ABSTRACT
The Metropolitan Region of Baixada Santista (MRBS) harbors one of the main port areas of Brazil: the Port of Santos. Due to the accelerated urban development in this region, the monitoring of biophysical parameters is fundamental. Therefore, this paper aims to i) estimate the soil surface temperature (Ts) and identify the Urban Heat Islands (UHI) formation; and ii) compare the Ts and the normalized difference vegetation index (NDVI) for MRBS from 1986 to 2016 using Landsat 5 and 8 images. Remote sensing tools are essential to meet the objectives of this work for providing both the spatial and temporal evaluation of a region. The spatial analysis was based on the NDVI to evaluate the vegetation density and size from five previously established classes (i.e., water bodies, urban grid, exposed soil and road corridors, shrub, and dense vegetation). The NDVI mapping showed a significant reduction in the cover area referred to the dense vegetation class (91.7%), while the urban grid category increased by 29.4%, resulting from the urban expansion and green cover reduction over the region during this period. Surface temperature thematic maps showed high-temperature values related to increased urbanization and decreased rainfall. Moreover, an 8°C rise in surface temperature over the last 30 years was registered due to the regional development, which has replaced natural soils by anthropic materials and reduced dense vegetation. This phenomenon has resulted in the formation and intensification of UHI, especially after the 2000s.
A Região Metropolitana da Baixada Santista (RMBS) abriga uma das principais zonas portuárias do Brasil, o Porto de Santos. Devido ao grau de urbanização dessa região, o monitoramento dos parâmetros biofísicos torna-se fundamental. Desta forma, este estudo tem como objetivo i) estimar a Temperatura de superfície terrestre (Ts) da RMBS, seguido da identificação da formação de ICU e ii) relacionar a Ts e o NDVI da RMBS no período de 1986 a 2016, a partir das imagens do Landsat 5 e 8. A análise espacial foi baseada no Normalized Difference Vegetation Index (NDVI), no sentido de verificar as condições da densidade e porte da vegetação a partir de cinco classes previamente estabelecidas (Corpo d'água, Malha urbana, Solo exposto e corredores viários, Substrato Arbustivo e Vegetação densa). Os mapas de NDVI indicam uma redução significativa na área de cobertura correspondente à classe vegetação densa, com o valor de cobertura de 91,7%. Por outro lado, a classe Malha urbana apresentou um aumento de 29,4%, resultantes da expansão urbana e da redução da cobertura verde na RMBS ao longo do período. Os mapas temáticos de Tsmostraram altos valores de temperatura, relacionados ao aumento da malha urbana e redução da precipitação. Além disso, houve um aumento de 8ºC na Ts nos últimos 30 anos, causados pelo avanço do desenvolvimento regional, associados à substituição do solo natural por materiais antrópicos e à redução da vegetação densa. Esses fatores resultaram no surgimento de ICU e sua intensificação a partir dos anos 2000.
Subject(s)
Temperature , Land Use , Time Series Studies , UrbanizationABSTRACT
Abstract INTRODUCTION: Meteorological influences along with the lack of basic sanitation has contributed to disease outbreaks, resulting in large socio-economic losses, especially in terms of dengue. This study aimed to evaluate the meteorological influences on the monthly incidence of dengue in Arapiraca-AL, Brazil during 2008-2015. METHODS: We used generalized linear models constructed via logistic regression to assess the association between the monthly incidence of dengue (MID) of and 8 meteorological variables [rainfall (R), air temperature (AT), dew point temperature (DPT), relative humidity (RH), pressure surface, wind speed (WS), wind direction (WD), and gust], based on data obtained from DATASUS and meteorological station databases, respectively. The dengue-1 model included R, AT, DPT, and RH and the dengue-2 model included AT, DPT, RH, WS, and WD. A MID >100 (classified as moderate incidence) indicated an abnormal month. RESULTS: Based on the dengue-1 model, variables with the highest odds ratio included R-lag1, DPT-lag1, and AT-lag1 with a 10.1, 18.3, and 26.7 times greater probability of a moderate MID, respectively. Based on the dengue-2 model, variables with the highest odds ratio were AT-lag1 and RH-lag0 indicating an 8.9 and 18.1 times greater probability of a moderate MID, respectively. CONCLUSIONS: AT, DPT, R, RH and WS influenced the occurrence of a moderate MID.