O fenômeno denominado Ilhas de Calor começou a ser percebido após o século XIX devido às grandes mudanças nas interações do homem com a superfície. Estudos relacionados à climatologia foram aprimorados com o avanço geotecnológico, possibilitando a análise de diversas áreas através do uso de métodos variados. Dessa forma, o objetivo deste estudo consiste na identificação de ilhas de calor urbanas por meio de técnicas de sensoriamento remoto, em diferentes localidades do estado catarinense: Joinville, Criciúma e Lages. Para melhor compreensão das superfícies urbanas dessas cidades, utilizaram-se os seguintes índices radiométricos: Normalized Density Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) e Built-up Index (BU), além da Temperatura da Superfície. Através da análise dos resultados obtidos, identificou-se que as ilhas de calor se formam em áreas que apresentam valores elevados de BU e reduzidos de NDVI. As ilhas formadas em Joinville e Criciúma ocorreram de forma semelhante, na porção central das cidades, devido à elevada densidade de elementos urbanísticos, enquanto na cidade de Lages, o fenômeno também se concentrou em fragmentos de solo exposto, afastados da malha urbana, devido ao predomínio da agricultura e silvicultura.

The growth of cities caused by the increase in population and human activities directly resulted in an increase in air temperature in the urban area, which can lead to changes in microclimatic patterns and consequently impact on the quality of life of the population. To mitigate these effects, the need for climate studies in urban areas becomes essential, as it is a measure that can guide public managers to make effective decisions aimed at the pursuit of sustainable urban planning. The objective of the present study was, through remote sensing, to verify possible alterations in the microclimatic patterns of the mountainous region of Santa Catarina, in the long term, through the analysis of data referring to the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). These data were obtained through the use of orbital images from the LANDSAT satellite (centered in 1997, 2009 and 2021) together with the processing carried out by the QGIS software. The results obtained from the NDVI show that there is a significant increase in the exposed soil class of 16.88% (Winter) and 11.63% (Summer) over the analyzed period (1997-2021). The LST also showed a significant increase for both winter and summer, especially the average temperature. Therefore, the results of this study demonstrate the importance of the contribution of Remote Sensing, as a tool that allows the monitoring and spatial analysis of long decades, which can serve as subsidies for public managers to act effectively in their communities, in the issue of urban planning.

This work is based on the hypothesis that in climate change scenarios, the hydrosedimentological cycle is affected and the amount of sediment produced in the basin is altered. The area used for the study is the Marombas watershed. Future flow was projected by the SWAT model under the RCP 4.5 and RCP 8.5 climate scenarios, from 2020 to 2099. The SWAT model was calibrated on a daily scale between 1979 and 1989 and validated between 1994 and 1997. The Nash-sutcliffe values ​​found were 0.91 for calibration and 0.74 for flow validation and 0.74. for calibration and 0.50 for sediment validation. The long-term hydrological projection (2020-2099) indicated an increase in monthly average flow of 10.4% and 16.5% for scenarios RCP 4.5 and RCP 8.5, respectively. The results showed an increase in modal flow (Q50) in both scenarios evaluated and in the occurrence of extreme events in RCP 8.5, as well as a reduction in the grantable flow (Q95 and Q98), in both scenarios. The results also indicated a trend of sediment production three times higher in the future than in the base period. This increase was attributed to the better temporal distribution of rainfall, resulting in higher soil moisture throughout the days, which is conducive to increased flow and intensified water erosion.

A quantificação da radiação solar é de suma importância, pois é a principal fonte de energia para os processos de aquecimento ou resfriamento da superfície terrestre e do ar, para a evapotranspiração e para o metabolismo das plantas e animais. Os equipamentos denominados de saldo radiômetros permitem mensurar o saldo de radiação, porém são limitados por fornecerem medidas pontuais. Diante disso, dados de sensoriamento remoto aparecem como ferramenta para fornecer estimativas distribuídas espacialmente. O objetivo do presente estudo foi estimar o saldo de radiação por meio de técnicas de sensoriamento remoto e comparar com estimativas de dados de uma estação de superfície. O estudo foi realizado no Parque Nacional de São Joaquim para o dia 30 de janeiro de 2014. Foram utilizadas: uma imagem do Satélite Landsat 8, modelo digital do terreno e dados meteorológicos obtidos em uma estação de superfície. O saldo de radiação obtido utilizando a imagem de satélite seguiu a metodologia do SEBAL e o saldo de radiação para a estação de superfície resultou da Equação de Brunt. Os maiores valores do saldo de radiação foram encontrados nas classes de água e floresta, enquanto os menores valores foram obtidos em locais de solo exposto e agricultura.

Water erosion is influenced by climate, soil, soil cover and soil conservation practices. These factors can be modified by natural (especially climate) and/or anthropogenic (especially soil, soil cover and conservation practices) actions. The relief factor also influences the water erosion and can also be partially modified by anthropic action. This study aimed to evaluate the impact of anthropogenic action due to the introduction of soil crop in no-tillage system on water erosion, and on the consequent flow of sediments in the water. The study was carried out in the Marombas river basin with an area of 3,939 km², using the Soil Water Assessment Tools (SWAT) model. The calibration and validation of the model for sediment production was carried out with a historical series of synthetic data. The data from this series were estimated by linear regression from sediment value load and the average daily flow obtained punctually in the basin’s outlet. The SWAT model was calibrated on a daily scale with data from 1979 to 1989 and was validated with data from 1994 and 1997. The SWAT model was suitable to represent the average daily flow and sediment flow in the Marombas watershed. The hypothesis of reduced sediment production with increasing soil crop in no-tillage system was accepted.