The High-resolution Carbon Geography of Perú for Forest Conservation and Management Gregory P. Asner Carnegie Institution for Science With special thanks to MINAM-DGOT, Wake Forest University, and the Peruvian Air Force COP-20 Lima, Perú 2014 2014 State-of-the-Science The High-resolution Carbon Geography of Perú 128,546,068 hectares (321 million acres) at 1-hectare resolution with uncertainty reported for every hectare throughout the country The Value of High Spatial Resolution Mapping • Resolves more activity at finer scales (i.e. degradation, regeneration) than does low resolution mapping • Creates a more direct bridge to field inventory, which leads to better plot network development and which feeds back to make better maps • Engages stakeholders at all scales (single hectare owners to multi-million hectare government agencies) The Critical Hectare • 10,000 m2 or about 2.5 acres • The most common unit of land management, ownership and tenure in the world. • People can intuitively understand something about as large as a hectare, and not often more • When aggregated to the hectarescale, studies show that careful fieldbased estimates of biomass converge on true forest biomass values with 1015% uncertainty. • But a tropical forest can easily contain > 600 trees per hectare (with diameters > 10 cm). Very hard to (re)measure on the ground! Limits of Field Inventory • Only valid at large scale if properly arranged (random, stratified random, systematic-aligned, etc) • Must constantly deal with inaccessibility, remoteness, standardization = expensive • Hard to maintain over time • Hard to engage stakeholders on-theground with plot inventory since it isn’t spatially explicit A Different Approach Three components: • Automated satellite analysis of forest cover, deforestation, degradation • New science and technology for high-resolution carbon mapping from the air • Long-term emissions monitoring at high resolution Asner 2009 (Environmental Research Letters) A way to massively sample forest structure LiDAR: Light Detection and Ranging Light Detection and Ranging (LiDAR) Carnegie Airborne Observatory LiDAR Easily Sees Structural Change LiDAR Easily Sees Structural Change LiDAR Easily Sees Structural Change Available Measurements of Forests from LiDAR Field-estimated (Plot) Carbon Stock LiDAR predictably calibrates to field-estimated carbon stock Airborne LiDAR Canopy Profile Asner et al. (2012): A Universal Airborne LiDAR Approach for Tropical Forest Carbon Mapping Tactical Use of Field Plots for LiDAR Calibration LiDAR-based forest biomass estimation is statistically indistinguishable from field-based biomass estimation Asner et al. (2014): Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric To save time & money, we build geophysical relationships between satellite and LiDAR data Geostatistical Modeling MINAM-DGOT Deforestation Mapping Training Satellite Data with Airborne LiDAR: Panama example Geostatistical Model Updated as Aircraft Collects Data CAO LiDAR (real-time carbon stocks in red) Deploy Aircraft & Sample Country Until Statistical Needs Met > 10.0% 5.0-10.0% 2.0-5.0% 1.0-2.0% 0.5-1.0% 5,000 hectares 0r n = 5,000! The High-resolution Carbon Geography of Perú 128,546,068 hectares (321 million acres) at 1-hectare resolution with uncertainty reported for every hectare throughout the country Years of work More Validation One day of flight Carbon Stocks in Every Hectare of Perú Uncertainty in Every Hectare of Perú Distribution of Carbon Stocks throughout Peru’s forests Natural Effects of Elevation on Aboveground Carbon Stocks Carbon Stocks by Jurisdiction Peru’s Balance Sheet Peru’s Balance Sheet Carbon Benefits of Expanding Protected Areas Gross Carbon Emissions from Deforestation and Degradation Madre de Dios 2000 2013  1 Tg = 1 million metric tonnes  Carbon emissions from forest disturbance average 47% of deforestation. Gross Carbon Emissions Balance Sheet from Deforestation and Degradation Madre de Dios Land Use Total Area (ha) Regional Carbon Flux (Tg C) Mean Flux Density (Mg C ha-1) S.D. Flux Density (Mg C ha-1) Gold mining 3,203 (0.238) 74.3 15.9 Forest disturbances 17,740 (1.360) 76.7 20.3 Deforestation 43,933 (2.930) 66.7 19.1 Secondary regrowth 63,173 1.932 30.6 16.7 Net flux (2.598) Many Sub-Projects Along the Way High-resolution Carbon Mapping Costs Rapid Increase in Coverage, and Decline in Costs Envir Res Letters 2009 PNAS 2010 Biogeosciences 2011 Frontiers in Ecology and Envir 2011 Remote Sensing of Envir 2011 Carbon Balance and Management 2012 Biogeosciences 2012 Carbon Balance and Management 2013 PLOS One 2014 Ecological Applications 2014 Carbon Balance 2014 PNAS 2014 Reaching the Large-Area, High-resolution Scale at Low Cost Additional Carnegie Resources Capacity Building  Satellite: 841 organizations in 127 countries  LiDAR: 33 organizations in 9 countries  Field inventory: 12 organizations in 5 countries CAO Maps Online http://cao.carnegiescience.edu/maps CAO LiDAR Training and Data Provision in California Colombia: IDEAM, IGAC, FAC Perú: MINAM, WWF-Perú, FAP Ecuador: IGM CLASlite Forest Monitoring Software • Software to automatically analyze imagery from 8 different satellites • Maps forest cover, deforestation, and forest degradation • Heritage of high accuracy mapping results from hundreds of national and sub-national studies worldwide • Today, 841 organizations in 127 countries with active CLASlite licenses. Top users are government: IDEAM (Colombia): 27 IPAM (Brazil): 27 ANAM (Panama): 26 MINAM (Peru): 25 INPA (Brazil): 18 CLASlite Iterative Science-to-Capacity Building Process CAPACITY BUILDING Broad capacity building through in-country workshops TECHNICAL DEVELOPMENT Version 1.0 – Version 2.3 Early Workshops in Multiple Countries Joint project implementation through institutional partnership and collaboration MINAM-DGOT Version 3.2 http://claslite.carnegiescience.edu https://class.claslite.carnegiescience.edu John D. and Catherine T. MacArthur Foundation Gordon and Betty Moore Foundation Avatar Alliance Foundation Grantham Fndn for the Protection of the Environment Mary Anne Nyburg Baker and G. Leonard Baker Jr. William Hearst III http://cao.carnegiescience.edu http://claslite.carnegiescience.edu Extra slides Geostatistical Modeling = Statistical + Geophysical Modeling Plot-scale LiDAR Validation (using local wood density) Regional-scale Validation (using regional basal-area-weighted wood density)