Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia

Abstract

Land surface temperature (LST) plays an important role in local, regional and global climate studies. LST controls the distribution of the budget for radiation heat between the atmosphere and the earth's surface. Therefore, it is important to evaluate abrupt changes in land use/land cover (LULC). Penang Island, Malaysia has been experiencing a rapid and drastic change in urban expansion over the past two decades due to growth in industrial and residential areas. The aim of this study was to investigate and evaluate the impact of LST with respect to land use changes in Penang Island, Malaysia. Three supervised classification techniques known as maximum likelihood, minimum distance-to-mean and parallelepiped were applied to the images to extract thematic information from the acquired scene by using PCI Geomatica 10.1 image processing software. These remote sensing classification techniques help to examine land-use changes in Penang Island using multi-temporal Landsat data for the period of 1999-2007. Training sites were selected within each scene and seven land cover classes were assigned to each classifier. The relative performance of each technique was evaluated. The accuracy of each classification map was assessed using a reference data set consisting of a large number of samples collected per category. Two Landsat satellite images captured in 1999 and 2007 were chosen to classify the LULC types using the maximum likelihood classification method, determined from visible and nearinfrared bands. The study revealed that the maximum likelihood classifier produced superior results and achieved a high degree of accuracy. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC. The results showed that the urban (highly built-up) area increased dramatically, and grassland area increased moderately. Inversely, barren land decreased obviously, and forest area decreased moderately. While urban (minimally built-up) area decreased slightly. These changes in LULC caused at significant difference in LST between urban and rural areas. Strong correlation values were observed between LST and NDVI for all LULC classes. The remote sensing technique used in this study was found to be efficient; it reduced the time for the analysis of the urban expansion, and it was found to be a useful tool to evaluate the impact of urbanisation with LST.

Sixty-Year Changes in Residential Landscapes in Beijing: A Perspective from Both the Horizontal (2D) and Vertical (3D) Dimensions

“…A wide range of remotely sensed data have been used in such studies, including AVHRR data with 8 km resolution, medium resolution imagery the 1 m IKONOS imagery. Most of these studies focus on urban expansion and its impacts on land cover/land use change [15][16][17][18][19][20]. Recently, there has been an increasing interest in quantifying the finescale landscape dynamics within well-developed urban areas [21][22][23].…”

Aerosol Optical Thickness and PM10 Study by Using a Handheld Spectroradiometer Over Penang

“…The state is geographically divided into two different entities -Penang Island (or "Pulau Pinang" in Malay Language) and a portion of mainland called "SeberangPerai" in Malay Language. Penang Island is an island of 293 square kilometres located in the Straits of Malacca and "SeberangPerai" is a narrow hinterland of 753 square kilometres (Tan, et al, 2010). The island and the mainland are linked by the 13.5 km long Penang Bridge and ferry.…”

“…The topography of "SeberangPerai" is mostly flat. Butterworth, the main town in "SeberangPerai", lies along the "Perai" River estuary and faces George Town at a distance of 3 km (2 miles) across the channel to the east (Tan, et al, 2010). The Penang Island climate is tropical, and it is hot and humid throughout the year.…”

“…The mean daily humidity varies between 60.9% and 96.8%. The average annual rainfall is about 267 cm and can be as high as 624 cm (Tan, et al, 2010).…”

Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques

“…Various studies have shown that the appli-cation of LULC change detection plays an important role in solving problems related to different domains such as changes in environmental services [4], urban development [5], and watershed characteristics [6][7].…”