Comparison between object based classfication and pixel based classfication technique to detect deforestation in Ulu Muda forest reserve, Kedah / Shahrul Nizam Jamaludin

• Main Author: Jamaludin, Shahrul Nizam
• Format: Thesis
• Language: English
• Published: 2018
• Subjects: SD Forestry; Remote sensing
• Online Access: http://ir.uitm.edu.my/id/eprint/21774/1/TD_SHAHRUL%20NIZAM%20JAMALUDIN%20AP%20R%2018_5.pdf; http://ir.uitm.edu.my/id/eprint/21774/

Remote sensing is moving toward mapping the Earth surface using the highly technology implement. The researcher has invented two types of classification that can be integrated with the modern technology. Those categories of classifications are pixel based classification and object based classification. Both methods purpose to analyse forest cover and changes especially deforestation activity but, due to the early stage of these methods, their abilities to classify land cover and monitor forest dynamics have not fully evaluated and investigate. Here, the strength for both methods was studied, to know which one is the best in detecting deforestation at Ulu Muda Forest Reserve, Kedah. The forest cover at Ulu Muda will be classified, where pixel based classification was done using the Erdas software while object based classification completed using the eCognition software. Satellite imagery from SPOT 5 and 6 with size pixel of 12 metre and 7 metre were used in change detection analysis. The accuracy assessment has been done to identify the overall accuracy of for both classifications including the user and producer accuracy. The higher value of that accuracy approaching to 100, the more accurate the classification can be said. The possible best method of classification in detecting deforestation activity will be determined and explained more its concept in this study.