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| Aims | Methods (remote sensing and GIS) | Concept illustrated | Geography and scale | Data sources | Software used | Major findings | Reference |
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| To find the association of P. argentipes with minimum NDVI in Bihar, India | NDVI, supervised classification | Spectral indices, land use/land cover | Patepur block in Vaishali district, Bihar, India, and Lohardaga block in Lohardaga district in Jharkhand, India, medium to fine | IRS-1C LISS III | ERDAS Imagine v9.3 | The usefulness of satellite remote sensing technology in generating the crucial information on spatial distribution of land use/land cover classes with special emphasis on indicator land cover classes thereby helping in prioritising the area to identify risk-prone areas of kala-azar through GIS application tools | Sudhakar et al., 2006 [16] |
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| To study the geoenvironmental parameters responsible for the propagation of sand fly vector (P. argentipes) in endemic (Vaishali district, Bihar, India) and nonendemic areas (Lohardaga district, Jharkhand, India) using remote sensing and GIS | NDVI, supervised classification | Spectral indices, land use/land cover | Vaishali district in Bihar, India, and Lohardaga district in Jharkhand, India, Medium to fine | IRS-1C LISS III | ERDAS imagine v9.3 | The study provided an insight into the microecosystem, that is the association of vegetation, water bodies, human settlements and associated peridomestic vegetation, and other land use features of relevance in distribution of vector sand fly | Paul et al., 2006 [29] |
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| To delineate the potential hydrological relationship between the vector and kala-azar transmission, the associations between inland water bodies, sand fly prevalence, and Leishmania infections | NDPI, nearest neighbour analysis, radial basic function interpolation | Spectral indices, geostatistics | Lalganj and Hajipur block (Vaishali district, Bihar, India), medium to fine | Landsat 5 TM | ERDAS imagine v9.3, ArcGIS v9.2 | (i) Kala-azar cases were strongly clustered along the nonperennial river banks, whereas a slightly random pattern was found along the perennial river banks
(ii) Most of the endemic villages were located between 250 m and 1 km away from the perennial river banks | Bhunia et al., 2011 [25] |
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| To delineate the suitable habitats of the VL vector, P. argentipes density in relation to environmental characteristics between different ecosystems was assessed in endemic (Bihar) and nonendemic (Jharkhand) Indian states | NDVI, supervised classification (Maximum Likelihood algorithm), spatial analysis, factor analysis | Spectral indices, land use/land cover overlay, buffering | Vaishali district in Bihar, India, and Lohardaga district in Jharkhand, India, medium and fine | IRS-LISS III, LISS IV | ERDAS Imagine v9.3, ArcGIS v9.2 | (i) In endemic site, the density of P. argentipes has a positive load on minimum NDVI, marshy land, and orchard/settlement, whereas agricultural fallow and water body surface have a negative load on the first factor in an endemic site
(ii) In the nonendemic site, results demonstrated that the density of P. argentipes has a positive load on settlement, water body, dense forest, and minimum NDVI, whereas agricultural fallow has a negative load on the first factor
(iii) Satellite-derived proxy for vegetation status derived from NDVI values showed that the majority of sand flies are present in the less dense vegetation zone | Kesari et al., 2011 [15] |
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| The study focused on examining relation with the environmental factors and vector distribution in a kala-azar endemic region in Bihar, India | SAVI, WI, LST, and supervised classification (Maximum Likelihood algorithm) | Spectral indices, land use/land cover | Muzaffarpur district (Bihar, India), medium to fine scale | Landsat TM | ERDAS Imagine v9.3 | The predictive value of remotely sensed data showed mean LST, minimum SAVI, mean SAVI, minimum WI, and maximum WI indices appear to be better for the forecast of the disease risk areas. | Bhunia et al., 2012 [12] |
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| To examine the relationship between land use/land cover classes and their suitability for vector habitats in areas endemic for kala-azar at different spatial scales | Thematic maps, supervised classification (Maximum Likelihood algorithm), and spatial analysis | Land use/land cover, overlay, buffering | Vaishali and Muzaffarpur district, (Bihar, India), large to fine scale | AVHRR, MODIS, Landsat TM, LISS IV | ERDAS Imagine v9.3, ArcGIS v9.2 | At national lever, areas associated with water bodies, closed shrub land, and settled areas are the main places where kala-azar can be expected
At state level, the highest information values were attained from bare areas or places with sparse or herbaceous vegetation, which indicates that these features contribute to the risk for kala-azar
At district level, the agricultural land, used or laying fallow, had less influence with regard to kala-azar endemicity
At village level, kala-azar affected areas as highly influenced by moist fallow, river/canal, and sandy areas | Bhunia et al., 2012 [22] |
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| The parameters associated with kala-azar endemic areas were considered for inclusion in a geoenvironmental risk model for predicting probable endemic areas for kala-azar | NDVI, WI, supervised classification (Maximum Likelihood algorithm), GIS, geostatistics, weighted index overlay method | Spectral analysis, land use/land cover analysis, buffering, local polynomial interpolation, index model | Vaishali district (Bihar, India), fine scale | Landsat TM | ERDAS Imagine v9.3, ArcGIS v9.2 | The kala-azar risk map generated based on environmental, climatic, entomological, and socioeconomic factors obtained from RS data and ground surveys shows promising potential for identifying areas associated with a high risk of kala-azar transmission
Information from the kala-azar risk map can be further used as a management guide to continuously monitor the status of the kala-azar intensity level | Bhunia et al., 2012 [27] |
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