Thresholding and Fuzzy Rule-Based Classification Approaches in Handling Mangrove Forest Mixed Pixel Problems Associated with in QuickBird Remote Sensing Image Analysis

Mangrove forest is an important costal ecosystem in the t ropical and sub-tropical coastal reg ions. It is among the most productivity, ecologically, environ mentally and biologically diverse ecosystem in the world. With the improvement of remote sensing technology such as remote sensing images, it provides the alternative for better way of mangrove mapping because covered wider area of ground survey. Image classification is the impo rtant part of remote sensing, image analysis and pattern recognition. It is defined as the extraction o f d ifferentiated classes; land use and land cover categories fro m raw remote s ensing digital satellite data. One p ixel in the satellite image possibly covers more than one object on the ground, within -class variability, or other complex surface cover patterns that cannot be properly described by one class. A pixel in remote sensing images might represent a mixture of class covers, within-class variability, or other co mplex surface cover patterns. However, this pixel cannot be correct ly described by one class. These may be caused by ground characteristics of the classes and the image spatial resolution. Therefore, the aim of this research is to obtain the optimal threshold value for each class of landuse/landcover using a combination of thresholding and fuzzy rule-based classification techniques. The proposed techniques consist of three main steps; selecting train ing site, identify ing threshold value and producing classificat ion map. In order to produce the final mangrove classification map, the accuracy assessment is conducted through ground truth data, spectroradiometer and expert judg ment. The assessment discovered the relationship between the image and condition on the ground, and the spectral signature of surface material in identifying the geographical object.