High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA

Bibliographic Data

ID15548615
AuthorsWenli Huang (0000-0002-5325-2629, Wuhan University, corresponding author), K A Dolan (University of Maryland, College Park), Katelyn Dolan (0000-0002-1119-2277), Anu Swatantran (0000-0003-0779-2779, University of Maryland, College Park), Kristofer Johnson (0000-0002-4015-6910, Northern Research Station), Hao Tang (0009-0008-7427-6321, University of Maryland, College Park), Jarlath O’Neil‐Dunne (0000-0002-5352-7389, University of Vermont), Ralph Dubayah (0000-0003-1440-6346, University of Maryland, College Park), G C Hurtt (0000-0001-7278-202X, University of Maryland, College Park)
Year2019
Volume14
Issue9
Pages095002-095002
Publication date2019-06-12
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ab2917
OpenAlexW2951297504
LanguageEN
Citations received8
References cited46

Accurate estimation of forest aboveground biomass at high-resolution continues to remain a challenge and long-term goal for carbon monitoring and accounting systems. Here, we present an exhaustive evaluation and validation of a robust, replicable and scalable framework that maps forest aboveground biomass over large areas at fine-resolution by linking airborne lidar and field data with machine learning algorithms. We developed this framework over multiple phases of bottom-up monitoring efforts within NASA’s Carbon Monitoring Program. Lidar data were collected by different local and federal agencies and provided a wall-to-wall coverage of three states in the USA (Maryland, Pennsylvania and Delaware with a total area of 157 865 km 2 ). We generated a set of standardized forestry metrics from lidar-derived imagery (i.e. canopy height model, CHM) to minimize inconsistency of data quality. We then estimated plot-scale biomass from field data that had the closet acquisition time to lidar data, and linked to lidar metrics using Random Forest models at four USDA Forest Service ecological regions. Additionally, we examined pixel-scale errors using independent field plot measurements across these ecoregions. Collectively, we estimate a total of ∼680 Tg C in aboveground biomass over the Tri-State region (13 DE, 103 MD, 564 PA) circa 2011. A comparison with existing products at pixel-, county-, and state-scale highlighted the contribution of trees over ‘non-forested’ areas, including urban trees and small patches of trees, an important biomass component largely omitted by previous studies due to insufficient spatial resolution. Our results indicated that integrating field data and low point density (∼1 pt m −2 ) airborne lidar can generate large-scale aboveground biomass products at an accuracy close to mainstream lidar forestry applications ( R 2 = 0.46–0.54, RMSE = 51.4–54.7 Mg ha −1 ; and R 2 = 0.33–0.61, RMSE = 65.3–100.9 Mg ha −1 ; independent validation). Local, high-resolution lidar-derived biomass maps such as products from this study, provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale mapping efforts and future development of a national carbon monitoring system

Agroforestry · Biomass (ecology · Canopy · Cartography · Forest Inventory · Forest management · Geography · Lidar · Random forest · Remote sensing · Sampling (signal processing · Scale (ratio · Computer Science · Environmental Science · Forest ecology and management · Forest Management and Policy · Remote Sensing and LiDAR Applications · Ecology · Forestry

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