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A temperatura de superfície terrestre nas áreas urbanas e rurais de Florínea-SP e Cabralia Paulista-SP

As cidades constituem um dos principais exemplos de alteração do ambiente natural, seja pela produção de uma paisagem antropizada, seja pela criação do Clima Urbano. O presente artigo tem como objetivo discutir e comparar a temperatura de superfície terrestre de duas cidades pequenas, com extensões semelhantes e diferentes usos do solo no entorno: Florínea com uso agrícola e Cabrália Paulista, principalmente, com reflorestamento. Os resultados mostraram que as áreas urbanas produzem um clima local com temperaturas de superfície terrestre mais elevadas do que em parte do seu entorno, como no caso de Cabrália Paulista onde na área urbana foi encontrada temperatura média de 17 ºC, sendo 8 ºC a mais do que nos reflorestamentos, que correspondem a aproximadamente 50 % da área de estudo e áreas naturais de 9 ºC. Somente as áreas de cultivo com 19 ºC e de pastagem com 21 ºC, superaram a área urbana. No caso de Florínea a área urbana apresenta temperaturas médias de 24 ºC e as áreas de cultivo mais desenvolvidas em seu entorno, temperaturas médias de 19 ºC. Nas áreas onde estava sendo feita a colheita as temperaturas chegaram a 24 ºC, no entanto em algumas destas áreas a temperatura média chegou a 27 ºC, o que pode estar relacionado com outras fases do cultivo ou outros fatores. Enfim, ficou comprovado que as 2 áreas urbanas são mais quentes e que as áreas de cultivo, elevam a temperatura na área de estudo de Florínea, e as áreas de reflorestamento diminuem na de Cabrália Paulista

Determination of land surface temperature using Landsat 8 images: Comparative study of algorithms on the city of Granada

[EN] The use of satellite images has become, in recent decades, one of the most common ways to determine the Land Surface Temperature (LST). One of them is through the use of Landsat 8 images that requires the use of single-channel (MC) and two-channel (BC) algorithms. In this study, the LST of a medium-sized city, Granada (Spain) has been determined over a year by using five Landsat 8 algorithms that are subsequently compared with ambient temperatures. Few studies compare the data source with the seasonal variations of the same metropolis, which together with its geographical location, high pollution and the significant thermal variations it experiences make it a suitable place for the development of this research. As a result of the statistical analysis process, the regression coefficients R2, mean square error (RMSE), mean error bias (MBE) and standard deviation (SD) were obtained. The average results obtained reveal that the LST derived from the BC algorithms (1.0 °C) are the closest to the ambient temperatures in contrast to the MC (-5.6 °C), although important variations have been verified between the different zones of the city according to its coverage and seasonal periods. Therefore, it is concluded that the BC algorithms are the most suitable for recovering the LST of the city under study.[ES] El empleo de imágenes satelitales se ha convertido, en las últimas décadas, en una de las formas más habituales para determinar la Temperatura de la Superficie Terrestre (TST). Una de ellas es mediante el empleo de imágenes Landsat 8 que requiere del uso de algoritmos del tipo monocanal (MC) y bicanal (BC). En este estudio se ha determinado la TST de una ciudad de tamaño medio, Granada (España) a lo largo de un año mediante el empleo de cinco algoritmos Landsat 8 que posteriormente se comparan con las temperaturas ambientales. Pocos estudios comparan la fuente de datos con las variaciones estacio-temporales de una misma metrópolis lo que unido a su situación geográfica, alta contaminación y las importantes variaciones térmicas que experimenta la convierten en un lugar adecuado para el desarrollo de esta investigación. Como resultado del proceso de análisis estadístico se obtuvieron los coeficientes de regresión R2, el error medio cuadrático (RMSE), sesgo medio del error (MBE) y la desviación estándar (DE). Los resultados medios obtenidos revelan que las TST derivada de los algoritmos BC (1,0 °C) son las más próximas a las temperaturas ambientales en contraposición con los MC (-5,6 °C) aunque se han verificado importantes variaciones entre las distintas zonas de la urbe según su cobertura y los periodos estacionales. Por todo ello, se concluye que los algoritmos BC son los más adecuados para recuperar la TST de la urbe objeto de estudio.Hidalgo-García, D. (2021). 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Assessing the cooling impact of tree canopies in an intensively modified tropical landscape

Ilmastonmuutoksella on ennustettu olevan kohtalokkaita seurauksia trooppisten alueiden hauraille ekosysteemeille, ja se uhkaa sekä maailmanlaajuista biodiversiteettiä että miljoonien ihmisten ruokaturvaa. Metsien on havaittu puskuroivan ilmaston lämpenemistä, ja mikroilmastolliset olosuhteet puiden latvustojen alla eroavat merkittävästi ympäröivästä makroilmastosta. Puut viilentävät lähiympäristöään monien biofysikaalisten mekanismien avulla, ja viilentävä vaikutus on havaittavissa myös metsän ulkopuolella kasvavilla puilla. Kaukokartoitus tarjoaa uusia mahdollisuuksia tutkia, kuinka topografia ja latvuspeite vaikuttavat lämpötiloihin sekä paikallisesti että alueellisesti. Tämän tutkimuksen tarkoituksena oli tutkia latvuspeitteen vaikutusta mikroilmastoon ja maan pinnan lämpötilaan Kenian Taitavuorilla. Eri latvuspeiton omaaville paikoille asennettiin 19 mikroilmaston mittaamiseen tarkoitettua sensoria, jotka tallensivat lämpötilaa. Lisäksi käytettiin Landsat 8 lämpöinfrapunasensorin (TIRS) tallentamaa dataa maan pinnan lämpötilasta (LST). Tutkimuksessa keskityttiin erityisesti päiväsajan keski- ja maksimilämpötiloihin, jotka mitattiin lämpötilasensoreilla kesä-heinäkuussa 2019. LST oli tallennettu 4. heinäkuuta 2019 ja laskettiin käyttämällä single-channel -metodia (SC). Lämpötiloja verrattiin korkean resoluution laserkeilausaineistoon (ALS) vuosilta 2014 ja 2015, jotta topografian ja latvuspeitteen vaikutuksia alueen lämpötiloihin voitaisiin tarkastella. Monimuuttujaregressiomallilla tutkittiin topografian ja latvuspeitteen yhteisvaikutuksia. Tulokset osoittavat negatiivisen lineaarisen suhteen päiväsaikaisten keski- ja maksimilämpötilojen ja latvuspeitteen välillä (R2 = 0.6–0.74). Kaikki lisäys latvuspeitteen määrään vaikutti negatiivisesti lämpötiloihin jokaisella mittauskorkeudella, vaikutuksen ollessa suurin pintalämpötiloihin. Ero 0 % ja 100 %:n latvuspeitteen alueilla oli keskilämpötiloissa 4.6–5.9 ˚C ja maksimilämpötiloissa 8.9–12.1 ˚C. Latvuspeite vaikutti negatiivisesti myös LST:en kulmakertoimella 5.0 ˚C. Latvuspeitteen vaikutus LST:en oli riippuvainen korkeudesta, ja merkittävä jakolinja löydettiin 1000 metrin korkeudelta, jossa latvuspeitteen vaikutus vuorilla laski puoleen verrattuna alankoihin. Tulosten perusteella voitiin päätellä, että puilla on merkittävä vaikutus sekä mikroilmastoon että maan pinnan lämpötilaan, mutta on riippuvainen korkeudesta. Tämä osoittaa, että puiden merkitys kasvaa mitä kuumemmasta ympäristöstä on kyse, ja että puuston säilyttäminen olisi erityisen tärkeää alankoalueilla. Metsän ulkopuolella kasvavat puut voivat lisätä kestävyyttä ilmastonmuutoksen edessä tutkimusalueella, ja jäljellä olevat metsäsirpaleet on syytä säilyttää alueellisen lämpötilan hallitsemiseksi.Global warming is expected to have detrimental consequences on fragile ecosystems in the tropics and to threaten both the global biodiversity as well as food security of millions of people. Forests have the potential to buffer the temperature changes, and the microclimatic conditions below tree canopies usually differ substantially from the ambient macroclimate. Trees cool down their surroundings through several biophysical mechanisms, and the cooling benefits occur also with trees outside forest. Remote sensing technologies offer new possibilities to study how tree cover affects temperatures both in local and regional scales. The aim of this study was to examine canopy cover’s effect on microclimate and land surface temperature (LST) in Taita Hills, Kenya. Temperatures recorded by 19 microclimate sensors under different canopy covers in the study area and LST estimated by Landsat 8 thermal infrared sensor (TIRS) were studied. The main interest was in daytime mean and maximum temperatures measured with the microclimate sensors in June-July 2019. The Landsat 8 imagery was obtained in July 4, 2019 and LST was retrieved using the single-channel method. The temperature records were combined with high-resolution airborne laser scanning (ALS) data of the area from years 2014 and 2015 to address how topographical factors and canopy cover affect temperatures in the area. Four multiple regression models were developed to study the joint impacts of topography and canopy cover on LST. The results showed a negative linear relationship between daytime mean and maximum temperatures and canopy cover percentage (R2 = 0.6–0.74). Any increase in canopy cover contributed to reducing temperatures at all microclimate measuring heights, the magnitude being the highest at soil surface level. The difference in mean temperatures between 0% and 100% canopy cover sites was 4.6–5.9 ˚C and in maximum temperatures 8.9–12.1 ˚C. LST was also affected negatively by canopy cover with a slope of 5.0 ˚C. It was found that canopy cover’s impact on LST depends on altitude and that a considerable dividing line existed at 1000 m a.s.l. as canopy cover’s effect in the highlands decreased to half compared to the lowlands. Based on the results it was concluded that trees have substantial effect on both microclimate and LST, but the effect is highly dependent on altitude. This indicates trees’ increasing significance in hot environments and highlights the importance of maintaining tree cover particularly in the lowland areas. Trees outside forests can increase climate change resilience in the area and the remaining forest fragments should be conserved to control the regional temperatures

Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature

Correção atmosférica de imagens termais utilizando perfis verticais de alta resolução simulados por um modelo de mesoescala

A estimativa da temperatura da superfície terrestre ( LST ) por sensoriamento remoto no infravermelho termal (TIR) é dependente d a realização de uma correção atmosférica apropriada que , em geral, necessita de perfis atmosféricos como dados de entrada. Dados globais de reanálise são uma alternativa prática para a obtenção desses perfis, mas podem apresentar limitações. Nesse contexto, o presente estudo teve como objetivo analisar a utilização do modelo numérico Weather Research and Forecasting (WRF) para gerar perfis verticais de alta resolução , refinando dados de reanálise , visando a correção atmosférica no TIR para o cálculo de valores de LST. Para tal, foram realizadas simulações com o modelo WRF com dados de reanálise do NCEP Climate Forecast System Version 2 (CFSv2) como condições iniciais e utilizando duas grades aninhadas com resoluções horizontais de 12 km (G12) e 3 km (G03). Para estimar a LST, foram empregados: o método da inversão direta da Equação de Transferência Radiativa (RTE) , o modelo MODTRAN e valores de radiância da banda 10 do Landsat 8 TIRS. A pesquisa avaliou o desempenho do modelo através dos perfis verticais, dos parâmetros atmosféricos de correção (transmitância atmosférica e radiâncias upwelling e downwelling ) e dos valores de LST, utilizando como referência dados de radiossondagens in situ , no sul do Brasil . Adicionalmente, foi executada uma análise de sensibilidade a dois esquemas de parametrização de camada limite planetária . Os resultados indicam que o modelo WRF simula de maneira satisfatória os perfis atmosféricos que, por consequência, geram parâmetros de correção e LST com baixos erros. Contudo, não existe melhora significativa nas métricas estatísticas entre os perfis extraídos diretamente da reanálise CFSv2 e os simulados pelo WRF . Em alguns casos, a utilização de um perfil de grade mais refinada resultou, até mesmo, em maiores erros. Os valores gerais de RMSE para a LST foram: 0,55 K ( CFSv2), 0,79 K ( WRF G12 ) e 0,82 K ( WRF G03 ). A escolha do esquema de camada limite mostrou - se caso - dependente. Conclui - se que não há necessidade especial de refinar a resolução dos perfis de reanálise visando estimativa de LST, por meio do método da RTE .The Land Surface Temperature (LST) retrieval from thermal infrared (TIR) remote sensing depends on performing an appropriate atmospheric correction. In general, this approach requires atmospheric profiles as input data. Global reanalysis data are a practical alternative to obtain these profiles, but they may have limitations. In this con text, this study aimed to assess the use of the Weather Research and Forecasting (WRF) numerical model to generate high - resolution vertical profiles, downscaling reanalysis data , to be applied in TIR atmospheric correction for LST retrieval . WRF simulations were carried out using NCEP Climate Forecast System Version 2 (CFSv2) reanalysis as initial conditions and two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03) . To retrieve the LST, we used: the Radiative Transfer Equation (RTE) based method , the MODTRAN model, and radiance values from Landsat 8 TIRS10 band . Th is research evaluated the model performance through vertical profiles, atmospheric correction parameters (atmospheric transmittance and upwelling and downwelling radiances) , and LST values, using in situ radiosonde data ( in Southern Brazil ) as reference. Moreover, a sensitivity analysis to two planetary boundary layer parameterization schemes was performed . The results indicate that the WRF model satisfactor il y simulates the atmospheric profiles that, consequently, generate correction param eters and LST with low errors. However, there is no significant improvement in statistical metrics between profiles extracted directly from the CFSv2 reanalysis and those simulated by WRF . In some cases, the use of a finer grid profile resulted even in larger errors. The LST overall RMSE values were: 0.55 K (CFSv2), 0.79 K (WRF G12) , and 0.82 K (WRF G03) . The boundary layer scheme choice proved to be case - dependent. We concluded that there is no special need to increase the resolution of reanalysis profiles in order to retrieve LST using the RTE - based method