DEFORESTATION ANALYSIS USING REMOTE SENSING

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.