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This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model among malaria prediction accuracy techniques in varying climate contexts. The results suggested that J48 had exhibited better skill than MLP with the 10-fold cross-validation method over the percentile split and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had a better association with malaria incidents than rainfall, and the performance was better during the monsoon and post-monsoon when the incidents are at the peak. [ABSTRACT FROM AUTHOR]

Essential oils isolated from 35 aromatic plants of Gorakhpur Division, India, were screened at 0.36 μl ml-1 against four dominant fungi Aspergillus flavus Link, A. niger van Tieghem, A. ochraceus Wilhelm and A. terreus Thom found on stored pigeon pea (Cajanus cajan Linn.) seeds. Chenopodium ambrosioides Linn. and Citrus aurantium Linn. oils exhibited strong antimycotic activity (100% mycelial inhibition). The minimum inhibitory concentration of C. ambrosioides oil was less (0.07 μl ml-1) than that of C. aurantium oil, against all the test fungi. Both the oils were fungicidal at higher doses (1.71-2.78 μl ml-1). C. ambrosioides oil exhibited broad fungitoxic spectrum against 16 other storage fungi of pigeon pea seeds. The oil remained effective with increased inoculum load, and physical factors had no adverse effect on its toxicity. C. ambrosioides oil proved superior to aluminium phosphide and ethylene dibromide, synthetic fumigants, at 0.29 μl ml-1 and significantly protected 1 kg of pigeon pea seeds from microbial attack for up to 6 months. Chenopodium oil can be recommended as an effective substitute of synthetic fumigants for storage of pigeon pea seeds. [ABSTRACT FROM AUTHOR]

The development of spark-ignited flame kernels in a turbulent field is strongly dependent upon the nature of the three-dimensional (3-D) turbulence adjacent to the ignition source. The turbulence scales vary in 3-D from shot to shot, resulting in successive flame kernels developing differently and thereby causing cyclic variations in, for example, spark ignition engines. It is necessary to quantify the scales affecting the flame at any instant for the accurate measurement of burn rate of such flame kernels, which requires visualization of the flame surface in 3-D. The experiments reported here employ a multiple sheet mie-scattered light technique to successfully characterize a nonstabilized expanding turbulent flame kernel in 3-D. A novel algorithm was developed to construct the flame surface in 3-D, which enabled accurate calculation of parameters such as flame surface density, reaction progress variables, and turbulent flame thickness. [ABSTRACT FROM AUTHOR]

Agriculture is the mainstay of rural populations, especially in the developing world. Increasing population and the demand for food, fodder and fuelwood have threatened the sustainability of the land resources. Without understanding farmers' perceptions on land-related issues, sustainable management of land resources is impossible because they have intimate knowledge of their land. Besides technical solutions to land-related problems, socio-economic appraisal also plays an important role for solutions to these problems. Therefore, the objectives of this article are to find farmers' perceptions on land degradation and to examine the socio-economic determinants of land degradation in the study area. In this study, a structured questionnaire was used to collect information on different socio-economic parameters affecting land degradation and farmers' perceptions on the status of land degradation in Pishin sub-basin, Pakistan. Farmers' perceptions are presented using simple descriptive statistics, whereas socio-economic determinants of land degradation in the study area were investigated using a binary logistic regression technique. The model predicted seven determinants of land degradation in the study area: household size, number of educated male members in the household, frequency of visits of extension workers, security of tenure, access to credit, cropping pattern and livestock population. [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]

Land use/land cover of the Earth is changing dramatically because of human activities and natural disasters. Information about changes is useful for updating land use/land cover maps for planning and management of natural resources, Several methods for land use/land cover change detection using time series Landsat imagery data were employed and discussed. Landsat 5 TM colour composites of 1990, 1993, 1996 and 1999 were employed for locating training samples for supervised classification in the coastal areas of Ban Don Bay, Surat Thani, Thailand. This study illustrated an increasing trend of shrimp farms. forest/mangrove and urban areas with a decreasing trend of agricultural and wasteland areas. Land use changes from one category to others have been clearly represented by the NDVI composite images, which were found suitable for delineating the development of shrimp farms and land use changes in Ban Don Bay. [ABSTRACT FROM AUTHOR]

Abstract: Air pollution is a serious problem in thickly populated and industrialized areas in Thailand, especially in Bangkok. The air pollution in Bangkok is abundant, especially in areas where pollution sources and the human population are concentrated. Economic growth and industrialization are proceeding at a rapid pace, accompanied by increasing emissions of air polluting sources. Furthermore, though the variety and quantities of polluting sources have increased dramatically, the development of a suitable method for monitoring the pollution causing sources has not followed at the same pace. Environmental impacts of air pollutants have impact on public health, vegetation, material deterioration etc. To prevent or minimize the damage caused by atmospheric pollution, suitable monitoring systems are urgently needed that can rapidly and reliably detect and quantify polluting sources for monitoring by regulating authorities in order to prevent further deterioration of the current pollution levels. Consequently, it is important that the current real-time air quality monitoring system, controlled by the Pollution Control Department (PCD), should be adapted or extended to aid in alleviating this problem. Nanotechnology has been applied to several industrial and domestic fields, for example, applications for gas monitoring systems, gas leak detectors in factories, fire and toxic gas detectors, ventilation control, breath alcohol detectors, and the like. Here we report an application example of studying air quality monitoring based on nanotechnology ‘solid state gas sensors’. So as to carry out air pollution monitoring over an extensive area, a combination of ground measurements through inexpensive sensors and wireless GIS will be used for this purpose. This portable device, comprising solid state gas sensors integrated to a Personal Digital Assistant (PDA) linked through Bluetooth communication tools and Global Positioning System (GPS), will allow rapid dissemination of information on pollution levels at multiple sites simultaneously. The AQ report generated can be then published using Internet GIS to provide a real-time information service for the PCD, for increased public awareness and enhanced public participation. The local deterministic and geostatistical interpolation methods have been used for spatial prediction, and to find out the most suitable method for studying air pollution, based on observations at each monitoring site. [Copyright &y& Elsevier]

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]

Morphological image transforms find their basis in the notions of mathematical morphology. An attempt is made to develop morphological transforms for enhancement of directional edges and lineaments from satellite imagery. Geostructural features are generally oriented in preferred directions. This information has been utilized in designing structuring elements for morphological transforms. Several shapes and sizes of structuring elements have been designed and applied to delineate lineaments in different litho-environments of the same study area. Top hat transform has been applied using the directional structuring elements for edge image at 0[sup o], 30[sup o], 60[sup o], 90[sup o], 120[sup o] and 150[sup o]. Geological lineaments such as faults could be easily identified on an edge image obtained using top hat transformation followed by an image superimposition technique. The lineament map was developed using directionally enhanced edge images and the results have been verified. [ABSTRACT FROM AUTHOR]