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.

No abstract

Oil palms are currently in high demand, which tend to increase even higher as a source of alternative energy for humans, especially in Southeast Asian countries. This leads to the study that focuses on the height measurement, using an unmanned aerial vehicle (UAV), and age analysis of oil palm trees planted within the experimental plots in order to predict their yield. The methodology described in the paper provides using Canopy Height Model (CHM) for height measurement and prediction of the oil palm yield by multiple linear regression. The results indicated that the errors caused by overlapping age ranges were found in 3 out of 12 experimental plots. Furthermore, the primary factors influencing the oil palm yield prediction included the age (9 years and above) and canopy density (over 41% of the area), while the secondary factors supporting more accuracy included the total plot area, canopy area, and canopy height, with the coefficient of determination or R-squared at 0.98. In this study, we learned that the aforementioned factors could be concluded from the data collected by an UAV, which reduced the time for measuring the height of each tree manually, resulting in more accurate yield prediction.

The objective of this research on the relationship between urbanization and road networks in the lower Northeastern region of Thailand was to compare the urban area in 2006, 2013 and 2016 using nighttime light satellite images from the National Oceanic and Atmospheric Administration (NOAA), acquired by the Defense Meteorological Satellite Program (DMSP/OLS) and the Suomi National Polar-orbiting Partnership (Suomi NPP). After that the relationship between urbanization and road network was identified using nighttime light satellite images from these satellites. The nighttime light data was used to determine the urbanization levels, which were then compared with Landsat 8 Satellite images taken in 2016 in order to find the Pearson correlation coefficient. The results indicated that areas with high urbanization identified from the nighttime light satellite images taken by the Suomi NPP Satellite had a day/night band reflectance of 172-255 indicated and were located primarily along the roads. The analysis of these data suggested that urbanization has a significantly positive relationship with the road network at 0.01 level, with R2 values of 0.800 for urbanization and 0.985 for the road network.

The objective of this research is to study urban expansion surrounding archaeological attractions by Normalized Difference Built-up Index (NDBI) technique at ancient civilization site of Haripunjaya Kingdom in Mueang Lamphun District, Lamphun Province, Thailand. From the survey area on October 18-20, 2022, the data was collected on important ancient sites that still appear traces around the city of Lamphun. The study found that there are a total of 13 archaeological sites, each of which is classified into 3 categories: 8 Ancient Religious sites, 4 Acient City Wall sites, and 1 Historical site. Then, surveys of urban and built-up land cover found that within the past 20 years, light urban and built-up land, urban areas and buildings with sparse density increased by 534.45%, or about 5 times, appearing around the old city in Nai Mueang sub-district and the area where the main road passes in a corridor pattern. In addition, the medium urban and built-up land area has also grown more than three times. It can be seen that urban expansion direction in the northern and central of the study area is most located in the 5 sub-district areas: Makhuea Chae, Ban Klang, Wiang Yong, Pa Sak, and Nai Mueang. The NDBI analysis revealed that the archaeological attractions that were most affected by urbanization were the Victory Shrine Pagoda. At present, it has become a historical site in the middle of the community area. It is located in the middle of the shopping mall parking lot, and there are buildings surrounding it, causing the archaeological site to be invaded and damaged greatly. The results of this study can be used to effectively manage cultural tourism planning, especially in the ancient civilization sites in Mueang Lamphun District, to be sustainable in the future.