High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA
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 b...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2019-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ab2917 |
| _version_ | 1797747993109069824 |
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| author | Wenli Huang Katelyn Dolan Anu Swatantran Kristofer Johnson Hao Tang Jarlath O’Neil-Dunne Ralph Dubayah George Hurtt |
| author_facet | Wenli Huang Katelyn Dolan Anu Swatantran Kristofer Johnson Hao Tang Jarlath O’Neil-Dunne Ralph Dubayah George Hurtt |
| author_sort | Wenli Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T15:58:33Z |
| format | Article |
| id | doaj.art-5af6ea2c8681444a9e0963d370ae425a |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:58:33Z |
| publishDate | 2019-01-01 |
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| series | Environmental Research Letters |
| spelling | doaj.art-5af6ea2c8681444a9e0963d370ae425a2023-08-09T14:44:44ZengIOP PublishingEnvironmental Research Letters1748-93262019-01-0114909500210.1088/1748-9326/ab2917High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USAWenli Huang0https://orcid.org/0000-0001-9608-1690Katelyn Dolan1https://orcid.org/0000-0002-1119-2277Anu Swatantran2https://orcid.org/0000-0003-0779-2779Kristofer Johnson3https://orcid.org/0000-0002-4015-6910Hao Tang4https://orcid.org/0000-0001-7935-5848Jarlath O’Neil-Dunne5https://orcid.org/0000-0002-5352-7389Ralph Dubayah6https://orcid.org/0000-0003-1440-6346George Hurtt7https://orcid.org/0000-0001-7278-202XDepartment of Geographical Sciences, University of Maryland , College Park, MD 20742, United States of America; School of Resources and Environmental Sciences, Wuhan University , Hubei 430079, People’s Republic of ChinaDepartment of Geographical Sciences, University of Maryland , College Park, MD 20742, United States of AmericaDepartment of Geographical Sciences, University of Maryland , College Park, MD 20742, United States of AmericaUSDA Forest Service, Northern Research Station, Newtown Square, PA 19073, United States of America; Forest and Agriculture Organization of the United Nations, Dhaka, BangladeshDepartment of Geographical Sciences, University of Maryland , College Park, MD 20742, United States of AmericaRubenstein School of the Environment and Natural Resources, University of Vermont , Burlington, VT 05405, United States of AmericaDepartment of Geographical Sciences, University of Maryland , College Park, MD 20742, United States of AmericaDepartment of Geographical Sciences, University of Maryland , College Park, MD 20742, United States of AmericaAccurate 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.https://doi.org/10.1088/1748-9326/ab2917forest aboveground biomasscarbon monitoring systemlidarforest inventory analysisMarylandDelaware |
| spellingShingle | Wenli Huang Katelyn Dolan Anu Swatantran Kristofer Johnson Hao Tang Jarlath O’Neil-Dunne Ralph Dubayah George Hurtt High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA Environmental Research Letters forest aboveground biomass carbon monitoring system lidar forest inventory analysis Maryland Delaware |
| title | High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA |
| title_full | High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA |
| title_fullStr | High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA |
| title_full_unstemmed | High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA |
| title_short | High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA |
| title_sort | high resolution mapping of aboveground biomass for forest carbon monitoring system in the tri state region of maryland pennsylvania and delaware usa |
| topic | forest aboveground biomass carbon monitoring system lidar forest inventory analysis Maryland Delaware |
| url | https://doi.org/10.1088/1748-9326/ab2917 |
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