Satellite-based woody canopy cover for Africa: Uncovering bias and recovering best estimates across years

Abstract

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.