Modeling of the land surface temperature as a function of the soil-adjusted vegetation index (original) (raw)
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Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data
Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.
Prediction of Land Surface Temperature (LST) Using Landsat Data: A Comparison of four Algorithms 2 3
The soft computing models for predicting land surface temperature (LST) changes, in 16 recent times, are very useful to evaluate and forecast rapidly climate change of the world. In this 17 study, several soft computing techniques such as the multivariate adaptive regression splines 18 (MARS), wavelet neural network (WNN), adaptive neuro-fuzzy inference system (ANFIS) and 19 dynamic evolving neuro-fuzzy inference system (DENFIS) are investigated and utilized to predict 20 the LST changes of Beijing area. The comparison of the aforementioned models is also presented.
Journal of the Indian Society of Remote Sensing, 2019
The land-cover type plays a decisive role for the land surface temperature (LST). Since cities and their satellite cities are composed of varying covers, including vegetation, built-up areas, buildings, roads, and bare areas, the main purpose of this research is to examine the LST in Tehran and its satellite cities and the cover type that contributes to increased or decreased temperature. The study investigated the relationship between NDVI, SAVI, NDBI, and NDBaI indices, as four biophysical variables, and LST over a period of 15 years (2001-2015) by the geographically weighted regression (GWR) model using imagery of Landsat 7. The results showed that the relationship between LST and NDBI is stronger than the associations with other variables. In 2010, biophysical variables had the greatest effect on LST. Using the GWR model, the local R 2 map was drawn for the studied area, showing that the highest value for the coefficient of determination belonged to Islamshahr and Shahriar because of the homogeneity of the land cover in these cities.
2012
Global climate destabilization as a result of the increased urbanization is one of today's most urgent issues. The detected Urban Heat Island phenomenon in urbanized areas, combined with the decreased vegetation and the anthropogenic heat discharge, is an example of this climate change and in order to take proper actions to reduce this effect, the urban environmental analysis is more than necessary. This paper aims at analyzing and exploring the relationship between land uses of a densely populated urban area with the Land Surface Temperature (LST) combining Worldview-2 and LANDSAT ETM+ Satellite Imagery. The available thermal band of the LANDSAT image is used to extract surface temperatures of the study area on a hot summer day. Continuously, the high resolution satellite image of Worldview-2 is used for extracting the land uses. Zonal statistics were applied highlighting the zones with high and low average temperatures. Additional statistical tests (correlation analysis, analysis of variance-ANOVA etc.) were applied, for evaluating the interaction between the temperature results with the land use types.
2020
the soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. e topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. e four models are assessed using statistical analysis, coefficient of determination (R 2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. e results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. e ANFIS model showed the highest performances in the training (R 2 � 0.99, RMSE � 0.78°C, MAE � 0.55°C) and testing (R 2 � 0.99, RMSE � 0.36°C, MAE � 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. e predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
Due to urbanization and changes in the urban thermal environment and because the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. In this regard, due to the unique properties of spatial data, in this study, a geographically weighted regression (GWR) was used to identify effective spatial factors. The GWR is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, the Landsat 8 satellite data on 18 August 2014 and Tehran land use data in 2006 was used for determining the land surface temperature and its effective factors. As a result, R 2 value of 0.765983 was obtained by taking the Gaussian kernel. The results showed that the industrial, military, transportation, and roads areas have the highest surface temperature.
IAMURE International Journal of Ecology and Conservation, 2020
The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU-LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone.