Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models
文献类型：SCI/SSCI论文
| 作者 | Huang C. |
| 发表日期 | 2013 |
| 关键词 | vegetation mapping Generalized Additive Models (GAMs) SPOT Receiver Operating Characteristic (ROC) Generalized Regression Analysis and Spatial Predictions (GRASP) Huanghe River Delta yanhe river catchment spatial prediction plant-communities biodiversity regression oregon images level delta |
| 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. |
| 出处 | Chinese Geographical Science |
| 卷 | 23 |
| 期 | 3 |
| 页 | 331-343 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1002-0063 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/30268]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Huang C.. Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models. 2013. |
入库方式： OAI收割
来源：地理科学与资源研究所
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