Mapping gullies, dunes, lava fields, and landslides via surface roughness

Korzeniowska, Karolina; Pfeifer, Norbert; Landtwing, Stephan

doi:10.1016/j.geomorph.2017.10.011

Cited by 40 publications

(19 citation statements)

References 64 publications

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“…As one of the most typical areas of loess deposit in the world, the Loess Plateau of China that features unique loess landform and severe soil erosion issue has attracted widespread attention for scholars in the fields of geomorphology, geology, hydrology, ecology, and soil and water conservation (Shi and Shao 2000;Hessel and van Asch 2003;Tang et al 2008; Li and Tang 2011;Wang et al 2013;Tang et al 2015;Xiong et al 2016). Gullies, as vessels of material and energy transportation during earth surface processes (Perron et al 2009;Willett et al 2014;Shruthi et al 2015;Xiong et al 2017), are the most active geomorphic units and widely distributed in the Loess Plateau and other regions (Korzeniowska et al 2018;Li et al 2019). Accompanying gully development, soil erosion arises and gully morphology changes constantly, which sculptures a complex and diverse combination of geomorphology (Valentin et al 2005;Wu and Cheng 2005;Castillo and Gómez 2016;Lv et al 2017;Zhu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial variation of gully development in the loess plateau of China based on the morphological perspective

Dai

et al. 2020

Earth Sci Inform

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Gullies characterized by frequent material exchange have profoundly affected the loess landforms evolution in the Loess Plateau of China. Accompanying gully development, various types of gullies have shaped the distinctive loess landforms. Thus, the spatial variations of gully morphology map the regional differences of gully development and loess landforms. Based on 6 key test areas with 5 m resolution DEM and 156 evenly distributed test areas with 25 m resolution DEM, a comprehensive gullyshape index system of topographic feature points, lines, and surfaces was constructed to describe spatial differences of the gully development. After comparing gully-shape indexes in the key test areas, an obvious correlation existed between these indexes and loess landform types. The gully development degree was low in Shenmu and Chunhua, high in Suide, Yanchuan, and Ganquan, while Yijun lay in-between. Using the method of Universal Kriging interpolation, a series of spatial interpolation maps of the gully-shape indexes were obtained, which revealed gully development degree in the whole Loess Plateau. Overall, The active areas of gully development were basically distributed along loess hilly ridges, loess hills, loess ridges, and in a few medium mountain areas, while the inactive areas were basically distributed in the thin loess coverage areas, including plains, river terraces, aeolian dunes, and a few loess platforms. Finally, the gully development regionalization was realized, which contained 6 subzones. These results provided significant references for loess landform research and soil erosion management in the Loess Plateau.

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Section: Introductionmentioning

confidence: 99%

Spatial variation of gully development in the loess plateau of China based on the morphological perspective

Dai

et al. 2020

Earth Sci Inform

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“…Land cover, as a landslide factor, is also the most influenced by human activities, since it is easy to manage and change [Akgun and Türk, 2010;Di Traglia et al, 2017]. Land cover type can also function as a measure for surface roughness when not relative measurements exist, although roughness can be more detailed [Korzeniowska et al, 2018;Di Traglia et al, 2018]. In future works this indicator be supplemented by roughness measurement with the use of topographic (DEMs) and SAR data if available [Catani et al, 2013].…”

Section: Ranking Variablesmentioning

confidence: 99%

Preliminary Risk Assessment at Ustica Based on Indicators of Natural and Human Processes

Alexandrakis

Vita²,

Vito³

2019

Annals of Geophysics

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Geomorphological phenomena have significant repercussions on environmental evolution, triggering changes in natural processes that might have a severe socioeconomic impact. To date, vulnerability estimations have been primarily based on natural processes, and secondarily by combining the exposure resulting from socioeconomic variables, which can assist in identifying areas under risk. The present investigation proposes a methodology to examine the risk from natural hazards by introducing social indicators as exposure factors. The methodology is based on a combination of socioeconomic and natural indicators. In this work the different indicators form indices that are used to make holistic risk estimation for both inland areas and coastal areas. This approach includes four sub-indices that contribute to the overall risk estimation. The sub-indices refer to the geomorphological characteristics, together with natural forcing, coastal erosion for the estimation of the vulnerability and socioeconomic indicators for the estimation of exposure. All variables are ranked on a 1-5 scale, with rank 5 indicating the highest, and are estimated in a GIS model. The main difficulty in making these estimations lies in assessing and ranking the socioeconomic indicators, and especially cultural heritage sites since their importance cannot be measured. The risk is estimated by using the vulnerability of the area and the socioeconomic sub-index that function as the exposure variable in the estimations. This work is an initial approach as part of the Brains2Islands project funded by Fondazione con il Sud and aims to develop a best practice guide for cultural heritage resilience to natural hazards, that will be tested and validated through field studies, using as a case study the island of Ustica, an area of high cultural and economic value, with ancient monuments.

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“…Apparently, the use of Light Detection and Ranging (LiDAR) data proved to be a viable alternative to optical data where gullies are covered by dense canopies. LiDAR-derived digital terrain models (DTMs) were successfully used to map gullies based on either automatic extraction, visual interpretation or both methods [30][31][32][33][34]. Whereas LiDAR data are desirable for gully mapping, the availability or costs associated with obtaining such data remains a challenge, especially in less developed countries like South Africa.…”

Section: Introductionmentioning

confidence: 99%

Mapping Permanent Gullies in an Agricultural Area Using Satellite Images: Efficacy of Machine Learning Algorithms

2021

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Gullies are responsible for detaching massive volumes of productive soil, dissecting natural landscape and causing damages to infrastructure. Despite existing research, the gravity of the gully erosion problem underscores the urgent need for accurate mapping of gullies, a first but essential step toward sustainable management of soil resources. This study aims to obtain the spatial distribution of gullies through comparing various classifiers: k-dimensional tree K-Nearest Neighbor (k-d tree KNN), Minimum Distance (MD), Maximum Likelihood (ML), and Random Forest (RF). Results indicated that all the classifiers, with the exception of ML, achieved an overall accuracy (OA) of at least 0.85. RF had the highest OA (0.94), although it was outperformed in gully identification by MD (0% commission), but the omission error was 20% (MD). Accordingly, RF was considered as the best algorithm, having 13% error in both adding (commission) and omitting pixels as gullies. Thus, RF ensured a reliable outcome to map the spatial distribution of gullies. RF-derived gully density map reflected the agricultural areas most exposed to gully erosion. Our approach of using satellite imagery has certain limitations, and can be used only in arid or semiarid regions where gullies are not covered by dense vegetation as the vegetation biases the extracted gullies. The approach also provides a solution to the lack of laser scanned data, especially in the context of the study area, providing better accuracy and wider application possibilities.

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Mapping gullies, dunes, lava fields, and landslides via surface roughness

Cited by 40 publications

References 64 publications

Spatial variation of gully development in the loess plateau of China based on the morphological perspective

Spatial variation of gully development in the loess plateau of China based on the morphological perspective

Preliminary Risk Assessment at Ustica Based on Indicators of Natural and Human Processes

Mapping Permanent Gullies in an Agricultural Area Using Satellite Images: Efficacy of Machine Learning Algorithms

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