Gully erosion susceptibility mapping considering seasonal variations of NDVI using a machine learning approach in the Mollisol region of China

IF 6.1 1区 农林科学 Q1 SOIL SCIENCE
{"title":"Gully erosion susceptibility mapping considering seasonal variations of NDVI using a machine learning approach in the Mollisol region of China","authors":"","doi":"10.1016/j.still.2024.106322","DOIUrl":null,"url":null,"abstract":"<div><div>Gully erosion is the most severe form of soil erosion, and mapping gully erosion susceptibility accurately and automatically is crucial for guiding policy decisions. Topography and vegetation cover were general factors for assessing gully susceptibility, yet little attention has been paid to the spatiotemporal variations in vegetation. This study aims to predict gully-prone areas using stable factors (topography, hydrology, soil, etc.) considering the monthly variability in vegetation based on a machine learning approach in the Mollisol region of China. A total of 1890 gully and non-gully points were extracted to establish an inventory database. Twelve treatments were conducted including the stable factors and individual NDVI from January to December, respectively. All potential factors were evaluated for contributing to the gully erosion prediction, and a set of rules based on accuracy, AUC, and kappa were used to evaluate the model performance. The results demonstrated that NDVI varied widely between gully and non-gully areas and the importance of NDVI varied in diverse months. NDVI in August was the most important explanatory factor (25 %) to gully occurrence mapping, followed by the plan curvature (14 %), and elevation (13 %), respectively. The gully-prone areas predicted by NDVI in August exhibited higher accuracy, followed by that in May and June. This was attributed to the greater difference in NDVI between the gully and non-gully areas in June (0.30), May (0.23), and August (0.16). Overall, the very low, low, moderate, high, and very high gully susceptibility levels occupied 35 %, 23 %, 18 %, 14 %, and 10 % of the study area, respectively. This study advances our understanding of spatial-temporal heterogeneity in NDVI among gully and non-gully areas that need to be considered in gully mapping. Further, an automatic and accurate gully mapping approach can provide valuable information to identify areas where urgent and appropriate measures should be applied.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soil & Tillage Research","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167198724003234","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOIL SCIENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Gully erosion is the most severe form of soil erosion, and mapping gully erosion susceptibility accurately and automatically is crucial for guiding policy decisions. Topography and vegetation cover were general factors for assessing gully susceptibility, yet little attention has been paid to the spatiotemporal variations in vegetation. This study aims to predict gully-prone areas using stable factors (topography, hydrology, soil, etc.) considering the monthly variability in vegetation based on a machine learning approach in the Mollisol region of China. A total of 1890 gully and non-gully points were extracted to establish an inventory database. Twelve treatments were conducted including the stable factors and individual NDVI from January to December, respectively. All potential factors were evaluated for contributing to the gully erosion prediction, and a set of rules based on accuracy, AUC, and kappa were used to evaluate the model performance. The results demonstrated that NDVI varied widely between gully and non-gully areas and the importance of NDVI varied in diverse months. NDVI in August was the most important explanatory factor (25 %) to gully occurrence mapping, followed by the plan curvature (14 %), and elevation (13 %), respectively. The gully-prone areas predicted by NDVI in August exhibited higher accuracy, followed by that in May and June. This was attributed to the greater difference in NDVI between the gully and non-gully areas in June (0.30), May (0.23), and August (0.16). Overall, the very low, low, moderate, high, and very high gully susceptibility levels occupied 35 %, 23 %, 18 %, 14 %, and 10 % of the study area, respectively. This study advances our understanding of spatial-temporal heterogeneity in NDVI among gully and non-gully areas that need to be considered in gully mapping. Further, an automatic and accurate gully mapping approach can provide valuable information to identify areas where urgent and appropriate measures should be applied.
利用机器学习方法绘制考虑到 NDVI 季节性变化的中国软土地区沟壑侵蚀易发性地图
沟壑侵蚀是最严重的土壤侵蚀形式,准确、自动地绘制沟壑侵蚀易发性地图对于指导决策至关重要。地形和植被覆盖是评估沟蚀易发性的一般因素,但人们很少关注植被的时空变化。本研究旨在基于机器学习方法,利用稳定因子(地形、水文、土壤等)并考虑植被的月度变化,预测中国软土地层地区的沟谷易发区。共提取了 1890 个沟谷和非沟谷点,建立了清单数据库。在 1 月至 12 月期间分别进行了 12 次处理,包括稳定因子和单个 NDVI。评估了所有潜在因子对冲沟侵蚀预测的贡献,并使用一套基于准确度、AUC 和 kappa 的规则来评价模型性能。结果表明,沟壑区和非沟壑区的 NDVI 差异很大,不同月份 NDVI 的重要性也不同。8 月份的 NDVI 是绘制沟谷发生图最重要的解释因子(25%),其次分别是平面曲率(14%)和海拔(13%)。用 8 月份的 NDVI 预测沟壑易发区的准确度较高,其次是 5 月和 6 月。这是因为 6 月(0.30)、5 月(0.23)和 8 月(0.16)的沟壑区和非沟壑区的 NDVI 差异较大。总体而言,极低、低、中、高和极高的沟壑易感等级分别占研究区域的 35%、23%、18%、14% 和 10%。这项研究加深了我们对沟壑区和非沟壑区 NDVI 时空异质性的理解,在绘制沟壑地图时需要考虑这些异质性。此外,自动和准确的沟壑绘图方法可提供宝贵的信息,以确定应采取紧急和适当措施的区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Soil & Tillage Research
Soil & Tillage Research 农林科学-土壤科学
CiteScore
13.00
自引率
6.20%
发文量
266
审稿时长
5 months
期刊介绍: Soil & Tillage Research examines the physical, chemical and biological changes in the soil caused by tillage and field traffic. Manuscripts will be considered on aspects of soil science, physics, technology, mechanization and applied engineering for a sustainable balance among productivity, environmental quality and profitability. The following are examples of suitable topics within the scope of the journal of Soil and Tillage Research: The agricultural and biosystems engineering associated with tillage (including no-tillage, reduced-tillage and direct drilling), irrigation and drainage, crops and crop rotations, fertilization, rehabilitation of mine spoils and processes used to modify soils. Soil change effects on establishment and yield of crops, growth of plants and roots, structure and erosion of soil, cycling of carbon and nutrients, greenhouse gas emissions, leaching, runoff and other processes that affect environmental quality. Characterization or modeling of tillage and field traffic responses, soil, climate, or topographic effects, soil deformation processes, tillage tools, traction devices, energy requirements, economics, surface and subsurface water quality effects, tillage effects on weed, pest and disease control, and their interactions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信