Remote Sensing Based Land Surface Temperature Analysis in Diverse Environment of Lalgudi Block

Introduction: Land Surface Temperature (LST) is a significant climatic variable and defined as how hot the "surface" of the Earth would feel to the physical touch in a particular location. A spatial analysis of the land surface temperature with respect to different land use/cover changes is vital to evaluate the hydrological processes. Methods: The objective of this paper is to assess the spatial variation of land surface temperature derived from thermal bands of the Landsat 8 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) by using split window algorithm. Place and Data: The study was conducted in Lalgudi block of Trichy District, Tamil Nadu, India. The block has diverse environment like forest area, barren land, river sand bed, water bodies, dry vegetation, cultivated areas (paddy, sugarcane, banana etc.) and settlements. Landsat 8 satellite images for four selected scenes (December 2014 & January 2015 and December 2017 & January 2018) were used to estimate the LST. Results: The spatial and temporal variation of Normalized Difference Vegetation Index (NDVI) and LST were estimated. The average NDVI values of cropped fields varied from 0.3 to 0.5 in all the scenes. The maximum value of LST ranging from 35 to 40°C was recorded in river sand bed. Subsequently, semi-urban settlements in the central part of Lalgudi block exhibited higher temperature ranging from 28 – 30°C. The LST of paddy crop and sugarcane was in the range of 23 to 25°C. The water bodies exhibited LST around 20°C. The coconut plantations, forest area and Prosopis juliflora showed LST value ranging from 24 – 29°C. This kind of block level monitoring studies helps in adopting suitable policies to overcome or minimize the problems triggered by increase in land surface temperature.

Spatial Assessment of Land Surface Temperature and Emissivity in the Tropical Littoral City of Port Harcourt, Nigeria

The study examined Land Surface Temperature (LST) and Land Surface Emissivity (LSE) in a tropical coastal city of Port Harcourt and its environs. Satellite remote sensing of multiple-wavelength origin was employed to derive data from the Landsat Enhance Thematic Mapper (ETM+). Statistical mean and range were used to show pattern of LST and LSE. The study established the relationship and characteristics of land use land cover, built-up area and influence of population on land surfaces. With population of over 3,095,342 persons occupying surface area of approximately 458,28 Km2, rapid vegetal and water body lost have put the city area under pressure of 4.7°C heat bias at the interval of 15 years. From rural fringes to the city center, LST varies with 9.3°C in wet season and 4.8°C in the dry season. During the dry season, LSE is severe in the southern part of the city contributed by water bodies, more vegetal cover and urban pavement materials. Emissivity in the wet season varied with 0.0136 and 0.0006 during the dry season but differs with 0.0165 between the two seasons. One critical finding is that LSE decreases from the rural fringes to the city center and LST increases from the rural fringes to the city center. It is recommended that urban greening at the city center should be practiced and the rural fringes should be explored by decongesting activities at the city center to the outskirts in order to ameliorate the effects of urban heat bias without further delay.

São Paulo urban heat islands have a higher incidence of dengue than other urban areas

Urban heat islands are characterized by high land surface temperature, low humidity, and poor vegetation, and considered to favor the transmission of the mosquito-borne dengue fever that is transmitted by the Aedes aegypti mosquito. We analyzed the recorded dengue incidence in Sao Paulo city, Brazil, in 2010-2011, in terms of multiple environmental and socioeconomic variables. Geographical information systems, thermal remote sensing images, and census data were used to classify city areas according to land surface temper- ature, vegetation cover, population density, socioeconomic status, and housing standards. Of the 7415 dengue cases, a majority (93.1%) mapped to areas with land surface temperature >28 ◦ C. The dengue incidence rate (cases per 100,000 inhabitants) was low (3.2 cases) in high vegetation cover areas, but high (72.3 cases) in low vegetation cover areas where the land surface temperature was 29 ± 2 ◦ C. Interestingly, a multiple cluster analysis phenogram showed more dengue cases clustered in areas of land surface temperature >32 ◦ C, than in areas characterized as low socioeconomic zones, high population density areas, or slum-like areas. In laboratory experiments, A. aegypti mosquito larval development, blood feeding, and oviposition associated positively with temperatures of 28-32 ◦ C, indicating these temperatures to be favorable for dengue transmission. Thus, among all the variables studied, dengue incidence was most affected by the temperature.

Appraisal of Phlebotomus argentipes habitat suitability using a remotely sensed index in the kala-azar endemic focus of Bihar, India

Visceral leishmaniasis, or kala-azar, is recognised as a serious emerging public health problem in India. In this study, environmental parameters, such as land surface temperature (LST) and renormalised difference vegetation indices (RDVI), were used to delineate the association between environmental variables and Phlebotomus argentipes abundance in a representative endemic region of Bihar, India. The adult P. argentipes were collected between September 2009-February 2010 using the hand-held aspirator technique. The distribution of P. argentipes was analysed with the LST and RDVI of the peak and lean seasons. The association between environmental covariates and P. argentipes density was analysed a multivariate linear regression model. The sandfly density at its maximum in September, whereas the minimum density was recorded in January. The regression model indicated that the season, minimum LST, mean LST and mean RDVI were the best environmental covariates for the P. argentipes distribution. The final model indicated that nearly 74% of the variance of sandfly density could be explained by these environmental covariates. This approach might be useful for mapping and predicting the distribution of P. argentipes, which may help the health agencies that are involved in the kala-azar control programme focus on high-risk areas.

Spatial epidemiological study on malaria epidemics in Hainan province / 中华流行病学杂志

Objective To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics. Methods Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct. , 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST)were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0. Results The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: Ⅰ(monthly, unit:1/1 000 000) = exp( - 1. 672 - 0. 399 × LST). Conclusion Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.