Chin J Plan Ecolo ›› 2012, Vol. 36 ›› Issue (10): 1106-1119.doi: 10.3724/SP.J.1258.2012.01106

• Research Articles • Previous Articles     Next Articles

Applying generalized additive model to integrate digital elevation model and remotely sensed data to predict the vegetation distribution

SONG Chuang-Ye1*, LIU Hui-Ming2, LIU Gao-Huan3, and HUANG Chong3   

  1. 1State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;

    2Satellite Eenvironment Center, Ministry of Environmental Protection, Beijing 100094, China;

    3State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2011-07-14 Revised:2012-08-28 Online:2012-09-26 Published:2012-10-01
  • Contact: SONG Chuang-Ye


Aims Our objective was to integrate vegetation survey data, remotely sensed data and environmental data based on generalized additive model to map vegetation and investigate whether coupling remotely sensed data and environmental data could improve the performance of generalized additive model.
Methods Altitude, slope, nearest distance to coastline, nearest distance to the Yellow River and spectral variables were selected as predictive variables. Generalized additive model was employed to describe the relation between the vegetation and predictive variables. Deviance (D2) was employed to test the goodness of curve fitting, and a probability map of each vegetation type produced by the fitted generalized additive model was assessed by cross-validation of receiver operating characteristic (ROC). Vegetation type of each grid cell was determined according to the maps of probability. We validated the final predicted vegetation type map by comparing the predicted vegetation type map with vegetation survey data.
Important findings Results indicated that integrating field sampling, digital elevation model and remotely sensed data based on generalized additive model was feasible to map vegetation. Coupling environmental variables and spectral variables improved the performance of generalized additive model. Prediction accuracy varied according to the plant community type, and plant community with dense cover was better predicted than sparse plant community. The selected generalized additive models for each vegetation type indicated that both environmental variables and spectral variables were important factors for predicting vegetation distribution. Drop contribution calculation indicated that the contribution of the same predictive variable for different vegetation types was different. This can be explained by the different spectral and environmental characteristics of vegetation types. The contribution of each predictive variable for the same vegetation type varied according to the modeling scene. This might be explained by the coupling effects of environmental variables and spectral variables.

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[1] Yu Feng-lan;Wang Jing-ping;Li Jing-min and Shan Xue-qin. The Isolation and Identification of Sterols and Other Constituents from Seed Fat of Sapium sebiferum[J]. Chin Bull Bot, 1989, 6(02): 121 -123 .
[2] LI Al-Fen;CHEN Min amd ZHOU Bai-Cheng. Advances and Problems in Studies of Photosynthetic Pigment-Protein Complexes of Brown Algae[J]. Chin Bull Bot, 1999, 16(04): 365 -371 .
[3] CHEN Xiao-Mei and GUO Shun-Xing. Research Advances in Plant Disease Resistive Material[J]. Chin Bull Bot, 1999, 16(06): 658 -664 .
[4] LI Ji-Quan JIN You-Ju SHEN Ying-Bai HONG Rong. The Effect of Environmental Factors on Emission of Volatile Organic Compounds from Plants[J]. Chin Bull Bot, 2001, 18(06): 649 -656 .
[5] . [J]. Chin Bull Bot, 2005, 22(增刊): 157 .
[6] Jianxia Li, Chulan Zhang, Xiaofei Xia, Liangcheng Zhao. Cryo-sectioning Conditions and Histochemistry Comparison with Paraffin Sectioning[J]. Chin Bull Bot, 2013, 48(6): 643 -650 .
[7] JIANG Yang-Ming, CUI Wei-Hong, and DONG Qian-Lin. Comprehensive evaluation and analysis of tobacco planting environment based on space technology[J]. Chin J Plan Ecolo, 2012, 36(1): 47 -54 .
[8] Hu Cheng-biao, Zhu Hong-guang, Wei Yuan-lian. A Study on Microorganism and Biochemical Activity of Chinese-fir Plantation on Different Ecological Area in Guangxi[J]. Chin J Plan Ecolo, 1991, 15(4): 303 -311 .
[9] Hong-Xin SU Fan BAI Guang-Qi LI. Seasonal dynamics in leaf area index in three typical temperate montane forests of China: a comparison of multi-observation methods[J]. Chin J Plan Ecolo, 2012, 36(3): 231 -242 .
[10] AN Ran, GONG Ji-Rui, YOU Xin, GE Zhi-Wei, DUAN Qing-Wei, YAN Xin. Seasonal dynamics of soil microorganisms and soil nutrients in fast-growing Populus plantation forests of different ages in Yili, Xinjiang, China[J]. Chin J Plan Ecolo, 2011, 35(4): 389 -401 .