The influx of nearly a million refugees from Myanmar’s Rakhine state to Cox’s Bazar, Bangladesh, in August 2017 put significant pressure on the regional landscape leading to land degradation due to biomass removal to provide shelter and fuel energy and posed critical challenges for both host and displaced population. This article emphasizes geospatial applications at different stages of addressing land degradation in Cox’s Bazar. A wide range of data and methods were used to delineate land tenure, estimate wood fuel demand and supply, assess land degradation, evaluate land restoration suitability, and monitor restoration activities. The quantitative and spatially explicit information from these geospatial assessments integrated with the technical guidelines for sustainable land management and an adaptive management strategy was critical in enabling a collaborative, multi-disciplinary and evidence-based approach to successfully restoring degraded landscapes in a displacement setting.

Reliable land cover information is essential for scaling plot-based measurements in national forest inventories (NFIs). This study compared the precision of key forest indicators in the Bangladesh NFI using remote sensing (RS)-derived and field-assigned land cover data. Field data from 1781 plots, collected as part of the Bangladesh NFI (2015–2019), were integrated with a 2015 national land cover map produced from SPOT-6/7, Landsat, and Sentinel-2 imagery. The precision of forest indicator estimates was evaluated across land cover domains and ecological zones. Results show that, under an unchanged NFI field measurement and estimation framework, RS-derived land cover reduced the width of confidence intervals (i.e., improved statistical precision) of estimates for most biomass related indicators, including above- and below-ground biomass, tree volume, basal area, and carbon pools, by 15–20% on average, with some reductions exceeding 50%. Improvements were less consistent for regeneration-related indicators (saplings, seedlings). The insights from this study highlight the advantages of remote sensing-derived land cover for improving NFI indicator precision, while underscoring the continued need for advancing ontology-driven approaches with necessary strengthening of field crew capacity to ensure the consistent application of land cover standards.

Reliable land cover (LC) information is essential for scaling plot-based measurements in National Forest Inventories (NFIs). This study assessed the impact of using remote sensing (RS)-derived LC compared with field-assigned LC on the precision of key forest indicators in Bangladesh’s NFI (2015–2019). Field data from 1,781 plots were integrated with a 2015 national LC map produced from SPOT-6/7, Landsat, and Sentinel-2 imagery. Sampling errors of forest indicator estimates were evaluated across LC classes and ecological zones. Results show that RS derived LC reduced sampling errors for most biomass related indicators, including above- and below-ground biomass, tree volume, basal area, and carbon pools, by 15–20% on average, with some reductions exceeding 50%. Improvements were less consistent for regeneration related indicators (saplings, seedlings). These findings highlight the advantages of RS derived LC for improving NFI precision, while underscoring the continued need for advancing ontology driven approaches with necessary strengthening of field crew capacity to ensure consistent application of land cover standards.