Open Access

ARTICLE

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

by Shahab Ul Islam¹, Saifullah Jan², Abdul Waheed^3,4,*, Gulzar Mehmood⁵, Mahdi Zareei⁶, Faisal Alanazi⁷

1 Department of Computer Science, IQRA National University, Peshawar, 25124, Pakistan
2 Department of Computer Science, City University of Science & Information Technology, Peshawar, 25124, Pakistan
3 Department of Information Technology, Hazara University, Mansehra, 21120, Pakistan
4 School of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea
5 Department of Computer Science and Information Technology, University of Malakand, 23021, Pakistan
6 Tecnologico de Monterrey, School of Engineering and Sciences, Zapopan, 45201, Mexico
7 Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, College of Engineering, Al Kharj, 16278, Saudi Arabia

* Corresponding Author: Abdul Waheed. Email:

Computers, Materials & Continua 2022, 70(2), 4123-4145. https://doi.org/10.32604/cmc.2022.019226

Received 07 April 2021; Accepted 15 July 2021; Issue published 27 September 2021

Abstract

Spatial and temporal information on urban infrastructure is essential and requires various land-cover/land-use planning and management applications. Besides, a change in infrastructure has a direct impact on other land-cover and climatic conditions. This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature (LST) during the years 1996 and 2019. For this purpose, firstly, satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper) plus and OLI (Operational Land Imager) of 30 m resolution were taken. Secondly, for classification and image processing, remote sensing (RS) applications ENVI (Environment for Visualising Images) and GIS (Geographic Information System) were used. Thirdly, for better visualization and more in-depth analysis of land sat images, pre-processing techniques were employed. For Land use and Land cover (LU/LC) four types of land cover areas were identified – vegetation area, water cover, urbanized area, and infertile land for the years under research. The composition of red, green, and near infra-red bands was used for supervised classification. Classified images were extracted for analyzing the relative infrastructure change. A comparative analysis for the classification of images is performed for SVM (Support Vector Machine) and ANN (Artificial Neural Network). Based on analyzing these images, the result shows the rise in the average temperature from 30.04°C to 45.25°C. This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km² from 1996 to 2019. It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

---

Keywords

---

Citations