Imtiaz Ahmad

Land Use and Land Cover (LULC) transformations driven by human activities significantly influence the thermal behavior and ecological function of urban environments. This study investigates the spatiotemporal dynamics of LULC, land surface temperature (LST), and key spectral indices—including the Normalized Difference Built-up Index (NDBI, for mapping impervious surfaces), the Normalized Difference Vegetation Index (NDVI, for assessing vegetation health and density), and the Normalized Difference Water Index (NDWI, for detecting surface water and moisture)—in Burewala City, Pakistan. Using multi-temporal Landsat imagery (2003, 2014, 2023), supervised classification and transition matrix analysis revealed quantitatively modest but environmentally significant urban expansion, with built-up areas increasing from 11.44% to 12.71% (a net gain of 19.05 km²), primarily through the conversion of 53.57 km² of bare soil. Although the classified area of vegetation slightly increased, NDVI values demonstrated a significant decline in vegetative health. Concurrently, mean LST rose substantially from 26.87 °C to 36.76 °C. Zonal analysis quantified a distinct thermal hierarchy among LULC classes, with built-up areas exhibiting the highest mean LST—exceeding vegetated surfaces by 3.6 °C in 2023. The spatiotemporal pattern of the Urban Thermal Field Variance Index (UTFVI, a measure of ecological thermal comfort) showed a marked expansion of areas experiencing strong heat island stress. Statistical analysis revealed strong correlations, most notably a robust and consistent negative relationship between LST and NDVI (R² = 0.82 to 0.76). The findings reveal that urban growth, coupled with the degradation of vegetation quality and loss of surface moisture, is a primary driver of elevated surface temperatures and worsening thermal comfort. These results underscore that effective mitigation of urban heat requires policies focused on enhancing vegetative health, restoring urban water cycles, and implementing targeted interventions in zones of high thermal stress, moving beyond conventional land-use planning.

Afghanistan, a semi-arid region with limited prior research. This study presents an integrated assessment of urban flood vulnerability by analyzing land use land cover change (LULCC), climate variability, and geophysical factors using remote sensing, GIS, and multi-criteria decision analysis. Landsat imagery indicates built-up areas increased from 124 km² in 2004 to 180 km² in 2024, while agricultural land decreased from 1,978 km² to 1,883 km². Climate data show rising temperatures and intensifying rainfall, exacerbating flood hazards. Geospatial analysis of elevation, slope, drainage density, and proximity to water bodies highlights the high vulnerability of low-lying areas. The Analytical Hierarchy Process (AHP) integrates diverse flood risk factors to produce accurate flood hazard maps. High and very-high flood susceptibility zones expanded from 5,706 km² in 2004 to 5,985 km² in 2024, whereas low-susceptibility zones declined from 131 km² to 112 km². These results underscore the need for adaptive land use planning, resilient drainage systems, and community-based flood risk reduction. The study provides actionable insights for sustainable flood management and demonstrates the value of combining GIS, remote sensing, and multi-criteria analysis in data-scarce, conflict-affected regions.