Rapid urbanization and industrialization in Lahore and Faisalabad have intensified air pollution issues, influencing nitrogen dioxide (NO ₂ ) concentrations, land surface temperature (LST), and vegetation. The study aims to comprehensively assess changes in NO ₂ , LST, and vegetation induced by industrialization, focusing on seasonal variations from 2019 to 2022. The study evaluates NO ₂ concentrations vegetation health using indices normalized difference vegetation index, enhanced vegetation index, atmospherically resistant vegetation index, and LST variations. The analysis reveals a notable increase in NO ₂ during both summer and winter, with approximately 0.021 (×10 ³ mol/m ² ) and 0.03 (×10 ³ mol/m ² ) rises observed in Lahore. In comparison, Faisalabad experienced more modest increases of around 0.0034 (×10 ³ mol/m ² ) and 0.007 (×10 ³ mol/m ² ) in the respective seasons. Simultaneously, vegetation indices decline in both cities, indicating substantial vegetation health deterioration. Moreover, a notable upward trend in LST occurred, with Lahore experiencing an increase of approximately 1.59 °C in summer and 0.92 °C in winter. Faisalabad also showed rises of around 1.64 and 0.54 °C in the corresponding seasons. Pearson correlation analysis highlights a robust negative correlation between NO ₂ and vegetation indices, underlining the impact of declining vegetation on air quality. A positive correlation between NO ₂ and LST indicates the interconnected nature of rising temperatures and air pollution. The findings emphasize the need for environmental regulations in Lahore and Faisalabad. Addressing rising NO ₂ levels and temperatures is critical for policymakers and urban planners. These insights contribute to the sustainable development goal 11, fostering strategies for sustainable cities and communities to combat pressing environmental challenges in these urban areas.

Rapid urbanization in Lahore, Pakistan, has led to significant ecological and thermal challenges, particularly the intensification of Urban Heat Island (UHI) effects and increased thermal stress as measured by the Urban Thermal Field Variance Index (UTFVI). This study employs a multi-temporal evaluation of Landsat satellite imagery and GIS-based analysis to investigate the Spatio-temporal trends in land-use and land-cover (LULC) changes from 1994 to 2024. We detected substantial changes in urban growth, vegetation cover, and barren areas using supervised classification (Random Forest) methods and remote sensing indices such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), NDBI (Normalized Difference Built-up Index), and NDWI (Normalized Difference Water Index). The classification models demonstrated high accuracy, with combined values ranging from 96.7% to 98.4%. Our results indicate a significant increase in metropolitan regions, from 608.66 km² (32.43%) in 1994 to 968.50 km² (51.6%) in 2024. This urban expansion correlates with rising land surface temperatures (LST), exacerbating UHI effects. The UTFVI analysis reveals a progressive rise in heat stress from 1994 to 2024. In 1994, most areas ranged from “Good” (0.020), indicating severe urban heat intensification, particularly in central and southern regions providing evidence of a significant increase in urban heat, especially in the center and southern areas of the city. These findings underscore the critical need for sustainable land management strategies to mitigate the adverse effects of Lahore's rapid urbanization on the environment. This study comprehensively explains unplanned urban growth's ecological and climatic consequences. The importance of balanced development in protecting natural ecosystems and enhancing urban resilience is highlighted, offering valuable insights for urban planners and policymakers.

An integrated sensor system comprised of a terrestrial laser scanner (TLS), corner reflectors (CRs), and high precision linear rail is utilized to validate ground-based synthetic aperture radar (GB-SAR) interferometric micro-displacement measurements. A rail with positioning accuracy of 0.1 mm is deployed to ensure accurate and controllable deformation. The rail is equipped with a CR on a sliding platform for mobility. Three smaller CRs are installed nearby, each with a reflective sticker attached to the CR’s vertex; the CRs present as high-amplitude points both in the GB-SAR images and the TLS point cloud to allow for accurate data matching. We analyze the GB-SAR zero-baseline repeated rail differential interferometry signal model to obtain 2D interferograms of the test site in time series, and then use TLS to obtain a 3D surface model. The model is matched with interferograms to produce more intuitive 3D products. The CR displacements can also be extracted via surface reconstruction algorithm. Finally, we compared the rail sensor measurement and TLS results to optimize coherent scatterer selection and filter the data. The proposed method yields accurate target displacement results via quantitative analysis of GB-SAR interferometry.