Suitability analysis for topographic factors in loess landslide research: a case study of Gangu County, China

Niu, Quanfu; Dang, Xinghai; Li, Yuefeng; Zhang, Yingxue; Lü, Xiaolin; Gao, Wenxing

doi:10.1007/s12665-018-7462-y

Cited by 15 publications

(4 citation statements)

References 27 publications

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“…Topographic factors were calculated from the advanced land observing satellite (ALOS) DEM data, with a cell size of 12.5 m by 12.5 m. The common variables included slope, aspect, and elevation. Other advanced variables were also used; for example, slope of slope (SOS) and the second derivative of surface elevation related to the horizontal surface, representing the terrain curve features [70]. According to the slope calculation theory, SOS re-calculates the slope of each point, based on the slope image from the DEM data, or the SOS = arctan (slope difference/horizontal distance).…”

Section: Extraction Of Topographic Factorsmentioning

confidence: 99%

“…According to the slope calculation theory, SOS re-calculates the slope of each point, based on the slope image from the DEM data, or the SOS = arctan (slope difference/horizontal distance). Second, surface roughness, the ratio of surface area at a specific region and its projected area, represented the fragmentation and degree of erosion [70]. Based on the slope image, surface roughness = 1/cos (slope).…”

Section: Extraction Of Topographic Factorsmentioning

confidence: 99%

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A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China

Zhenlong

et al. 2018

Remote Sensing

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Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems.

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Section: Extraction Of Topographic Factorsmentioning

confidence: 99%

Section: Extraction Of Topographic Factorsmentioning

confidence: 99%

A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China

Zhenlong

et al. 2018

Remote Sensing

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“…Regarding the investigation of regional landslide susceptibility in the Loess Plateau of China, some researchers have made efforts, for instance, Niu et al (2018), Tang et al (2020) and Mao et al (2022). However, most of these studies were conducted at a county scale (<500 km 2 ), and the ones over larger areas are missing.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping in the Loess Plateau of northwest China using three data-driven techniques-a case study from middle Yellow River catchment

Guo

Tian

et al. 2023

Front. Earth Sci.

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Accurate landslide susceptibility maps are relevant for stakeholders to take effective measures and perform land use planning. The present research aims at using three data-driven approaches to generate landslide susceptibility map for the middle Yellow River catchment (northwest China) and comparing their performances, including the weighted information value (WIV), where the IV model was integrated with analytic hierarchy process (AHP), the support vector machine (SVM) and the random forest (RF) models. A landslide inventory map including 684 historical landslides was generated first by visual interpretation of remote sensing images combined with a field survey. A total of 14 thematic layers were applied to serve as the landslides influencing factors. The Pearson correlation coefficient analyzed the correlation among these factors, and the C5.0 decision tree algorithm determined the factor importance. The results demonstrated the correlation between every two factors were all less than 0.5. Three factors (including distance to road, distance to river, and slope) were the most important contributions to the landslide occurrences in the region, whereas five factors (including NDWI, plan curvature, profile curvature, surface roughness, and aspect) had minor importance. All the models predict that most of the historical landslides are identified in moderate and high susceptibility areas. For the prediction percentage of landslides in high susceptibility areas, both SVM and RF models exceed 70%. The RF model represented the best performance, with high susceptibility zones accounting for 21.9% and landslide numbers accounting for 90.5%. The comparison among the receiver operating characteristic curves indicated that the accuracy was higher in the RF model than in the other two models: the area under the curve (AUC) for the RF was 0.904, whereas that for the WIV and the SVM were 0.845 and 0.847, respectively. Hence, the RF was proven suitable for assessing the landslide susceptibility in the region. Current results can provide valuable references for future studies and landslide risk mitigation strategies.

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“…The LSM requires a multi-criteria decision-making (MCDM) approach to generate maps with high levels of accuracy and reliability, which can then be used as input for disaster management plans [3]. GIS-based MCDM is an important geospatial analysis method which combines geospatial and non-spatial data to produce LSMs of an area [4]. The GIS tool integrated with MCDM methods provides a geospatial framework to organise these various thematic layers into a hierarchical structure and examine the relationships between the different geospatial components [5].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Spatial Multi-Criteria Evaluation Method for Mapping Landslide Susceptible Areas in Kullu Valley, Himalayas

2019

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In this paper we report our results from analysing a hybrid spatial multi-criteria evaluation (SMCE) method for generating landslide susceptibility mapping (LSM). This study is the first of its kind in the Kullu valley, Himalayas. We used eight related geospatial conditioning factors from three main groups: geological, morphological and topographical factors. Our landslide inventory dataset has a total of 149 GPS points of landslide locations, collected based on a field survey in July 2018. The relationships between landslide locations and conditioning factors were determined using the GIS-based statistical methods of frequency ratio (FR), multi-criteria decision-making (MCDM) and the integration method of hybrid SMCE. We compared the performance of applied methods by dividing the inventory into testing (70%) and validation (30%) datasets. The area under the curve (AUC) was used to validate the results. The integration method of hybrid SMCE gave the highest accuracy rate (0.910) compared to the other two methods, with 0.797 and 0.907 accuracy rates for the analytical hierarchy process (AHP) and FR, respectively. The applied methodologies are easily transferable to other areas, and the resulting landslide susceptibility maps (LSMs) can be useful for risk mitigation and development planning purposes in the Kullu valley, Himalayas.

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Suitability analysis for topographic factors in loess landslide research: a case study of Gangu County, China

Cited by 15 publications

References 27 publications

A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China

A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China

Landslide susceptibility mapping in the Loess Plateau of northwest China using three data-driven techniques-a case study from middle Yellow River catchment

A Hybrid Spatial Multi-Criteria Evaluation Method for Mapping Landslide Susceptible Areas in Kullu Valley, Himalayas

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