Forest plantation mapping in the Southern Highlands, Tanzania 2016

Recent years have witnessed the practical value of open-access Earth observation data catalogues and software in land and forest mapping. Combined with cloud-based computing resources, and data collection though the crowd, these solutions have substantially improved possibilities for monitoring changes in land resources, especially in areas with difficult accessibility and data scarcity. In this study, we developed and tested a participatory mapping methodology utilizing the open data catalogues and cloud computing capacity to map the previously unknown extent and species composition of forest plantations in the Southern Highlands area of Tanzania, a region experiencing a rapid growth of smallholder-owned woodlots. A large reference data, focusing on plantation coverage, species and age information, was collected in a two-week Participatory GIS campaign where 22 Tanzanian experts interpreted high-resolution satellite images in Google Earth with Open Foris Collect Earth tool developed by FAO. The collected samples were used as training data to classify a multi-sensor image stack of Landsat 8 OLI (2013-2015), Sentinel-2 (2015-2016), Sentinel-1 (2015), and SRTM derived elevation and slope data layers into 30m resolution plantation map. The results show that the plantation area was estimated with high overall accuracy (85%). The interpretation accuracy of local experts was high considering general definition of plantation declining with increased details in interpretation attributes. The results showcase the unique value of local expert participation, enabling the collection of thousands of reference samples over a large geographical area in a short period of time simultaneously building the capacity of the experts. However, sufficient training prior the data collection is crucial for the interpretation success especially when detailed interpretation is conducted in complex landscapes. Since the methodology is built on open-access data and software, it presents a highly feasible solution for repetitive land resource mapping applicable at different spatial scales globally.