Monitor the Land Use Change and Prediction Using CA-Markov Model in Li Pe Island, Satun Province, Thailand

Waiyasusri, Katawut; Kulpanich, Nayot; Worachairungreung, Morakot; Sae-Ngow, Pornperm

doi:10.1007/978-3-030-33900-5_5

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Many researchers' index settings are more general, and their characteristic signs are not noticeable. ere is limited research on period dynamic forecasting [27][28][29][30][31][32][33]. Although there are few researchers working on Hainan-related themes, they are far from matching the new criteria of the free trade portage in terms of research.…”

Section: Introductionmentioning

confidence: 99%

Rapid Prediction Algorithm for Economic Development Trend of Tourism Using Markov Chain

Qiao

2022

Mobile Information Systems

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The environment for tourist development will be increased further if the people-centered development concept is fully implemented, and people’s desire for a colorful tourism life will be boosted. This includes the new kinetic energy of digitalization and networking, which has contributed more and more to economic development. The people-centered development concept also includes the sudden new crown pneumonia outbreak that has had a major influence on the tourism sector; the tourism industry’s robust expansion will not change as a result. There is a reason to assume that tourism will become a key business that contributes to the long-term economic and social growth of China. With the Markov chain to forecast the development tendency of the tourism economy, this paper constructs characteristic indicators such as the number of duty-free shopping on outlying islands, the per capita spending of outlying island rabbit tax shopping, and the number of ecological civilization villages, using the era of free trade ports as a backdrop. The experimental results shows that the proposed idea of this paper is realistic and can have a significant impact towards designing a national ecological civilization pilot zone, international tourism consumption center, and the creation of tropical rainforest national parks.

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Section: Introductionmentioning

confidence: 99%

Rapid Prediction Algorithm for Economic Development Trend of Tourism Using Markov Chain

Qiao

2022

Mobile Information Systems

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“…From the land cover classification using NDVI and NDBI methods, the result is a land cover model that covers the study area, which is an important result that helps us to know what appears in the spatial database to create a database in GIS, then analyzes the land cover for each period to check for changes in the ground cover [41][42], as in Eq. 7.…”

Section: ) Dynamic Annual Change Of Lucmentioning

confidence: 99%

Monitoring the Land Cover Changes in Mangrove Areas and Urbanization using Normalized Difference Vegetation Index and Normalized Difference Built-up Index in Krabi Estuary Wetland, Krabi Province, Thailand

Waiyasusri

2021

App. Envi. Res.

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Krabi Estuary Wetland (KEW) is an outstanding wetland with an estuary environment. At present, the tourism industry has rapidly grown, resulting in the impact of land cover changes. This research aims to assess the changes that have occurred in the KEW from 1999 to 2020 using NDVI and NDBI for monitoring changes in mangrove areas and urbanization in Krabi Province, Thailand. Landsat satellite images in years 1999, 2009 and 2020 were classified by using a band ratio to create land cover maps. The results show that NDVI between 0.41–1.00 clearly shows the mangrove forest area, while NDBI between 0.01–0.40 shows urban and built-up land, and 0.41–1.00 appears as bare land. The NDVI overall accuracy assessment is 82.88%, 97.46% and 88.25% with Kappa values of 0.64, 0.92, and 0.85 for year 1999, 2009 and 2020, respectively. The NDBI overall accuracy assessment is 92.81%, 77.11% and 64% with Kappa values of 0.93, 0.77, and 0.63 for year 1999, 2009 and 2020, respectively. In addition, areas that are sensitive to land-cover change appear around the Chi rat River, Pak Nam Krabi River, and Yuan River, which are tourist areas close to the Krabi and Ao Nang communities. Therefore, it is necessary to speed up the problem solving and find measures to prevent mangrove forest degradation in these 3 mangrove forest areas so that the mangrove forest areas will not decrease rapidly in the future. This research can be valuable for land-cover management in the KEW by policy and decision makers.

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Monitor the Land Use Change and Prediction Using CA-Markov Model in Li Pe Island, Satun Province, Thailand

Cited by 2 publications

References 14 publications

Rapid Prediction Algorithm for Economic Development Trend of Tourism Using Markov Chain

Rapid Prediction Algorithm for Economic Development Trend of Tourism Using Markov Chain

Monitoring the Land Cover Changes in Mangrove Areas and Urbanization using Normalized Difference Vegetation Index and Normalized Difference Built-up Index in Krabi Estuary Wetland, Krabi Province, Thailand

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