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Quantifying Geomorphological Processes Using Remote Sensing Techniques
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Quantifying Geomorphological Processes Using Remote Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 20966

Special Issue Editor

Prof. Dr. Weiming Cheng

E-Mail Website
Guest Editor
Chinese Academy of Sciences, Beijing, China
Interests: GIS; digital topography and geomorphology analysis; information extraction; digital mapping; geomorphological application under eco-environmental change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of globalization, based on the classical problems, geomorphology study should develop the geomorphological theory, technique and methodology of the new era with the aid of remote sensing, big data, AI and other techniques. Moreover,  geomorphological application researches at a global scale should be conducted, which can provide a scientific basis for global change estimation of ecology and environment, the layout of green silk road industrial park and infrastructure site selection.

With the continuous promotion of remote sensing and sample study to Mars, Venus, the Moon and other stars, on the basis of the classical global geomorphological study, the studies on the aspects such as the mechanism and effect of geomorphological formation and evolution, geology and geomorphology and environmental evolution, evolution and effect of the impact geomorphology on the stars, can serve earth system science research better.

This Special Issue aims at studies covering quantitative geomorphology and planetary geomorphology researches using different remote sensing data acquired by sensors and platforms. Topics may cover digital geomorphology researches at different scales, from regional to global even planetary extent. Meanwhile, digital topographic analysis can also be incorporated by using DEM data from different sources and resolutions.

The subject of the Special Issues focuses on quantitative geomorphology researches using different data sources and techniques. Undoubtedly, the remote sensing method is one of the most important ways to acquire these data and techniques. For example, remote sensing images in a long time-series; DEM data acquired by stereo-images or InSAR methods. Hence, the subject of this Special Issue has close relations to the scope of the Remote Sensing  journal.

Articles may  address, but are not limited, to the following topics:

  • Geomorphological classification and mapping
  • Geomorphological information tupu
  • Geomorphological disaster
  • Permafrost change monitoring
  • Digital topographic analysis
  • Ground subsidence monitoring
  • Lunar carters extraction
  • Water inrush disaster

Suggested article types for submissions include research papers, review papers, technical letters or other proper formats.

Prof. Dr. Weiming Cheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantitative geomorphology
  • planetary geomorphology
  • remote sensing
  • DEM
  • GIS
  • impact geomorphology
  • ecological and environmental change
  • geology
  • geomorphological evolution
  • geomorphological classification and Mapping

Published Papers (11 papers)

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Research

22 pages, 8449 KiB  
Article
Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China
by Menghua Li, Xiebing Yin, Bo-Hui Tang and Mengshi Yang
Remote Sens. 2023, 15(7), 1952; https://doi.org/10.3390/rs15071952 - 06 Apr 2023
Cited by 2 | Viewed by 1685
Abstract
The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products [...] Read more.
The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs’ accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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19 pages, 49764 KiB  
Article
Improved Automatic Classification of Litho-Geomorphological Units by Using Raster Image Blending, Vipava Valley (SW Slovenia)
by Galena Jordanova and Timotej Verbovšek
Remote Sens. 2023, 15(2), 531; https://doi.org/10.3390/rs15020531 - 16 Jan 2023
Viewed by 1638
Abstract
Automatic landslide classification based on digital elevation models has become a powerful complementary tool to field mapping. Many studies focus on the automatic classification of landslides’ geomorphological features, such as their steep main scarps, but in many cases, the scarps and other morphological [...] Read more.
Automatic landslide classification based on digital elevation models has become a powerful complementary tool to field mapping. Many studies focus on the automatic classification of landslides’ geomorphological features, such as their steep main scarps, but in many cases, the scarps and other morphological features are difficult for algorithms to detect. In this study, we performed an automatic classification of different litho-geomorphological units to differentiate slope mass movements in field maps by using Maximum Likelihood Classification. The classification was based on high-resolution lidar-derived DEM of the Vipava Valley, SW Slovenia. The results show an improvement over previous approaches as we used a blended image (VAT, which included four different raster layers with different weights) along with other common raster layers for morphometric analysis of the surface (e.g., slope, elevation, aspect, TRI, curvature, etc.). The newly created map showed better classification of the five classes we used in the study and recognizes alluvial deposits, carbonate cliffs (including landslide scarps), carbonate plateaus, flysch, and slope deposits better than previous studies. Multivariate statistics recognized the VAT layer as the most important layer with the highest eigenvalues, and when combined with Aspect and Elevation layers, it explained 90% of the total variance. The paper also discusses the correlations between the different layers and which layers are better suited for certain geomorphological surface analyses. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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24 pages, 7423 KiB  
Article
Ground Subsidence Monitoring in a Mining Area Based on Mountainous Time Function and EnKF Methods Using GPS Data
by Shifang Zhang and Jin Zhang
Remote Sens. 2022, 14(24), 6359; https://doi.org/10.3390/rs14246359 - 15 Dec 2022
Cited by 3 | Viewed by 1316
Abstract
Ground subsidence is an important geomorphological phenomenon in mining areas. It is difficult to monitor and predict ground subsidence with high precision, especially in mountainous mining areas. Taking the mining workface of a mountainous coalfield in Taiyuan City, in the Shanxi Province of [...] Read more.
Ground subsidence is an important geomorphological phenomenon in mining areas. It is difficult to monitor and predict ground subsidence with high precision, especially in mountainous mining areas. Taking the mining workface of a mountainous coalfield in Taiyuan City, in the Shanxi Province of China as an example, this research selects five typical points from GPS observation data along the strike section. Based on the materials, the ground subsidence processes at these typical points are monitored and predicted using the mountainous time function method. Acquired from the mountains time function is a recurrence equation, which is regarded as the state equation, and the Ensemble Kalman (EnKF) method is conducted accordingly. Finally, the performance of the two methods is evaluated and compared using error curves and indexes. This research presents a recurrence equation based on the mountainous time function method and establishes the EnKF method for ground subsidence monitoring and prediction. Meanwhile, compared to the mountainous time function method, the values of the ME, MAE, RMSE and MAPE indexes are largely improved for the EnKF method. Hence, this research not only presents an effective method for ground subsidence monitoring in mountainous mining areas, but also provides theoretical support for safe coal mining and environmental protection. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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19 pages, 4678 KiB  
Article
Embedded Feature Selection and Machine Learning Methods for Flash Flood Susceptibility-Mapping in the Mainstream Songhua River Basin, China
by Jianuo Li, Hongyan Zhang, Jianjun Zhao, Xiaoyi Guo, Wu Rihan and Guorong Deng
Remote Sens. 2022, 14(21), 5523; https://doi.org/10.3390/rs14215523 - 02 Nov 2022
Cited by 10 | Viewed by 1700
Abstract
Mapping flash flood susceptibility is effective for mitigating the negative impacts of flash floods. However, a variety of conditioning factors have been used to generate susceptibility maps in various studies. In this study, we proposed combining logistic regression (LR) and random forest (RF) [...] Read more.
Mapping flash flood susceptibility is effective for mitigating the negative impacts of flash floods. However, a variety of conditioning factors have been used to generate susceptibility maps in various studies. In this study, we proposed combining logistic regression (LR) and random forest (RF) models with embedded feature selection (EFS) to filter specific feature sets for the two models and map flash flood susceptibility in the mainstream basin of the Songhua River. According to the EFS results, the optimized feature sets included 32 and 28 features for the LR and RF models, respectively, and the composition of the two optimal feature sets was similar and distinct. Overall, the relevant vegetation cover and river features exhibit relatively high effects overall for flash floods in the study area. The LR and RF models provided accurate and reliable flash flood susceptibility maps (FFSMs). The RF model (accuracy = 0.8834, area under the curve (AUC) = 0.9486) provided a better prediction capacity than the LR model (accuracy = 0.8634, AUC = 0.9277). Flash flood-prone areas are mainly distributed in the south and southwest and areas close to rivers. The results obtained in this study is useful for flash flood prevention and control projects. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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27 pages, 15141 KiB  
Article
Assessing Through-Water Structure-from-Motion Photogrammetry in Gravel-Bed Rivers under Controlled Conditions
by Chendi Zhang, Ao’ran Sun, Marwan A. Hassan and Chao Qin
Remote Sens. 2022, 14(21), 5351; https://doi.org/10.3390/rs14215351 - 26 Oct 2022
Cited by 2 | Viewed by 1529
Abstract
Structure-from-Motion (SfM) photogrammetry has become a popular solution for three-dimensional topographic data collection in geosciences and can be used for measuring submerged bed surfaces in shallow and clear water systems. However, the performance of through-water SfM photogrammetry has not been fully evaluated for [...] Read more.
Structure-from-Motion (SfM) photogrammetry has become a popular solution for three-dimensional topographic data collection in geosciences and can be used for measuring submerged bed surfaces in shallow and clear water systems. However, the performance of through-water SfM photogrammetry has not been fully evaluated for gravel-bed surfaces, which limits its application to the morphodynamics of gravel-bed rivers in both field investigations and flume experiments. In order to evaluate the influence of bed texture, flow rate, ground control point (GCP) layout, and refraction correction (RC) on the measurement quality of through-water SfM photogrammetry, we conducted a series of experiments in a 70 m-long and 7 m-wide flume with a straight artificial channel. Bed surfaces with strongly contrasting textures in two 4 m-long reaches were measured under five constant flow regimes with three GCP layouts, including both dry and underwater GCPs. All the submerged surface models with/without RC were compared with the corresponding dry bed surfaces to quantify their elevation errors. The results illustrated that the poorly sorted gravel-bed led to the better performance of through-water SfM photogrammetry than the bed covered by fine sand. Fine sediment transport caused significant elevation errors, while the static sand dunes and grain clusters did not lead to noticeable errors in the corrected models with dry GCPs. The elevation errors of the submerged models linearly increased with water depth for all the tested conditions of bed textures, GCP layouts, and discharges in the uncorrected models, but the slopes of the increasing relations varied with texture. The use of underwater GCPs made significant improvements to the performance of direct through-water SfM photogrammetry, but counteracted with RC. The corrected models with dry GCPs outperformed the uncorrected ones with underwater GCPs, which could still be used to correct the underestimation in surface elevation caused by RC. Based on the new findings, recommendations for through-water SfM photogrammetry in measuring submerged gravel-bed surfaces were provided. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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Graphical abstract

25 pages, 24617 KiB  
Article
Terrestrial and Airborne Structure from Motion Photogrammetry Applied for Change Detection within a Sinkhole in Thuringia, Germany
by Helene Petschko, Markus Zehner, Patrick Fischer and Jason Goetz
Remote Sens. 2022, 14(13), 3058; https://doi.org/10.3390/rs14133058 - 25 Jun 2022
Cited by 1 | Viewed by 1674
Abstract
Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D [...] Read more.
Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D point clouds and derived detectable changes over the years are comparable, even though different sensors and data collection methods were applied. Analyzing this, we took images of a sinkhole terrestrially with a Nikon D3000 and aerially with a DJI drone camera in 2017, 2018, and 2019 and computed 3D point clouds and precision maps using Agisoft PhotoScan and the SfM_Georef software. Applying the “multiscale model to model cloud comparison using precision maps” plugin (M3C2-PM) in CloudCompare, we analyzed the differences between the point clouds arising from the different sensors and data collection methods per year. Additionally, we were interested if the patterns of detectable change over the years were comparable between the data collection methods. Overall, we found that the spatial pattern of detectable changes of the sinkhole walls were generally similar between the aerial and terrestrial surveys, which were performed using different sensors and camera locations. Although the terrestrial data collection was easier to perform, there were often challenges due to terrain and vegetation around the sinkhole to safely acquire adequate viewing angles to cover the entire sinkhole, which the aerial survey was able to overcome. The local levels of detection were also considerably lower for point clouds resulting from aerial surveys, likely due to the ability to obtain closer-range imagery within the sinkhole. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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Graphical abstract

22 pages, 16961 KiB  
Article
An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
by Jinlong Hu, Mingliang Luo, Leichao Bai, Jinliang Duan and Bing Yu
Remote Sens. 2022, 14(12), 2776; https://doi.org/10.3390/rs14122776 - 09 Jun 2022
Cited by 4 | Viewed by 2383
Abstract
Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted separately for each type, while the internal [...] Read more.
Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted separately for each type, while the internal connections between the terrain feature points were ignored. Therefore, this work proposes an integrated algorithm for extracting terrain feature-point clusters, including the peaks, saddles and runoff nodes, based on the DEM data. This method includes two main processes: positive terrain-constrained ridgeline extraction and terrain feature-point cluster extraction. Firstly, a threshold determination method of flow accumulation in the hydrological analysis is proposed by combining morphological characteristics with runoff simulation, and the ridgelines are extracted based on this threshold. Subsequently, the peaks and their control areas are extracted by space segmentation. Meanwhile, the saddles and runoff nodes are obtained by spatial intersection. Finally, the integrated terrain feature-point clusters are obtained by merging the three extracted terrain feature points. This method was experimented with in the six typical sample areas in Shaanxi Province and verified its results by contour lines and optical images. It shows that the spatial positions of the extracted terrain feature clusters are accurate, and the coupling relationships are great. Finally, the experiments show that the statistical attributes of point clusters and their spatial distribution trends have an obvious correlation with geomorphic types and geomorphic zoning, which can provide an important reference for geomorphic zoning and mapping. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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20 pages, 9728 KiB  
Article
New Insights into Ice Avalanche-Induced Debris Flows in Southeastern Tibet Using SAR Technology
by Siyuan Luo, Junnan Xiong, Shuang Liu, Kaiheng Hu, Weiming Cheng, Jun Liu, Yufeng He, Huaizhang Sun, Xingjie Cui and Xin Wang
Remote Sens. 2022, 14(11), 2603; https://doi.org/10.3390/rs14112603 - 29 May 2022
Cited by 4 | Viewed by 2249
Abstract
Drastic climate change has led to glacier retreat in southeastern Tibet, and the increased frequency and magnitude of heavy rainfall and intense snow melting have intensified the risk of ice avalanche-induced debris flows in this region. To prevent and mitigate such hazards, it [...] Read more.
Drastic climate change has led to glacier retreat in southeastern Tibet, and the increased frequency and magnitude of heavy rainfall and intense snow melting have intensified the risk of ice avalanche-induced debris flows in this region. To prevent and mitigate such hazards, it is important to derive the pre-disaster evolutionary characteristics of glacial debris flows and understand their triggering mechanisms. However, ice avalanche-induced debris flows mostly occur in remote alpine mountainous areas that are hard for humans to reach, which makes it extremely difficult to conduct continuous ground surveys and optical remote sensing monitoring. To this end, synthetic aperture radar (SAR) images were used in this study to detect and analyze the pre-disaster deformation characteristics and spatial evolution in the Sedongpu Basin and to detect changes in the snowmelt in the basin in order to improve our understanding of the triggering mechanism of the ice avalanche-induced debris flows in this region. The results revealed that the maximum average deformation rate in the basin reached 57.3 mm/year during the monitoring period from January 2016 to October 2018. The deformation displacement in the gully where the ice avalanche source area was located was intimately correlated with the summer snowmelt and rainfall and was characterized by seasonal accumulation. Clear acceleration of the deformation was detected after both the most recent earthquake and the strong rainfall and snowmelt processes in the summer of 2018. This suggests that earthquakes, snowmelt, and rainfall were significant triggers of the Sedongpu ice avalanche-induced debris flows. The results of this study provide new insights into the genesis of the Sedongpu ice avalanche-induced debris flows, which could assist in disaster warning and prevention in alpine mountain regions. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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20 pages, 14921 KiB  
Article
INDMF Based Regularity Calculation Method and Its Application in the Recognition of Typical Loess Landforms
by Sheng Jiang, Xiaoli Huang and Ling Jiang
Remote Sens. 2022, 14(9), 2282; https://doi.org/10.3390/rs14092282 - 09 May 2022
Viewed by 1452
Abstract
The topographical morphology of the loess landform on the Loess Plateau exhibits remarkable textural features at different spatial scales. However, existing topographic texture analysis studies on the Loess Plateau are usually dominated by statistical characteristics and are missing structural characteristics. At the same [...] Read more.
The topographical morphology of the loess landform on the Loess Plateau exhibits remarkable textural features at different spatial scales. However, existing topographic texture analysis studies on the Loess Plateau are usually dominated by statistical characteristics and are missing structural characteristics. At the same time, there is a lack of regularity calculation methods for DEM digital terrain analysis. Taking the Loess Plateau as the study area, a regularity calculation method based on the improved normalized distance matching function (INDMF) is proposed and applied to the classification of a loess landform. The regularity calculation method used in this study (INDMF regularity) mainly includes two key steps. Step 1 calculates the INDMF sequence value and the peak and valley values for the terrain data. Step 2 calculates the significant peak and valley, constructs the significant peak and valley sequences, and then obtains the regularity using the normalised ratio value. The experimental results show that the proposed method has good anti-interference ability and can effectively extract the regularity of the main landform unit. Compared with previous methods, adding structural features (i.e., INDMF regularity) can effectively distinguish loess hill and loess ridge in the hilly and gully region. For the loess hill and loess ridge, the recognition rates of the proposed method are 84.62% and 92.86%, respectively. Combined with the existing topographic characteristics, the proposed INDMF regularity is a topographic structure feature extraction method that can effectively discriminate between loess hill and loess ridge areas on the Loess Plateau. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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31 pages, 5858 KiB  
Article
The Applicability of Time-Integrated Unit Stream Power for Estimating Bridge Pier Scour Using Noncontact Methods in a Gravel-Bed River
by Laura A. Hempel, Helen F. Malenda, John W. Fulton, Mark F. Henneberg, Jay R. Cederberg and Tommaso Moramarco
Remote Sens. 2022, 14(9), 1978; https://doi.org/10.3390/rs14091978 - 20 Apr 2022
Cited by 1 | Viewed by 1822
Abstract
In near-field remote sensing, noncontact methods (radars) that measure stage and surface water velocity have the potential to supplement traditional bridge scour monitoring tools because they are safer to access and are less likely to be damaged compared with in-stream sensors. The objective [...] Read more.
In near-field remote sensing, noncontact methods (radars) that measure stage and surface water velocity have the potential to supplement traditional bridge scour monitoring tools because they are safer to access and are less likely to be damaged compared with in-stream sensors. The objective of this study was to evaluate the use of radars for monitoring the hydraulic conditions that contribute to bridge–pier scour in gravel-bed channels. Measurements collected with a radar were also leveraged along with minimal field measurements to evaluate whether time-integrated stream power per unit area (Ω) was correlated with observed scour depth at a scour-critical bridge in Colorado. The results of this study showed that (1) there was close agreement between radar-based and U.S. Geological Survey streamgage-based measurements of stage and discharge, indicating that radars may be viable tools for monitoring flow conditions that lead to bridge pier scour; (2) Ω and pier scour depth were correlated, indicating that radar-derived Ω measurements may be used to estimate scour depth in real time and predict scour depth based on the measured trajectory of Ω. The approach presented in this study is intended to supplement, rather than replace, existing high-fidelity scour monitoring techniques and provide data quickly in information-poor areas. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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17 pages, 11313 KiB  
Article
Large-Scale Detection of the Tableland Areas and Erosion-Vulnerable Hotspots on the Chinese Loess Plateau
by Kai Liu, Jiaming Na, Chenyu Fan, Ying Huang, Hu Ding, Zhe Wang, Guoan Tang and Chunqiao Song
Remote Sens. 2022, 14(8), 1946; https://doi.org/10.3390/rs14081946 - 18 Apr 2022
Cited by 8 | Viewed by 2152
Abstract
Tableland areas, featured by flat and broad landforms, provide precious land resources for agricultural production and human settlements over the Chinese Loess Plateau (CLP). However, severe gully erosion triggered by extreme rainfall and intense human activities makes tableland areas shrink continuously. Preventing the [...] Read more.
Tableland areas, featured by flat and broad landforms, provide precious land resources for agricultural production and human settlements over the Chinese Loess Plateau (CLP). However, severe gully erosion triggered by extreme rainfall and intense human activities makes tableland areas shrink continuously. Preventing the loss of tableland areas is of real urgency, in which generating its accurate distribution map is the critical prerequisite. However, a plateau-scale inventory of tableland areas is still lacking across the Loess Plateau. This study proposed a large-scale approach for tableland area mapping. The Sentinel-2 imagery was used for the initial delineation based on object-based image analysis and random forest model. Subsequently, the drainage networks extracted from AW3D30 DEM were applied for correcting commission and omission errors based on the law that rivers and streams rarely appear on the tableland areas. The automatic mapping approach performs well, with the overall accuracies over 90% in all four investigated subregions. After the strict quality control by manual inspection, a high-quality inventory of tableland areas at 10 m resolution was generated, demonstrating that the tableland areas occupied 9507.31 km2 across the CLP. Cultivated land is the dominant land-use type on the tableland areas, yet multi-temporal observations indicated that it has decreased by approximately 500 km2 during the year of 2000 to 2020. In contrast, forest and artificial surfaces increased by 57.53% and 73.10%, respectively. Additionally, we detected 455 vulnerable hotspots of the tableland with a width of less than 300 m. Particular attention should be paid to these areas to prevent the potential split of a large tableland, accompanied by damage on roads and buildings. This plateau-scale tableland inventory and erosion-vulnerable hotspots are expected to support the environmental protection policymaking for sustainable development in the CLP region severely threatened by soil erosion and land degradation. Full article
(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)
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