Evaluation of the Reliability of Classifiers for the Mapping of Mangrove Forest using Landsat TM Images
In Indonesia land use management of many areas has undergone significant changes due to a variety of human activities. The historical land use management at EMRP (Ex Mega Rice Project) area, Central Kalimantan, has been expanded for crops production in 1996/1997 and usage is divided into many classes. Mangrove as a potential carbon sink, having the largest amount of biomass found with in the IndoPacific area, was focused on as potential rehabilitated land to promote biomass restoration. As a result it has become a main topic of research. Several Landsat images taken in 1997, 2006 and 2010 were used and classified to monitor mangrove distribution using Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) methods, then comparisons were made. The land cover type includes, forest, mangrove forest, water, cloud, and other land use (bareland, settlement, bush shrub swamp, dry land agriculture, savanna, paddy field, and bush shrub). Result shows that SVM based on statistical and vector approach appears to be a superior method, with a high accuracy of 0.95±0.003. The smallest standard deviation of classification accuracy also showed that SVM is relatively more stable in mangrove forest mapping.
classification rehabilitation mangrove land use
Chinsu Lin Desi Trianingsih
Dept. of Forestry and Natural Resources National Chiayi University Chiayi, Taiwan
国际会议
三峡
英文
3163-3166
2012-05-18（万方平台首次上网日期，不代表论文的发表时间）