Near-real time aboveground carbon emissions in Peru

• Published in: PloS one Vol. 15; no. 11; p. e0241418
• Main Authors: Csillik, Ovidiu, Asner, Gregory P
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.11.2020; Public Library of Science (PLoS)
• Subjects: Airborne radar; Biology and Life Sciences; Carbon; Carbon - metabolism; Carbon emissions; Carbon sequestration; Climate Change; Clouds; Computer and Information Sciences; Conservation of Natural Resources; Control; Deep learning; Deforestation; Dry season; Earth Sciences; Ecology and Environmental Sciences; Emissions; Emissions (Pollution); Engineering and Technology; Environmental management; Environmental Monitoring; Estimates; Evolution; Fluxes; Forest degradation; Forest management; Forest resources; Forests; Humans; Land degradation; Lidar; Methods; Neural networks; Observatories; People and places; Peru - epidemiology; Physical Sciences; Pollution monitoring; Radar; Radar imaging; Regression analysis; Remote sensing; Satellite Imagery; Satellites; Spatial analysis; Spatial discrimination; Spatial resolution; Topography; Trees; Tropical Climate; Tropical forests; Vegetation; Workflow; Peru; United States > US; Arizona
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0241418

Monitoring aboveground carbon stocks and fluxes from tropical deforestation and forest degradation is important for mitigating climate change and improving forest management. However, high temporal and spatial resolution analyses are rare. This study presents the most detailed tracking of aboveground carbon over time, with yearly, quarterly and monthly estimations of emissions using the stock-difference approach and masked by the forest loss layer of Global Forest Watch. We generated high spatial resolution (1-ha) monitoring of aboveground carbon density (ACD) and emissions (ACE) in Peru by incorporating hundreds of thousands of Planet Dove satellite images, Sentinel-1 radar, topography and airborne LiDAR, embedded into a deep learning regression workflow using high-performance computing. Consistent ACD results were obtained for all quarters and months analyzed, with R2 values of 0.75-0.78, and root mean square errors (RMSE) between 20.6 and 22.0 Mg C ha-1. A total of 7.138 Pg C was estimated for Peru with annual ACE of 20.08 Tg C between the third quarters of 2017 and 2018, respectively, or 23.4% higher than estimates from the FAO Global Forest Resources Assessment. Analyzed quarterly, the spatial evolution of ACE revealed 11.5 Tg C, 6.6 Tg C, 8.6 Tg C, and 10.1 Tg C lost between the third quarters of 2017 and 2018. Moreover, our monthly analysis for the dry season reveals the evolution of ACE at unprecedented temporal detail. We discuss environmental controls over ACE and provide a spatially explicit tool for enhanced forest carbon management at scale.