A Forest Monitoring System for Tanzania

: Mapping Change and Extent

Abstract

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. Tanzania is undergoing substantial forest cover changes, but monitoring is limited, partly due to a lack of remote sensing knowledge, tools, and methods. This study has demonstrated a comprehensive approach for creating a national-scale forest monitoring system using Earth Observation data to inform decision-making, policy formulation, and combat biodiversity loss. A Maximum Entropy model was used to predict forest change under different climate change scenarios (RCP 4.5 and RCP 8.5 for 2055 and 2085). This analysis identified that these landscapes will experience increased isolation and reduced connectivity. For example, upland forests, essential refugia of species, and endemism were predicted to almost halve in extent by 2085. A national 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 46% of the country’s area is covered with forest. Of those forested areas, 45% were found within nationally protected areas. Using a novel methodology where habitat suitability analysis was used to constrain the classification, 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 Landsat 8 imagery was used along with the XGBoost classifiers trained to define the national forest extent. The Landsat 8 scenes were individually downloaded and processed and the identified changes were summarised on an annual basis. Forest losses identified for 2019 were found to total 157,204 hectares, with an overall accuracy of 82%. Forest loss within Tanzania has already triggered ecological problems and alterations in ecosystem types and species loss. Therefore, the importance of 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.

Date of Award	2023
Original language	English
Awarding Institution	Aberystwyth University
Supervisor	Pete Bunting (Supervisor) & Andy Hardy (Supervisor)