Faculty of Geoinformatics / คณะภูมิสารสนเทศศาสตร์ http://ir.buu.ac.th/dspace/handle/1513/45 2024-09-05T11:24:49Z 2024-09-05T11:24:49Z http://ir.buu.ac.th/dspace/handle/1513/745 2023-04-12T02:58:43Z 0017-01-01T00:00:00Z

Title: SPATIAL ANALYSIS OF CRIME IN THREE PROVINCES, SOUTHERN THAILAND: HOTSPOT, FACTORS, AND ACCESSIBILITY ; การวิเคราะห์เชิงพื้นที่ของอาชญากรรมในพื้นที่สามจังหวัดชายแดนใต้ประเทศไทย:ความหนาแน่น ปัจจัย และการเข้าถึง Abstract: Thailand has faced with problems of criminal and disorder situations in Three southern border provinces in Pattani, Yala, and Narathiwat since 2004 until present. In overall, such situations seem continuously violent since troublemakers still attempt to harm innocent people and fight back to the government officers in every chance. Consequently, the criminal problems in these areas are complicated with more and more violence as reported in various media. These problems cause people’s fear and mental instability and they have effects on the national economy, societies, and security. Using a map to explain the areas with criminal density can support a decision-making process of planners and police officers to plan for criminal prevention in risk areas. This study collected the data of disordering incidents from security agencies and reliable sources during 2017 – 2020 and processed them with the geography information system (GIS) by using spatial statistics for examining the criminal patterns. The purpose was to explain the criminal cases in Three southern border provinces to find out the patterns of criminal behaviors in shooting, bombing, drugs, physical violence, and arson. In addition, Kernel Density Estimation (KDE) was also used to display distribution of criminal patterns, hotspots classified according to offences, criminal periods, and criminal density in each area. After that the correlation coefficient and regression analyses were used to find out relationship of different factors with effects on crime incidents  in Three southern border provinces whereas. The two-step floating catchment area (2SFCA) was used to analyze the accessibility index of police stations and checkpoints in criminal areas.  The results were presented in statistics and maps on particular issues to be explained.  The study results showed that the crime cases took place in a clustered patterned at repeated criminal areas or nearby previous criminal areas. In the analysis of high-risk areas (hot spots), shooting cases took place at the highest rate of other cases, followed by bombing cases, physical violence cases, arson cases, and drug cases. 1) The highest density of shooting cases was found at Mueang District, Yala Province in community and city areas, frequently at night during > 06 pm – 12 pm. 2) The highest density of bombing cases was found at Mueang Yala District, Yala Province at roadsides and police officers’ traffic paths, frequently during daytime at 06 am – 12 pm. 3) The highest density of physical violence was found at Nong Chik District, Pattani Province in public places and blind spots, frequently during daytime at >12 am – 06 pm. 4) The highest density of arson cases was found at Mueang Pattani District, Pattani Province in the areas with buildings and workplaces, frequently at night during > 12 pm - 06 am. And 5) the highest density of drug cases was found at Takbai District, Narathiwat Province at the border areas of Thailand, frequently at night during > 06 pm – 12 pm. In overall, the least criminal and disorder incidents was found in Yala Province in comparison with Pattani and Narathiwat Provinces. Regarding the accessibility index of police stations and checkpoints on criminal incidents, the police station which should highly servient to cope with crimes in the area was Mueang Pattani Police Station in Pattani Province, followed by Ra-ngae Police Station in Narathiwat, Ma Yor Police Station in Pattani Province, and Reuso Police Station in Narathiwat respectively. The results of this study can be used for guidelines in criminal prevention or reduction, and they are useful for police officers in planning for criminal prevention in Three southern border provinces.     ; - Description: Master Degree of Science (M.Sc.); วิทยาศาสตรมหาบัณฑิต (วท.ม.)

0017-01-01T00:00:00Z http://ir.buu.ac.th/dspace/handle/1513/624 2023-01-10T02:55:26Z 0011-01-01T00:00:00Z

Title: Change Detection for Surface Mining Boundary Based on Multi-source Remote Sensing Images; การตรวจสอบการเปลี่ยนแปลงของขอบเขตการทำเหมืองแร่แบบเปิด โดยการประยุกต์ใช้แหล่งข้อมูลจากการสำรวจระยะไกล  Abstract: Mining is an important industry in Thailand. It can levy gross mineral royalties an average of 3 billion Thai baht per year, and The Minerals Act, B.E. 2560, regulates the industry. The Department of Primary Industries and Mines (DPIM), part of the Ministry of Industry, is in charge of monitoring and promoting the mining industry, including mineral trading, as well as establishing safety and pollution-control regulations. In the past, mining outside the permissible limits frequently occurred. It negatively affects royalty storage and the environment. Because of mining supervision, there are also limitations to tools, methods, personnel, expenses, as well as regulatory frequency. This study applied data from freely available satellite data and open-source software. To assess the suitability of satellite technology applications to detect changes in horizontal and vertical mining in small mining areas to suit mining areas in Thailand. There are two study areas, selected from the average size of all mines currently mining. The satellite data used in this study include Sentinel-1, Sentinel-2, and Landsat 8. The validating data is from the allowed agent to ensure the reliability and accuracy comprising topographical mining data measured by unmanned aircraft (UAV) from DPIM which have high resolution, and the Digital Elevation Model (DEM) from the Royal Thai Survey Department (RTSD). The research methodology used in this study is to extract the boundary of horizontal mining by applying Sentinel-2 data and Landsat 8 by Mean-Shift algorithm and classifying mining areas with Random Forest (RF) algorithms obtains classified into two classes: Mining and Non-mining. The performance of the classification result was assessed based on the confusion matrix formed using the 32 observations for study area 1 and 34 observations for study area 2 from the test samples. The overall accuracy was calculated using the confusion matrix. The vertical boundary mining analysis has applied Sentinel-1 data to extract DEM using InSAR techniques. Then used the DEM compared with RTSD DEM, statistically analyzed by using a coefficient of determination (R²) and root mean squared error (RMSE). Analyzing changes in vertical mining using DEM data obtained from the InSAR technique and analyzing the volume changes of two periods. The result of horizontal mining boundary extraction from Sentinel-2 and Landsat 8, In the first study area round 1 has an overall accuracy of 95.66% and 86.57%. Round 2 of the first study area is 97.47% and 96.50%. In the second study area, round 1, the overall accuracy is 100% and 99.35%. Round 2 of the second study area is 99.26% and 95.90% respectively. Based on validation results, the satellite data from Sentinel-2 is more accurate than the horizontal boundary of mining compared to Landsat 8 data. When using the horizontal boundary of mining from sentinel-2 data to analyze changes in horizontal mining areas, the data of the mining area was used to analyze the changes in the horizontal mining area. In study area 1, mining expansion was 11.64% of the original mining area, according to the reference. And in study area 2, mining expansion was 11.79% of the original mining area, according to the reference. The result of DEM extraction has obtained the result as 14 m resolution of DEM and correlates when compares to DEM from UAV. The result found that the R² and RMSE values are 0.6038 and 34.279 for the study area 1 of the first round, 0.5621 and 35.731 for study area 1 of the second round, 0.2947 and 55.704 for study area 2 of the first round, and 0.2666 and 57.603 for the study area 1 of the second round.  However, the DEM extracted from the Sentinel-1 is highly accurate, but it is not enough to need of vertical mining change analysis of a small mining area. Finally, the application and method of this research to use in change detection of horizontal and vertical surface mining boundaries. Sentinel-2 has a medium level of suitability for change detection of horizontal mining boundary since the change characteristic is similar to the reference data. Landsat-8 is not a suitable choice for horizontal change detection in small area mining and Sentinel-1 is not suitable for detecting the change in vertical mining in the small mining areas.; - Description: Master Degree of Science (M.Sc.); วิทยาศาสตรมหาบัณฑิต (วท.ม.)

0011-01-01T00:00:00Z http://ir.buu.ac.th/dspace/handle/1513/623 2023-01-10T02:55:03Z 0011-01-01T00:00:00Z

Title: STUDY OF LAND SUBSIDENCE BY INSAR TIME SERIES OF ALOS-2, SENTINEL-1 AND GNSS CORS STATIONS IN CHAOPRAYA BASIN, SAMUTPRAKAN, THAILAND; การศึกษาการทรุดตัวของจังหวัดสมุทรปราการ ด้วยข้อมูลอนุกรมเวลาโดยใช้ข้อมูลดาวเทียม ALOS-2 Sentinel-1 และสถานี CORS  Abstract: Samutprakan province is one of the economically most important provinces in Thailand. The province is located in the northern gulf of Thailand near Bangkok. This province is facing flooding from sea level rise. This problem is getting closer to the capital city: Bangkok. Samutprakan and surrounding provinces are facing sea level rise, flooding, and land subsidence. The land subsidence is an important factor of flooding in Samutprakan, shown by case studies showing that the land subsidence in Samutprakan is caused by many factors such as the use of ground water, land reclaiming, and movements of the Earth surface. The Department of Groundwater Resources started to do research between 1978 - 1981 and they found land subsidence of more than 10 cm per year in Bangkok and Samutprakan. After that, the study of land subsidence has been widespread to many Universities in Thailand such as King Mongkut University of Technology vicinity (Bangmod), Chulalongkorn University, and Kasetsart University to work on monitoring land subsidence in the central part of Thailand. This study will identify the movement ratio of land subsidence rate in the last six years by using Interferometric Synthetic Aperture Radar; InSAR time series technique from ALOS-2 satellite, Sentinel-1 and Precise Point Positioning (PPP) from GNSS CORS stations to identify the rate of land subsidence and compare the land subsidence with three difference methods above in last 6 years of Samutprakan province Thailand. The InSAR time series technique has been used for many decades to measure earth surface deformation with high resolution and high accuracy. The InSAR time series will correct data by using radio detection and ranging (RADAR) to send electromagnetic wave to object to the earth surface and reflect to satellite’s antenna itself. The satellite images will be collected by sun-synchronous orbit satellite in many modes of transmission such as HH, VV, HV, VH and from ascending and descending orbits. Many scenes of satellite images in s time series will be processed by interferometric phase measurements which wrapped and unwrapped phases and compared with many scenes of images to find the subsidence rate of objects on earth surface with high accuracy number in millimeter. The Precise Point Positioning (PPP) and GNSS stations Continuously Operating Reference (CORS) or a permanent GNSS satellite receiver station. These stations receive signals 24 hours a day, 365 days a year to use the information that obtained to refer to the coordinates. It is a reference station for RTK (Real-Time Kinematic) and Network RTK (VRS) surveys. All data will be combined before PPP processing to remove GNSS errors and get the highest position accuracy from one receiver. Accuracy data from many stations and many times will be presented the difference of GNSS CORS stations in Latitude, Longitude, and Height to present the difference numbers of time series. Both techniques are used to identify data from difference sources to find the subsidence rates of the study area in Samutprakan, in order to allocate the suitable area for industrial constructions and farming areas. The result from this thesis will benefit the residents in Samutprakan and the planning of industrial areas in the next 6 to 12 years and indicate the most accuracy methods from these three methods.; - Description: Master Degree of Science (M.Sc.); วิทยาศาสตรมหาบัณฑิต (วท.ม.)

0011-01-01T00:00:00Z http://ir.buu.ac.th/dspace/handle/1513/622 2023-01-10T02:54:42Z 0011-01-01T00:00:00Z

Title: FLOOD RISK MAPPING USING REMOTE SENSING AND HYDRAULIC MODELLING IN KHLONG BANG SAPHAN YAI RIVER BASIN, THAILAND; การทำแผนที่ความเสี่ยงน้ำท่วมโดยใช้การสำรวจระยะไกล (Remote Sensing) และแบบจำลองทางชลศาสตร์ (Hydraulic Modelling) ในพื้นที่ลุ่มน้ำคลองบางสะพานใหญ่ ประเทศไทย Abstract: Over the past decades, floods have been one of the most common and damaging natural disasters in Thailand and in my study area. The Khlong Bang Saphan Yai River Basin is in the southern region of Thailand, which is influenced by the monsoons and rainstorms, resulting in exposure to flooding yearly. As a result, it impacts human lives, economic loss, and severe damage to communities and agriculture. Significantly, floods pose a threat to the exposures and vulnerabilities in the Khlong Bang Saphan Yai River Basin. Therefore, this research focuses on flood risk assessment in the river basin for flood management and risk mitigation. In this research, the Sentinel-1 SAR remote sensing techniques were combined with the HEC-RAS 1D modelling for flood risk mapping of the Khlong Bang Saphan Yai river basin in Thailand for flood events. The flood inundation maps from the HEC-RAS model were validated by using the flood extent maps from the Sentinel-1 SAR remotely sensed technique based on the histogram thresholding analysis. The HEC-RAS models were used for flood inundation simulation in this study area. It also used the topographic data (ALOS 30m DSM) for defining the geometry data (network river, riverbank, flow path, cross-section, and hydraulic structures) input into the HEC-RAS model. The hydraulic model was used to simulate the unsteady flow of the expected flood hydrographs. The observed water level data were used to calibrate and validate the performance of the HEC-RAS model. It also requires the geometry data, Manning roughness coefficient “n”, and daily discharge data at the upstream (GT.7) station, which is the input data. Such flood simulation results (2018 and 2019) were calibrated and validated using four performance indicators (NSE, RMSE, R2, and PBIAS) and the water level and discharge data from the observed station at the downstream station (GT.20) for performing the HEC-RAS model. Moreover, the flood inundation maps were required to be validated using the flood extent area from the Sentinel-1 SAR imagery data for comparison and analysis. The Sentinel-1 SAR data were used to extract flood extent by using the histogram thresholding analysis in the SNAP software, and the flood extent results were used to calibrate and validate flood inundation maps from the HEC-RAS models. The flood extent maps from the Sentinel-1 SAR technique were compared and referenced by using the permanent water data from the Sentinel-1 data technique based after flooding. When the validation outputs for the 2019 flood event were compared to the flood extent maps derived from SAR image data, the overlapping area for the 2019 flood event was 23.18 percent. And it is a closed verified check for the flood area, with the total flood inundation area of the 2019 flood event being 0.27 sq.km. The flood inundation from the HEC-RAS model is like the flood extent from the Sentinel-1 SAR images. The research results serve as a valuable reference to support policy and decision-making for future planning and development in the current study area.; - Description: Master Degree of Science (M.Sc.); วิทยาศาสตรมหาบัณฑิต (วท.ม.)

0011-01-01T00:00:00Z