Land use changes detection using supervised classfication and post classfication method / Nur Liyana Yusof

Yusof, Nur Liyana (2018) Land use changes detection using supervised classfication and post classfication method / Nur Liyana Yusof. Degree thesis, Universiti Teknologi Mara Perlis.

[img]
Preview
Text
TD_NUR LIYANA YUSOF AP R 18_5.pdf

Download (278kB) | Preview

Abstract

The purpose of this study was to examine the land use land cover in Klang Selangor by using supervised classification and post classification method. The comparison of time series data between year 2005 to 2010 and 2010 to 2016 have been carried out to identify the changes of land use land cover. The data use in this study were Landsat Satellite Imagery (TM and Oli-Tirs). Land use have been divided into five main categories representing water body, forest, agriculture, bare soil and built area. The classification of land use land cover using method supervised classification and post classification then make comparison using both method. After that, the relationship between land use land cover with land surface temperature was derived from linear correlation coefficient. Calculate land surface temperature from satellite imagery using a formula. Comparison data from Development Rural and Urban (JPBD) with the data derived from satellite imagery.

Item Type: Thesis (Degree)
Creators:
CreatorsEmail
Yusof, Nur LiyanaUNSPECIFIED
Subjects: H Social Sciences > HD Industries. Land use. Labor > Land use
T Technology > TD Environmental technology. Sanitary engineering > Remote sensing
Divisions: Universiti Teknologi MARA, Perlis > Arau Campus > Faculty of Architecture, Planning and Surveying (R)
Item ID: 21688
Uncontrolled Keywords: Land use change ; supervised classification ; post classification ; Landsat Satellite Imagery (TM and Oli-Tirs)
Last Modified: 25 Sep 2018 05:11
Depositing User: Perpustakaan Dato' Jaafar Hassan UiTM Cawangan Perlis
URI: http://ir.uitm.edu.my/id/eprint/21688

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year