Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province

文献类型： 外文期刊

第一作者： Feng, Shanshan

作者： Feng, Shanshan;Jiang, Shun;Liu, Xu;Zhang, Lei;Gan, Yangying;Zhou, Canfang;Feng, Shanshan;Jiang, Shun;Liu, Xu;Zhang, Lei;Gan, Yangying;Xia, Ning;Zhou, Canfang;Xia, Ning;Wu, Wenbin;Wu, Wenbin

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关键词： Spatial resolution; Accuracy; Remote sensing; Data mining; Normalized difference vegetation index; Meters; Urban areas; Crops; Graphical models; Distribution functions; Abandoned cropland; land use classification; normalized difference vegetation index (NDVI) maximum value; unplanted cropland; Zengcheng

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：5.3； 五年影响因子：5.6 ）

ISSN： 1939-1404

年卷期： 2025 年 18 卷

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收录情况： SCI

摘要： The accurate extraction and temporal monitoring of abandoned croplands are essential for the effective scientific management of such abandonments. Currently, analyzing time-series normalized difference vegetation index (NDVI) variations serves as a widely used method for abandoned cropland extraction. However, acquiring complete cloud-free images to establish time-series NDVI data across the entire crop growth cycle is usually challenging. To enhance the accuracy of abandoned cropland extraction, the method of annual maximum of NDVI value was proposed to extract abandoned cropland using Gaofen-2 (GF-2) and Sentinel-2 imagery in Zengcheng District, Guangdong Province, China. The method involves the following steps. First, the GF-2 images were used to generate land use map by object-based image analysis method. Subsequently, the images of annual maximum of NDVI value from 2018 to 2022 of Zengcheng District were calculated based on Sentinel-2 data on the Google Earth Engine platform. Crucially, the optimal threshold distinguishing planted cropland and unplanted cropland was determined. After conducting repeated comparisons from Google Earth images and field survey data, it was determined that an object with an annual maximum NDVI value below 0.4 should be regarded as an unplanted object. With this threshold (NDVI = 0.4), the spatial distribution of unplanted cropland within year was identified. Finally, croplands that remained unplanted for two or more consecutive years were extracted as abandonment. The extraction results and accuracy assessment showed that our method achieved an overall accuracy ranging from 0.80 to 0.85. In summary, this study presents a novel approach for accurate abandonment extraction based on available NDVI data.

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