Mapping Cropland Abandonment in Mountainous Areas in China Using the Google Earth Engine Platform

Abstract

first_pageDownload PDF 下载PDFsettingsOrder Article Reprints 订购文章重印本 Open AccessArticle 开放获取文章 Mapping Cropland Abandonment in Mountainous Areas in China Using the Google Earth Engine Platform 使用Google Earth Engine平台绘制中国山区耕地撂荒地图 by Suchen Xu 1,Wu Xiao 1,*ORCID,Chen Yu 2,Hang Chen 1ORCID andYongzhong Tan 1 作者：Suchen 1，Wu Xiao 1，* ORCID ，Chen Yu 2，Hang 1 ORCID 和Yongzhong Tan 1 1 Department of Land Management, Zhejiang University, Hangzhou 310058, China 浙江大学土地管理系， 杭州310058， 中国 2 Sichuan Institute of Land Science and Technology, Chengdu 610065, China 四川省土地科学技术研究院， 成都610065， 中国 * Author to whom correspondence should be addressed. 应向其发送信件的作者。 Remote Sens. 2023, 15(4), 1145; https://doi.org/10.3390/rs15041145 遥感 2023， 15（4）， 1145;https://doi.org/10.3390/rs15041145 Submission received: 22 November 2022 / Revised: 25 January 2023 / Accepted: 16 February 2023 / Published: 20 February 2023 提交截止日期：2022 年 11 月 22 日 / 修订日期：2023 年 1 月 25 日 / 接受日期：2023 年 2 月 16 日 / 发布日期：2023 年 2 月 20 日 (This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine) （本文属于《利用Google Earth Engine遥感土地利用与土地变化》专刊） Downloadkeyboard_arrow_down Browse Figures Review Reports Versions Notes 下载keyboard_arrow_down 浏览数字审查 报告 版本 说明 Abstract 抽象 Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands’ status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km2, within which 9252 km2 of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner.

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