Ecosystem evaluation (1989-2012) of Ramsar wetland Deepor Beel using satellite-derived indices.

The unprecedented urban growth especially in developing countries has laid immense pressure on wetlands, finally threatening their existence altogether. A long-term monitoring of wetland ecosystems is the basis of planning conservation measures for a sustainable development. Deepor Beel, a Ramsar wetland and major storm water basin of the River Brahmaputra in the northeastern region of India, needs particular attention due to its constant degradation over the past decades. A rule-based classification algorithm was developed using Landsat (2011)-derived indices, namely Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), Normalised Difference Pond Index (NDPI), Normalised Difference Vegetation Index (NDVI) and field data as ancillary information. Field data, ALOS AVNIR and Google Earth images were used for accuracy assessment. A fuzzy accuracy assessment of the classified data sets showed an overall accuracy of 82 % for MAX criteria and 90 % for RIGHT criteria. The rules were used to classify major wetland cover types during low water season (January) in 1989, 2001 and 2012. The statistical analysis of the classified wetland showed heavy manifestation in aquatic vegetation and other features indicating severe eutrophication over the past 23 years. This degradation was closely related to major contributing anthropogenic factors, such as a railway line construction, growing croplands, waste disposal and illegal human settlements in the wetland catchment. In addition, the landscape development index (LDI) indicated a rapid increase in the impact of the surrounding land use on the wetland from 1989 to 2012. The techniques and results from this study may prove useful for top-down landscape analyses of this and other freshwater wetlands.

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.

Analyzing the spatio-temporal relationship between dengue vector larval density and land-use using factor analysis and spatial ring mapping.

BACKGROUND: Dengue, a mosquito-borne febrile viral disease, is found in tropical and sub-tropical regions and is now extending its range to temperate regions. The spread of the dengue viruses mainly depends on vector population (Aedes aegypti and Aedes albopictus), which is influenced by changing climatic conditions and various land-use/land-cover types. Spatial display of the relationship between dengue vector density and land-cover types is required to describe a near-future viral outbreak scenario. This study is aimed at exploring how land-cover types are linked to the behavior of dengue-transmitting mosquitoes. METHODS: Surveys were conducted in 92 villages of Phitsanulok Province Thailand. The sampling was conducted on three separate occasions in the months of March, May and July. Dengue indices, i.e. container index (C.I.), house index (H.I.) and Breteau index (B.I.) were used to map habitats conducible to dengue vector growth. Spatial epidemiological analysis using Bivariate Pearson's correlation was conducted to evaluate the level of interdependence between larval density and land-use types. Factor analysis using principal component analysis (PCA) with varimax rotation was performed to ascertain the variance among land-use types. Furthermore, spatial ring method was used as to visualize spatially referenced, multivariate and temporal data in single information graphic. RESULTS: Results of dengue indices showed that the settlements around gasoline stations/workshops, in the vicinity of marsh/swamp and rice paddy appeared to be favorable habitat for dengue vector propagation at highly significant and positive correlation (p = 0.001) in the month of May. Settlements around the institutional areas were highly significant and positively correlated (p = 0.01) with H.I. in the month of March. Moreover, dengue indices in the month of March showed a significant and positive correlation (p <= 0.05) with deciduous forest. The H.I. of people living around horticulture land were significantly and positively correlated (p = 0.05) during the month of May, and perennial vegetation showed a highly significant and positive correlation (p = 0.001) in the month of March with C.I. and significant and positive correlation (p <= 0.05) with B.I., respectively. CONCLUSIONS: The study concluded that gasoline stations/workshops, rice paddy, marsh/swamp and deciduous forests played highly significant role in dengue vector growth. Thus, the spatio-temporal relationships of dengue vector larval density and land-use types may help to predict favorable dengue habitat, and thereby enables public healthcare managers to take precautionary measures to prevent impending dengue outbreak.

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 (NO(3) (-)-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 NO(3) (-)-N allows it to leach into groundwater faster, so there is positive relationship between the percentage of sands and NO(3) (-)-N concentration in groundwater wells. This study aimed to estimate the effect of soil texture on NO(3) (-)-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 NO(3) (-)-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 NO(3) (-)-N on various soil textures. Through experimentation, it was found that NO(3) (-)-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 NO(3) (-)-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.