During 29–31 September 2019, tropical storm Podul moved into the Kaeng Lawa sub-watershed (KLs), the upstream area of the Chi watershed, causing the worst flooding in 40 years. This study was carried out to analyze the watershed characteristic (WC) variables and prioritize the risks of land-use patterns in KLs, Khon Kaen Province, using a watershed delineation approach. As a result of this study, of the 11 sub-watersheds in the Kaeng Lawa watershed, only KL03 and KL04 were deemed medium priority within their drainage and storage capacity systems. KL01, in the upstream sub-watershed, displayed very low priority. The pattern of land-use that appeared most in KL01 sub-watershed was deforestation, where the upper forest area appeared to show a 63% decrease from 2002 to 2017. The decreased forest area was replaced with agricultural area, for crops such as sugarcane and para-rubber, and fruit farms. Moreover, increases in urban area expansion were found in the downstream area in the north of KLs. The findings of this study reveal that severe flooding in this area was caused not only by tropical storm Podul, but also by the low prioritization of watershed characteristics and patterns of land-use that resulted in decreasing forested area in this watershed area. Consequently, these factors have influenced watershed storage and caused an accumulation of water volume, which regularly results in floods. Thus, flood mitigation should be implemented urgently, in the very low priority areas of the study area first.

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Spatial evolution can be traced by land-use change (LUC), which is a frontier issue in the field of geography. Using the limited areas of Koh Chang in Thailand as the research case, this study analyzed the simulation of its spatial evolution from a multi-scenario perspective on the basis of the 1900–2020 thematic mapper/operational land imager (TM/OLI) remote sensing data obtained through the transfer matrix model, and modified LUC and the dynamic land-use change model (Dyna-CLUE). Over the past 30 years, the expansion of recreation areas and urban and built-up land has been very high (2944.44% and 486.99%, respectively) along the western coast of Koh Chang, which replaced the original mangrove forests, orchards, and communities. Logistic regression analysis of important variables affecting LUC revealed that population density variables and coastal plain topography significantly affected LUC, which showed strong β coefficients prominently in the context of a coastal tourist city. The results of the LUC and logistic regression analyses were used to predict future LUCs in the Dyna-CLUE model to simulate 2050 land-use in three scenarios: (1) natural evolution scenario, where a large patch expansion of agricultural land extends along the edge of the entire forest boundary around the island, particularly the southwestern areas of the island that should be monitored; (2) reserved area protection scenario, where the boundary of the conservation area is incorporated into the model, enabling forest preservation in conjunction with tourism development; and (3) recreation area growth scenario, where the southern area is the most susceptible to change at the new road crossing between Khlong Kloi village to Salak Phet village, and where land-use of the recreation area type is expanding. The model-projected LUC maps provide insights into possible changes under multiple pathways, which could help local communities, government agencies, and stakeholders jointly allocate resource planning in a systematic way, so that the development of various infrastructures to realize the potential impact on the environment is a sustainable coastal tourist city development.

This research investigates the application of logistic regression analysis for flood prone risk mapping in the Lam Se Bok watershed area. The study found that floods have occurred as many as 15 times since 2005. In 2019, flooding covered 200.01 km 2 of the watershed (5.51% of the total watershed). Among the areas that flood every year, 15 floods occurred in the lower part of the LSBW basin in Na Udom village, Khok Sawang and Fa Huan village, Rai Khi sub-district, which are in the south of Lue Amnat District, Amnat Charoen Province, as well as in parts of Dum Yai sub-district, Muang Sam Sip district, Ubon Ratchathani. Logistic regression analysis was used to determine the influence of certain variables on this flooding. The variables showing positive β values were mean annual precipitation and distance to a road. The variables showing negative β values included elevation, terrain, slope, soil drainage, distance to stream, land-use, and distance to village, respectively. All of these variables can be analyzed for their Flood Prone Risk area in GIS. The study found that floodprone areas at the very high-level flood prone risk areas, with a total area of 638.59 km 2 (17.59%), high level flood prone risk areas cover an area of 1,848.10 km2 (50.92%). Medium flood prone risk areas cover 794.95 km2 (21.90%). Low flood prone risk areas cover 310.86 km2 (8.56%), the least vulnerable to flooding encompassed 46.35 km2 (1.27%)., and occurred in areas with low elevation and areas with high annual average rainfall when the variable was located in the middle and downstream parts of the LSBW river basin.

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.