Remote Sensing Data Reveals Eco-Environmental Changes in Urban Areas of Klang Valley, Malaysia: Contribution from Object Based Analysis

• Published in: Journal of the Indian Society of Remote Sensing 2013-12, Vol.41 (4), p.981-991
• Main Authors: Tehrany, Mahyat Shafapour, Pradhan, Biswajeet, Jebur, Mustafa Neamah
• Format: Article
• Language: English
• Subjects: decision support systems; Earth and Environmental Science; Earth Sciences; Elaeis guineensis; heat transfer; land cover; Landsat; remote sensing; Remote Sensing/Photogrammetry; Research Article; soil; spatial data; surface temperature; urban areas; urban planning; urbanization; vegetation
• ISSN: 0255-660X; 0974-3006
• DOI: 10.1007/s12524-013-0289-9

Understanding the growth and changes in urban environments are the most dynamic system on the earth’s surface is critical for urban planning and sustainable management. This study attempts to present a space-borne satellite-based approach to demonstrate the urban change and its relation with land surface temperature (LST) variation in urban areas of Klang valley, Malaysia. For this purpose an object-based nearest neighbour classifier (S-NN) approach was first applied on SPOT 5 data acquired on 2003 and 2010 and subsequently five land cover categories were extracted. The overall accuracies of the classified maps of 2003 and 2010 were 90.5 % and 91 % respectively. The classified maps were then used as inputs to perform the post classification change detection. The results revealed that the post-classification object-based change detection analysis performed reasonably well with an overall accuracy of 87.5 %, with Kappa statistic of 0.81 %. The changes represented that the urban expanded by 10 % over the period, whereas the urban expansion had caused reduction in soil (1.4 %) and vegetation (11.4 %), and growth in oil palm (2 %), and water (0.7 %). Additionally decision tree method was used to derive the surface heat fluxes from thermal infrared Landsat TM and ETM+bands. Subsequently, a comparison was made with classified result from SPOT 5 images. Results showed high correlation between urban growth and LST.