ABSTRACT
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.
ABSTRACT
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.