Geomorphometry and Terrain Analysis

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 44436

Special Issue Editors

Department of Geoinformatics, University of Salzburg, Salzburg, Austria
Interests: terrain analysis; visualization of terrain characteristics; multi-resolution DEMs; terrain-constrained processes; surface feature extraction; DEM-DSM accuracy
Special Issues, Collections and Topics in MDPI journals
School of Geography, Nanjing Normal University, Wenyuan Road 1#, Nanjing 210023, Jiangsu Province, China
Interests: geomorphometry; landform evolution modeling; machine learning; geomorphology; GIS

Special Issue Information

Dear Colleagues,

Terrain is considered one of the most important natural geographical features and a key factor in physical processes. Geomorphometry, as the science of digital terrain sensing, quantification, and analysis, provides important toolsets for geomorphology, hydrology, soil science, as well as general geographical information science (GIS). With the progress of elevation data acquisition techniques and terrain analysis methods, surface morphology and its corresponding processes are investigated and interpreted across different scales (from micro to macro) and aspects (such as from surface morphology to formation processes), which significantly contribute to a deeper understanding of Earth surface processes. However, current geomorphometry research is still more focused on morphological metrics and phenomena rather than the mechanisms and processes of terrain morphodynamics. This Special Issue focuses on the ideas of transforming current geomorphometry research from the study of surface morphology to the study of dynamic landform processes. Submissions related to new ideas of terrain modeling, feature extraction and terrain analysis, and their applications in different fields of hydrology and topoclimate, geomorphology, soil science, and radiative processes are also welcome.

Topics include but are not limited to:

  • Terrain analysis (DTA) and feature extraction from digital elevation models;
  • Landform classification across scales by OBIA or machine learning;
  • Scale effects and accuracy assessment of terrain representation and terrain analysis;
  • Progress of DEM and DTA applications in geomorphodynamics;
  • Landform evolution modeling based on terrain morphogenesis;
  • Application of DTA in different fields of hydrology and topoclimate, soil science, and radiative transfer processes;
  • Coupling of DEM with above- and sub-surface representation and dynamics;
  • UAV-based terrain data acquisition and change analysis.

Prof. Josef Strobl
Prof. Liyang Xiong
Guest Editors

Manuscript Submission Information

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Keywords

  • geomorphometry
  • terrain analysis
  • terrain units
  • geomorphodynamics
  • DEM
  • DSM
  • UAV
  • LiDAR
  • landform evolution modeling
  • Earth surface processes
  • scale and accuracy

Published Papers (18 papers)

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Research

18 pages, 7596 KiB  
Article
Similarity Analysis: Revealing the Regional Difference in Geomorphic Development in Areas with High and Coarse Sediment Yield of the Loess Plateau in China
by Hui Yang, Jinhong Liu, Leichao Bai and Mingliang Luo
ISPRS Int. J. Geo-Inf. 2022, 11(4), 227; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11040227 - 28 Mar 2022
Cited by 2 | Viewed by 1703
Abstract
The development of loess landforms is controlled by underlying, pre-existing paleotopography. Previous studies have focused on the inheritance of loess landform and the control of underlying paleotopography on modern terrain based on the digital elevation model (DEM), while the similarities and differences between [...] Read more.
The development of loess landforms is controlled by underlying, pre-existing paleotopography. Previous studies have focused on the inheritance of loess landform and the control of underlying paleotopography on modern terrain based on the digital elevation model (DEM), while the similarities and differences between modern terrain and underlying paleotophotography have not been directly spatialized. In this study, areas with high and coarse sediment yield (AHCSY) in the Loess Plateau of China were selected to form the study area, and the DEM of the study area’s underlying paleotophotography was reconstructed using detailed geological maps, loess thickness maps, and underlying paleotopographic information. The hypsometric integral (HI) and spatial similarity analysis methods were used to compare the spatialized difference between underlying and modern terrain of the Loess Plateau from the perspectives of the landform development stage and surface elevation, respectively. The results of the HI method demonstrate that essentially, there are similarities between the geomorphologic development stages of underlying and modern terrain, and only some local differences exist in some special areas. The results regarding the spatialized coefficient of relative difference and the Jensen–Shannon divergence demonstrate that the thicker the loess is, the weaker the similarity is, and vice versa. Meanwhile, according to the present loess landform division, the order of regional similarity from low to high is as follows: loess tableland, broken loess tableland, hilly regions, dunes, and the Yellow River Trunk. The use of the similarity analysis method to analyze similarities between underlying and modern terrain plays an important role in revealing the inheritance of loess landforms. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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18 pages, 10927 KiB  
Article
Fusion Scheme and Implementation Based on SRTM1, ASTER GDEM V3, and AW3D30
by Shangmin Zhao, Jiao Liu, Weiming Cheng and Chenghu Zhou
ISPRS Int. J. Geo-Inf. 2022, 11(3), 207; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11030207 - 18 Mar 2022
Cited by 3 | Viewed by 2323
Abstract
Multi-source data fusion can help to weaken the original data’s shortcomings while improving data accuracy. The experimental area in this research is Taiyuan City in Shanxi Province, China. Using SRTM1 DEM, ASTER GDEM V3, and AW3D30 DEM, the optimal resolution of the Fused [...] Read more.
Multi-source data fusion can help to weaken the original data’s shortcomings while improving data accuracy. The experimental area in this research is Taiyuan City in Shanxi Province, China. Using SRTM1 DEM, ASTER GDEM V3, and AW3D30 DEM, the optimal resolution of the Fused DEM in the research area is determined by analyzing the topographic factor information entropy. Then the optimally weighted fusion coefficient of the DEM with root mean square error (RMSE) as the criterion under different slope classes is determined by traversal exploration and quantitatively evaluates the fusion effect. The results show that the optimal resolution of the Fused DEM is 40 m under the terrain feature constraint of Taiyuan city. The fused DEM decreases by 33.8%, 57.9%, and 11.5% for mean absolute error (MAE), 36.3%, 54.6%, and 1.4% for standard deviation (STD), and 32.8%, 54.2%, and 9.7% for root mean square error (RMSE) compared with SRTM1, ASTER GDEM V3, and AW3D30. The weighted average fusion of multiple intensities increased the accuracy of the original data. The reduced topographic factor errors, such as slope, profile curvature, and TPI, improved the Fused DEM’s topographic representation capacity. Furthermore, the results confirm the high accuracy of Fused DEM in complex mountainous regions. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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14 pages, 3353 KiB  
Article
Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province
by Ding Ma and Shangmin Zhao
ISPRS Int. J. Geo-Inf. 2022, 11(3), 154; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11030154 - 22 Feb 2022
Cited by 9 | Viewed by 2162
Abstract
It is of great significance for the monitoring and protection of the original ecological environment in coal mining areas to identify the ground subsidence and quantify its influence on the surface vegetation. The surface deformation and vegetation information were obtained by using spaceborne [...] Read more.
It is of great significance for the monitoring and protection of the original ecological environment in coal mining areas to identify the ground subsidence and quantify its influence on the surface vegetation. The surface deformation and vegetation information were obtained by using spaceborne SAR and Landsat OLI images in the Xishan Coalfield. The relative change rate, coefficient of variation, and trend analysis methods were used to compare the vegetation growth trends in the subsidence center, subsidence edge, and non-subsidence zones; and the vegetation coverage was predicted by the pixel dichotomy and grey model from 2021 to 2025. The results indicated that the proportions of vegetation with high fluctuation and serious degradation were 6.60% and 5.64% in the subsidence center, and its NDVI values were about 10% lower than that in the subsidence edge and non-subsidence zones. In addition, vegetation coverage showed a wedge ascending trend from 2013 to 2020, and the prediction values of vegetation coverage obtained by GM (1,1) model also revealed this trend. The residuals of the predicted values were 0.047, 0.047, and 0.019 compared with the vegetation coverage in 2021, and the vegetation coverage was the lowest in the subsidence center, which was consistent with the law obtained by using NDVI. Research suggested that ground subsidence caused by mining activities had a certain impact on the surface vegetation in the mining areas; the closer to the subsidence center, the greater the fluctuation of NDVI, and the stronger the vegetation degradation trend; conversely, the smaller the fluctuation, and the more stable the vegetation growth. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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17 pages, 7762 KiB  
Article
Terrain Skeleton Construction and Analysis in Loess Plateau of Northern Shaanxi
by Min Li, Ting Wu, Weitao Li, Chun Wang, Wen Dai, Xu Su and Yuanyuan Zhao
ISPRS Int. J. Geo-Inf. 2022, 11(2), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11020136 - 15 Feb 2022
Cited by 3 | Viewed by 2450
Abstract
A terrain skeleton determines the overall structure and characteristics of the terrain and indicates the presence of significant terrain features, such as ridges and valleys. It plays an important role in terrain representation and reconstruction, hydrological analysis of watersheds, and other scientific studies [...] Read more.
A terrain skeleton determines the overall structure and characteristics of the terrain and indicates the presence of significant terrain features, such as ridges and valleys. It plays an important role in terrain representation and reconstruction, hydrological analysis of watersheds, and other scientific studies and engineering applications. Previous studies of terrain skeleton have been mostly focused on the extraction of terrain skeletons, ignoring their important effect on terrain analysis. Therefore, this work proposes a new terrain skeleton, which includes three types of terrain skeleton points and two types of terrain skeleton lines. The terrain control points are peak, saddle, and valley nodes, while the terrain skeleton lines are connection lines of peaks and saddles and connection lines of saddles and valley nodes. The terrain skeleton connects isolated terrain control points together. The data structure is designed, and three analysis indicators, namely, nearest-neighbor index, topological connectivity index and landscape shape index are selected. Results show that the three selected indicators can reflect the spatial structure of the terrain skeleton and describe the landform development to a certain extent. Different areas of the same landform, such as the two sample areas in Shenmu County, show variations. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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30 pages, 9647 KiB  
Article
Topographic Characteristics of Drainage Divides at the Mountain-Range Scale—A Review of DTM-Based Analytical Tools
by Kacper Jancewicz, Milena Różycka, Mariusz Szymanowski, Maciej Kryza and Piotr Migoń
ISPRS Int. J. Geo-Inf. 2022, 11(2), 116; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11020116 - 06 Feb 2022
Cited by 4 | Viewed by 2498
Abstract
We review DTM-based measures that can be applied to study the main drainage divides of mountain ranges. Both measures proposed in the past and new or modified approaches are presented, in order to show an ensemble of tools and jointly discuss their information [...] Read more.
We review DTM-based measures that can be applied to study the main drainage divides of mountain ranges. Both measures proposed in the past and new or modified approaches are presented, in order to show an ensemble of tools and jointly discuss their information potential and problematic issues. The first group focuses on the main drainage divide (MDD) as a line running along the range and includes elevation profile, sinuosity, and orientation. The second one includes measures used to compare morphometric properties of two parts of the range, located on the opposite sides of the MDD, such as range asymmetry, morphometric properties of drainage basins, and the position of MDD versus maximum elevation within the range. In the third group, morphometric properties of the terrain immediately adjacent to the MDD are considered. These include properties of areas located far beyond the range symmetry line, topographic asymmetry, longitudinal stream profiles, and relief types derived from automatic landform classifications. The majority of these tools supports identification of sectors of the MDD, anomalous in terms of elevation, symmetry of the range, or the geomorphic context. All these measures were applied to the test area of the Sudetes range in Central Europe. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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18 pages, 6307 KiB  
Article
An Exploration of Loess Landform Development Based on Population Ecology Method
by Ling Yang, Xin Yang and Jiaming Na
ISPRS Int. J. Geo-Inf. 2022, 11(2), 104; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11020104 - 02 Feb 2022
Cited by 2 | Viewed by 1697
Abstract
The study of gully characteristics is one of the most effective ways to explore the loess landform development in the Loess Plateau of China. However, current studies mostly focus on gullies’ overall characteristics and ignore the different composition of the whole gully system. [...] Read more.
The study of gully characteristics is one of the most effective ways to explore the loess landform development in the Loess Plateau of China. However, current studies mostly focus on gullies’ overall characteristics and ignore the different composition of the whole gully system. Therefore, a new perspective is provided in this paper for exploring loess landform development from the population characteristics of the gully system. Firstly, different types of gullies were extracted based on DEM and high-resolution images in three sample watersheds, including hillslope ephemeral gully, bank gully and different-level valley gully. Secondly, population characteristics from the amount, length, age structure and convergent relationship were calculated and analyzed by referring to the biological population in ecology. Finally, the development stages of loess landform in three watersheds were explored based on their population characteristics. The results showed that: (1) The population characteristics, including number density, length density, age structure and convergence, were obviously different in three sample watersheds. (2) The development differences of three watersheds were obtained by synthesizing all population characteristics: Linjiajian was the most developed and oldest watershed, followed by Yangjiaju and then Wangjiagou. (3) The comparison based on the existing soil erosion intensity map and predisposing factors proved that the findings of this paper were more reasonable than that of the traditional hypsometric integral. This research provides a new quantitative-based approach to explore the development degree of loess landform from the gully population, and is a beneficial attempt to combine geomorphology and ecology, further supplementing and improving the study of loess landform development. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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31 pages, 37064 KiB  
Article
Surface Elevation Changes Estimation Underneath Mangrove Canopy Using SNERL Filtering Algorithm and DoD Technique on UAV-Derived DSM Data
by Norhafizi Mohamad, Anuar Ahmad, Mohd Faisal Abdul Khanan and Ami Hassan Md Din
ISPRS Int. J. Geo-Inf. 2022, 11(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11010032 - 31 Dec 2021
Cited by 9 | Viewed by 2618
Abstract
Estimating surface elevation changes in mangrove forests requires a technique to filter the mangrove canopy and quantify the changes underneath. Hence, this study estimates surface elevation changes underneath the mangrove canopy through vegetation filtering and Difference of DEM (DoD) techniques using two epochs [...] Read more.
Estimating surface elevation changes in mangrove forests requires a technique to filter the mangrove canopy and quantify the changes underneath. Hence, this study estimates surface elevation changes underneath the mangrove canopy through vegetation filtering and Difference of DEM (DoD) techniques using two epochs of unmanned aerial vehicle (UAV) data carried out during 2016 and 2017. A novel filtering algorithm named Surface estimation from Nearest Elevation and Repetitive Lowering (SNERL) is used to estimate the elevation height underneath the mangrove canopy. Consequently, DoD technique is used to quantify the elevation change rates at the ground surface, which comprise erosion, accretion, and sedimentation. The significant findings showed that region of interest (ROI) 5 experienced the highest volumetric accretion (surface raising) at 0.566 cm3. The most increased erosion (surface lowering) was identified at ROI 8 at −2.469 cm3. In contrast, for vertical change average rates, ROI 6 experienced the highest vertical accretion (surface raising) at 1.281 m. In comparison, the most increased vertical erosion (surface lowering) was spotted at ROI 3 at −0.568 m. The change detection map and the rates of surface elevation changes at Kilim River enabled authorities to understand the situation thoroughly and indicate the future situation, including its interaction with sea-level rise impacts. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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15 pages, 30660 KiB  
Article
Combining Global Geopotential Models, Digital Elevation Models, and GNSS/Leveling for Precise Local Geoid Determination in Some Mexico Urban Areas: Case Study
by Norberto Alcantar-Elizondo, Ramon Victorino Garcia-Lopez, Xochitl Guadalupe Torres-Carillo and Guadalupe Esteban Vazquez-Becerra
ISPRS Int. J. Geo-Inf. 2021, 10(12), 819; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10120819 - 04 Dec 2021
Cited by 1 | Viewed by 2815
Abstract
This work shows improvements of geoid undulation values obtained from a high-resolution Global Geopotential Model (GGM), applied to local urban areas. The methodology employed made use of a Residual Terrain Model (RTM) to account for the topographic masses effect on the geoid. This [...] Read more.
This work shows improvements of geoid undulation values obtained from a high-resolution Global Geopotential Model (GGM), applied to local urban areas. The methodology employed made use of a Residual Terrain Model (RTM) to account for the topographic masses effect on the geoid. This effect was computed applying the spherical tesseroids approach for mass discretization. The required numerical integration was performed by 2-D integration with 1DFFT technique that combines DFT along parallels with direct numerical integration along meridians. In order to eliminate the GGM commission error, independent geoid undulations values obtained from a set of GNSS/leveling stations are employed. A corrector surface from the associated geoid undulation differences at the stations was generated through a polynomial regression model. The corrector surface, in addition to the GGM commission error, also absorbs the GNSS/leveling errors as well as datum inconsistencies and systematic errors of the data. The procedure was applied to five Mexican urban areas that have a geodetic network of GNSS/leveling points, which range from 166 to 811. Two GGM were evaluated: EGM2008 and XGM2019e_2159. EGM2008 was the model that showed relatively better agreement with the GNSS/leveling stations having differences with RMSE values in the range of 8–60 cm and standard deviations of 5–8 cm in four of the networks and 17 cm in one of them. The computed topographic masses contribution to the geoid were relatively small, having standard deviations on the range 1–24 mm. With respect to corrector surface estimations, they turned out to be fairly smooth yielding similar residuals values for two geoid models. This was also the case for the most recent Mexican gravity geoid GGM10. For the three geoid models, the second order polynomial regression model performed slightly better than the first order with differences up to 1 cm. These two models produced geoid correction residuals with a standard deviation in one test area of 14 cm while for the others it was of about 4–7 cm. However, the kriging method that was applied for comparison purposes produced slightly smaller values: 8 cm for one area and 4–6 cm for the others. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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17 pages, 3465 KiB  
Article
OBIA-Based Extraction of Artificial Terrace Damages in the Loess Plateau of China from UAV Photogrammetry
by Xuan Fang, Jincheng Li, Ying Zhu, Jianjun Cao, Jiaming Na, Sheng Jiang and Hu Ding
ISPRS Int. J. Geo-Inf. 2021, 10(12), 805; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10120805 - 30 Nov 2021
Cited by 3 | Viewed by 1879
Abstract
Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. [...] Read more.
Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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12 pages, 4644 KiB  
Article
Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy
by Dario Gioia, Maria Danese, Giuseppe Corrado, Paola Di Leo, Antonio Minervino Amodio and Marcello Schiattarella
ISPRS Int. J. Geo-Inf. 2021, 10(11), 725; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10110725 - 27 Oct 2021
Cited by 9 | Viewed by 2660
Abstract
Automatic procedures for landform extraction is a growing research field but extensive quantitative studies of the prediction accuracy of Automatic Landform Classification (ACL) based on a direct comparison with geomorphological maps are rather limited. In this work, we test the accuracy of an [...] Read more.
Automatic procedures for landform extraction is a growing research field but extensive quantitative studies of the prediction accuracy of Automatic Landform Classification (ACL) based on a direct comparison with geomorphological maps are rather limited. In this work, we test the accuracy of an algorithm of automatic landform classification on a large sector of the Ionian coast of the southern Italian belt through a quantitative comparison with a detailed geomorphological map. Automatic landform classification was performed by using an algorithm based on the individuation of basic landform classes named geomorphons. Spatial overlay between the main mapped landforms deriving from traditional geomorphological analysis and the automatic landform classification results highlighted a satisfactory percentage of accuracy (higher than 70%) of the geomorphon-based method for the coastal plain area and drainage network. The percentage of accuracy decreased by about 20–30% for marine and fluvial terraces, while the overall accuracy of the ACL map is 69%. Our results suggest that geomorphon-based classification could represent a basic and robust tool to recognize the main geomorphological elements of landscape at a large scale, which can be useful for the advanced steps of geomorphological mapping such as genetic interpretation of landforms and detailed delineation of complex and composite geomorphic elements. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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21 pages, 17996 KiB  
Article
Quantification of Loess Landforms from Three-Dimensional Landscape Pattern Perspective by Using DEMs
by Hong Wei, Sijin Li, Chenrui Li, Fei Zhao, Liyang Xiong and Guoan Tang
ISPRS Int. J. Geo-Inf. 2021, 10(10), 693; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10100693 - 14 Oct 2021
Cited by 12 | Viewed by 2280
Abstract
Quantitative analysis of the differences and the exploration of the evolution models of different loess landform types are greatly important to the in-depth understanding of the evolution process and mechanism of the loess landforms. In this research, several typical loess landform areas in [...] Read more.
Quantitative analysis of the differences and the exploration of the evolution models of different loess landform types are greatly important to the in-depth understanding of the evolution process and mechanism of the loess landforms. In this research, several typical loess landform areas in the Chinese Loess Plateau were selected, and the object-oriented image analysis (OBIA) method was employed to identify the basic loess landform types. Three-dimensional (3D) landscape pattern indices were introduced on this foundation to measure the morphological and structural features of individual loess landform objects in more detail. Compared with the traditional two-dimensional (2D) landscape pattern indices, the indices consider the topographic features, thereby providing more vertical topographic information. Furthermore, the evolution modes between different loess landform types were discussed. Results show that the OBIA method achieved satisfying classification results with an overall accuracy of 88.12%. There are evident differences in quantitative morphological indicators among loess landform types, especially in indicators such as total length of edge, mean patch size, landscape shape index, and edge dimension index. Meanwhile, significant differences are also found in the combination of loess landform types corresponding to different landform development stages. The degree of surface erosion became increasingly significant as loess landforms developed, loess tableland area rapidly reduced or even vanished, and the dominant loess landform types changed to loess ridge and loess hill. Hence, in the reconstruction and management of the Loess Plateau, the loess tableland should be the key protected loess landform type. These preliminary results are helpful to further understand the development process of loess landforms and provide a certain reference for regional soil and water conservation. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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19 pages, 3910 KiB  
Article
Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models
by Annan Yang, Chunmei Wang, Guowei Pang, Yongqing Long, Lei Wang, Richard M. Cruse and Qinke Yang
ISPRS Int. J. Geo-Inf. 2021, 10(10), 680; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10100680 - 09 Oct 2021
Cited by 29 | Viewed by 2527
Abstract
Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve [...] Read more.
Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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20 pages, 4645 KiB  
Article
Extracting Terrain Texture Features for Landform Classification Using Wavelet Decomposition
by Yuexue Xu, Shengjia Zhang, Jinyu Li, Haiying Liu and Hongchun Zhu
ISPRS Int. J. Geo-Inf. 2021, 10(10), 658; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10100658 - 30 Sep 2021
Cited by 11 | Viewed by 1970
Abstract
Accurate landform classification is a crucial component of geomorphology. Although extensive classification efforts have been exerted based on the terrain factor, the scale analysis to describe the macro and micro landform features still needs standard measurement. To obtain the appropriate analysis scale of [...] Read more.
Accurate landform classification is a crucial component of geomorphology. Although extensive classification efforts have been exerted based on the terrain factor, the scale analysis to describe the macro and micro landform features still needs standard measurement. To obtain the appropriate analysis scale of landform structure feature, and then carry out landform classification using the terrain texture, the texture feature is introduced for reflecting landform spatial differentiation and homogeneity. First, applying the ALOS World 3D-30m (AW3D30) DEM and selecting typical landforms of the southwest Tibet Plateau, the discrete wavelet transform (DWT), which acts as the texture feature analysis method, is executed to dissect the multiscale structural features of the terrain texture. Second, through the structural indices of reconstructed texture images, the optimum decomposition scale of DWT is confirmed. Under these circumstances, wavelet coefficients and wavelet energy entropy are extracted as texture features. Finally, the random forest (RF) method is utilized to classify the landform. Results indicate that the texture feature of DWT can achieve higher classification accuracy, which increases by approximately 11.8% compared with the gray co-occurrence matrix (GLCM). Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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13 pages, 2668 KiB  
Article
Zero Watermarking for the TIN DEM Data Based on the Edge Length
by Qifei Zhou, Changqing Zhu and Na Ren
ISPRS Int. J. Geo-Inf. 2021, 10(8), 559; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10080559 - 18 Aug 2021
Cited by 4 | Viewed by 2286
Abstract
How to keep the fidelity of the digital elevation model (DEM) data is a crucial problem in the current watermarking research, as the watermarked DEM data need to preserve their accuracy. We proposed a zero watermarking method for the triangulated irregular network (TIN) [...] Read more.
How to keep the fidelity of the digital elevation model (DEM) data is a crucial problem in the current watermarking research, as the watermarked DEM data need to preserve their accuracy. We proposed a zero watermarking method for the triangulated irregular network (TIN) DEM data. It takes full advantage of the characteristics of the edge length in the TIN DEM data. First, the radio of the edge lengths is quantified to the watermark index, and then the comparison of the edge lengths is quantified to the watermark bit. Finally, the watermark is constructed by combing the watermark bits according to the watermark indices with the help of the majority voting mechanism. In the method, the TIN DEM data are only used to construct the watermark, not to be embedded by the watermark. Therefore, the data quality is preserved to the greatest extent. Experiments verify the theoretical achievements of this method and demonstrate the method is lossless to the TIN DEM data. Simulation results also show that the method has good robustness on translation, rotation, scaling, and cropping attacks. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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17 pages, 2839 KiB  
Article
A Study on Prediction Model of Gully Volume Based on Morphological Features in the JINSHA Dry-Hot Valley Region of Southwest China
by Dan Yang, Kai Mu, Hui Yang, Mingliang Luo, Wei Lv, Bin Zhang, Hui Liu and Zhicheng Wang
ISPRS Int. J. Geo-Inf. 2021, 10(5), 300; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050300 - 05 May 2021
Cited by 5 | Viewed by 2062
Abstract
Gully erosion is well-developed in the Jinsha dry-hot valley region, which has caused serious soil losses. Gully volume is regarded as an effective indicator that can reflect the development intensity of gully erosion, and the evolutionary processes of gullies can be predicted based [...] Read more.
Gully erosion is well-developed in the Jinsha dry-hot valley region, which has caused serious soil losses. Gully volume is regarded as an effective indicator that can reflect the development intensity of gully erosion, and the evolutionary processes of gullies can be predicted based on the dynamic variation in gully volume. Establishing an effective prediction model of gully volume is essential to determine gully volume accurately and conveniently. Therefore, in this work, an empirical prediction model of gully volume was constructed and verified based on detailed morphological features acquired by elaborate field investigations and measurements in 134 gullies. The results showed the mean value of gully length, width, depth, cross-section area, volume, and vertical gradient decreased with the weakness of the activity degree of the gully, although the decrease in processes of these parameters had some differences. Moreover, a series of empirical prediction models of gully volume was constructed, and gully length was demonstrated to be a better predictor than other morphological features. Lastly, the effectiveness test showed the model of V = aL^b was the most effective in predicting gully volume among the different models established in this study. Our results provide a useful approach to predict gully volume in dry-hot valley regions. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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19 pages, 14038 KiB  
Article
Accuracy Comparison on Culvert-Modified Digital Elevation Models of DSMA and BA Methods Using ALS Point Clouds
by Nadeem Fareed and Chi-Kuei Wang
ISPRS Int. J. Geo-Inf. 2021, 10(4), 254; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040254 - 09 Apr 2021
Cited by 5 | Viewed by 2574
Abstract
High-resolution digital elevation models (HR-DEMs) originating from airborne laser scanning (ALS) point clouds must be transformed into Culvert-modified DEMs for hydrological and geomorphological analysis. To produce a culvert-modified DEM, information on the locations of drainage structures (DSs) (e.g., bridges and culverts) is essential. [...] Read more.
High-resolution digital elevation models (HR-DEMs) originating from airborne laser scanning (ALS) point clouds must be transformed into Culvert-modified DEMs for hydrological and geomorphological analysis. To produce a culvert-modified DEM, information on the locations of drainage structures (DSs) (e.g., bridges and culverts) is essential. Nevertheless, DS mapping techniques, whether in connection with the development of new methods or an application setting of existing methods, have always been complicated. Consequently, wide area DS data are rare, making it challenging to produce a culvert-modified DEM in a wide area capacity. Alternatively, the breach algorithm (BA) method is a standard procedure to obtain culvert-modified DEMs in the absence of DS data, solving the problem to some extent. This paper addresses this shortcoming using a newly developed drainage structure mapping algorithm (DSMA) for obtaining a culvert-modified DEM for an area of 36 km2 in Vermont, USA. Benchmark DS data are used as a standard reference to assess the performance of the DSMA method compared to the BA method. A consistent methodological framework is formulated to obtain a culvert-modified DEM using DS data, mapped using the DSMA and resultant culvert-modified DEM is then compared with BA method respectively. The DSs found from the culvert-modified DEMs were reported as true positive (TP), false positive (FP), and false negative (FN). Based on TP, FP, and FN originating from the culvert-modified DEMs of both methods, the evaluation metrics of the false positive rate (FPR) (i.e., the commission error) and false negative rate (FNR) (i.e., the omission error) were computed. Our evaluation showed that the newly developed DSMA-based DS data resulted in an FPR of 0.05 with federal highway authorities (FHWA) roads and 0.12 with non-FHWA roads. The FNR with FHWA roads was 0.07, and with non-FHWA roads, it was 0.38. The BA method showed an FPR of 0.28 with FHWA roads and 0.62 with non-FHWA roads. Similarly, the FNR for the BA method was 0.32 with FHWA roads and 0.61 with non-FHWA roads. The statistics based on the FPR and FNR showed that the DSMA-based culvert-modified DEM was more accurate compared with the BA method, and the formulated framework for producing culvert-modified DEMs using DSMA-based DS data was robust. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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41 pages, 8409 KiB  
Article
DEM Based Study on Shielded Astronomical Solar Radiation and Possible Sunshine Duration under Terrain Influences on Mars by Using Spectral Methods
by Siwei Lin and Nan Chen
ISPRS Int. J. Geo-Inf. 2021, 10(2), 56; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020056 - 30 Jan 2021
Cited by 4 | Viewed by 2254
Abstract
Solar radiation may be shielded by the terrain relief before reaching the Martian surface, especially over some rugged terrains. Yet, to date, no comprehensive studies on the spatial structure of shielded astronomical solar radiation (SASR) and the possible sunshine duration (PSD) on Mars [...] Read more.
Solar radiation may be shielded by the terrain relief before reaching the Martian surface, especially over some rugged terrains. Yet, to date, no comprehensive studies on the spatial structure of shielded astronomical solar radiation (SASR) and the possible sunshine duration (PSD) on Mars have been conducted by previous researchers. Previous studies generally ignored the influences of the terrain on the SASR and PSD, which resulted in a corresponding unexplored field on SASR. The purpose of this paper is to study the Martian spatial-temporal structure of SASR and the PSD under terrain influences. In this paper, the theory of Earth’s SASR, the previous Martian SASR model and the theory of planetary science were combined to propose the SASR model that can be applied to Mars. Then, with the spectrum method theory of geography, we defined two new concepts of spectrums to explore the spatial-temporal distribution of SASR and PSD in different Martian landforms. We found SASR and PSD on Mars were significantly influenced by terrain relief and latitude and showed sufficient regularity, which can be concluded as a gradual attenuation with terrain relief and a regularity of latitude anisotropy. The latitude anisotropy feature is a manifestation of the terrain shielding effect. With the latitude varying, SASR and PSD at different temporal scale generally showed different features with those of Earth, which may be attributed to the imbalanced seasons caused by Martian moving orbits and velocity. Compared to PSD, SASR showed more regular variation under terrain relief and was more influenced by the terrain relief which revealed that SASR is more sensitive to terrain relief than PSD. Additionally, the critical area is a quantitative index to reflect the stable spatial structure of SASR and PSD in different landforms and may be viewed as the minimum test region of sample areas. The corresponding result of the experiments herein indicated that either spectrum can effectively depict the spatial-temporal distribution of SASR and PSD on Mars under terrain relief and deepen the understanding of the variation of SASR and PSD influences by terrain. The critical area of either spectrum can be employed to explore and determine the stable spatial structure of SASR and PSD in different landforms. The proposed Martian SASR model and the new spectral method theory shed new light on revealing the spatial-temporal structure of SASR and PSD under terrain influences on Mars. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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17 pages, 3763 KiB  
Article
Sensitivity Assessment of Spatial Resolution Difference in DEM for Soil Erosion Estimation Based on UAV Observations: An Experiment on Agriculture Terraces in the Middle Hill of Nepal
by Chhabi Lal Chidi, Wei Zhao, Suresh Chaudhary, Donghong Xiong and Yanhong Wu
ISPRS Int. J. Geo-Inf. 2021, 10(1), 28; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010028 - 13 Jan 2021
Cited by 16 | Viewed by 2960
Abstract
Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable [...] Read more.
Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable agriculture and environmental management. Currently, many soil erosion estimations are mainly based on the freely available medium or coarse resolution digital elevation model (DEM) data that neglect micro topographic modification of the agriculture terraces. The development of unmanned aerial vehicle (UAV) technology enables the development of high-resolution (centimeter level) DEM to present accurate topographic features. To demonstrate the sensitivity of soil erosion estimates to DEM resolution at this high-resolution level, this study tries to evaluate soil erosion estimation in the Middle Hill agriculture terraces in Nepal based on UAV derived high-resolution (5 × 5 cm) DEM data and make a comparative study for the estimates by using the DEM data aggregated into different spatial resolutions (5 × 5 cm to 10 × 10 m). Firstly, slope gradient, slope length, and topographic factors were calculated at different resolutions. Then, the revised universal soil loss estimation (RUSLE) model was applied to estimate soil erosion rates with the derived LS factor at different resolutions. The results indicated that there was higher change rate in slope gradient, slope length, LS factor, and soil erosion rate when using DEM data with resolution from 5 × 5 cm to 2 × 2 m than using coarser DEM data. A power trend line was effectively used to present the relationship between soil erosion rate and DEM resolution. The findings indicated that soil erosion estimates are highly sensitive to DEM resolution (from 5 × 5 cm to 2 × 2 m), and the changes become relatively stable from 2 × 2 m. The use of DEM data with pixel size larger than 2 × 2 m cannot detect the micro topography. With the insights about the influencing mechanism of DEM resolution on soil erosion estimates, this study provides important suggestions for appropriate DEM data selection that should be investigated first for accurate soil erosion estimation. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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