A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA
文献类型：期刊论文
| 作者 | Hudak, Andrew T.1; Fekety, Patrick A.4; Kane, Van R.2; Kennedy, Robert E.3; Filippelli, Steven K.4; Falkowski, Michael J.4; Tinkham, Wade T.5; Smith, Alistair M. S.6; Crookston, Nicholas L.; Domke, Grant M.7 |
| 刊名 | ENVIRONMENTAL RESEARCH LETTERS
 |
| 出版日期 | 2020-09-01 |
| 卷号 | 15期号:9页码:17 |
| ISSN号 | 1748-9326 |
| 关键词 | Commercial Off-The-Shelf (COTS) lidar Forest Inventory and Analysis (FIA) landsat image time series LandTrendr monitoring reporting verification (MRV) |
| DOI | 10.1088/1748-9326/ab93f9 |
| 通讯作者 | Hudak, Andrew T.(andrew.hudak@usda.gov) |
| 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 (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. |
| WOS关键词 | CONTERMINOUS UNITED-STATES ; LANDSAT TIME-SERIES ; NEAREST-NEIGHBOR IMPUTATION ; DISCRETE-RETURN LIDAR ; FOREST INVENTORY DATA ; CLIMATE-CHANGE ; WOODY BIOMASS ; ALOS PALSAR ; BASAL AREA ; PLOT SIZE |
| 资助项目 | NASA Carbon Monitoring Systems (CMS) Program Award[NNH15AZ06I] ; Joint Fire Science Program (JFSP) Fire and Smoke Model Evaluation Experiment (FASMEE) Project[15-S-01-01] ; University of Minnesota[15-JV-044] ; Colorado State University[16-JV-061] ; Oregon State University[15-JV-041] ; University of Idaho[15-JV-040] |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | IOP PUBLISHING LTD |
| WOS记录号 | WOS:000565760100001 |
| 资助机构 | NASA Carbon Monitoring Systems (CMS) Program Award ; Joint Fire Science Program (JFSP) Fire and Smoke Model Evaluation Experiment (FASMEE) Project ; University of Minnesota ; Colorado State University ; Oregon State University ; University of Idaho |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/157912]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Hudak, Andrew T. |
| 作者单位 | 1.USDA Forest Serv, Rocky Mt Res Stn, Forestry Sci Lab, 1221 South Main St, Moscow, ID 83843 USA 2.Univ Washington, Sch Environm & Forest Sci, Seattle, WA 98195 USA 3.Oregon State Univ, Coll Earth Ocean & Atmospher Sci, Ocean Adm Bldg,104,101 SW 26th St, Corvallis, OR 97331 USA 4.Colorado State Univ, Nat Resources Ecol Lab, Ft Collins, CO 80523 USA 5.Colorado State Univ, Dept Forest & Rangeland Stewardship, Ft Collins, CO 80523 USA 6.Univ Idaho, Dept Forest Rangeland & Fire Sci, Idaho Falls, ID USA 7.USDA Forest Serv, Northern Res Stn, Moscow, ID 83843 USA 8.Northwest Management Inc, Moscow, ID 83843 USA 9.Washington State Dept Nat Resources, Olympia, WA USA 10.USDA Forest Serv, Pacific Northwest Res Stn, 3200 SW Jefferson Way, Corvallis, OR 97331 USA |
| 推荐引用方式 GB/T 7714 | Hudak, Andrew T.,Fekety, Patrick A.,Kane, Van R.,et al. A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA[J]. ENVIRONMENTAL RESEARCH LETTERS,2020,15(9):17. |
| APA | Hudak, Andrew T..,Fekety, Patrick A..,Kane, Van R..,Kennedy, Robert E..,Filippelli, Steven K..,...&Dong, Jinwei.(2020).A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA.ENVIRONMENTAL RESEARCH LETTERS,15(9),17. |
| MLA | Hudak, Andrew T.,et al."A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA".ENVIRONMENTAL RESEARCH LETTERS 15.9(2020):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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