Analysis of the relationship between NDVI and climate variables in minnesota using geographically weighted regression and spatial interpolation

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

In order to better understand the effects of climate change on ecosystems, the relationship between Normalized Difference Vegetation Index (NDVI) and atmospheric constituents have been explored widely by scientists using the global technique of Ordinary Least Squared (OLS) regression analysis. However, recent studies exploring such relationships at different spatial scales have revealed that local statistical approaches are more appropriate when the assumption of spatial stationarity is invalid. This study aims to explore the relationships between NDVI and the local level atmospheric constituents consisting of precipitation and temperature in the state of Minnesota from 1990 to 1997 using Geographically Weighted Regression (GWR) and spatial interpolation techniques. The analysis focuses on the summer months, when such relationships are more apparent in northern mid-latitude regions. In comparison to traditional OLS, there is a substantial improvement in the analysis using GWR with the average r2 value improved from 0.24 to 0.67. The overall relationship between the different atmospheric constituents and NDVI were broadly consistent with the different types of land uses across the state with the highest correlation located in forested areas. The spatial patterns of the association between different climatic variables and NDVI in the form of regression coefficients were not very consistent over the years as result of inter-annual variations in the local climate.

Original languageEnglish (US)
Title of host publicationAmerican Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions
Pages784-789
Number of pages6
Volume2
StatePublished - 2007
Externally publishedYes
EventASPRS Annual Conference 2007: Identifying Geospatial Solutions - Tampa, FL, United States
Duration: May 7 2007May 11 2007

Other

OtherASPRS Annual Conference 2007: Identifying Geospatial Solutions
CountryUnited States
CityTampa, FL
Period5/7/075/11/07

Fingerprint

NDVI
interpolation
Interpolation
climate
Land use
Regression analysis
Climate change
Ecosystems
annual variation
regression analysis
land use
climate change
analysis
ecosystem
summer
temperature
Temperature

ASJC Scopus subject areas

  • Information Systems
  • Computers in Earth Sciences

Cite this

Yuan, F., & Roy, S. S. (2007). Analysis of the relationship between NDVI and climate variables in minnesota using geographically weighted regression and spatial interpolation. In American Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions (Vol. 2, pp. 784-789)

Analysis of the relationship between NDVI and climate variables in minnesota using geographically weighted regression and spatial interpolation. / Yuan, Fei; Roy, Shouraseni S.

American Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions. Vol. 2 2007. p. 784-789.

Research output: Chapter in Book/Report/Conference proceedingChapter

Yuan, F & Roy, SS 2007, Analysis of the relationship between NDVI and climate variables in minnesota using geographically weighted regression and spatial interpolation. in American Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions. vol. 2, pp. 784-789, ASPRS Annual Conference 2007: Identifying Geospatial Solutions, Tampa, FL, United States, 5/7/07.
Yuan F, Roy SS. Analysis of the relationship between NDVI and climate variables in minnesota using geographically weighted regression and spatial interpolation. In American Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions. Vol. 2. 2007. p. 784-789
Yuan, Fei ; Roy, Shouraseni S. / Analysis of the relationship between NDVI and climate variables in minnesota using geographically weighted regression and spatial interpolation. American Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions. Vol. 2 2007. pp. 784-789
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