Introduction BIOCLIME WP3 report Remote Sensing Solutions GmbH Final

RSS - Remote Sensing Solutions GmbH 2 Executive summary With the Biodiversity and Climate Change Project BIOCLIME, Germany supports Indonesias efforts to reduce greenhouse gas emissions from the forestry sector, to conserve forest biodiversity of High Value Forest Ecosystems, maintain their Carbon stock storage capacities and to implement sustainable forest management for the benefit of the people. Germanys immediate contribution will focus on supporting the Province of South Sumatra to develop and implement a conservation and management concept to lower emissions from its forests, contributing to the GHG emission reduction goal Indonesia has committed itself until 2020. One of the important steps to improve land-use planning, forest management and protection of nature is to base the planning and management of natural resources on accurate, reliable and consistent geographic information. In order to generate and analyze this information, a multi-purpose monitoring system is required. The concept of the monitoring system consists of three components: historical, current and monitoring. This report presents the outcomes of the work package 3 “Aboveground biomass and tree community composition modelling” which is part of the current component. The main objectives of WP 3 were:  Filtering of the LiDAR 3D point clouds provided by the project into vegetation and non- vegetation points.  Derive Digital Surface Models DSM, Digital Terrain Models DTM and Canopy Height Models CHM from the airborne LiDAR data.  Advice BIOCLIME in the collection of forest inventory data to calibrate the LiDAR derived aboveground biomass model.  Derive an aboveground biomass model from the airborne LiDAR data provided by the project in combination with forest inventory data provided by the project.  Derive local aboveground biomass values for different vegetation classes from this LiDAR based aboveground biomass model.  Derive a tree a community composition model of Lowland Dipterocarp Forest at various degradation stages from LiDAR data provided by the project in combination with tree speciesgenera diversity data collected in the field provided by the project. The results of the workpackage were a set of local aboveground biomass AGB values Emission factors derived from the LiDAR based aboveground biomass model for almost all identified vegetation cover classes. It was shown that aboveground biomass variability within vegetation classes can be very high e.g. Primary Dryland Forest has a standard deviation for aboveground biomass of ±222.5 tha. Areas with the highest aboveground biomass AGB values were located within and around the Kerinci Seblat National Park. The tree community composition modelling results indicate that the similarity in tree community composition can be predicted and monitored by means of airborne LiDAR. In addition to using airborne LiDAR data as mapping tool for aboveground biomass this data could be further developed to provide a biodiversity mapping tool, so that biodiversity assessments could be carried out simultaneously with aboveground biomass analyses same dataset. A further advantage of the approach is that the tree community composition can be carried out without identifying individual tree crowns in remotely sensed imagery.