Monitoring the impacts of spatio-temporal land-use changes on the regional climate of city Faisalabad, Pakistan

Abstract

The eco-environment in city Faisalabad has attained much consideration due to rapid urbanization and land-use changes in past few years. The purpose of this study is to analyse the impacts of land-use changes on regional climate by linking them with temperature and precipitation trend using spatio-temporal analysis and statistical method. Geospatial and remote sensing technique provides essential tools which can be used to interpret the land-use changes. Spatio-temporal land-use changes from 1985 to 2016 were analysed using unsupervised image classification technique. Land-use maps were classified into three main classes, namely agriculture, built-up and open land. The results indicate that agricultural land has continuously decreased by 37.79-10.23%, built-up area has increased by 33. 07-88.19% and open land has decreased by 29.13-1.5% from 1985 to 2016. Statistical analysis indicates that transformation of agriculture and open land into built-up land has led to rise in temperatures and annual rainfall trend in Faisalabad. Average increase in annual T Min , T Max and T Mean was 1.2, 0.3 and 0.7°C, respectively from 1985 to 2016. Overall contribution of urban warming to total annual T Min , T Max and T Mean was 12.31%, 1.95% and 5.42%, respectively. Annual rainfall is also increasing and it has increased by 120 mm from 1985 to 2016. Results indicate that temporal variations of annual temperature and rainfall are consistent with land-use changes in city Faisalabad, and significant correlation coefficients were found between them. It is recommended that this type of study is helpful for urban planners to control the urbanization properly especially in larger cities of Pakistan. ARTICLE HISTORY

Spatiotemporal shifts in thermal climate in responses to urban cover changes: a-case analysis of major cities in Punjab, Pakistan

“…In ArcGIS 10.3, the accuracy assessment protocol for LULC maps was carried out. For each class, we randomly collected 100 pixels from random locations on Google Earth each year , Zhang et al 2013) and followed previously published literature and expert knowledge (Yuan et al 2005, Gorelick et al 2017, Arshad et al 2019. In order to test their accuracy using various tools in ArcGIS software, the pixel values in the image were compared with the ground truth samples for each land-use class.…”

“…There are previous studies have been published on the thermal evaluation of the Punjab Plain urban areas (land drained by the Indus River and its tributaries), focusing on the city's single-core urban agglomeration zone, and little attention has been paid to identifying factors contributing to increase LST and SUHII phenomena. Arshad et al (2019) studied the impact of LULC changes on the regional climate of Faisalabad and concluded that urban cover caused higher regional temperature (Arshad et al 2019). Sajid, Ahmad, and Javed (2020) have considered the NDVI as a LULC change factor to know the urban contribution to LST of Lahore, Faisalabad, and Multan cities in Pakistan (Sajid, Ahmad, Javed 2020).…”

“…Pakistan is highly exposed to climate change and faced major extreme events, in order to follow the UN sustainable goals and there is significant need to make sustainable urban cities. In recent years, extensively LULC changed in the major cities of Punjab and affected the regional/local urban climate that causes heatwaves, diseases, social life disturbance (Sadiq Khan et al 2020;Arshad et al 2019).…”

An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data

“…act as positive catalysts for rising up the LST. Recently, some important research works on LST were conducted in most of the Indian sub-continent countries like India, Pakistan and Sri Lanka (Senanayake et al 2013;Guha et al 2017Ranagalage et al 2017;Arshad et al 2019;Ranagalage, Estoque, Handayani, et al 2018;Dissanayake et al 2019c;Govil et al 2019;Mehmood and Butt 2019;Ranagalage et al 2019;Imran and Mehmood 2020). Normalized difference water index (NDWI) is the most popular index for the extraction of water body that is invariably applied in land use and LST related studies (McFeeters 1996(McFeeters , 2013Chen et al 2006;Weng and Quattrochi 2006;Essa et al 2012;Chen et al 2013;Yuan et al 2017).…”

Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata

“…LUCC models generally use complex processes to represent land use and land cover on a spatial grid (Popp et al, ; Verburg et al, ). Global‐scale LUCC models are now considered to be crucial inputs for various assessments of anthropogenic environmental change, including climate change, biological diversity, and mitigation (Arshad et al, ; Hanaček & Rodríguez‐Labajos, ; Hirsch et al, ; Meiyappan et al, ; Pelletier et al, ; Sleeter et al, ; Wolffa et al, ).…”