Global Pattern of Vegetation Homogeneity and Its Impact on Land Surface Temperature

Ehsan Rahimi, Pinliang Dong and Chuleui Jung ()
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Ehsan Rahimi: Agricultural Science and Technology Institute, Andong National University, Andong 36729, Republic of Korea
Pinliang Dong: Department of Geography and the Environment, University of North Texas, Denton, TX 76205, USA
Chuleui Jung: Agricultural Science and Technology Institute, Andong National University, Andong 36729, Republic of Korea

Land, 2025, vol. 14, issue 2, 1-15

Abstract: Recent advancements in texture-based metrics have improved the representation of landscape heterogeneity, yet global-scale analyses of the relationship between vegetation homogeneity and land surface temperature (LST) remain limited. This study addresses this gap by examining the correlation between Enhanced Vegetation Index (EVI)-derived texture metrics and LST worldwide. We used texture-based metrics from the EVI to assess landscape homogeneity, with LST data from the 2015 MODIS MOD11A1 V6.1 product at a 1 km spatial resolution. Correlation analyses and nonlinear regression models were applied to explore how EVI homogeneity relates to LST across latitudes. Our findings reveal a significant positive correlation between EVI homogeneity and LST, with the strongest association in the Northern Hemisphere (R 2 = 49.3%), followed by a moderate relationship in the Southern Hemisphere (R 2 = 21.1%). In tropical regions (−10° to 10° latitudes), the association is weaker but still significant (R 2 = 15.1%). The distribution of EVI homogeneity follows a Gaussian curve, peaking in mid-latitudes (from −35° to −15° in the Southern Hemisphere and from 15° to 35° in the Northern Hemisphere), while tropical regions exhibit consistently low homogeneity with minimal variation. Our results indicate that regions with higher EVI homogeneity, representing less fragmented vegetation, tend to experience higher LST, whereas areas with more fragmented vegetation (lower homogeneity) exhibit cooler temperatures. Our findings offer valuable insights into the role of vegetation structure in regulating surface temperature across diverse ecosystems. The study highlights the potential for texture-based metrics to enhance environmental monitoring, contributing to improved climate adaptation strategies and sustainable land management practices globally.

Keywords: landscape ecology; enhanced vegetation index; texture metrics; landscape heterogeneity latitudinal variation; MODIS data (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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