A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA
This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and cont...
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| Published in: | Environmental research letters Vol. 15; no. 9; pp. 95003 - 95019 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Bristol
IOP Publishing
01.09.2020
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| ISSN: | 1748-9326, 1748-9326 |
| Online Access: | Get full text |
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| Abstract | This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R2 = 0.8, RMSE = 115 Mg ha−1, Bias = 2 Mg ha−1). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R2 = 0.8, RMSE = 152 Mg ha−1, Bias = 9 Mg ha−1), including higher AGB values (>400 Mg ha−1) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps. |
|---|---|
| AbstractList | This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R
2
= 0.8, RMSE = 115 Mg ha
−1
, Bias = 2 Mg ha
−1
). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R
2
= 0.8, RMSE = 152 Mg ha
−1
, Bias = 9 Mg ha
−1
), including higher AGB values (>400 Mg ha
−1
) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders.
Social media abstract
Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps. This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R2 = 0.8, RMSE = 115 Mg ha−1, Bias = 2 Mg ha−1). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R2 = 0.8, RMSE = 152 Mg ha−1, Bias = 9 Mg ha−1), including higher AGB values (>400 Mg ha−1) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps. This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R ^2 = 0.8, RMSE = 115 Mg ha ^−1 , Bias = 2 Mg ha ^−1 ). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R ^2 = 0.8, RMSE = 152 Mg ha ^−1 , Bias = 9 Mg ha ^−1 ), including higher AGB values (>400 Mg ha ^−1 ) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps. |
| Author | Falkowski, Michael J McGaughey, Robert J Filippelli, Steven K Churchill, Derek J Gould, Peter J Dong, Jinwei Corrao, Mark V Tinkham, Wade T Kennedy, Robert E Crookston, Nicholas L Bright, Benjamin C Kane, Jonathan T Domke, Grant M Kane, Van R Hudak, Andrew T Fekety, Patrick A Smith, Alistair M S |
| Author_xml | – sequence: 1 givenname: Andrew T orcidid: 0000-0001-7480-1458 surname: Hudak fullname: Hudak, Andrew T email: andrew.hudak@usda.gov organization: Author to whom any correspondence should be addressed – sequence: 2 givenname: Patrick A surname: Fekety fullname: Fekety, Patrick A organization: Colorado State University, Natural Resources Ecology Laboratory, Fort Collins , Colorado, United States of America – sequence: 3 givenname: Van R surname: Kane fullname: Kane, Van R organization: University of Washington, School of Environmental and Forest Sciences , Seattle, WA 98195, United States of America – sequence: 4 givenname: Robert E orcidid: 0000-0002-5507-474X surname: Kennedy fullname: Kennedy, Robert E organization: Oregon State University, College of Earth, Ocean, and Atmospheric Sciences , Corvallis, Oregon, United States of America – sequence: 5 givenname: Steven K orcidid: 0000-0001-7291-0888 surname: Filippelli fullname: Filippelli, Steven K organization: Colorado State University, Natural Resources Ecology Laboratory, Fort Collins , Colorado, United States of America – sequence: 6 givenname: Michael J surname: Falkowski fullname: Falkowski, Michael J organization: Colorado State University, Natural Resources Ecology Laboratory, Fort Collins , Colorado, United States of America – sequence: 7 givenname: Wade T surname: Tinkham fullname: Tinkham, Wade T organization: Colorado State University, Department of Forest and Rangeland Stewardship, Fort Collins , Colorado 80523, United States of America – sequence: 8 givenname: Alistair M S surname: Smith fullname: Smith, Alistair M S organization: University of Idaho, Department of Forest, Rangeland, and Fire Sciences , Idaho, United States of America – sequence: 9 givenname: Nicholas L surname: Crookston fullname: Crookston, Nicholas L organization: Private Forestry Consultant , Moscow, Idaho, United States of America – sequence: 10 givenname: Grant M surname: Domke fullname: Domke, Grant M organization: USDA Forest Service, Northern Research Station , St. Paul, Minnesota, United States of America – sequence: 11 givenname: Mark V surname: Corrao fullname: Corrao, Mark V organization: Northwest Management, Inc. , Moscow, ID 83843, United States of America – sequence: 12 givenname: Benjamin C surname: Bright fullname: Bright, Benjamin C organization: USDA Forest Service, Northern Research Station , St. Paul, Minnesota, United States of America – sequence: 13 givenname: Derek J surname: Churchill fullname: Churchill, Derek J organization: Washington State Department of Natural Resources , Olympia, Washington, United States of America – sequence: 14 givenname: Peter J surname: Gould fullname: Gould, Peter J organization: Washington State Department of Natural Resources , Olympia, Washington, United States of America – sequence: 15 givenname: Robert J surname: McGaughey fullname: McGaughey, Robert J organization: USDA Forest Service, Pacific Northwest Research Station , Seattle, Washington, United States of America – sequence: 16 givenname: Jonathan T surname: Kane fullname: Kane, Jonathan T organization: University of Washington, School of Environmental and Forest Sciences , Seattle, WA 98195, United States of America – sequence: 17 givenname: Jinwei orcidid: 0000-0001-5687-803X surname: Dong fullname: Dong, Jinwei organization: Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resource Research, Chaoyang District , Beijing, People's Republic of China |
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| Snippet | This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS... |
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| SubjectTerms | Algorithms Bias Biomass Carbon Climate models Commercial Off-The-Shelf (COTS) lidar Decision trees Elevation Estimates Forest Inventory and Analysis (FIA) Forest management Forestry Forests Landsat landsat image time series Landsat satellites Landscape LandTrendr Learning algorithms Lidar Machine learning Monitoring Monitoring systems National forests Remote sensing reporting Satellite imagery verification (MRV) |
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| Title | A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA |
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