Publications

Zhang, H; Zheng, XT; Ghaffar, B; Quddoos, A; Naz, I; Aslam, RW; Fatima, Z; Abdullah-Al-Wadud, M; Zulqarnain, RM (2025). Monitoring of Fire-Affected Buildings and Air-Quality Assessment: A Remote-Sensing Study Using Ground-Based interferometric Radar and Google-Earth-Engine. RANGELAND ECOLOGY & MANAGEMENT, 101, 28-42.

Abstract
This study investigates the environmental and structural impacts of crop and rangeland residue burning in Punjab during April and May 2023, utilizing ground-based interferometric radar alongside atmospheric and land use datasets. The radar system was employed to monitor structural deformations in buildings affected by fire, providing critical insights into the broader impacts of the fires on infrastructure. Multiple ground-based interferometric radars for distributed monitoring can effectively obtain the three-dimensional deformation components of structural targets in real fire scenes. The monitoring results conform to the displacement variation law of existing portal steel frame structures during fire deformation, and can provide effective data support for building structure collapse warning under fire. This approach offers a novel method for assessing the structural integrity of buildings during and after fire events, complementing traditional environmental monitoring. Environmental data show a marked increase in atmospheric pollutants such as NO2, CH4, and CO in May 2023, directly linked to the residue burning. Active fire data from FIRMS and MODIS revealed a 98% increase in fire events compared to April, while land surface temperature analysis showed a rise of 1.54%, with affected areas reaching up to 45.9 degrees C. LULC analysis indicated a 23.3% decrease in cropland and rangeland areas, reflecting extensive land clearing. These findings highlight the urgent need for sustainable agricultural management and policies to mitigate the environmental and infrastructural damage caused by residue burning in Punjab. (c) 2025 The Society for Range Management. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

DOI:
10.1016/j.rama.2025.03.006

ISSN:
1551-5028