Short Course 30: Open access data in remote sensing: applications for carbon estimates in REDD+ projects

Accurate estimates of emitted equivalent carbon are one of the foundations of integrity and effectiveness in current nature- based carbon projects. Remote sensing, using advanced technologies such as drones equipped with LiDAR, is a powerful tool in carbon quantification in tropical forests. Recently, global canopy height products have been made available for free, enabling their monitoring with the application of deep learning techniques. In this context, it is possible to obtain accurate carbon maps for extensive areas through the calibration of these products with field data. Thus, the course aims to train professionals to develop carbon maps and validate them with regional field and LiDAR data. The course will feature REDD+ (forest conservation) and ARR (forest restoration) projects as a background, highlighting the main demands and methodological standards required for credit verification. We will provide insights and practical examples of how remote sensing can be applied in carbon quantification and baseline construction for credit generation in the monitoring  and verification stages of projects. Participants will be able to assess the quality of pre-existing and freely accessible products (obtained via Google Earth Engine) in GIS (QGIS) and statistical R environment.