Monitoring tropical forest carbon stocks and emissions using Planet satellite data

Tropical forests are crucial for mitigating climate change, but many forests continue to be driven from carbon sinks to sources through human activities. To support more sustainable forest uses, we need to measure and monitor carbon stocks and emissions at high spatial and temporal resolution. We developed the first large-scale very high-resolution map of aboveground carbon stocks and emissions for the country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into a random forest machine learning regression workflow, obtaining an R2 of 0.70 and RMSE of 25.38 Mg C ha−1 for the nationwide estimation of aboveground carbon density (ACD). The diverse ecosystems of Peru harbor 6.928 Pg C, of which only 2.9 Pg C are found in protected areas or their buffers. We found significant carbon emissions between 2012 and 2017 in areas aggressively affected by oil palm and cacao plantations, agricultural and urban expansions or illegal gold mining. Creating such a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries will serve as a transformative tool to quantify the climate change mitigation services that forests provide.

[1]  Matthew F. McCabe,et al.  High-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture , 2016, Remote. Sens..

[2]  Sandra Eckert,et al.  Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data , 2012, Remote. Sens..

[3]  M. Keller,et al.  Post-drought decline of the Amazon carbon sink , 2018, Nature Communications.

[4]  Roberta E. Martin,et al.  Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion , 2012 .

[5]  Pramaditya Wicaksono,et al.  Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment , 2018, Fine Resolution Remote Sensing of Species in Terrestrial and Coastal Ecosystems.

[6]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[7]  V. Radeloff,et al.  Image texture as a remotely sensed measure of vegetation structure , 2012 .

[8]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[9]  G. Asner,et al.  High-resolution mapping of forest carbon stocks in the Colombian Amazon , 2012 .

[10]  G. Asner,et al.  Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric , 2014 .

[11]  Hideki Saito,et al.  Object-Based Mapping of Aboveground Biomass in Tropical Forests Using LiDAR and Very-High-Spatial-Resolution Satellite Data , 2018, Remote. Sens..

[12]  Jin Liu,et al.  The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data , 2018, Remote. Sens..

[13]  G. Powell,et al.  High-resolution forest carbon stocks and emissions in the Amazon , 2010, Proceedings of the National Academy of Sciences.

[14]  Roberta E. Martin,et al.  A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping , 2014, PloS one.

[15]  Arko Lucieer,et al.  Modelling LiDAR derived tree canopy height from Landsat TM, ETM+ and OLI satellite imagery - A machine learning approach , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Roberta E. Martin,et al.  Targeted carbon conservation at national scales with high-resolution monitoring , 2014, Proceedings of the National Academy of Sciences.

[17]  P. Couteron,et al.  Predicting tropical forest stand structure parameters from Fourier transform of very high‐resolution remotely sensed canopy images , 2005 .

[18]  G. Asner,et al.  An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR , 2018 .

[19]  R. Houborg,et al.  A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data , 2018 .

[20]  Nicolas Barbier,et al.  Inverting Aboveground Biomass-Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery , 2017, Remote. Sens..

[21]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[22]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

[23]  Wenjiang Huang,et al.  Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets , 2018, Sensors.

[24]  Craig C. Brelsford,et al.  Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning , 2018, Biogeosciences.

[25]  G. Foody,et al.  Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions , 2012 .

[26]  C. Proisy,et al.  Assessing aboveground tropical forest biomass using Google Earth canopy images. , 2012, Ecological applications : a publication of the Ecological Society of America.

[27]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[28]  Roberta E. Martin,et al.  Coral reef atoll assessment in the South China Sea using Planet Dove satellites , 2017 .

[29]  Vanessa Helena de Souza Zago,et al.  HDL Size is More Accurate than HDL Cholesterol to Predict Carotid Subclinical Atherosclerosis in Individuals Classified as Low Cardiovascular Risk , 2014, PloS one.

[30]  Jungho Im,et al.  Forest biomass estimation from airborne LiDAR data using machine learning approaches , 2012 .

[31]  David E. Knapp,et al.  Estimating aboveground carbon density across forest landscapes of Hawaii: Combining FIA plot-derived estimates and airborne LiDAR , 2018, Forest Ecology and Management.

[32]  Nicolas Barbier,et al.  Linking canopy images to forest structural parameters: potential of a modeling framework , 2012, Annals of Forest Science.

[33]  Hugh Eva,et al.  The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania , 2019, Forests.

[34]  A. Baccini,et al.  Improving pantropical forest carbon maps with airborne LiDAR sampling , 2013 .

[35]  G. Asner,et al.  Evaluating uncertainty in mapping forest carbon with airborne LiDAR , 2011 .

[36]  Jingjing Zhou,et al.  The Effects of GLCM parameters on LAI estimation using texture values from Quickbird Satellite Imagery , 2017, Scientific Reports.

[37]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[38]  A. Gentry Tree species richness of upper Amazonian forests. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Sandra A. Brown,et al.  Monitoring and estimating tropical forest carbon stocks: making REDD a reality , 2007 .

[40]  Wu Ma,et al.  Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images , 2016, Remote. Sens..

[41]  P. Defourny,et al.  Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery , 2006 .

[42]  O. Phillips,et al.  Continental-scale patterns of canopy tree composition and function across Amazonia , 2006, Nature.

[43]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[44]  Nicolas Barbier,et al.  Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach. , 2014, Ecological applications : a publication of the Ecological Society of America.

[45]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[46]  H. Schroeder,et al.  Governing and implementing REDD , 2011 .

[47]  Gregory P. Asner,et al.  Tropical forest carbon assessment: integrating satellite and airborne mapping approaches , 2009 .

[48]  Sajid Ghuffar,et al.  DEM Generation from Multi Satellite PlanetScope Imagery , 2018, Remote. Sens..

[49]  Ghislain Vieilledent,et al.  Human and environmental controls over aboveground carbon storage in Madagascar , 2012, Carbon Balance and Management.

[50]  R. Valentini,et al.  Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data , 2014 .

[51]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[52]  Roberta E. Martin,et al.  Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo , 2018 .

[53]  Richard Condit,et al.  Error propagation and scaling for tropical forest biomass estimates. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[54]  Vicente Pérez-Muñuzuri,et al.  Extreme Wave Height Events in NW Spain: A Combined Multi-Sensor and Model Approach , 2017, Remote. Sens..

[55]  Maria Petrou,et al.  ESTIMATION OF VEGETATION HEIGHT THROUGH SATELLITE IMAGE TEXTURE ANALYSIS , 2012 .

[56]  J. Evans,et al.  Modeling Species Distribution and Change Using Random Forest , 2011 .

[57]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[58]  Claudia Notarnicola,et al.  Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data , 2015, Remote. Sens..

[59]  Arnon Karnieli,et al.  redicting forest structural parameters using the image texture derived from orldView-2 multispectral imagery in a dryland forest , Israel , 2011 .