Please use this identifier to cite or link to this item:
|Title:||MANGROVE FORESTS CHANGES AND RESPONSES TO SEA LEVEL RISE BASED ON REMOTE SENSING AND GIS IN PKWS, CAMBODIA|
การศึกษาการเปลี่ยนแปลงของพื้นที่ป่าชายเลน ที่มีผลกระทบมาจากระดับน้ำทะเล ด้วยวิธีการรับรู้ระยะไกลและเทคนิคทางภูมิสารสนเทศในพื้นที่เขตรักษาพันธุ์สัตว์ป่า PEAM KRASOP, กัมพูชา
Burapha University. Faculty of Geoinformatics
Sea Level Rise
|Abstract:||Cambodia is a country that can be found rich in mangrove forests area. Cambodia’s mangrove forests are found along a coastline of 435 km that consists of Koh Kong province, Kampot, Sihanouk Ville, and Kep province. Mangrove forests in Cambodia are considered essential forests that provide food sources, shelters, and nurseries along the coastal zone. These mangrove ecosystems have decline and change to shrimp farming, salt farming, charcoal production, pollution, illegal logging, and threatened to climate change such Sea level rise (SLR). Sea level rise can be as a parameter to assess the vulnerability of coastal mangroves in Cambodia due to be Sea level rise can lead a significant impact on mangrove ecosystems.
This research reveals the mangrove forests extraction and change from 2015 to 2020. Then, the vulnerable area of mangrove forests due to three different SLR scenarios in Peam Krasop Wildlife Sanctuary (PKWS) in Cambodia. To extract the mangrove forests area in this study, Sentinel-2 multi-temporal data from 2015 to 2020 were used to classify and identify the mangrove forests and other classes using Random Forest classifier Machine Learning in the Dzetsaka plugin in QGIS. For analyzed and produced the changes of mangrove forests map between 2015 to 2020 using MOLUSCE in QGIS. To predict the vulnerable area of mangrove forests by future SLR, a model of Geospatial Model based on SRTM DEM and IPCC’s SLR scenarios will be used to delineate mangrove areas in 2020. Three different SLR scenarios have been adopted in this study such as SLR 40 cm, SLR 60 cm, SLR 1 m. For DEM data was SRTM that download from USGS, and this SRTM was created, manipulated, and processed in ArcMap.
The experimental results are satisfactory, mangrove forest areas were estimated at 7157.90 ha in 2015, 7495.21 ha in 2016, 7337.47 ha in 2017, 6436.26 ha in 2018, 6761.66 ha in 2019, and 7045.64 ha in 2020.
Furthermore, mangrove forests in PKWS in this study were changed from 7150.90 ha to 7495.21 ha (2015-2016), between this period, mangrove forests were significant increased by 337.31 ha. Mangrove forests in PKWS were changed from 7495.21 ha to 7337.47 ha (2016-2017), between this period, mangrove forests were extremely decreased by 157.74 ha. Similarly, mangrove forests have continued to decrease 901.21 ha from 7337.47 ha to 6436.26 ha (2017-2018). However, mangrove forests started to increase 325.40 ha in PKWS in 2019, mangrove forests were changed from 6436.26 ha to 6761.66 ha (2018-2019). Between 2019 and 2020, mangrove forests have increased by approximately 283.98 ha mangrove forests have changed from 6761.66 ha to 7045.64 ha (2019-2020). The total long-term change in mangrove forests areas in PKWS from 2015 to 2020, mangrove forests were decreased 112.26 ha from 7157.90 ha to 7045.64 ha.
Based on the result of the study finds that when sea level rise by 40 and 60 cm, the mangrove forests areas are projected to be inundated or impacted on areas about 40.44 ha at the end of the twenty-first century, and mangrove forests areas are predicted future inundated by 53.14 ha beside increasing 1 m for high Sea level rise scenarios respectively.|
|Description:||Master Degree of Science (M.Sc.)|
|Appears in Collections:||Faculty of Geoinformatics|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.