Qualitative comparison and interrelationship of the normalized difference vegetation index by different satellite data for Karachi metropolis

The Sustainable Development Goal (SDG) (Goal 15: Life on land) seeks to protect, restore, and promote the conservation and sustainable use of terrestrial ecosystems. The assessment of urban vegetation contributes to SDGs by promoting health (Goal 3), sustainable cities (Goal 11), and climate resilience (Goal 13), fostering well-being, mitigating environmental issues, and enhancing biodiversity in line with global sustainability efforts. Urban vegetation assessment aligns with this goal by contributing to the preservation and enhancement of biodiversity, fostering well-being, mitigating environmental issues, and promoting climate resilience. Collaboration among stakeholders is crucial for integrating urban vegetation assessment into urban planning and achieving SDGs. This study elucidates the statistical interrelationship of the normalized difference vegetation index (NDVI) derived from multisensory remote sensing data for the Karachi metropolis spanning from 2015 to 2020, in line with SDG 15. NDVI computations were performed using Sentinel-2 and Landsat-8 imagery, while Moderate Resolution Imaging Spectroradiometer(MODIS) NDVI products (MOD13Q1, MOD13A1, and MOD13A3) and Visible Infrared Imaging Radiometer Suite (VIIRS) (VNP13A3) NDVI product were acquired from Land Processes Distributed Active Archive Center. Statistical analyses encompassed raster datasets, incorporating measures such as mean, minimum, maximum, standard deviation, covariance, and correlation across all NDVI datasets. Sentinel-2 imagery was designated as the baseline for correlation assessment. Two hundred random sample points were generated for statistical evaluation and regression analysis, and NDVI values were extracted as vector data alongside tabular values from all datasets. The findings reveal that Sentinel-2 imagery exhibits superior mean NDVI values and fewer outliers, furthering the objectives of SDG 15. Furthermore, the regression model between Sentinel-2 and Landsat-8 accounts for 78% of the association around the values, underscoring the importance of these insights for monitoring urban vegetation dynamics and guiding sustainable urban planning and environmental management strategies in Karachi and similar metropolitan areas worldwide.