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
作者: Deji Wuyun;Mengqi Duan;Liang Sun;Luís Guilherme Teixeira Crusiol;Nitu Wu;Zhongxin Chen
作者机构:
关键词: Cropland abandonment;Drought;Inner Mongolia;LandTrendr algorithm;Vegetation Health Index
期刊名称: Computers and Electronics in Agriculture
ISSN: 0168-1699
年卷期: 2024 年 227 卷
页码:
收录情况: SCIE(2024版) ; ; EI(2024版)
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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