气候变化领域集成服务门户(CCPortal): Satellite-based woody canopy cover for Africa: Uncovering bias and recovering best estimates across years

CCPortal

DOI	10.1016/j.srs.2024.100124
	Satellite-based woody canopy cover for Africa: Uncovering bias and recovering best estimates across years
	Kahiu, Njoki; Anchang, Julius; Prihodko, Lara; Yu, Qiuyan; Hanan, Niall
发表日期	2024
ISSN	2666-0172
起始页码	9
卷号	9
英文摘要	Woody plants in both forested and non-forested areas are vital for carbon storage, climate change mitigation, biodiversity conservation, and provision of ecosystem services. Accurate mapping of woody cover (WC) is crucial for understanding global environmental dynamics, but despite advancements in Earth observation (EO), challenges persist in WC mapping, particularly in spatially heterogeneous mixed tree-grass systems, characterized by low density and low stature (LDLS, i.e., savannas and dryland ecosystems) woody plants. This study aims to guide users in selecting appropriate WC products for their analytical needs, particularly in LDLS ecosystems, and encourage WC product developers to consider incorporating dryland woody vegetation into their product development, utilizing modern EO data and techniques. To achieve this, we assessed existing WC products for the biome diverse Sub-Saharan Africa (SSA), for epoch 2005-2010 (EP01) and 2015-2020 (EP02). Our analysis focused on LDLS, which are often overlooked in EO products. We provide error assessments for available WC products at continental and regional scales, in both epochs, providing data for optimal dataset selection. Our results show that WC products that exclude low stature woody vegetation (<5 m height) from training data tend to underestimate WC in drylands, particularly in areas where WC is <40%. However, in general models tend to underestimate cover in dense WC ecosystems. This could potentially be attributed to systematic bias in machine learning regression models, lack of sufficient training data, and increased prevalence of cultivation, and cloud contamination in more humid regions.
英文关键词	Africa; Drylands; Earth observation; Validation; Woody cover
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001193279600001
来源期刊	SCIENCE OF REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300198
作者单位	New Mexico State University
推荐引用方式 GB/T 7714	Kahiu, Njoki,Anchang, Julius,Prihodko, Lara,et al. Satellite-based woody canopy cover for Africa: Uncovering bias and recovering best estimates across years[J],2024,9.
APA	Kahiu, Njoki,Anchang, Julius,Prihodko, Lara,Yu, Qiuyan,&Hanan, Niall.(2024).Satellite-based woody canopy cover for Africa: Uncovering bias and recovering best estimates across years.SCIENCE OF REMOTE SENSING,9.
MLA	Kahiu, Njoki,et al."Satellite-based woody canopy cover for Africa: Uncovering bias and recovering best estimates across years".SCIENCE OF REMOTE SENSING 9(2024).