中国地理科学(英文版) ›› 2013, Vol. 23 ›› Issue (3): 331-343.doi: 10.1007/s11769-013-0590-0

• 论文 • 上一篇    下一篇

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

SONG Chuangye1, HUANG Chong2, LIU Huiming3   

  1. 1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;
    2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    3. Satellite Environment Centre, Ministry of Environmental Protection, Beijing 100094, China
  • 收稿日期:2012-04-18 修回日期:2012-09-21 出版日期:2013-05-29 发布日期:2013-05-30
  • 通讯作者: SONG Chuangye. E-mail: songcy@ibcas.ac.cn E-mail:songcy@ibcas.ac.cn
  • 基金资助:

    Under the auspices of National Natural Science Foundation of China (No. 41001363)

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

SONG Chuangye1, HUANG Chong2, LIU Huiming3   

  1. 1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;
    2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    3. Satellite Environment Centre, Ministry of Environmental Protection, Beijing 100094, China
  • Received:2012-04-18 Revised:2012-09-21 Online:2013-05-29 Published:2013-05-30
  • Contact: SONG Chuangye. E-mail: songcy@ibcas.ac.cn E-mail:songcy@ibcas.ac.cn
  • Supported by:

    Under the auspices of National Natural Science Foundation of China (No. 41001363)

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.

关键词: vegetation mapping, Generalized Additive Models (GAMs), SPOT, Receiver Operating Characteristic (ROC), Generalized Regression Analysis and Spatial Predictions (GRASP), Huanghe River Delta

Abstract:

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.

Key words: vegetation mapping, Generalized Additive Models (GAMs), SPOT, Receiver Operating Characteristic (ROC), Generalized Regression Analysis and Spatial Predictions (GRASP), Huanghe River Delta