Monitoring Land Cover Changes In The Tropics Using Satellite Remote Sensing Data

Ahmad, Asmala and Mohamad Hashim, Ummi Kalsom and Sakidin, Hamzah and Sufahani, Suliadi Firdaus and Mat Amin, Abd Rahman (2019) Monitoring Land Cover Changes In The Tropics Using Satellite Remote Sensing Data. International Journal of Innovative Technology and Exploring Engineering, 8 (12). pp. 3631-3636. ISSN 2278-3075

Text L38091081219 IJITEE LAND COVER CHANGES MONITORING.PDF Download (599kB)

Abstract

Changes in land cover are inevitable phenomena that occur in all parts of the world. Land cover changes can occur due to natural phenomena that include runoff, soil erosion and sedimentation besides man-made phenomena that include deforestation, urbanization and conversion of land covers to suit human needs. Several works on change detection have been carried out elsewhere, however there were lack of effort in analyzing the issues that affect the performance of existing change detection techniques. The study presented in this paper aims to detect changes of land covers by using remote sensing satellite data. The study involves detection of land cover changes using remote sensing techniques. This makes use satellite data taken at different times over a particular area of interest. The data has resolution of 30 m and records surface reflectance at approximately 0.4 to 0.7 micrometers wavelengths. The study area is located in Selangor, Malaysia and occupied with tropical land covers including coastal swamp water, sediment plumes, urban, industry, water, bare land, cleared land, oil palm, rubber and coconut. Initially, region of interests (ROI) were drawn on each of the land covers in order to extract the training pixels. Landsat satellite bands 1, 2, 3, 4, 5 and 7 were then used as the input for the three supervised classification methods namely Support Vector Machine (SVM), Maximum Likelihood (ML) and Neural Network (NN). Different sizes of training pixels were used as the input for the classification methods so that the performance can be better understood. The accuracy of the classifications was then assessed by analyzing the classifications with a set of reference pixels using a confusion matrix. The classification methods were then used to identify the conversion of land cover from year 2000 to 2005 within the study area. The outcomes of the land cover change detection were reported in terms quantitative and qualitative analyses. The study shows that SVM gives a more accurate and realistic land cover change detection compared to ML and NN mainly due to not being much influenced by the size of the training pixels. The findings of the study serve as important input for decision makers in managing natural resources and environment in the tropics systematically and efficiently.

Item Type:	Article
Uncontrolled Keywords:	Supervised classification, Change detection, Classification accuracy, Land cover, Remote sensing
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	07 Dec 2020 15:31
Last Modified:	07 Dec 2020 15:31
URI:	http://eprints.utem.edu.my/id/eprint/24533
Statistic Details:	View Download Statistic

Actions (login required)

View Item