The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data
文献类型：期刊论文
| 作者 | Jin, Shichao1; Su, Yanjun1,3; Gao, Shang1; Hu, Tianyu; Liu, Jin; Guo, Qinghua1,3
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2018 |
| 卷号 | 10期号:8 |
| 关键词 | canopy height Random Forest LiDAR multi-source vegetation type location scale |
| DOI | 10.3390/rs10081183 |
| 文献子类 | Article |
| 英文摘要 | Canopy height is an important forest structure parameter for understanding forest ecosystem and improving global carbon stock quantification accuracy. Light detection and ranging (LiDAR) can provide accurate canopy height measurements, but its application at large scales is limited. Using LiDAR-derived canopy height as ground truth to train the Random Forest (RF) algorithm and therefore predict canopy height from other remotely sensed datasets in areas without LiDAR coverage has been one of the most commonly used method in large-scale canopy height mapping. However, how variances in location, vegetation type, and spatial scale of study sites influence the RF modelling results is still a question that needs to be addressed. In this study, we selected 16 study sites (100 km(2) each) with full airborne LiDAR coverage across the United States, and used the LiDAR-derived canopy height along with optical imagery, topographic data, and climate surfaces to evaluate the transferability of the RF-based canopy height prediction method. The results show a series of findings from general to complex. The RF model trained at a certain location or vegetation type cannot be transferred to other locations or vegetation types. However, by training the RF algorithm using samples from all sites with various vegetation types, a universal model can be achieved for predicting canopy height at different locations and different vegetation types with self-predicted R-2 higher than 0.6 and RMSE lower than 6 m. Moreover, the influence of spatial scales on the RF prediction accuracy is noticeable when spatial extent of the study site is less than 50 km(2) or the spatial resolution of the training pixel is finer than 500 m. The canopy height prediction accuracy increases with the spatial extent and the targeted spatial resolution. |
| 学科主题 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版地 | BASEL |
| 电子版国际标准刊号 | 2072-4292 |
| WOS关键词 | LANDSAT ETM+ DATA ; AIRBORNE LIDAR ; TREE HEIGHT ; ABOVEGROUND BIOMASS ; TM DATA ; COVER ; CARBON ; DENSITY ; SCALE ; TIME |
| 语种 | 英语 |
| WOS记录号 | WOS:000443618100019 |
| 出版者 | MDPI |
| 资助机构 | National Key R&D Program of China [2016YFC0500202, 2017YFC0503905] ; Frontier Science Key Programs of the Chinese Academy of Sciences [QYZDY-SSW-SMC011] ; National Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41471363, 31741016] ; CAS Pioneer Hundred Talents Program ; US National Science FoundationNational Science Foundation (NSF) [DBI 1356077] |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/20369]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Calif Merced, Sch Engn, Sierra Nevada Res Inst, Merced, CA 95343 USA 3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jin, Shichao,Su, Yanjun,Gao, Shang,et al. The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data[J]. REMOTE SENSING,2018,10(8). |
| APA | Jin, Shichao,Su, Yanjun,Gao, Shang,Hu, Tianyu,Liu, Jin,&Guo, Qinghua.(2018).The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data.REMOTE SENSING,10(8). |
| MLA | Jin, Shichao,et al."The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data".REMOTE SENSING 10.8(2018). |
入库方式： OAI收割
来源：植物研究所
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