Effects of Normalized Difference Vegetation Index and Related Wavebands′ Characteristics on Detecting Spatial Heterogeneity Using Variogram-based Analysis

作     者：WEN Zhaofei ZHANG Ce ZHANG Shuqing DING Changhong LIU Chunyue PAN Xin LI Huapeng SUN Yan 

作者机构：Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun 130012 China Graduate University of Chinese Academy of Sciences Beij＇ing 100049 China Laboratory of Geographical Resources and Environmental Remote Sensing College of Geographical Sciences Harbin Normal University Harbin 150025 China Electrical and Electronic Teaching and Research Office Aviation University of Air Force Changehun 130022 China 

出 版 物：《Chinese Geographical Science》 (中国地理科学（英文版）)

年 卷 期：2012年第22卷第2期

页      面：188-195页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 09[农学] 0903[农学-农业资源与环境] 0706[理学-大气科学] 090301[农学-土壤学] 

基　　金：Under the auspices of National Key Technology Research and Development Program of China (No.2009BADB3B01-05) Knowledge Innovation Programs of Chinese Academy of Sciences (No. KSCX1-YW-09-13) 

主　　题：spatial variation spatial structure NDVI characteristic semivariogram model semivariogram analysis 

摘      要：Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multiplicity of spatial heterogeneity by using various environmental variables. How these variables affect their corresponding spatial heterogeneities, however, have received little attention. In this paper, we examined the effects of characteristics of normalized difference vegetation index （NDVI） and its related bands variable images, namely red and near infrared （NIR）, on their corresponding spatial heterogeneity detection based on variogram models. In a coastal wetland region, two groups of study sites with distinct fractal vegetation cover were tested and analyzed. The results show that： l） in high fractal vegetation cover （H-FVC） area, NDV! and NIR variables display a similar ability in detecting the spatial he- terogeneity caused by vegetation growing status structure; 2） in low fractal vegetation cover （L-FVC） area, the NIR and red variables outperform NDVI in the survey of soil spatial heterogeneity; and 3） generally, NIR variable is ubiquitously applicable for vegetation spatial heterogeneity investigation in different fractal vegetation covers. Moreover, as variable selection for remote sensing applications should fully take the characteristics of variables and the study object into account, the proposed variogram analysis method can make the variable selection objectively and scientifically, especially in studies related to spatial heterogeneity using remotely sensed data.