A Forest Monitoring System for Tanzania

Tropical forests provide essential ecosystem services related to human livelihoods. However, the distribution and condition of tropical forests are under significant pressure, causing shrinkage and risking biodiversity loss across the tropics. Tanzania is currently undergoing significant forest cover changes, but monitoring is limited, in part due to a lack of remote sensing knowledge, tools and methods. This study has demonstrated a comprehensive approach to creating a national-scale forest monitoring system using Earth Observation data to inform decision making, policy formulation, and combat biodiversity loss. A systematically wall-to-wall forest baseline was created for 2018 through the application of Landsat 8 imagery. The classification was developed using the extreme gradient boosting (XGBoost) machine-learning algorithm, and achieved an accuracy of 89% and identified 45.76% of the country’s area to be covered with forest. Of those forested areas, 45% was found within nationally protected areas. Utilising an innovative methodology based on a forest habitat suitability analysis, the forest baseline was classified into forest types, with an overall accuracy of 85%. Woodlands (open and closed) were found to make up 79% of Tanzania’s forests. To map changes in forest extent, an automated system for downloading and processing of the Landsat imagery was used along with the XGBoost classifiers trained to define the national forest extent, where Landsat 8 scenes were individually downloaded and processed and the identified changes summarised on an annual basis. Forest loss identified for 2019 was found to be 157,204 hectares, with an overall accuracy of 82%. These forest losses within Tanzania have already triggered ecological problems and alterations in ecosystem types and species loss. Therefore, a forest monitoring system, such as the one presented in this study, will enhance conservation programmes and support efforts to save the last remnants of Tanzania’s pristine forests.