GIS (Geographic Information System) application for distribution of rubber plantation in Surat Thani, Thailand
The preservation and sustainable management of forest and other land cover ecosystems are very important to every country (Kuhlman, Farrington, Kuhlman, & Farrington, 2010). For this research, rubber trees were subject that been focus on. In the meantime, it will help addressing two major issues...
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| Format: | Undergraduate Final Project Report |
| Published: |
2020
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| Online Access: | http://discol.umk.edu.my/id/eprint/4082/ |
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| Summary: | The preservation and sustainable management of forest and other land cover ecosystems are very important to every country (Kuhlman, Farrington, Kuhlman, & Farrington, 2010). For this research, rubber trees were subject that been focus on. In the meantime, it will help addressing two major issues to the country which is climate change (Willmott & Matsuura, 2005) and natural disasters. The rubber trees are dominantly distributed in Surat Thani provinces than other provinces. At present, the rubber sector encounters lack of demand and large supplies rubber stock. Natural rubber being replace with synthetic rubber. The price of natural rubber in market also decreasing. To curb the problem, most of rubber plantation holder start to replace their crop to more profitable crop. The changes occur rapidly and lead to massive bare land in the province which in the same time lead to temperature rising. This study is aimed to analyse the spatial distribution of rubber plantation for 2007, 2014 and 2019 in Surat Thani, Thailand. Geospatial data from remote sensors are used to deal with the time and labour consuming problem due to the large scale of spatial coverage and the need of continuous temporal data. Remote sensing imagery that have been used in this study is Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI). The image from the optical sensor was used to sense the land cover (Paiboonvorachat & Oyana, 2011) and further classified for rubber plantation land cover changes. Maximum Likelihood classifier (MLC) was used for the classification and Google Earth Pro was used for the validation. The accuracy was tested during the post classification using the confusion matrix (Sallaba, 2009). The result may help the government in the agricultural sector for future management concern. |
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