Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data
文献类型：期刊论文
| 作者 | Ni, Xiliang1; Zhou, Yuke1; Cao, Chunxiang1; Wang, Xuejun1; Shi, Yuli1; Park, Taejin1; Choi, Sungho1; Myneni, Ranga B.1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2015 |
| 卷号 | 7期号:7页码:2569-2582 |
| 通讯作者 | Zhou, YK (reprint author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China. |
| 英文摘要 | Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., continental and global) is problematic because existing LiDAR instruments are still data-limited and expensive. This paper proposes a new approach based on an artificial neural network (ANN) for modeling of forest canopy heights over the China continent. Our model ingests spaceborne LiDAR metrics and multiple geospatial predictors including climatic variables (temperature and precipitation), forest type, tree cover percent and land surface reflectance. The spaceborne LiDAR instrument used in the study is the Geoscience Laser Altimeter System (GLAS), which can provide within-footprint forest canopy heights. The ANN was trained with pairs between spatially discrete LiDAR metrics and full gridded geo-predictors. This generates valid conjugations to predict heights over the China continent. The ANN modeled heights were evaluated with three different reference data. First, field measured tree heights from three experiment sites were used to validate the ANN model predictions. The observed tree heights at the site-scale agreed well with the modeled forest heights (R = 0.827, and RMSE = 4.15 m). Second, spatially discrete GLAS observations and a continuous map from the interpolation of GLAS-derived tree heights were separately used to evaluate the ANN model. We obtained R of 0.725 and RMSE of 7.86 m and R of 0.759 and RMSE of 8.85 m, respectively. Further, inter-comparisons were also performed with two existing forest height maps. Our model granted a moderate agreement with the existing satellite-based forest height maps (R = 0.738, and RMSE = 7.65 m (R-2 = 0.52, and RMSE = 8.99 m). Our results showed that the ANN model developed in this paper is capable of estimating forest heights over the China continent with a satisfactory accuracy. Forth coming research on our model will focus on extending the model to the estimation of woody biomass. |
| 研究领域[WOS] | Remote Sensing |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000360919900009 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38156]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Ni, Xiliang 2.Cao, Chunxiang] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 3.[Zhou, Yuke] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.[Wang, Xuejun] State Forest Adm China, Survey Planning & Design Inst, Beijing 100714, Peoples R China 5.[Shi, Yuli] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing, Nanjing 210044, Jiangsu, Peoples R China 6.[Park, Taejin 7.Choi, Sungho 8.Myneni, Ranga B.] Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA |
| 推荐引用方式 GB/T 7714 | Ni, Xiliang,Zhou, Yuke,Cao, Chunxiang,et al. Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data[J]. REMOTE SENSING,2015,7(7):2569-2582. |
| APA | Ni, Xiliang.,Zhou, Yuke.,Cao, Chunxiang.,Wang, Xuejun.,Shi, Yuli.,...&Myneni, Ranga B..(2015).Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data.REMOTE SENSING,7(7),2569-2582. |
| MLA | Ni, Xiliang,et al."Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data".REMOTE SENSING 7.7(2015):2569-2582. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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