Evaluating Agreement Between Global Satellite Data Products for Forest Monitoring in Madagascar - Oladimeji Mudele; Marissa L. Childs; Jayden Personnat; Christopher D. Golden (Remote Sensing)
Producing high-quality local land cover data can be cost-prohibitive, leaving gaps in reliable estimates of forest cover and loss for environmental policy and planning. Remote sensing data (RSD) offer accessible, globally consistent layers for forest mapping. However, being able to produce reliable RSD-based land cover products with high local fidelity requires ground truth data, which are scarce and cost-intensive to obtain in settings like Madagascar. Global land cover datasets that rely on models trained mostly in well-studied regions claim to alleviate the problem of label scarcity. However, studies have shown that these products often fail to fulfill this promise. Given downstream studies focused on Madagascar still rely on these global land cover products, in this study we compared seven global RSD products measuring forest extent and change in Madagascar to explore levels of similarity across different forest ecoregions over multiple years. We also conducted temporal correlation analysis by checking the correlation between forest area from the different products. We found that agreement levels among the different data products varied by forest type and region, with higher disagreement levels in drier forest ecosystems (dry and spiny forests) than in more humid ones (moist forests and mangroves). For instance, if high agreement is defined as a pixel being classified as a forest by all or all but one product in a year, the average percentage of high-agreement pixels between 2016 and 2020 is just about 8% in the spiny forest and 16% in the dry forest region. These findings underscore the limitations of global RSD products and the importance of localized data for accurate forest monitoring, building justification for efforts to develop a local forest cover product for Madagascar. Our temporal similarity analysis indicates that, although pixel-level maps may show low agreement, temporal aggregates tend to be highly correlated in most cases. We synthesized these results with existing applications of global RSDs in Madagascar to propose practical recommendations for end-users of these products in Madagascar.