Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models
文献类型：期刊论文
| 作者 | Song Chuangye; Huang Chong2; Liu Huiming1 |
| 刊名 | CHINESE GEOGRAPHICAL SCIENCE
 |
| 出版日期 | 2013 |
| 卷号 | 23期号:3页码:331-343 |
| 关键词 | vegetation mapping Generalized Additive Models (GAMs) SPOT Receiver Operating Characteristic (ROC) Generalized Regression Analysis and Spatial Predictions (GRASP) Huanghe River Delta |
| ISSN号 | 1002-0063 |
| DOI | 10.1007/s11769-013-0590-0 |
| 文献子类 | Article |
| 英文摘要 | This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision. |
| 学科主题 | Environmental Sciences |
| 出版地 | NEW YORK |
| 电子版国际标准刊号 | 1993-064X |
| WOS关键词 | YANHE RIVER CATCHMENT ; BIODIVERSITY ; REGRESSION ; COMMUNITY |
| WOS研究方向 | Science Citation Index Expanded (SCI-EXPANDED) |
| 语种 | 英语 |
| WOS记录号 | WOS:000318564600007 |
| 出版者 | SPRINGER |
| 资助机构 | National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/28035]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song Chuangye,Huang Chong,Liu Huiming. Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models[J]. CHINESE GEOGRAPHICAL SCIENCE,2013,23(3):331-343. |
| APA | Song Chuangye,Huang Chong,&Liu Huiming.(2013).Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models.CHINESE GEOGRAPHICAL SCIENCE,23(3),331-343. |
| MLA | Song Chuangye,et al."Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models".CHINESE GEOGRAPHICAL SCIENCE 23.3(2013):331-343. |
入库方式： OAI收割
来源：植物研究所
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