Estimation of the Effect of Soil Texture on Nitrate-Nitrogen Content in Groundwater Using Optical Remote Sensing

The use of chemical fertilizers in Thailand increased exponentially by more than 100-fold from 1961 to 2004. Intensification of agricultural production causes several potential risks to water supplies, especially nitrate-nitrogen (NO3−-N) pollution. Nitrate is considered a potential pollutant because its excess application can move into streams by runoff and into groundwater by leaching. The nitrate concentration in groundwater increases more than 3-fold times after fertilization and it contaminates groundwater as a result of the application of excess fertilizers for a long time. Soil texture refers to the relative proportion of particles of various sizes in a given soil and it affects the water permeability or percolation rate of a soil. Coarser soils have less retention than finer soils, which in the case of NO3−-N allows it to leach into groundwater faster, so there is positive relationship between the percentage of sands and NO3−-N concentration in groundwater wells. This study aimed to estimate the effect of soil texture on NO3−-N content in groundwater. Optical reflectance data obtained by remote sensing was used in this study. Our hypothesis was that the quantity of nitrogen leached into groundwater through loam was higher than through clay. Nakhon Pathom province, Thailand, was selected as a study area where the terrain is mostly represented by a flat topography. It was found that classified LANDSAT images delineated paddy fields as covering 29.4% of the study area, while sugarcane covered 10.4%, and 60.2% was represented by “others”. The reason for this classified landuse was to determine additional factors, such as vegetation, which might directly affect the quantity of NO3−-N in soil. Ideally, bare soil would be used as a test site, but in fact, no such places were available in Thailand. This led to an indirect method to estimate NO3−-N on various soil textures. Through experimentation, it was found that NO3−-N measured through the loam in sugarcane (I = 0.0054, p < 0.05) was lower than clay represented by paddies (I = 0.0305, p < 0.05). This had a significant negative impact on the assumption. According to the research and local statistical data, farmers have always applied an excess quantity of fertilizer on paddy fields. This is the main reason for the higher quantity of NO3−-N found in clay than loam in this study. This case might be an exceptional study in terms of quantity of fertilizers applied to agricultural fields

CA-Markov Analysis of Constrained Coastal Urban Growth Modeling: Hua Hin Seaside City, Thailand

Thailand, a developing country in Southeast Asia, is experiencing rapid development, particularly urban growth as a response to the expansion of the tourism industry. Hua Hin city provides an excellent example of an area where urbanization has flourished due to tourism. This study focuses on how the dynamic urban horizontal expansion of the seaside city of Hua Hin is constrained by the coast, thus making sustainability for this popular tourist destination—managing and planning for its local inhabitants, its visitors, and its sites—an issue. The study examines the association of land use type and land use change by integrating Geo-Information technology, a statistic model, and CA-Markov analysis for sustainable land use planning. The study identifies that the land use types and land use changes from the year 1999 to 2008 have changed as a result of increased mobility; this trend, in turn, has everything to do with urban horizontal expansion. The changing sequences of land use type have developed from forest area to agriculture, from agriculture to grassland, then to bare land and built-up areas. Coastal urban growth has, for a decade, been expanding horizontally from a downtown center along the beach to the western area around the golf course, the southern area along the beach, the southwest grassland area, and then the northern area near the airport

Spatial Diffusion of Influenza Outbreak-Related Climate Factors in Chiang Mai Province, Thailand

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks