Remote sensing images are key tools in environmental and urban studies, where precise natural resource monitoring is becoming increasingly important. However, using medium-resolution images, such as Landsat 9, frequently presents difficulties in delivering correct spatial data, such as the distribution of vegetation and other land coverings, due to the pressing need for improving remote sensing techniques to satisfy the needs and assess environmental changes in increasingly metropolitan regions. In this study, spectral and spatial information (vegetation indices and modified water index) driven from Landsat 9 images were compared to spectral information from high-resolution Pleiades images, which was evaluated using linear regression between the spectral information samples from Landsat and Pleiades indices. The results revealed a limited correlation between traditional Landsat 9 data and high-resolution Pleiades spectral indices. However, the NDVSI spectral index derivative from Landsat data showed efficiency and accuracy in estimating vegetation parameters, with a correlation coefficient (R2 =0.5) with the Pleiades. Unlike the usual NDVI metric with a correlation coefficient (R2 = 0.2). These findings highlight the necessity of enhancing medium-resolution remote sensing data in urban settings, as it adds to improving and estimating natural resources.
The Effect of Sensor Resolution (Landsat 9 and Pleiades) on Spectral Indices Information: Comparison and Analysis
Spectral Indices
Landsat9
Pleiades
NDVI
NDVSI
MNDWI
IDW