Change detection of tree cover and burned areas at apiary sites in the Kwahu Afram Plains, Ghana

Bees for Development (BfD) and its local partners have supported beekeeping initiatives in the Kwahu Afram Plains, Ghana, since 2019, with the objective of promoting forest conservation and empowering beekeepers to reduce drivers of forest loss while enhancing forest recovery. Evaluating the conservation results of such community-based interventions requires independent, spatially explicit evidence of environmental outcomes. Key indicators of success include the absence of tree loss and burned areas within a 250 m radius of established apiary sites.

While satellite-based change detection of tree cover and burned areas is well established at regional and global scales, fine-scale monitoring at localized, spatially distributed sites remains relatively understudied and methodologically challenging. Such analyses require careful calibration and validation to detect subtle changes at small spatial extents. In this study, we assess the performance of multiple change detection algorithms for monitoring tree cover loss and fire disturbance within a 500 m diameter surrounding apiary locations, with a focus on fine-scale detection.

We integrate multi-sensor satellite data from Landsat, Sentinel-1, and Sentinel-2, and apply statistical time-series approaches including AVOCADO and BEAST. Algorithm performance is evaluated using high-resolution reference data from PNEO, WorldView, and Google Earth imagery, complemented by field-based ground observations. The study investigates different change detection methods, especially for localized, fine-scale conservation impact assessment and provides insight into practical and independent monitoring frameworks for community-led  conservation initiatives.