On the application of remote sensing towards the estimation of cultivated land lost to urbanization

ABSTRACT

In this research work, a 40-km² SPOT-5 High-Resolution Imagery (HRI) of the Warsak locality in district Peshawar, Pakistan, was utilized to approximate the quantity of cultivated land lost to urbanization, due to the construction of new homes and buildings. The imagery from a period of 2005 to 2015 for wheat crop was taken, specifically during the months of March and June when the crop is rich green and golden ripe respectively. eCognition ® program’s Object-Oriented Classification Method (OOCM) was employed for recognition of land versus buildings. Nearest Neighbour (NN), Support Vector Machine (SVM), Decision Trees (DT) and Random Forests (RF) were utilized for the classification process. The results demonstrated that the urbanized area had increased by approximately 28 per cent in the area considered. Moreover, the efficacy of the proposed method is depicted by an accuracy of 97.9 per cent and a Kappa Statistics of 0.975 for the SVM classifier.

KEYWORDS:

• Crop estimation
• urbanization
• remote sensing
• SPOT-5 satellite imagery
• high-resolution imagery (HRI)
• machine learning
• classification

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Dr Aftab Khan is working as an assistant professor in the Department of Computer Systems Engineering (DCSE), University of Engineering and Technology (UET), Peshawar since December 2013. He received his B.E. degree in computer system engineering in 2009 from the College of Electrical and Mechanical Engineering, a constituent college of the National University of Sciences and Technology (NUST), Islamabad, Pakistan. Afterwards, he completed his Ph.D. research study in ‘Single-Image Blind Deblurring and Restoration Techniques’ from The University of Manchester, UK in 2014. His research interests include digital image restoration, blind image deblurring, medical image processing and digital image and video compression.

Mr SherAfgan Khattak did his graduation and post-graduation from the Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Pakistan. He is serving as a Subject specialist of Computer Science in a Government Institution. He has in-depth knowledge of Machine learning and pattern recognition. He is environmentalist and actively working on deterring the negative effects of urbanization on environment through active plantation drives. Moreover, in community, he is among the pioneer of introducing Early Age programming for government school kids under the banner ‘Anyone can code’.

Mr Muhammad Waleed graduated from the University of Engineering and Technology (UET) in 2017 and 2014 with his M.Sc. and B.Sc., respectively. He did his Intermediate (Pre-Engineering) from 2008 to 2010 at Government College, Peshawar. He is currently pursuing his Ph.D. from UET as well in the field of image processing. His interests include image processing, networking, data compression, etc. He is also a teaching assistant at UET Peshawar.

Dr Ashfaq Khan is working as Assistant professor in the Department of Mechanical Engineering, University of Engineering and Technology (UET), Peshawar since January 2012. He is the first-ever Ph.D. graduate under UET’s development project, ‘Jalozai Campus, UET Peshawar’ and has returned before his due time after completing his Ph.D. from Manchester University, UK. Soon after his Ph.D. he worked as Research Visitor at UoM where he was able to continue his research work. In January 2012, he joined UET as Assistant professor.

Umair Khan is a graduate student within the Computer Systems Engineering programme at the University of Engineering and Technology (UET), Peshawar, Pakistan. He is a student of MS program in Computer System Engineering at UET Peshawar, Pakistan. He is also currently working as a research associate in COMSATS Institute of Information Technology, Attock. Umair's research concentrates on advanced concepts of image processing and machine learning.

ORCID

Aftab Khan http://orcid.org/0000-0002-1797-0965