#remote sensing

Rapid urbanization and morphological transformations in Cagayan de Oro City have substantially intensified the Surface Urban Heat Island (SUHI) effect. This research quantifies the spatiotemporal evolution of the city’s thermal environment from 2016 to 2024 using an empirical, multi-tiered geospatial framework. Sentinel-2A multispectral data mapped six land cover classes, validated by a simple random sampling of 300 independent points, while Landsat 8 TIRS data retrieved kinetic Land Surface Temperature (LST) via single-channel emissivity modeling. Spatial statistics expose a profound terrestrial transition, primarily driven by a 27.15% contraction of forest canopy alongside a 441.40% surge in Bare Ground and a 5.20% expansion of Built-up areas. This physical restructuring catalyzed a 2.26°C escalation in the mean basin LST. Bivariate analyses confirmed the Normalized Difference Vegetation Index (NDVI) exerts a stronger suppressive influence on LST variance (R2= 0.4174) than the warming effect of the Normalized Difference Built-up Index (R2= 0.3190). Translating radiometry into ecological vulnerability, the Urban Thermal Field Variance Index (UTFVI) revealed an aggressive geographic diffusion of thermal stress. Furthermore, Contribution Index (CI) assessments determined Forests are the sole remaining thermal sink, with the overall Landscape Effect Index (LI) degrading to 0.981, signaling an inverted thermal regime. Getis-Ord Gi* hotspot analysis anchored these patterns, demonstrating 67.5% of administrative units have coalesced into 99% Confidence Hot Spots. Mitigating this self-reinforcing SUHI effect demands a shift toward spatial de-clustering, strict zone to protect cold spots, and extensive green infrastructure deployment.

In this study, district Mansehra, Pakistan was chosen as the study area. The main objectives of this research are to assess the extent and the changes in the rate of deforestation in Mansehra since past 20 years. It also examine the impacts of deforestation on the Climate by establishing and mapping the magnitude and rates of land cover changes that had occurred in the study area. Landsat satellite images were taken as secondary data and they were foremost for the classification process. Remote sensing data together with GIS techniques have made it conceivable to display and oversee remotely detected information in various scales. The images taken for classification are Landsat 5 TM for the year 1998 and 2008 and from Landsat 8 OLI/TIRS TM for the year 2017. Climatic data from 1988 to 2017 was collected from Pakistan Meteorological Department (PMD) of Mansehra District. The Forest Cover was 14% (601 Sq. Km) in 1998, 15% (668 Sq. Km) in 2008 and 5% (194 Sq. Km) in 2017. Taking everything together, anyway the results exhibit that some land cover types experienced extending rates and sizes of changes however in the others the inverse is legitimate, results revealed that the size and the rates of land cover changes for forest areas experienced an exceptional fluctuations in these 20 years. Maximum temperature of Mansehra increased at an alarming rate from 25.82°C in 1988 and 24.8°C in 1998 to 25.667°C in 2008 and 27.304°C in 2016 and 26.739°C in the year 2017. Mean minimum temperature showed fluctuation of 1° during these 30 years. In some years mean minimum temperature showed decrease from 12° to 11° and this change is not confined to a specific year. The rainfall was 5.129mm in 1988 and then rainfall increased from 3.6 mm in the year 1998 when there was 14% forest Cover to 4.8mm in 2008 when there was 15% Forest Cover but declined to 2.9mm in the year 2017 when there was only 5% forest left in Mansehra.