Pixel-wise parameter assignment in LandTrendr algorithm: Enhancing cropland abandonment monitoring using satellite-based NDVI time-series

文献类型： 外文期刊

第一作者： Wuyun, Deji

作者： Wuyun, Deji;Duan, Mengqi;Sun, Liang;Crusiol, Luis Guilherme Teixeira;Wu, Nitu;Chen, Zhongxin

作者机构：

关键词： Cropland abandonment; Inner Mongolia; Drought; Vegetation Health Index; LandTrendr algorithm

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2024 年 227 卷

页码：

收录情况： SCI

摘要： Effective global agricultural land management is crucial for ensuring food security amidst rapid population growth, especially in Northern China's semi-arid and desert regions, where uncontrolled fallowing has led to increased cropland abandonment. Traditional remote sensing methods often face accuracy challenges in these harsh climatic conditions. This study introduces a novel approach to enhance the Normalized Difference Vegetation Index (NDVI) by reconstructing the magnitude image using ground-truth samples and regional-scale Vegetation Health Index (VHI) data. This allows for flexible, pixel-level parameter assignment to detect cropland transitions more accurately. Rather than relying on single-value assessments to differentiate between active and inactive croplands, we employ pixel-wise magnitude images, integrated into the LandTrendr algorithm for trajectory-based change detection, to better address the variability of large, diverse agricultural regions. Tested on the Google Earth Engine (GEE) platform in Inner Mongolia-a representative arid and semi-arid region of Northern China-our method identified the year of cropland abandonment with 82.02 % accuracy and showed a correlation (R2) of 0.6065 between observed and actual abandonment durations. This research extends the applicability of the LandTrendr algorithm, offering a robust solution for optimizing land use change detection across regions with significant climatic variation.

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