干旱区生态环境知识资源中心(Arid): An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning

Arid

DOI	10.1007/s10661-024-12677-0
	An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning
	Mustapha, Musa; Zineddine, Mhamed
通讯作者	Mustapha, M
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2024
卷号	196 期号:6
英文摘要	Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA's ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.
英文关键词	Drought VHI Climate change Remote sensing LULC changes MODIS Sentinel-2 Machine learning Morocco
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001214900600002
WOS关键词	TEMPERATURE CONDITION INDEXES ; GOOGLE EARTH ENGINE ; RANDOM FOREST ; VEGETATION INDEX ; IMAGE CLASSIFICATION ; URBAN AREAS ; L-BAND ; METAANALYSIS ; PRODUCT ; HEALTH
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403600
推荐引用方式 GB/T 7714	Mustapha, Musa,Zineddine, Mhamed. An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning[J],2024,196(6).
APA	Mustapha, Musa,&Zineddine, Mhamed.(2024).An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning.ENVIRONMENTAL MONITORING AND ASSESSMENT,196(6).
MLA	Mustapha, Musa,et al."An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning".ENVIRONMENTAL MONITORING AND ASSESSMENT 196.6(2024).