi-dimensional Moving Window (PMMW) algorithm that only requires Digital Elevation Model (DEM) data of a watershed to efficiently locate potentially optimal hydropower sites. The methodology requires only open source DEM data; therefore, it can be used even in remotest watersheds of the world where in situ measurements are scarce or not available at all. We used three parameters in this algorithm, and tested the method using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Shuttle Radar Topography Mission (SRTM) derived DEMs. Our case study on the Morony Watershed, Montana, USA shows that (1) along with 6 out of the 7 existing hydropower plants being successfully located, 12 new potential hydropower sites were also identified, using a clearance of 1 km, diversion of 90 m, and Hydropower Index (HI) threshold of 109 m as the criteria. For the 12 new potential hydropower sites, 737.86 Megawatts (MW) ± 84.56 MW untapped hydropower potential in the Morony Watershed was also derived; (2) SRTM DEM is more suitable for determining the potential hydropower sites; (3) although the ASTER and SRTM DEMs provide elevation data with high accuracy, micro-scale elevation differences between them at some locations may have a profound impact on the HI.

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

ABSTRACT The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.

SRTM DEM Correction Using Ensemble Machine Learning Algorithm

The Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) is a widely utilized product for geological, climatic, oceanic, and ecological applications. However, the accuracy of the SRTM DEM is constrained by topography and vegetation. Using machine learning models to correct SRTM DEM with high-accuracy reference elevation observations has been proven to be useful. However, most of the reference observation-aided approaches rely on either parametric or non-parametric regression (e.g., a single machine learning model), which may lead to overfitting or underfitting and limit improvements in the accuracy of SRTM DEM products. In this study, we presented an algorithm for correcting SRTM DEM using a stacking ensemble machine learning algorithm. The proposed algorithm is capable of learning how to optimally combine the predictions from multiple well-performing machine learning models, resulting in superior performance compared to any individual model within the ensemble. The proposed approach was tested under varying relief and vegetation conditions in Hunan Province, China. The results indicate that the accuracy of the SRTM DEM productions improved by approximately 46% using the presented algorithm with respect to the original SRTM DEM. In comparison to two conventional algorithms, namely linear regression and artificial neural network models, the presented algorithm demonstrated a reduction in root-mean-square errors of SRTM DEM by 28% and 12%, respectively. The approach provides a more robust tool for correcting SRTM DEM or other similar DEM products over a wide area.

Characterizing the surge behaviour and associated ice-dammed lake evolution of the Kyagar Glacier in the Karakoram

Abstract. Glacier surges are prevalent in the Karakoram and occasionally threaten local residents by inundating land and initiating mass movement events. The Kyagar Glacier is well known for its surge history, and in particular its frequent blocking of the downstream valley, leading to a series of high-magnitude glacial lake outburst floods (GLOFs). Although the surge dynamics of the Kyagar Glacier have been broadly described in the literature, there remains an extensive archive of remote sensing observations that have great potential for revealing specific surge characteristics and their relationship with historic lake outburst floods. In this study, we propose a new perspective on quantifying the surging process using successive digital elevation models (DEMs), which could be applied to other sites where glacier surges are known to occur. Advanced Spaceborne Thermal Emission and Reflection Radiometer DEMs, High Mountain Asia 8-meter DEMs, and the Shuttle Radar Topography Mission DEM were used to characterize surface elevation changes throughout the period from 2000 to 2021. We also used Landsat time series imagery to quantify glacier surface velocities and associated lake changes over the course of two surge events between 1989 and 2021. Using these datasets, we reconstruct the surging process of the Kyagar Glacier in unprecedented detail and find a clear signal of surface uplift over the lower glacier tongue, along with uniformly increasing velocities, associated with the period of surge initiation. Seasonal variations in surface flow are still evident throughout the surge phase, indicating the presence of water at the glacier bed. Surge activity of the Kyagar Glacier is strongly related to the development and drainage of the terminal ice-dammed lake, which itself is controlled by the drainage system beneath the glacier terminus.

Morphometric Analysis of Panjhara River Basin With Use of GIS for Development of Watershed Plan

Objectives: Geomorphological changes that occur within a watershed have an influence on the availability and scarcity of water. As a consequence, a quantitative analysis of the watershed geometry is necessary to ascertain the impact of geomorphological processes on the catchment’s hydrology. Methods: Remote sensing (RS) and geographic information systems (GIS)- based strategies have gained in popularity recently because they help strategists and decision-makers make precise and effective decisions. The Panjhara River basin, tributary of Tapi River was selected for this study. The research demonstrates that, for improved planning and maintenance, GIS and RS data may be utilized to analyze and approximatively measure the duration and pace of erosional activities in a Panjhara River basin. The technique uses a 30-meter shuttle radar topography mission digital elevation model (SRTMDEM) and Survey of India toposheet for efficient and quick extraction of morphometric data and hypsometric analysis. Findings: The linear, areal, and relief aspects of the Panjhara River catchment were subjected to the twentysix morphometric criteria. The Re, Rc, and Ff all imply an elongated basin form. According to statistical study, there is a strong correlation between stream order and stream number as well as between stream order and stream length. The shape of the hypsometric curves and the findings of the computed hypsometric integrals represent the erosive phases of the Panjhara River basin. Novelty: The research finds that stakeholders involved in watershed development and management initiatives may find the results of morphometric and hypsometric analyses valuable. Keywords: Morphometric Analysis; Hypsometric Analysis; Stream; GIS

Mapping and analysis of structural lineaments using SRTM radar data and Landsat-8 OLI image: an example from the Telouet–Tighza area, Marrakech High Atlas, Morocco

This study provides the first evaluation of the potential of both the Landsat-8 Operational Land Imager (OLI) sensor images and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data for automated lineament extraction in the south side of the Marrakech High Atlas (Telouet–Tighza area). After image corrections, enhancement methods such as principal component analysis, band composite (BC), and directional filtering were adopted to create new images that provided high visibility of linear structures. The new Landsat BC image used in this study was selected based on the calculation of the optimum index factor and correlation index. In addition to the Landsat image, the SRTM DEM was used to detect structural lineaments in the area by generating shaded relief images. Multisource data, such as band ratio image, geological maps, and fieldwork, were used to eliminate the nongeological lineaments extracted. The results indicate that an automated method was applied successfully for lineament mapping in this area, by detailing the main tectonic faults. Moreover, new lineaments are identified and are validated by fieldwork. Structural lineaments extracted show compatibility in their direction, length, distribution, and density with the tectonic evolution of the study area. A total of 2945 lineaments were extracted with major ENE–WSW and predominant E–W directions. The new structural map shows more structural information compared with the geological map of this area and exemplifies the performance of Landsat-8 OLI bands and SRTM data in this kind of study.

Remote-Sensing Extraction of Small Water Bodies on the Loess Plateau

The mixed pixel of low-resolution remote-sensing image makes the traditional water extraction method not effective for small water body extraction. This study takes the Loess Plateau with complex terrain as the research area and develops a multi-index fusion threshold segmentation algorithm (MFTSA) for a large-scale small water body extraction algorithm based on GEE (Google Earth Engine). MFTSA uses the AWEI (automated water extraction index), MNDWI (modified normalized difference water index), NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index) for multi-index synergy to extract small water bodies. It also uses slope data generated by the SRTM (Shuttle Radar Topography Mission digital elevation model) and NIR band reflectance to eliminate suppressing high reflectivity noise and shadow noise. An MFTSA algorithm was proposed and the results showed that: (1) The overall extraction accuracy of the MFTSA algorithm on the Loess Plateau was 98.14%, and the correct extraction rate of small water bodies was 92.82%. (2) Compared with traditional water index methods and classification methods, the MFTSA algorithm could extract small water bodies with higher integrity and clearer and more accurate boundaries. (3) The MFTSA algorithm was used to extract a total of 69,900 small water bodies on the Loess Plateau, accounting for 97.63% of the total water bodies, and the area was 482.11 square kilometers, accounting for 16.50% of the total water bodies.

DEM BASED COMPUTATION OF TOPOGRAPHIC SURFACE ROUGHNESS TO REVEAL INCISION IN GILGIT-BALTISTAN,PAKISTAN

This research investigates interrelationship between landscape evolution and neotectonics in the Gilgit-Baltistan (GB) region based on surface roughness (SR). We analyzed the topographic surface features such as flat area, surface area and surface ratio using Shuttle RadarTopographic Mission-Digital Elevation Model (SRTM DEM) with a spatial resolution of 90 meters. The objective is to make a SR map and to identify areas influenced by neotectonics. The DEM based spatial distribution of slope gradients and slope orientations along with the aspect map were combined to portray the vertical dissection. Lower values of vertical dissection indicate relatively flat topographic basins which generally mean a region of with intermediate slope gradient and slope orientation. Areas with high vertical dissection values correspond to sharp changes in the slope gradients and slope orientations, which could be due to abrupt scrap edges (possibly neotectonic or lithological control). Higher vertical dissection values in GB are wide spread and are oriented SE- NW in Ghanche, Skardu, Hunza, Gilgit and northern Ghizer districts along Karakorum, South Pamirs and Hindu-Kush ranges. While a few higher roughness values are aligned NE- SW in Diamir and Gilgit districts along the Raikot Fault. The surface roughness map is capable of identifying deeply incised valleys, tectonic uplifts and depressions.

The ATL08 as a height reference for the global digital elevation models

ABSTRACT High-quality height reference data are embedded in the accuracy verification processes of most remote sensing terrain applications. The Ice, Cloud, and Land elevation Satellite 2 (ICESat-2)/ATL08 terrain product has shown promising results for estimating ground heights, but it has not been fully evaluated. Hence, this study aims to assess and enhance the accuracy of the ATL08 terrain product as a height reference for the newest versions of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and TanDEM-X (TDX) DEMs over vegetated mountainous areas. We used uncertainty-based filtering method for the ATL08 strong and weak beams to enhance their accuracy. Then, the results were evaluated against a reference airborne LiDAR digital terrain model (DTM), by selecting 10,000 points over the entire area and comparing the accuracy of ASTER, SRTM, and TDX DEMs assessed by the LiDAR DTM to the accuracy of the ASTER, SRTM, and TDX DEMs assessed by the ATL08 strong beams, weak beams, and all beams. We also detected the impact of the terrain aspect, slope, and land cover types on the accuracy of the ATL08 terrain elevations and their relationship with height errors and uncertainty. Our findings show the accuracy of the ATL08 strong beams was enhanced by 43.91%; while the weak beams accuracy was enhanced by 74.05%. Furthermore, slope strongly influenced ATL08 height errors and height uncertainty; especially on the weak beams. The errors induced by the slope significantly decreased when the uncertainty levels were reduced to <20 m. The evaluations of ASTER, SRTM, and TDX DEMs by ATL08 strong and weak beams are close to those assessed by LiDAR DTM points within 0.6 m for the strong beams. These findings indicate that ATL08 strong beams can be used as a height reference over vegetated mountainous regions.

Multivariable Integrated Evaluation of Hydrodynamic Modeling: A Comparison of Performance Considering Different Baseline Topography Data

AbstractContinental‐scale river hydrodynamic modeling helps to understand the global hydrological cycle, and model evaluation is essential for calibration and performance assessment. Although many models have been evaluated using several variables separately, methods for the integrated multivariable assessment have yet to be established. We proposed an integrated multivariable evaluation method and an overall basin skill score (OSK) metric, which integrates the sub‐basin skill score, calculated by combining multiple variables' normalized Nash‐Sutcliffe efficiency values. The sub‐basin approach and OSK assess the model performance at the sub‐basin and basin scales and overcome the observation‐related limitations of different spatial dimensions. Evaluation of the CaMa‐Flood global river model for the Amazon Basin considering three variables (discharge, water surface elevation, and flooded area) and two baseline topography data (Shuttle Radar Topography Mission (SRTM) and Multi‐Error‐Removed Improved‐Terrain (MERIT) digital elevation models (DEMs)), shows that the proposed methodology can overcome the limitation of single‐variable evaluation in identifying multiple best parameter sets due to low sensitivity. CaMa‐Flood considering MERIT DEM achieves a single optimal parameter set with a maximum OSK of 0.57 compared with 0.52 for the SRTM DEM, and it shows the importance of accuracy of topography data for improvement in the performance of river hydrodynamic modeling. The proposed multivariable integrated evaluation with better topography data proved to help reduce equifinality and estimate the best parameter by maintaining the physical relationships among variables.

The use of spatial data and satellite information in legal compliance and planning in forest management.

A key part of native forest management in designated wood production areas is identifying locations which must be exempt from logging. Forest laws, government regulations, and codes of practice specify where logging is and is not permitted. Assessing compliance with these regulations is critical but can be expensive and time consuming, especially if it entails field measurements. In some cases, spatial data products may help reduce the costs and increase the transparency of assessing compliance. However, different spatial products can vary in their accuracy and resolution, leading to uncertainty in forest management. We present the results of a detailed case study investigating the compliance of logging operations with laws preventing cutting on slopes exceeding 30°. We focused on two designated water catchments in the Australian State of Victoria which supply water to the city of Melbourne. We compared slopes that had been logged on steep terrain using spatial data based on a Digital Elevation Model (DEM) derived from LiDAR, a 1 arc second DEM derived from the Shuttle Radar Topography Mission, and a Digital Terrain Model (DTM) with a resolution of 10m. While our analyses revealed differences in slope measurements among the different spatial products, all three datasets (and the on-site slope measurements) estimated the occurrence of widespread logging of forests on slopes >30° in both water catchments. We found the lowest resolution Shuttle Radar Topography Mission DEM underestimated the steepness of slopes, whilst the DTM was variable in its estimates. As expected, the LiDAR generated slope calculations provided the best fit with on-site measurements. Our study demonstrates the value of spatial data products in assessing compliance with logging laws and codes of practice. We suggest that LiDAR DEMs, and DTMs also can be useful in proactive forest planning and management by helping better identify which areas should be exempt from cutting before logging operations commence.