Detection and Prediction of Peatland Cover Changes Using Support Vector Machine and Markov Chain Model
Abstract: Detection and prediction
of peatland cover changes should be conducted due to high rate deforestation in
Indonesia. In this work we applied Support Vector Machine (SVM) and Markov
Chain Model on multitemporal satellite data to generate the correspondings
detection and prediction. The study area is located in the Rokan Hilir
district, Riau Province. SVM classification technique used to extract information
from satellite data for the years 2000, 2004, 2006, 2009 and 2013. The Markov
Chain Model was used to predict future peatland cover. The SVM classification
result showed that the mean Kappa coefficient of peatland cover classification
is 0.97. Between years 2000 and 2013, the wide of non vegetation areas and
sparse vegetation areas have increased up to 307% and 22%, respectively. While the
wide of dense vegetation areas have decreased up to 61%. We found that a 3
years interval used in the Markov Chain Model leads to more accurate results for
predicting peatland cover changes.
Author: Ulfa Khaira
Journal Code: jptkomputergg160187
