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This investigation is aimed in contribute the concepts and methods of the innovative development and application of Geographical Information System (GIS) and Remote Sensing regarding Malaria prevalence. Kanchanaburi province, Thailand is chosen 0.3 study area. The input data are based on the geospatial factors including climatic aspects, physical environment, and statistical record of Malaria cases. Afterwards data preprocessing needs to be carried out. Malaria cases map and information value (i-value) approach will he used in order to identify the relation between physical and social factors, and also statistical analysis will be carried out so as to define the relation of climatic factor. The final output based on those approaches is a Malaria risk map which is classified into three classes including high risk level, moderate risk level and low risk level. This study also develops the model based on i-values which are formed as thematic layers in GIS database and are computed using different map analysis techniques, for example, map crossing. [ABSTRACT FROM AUTHOR]

Quantitative analysis morphometric parameters such as stream length, drainage density (DD), bifurcation ratio (Rb), slope, shape factors are good indicators for morphological evolution of a basin. Relationships of these parameters with structure, lithology and geomorphology are enhancing the factors which control the erosion behavior of the area and further use for proper management and development programs. In this study LANDSAT ETM+ data is used for DIP techniques such as NDVI and lineaments enhancement, and GIS techniques to extract different models. In this study the Dashtgol basin which is a watershed overlooking the reservoir of the great Shahid Abasspour dam in the south west Iran has selected to evaluate geomorphic factors. This basin is located within the ZFB. It exhibits complex geological and geomorphic configuration. The information from remotely sensed and ground truth data are analysed in GIS environment. Further, the study, area is divided in 10 hydrologic divisions. The results show that the maximum erosion belongs to the sixth hydrologic division (27.5 t/h per year) and the minimum erosion belongs to second hydrologic division (14.8 t/h per year). Geologic interpretation and geomorphic analysis in GIS environment on the Sabzab river profile shows that the tectonic process is a factor to increase the rates of sedimentation and erosion in the study area. [ABSTRACT FROM AUTHOR]

The classification of irrigated crops by remote sensing requires the use of time series data, since the timing. cropping intensity and duration of cropping is quite variable over the course of a year. Rice is the dominant irrigated crop in tropical and sub-tropical Asia, where rainfall is high, but is seasonal and often uni-modal. Existing crop classification methods for rice are not able to distinguish between rainfed and irrigated crops, leading to errors in classification and estimated irrigated area. This paper describes a technique, a 'peak detector algorithm', to successfully discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand. The methodology uses a three-year time series of Satellite pour l'Observation de Ia Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data (10 day composites) to identify cropping intensity (number, timing and peak values). Peak NDVI is then lag-correlated with long term average rainfall data. There is a high correlation at a 40-50 day lag, between a peak rainfall and a 'single' peak NDVI of rainfed rice. In irrigated areas, there are multiple peaks, and multiple correlations with low values for at least 90 days after peak rainfall. The methodology currently uses a mask to remove un-cropped and non-rice areas, which is derived from existing Geographical Information Systems (GIS). The method achieves a classification accuracy of 89% or better against independent groundtruth data. The procedure is designed as a second level of analysis to refine classifications using other techniques of mapping irrigated area at global and regional scales. [ABSTRACT FROM AUTHOR]

The widely available laboratory spectrometers detect targets at spectral regions restricted to visible and near-infrared (VNIR). The spectral response of soils in this region is predominantly featureless and obstructs the exploitation of absorption features as diagnostic criterion. In this study, polynomial based modelling was developed as an alternative method of estimating soil organic matter (OM) from VNIR spectral region. Forty-one core samples, collected from Lop Buri, Thailand, were subjected to chemical and radiometric analysis. Computations were made across four categories of synthesized bandwidths. The selection procedure identified bands at 960, 1100 and 520 nm as OM sensitive. The widening interval of bandwidth has corresponded with diminishing predictive power, termed 'bandwidth decay effect'. The use of polynomial models and their validations showed a higher performance than the analysis made with multiple regressions analysis. The polynomial based approach offers a fresh opportunity for modelling other non-photoactive soil nutrient parameters. Furthermore, it may form the basis for integration of spectrometers and satellite sensors, aimed at mapping of non-vegetated soils. [ABSTRACT FROM AUTHOR]

Understanding the spectral characteristics of tea plants is important in monitoring tea plantations by remote sensing. Westhall tea estate, Kataboola, situated in the Kandy district in Sri Lanka, was selected as the study site. The Leaf Area Index (LAI) of the tea canopy and spectral reflectance of different types of tea clones for different pruning ages were studied in 50 tea fields in the estate. An empirical model between Normalized Difference Vegetation Index (NDVI) (spectrophotometer derived) and LAI (field measured) of the tea canopy was developed. This study has revealed that different tea clones have unique spectral characteristics depending on the tea canopy structure, size, greenness and maturity of the leaves. These spectral variations were also studied using IRS-1C image acquired on the sampling date. Satellite sensor records have shown variation in spectral characteristics for different types of clonal and seedling tea, but these variations are not as prominent as the result offered by spectrophotometer-based observations. [ABSTRACT FROM AUTHOR]

Remote sensing data from IRS-1D LISS-III were used for bathymetric mapping in Kakinada Bay, India. A global positioning system was employed to locate the accurate sampling points for sea depth. An echo sounder was used to collect sea depth information. Suitability of wavelength bands for bathymetry was examined. An empirical model between sea depth and spectral reflectance was determined based on least squares regression analysis. The influence of turbidity on sea depth measurements using remote sensing data was investigated and a correcting factor (turbidity influence factor, TIF) developed. The regression model was verified using a Chi-square test. The depth of Kakinada Bay was determined using the empirical model and a Band 1 (0.52-0.59 μm) image corrected for turbidity. A bathymetric map of Kakinada was prepared using this depth information. [ABSTRACT FROM AUTHOR]

Hope Island is a long spit formed in Kakinada Bay, India. It acts as a barrier to direct sea currents thus protecting Kakinada town, port and mangroves along the coast. Erosion was noticed at one section of the Island on visual interpretation of IRS-ID LISS-III imagery. In the absence of sea depth data it was difficult to analyse the extent of erosion along the height of the Island above the seabed. Remote sensing based bathymetric estimates were made and used to investigate the erosion problem at selected locations. The work considered the use of remote sensing for determining three-dimensional coastal erosion along slopes of Hope Island. [ABSTRACT FROM AUTHOR]