Land Use Trends Analysis Using SPOT-5 Images and Its Effect on the Landscape of Cameron Highland, Malaysia
Mohd Hasmadi Ismail1*, Che Ku Akmar Che Ku Othman1, Ismail Adnan Abd Malek1 and Saiful Arif Abdullah2
1Forest Surveying and Engineering Laboratory, Faculty of Forestry
Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
*email:
2Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
Abstract
A large part of the mountain steep land in Peninsular Malaysia is covered by forests. Cameron Highland is a mountainous region with a climate favourable to the cultivation of tea, sub-tropical vegetable and flowers. However rapid economic growth and land use practices has altered the environment landscape of the area. This study was carried out to examine the rate of loss and pattern of fragmentation of the tropical mountain forests in Cameron Highlands. Temporal remotely sensed data (SPOT-5 images) from 2000, 2005 and 2010 were used in a GIS to calculate landscape indices. Results showed increases in the class area (15,384 ha to 15,691 ha), number of patches (499 to 545) and patch density (1.8 to 2.0 patches/100 ha). The largest patch index increase (34% to 40%) was associated with the decrease in the area of mean patch (30 ha to 28 ha). The observed landscape trends indicate slight increase of forest loss and fragmentation, particularly during the years 2005-2010 periods. Approximately 2 % of the forest cover in Cameron Highland had been lost in 10 years, and a proportion of the remaining forests had been degraded as a result of agricultural practices. Combining landscape ecology and remote sensing has the potential to provide a significant way in assessing the dynamic of highland landscapes. It is suggested that conservation efforts should be focused on the management of the natural system and the management of the external influences particularly restoration and sustainable forest exploitation in the highland.
Keywords – Cameron Highland, land cover change, landscape pattern, remote sensing.
1. INTRODUCTION
Global environmental change is a result of land cover change (Skole et al., 1997). The ability to monitor land cover change at a variety of scales provides essential information required to assist in sustainable land management. In recent years, land management has moved towards a landscape approach which reflects mix of social, environmental and economic values. In landscape ecology, landscape scale is divided into ecological processes and human use through developed infrastructure, ownership and management resources. Our landscape is continuously changing due to both natural and human disturbances. Landscape changes often occur gradually over time as a series of small, localized events. The structure and function of a landscape can be perceived differently at different scales and it is important for the observer to decide upon appropriate scales for a study (Turner, 1989). The relationship between human behavior and forest change poses a major research challenge for development projects, policy makers and environmental organizations that aim to improve forest management (Jane Southworth et al., 2002).
Landscape mapping is often the first step in many remote sensing projects (e.g., Watson and Wilcock, 2001; Zha and Ni, 2003). Many landscape metrics used in remote sensing change detection are based on the ecology and these metrics have been developed for quantifying landscape structure. Landscape metrics is a number or indices that describe the landscape configuration and composition to formulate and analyse either individual patches or the whole landscape. Landscape metrics are very important to detect the pattern of change that is not readily visible to the human eye or easily detectable by human analyst. The metrics can be used to assess ecosystem health or as variables for models that support environmental assessment and planning efforts (Herzog et al., 2001, Patil et al., 2001). These metrics fall into two general categories: those that quantify the composition of the map without reference to spatial attributes, and those that quantify the spatial configuration of the map, requiring spatial information for their calculation (McGarigal and Marks, 1995; Gustafson, 1998). Using satellite imagery such as SPOT or Landsat, aerial photography, and geographic information systems, landscape ecologists are able to examine how the landscape has changed over time and how it is likely to change in the future. Once landscape changes are identified or predicted, the causes and the ecological and societal consequences of such changes can be examined.
Roy and Joshi (2002) clearly state that changing the landscape pattern through fragmentation can disrupt ecological processes that depend on movement within the landscape. Tropical mountain forests are among the most fragile and highly threatened of all tropical forest ecosystems (Bruijnzeel, 2001). Forest landscape models have benefited greatly from technological advances, including increased computing capacity, the development of GIS, remote sensing, and software engineering. The forest ecological processes and their interactions in forest landscape models can be represented by well-designed computer software (He et al., 2000).
Previous studies reported that Cameron Highlands face various environmental problems caused by human activities like agriculture, urbanization, infrastructure development and deforestation which contribute to degradation of the highland landscape and severe upland soil erosions (Aminuddin et al., 2005; Che Ku Akmar and Mohd Hasmadi, 2010). To date there are limited studies on landscape pattern or changes in mountain area in Malaysia. In this paper, the rate of forest loss and pattern of landscape fragmentation in tropical mountain forest of Cameron Highland was examined by comparing temporal SPOT-5 images in year 2000, 2005 and 2010. The landscape structure changes were assessed based on their spatial configuration over time using selected landscape metric or indices. The information obtained may be directly or indirectly useful to the management and development strategies for environmental sustainability of the highlands.
2. METHODOLOGY
2.1 Study area
The study area covers the western region of the Cameron Highlands district, State of Pahang, Peninsular Malaysia (Figure 1). The area is located between 40 35’ 55.40” N latitude and 1010 29’ 07.05” E longitude. The study area covers an area of about 27009.8 ha from the total area of the Cameron Highland district (71,225 ha). The elevation in the study area ranges between 1070 m and 1830 m above mean sea level .The highland has a steep slope where 66% is more than 200. The mean temperatures about 240C in th daytime and 14 0C at night. The average annual rainfall is 2660 mm with two peaks in May and October. Cameron Highlands is drained by three main rivers namely Sg. Telom, Sg. Bertam and Sg. Lemoi. Two of the main economic activities in Cameron Highlands are tourism and agriculture.
Figure 1: Location and satellite imagery (inset) of Cameron Highlands,
Peninsular Malaysia.
2.2 Land cover classification
Three SPOT-5 imageries with 20 meter raster grid resolution were acquired for the years 2000, 2005 and 2010 respectively. Each image was geometrically, atmospherically and topographically corrected. The images were analyzed using ERDAS Imagine 9.1, ArcGIS 9.3 software. The land cover in Cameron Highlands were first defined into five classes; (1) water body,(2) tea plantation, (3) secondary forest/shrubs, (4) mixed agriculture/residential/road, and (5) primary forest. The classification of was automatically generated using supervised-maximum likelihood classifier. Supervise image classification is a method in which the analyst initially defines small areas, called training sites, on the image which are representative of each desired land cover category (Kucukmehmetoglu and Geymen, 2008). The classifiers then recognize the spectral values or signatures associated with these training sites. After the signatures for each land use/land cover category have been defined, the software then uses these signatures to classify the remaining pixels. Land use/land covers classification in each images were generated using combined bands of 4, 3 and 1. Using AOI (Area of Interest) the spectral signature and spectral separability among classes were selected. The land cover classes then were verified following ground verification or truthing. The classified images were finally filtered by using 3 X 3 of median statistical filtering approach to reduce pixel overlaying of minor or isolated classes.
2.3 Accuracy assessment
Accuracy assessment is an essential component of the investigation to quantify whether the data quality of one classification method is superior over the others (Sader et al, 2005). Accuracy assessment is the process in which the image is partitioned into a set number of groups (classes) based on the values of the pixels in one or more image channels. The accuracy was set at more than 80% as the threshold for accurate values. A total of 255 reference pixels class were selected in a stratified random sampling approach for each class to assess the accuracy. Accuracy assessment involves identifying a set of sample locations (ground verification points) that would be visited in the field. Then the land cover identified in the field was compared to the one mapped in the image during supervised classification for the same location by means of error or confusion matrices (Jensen, 1986; Stehman, 1996). Based on the confusion matrices, different accuracy measures were calculated: producer’s accuracy, user’s accuracy, and overall accuracy. In order to summarise the classification results, overall accuracies with 95% confidence intervals were also generated. KAPPA analysis yields (Khat statistic) were also calculated to measure agreement or accuracy as suggested by Congalton (1991).
2.4 Landscape pattern analysis
There are more than a hundred of indices for quantifying landscape pattern. A group of selected metrics (or indices) can be useful to interpret the landscape changes and considered relative to the type of patches (Apan and Peterson, 1998). Landscape pattern metrics were run on images for year studied. This analysis is designed to generate spatial-temporal indicators of landscape pattern as part of the scale-pattern-process paradigm (Walsh et al., 1998). Six landscape metrics were chosen in this study. The six landscape metrics are Class Area (CA), Percent of landscape (PLAND), Number of patches (NP), Patch Density (PD), Mean patch area (AREA_MN) and Largest Patch Index (LPI). The selected landscape metrics is presented in Table1. The spatial metrics were generated by FRAGSTATS Version 3.3, a software package that calculates a number of spatial structures at three levels; the entire landscape, class and patch levels (McGarigal and Marks, 1995; McGarigal et al., 2002).
Table 1: The selected landscape metrics used in this study
No. / Landscape metrics / Abbreviation / Unit / Description1. / Class Area / CA / Hectares
(ha) / The sum of areas of all patches of the corresponding patch type.
2. / Percent of landscape / PLAND / Percentage
(%) / It is equals the percentage of the landscape comprised of the corresponding patch type.
%LAND = (CA/TLA)* 100
-TLA (Total Landscape Area)
Pi = proportion of the landscape occupied
by patch type (class) i.
aij = area (m2) of patch ij.
A = total landscape area (m2).
3. / Number of patches / NP / No / The number of patches for each individual class. The higher NP indicates greater fragmentation.
NP = N
N = total number of patches in the landscape.
4. / Patch Density / PD / Number per 100 hectares / Equals the number of patches in the landscape, divided by total landscape area (m2), and multiplied by 10,000 and 100 (to convert to 100 hectares).
N = total number of patches in the landscape.
A = total landscape area (m2).
5. / Mean patch area / AREA_MN / No / It equals to means of patch areas (ha)
6. / Largest Patch Index / LPI / Percentage
(%) / Equals the area (m2) of the largest patch of the corresponding patch type divided by total landscape area (m2), multiplied by 100 (to convert to a percentage).
aij = area (m2) of patch ij.
A = total landscape area (m2).
3. RESULTS AND DISCUSSION
3.1 Land use land cover change
The trends of land cover change between years 2000, 2005 and 2010 in Cameron Highlands were varied for each class. Changes in land cover (Figure 2) were derived from area estimates using land cover maps (Figure 3).
Figure 2: Estimated area of land cover types in years 2000, 2005, and 2010 in the
Cameron Highlands
Figure 3: Spatial variation of land use/land cover Cameron Highlands in years 2000, 2005, and 2010.
Comparing the three classification maps in general, the landscape change is not significant in the study area. Primary forest had increased slightly from year 2000 to year 2005, but decreased after that toward year 2010. This demonstrated the recovery process of the secondary forest/shrub in Cameron Highland and also in some part of the abandoned mixed agriculture area. The expansion of tea plantation is expected as shown in this study. Cameron Highland is the most famous tea producer in Malaysia where more than 2000 ha of the highland is covered by tea plantation. According to Jamilah et al., (2006), the tea plantation landscape is the most preferred scene in Cameron Highlands. The scenic view of the tea plantation has been known ever since tea was introduced to Cameron Highlands in 1929. So, this land use has maintained as the main scenic icon of Cameron Highlands and had increased in aerial extent gradually. It is noted that the other mixed agriculture/residential/road are showing a decreased trend in the periods of study. This due to the transformation of this class into other land covers type such as secondary forest and tea plantation. Meanwhile, the water body showed a minor increase (less than 100 ha) from years 2000 to 2010.
3.1 Accuracy assessment
The overall accuracies of the supervised classification of the satellite images accounted for 94% ( for year 2000), 91% ( for year 2005) and 88% ( for year 2010), respectively (Table 2). Meanwhile the Kappa statistics were 0.90, 0.80, and 0.79 respectively for the years 2000, 2005 and 2010 classifications.