Document Type
Article
Publication Date
11-29-2024
Abstract
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” (<0.005) to “Normal” (0.005–0.010), but by 2024, large portions of the city showed “Worst” conditions (>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.
Recommended Citation
Z. Hao et al., "Multi-Temporal Analysis of Urbanization-Driven Slope and Ecological Impact Using Machine-Learning and Remote Sensing Techniques," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, https://doi.org/10.1109/JSTARS.2024.3509133
Peer Reviewed
1
Copyright
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Included in
Climate Commons, Environmental Health and Protection Commons, Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Remote Sensing Commons
Comments
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing in 2024 following peer review. This article may not exactly replicate the final published version. The definitive publisher-authenticated version is available online at https://doi.org/10.1109/JSTARS.2024.3509133.