Abstract
This study aimed to investigate the spatial autocorrelation between precipitation and vegetation indices in the Bandar Abbas basin. For this purpose, the vegetation indices of DVI, EVI, IPVI, NDVI, NDWI, RVI, SAVI, TCI, VCI, and VHI were derived from Landsat satellite images over 20 years were studied. Precipitation data corresponding to rain gauge stations was extracted. The Pearson correlation coefficient and the GI * and I indices were used to investigate the relationship between precipitation and spatial autocorrelation. Moreover, the Pearson correlation coefficient was used to investigate the relationship between precipitation and vegetation indices, and the GI * and I indices was used to correlate spatial autocorrelation patterns. The results showed that SAVI, VHI, VCI, and NDWI were most correlated with precipitation among the Bandar Abbas basin's vegetation indices, with the SAVI index being more closely correlated than the others. However, precipitation had the least impact on the TCI index. The spatial autocorrelation of rainfall with the vegetation indices, except for the IPVI index, had a scattered pattern in the study area’s southern and eastern parts. Of the indices studied in terms of spatial pattern, the IPVI and NDWI indices formed a positive spatial correlation pattern with precipitation over a greater spatial range.
Highlights
In the international approaches to vegetation to estimate agricultural production at regional and national levels is the use of remote sensing technology
The spatial variability of precipitation with the vegetation index IPVI showed that unlike the vegetation indices difference vegetation index (DVI) and Enhanced Vegetation Index (EVI) that had the highest relationship with precipitation in the eastern and southern regions of the study area, the vegetation index IPVI had the highest relationship with precipitation in the northern
In the vegetation index Normalized Difference Vegetation Index (NDVI), the coefficient of interchangeability of precipitation with this index was weak in all regions of the study area, except in the southeast
Summary
In the international approaches to vegetation to estimate agricultural production at regional and national levels is the use of remote sensing technology. According to Carvalho et al, (2008), remotely sensed data is a regular source of information and it is important for the systematic monitoring of the vegetation dynamics. The Normalized Difference Vegetation Index (NDVI), derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, can capture the surface vegetation greenness and coverage (Yengoh et al, 2015), and has been widely used in vegetation dynamics monitoring (Bing et al, 2014; Lin et al, 2020). The satellite Landsat-8 has been equipped with two sensors: The Operational Land Imager (OLI), designed in order to operate in continuity with TM and ETM+; and the Thermal Infrared Sensor (TIRS), which features two bands in the thermal infrared region. The main differences between OLI and Mehran Safa, IJSRM Volume 09 Issue 12 December 2021 [www.ijsrm.in]
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