Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass
文献类型：期刊论文
| 作者 | Li, Le2; Guo, Qinghua1 ; Tao, Shengli5; Kelly, Maggi4; Xu, Guangcai
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| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2015 |
| 卷号 | 102页码:198-208 |
| 关键词 | Lidar Forest biomass MODIS Terrestrial Scale |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2015.02.007 |
| 文献子类 | Article |
| 英文摘要 | Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: (1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, (2) linear regression, among four tested models, achieve the best performance (R-2 = 0.74; RMSE = 183.57 Mg/ha); (3) for MODIS-derived vegetation indices at varied spatial resolution (250-1000 m), accumulated NDVI, accumulated LAI, and accumulated FPAR could explain 53-74% variances of forest AGB, whereas accumulated NDVI derived from 1 km MODIS products gives higher R-2 (74%) and lower RMSE (13.4 Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. |
| 学科主题 | Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版地 | AMSTERDAM |
| 电子版国际标准刊号 | 1872-8235 |
| WOS关键词 | LANDSAT TM DATA ; CANOPY-HEIGHT ; IMPROVED STRATEGY ; AIRBORNE LIDAR ; CLIMATE-CHANGE ; INVENTORY DATA ; STEM VOLUME ; BASAL AREA ; CARBON ; VEGETATION |
| WOS研究方向 | Science Citation Index Expanded (SCI-EXPANDED) |
| 语种 | 英语 |
| WOS记录号 | WOS:000352665500017 |
| 出版者 | ELSEVIER |
| 资助机构 | USDA Forest Service Region 5 ; USDA Forest Service Pacific Southwest Research Station ; US Fish and Wildlife Service ; California Department of Water Resources ; California Department of Fish and Game ; California Department of Forestry and Fire Protection ; Sierra Nevada Conservancy ; State Key Laboratory of Earth Processes and Resource Ecology ; National Natural Science Foundation Project of China [41471363] |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/25687]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Beijing Normal Univ, State Key Lab Earth Proc & Resource Ecol, Beijing 100875, Peoples R China 2.Chinese Acad Sci Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA 4.Peking Univ, Dept Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China 5.Univ Calif Merced, Sch Engn, Sierra Nevada Res Inst, Merced, CA 95344 USA |
| 推荐引用方式 GB/T 7714 | Li, Le,Guo, Qinghua,Tao, Shengli,et al. Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2015,102:198-208. |
| APA | Li, Le,Guo, Qinghua,Tao, Shengli,Kelly, Maggi,&Xu, Guangcai.(2015).Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,102,198-208. |
| MLA | Li, Le,et al."Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 102(2015):198-208. |
入库方式： OAI收割
来源：植物研究所
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