Remote Sensing (Sep 2024)

Changes in Vegetation Cover and the Relationship with Surface Temperature in the Cananéia–Iguape Coastal System, São Paulo, Brazil

  • Jakeline Baratto,
  • Paulo Miguel de Bodas Terassi,
  • Emerson Galvani

DOI
https://doi.org/10.3390/rs16183460
Journal volume & issue
Vol. 16, no. 18
p. 3460

Abstract

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The objective of this article is to investigate the possible correlations between vegetation indices and surface temperature in the Cananéia–Iguape Coastal System (CICS), in São Paulo (Brazil). Vegetation index data from MODIS orbital products were used to carry out this work. The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) were acquired from the MODIS/Aqua sensor (MYD13Q1) and the leaf area index (LAI) from the MODIS/Terra (MOD15A2H). Surface temperature data were acquired from MODIS/Aqua (MYD11A2). The data were processed using Google Earth Engine and Google Colab. The data were collected, and spatial and temporal correlations were applied. Correlations were applied in the annual and seasonal period. The annual temporal correlation between vegetation indices and surface temperature was positive, but statistically significant for the LAI, with r = 0.43 (90% significance). In the seasonal period, positive correlations occurred in JFM for all indices (95% significance). Spatially, the results of this research indicate that the largest area showed a positive correlation between VI and LST. The hottest and rainiest periods (OND and JFM) had clearer and more significant correlations. In some regions, significant and clear correlations were observed, such as in some areas in the north, south and close to the city of Iguape. This highlights the complexity of the interactions between vegetation indices and climatic attributes, and highlights the importance of considering other environmental variables and processes when interpreting changes in vegetation. However, this research has significantly progressed the field, by establishing new correlations and demonstrating the importance of considering climate variability, for a more accurate understanding of the impacts on vegetation indices.

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