Tanzania dedicates a substantial proportion (38%) of its territory to conservation, with a large number of Protected Areas (PAs) managed under various regimes. Nevertheless, the country still experiences high rates of deforestation, which threaten the ecological integrity and socio-economic benefits of its forests. We utilized the Global Forest Change Dataset (2012–2022) and implemented a Propensity Score Matching (PSM) approach followed by a series of binomial logit regression modeling. Our objectives were to evaluate (1) the likelihood of PAs in avoiding deforestation compared with unprotected forest landscapes, (2) the variability in effectiveness among the different PA management regimes in avoiding deforestation, (3) evidence of leakage, defined here as the displacement of deforestation beyond PA boundaries as a result of protection inside PAs. Our findings reveal that, despite ongoing deforestation within and outside of PAs, conservation efforts are, on average, three times more likely to avoid deforestation compared with unprotected landscapes. However, the effectiveness of avoiding deforestation significantly varies among the different management regimes. National Parks and Game Reserves are nearly ten times more successful in avoiding deforestation, likely because of the stringent set of regulations and availability of resources for implementation. Conversely, Nature Forest Reserves, Game Controlled Areas, and Forest Reserves are, on average, only twice as likely to avoid deforestation, indicating substantial room for improvement. We found little evidence of the overall leakage as a consequence of protection. These results highlight the mixed success of Tanzania’s conservation efforts, suggesting opportunities to enhance the effectiveness of many less protected PAs. We conclude by proposing potential strategic pathways to enhance further the climate and ecosystem benefits of conservation in Tanzania.

Species-level phenology models are essential for predicting shifts in tree species under climate change. This study quantified phenological differences among dominant miombo tree species and modeled seasonal variability using climate variables. We used TIMESAT version 3.3 software and the Savitzky–Golay filter to derive phenology metrics from bi-monthly PlanetScope Normalized Difference Vegetation Index (NDVI) data from 2017 to 2024. A repeated measures Analysis of Variance (ANOVA) assessed differences in phenology metrics between species, while a regression analysis modeled the Start of Season (SOS) and End of Season (EOS). The results show significant seasonal and species-level variations in phenology. Brachystegia spiciformis differed from other species in EOS, Length of Season (LOS), base value, and peak value. Surface solar radiation and skin temperature one month before SOS were key predictors of SOS, with an adjusted R-squared of 0.90 and a Root Mean Square Error (RMSE) of 13.47 for Brachystegia spiciformis. SOS also strongly predicted EOS, with an adjusted R-squared of 1 and an RMSE of 3.01 for Brachystegia spiciformis, indicating a shift in the growth cycle of tree species due to seasonal variability. These models provide valuable insights into potential phenological shifts in miombo species due to climate change.

Remote sensing is essential for mapping mangrove forest ecosystems, yet accuracy and spatial resolution remain limited, particularly in Africa, where mangroves are fragmented, reference data and cloud-free images are scarce. The state-of-the-art of mangrove forest mapping relies on native-resolution imagery using machine learning. This study evaluates deep learning methods using aggregated multi-date satellite imagery to improve mangrove mapping and national-scale area estimation. Super-resolution and semantic segmentation techniques were applied to Sentinel-2 (S2) and Sentinel-1 (S1) images, with training data derived from field observations and high-resolution drone and satellite imagery. Coastal regions of Tanzania and Ghana were used as representative study areas for eastern and western Africa, respectively. Results show strong classification performance, with S2 imagery alone achieving an Intersection over Union (IoU) of 85.8%. Adding S1 provided modest gains (IoU 86.3%), while super-resolution significantly improved the accuracy (IoU 88.5%). The combined super-resolved S2 and S1 dataset yielded the highest performance (IoU 89.2%). Wall-to-wall mapping and statistically rigorous validation and area estimation produced accurate results with quantified uncertainty. Tanzania showed high consistency (F1 = 96%; 1207 km², 95% CI [1151.7, 1263]), while Ghana exhibited slightly higher uncertainty (F1 = 92%; 92.6 km², 95% CI [87.5, 97.7]). Integrating deep learning with multi-date, multi-sensor fusion and super-resolution enables accurate mangrove mapping and area estimation for improved coastal management and restoration.

Remote sensing is essential for mapping mangrove forest ecosystems, yet accuracy and spatial resolution remain constrained, particularly in Africa, where mangroves are fragmented, reference data and cloud-free images are scarce. The state-of-the-art of mangrove forest mapping relies on native-resolution, single-date imagery and machine learning. This study evaluates deep learning methods using aggregated multi-date satellite imagery to improve mangrove mapping and national-scale area estimation. Semantic segmentation and super-resolution techniques were applied to Sentinel‑2 (S2) and Sentinel‑1 (S1) images, with training data derived from field observations and high-resolution drone and satellite imagery. Coastal regions of Tanzania and Ghana were used as representative study areas for eastern and western Africa, respectively. Results show strong classification performance, with S2 imagery alone achieving an Intersection over Union (IoU) of 85.8%. Adding S1 provided modest gains (86.3%), while super-resolution significantly improved the accuracy (88.5%). The combined super-resolved S2 and S1 dataset yielded the highest performance (89.2%). Wall-to-wall mapping and statistically rigorous validation and area estimation produced accurate results with quantified uncertainty. Tanzania showed high consistency (F1 = 96%; 1207 km², 95% CI [1151.7, 1263]), while Ghana exhibited slightly higher uncertainty (F1 = 92%; 92.6 km², 95% CI [87.5, 97.7]). Integrating deep learning with multi-date, multi-sensor fusion and super-resolution enables accurate mangrove mapping and area estimation for improved coastal management and restoration.