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DOI10.1088/1748-9326/ab93f9
A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA
Hudak A.T.; Fekety P.A.; Kane V.R.; Kennedy R.E.; Filippelli S.K.; Falkowski M.J.; Tinkham W.T.; Smith A.M.S.; Crookston N.L.; Domke G.M.; Corrao M.V.; Bright B.C.; Churchill D.J.; Gould P.J.; McGaughey R.J.; Kane J.T.; Dong J.
发表日期2020
ISSN17489318
卷号15期号:9
英文摘要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. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词Commercial Off-The-Shelf (COTS) lidar; Forest Inventory and Analysis (FIA); landsat image time series; LandTrendr; monitoring; reporting,and verification (MRV)
语种英语
scopus关键词Biomass; Carbon; Climate models; Decision trees; Forestry; Machine learning; Magnesium; Optical radar; Random forests; Aboveground biomass; Landscape level; Landscape model; Monitoring system; National forest inventories; Predictor variables; Stratified random sample; US Forest Service; Monitoring; aboveground biomass; ecosystem service; environmental monitoring; forest ecosystem; lidar; machine learning; pixel; satellite altimetry; spectral analysis; United States
来源期刊Environmental Research Letters
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/153844
作者单位Usda Forest Service, Rocky Mountain Research Station, Forestry Sciences Lab, 1221 South Main St., Moscow, ID 83843, United States; University of Washington, School of Environmental and Forest Sciences, Seattle, WA 98195, United States; Oregon State University, College of Earth and Atmospheric Sciences, Ocean Administration Building, 101 SW 26th St Ocean, 104, Corvallis, OR 97331, United States; Colorado State University, Natural Resources Ecology Laboratory, Fort Collins, CO, United States; Colorado State University, Department of Forest and Rangeland Stewardship, Fort Collins, CO 80523, United States; University of Idaho, Department of Forest, Rangeland, and Fire Sciences, Idaho, United States; Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resource Research, Chaoyang District, Beijing, China; Usda Forest Service, Northern Research Station, Moscow, ID 83843, United States; Northwest Management Inc., Moscow, ID 83843, United States; Washington State Department of Natural R...
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Hudak A.T.,Fekety P.A.,Kane V.R.,et al. A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA[J],2020,15(9).
APA Hudak A.T..,Fekety P.A..,Kane V.R..,Kennedy R.E..,Filippelli S.K..,...&Dong J..(2020).A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA.Environmental Research Letters,15(9).
MLA Hudak A.T.,et al."A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA".Environmental Research Letters 15.9(2020).
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