scholarly journals Análise multitemporal de uso, ocupação e cobertura da terra na zona Leste da cidade de Caxias/Maranhão/Brasil

2021 ◽  
Vol 14 (3) ◽  
pp. 1415
Author(s):  
Patricia Barbosa Pereira ◽  
Hikaro Kayo de Brito Nunes ◽  
Francisco De Assis da Silva Araújo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Area ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Land Occupation ◽  
Multi Temporal ◽  
Landsat 5 Tm ◽  
The City

Com o avanço da quantidade de habitantes no espaço urbano surgem novas formas de modificações no ambiente, e, assim, há o favorecimento da intensificação do processo de antropização, como a supressão da cobertura vegetal, a descaracterização do relevo e danos aos cursos d’água. Frente a isso, o objetivo deste estudo é analisar e quantificar, em escala multitemporal, a dinâmica de uso, ocupação e cobertura da terra da cidade de Caxias/MA com foco na zona Leste por meio de ferramentas obtidas junto ao Sensoriamento Remoto. A metodologia utilizada foi pesquisa bibliográfica, documental e cartográfica. Os mapas temáticos foram confeccionados através da interpretação de imagens obtidas dos satélites Landsat 5 TM (Thematic Mapper) para o ano de 2000 e o Landsat 8 OLI (Operational Land Imager) para 2017, por meio do plugin SCP (Semi-Automatic Classification) do software QGIS 2.18.8. Com os resultados obtidos, constatou-se que, a vegetação secundária continuou representando a maior área, apesar da área urbana ter crescido (de 33% a 35%). Isso é caraterizado devido à grande área verde no bairro Pai Geraldo e no bairro Baixinha onde está localizada uma fazenda. Diante dos dados e com as etapas de sensoriamento remoto, de campo e de laboratório, este estudo representou uma análise de uso, ocupação e cobertura da terra ocorrida, onde, a partir dela constatou-se as mais diversas atividades desenvolvidas na área, relacionando, ainda, com distintos riscos e impactos socioambientais. Assim, reforça-se a necessidade de novos estudos e a contribuição do sensoriamento remoto para o alcance dos objetivos.  Analyze multi-temporal the dynamics of use, land occupation and coverage the on east Zone of the city of Caxias/MA/Maranhão/Brazil  A B S T R A C TWith the advancement of the amount of people in the urban area there are new forms of changes in environment, and thus there favoring intensifying anthropization process as suppression of vegetation, the relief adulteration and damage to water courses. Faced with this, the general objective of this study was to analyze in multi-temporal scale, the dynamics of use, land occupation and coverage of the city of Caxias/MA with a focus on east Zone East through tools obtained from the Remote Sensing. The methodology used was literature, documentary and cartographic. Thematic maps were made by interpreting images obtained from the Landsat 5 TM (Thematic Mapper) satellites for the year 2000 and the Landsat 8 OLI (Operational Land Imager) for 2017, using the SCP (Semi-Automatic Classification) plugin of the QGIS software 2.18.8. Through the results obtained, it was found that in the zona Leste secondary vegetation continued to represent the largest area, despite the urban area having grown (from 33% to 35%). It is characterized because of the large green area in the Pai Geraldo district and Baixinha where it is located a farm. In the face and the remote sensing steps, field and laboratory, this study represents an analysis of use, occupation and land cover occurred in two areas of the city of Caxias/MA where, from there it was found the most diverse activities in the area, also with different risks and environmental impacts. Thus, it reinforces the need for further studies and remote sensing to the achievement of goals.Keywords: land cover; remote sensing; East zone; Caxias/MA.

scholarly journals ALTERAÇÃO NA COBERTURA FLORESTAL DA REGIÃO NOROESTE DA AMAZÔNIA MATOGROSSENSE

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 520
Author(s):  
Luani Rosa de Oliveira Piva ◽  
Rorai Pereira Martins Neto
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Forest Conversion ◽  
Landsat 8 ◽  
Amazon Forest ◽  
Landsat 8 Oli ◽  
Mato Grosso ◽  
The Past ◽  
Landsat 5 Tm

Nos últimos anos, a intensificação das atividades antrópicas modificadoras da cobertura vegetal do solo em território brasileiro vem ocorrendo em larga escala. Para fins de monitoramento das alterações da cobertura florestal, as técnicas de Sensoriamento Remoto da vegetação são ferramentas imprescindíveis, principalmente em áreas extensas e de difícil acesso, como é o caso da Amazônia brasileira. Neste sentido, objetivou-se com este trabalho identificar as mudanças no uso e cobertura do solo no período de 20 anos nos municípios de Aripuanã e Rondolândia, Noroeste do Mato Grosso, visando quantificar as áreas efetivas que sofreram alterações. Para tal, foram utilizadas técnicas de classificação digital de imagens Landsat 5 TM e Landsat 8 OLI em três diferentes datas (1995, 2005 e 2015) e, posteriormente, realizada a detecção de mudanças para o uso e cobertura do solo. A classificação digital apresentou resultados excelentes, com índice Kappa acima de 0,80 para os mapas gerados, indicando ser uma ferramenta potencial para o uso e cobertura do solo. Os resultados denotaram uma conversão de áreas florestais principalmente para atividades antrópicas agrícolas, na ordem de 472 km², o que representa uma perda de 1,3% de superfície de floresta amazônica na região de estudo.Palavras-chave: conversão de áreas florestais; uso e cobertura do solo; classificação digital; análise multitemporal. CHANGE IN FOREST COVER OF THE NORTHWEST REGION OF AMAZON IN MATO GROSSO STATE ABSTRACT: In the past few years, the intensification of anthropic activities that modify the soil-vegetation cover in Brazil’s land has been occurring on a large scale. To monitor the forest cover changes, the techniques of Remote Sensing of vegetation are essential tools, especially in large areas and with difficult access, as is the case of the Brazilian Amazon. The aim of this work was to identify the changes in land use and land cover, over the past 20 years, in the municipalities of Aripuanã and Rondolândia, Northwest of Mato Grosso State, in order to quantify the effective altered areas. Landsat 5 TM and Landsat 8 OLI digital classification images techniques were used in three different dates (1995, 2005 and 2015) and, later, the detection to the land use and land cover changes. The digital classification showed excellent results, with kappa index above 0.80 for the generated maps, indicating the digital classification as a potential tool for land use and land cover. Results reflect the conversion of forest areas mainly for agricultural activities, in the order of 472 km², representing a loss of 1.3% of Amazon forest surface in the study region.Keywords: forest conversion; land use and land cover; digital classification; multitemporal analysis.

scholarly journals Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors

2020 ◽  
Vol 12 (8) ◽  
pp. 1263 ◽  
Author(s):  
Yingfei Xiong ◽  
Shanxin Guo ◽  
Jinsong Chen ◽  
Xinping Deng ◽  
Luyi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
The Cross ◽  
Model Training

Detailed and accurate information on the spatial variation of land cover and land use is a critical component of local ecology and environmental research. For these tasks, high spatial resolution images are required. Considering the trade-off between high spatial and high temporal resolution in remote sensing images, many learning-based models (e.g., Convolutional neural network, sparse coding, Bayesian network) have been established to improve the spatial resolution of coarse images in both the computer vision and remote sensing fields. However, data for training and testing in these learning-based methods are usually limited to a certain location and specific sensor, resulting in the limited ability to generalize the model across locations and sensors. Recently, generative adversarial nets (GANs), a new learning model from the deep learning field, show many advantages for capturing high-dimensional nonlinear features over large samples. In this study, we test whether the GAN method can improve the generalization ability across locations and sensors with some modification to accomplish the idea “training once, apply to everywhere and different sensors” for remote sensing images. This work is based on super-resolution generative adversarial nets (SRGANs), where we modify the loss function and the structure of the network of SRGANs and propose the improved SRGAN (ISRGAN), which makes model training more stable and enhances the generalization ability across locations and sensors. In the experiment, the training and testing data were collected from two sensors (Landsat 8 OLI and Chinese GF 1) from different locations (Guangdong and Xinjiang in China). For the cross-location test, the model was trained in Guangdong with the Chinese GF 1 (8 m) data to be tested with the GF 1 data in Xinjiang. For the cross-sensor test, the same model training in Guangdong with GF 1 was tested in Landsat 8 OLI images in Xinjiang. The proposed method was compared with the neighbor-embedding (NE) method, the sparse representation method (SCSR), and the SRGAN. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were chosen for the quantitive assessment. The results showed that the ISRGAN is superior to the NE (PSNR: 30.999, SSIM: 0.944) and SCSR (PSNR: 29.423, SSIM: 0.876) methods, and the SRGAN (PSNR: 31.378, SSIM: 0.952), with the PSNR = 35.816 and SSIM = 0.988 in the cross-location test. A similar result was seen in the cross-sensor test. The ISRGAN had the best result (PSNR: 38.092, SSIM: 0.988) compared to the NE (PSNR: 35.000, SSIM: 0.982) and SCSR (PSNR: 33.639, SSIM: 0.965) methods, and the SRGAN (PSNR: 32.820, SSIM: 0.949). Meanwhile, we also tested the accuracy improvement for land cover classification before and after super-resolution by the ISRGAN. The results show that the accuracy of land cover classification after super-resolution was significantly improved, in particular, the impervious surface class (the road and buildings with high-resolution texture) improved by 15%.

scholarly journals Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollutants ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Regional Studies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

scholarly journals Thermal Remote Sensing: A tool to Determine Temporal Land Surface Temperature in Hawassa City, Ethiopia

2020 ◽  
Author(s):  
Mikias Biazen Molla
Keyword(s):  
Remote Sensing ◽  
Standard Deviation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Thermal Remote Sensing ◽  
The City

Abstract This investigation was conducted for the estimation of the temporal land surface temperature value using thermal remote sensing of Landsat-8 (OLI) Data in Hawassa City Administration, Ethiopia. Satellite datasets of Landsat-7 (ETM+) for 22nd March 2002 and Landsat-8 (OLI) of 22nd March 2019 were taken for this study. Different algorisms were used to estimate the Normalized Difference Vegetation Index threshold from the Red and Near-Infrared band and the ground earth's surface emissivity esteem is legitimately recovered from the thermal infrared by coordinating with the outcome got from MODIS information. The land use land cover map of the city was prepared with better accuracy using the on-screen classification technique. The spatial distribution of surface temperature of the city range from 6.62°C to 22.54°C with a mean of 14.58°C and a standard deviation of 11.25 in the year of march 22nd 2002. The LST result derived from Landsat 8 for March 22nd, 2019, ranges from 11.97°C to 35.5°C with a mean of 23.735 °C and a standard deviation of 16.64. In both years the higher LST values correspond to built-up/settlement and bare/open lands of the city; whereas, lower LST values were observed in vegetation (trees/woodlot, shrubs, and grass forested) area. Urban expansion (built-up area roads, and another impervious surface), decline in vegetation levels due to deforestation and increasing population density. Increasing an evergreen tree and green space coverage, design and develop city parks and rehabilitate the existing degraded natural environments are among the recommended strategy to reduce the rate of LST.

scholarly journals Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

2017 ◽  
Vol 1 (2) ◽  
pp. 355-366
Author(s):  
E. O. Makinde ◽  
A. D. Obigha
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Landsat 7 ◽  
Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

scholarly journals URBANIZATION AND IT IMPACTS TO LAND SURFACE TEMPERATURE ON SMALL MEDIUM SIZE CITY FOR YEAR 1991, 2011 AND 2018: CASE STUDY KOTA KINABALU

2020 ◽  
Vol 6 (1) ◽  
pp. 58-76
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hot Spot ◽  
Medium Size ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Urban Heat ◽  
Landsat 5 Tm

Transformation of land cover vegetation toward urban areas causes the temperature at urban higher to compare to suburban and rural areas, namely urban heat island (UHI) effect. The UHI has a negative impact, such a stroke heat, air pollution, green gasses emission, and electric consumption. UHI studies at a tropical country still limited due to the containment of cloud cover. Besides that, studies only focus on big cities which have residents above than 2 million. The outcome this studied important to enhance our knowledge of urban heat effect at small-medium cities and guidelines to policymaker and urban planner to discover there has effectively taken to decrease the effect of urban heat at the hot spot area. The main goal of this research about to discovered influence of urban growth and selected urban index, namely the Normalized Difference Built Index (NDBI) to LST. NDBI is an index which denotes intensity of urban built up. In the first step, we generate the LST and NDBI from Landsat 8 OLI at year 2018 and Landsat 5 TM for the year 2011 and 1991. Second, we applied the unsupervised classification of Landsat 8 OLI and Landsat 5 TM to generate the land cover maps for the years 1991, 2011, and 2018. Third of our method to examine the relationship between Land surface temperature (LST) and NDBI.  The higher value NDBI is a hot spot, and the low value is a cold spot. In the last step, we applied for Change Detection analysis using GIS to examine the land cover change between 1991 and 2018.  Our results show the higher the value of NDBI and LST at the centre of the city and the lowest value at vegetation land cover. The transformation of land cover vegetation to urban increase at countryside area and out-of-town and significantly increase of distribution of UHI. On another hand, the shows positive relationships between LST and NDBI. The output of the study provides a guideline for policymakers and town designers to develop to toward city zero carbon, sustainable and health.

scholarly journals Transformações do uso e cobertura da terra na bacia hidrográfica do rio Murucupi, Barcarena, Pará

2020 ◽  
Vol 13 (5) ◽  
pp. 2340
Author(s):  
Layse Gomes Furtado ◽  
Gundisalvo Piratoba Morales ◽  
Davi Farias Da Silva ◽  
Altem Nascimento Pontes
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Environmental Impacts ◽  
Population Expansion ◽  
Forest Vegetation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Urbanized Area ◽  
Urbanized Areas ◽  
Landsat 5 Tm

A importância de estudos que relacionem as alterações de cobertura, uso e manejo da terra são fundamentais para compreender a dinâmica da paisagem em determinadas áreas. O presente trabalho teve por objetivo analisar as transformações do uso e cobertura da terra na bacia hidrográfica do rio Murucupi, no município de Barcarena/PA, ao longo dos últimos 29 anos. Por meio de geotecnologias foi realizada a identificação, classificação e quantificação dos tipos de uso e cobertura da terra, utilizando imagens de satélite Landsat 5 TM e Landsat 8 OLI, correspondentes aos anos de 1990, 2000, 2010 e 2019. Após o processamento das imagens, os elementos encontrados no interior da bacia foram classificados e analisados para a compreensão do seu processo de evolução ou regressão. Os resultados apontaram que houve um crescimento progressivo de áreas urbanizadas desde 1990, em detrimento da vegetação florestal. Porém, somente em 2000, foi observada a crescente evolução das áreas sem vegetação, onde 21,19% correspondia a área urbanizada e 4,08% a área de solo exposto. Em 2010, foi registrado uma perda superior a 50% de vegetação florestal, dando espaço, principalmente, para área urbanizada (35,01%). Diferentemente do ano 2000, em 2019 mais de 50% (1,505 ha) da bacia encontrava-se antropizada. Conclui-se que a maioria de áreas de floresta foram convertidas em áreas urbanizadas, onde a expansão delas é proporcional a expansão industrial e populacional do munícipio. Tais mudanças propiciaram diversos impactos ambientais, dentre eles o intenso desflorestamento e a poluição do solo e do rio Murucupi por dejetos domésticos e industriais. Land use and land cover transformations in the Murucupi river basin, Barcarena, Pará A B S T R A C TThe importance of studies that relate to changes in land cover, use and management are fundamental to understand the dynamics of the landscape in certain areas. The present work aims to analyze the transformations of land use and coverage in the hydrographic basin of the Murucupi River, in the municipality of Barcarena / PA, over the past 29 years. Geotechnologies were used to identify, classify and quantify the types of land use and land cover, using satellite images Landsat 5 TM and Landsat 8 OLI, corresponding to the years 1990, 2000, 2010 and 2019. After processing the images, the elements found inside the basin were classified and analyzed to understand their evolution or regression process. The results showed that there has been a progressive growth in urbanized areas since 1990, to the detriment of forest vegetation. However, only in 2000, there was an increasing evolution of areas without vegetation, where 21.19% corresponded to the urbanized area and 4.08% to the exposed soil area. In 2010, a loss of more than 50% of forest vegetation was recorded, giving space, mainly, to the urbanized area (35.01%). Unlike the year 2000, in 2019 more than 50% (1,505ha) of the basin was anthropized. It is concluded that the majority of forest areas have been converted into urbanized areas, where their expansion is proportional to the industrial and population expansion of the municipality. Such changes have led to several environmental impacts, including soil and Murucupi river pollution by domestic and industrial waste.Keywords: Landscape dynamics, Geotechnologies, Environmental impacts.

scholarly journals DINAMIKA SUHU PERMUKAAN DAN KERAPATAN VEGETASI DI KOTA CIREBON

2019 ◽  
Vol 6 (1) ◽  
pp. 23-31
Author(s):  
Moh Dede ◽  
Galuh Putri Pramulatsih ◽  
Millary Agung Widiawaty ◽  
Yanuar Rizky Rizky Ramadhan ◽  
Amniar Ati
Keyword(s):  
Transfer Equation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Radiative Transfer Equation ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Multi Temporal ◽  
Urban Heat ◽  
Landsat 5 Tm

Peningkatan suhu udara merupakan dampak dari pemanasan global serta berkurangnya vegetasi. Pada kawasan perkotaan, peningkatan suhu udara secara signifikan dapat memunculkan fenomena urban heat island yang dalam jangka panjang mampu mengubah iklim mikro. Estimasi suhu permukaan dan kerapatan vegetasi diperoleh dari data satelit penginderaan jauh secara multi-temporal. Penelitian ini bertujuan untuk menganalisis dinamika suhu permukaan dan kerapatan vegetasi di Kota Cirebon. Penelitian ini memanfaatkan data citra Landsat-5 TM dan Landsat-8 OLI yang divalidasi dengan data MODIS pada periode tahun 1998, 2008, serta 2018. Nilai suhu permukaan diekstraksi dengan radiative transfer equation, sedangkan informasi kerapatan vegetasi diperoleh dengan normalized difference vegetation index (NDVI). Interaksi antara suhu permukaan dan kerapatan vegetasi diketahui melalui analisis korelasi spasial. Sepanjang tahun 1998 hingga 2018 terjadi peningkatan suhu permukaan sebesar 1.18 oC yang disertai dengan menurunnya area bervegetasi rapat hingga 12.683 km2. Penelitian ini juga menunjukkan korelasi negatif yang signifikan antara suhu permukaan dan kerapatan vegetasi di Kota Cirebon. Suhu permukaan tertinggi terpusat pada CBD, pelabuhan, area rawan kemacetan, kawasan industri, dan terminal. Berdasarkan kajian ini, upaya menanggulangi suhu permukaan di Kota Cirebon perlu ditangani melalui penyediaan ruang terbuka hijau, green belt, maupun reforestrasi.

scholarly journals Spatio-Temporal Analysis of UHI using Geo-Spatial Techniques: A case study of Ahmedabad

2014 ◽  
Vol XL-8 ◽  
pp. 997-1002 ◽  
Author(s):  
A. Vyas ◽  
B. Shastri ◽  
Y. Joshi
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Heat Island ◽  
Urban Area ◽  
Rural Areas ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Heat Island ◽  
Urban Heat ◽  
The City

As per the current estimates, nearly half of the world’s population lives in the cities, by 2030 it is calculated to increase to 70%. This calls for a need of more sustainable structure in the urban areas as to support increase in the urban population. Urban Heat Island is one such conspicuous phenomenon which has its significance at local regional and also at the global levels. It is a microscale temperature variation between urban and rural areas, in which urban area are warmer compare to surrounding rural area. The temperature difference between the urban and the rural areas are usually modest, averaging less than 1°C, but occasionally rising to several degrees when urban, topographical and meteorological conditions are favorable for the UHI to develop. It is defined as the phenomena where in the occurrence of surface and atmospheric modifications due to the urbanization causes modification in the thermal climatic conditions which results into warmer areas as compared to the surrounding non urbanized areas, particularly in night. In that case urban built forms such as buildings, roofs, pavements etc. absorb more solar heat/radiation and remain warmer throughout the day time and slowly release energy during night time. The two major causes are rapid urbanization and anthropogenic heat generated due to transport and industrial activities. Urban Heat Island is a crucial subject for global environment. Urbanization has significant effects on local weather and climate. Among these effects one of the most popular is the urban heat island, for which the temperatures of the central urban locations are several degrees higher than those of nearby rural areas of similar elevation. Satellite data provides important inputs for estimating regional surface albedo and evapo-transpiration required in the studies related to surface energy balance. <br><br> The phenomenon of UHI affects environment and population in so many ways it can also be considered as an active element that cause vulnerabilities to human health, the marginal population affected largely as the natural environment is their only home or their main shelter. Furthermore elderly people also affected in greater amount as their weakening immunes system. Major effects of UHI on environment include: a) Air Quality, b) Energy consumption and c) Human health. <br><br> To study the causes and effect of UHI of any urban area, the first step is to demarcate the spatial distribution of UHI and its intensity over different time period of the day as well as difference in the temperature of urban area with the surrounding rural areas. Secondly, study of land use land cover change in the area also helps in identifying causes of heat accumulation for particular region. After marking up of intensity, analysis of different zones for understanding the relationship between UHI and urban morphological features can be done which further became suggestive towards planning of urban center that mitigates the effect of UHI. Mainly two approaches are there to demarcate UHI study as: <br><br> &ndash; Field data collection and observations <br> &ndash; Remote sensing data analysis <br><br> For a long period of time observations from interior of the city and outwards of it can analyze by a climatic methods, by observing many days as well as many times of a day continuously to analyze the daily variation law of the heat island effects. As the city is for its developmental approaches may cover an area of hundreds of square kilometers, the ground observation data is not able to provide enough detail about the urban heat island distribution characteristics. The most precise method is the Satellite Remote Sensing method. The UHI phenomenon can be analyzed by using the thermal infrared data obtained meteorological satellite sensing. The atmospheric attenuation can be corrected for the remote sensing data by use of meteorological soundings and ground observation data. Ideally the heat island effect over a city is not same for any other city. <br><br> Satellite images from AVHRR Advanced Very High Resolution Radiometer) or ENVISAT AATSR provides thermal infrared data and comparatively easy to acquire, process and analyze. In the case of Ahmedabad city, land cover changes over the time is to be studied by classifying the image and then temperature can be derived by using a quadratic regression model from Malaret at al. (1985). Band 6 produces the images that show the relative difference emitted thermal energy that correlate in part with the effects of solar heating on surface of varying composition and orientation. The surface temperatures are suitable to detect UHI at Urban canopy level. Nichol (1996) found that surface temperatures extracted are moreover similar to the actual ambient air temperatures recorded. <br><br> The paper has narrated analylitical framework on which the research has been carried out. The result derived on Land Surface Temperature variation causing Urban Heat Island, its relationship with the land use land cover. A time series data has been used. Authors are thankful to Ms. Darshana Rawal, Ms. Pallavi Knahdewal and Mr. Hardik Panchal.

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