Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

October 18, 2017 - Zhiqiang Zhao; Jiangbo Gao; Yanglin Wang; Jianguo Liu<liuji@msu.edu>; Shuangcheng Li

Journal or Book Title: Theoretical and Applied Climatology

Volume/Issue: 120 / 3-4

Page Number(s): 507-519

Year Published: 2015

At landscape scale, the normalized difference vegetation index (NDVI) can be used to indicate the vegetation’s dynamic characteristics and has been widely employed to develop correlated and dependent relationships with the climatic and environmental factors. However, studies show that NDVI-environment relationships always emerge with complex features such as nonlinearity, scale dependency, and nonstationarity, especially in highly heterogeneous areas. In this study, we used geographically weighted regression (GWR), a local modeling technique to estimate regression models with spatially varying relationships, to investigate the spatially nonstationary relationships between NDVI and climatic factors at multiple scales in North China. The results indicate that all GWR models with appropriate bandwidth represented significant improvements of model performance over the ordinary least squares (OLS) models. The spatial relationships between NDVI and climatic factors varied significantly over space and were more significant and sensitive in the ecogeographical transition zone. Clear spatial patterns of slope parameters and local coefficient of determination (R2) were found from the results of the GWR models. Moreover, the spatial patterns of the local R2 of NDVI-precipitation are much clearer than the R2 of NDVI-temperature in the semi-arid and subhumid areas, which mean that precipitation has more significant influence on vegetation in these areas. In conclusion, the study revealed detailed site information on the variable relationships in different parts of the study area, especially in the ecogeographical transition zone, and the GWR model can improve model ability to address spatial, nonstationary, and scale-dependent problems in landscape ecology.

DOI: 10.1007/s00704-014-1188-x

Type of Publication: Journal Article

Authors

You Might Also Be Interested In

Accessibility Questions:

For questions about accessibility and/or if you need additional accommodations for a specific document, please send an email to ANR Communications & Marketing at anrcommunications@anr.msu.edu.

Tag List
  • center for systems integration and sustainability
Michigan State University
  • Spartans Will.
  • © Michigan State University

MSU is an affirmative-action, equal-opportunity employer, committed to achieving excellence through a diverse workforce and inclusive culture that encourages all people to reach their full potential.

Michigan State University Extension programs and materials are open to all without regard to race, color, national origin, gender, gender identity, religion, age, height, weight, disability, political beliefs, sexual orientation, marital status, family status or veteran status. Issued in furtherance of MSU Extension work, acts of May 8 and June 30, 1914, in cooperation with the U.S. Department of Agriculture. Quentin Tyler, Director, MSU Extension, East Lansing, MI 48824. This information is for educational purposes only. Reference to commercial products or trade names does not imply endorsement by MSU Extension or bias against those not mentioned.

The 4-H Name and Emblem have special protections from Congress, protected by code 18 USC 707.

We comply with the Federal Trade Commission 1998 Children’s Online Privacy Protection Act (COPPA).