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

Key words: 
• spatial variation /
• spatial structure /
• NDVI characteristic /
• semivariogram model /
• semivariogram analysis

Abstract: 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 he- terogeneities, 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: 1) in high fractal vegetation cover (H-FVC) area, NDVI 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.