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EN
The purpose of this research was to investigate the intricate connections among land use change, land surface temperature, and the distribution of partridges (Alectoris barbara), employing a comprehensive analysis of various environmental factors. Indeed, a variety of geospatial techniques have been used to analyze the spatio-temporal trends in temperature as a function of different classes of vegetation cover, and the geographic distribution of ecological niches for this species in Meknes province was modeled using Maxent 3.2 (Maximum Entropy) software. The study spanned a 22-year timeframe, from 2000 to 2021, during which alterations in each land use category were identified through the utilization of various sensors, incorporating Landsat 7 ETM+ and Landsat 8 OLI/TIRS in the analysis. The results induced a significant change in the land surface temperature (LST) with a range of 15.85–36.20°C, 12.76–38.24°C and 25.73–47.79°C for the years 2000, 2010 and 2020, respectively. However, this change was negatively correlated with the normalized difference vegetation index (NDVI). This decline in vegetation, in turn, manifests as a significant factor contributing to the diminution of partridge distribution. By empirically establishing these connections, the research not only underscores the impact of temperature-induced vegetation changes on partridge habitat but also enhances comprehension of the intricate ecological dynamics governing species distribution in the context of evolving land use patterns.
EN
The continuous process of urbanization and climate change has led to severe urban heat island (UHI) effects. Constructing parks with cooling capabilities is considered an effective measure to alleviate UHI effects. However, most studies only quantify the cooling effect from a maximum value perspective, lacking a measure of temperature diffusion in space. This study combines the perspectives of maximum value and accumulation to define a cold island index, quantifying the cooling effect of 40 urban parks in the main urban area of Xi'an city. The results show that, on average, urban parks can reduce the surrounding environment by approximately 2.3℃, with a cooling range of about 127.1ha, which is three times the park area. Different factors drive the measurement of the cooling effect using different cold island indexes, but all indexes are highly correlated with green space area. There are significant differences in the cooling effect among different types of parks, and overall, ecological parks have the best cooling effect. The directional spread of internal cold islands in parks is most related to park shape, while external spread is related to surrounding green spaces. The research results have practical value in the construction of parks with cooling effects and the sustainable development of cities in urban planning processes.
EN
The main objective of this study is to show which of the LST-NDVI and LST-NDBI relationships can determine the most accurate index that can be used as an indicator of the effects of urban heat islands in the municipality of Guelma, using Landsat data. 8 OLI/TIRS and the geographic information system. The application of the calculation formulas made it possible to extract the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built up Index (NDBI) of the municipality of Guelma for the four seasons of 2019. This calculation led to the determination of the relationship between all three indicators. The results obtained show a strong correlation between the LST and the NDBI for the four seasons of the year. They suggest that the NDBI is an accurate indicator of the heat island effect in Guelma. This indicator can serve as a tool for future urban planning by those in charge of this department. However, there is currently and urgent need to strengthen strategies for reducing the effects of urban heat islands in order to preserve the quality of urban life of the inhabitants and by setting up emergency programs.
EN
Land surface temperature (LST) estimation is a crucial topic for many applications related to climate, land cover, and hydrology. In this research, LST estimation and monitoring of the main part of Al-Anbar Governorate in Iraq is presented using Landsat imagery from five years (2005, 2010, 2015, 2016 and 2020). Images of the years 2005 and 2010 were captured by Landsat 5 (TM) and the others were captured by Landsat 8 (OLI/TIRS). The Single Channel Algorithm was applied to retrieve the LST from Landsat 5 and Landsat 8 images. Moreover, the land use/land cover (LULC) maps were developed for the five years using the maximum likelihood classifier. The difference in the LST and normalized difference vegetation index (NDVI) values over this period was observed due to the changes in LULC. Finally, a regression analysis was conducted to model the relationship between the LST and NDVI. The results showed that the highest LST of the study area was recorded in 2016 (min = 21.1°C, max = 53.2°C and mean = 40.8°C). This was attributed to the fact that many people were displaced and had left their agricultural fields. Therefore, thousands of hectares of land which had previously been green land became desertified. This conclusion was supported by comparing the agricultural land areas registered throughout the presented years. The polynomial regression analysis of LST and NDVI revealed a better coefficient of determination (R2) than the linear regression analysis with an average R2 of 0.423.
EN
The rapid increase in the urbanisation process and other developmental activities across the globe have increased the land surface temperature of built-up areas which is considered as an emerging urban environmental problem. The rapid unplanned urban sprawl has influenced the land use and land cover of the urban area leading to the development of the phenomenon of Urban Heat Island. The present study highlights how land use and land cover changes have impacted the land surface temperature and urban heat islands phenomenon in the greater Imphal city of Manipur in India. The study was carried out with multi-spectral and multi-temporal satellite imageries of 1988, 2000, 2011, and 2021, respectively. The extracted information from the rectified imageries highlights a significant increase in the land surface temperature in the built-up area of the city. The findings illustrate that the maximum and minimum LST of the Imphal urban area has significantly increased from 28.77 to 31.25℃ and 10.44 to 11.47℃, respectively, for the month of February from 1988 to 2021. The increase in land surface temperature is directly attributed to the increased built-up area (24.06% to 44.85%) and reduction in the urban forest cover (28.17% to 16.65%). Cumulatively, there is a 2.44℃ and 1.03℃ rise in maximum and minimum LST over three decades. The variability in the LST shows positive correlations with the NDBI and negative correlations with NDVI. The study witnessed nearly about 0.74℃ (maximum) and 0.31℃ (minimum) decadal changes in the overall LST in the greater Imphal area.
EN
Population growth and urbanization lead to urban heat island (UHI) phenomenon. Urbanization is occurring at a very high rate in the Surat city. Thus, the study of the urbanization impact on the UHI effect for the Surat city is performed in the present study through studying the impact of land use land cover on the land surface temperature of urban and sub-urban areas of the Surat city over the period May 1998 to May 2018. Also, these effects are compared with that of a nearby sub urban taluka Kamrej, which showed that temperature in urban areas is more than that of the sub-urban areas. Aforesaid facts clearly showing the existence of the UHI effect in the Surat city. As urbanization contributes to climate change, its effects on rainfall are studied by comparing rainfall trends of urban and sub-urban areas of the Surat city and nearby sub-urban area Kamrej. Trend analysis showed that trend magnitude values are higher for the urban areas than sub-urban areas, indicating that UHI effect increases rainfall in urban areas. Hotspot analysis is also performed for the Surat city corresponding to May 2018 to recognize hot spots and cold spots. As the Surat city is highly urbanized, thus, hotspots are more than cold spots.
EN
In order to analyze the impact of land use and land cover change on land surface temperature (LST), remote sensing is the most appropriate tool. Land use/cover change has been confirmed to have a significant impact on climate through various aspects that modulate LST and precipitation. However, there are no studies which illustrate this link in the Fez-Meknes region using satellite observations. Thus, the aim of this study was to monitor LST as a function of the land use change in the Saïss plain. In the study, 12 Landsat images of the year 2019 (one image per month) were used to represent the variation of LST during the year, and 2 images per year in 1988, 1999 and 2009 to study the interannual variation in LST. The mapping results showed that the land use/cover in the region has undergone a significant evolution; an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. On the basis of statistical analyses, LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of land use type. The relationship between LST and NDVI shows a negative correlation (LST decreases when NDVI increases). This explains the increase in LST in rangelands and arable land, while it decreases in irrigated crops and arboriculture.
EN
The unsusual rise in the land surface temperature is playing a vital role toward the rapid and intense changes in global climate. Occurrence of certain land use land cover and alternative changes in them is the prime cause of bringing extreme changes in temperature. In this research, using 30-year long time series (1988–2018) data from Landsat satellites for understanding relation of mean temperature with the two largest and main controlling land use classes (vegetation and built-up) in a rapidly urbanizing district, Lahore. For this purpose, multivariate statistical approaches of scatter plot and correlation coefcient were employed. Temperatures, vegetative and built-up areas were derived using a combination of diferent spatiotemporal tools in a specifcally designed model. Critical analysis suggests breaking up of investigation timeline in two portions based on changing trend. A 23-year period from 1989 to 2011 (Temporal Window-1) and a 6-year period from 2013 to 2018 (Temporal Window-2) were tested separately for the same arguments. Vegetative area showed an increase throughout the temporal window-1 and then a rapid decrease from 2013 to 2018, while built-up area and mean temperature presented an ever-increasing trend during both temporal windows but with much higher rates in second window. Correlation of tempera ture with the both the investigated thermal controls has found getting strong in Temporal Window-2 showing that relation of these landcover areas with temeperature is not linear and severity is increasing with time. Moreover, temperature is found to be strongly dependent upon changes in built-up areas than that of vegetative areas. So, an increase in built-up area has much more devastating efects over the temperature rather than decrease in vegetative area. It was concluded that Lahore district is contributing in global warming more rapidly than it had ever done before.
EN
At present, the climate has constantly been changing, especially the increase in global average temperature that results in the risk of severe climatic conditions such as heat wave, drought and flood. The objective of this study is to estimate land surface temperature (LST) by applying Landsat satellite data in Mueang Maha Sarakham District, Maha Sarakham Province, Thailand. The study focuses on investigating the temperature changes for the years 2006 and 2015. The research was conducted by analyzing the satellite data in the thermal infrared band with a geo-informatics package software mutually with mathematical models. The operation results indicated that the average LST was at 26.28°C in 2006 and 27.15°C in 2015. In order to verify the accuracy of the data in this study, the results of the annual satellite data analysis were brought to find out a statistical correlation with the LST data from the Meteorological Station of Thai Meteorological Department (TMD). The results indicated that there was a correlation of the data at a high level in 2006 and 2015. The results of this study indicated that the satellite data analysis method is reliable and can be used to analyze, track, and verify data to predict surface temperatures effectively.
EN
Currently, more than half of the world’s population is living in cities. Rapid and unplanned urbanization became a common scenario in rapidly developing countries such as those in Asia. Decline in vegetation coverage and increase in local air and land surface temperatures are among the adverse effects of unplanned urban growth. We used Landsat data for the period 1991–2017 to estimate the expansion of urban areas in terms of vegetation loss and the development of small-scale urban heat islands in developing cities in Kerala state of India. For the last 27 years, unplanned urbanization in Kerala state has increased and this resulted in the enhanced loss of vegetation and, possibly, resulted in the increase in land surface temperature (LST). Our results indicate that vegetation coverage, particularly near the urban areas, has been decreased by 5.8%, 10.4%, and 9.6% in Ernakulam, Trichur, and Kozhikode districts, respectively. The land surface temperatures also have been increased during the study period. It is interesting to note that higher increase in LST and higher reduction in vegetation coverage were observed in Trichur and Kozhikode districts compared with highly populated and urbanized Ernakulam district.
EN
This research aimed to present the technique for land surface temperature analysis with the data from Landsat-8 Operational Land Imager (OLI) /Thermal Infrared Sensors (TIR) in Meuang Maha Sarakham District, Maha Sarakham Province, Northeast Thailand. The research was conducted as following three steps: 1) Collecting the satellite data in thermal infrared band from Landsat-8 TIR satellite to adjust the value of Top of Atmosphere (ToA) Reflectance and then analyzing the land Surface temperature 2) Collecting multi-band data from Landsat-8 OLI satellite to adjust the value of Top of Atmosphere (ToA) Reflectance and then analyzing values of Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC) and Land surface Emissivity (LSE) 3) Bringing the results of 1) and 2) to analyze the land surface temperature with split window algorithm. The research results indicated that the analysis of the data from Landsat-8 OLI/TIR satellites in 18 March 2015 indicated a mean temperature of 33.57 °C.
EN
The Earth observation satellite imaging systems have known limitations, especially regarding their spatial and temporal resolution. Therefore, approaches which aim to combine data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution are of high interest. This allows for joint utilization of the advantages of both these types of sensors. As there are several ways to achieve this goal, in this paper two approaches, direct and inverse, of downscaling the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR) were evaluated. The applied downscaling methods utilize biophysical properties of the surface sensed using short wave infrared and thermal band. The presented algorithm evaluation was performed on the basis of a specific test case: the coastal zone area of the Gulf of Gdańsk, Poland. In this context, the objective presented in the study was to compare two methods of downscaling for a specific test case in order to evaluate how the proposed approaches cope with the specific conditions of the coastal zone area.
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