Assessing in-house capacity for participatory GIS in community-based measuring reporting and verification (MRY) systems in Uganda

Abstract

Mabira Forest is the biggest forest conservation in Uganda, the forest provides a long-term benefit of carbon storage however it is currently facing continuous degradation bringing it to reduction in such benefits. Currently over 4,755 hectares have been lost to human activities like lumbering, charcoal burning and clearing land for farming. Different projects like the Reduction of Emissions from Deforestation and forest Degradation (REDD+) have been introduced in order to support the adequate monitoring of carbon sequestration as well as climate change mitigation, thus supporting financial benefits of carbon sequestration data derived. This research therefore sought to investigate the integration of Participatory GIS in Community-Based Measuring Reporting & Verification (MRV) in the community of Mabira forest for an improved National MRV System on forest Carbon stocks. Its objectives were to assess local communities’ capacity to participate in PMRV, to calculate the forest inventory and remote sensed carbon stock and to define the relationship between carbon stocks from community-based data and remotely sensed data. Both primary and secondary methods of data collection were used including focused group discussions; participatory forest mapping and remote sensing were applied in the achievement of the research interests. An allometric equation utilizing the datasets of height, diameter at breast height and wood density and a discriminative classifier algorithm known as Support Vector Machine (SVM) and InVEST model, were applied in the achievement of field derived and remote sensing carbon stock respectively. Pearson correlation was applied so as to define the relationship between the field derived carbon stock contributed by the community and remote sensing derived carbon stock. Over 200 trees were sampled through field stratification and a maximum of 202.39 t C ha-1 carbon stock was estimated. Also, a total carbon stock of 321.167tC ha-1 was derived from the satellite image representing the dense canopy. It was also indicated that least carbon existed in the tree species with DBH<10cm. A relationship coefficient of 0.90 was obtained indicating a very strong positive relationship between the field inventories carbon stock and that obtained from satellite imagery. This concluded a relative accuracy between the remote sensing method and the field inventory methods in estimating carbon stock, this showed that if the community is equipped with tools and sensitization, it is able to determine the field inventory parameters to support MRV for national systems on forest carbon stocks.