干旱区生态环境知识资源中心(Arid): Mediterranean basin vegetation forecasting approaches: accuracy analysis & climate-land cover-sensor nexus impacts

Arid

DOI	10.1080/01431161.2023.2217984
	Mediterranean basin vegetation forecasting approaches: accuracy analysis & climate-land cover-sensor nexus impacts
	Naciri, H.; Ben Achhab, N.; Ezzaher, F-E.; Sobrino, J. A.; Raissouni, N.
通讯作者	Ben Achhab, N
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2023
英文摘要	From land degradation and desertification to cyclones and tropical storms, and so on, the repercussions of global change have become increasingly severe in recent years. Such environmental impacts require continuous assessment and monitoring. Thus, to study and analyse these impacts, a variety of time-series forecasting approaches have been developed, including statistical ones [i.e. Moving Average (MA), Auto-Regressive Integrated Moving Average (ARIMA), etc.], and machine-learning approaches such as Recurrent Neural Networks (RNN), Long-Short-Term Memory Network (LSTM) and Convolutional Neural Networks (CNN). In this study, accuracy of the most used forecasting approaches (i.e. MA, LSTM and Conv-LSTM) has been quantified, and three impacts (i.e. climate regions, land cover and satellite sensors) have been brought to light. Firstly, eight Mediterranean regions were selected (i.e. two hot arid regions, two cold arid regions, two regions with temperate hot summers and two regions with temperate warm summers) based on Koppen climate classification. Secondly, 654 hyperspectral images retrieved from three different satellites (i.e. Sentinel-2, Landsat-8 and MODIS) from 2016 to 2022 were computed in order to predict 29 vegetation biophysical indices (i.e. NDVI, GNDVI, EVI, CVI, etc.). Accordingly, more than 18,000 images were computed, resulting in 696 time series forecasted using the aforementioned approaches. Finally, 2088 forecasted time series have been determined, and their accuracy has been compared. As a result, Landsat-8 and Sentinel-2 images had the highest forecasting accuracy in the three approaches, reaching 86% of the computed indices being over 50% accurately predicted in LSTM model, while MODIS data had the highest forecasting accuracy only in MA model, with a percentage of 72% of the computed indices being over 50% accurately predicted. Furthermore, we have identified that region climate impacts vegetation forecasting accuracy. For instance, arid regions showed low accuracies across all models, while temperate regions showed higher accuracies in the Mediterranean region.
英文关键词	Mediterranean basin time series statistical forecasting machine learning forecasting forecasting accuracy remote sensing climate impacts
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000997927700001
WOS关键词	LEAF-AREA INDEX ; RECURRENT NEURAL-NETWORKS ; GLOBAL VEGETATION ; REFLECTANCE ; IMAGERY ; MODELS ; TRENDS ; NDVI
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397040
推荐引用方式 GB/T 7714	Naciri, H.,Ben Achhab, N.,Ezzaher, F-E.,et al. Mediterranean basin vegetation forecasting approaches: accuracy analysis & climate-land cover-sensor nexus impacts[J],2023.
APA	Naciri, H.,Ben Achhab, N.,Ezzaher, F-E.,Sobrino, J. A.,&Raissouni, N..(2023).Mediterranean basin vegetation forecasting approaches: accuracy analysis & climate-land cover-sensor nexus impacts.INTERNATIONAL JOURNAL OF REMOTE SENSING.
MLA	Naciri, H.,et al."Mediterranean basin vegetation forecasting approaches: accuracy analysis & climate-land cover-sensor nexus impacts".INTERNATIONAL JOURNAL OF REMOTE SENSING (2023).