Land Cover Classification

Three SPOT 5 image series with 20-m 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 was automatically generated using a supervised maximum – likelihood classifier. Supervised image classification is a method in which the analyst initially defines small areas, called training sites, on the image that 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 cover classification in each image was gener­ated using combined bands of four, three, and one. 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.