Evaluating satellite data and deep learning for identifying direct deforestation drivers in Cameroon

Abstract

Deforestation rates have been increasing in the Congo Basin in recent years, especially in Cameroon. To support actions to slow deforestation, Earth Observation (EO) has been used extensively to detect forest loss, but approaches to automatically identify specific drivers of deforestation in a level of detail that allows for intervention prioritisation (e.g. focusing on specific areas and actions, designing measures to address specific drivers) have been rare. In this paper, using a new country-specific dataset created for this task, we test whether deep learning with optical satellite data can reliably identify direct drivers of deforestation in Cameroon. We compare the effectiveness of two types of freely available optical satellite imagery of differing spatial resolutions: Landsat-8 (30 m) and NICFI PlanetScope (4.77 m). Since it can be challenging to know which collections are best suited for specific applications, we tested different ones to find the optimal approach. Our detailed classification strategy includes fifteen direct deforestation drivers for forest loss events taking place between 2015 and 2020. We obtain a macro-average F1 score of 0.77 with Landsat-8 data, and a macro-average F1 score of 0.65 with NICFI PlanetScope. Despite a coarser spatial resolution, Landsat-8 performs better than NICFI PlanetScope overall, including for small-scale drivers, although results vary by class. Using only a single-image approach, we achieve F1 scores above 0.65 for all classes except ‘Oil palm plantation’, ‘Hunting’ and ‘Fruit plantation’ with Landsat-8. Our results demonstrate the potential of this approach to monitor and analyse land-use changes leading to deforestation with more refined classes than before. Further, our study demonstrates the potential of leveraging existing available datasets and straightforwardly adapting a generalised framework for other regions experiencing rapid deforestation with only a relatively small amount of location-specific data.