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Deforestation is becoming a great danger to the existence of our mother world, Earth, in recent years. Clearance and removal of tress without sufficient replacement resulted in a wide range change of forestlands all over the world. Because of that forest qualities are being degraded continuously, plant and animal lives are being affected negatively. Deforestation also imposed its negative impact on soil, climate and environment. Population expansion, industrialization and scarcity of land are the major causes for the deforestation. However economical and natural reasons like, dependences for heat and energy, wildfire and drought also contribute for the aggravation of this problem. Deforestation affects human life and environment in a negative way. Therefore it is a global desire to come up with a structured way of checking, monitoring and fighting this great danger worldwide before it is too late to take things back to order. This project presents a fuzzy c-mean clustering segmentation based deforestation analysis using remotely sensed data. Remote sensing has given a very unique and reliable source of data for forestation and deforestation monitoring and which can be extracted easily, cheaply and timely. This study gives and analysis of OTSU and Fuzzy c-mean segmentation based effective deforestation analysis methods which deploys satellite images to study and analyze deforestation rates.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Microelectronics - Sensor Development
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 30 Sep 2013 16:20
Last Modified: 25 Jan 2017 09:42
URI: http://utpedia.utp.edu.my/id/eprint/7135

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