In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1 hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform Lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.

The ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) is a platform designed to meet the need of the international scientific community to collaborate on the generation and analysis of increasingly massive datasets from space-based, airborne, and field observations for aboveground terrestrial carbon dynamics. The MAAP is an open-source, cloud-based platform that distinguishes itself from other science platforms by being agnostic to any science disciplines and allows scientists to write, develop, and execute their own algorithms in shared workspaces that are tailored to their specific area of research. We present an overview of the capabilities of the Pilot implementation of MAAP. Users can explore and visualize ESA and NASA data that is ingested in the MAAP data catalog, develop and test algorithms to generate new datasets and act upon existing ones, launch matured algorithms as large-scale processing jobs, and analyze the results. This allows scientists to follow the entire scientific process within the platform, from the conception of a hypothesis to the creation of scientific results in a version-controlled and reproducible environment. Users can document their process, collaborate with other users, and share algorithms and datasets. We will conclude with an outlook on the features that the next phase of development will bring to the platform.

Urban population in sub Saharan Africa (SSA) is rapidly growing. While only 30% of its population lived in urban centers in 2000, this figure will reach 60% by year 2050. Urban energy demand is closely tied to forest degradation. Charcoal is the main source of cooking fuel for eighty percent of African urban households and its overall consumption is expected to rise by 2040. Charcoal production is already the main driver of forest degradation in SSA. REDD+ guidelines encourage countries to identify and describe individual activities and drivers causing forest degradation as an initial step to define suitable methods for measuring and monitoring and formulate appropriate strategies and policies. Yet, forest degradation associated to charcoal production remains largely under reported. Charcoal production results in partial removals of forest cover that do not necessarily involve significant variations of the spectral signal. As a consequence, efforts to monitor forest degradation associated to charcoal production with medium resolution data has proved elusive. We present initial results of our effort to monitor and quantify carbon emissions from forest degradation due to charcoal production in SSA. Our work combines time series of multi sensor medium (20 – 30m), high (2m) and very high (0.5m) spatial resolution sensors with field data to characterize the spatial and temporal dynamics of charcoal production in charcoal production sites across SSA. The integration of these datasets provides the means to map, monitor and measure charcoal kilns, and subsequently quantify the magnitude and intensity of aboveground biomass removals associated to charcoal production at a level of detail and precision not reported previously. Our initial results reveal that charcoal production accounts for a larger share of greenhouse gas emission than previously reported, highlight its negative impacts on the ecosystem, and question the long-term sustainability of charcoal production under current and future urban energy demands. This work is a first step towards the development of a monitoring, reporting and verification system specific to forest degradation in the SSA context.