IGSNRR OpenIR
predictivevegetationmappingapproachbasedonspectraldatademandgeneralizedadditivemodels
Song Chuangye1; Huang Chong2; Liu Huiming3
2013
Source Publicationchinesegeographicalscience
ISSN1002-0063
Volume23Issue:3Pages:331
AbstractThis 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.
Language英语
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/122766
Collection中国科学院地理科学与资源研究所
Affiliation1.中国科学院植物研究所
2.中国科学院地理科学与资源研究所
3.Satellite Environment Centre, Ministry of Environmental Protection
Recommended Citation
GB/T 7714
Song Chuangye,Huang Chong,Liu Huiming. predictivevegetationmappingapproachbasedonspectraldatademandgeneralizedadditivemodels[J]. chinesegeographicalscience,2013,23(3):331.
APA Song Chuangye,Huang Chong,&Liu Huiming.(2013).predictivevegetationmappingapproachbasedonspectraldatademandgeneralizedadditivemodels.chinesegeographicalscience,23(3),331.
MLA Song Chuangye,et al."predictivevegetationmappingapproachbasedonspectraldatademandgeneralizedadditivemodels".chinesegeographicalscience 23.3(2013):331.
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