Integrating multiple land cover maps through a multi-criteria analysis to improve agricultural monitoring in Africa.

ABSTRACT: Monitoring agricultural land cover is highly relevant for global early warning systems such as ASAP (Anomaly hot Spots of Agricultural Production), because it represents the basis for detecting production deficits in food security assessment. Given the significant inconsistencies among existing land cover datasets, there is a need to obtain a more accurate representation of the spatial distribution and extent of agricultural area in Africa. In this research, we explore a fusion approach that combines the strength of individual datasets and minimises their limitations. Specifically, a semi-automatic method is developed, relying on multi-criteria analysis (MCA) complemented with manual fine-tuning using the best-rated datasets, to generate two hybrid and static agricultural masks - one for c

SUBMITTER: Perez-Hoyos A 

PROVIDER: S-EPMC7497230 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature