Automated mapping of landforms through the application of supervised classification to lidAR-derived DEMs and the identification of earthquake ruptures

Wei, Zhenyu; He, Huaiyu; Hao, Haijian; Gao, Wei

doi:10.1080/01431161.2017.1372861

Cited by 8 publications

(10 citation statements)

References 51 publications

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“…Drăguţ and Eisank (2012) Digital terrain analysis (DTA) extracted land surface parameters (LSPs) and terrain factors from DEMs to depict macro-or microscale topographic characteristics quantitatively (Wilson, 2012). The benefit of terrain factors extracted via DTA for terrain classification and mapping was confirmed in several studies (Drăguţ & Eisank, 2012;Li et al, 2017;Wei, He, Hao, & Gao, 2017). As a commonly used terrain factor, terrain texture can express the resemblance of similar landforms and recognize regional discrepancies among different landforms at a certain scale for reliable depictions and discrimination of various terrains, especially macroscale terrain information (Ding, Na, Huang, Tang, & Liu, 2018;Liu et al, 2017;Tao & Strobl, 2012).…”

Section: Introductionmentioning

confidence: 94%

Object‐based large‐scale terrain classification combined with segmentation optimization and terrain features: A case study in China

Ding

Zhao

et al. 2021

Transactions in GIS

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Terrain classification involves essential tasks in geomorphology, landscape investigation, regional planning, and hazard prediction. Most existing methods are based on a simple thresholding approach. However, such an approach is limited in terms of accuracy and robustness, especially for large‐scale tasks. To overcome this limitation, this article proposes an object‐based framework combined with the random forest. Six terrain factors, namely terrain relief, surface roughness, elevation, elevation coefficient variation, shaded relief, and accumulative curvature, are first selected by correlation analysis using Sheffield's entropy. The obtained segmentation result is then optimized by Moran's I and the weighted variance, combining both terrain factors and textures derived from digital elevation models. Then, the features are selected among both terrain factors and their gray‐level co‐occurrence matrix textures. Finally, the features are fed into the random forest classifier. Seven landform types are classified, including plain, hill, low mountain, low‐middle mountain, high‐middle mountain, high mountain, and extremely high mountain. A case study in China was conducted and achieved an overall accuracy of 80.53% compared with the official landform atlas, which is better performance over the compared semi‐automatic methods. The transferability of our framework was further confirmed by an additional application in provincial‐scale mapping with a different classification system.

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Section: Introductionmentioning

confidence: 94%

Object‐based large‐scale terrain classification combined with segmentation optimization and terrain features: A case study in China

Ding

Zhao

et al. 2021

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“…Compared with largescale DEM data mentioned above, DEMs derived from LiDAR are of great advantage with respect to the detailed description of microscopic topographic elements related to tectonic activities and surface processes. Therefore, Wei et al (2017) utilized high-resolution LiDAR data from the Dushanzi anticline for automated landform mapping research (Wei et al, 2017).…”

Section: Automated Geomorphological Mappingmentioning

confidence: 99%

“…In recent years, Chinese scholars have carried out active fault mapping and fault activity studies in the Haiyuan and Altyn Tagh fault zones using LiDAR data (Zhang et al, 2013;Chen et al, 2014;Ren et al, 2015;Chen et al, 2018;Kang et al, 2019). Our research group carried out surveys in the Dushanzi anticline zone of the northern Tian Shan, including the morphological dating of terraces risers (Wei et al, 2015), paleoseismicity investigation using the fault scarp (Wei et al, 2019), and the automated mapping of landforms (Wei et al, 2017). In this paper, we summarized the application of LiDAR in active tectonics of China by reviewing the specific content of these works.…”

Section: Introductionmentioning

confidence: 99%

Review on the Application of Airborne LiDAR in Active Tectonics of China: Dushanzi Reverse Fault in the Northern Tian Shan

Wei

Sun

2022

Front. Earth Sci.

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High-resolution topographic data are fundamental for active tectonics studies. Within the last 2 decades, the airborne light detection and ranging (LiDAR) system provided a solution for the accurate and efficient acquisition of detailed geomorphic features. The use of LiDAR data for the identification of microstructural and geomorphic features, fault zone activity analysis, and earthquake disaster assessment remains challenging and has been the focus of active tectonics studies. Based on the LiDAR data of Dushanzi anticline–a reverse fault zone in Xinjiang, China, our group carried out a significant number of active tectonic research studies. By reviewing the specific content of these works, we summarized the main application of LiDAR for a specific structure, the Dushanzi Reverse Fault in the northern Tianshan. In addition, other applications of LiDAR in active tectonics are summarized in this paper. These studies show that high-resolution LiDAR data facilitate detailed studies of the fault activity and paleoseismicity. We hope more researchers can realize the advantages of LiDAR technology in active tectonics research and apply LiDAR technology into their own practical work, so as to promote the development of active tectonics research.

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“…Most of them simply require a DEM, and the availability of high-resolution DEMs at a global scale has promoted the proliferation of many applications in the fields of geomorphology, geology, archaeology, and urban science [1][2][3][4][5][6]. Indeed, several works have taken advantage of automated landform classification for the reconstruction of issues related to the complex relationships between the spatial distribution of landforms and landscape evolution [7][8][9], seismotectonics [10], geoarchaeology [6,11], geodiversity [12], and urban planning [13]. Although such studies have demonstrated the usefulness of automatic procedures of landform extraction, traditional geomorphological analysis cannot be disregarded for the accurate preparation of detailed landform maps.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy

Gioia

Danese

Corrado

et al. 2021

IJGI

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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.

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Automated mapping of landforms through the application of supervised classification to lidAR-derived DEMs and the identification of earthquake ruptures

Cited by 8 publications

References 51 publications

Object‐based large‐scale terrain classification combined with segmentation optimization and terrain features: A case study in China

Object‐based large‐scale terrain classification combined with segmentation optimization and terrain features: A case study in China

Review on the Application of Airborne LiDAR in Active Tectonics of China: Dushanzi Reverse Fault in the Northern Tian Shan

Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy

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