Search

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]

The distribution patterns of malaria incidence at a village level in Thailand were demonstrated with the use of a geographical information system (GIS), and provided the study of the malaria situation at a household level. Mosaic imageries from aerial photographs were used to create maps that contained X and Y coordinates. These digitized base maps were kept as computerized files. Standard Distance Ellipse (SDE) was used to measure the prevalence of dispersion around the mean center of malaria cases and points. Households in the SDE were at greater risk of malaria infection than those located outside the SDE. The spatial pattern of malaria incidence was investigated using spatial autocorrelation using Geary's ratio and Moran's index. Five of seven villages had a clustered spatial distribution of malaria incidence, the vector point of which had a 2-3km range from the patient's houses. Only one village had a significant clustered spatial distribution of malaria incidence (p<0.05). Control efforts should be focused on high-risk areas, especially those households with the heaviest caseloads. This approach would probably be more cost effective than the conventional malaria control methods. This SDE analytical technique would be a novel and useful epidemiological control method for use by public health administrators. The ellipsoidal areas required malaria control intervention., (Copyright 2010 Elsevier B.V. All rights reserved.)

A remote sensing (RS)-based Geographic Information System (GIS) was used to characterize the breeding habitats of Anopheles minimus species A and C in five different districts of Kanchanaburi Province in western Thailand. The GIS and RS were used to monitor the area for the presence and absence of An. minimus A and C in five major land areas, forest, agriculture, urban, water and bare land. The results show that An. minimus A survives both in dense canopy forest and in open fields where agriculture is dominant. A scatter plot of land-use/land-cover for An. minimus, considering proximities to the forest and proximities to agriculture, suggests that An. minimus A has a wider habitat preference, ranging from dense canopy forest to open agricultural fields. A scatter plot for An. minimus C, on the other hand, showed a narrow habitat preference. A scatter plot for proximities performed on separate populations of An. minimus species A, one in the north and the other in the south, showed that there was an association in the northern population with the forest and in the southern population with agricultural areas. There were no statistically significant differences in the scatter plot of proximities to urban areas and water bodies with the An. minimus A north, south, and An. minimus C. LANDSAT TM satellite data classification was used to identify larval habitats that produce An. minimus A and C and analyze proximities between land-use/land-cover classes and locations of larval habitats. An. minimus A has a wide habitat preference, from dense canopy forest to open agricultural fields, while An. minimus C has a narrow habitat preference.

We mapped overall malaria cases and located each field observed major malaria vector breeding habitat using Global Positioning System (GPS) instruments from September 2000 to October 2003 around the three malaria-endemic villages of Ban Khun Huay, Ban Pa Dae, and Ban Tham Seau, Mae Sod district, Tak Province, Thailand. The land-use/land-cover classifications of the three villages and surrounding areas were performed on IKONOS satellite images acquired on 12 November 2001 with a spatial resolution of 1 x 1 m. Stream network was delineated and displayed. Proximity analysis was performed on the locations of the houses with and without malaria cases within a 1.5 km buffer from An. minimus immature mosquito breeding habitats, mainly stream margins. The 1.5 km used in our proximity analysis was arbitrarily estimated based on the An. minimus flight range. A statistical t-test at 5% significance level was performed to evaluate whether houses with malaria cases have higher proximities to streams than houses without malaria cases. The result shows no significant difference between proximity to streams between houses with malaria cases and houses without malaria cases. We suspect that the actual flight range of An. minimus may be greater than 1.5 km. The An. minimus larval habitat deserves more detailed investigation. Further studies on human behavior contrary to that required for adequate malaria control among these three villages are also recommended.