Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India

This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST.The Maximum Likelihood Classification(MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57°C, and the Waterbodies are noticed with a low LST of 28.9°C. However, the overall rate of LST decreased by 0.56°C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.

Application of Remote Sensing Techniques and Geographic Information Systems to Analyze Land Surface Temperature in Response to Land Use/Land Cover Change in Greater Cairo Region, Egypt

The Greater Cairo Region (GCR), Egypt has experienced rapid urban expansion and broad development over the past several decades. Due to such development, this region faces many environmental consequences. In order to mitigate such consequences, it is essential to examine the historical change to measure the urban sprawl of GCR, and its effect on land surface temperature (LST). The objective of this study is to fulfill this goal. It does so by generating land use/land cover (LULC) maps derived from Landsat 5 TM for 1990 and 2003 and Landsat 8 OLI for 2016, using several classification techniques. A spectral radiance model and a web-based atmospheric correction model were used to successfully evaluate LST from thermal bands of Landsat data. Overall accuracy of Landsat derived land use data were 90.3%, 96.5% and 94.9% for years 1990, 2003 and 2016, respectively. The LULC change analysis revealed vegetation loss to urban land by an amount of 7.73% and from barren lands to urban uses by 8.70% within a 26-year timespan (1990-2016). This rapid urban growth significantly decreases vegetation areas, consequently increasing the LST and modifying the urban microclimate. Results from this study can help policy-makers characterize the evolution of urban construction for future developments.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang,China

Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Estimate Land Surface Temperature in Relation to Land Use Types and Geological Formations Using Spectral Remote Sensing Data in Northeast Jordan

Land Surface Temperature (LST) is one of the important indicators to understand the spatial changes and surface processes on the earth surface that leads to actual assessment of environmental quality from local to global scales. The relation between spatial analysis of the land surface temperature and existing land use/land cover changes is important to evaluate the climate processes. Monitoring of this relation in the arid and semi-arid regions is necessary to make an appropriate decision about Land surface temperature and environmental status. In this paper, generally the split-window algorithm is used to estimate LST from thermal bands of the Landsat Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) using remote sensing and Geographic Information System (GIS) techniques as well as meteorological data through Moderate Resolution Imaging Spectroradiometer (MODIS). The results show the relationships between land use types and land surface temperature. MODIS data were analyzed. The relationship between MODIS and Landsat data temperature is moderate relation and the (R2 = 0.5109) according on 200 random points were selected. This research concludes that the maximum temperatures of the land use types in MODIS and Landsat data for the rock formation are 59°and 45°respectively, whereas the maximum temperatures of the geological formation in MODIS and Landsat data for the basalt are 59°and 45°respectively. In conclusion, the MODIS and Landsat OLI and TIRS Data have high ability to distinguish the land use types. The correlation coefficient of the relation between the surface temperature with rock was (R2 = 0.6197). Therefore, it is found that there is an ability to monitor the spatial and temporal changes for land surface temperature and thus it can be useful to environmental studies.

Impact of Land Use/Land Cover Change on Surface Temperature Condition of Awka Town, Nigeria

This paper is aimed at identifying the land use/cover types in Awka in relation to their temporal dynamics, the extent of land use change in the city and effects of land use change on surface temperature. Multitemporal Landsat TM, ETM+ and OLI imageries were obtained at 15 years interval for 1986, 2000 and 2015 respectively. Image classification was conducted using supervised classification method. The result showed that built-up area has been on the increase, expanding from 9.55 sqkm in 1986 to 21.3 sqkm in 2000 and 21.45 sqkm in 2015. Cultivated lands have maintained a steady decline since 2000 having lost about 3.29 sqkm of its area. Similarly, vegetation, made up of dense, savanna and riparian, has been on a decline from a total of 33.69sqkm in 1986 to 21.407 sqkm losing about 12.29 sqkm of its area and increased by a mere 4.07 sqkm in 2015. These alterations had given rise to an average increase of 2.2°C in surface radiant temperature. This study recommends that relevant government planning agencies (ACTDA, ASHDC, etc.) should factor in the concept of greening and green spaces into their development policies and strategies to ensure that fair, conducive microclimate and sustainable environment is maintained in the Awka urban area.

Impact of the Zhalong Wetland on Neighboring Land Surface Temperature Based on Remote Sensing and GIS

Wetlands play a key role in regulating local climate as well as reducing impacts caused by climate change. Rapid observations of the land surface temperature(LST) are, therefore, valuable for studying the dynamics of wetland systems. With the development of thermal remote sensing technology, LST retrieval with satellite images is a practicable way to detect a wetland and its neighboring area’s thermal environment from a non-point visual angle rather than the traditional detection from a point visual angle. The mono-windows(MW) method of retrieving LST was validated. On the basis of estimated LST, we used Geographical Information System(GIS) technology to study the impact of wetland reclamation on local temperatures at a regional scale. Following that, correlations between LST and the wetland were analyzed. The results show that: 1) It is feasible to retrieve the LST from Landsat 8 OLI satellite images with MW model. The model was validated with the land surface temperature observed in four meteorological stations when the satellite scanned the study region. The satellite retrieval error was approximately 1.01°C. 2) The relationship between the spatial distribution of land surface temperatures and the Zhalong wetland was analyzed based on GIS technology. The results show that wetland has an obvious influence on LST, and that this influence decreases with increasing distance from the wetland. When the distance from the wetland was less than 500 m, its influence on LST was significant. Results also illustrated that the effect of the wetland’s different land use/land cover’s LST distribution varied with different seasons.

Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Onitsha, South East Nigeria

The increasing urbanization generally is brought about by many eco-environmental problems, such as the drastic change of land use and development of urban heat island. This study identified the pattern of land cover changes in Onitsha, South East Nigeria, and assessed the trend in temperature over the area from 1986 to 2016, simulated the land cover changes for 2030 and 2044, and estimated their impacts on land surface temperature (LST). These were with the view to determining the effects of changes in land use/cover on the LST in the area. Primary and secondary data were used for the study. The primary data were coordinates of geographic features within the study area, collected through the use of global positioning system. The secondary data were minimum and maximum temperature data from the Nigerian Meteorological Agency (NIMET), high-resolution Quick-bird imagery, Landsat TM/ETM imageries for four epochs (1986, 2002, and 2016) with path and row of 189 and 56. Individual components (bare surface, built-up, vegetation and surface water bodies) in the study area were extracted from the images. Radiometric correction was first performed (for 2016 landsat imagery) to fill the line scan corrector (LSC) gaps using Focal Analysis tool in ERDAS Imagine 14.0. This was followed with clipping of the satellite imagery to extract only those portions that are within the boundary of the study area. Supervised image classification was done for the three series of remotely sensed imageries to extract the spatial pattern of land cover change. The results revealed that the study area has been growing to a relatively compact urban agglomeration. The concentration of built-up area at the western and the central part of the study area has been getting larger and more aggregated. Built-up area increased by 11.49% from 1986 to 2002 and 5.68% from 2002 to 2016 while vegetation decreased by 6.03% from 50.26% in 1986 to 44.23% in 2002. The decrease further continued from 44.23% in 2002 to 29.79% in 2016 (change of 14.44%). Expansion to spread into the sub-urban and rural communities bounding the metropolis, converting agricultural and green area to built-up areas. The land cover trend of the study area from the period of 1986-2016 shows dramatic changes for the dominant land cover types. The analyzed trend in temperature change shows that the central parts of the city have a temperature higher than the outlying parts. The results indicated that the average temperature of the study area increased from 24&#176C in 1986 to 28&#176C in 2016. The simulated land cover also showed a decreasing trend in vegetal cover. The study concluded that the increase in built-up area caused an annual increase in land surface temperature (0.31&#176C) over the study period.

Impact of Land Use Changes on Surface Warming in China

Land use changes such as urbanization, agriculture, pasturing, deforestation, desertification and irrigation can change the land surface heat flux directly, and also change the atmospheric circulation indirectly, and therefore affect the local temperature. But it is difficult to separate their effects from climate trends such as greenhouse-gas effects. Comparing the decadal trends of the observation station data with those of the NCEP/NCAR Reanalysis (NNR) data provides a good method to separate the effects because the NNR is insensitive to land surface changes. The effects of urbanization and other land use changes over China are estimated by using the difference between the station and the NNR surface temperature trends. Our results show that urbanization and other land use changes may contribute to the observed 0.12℃ (10yr)-1 increase for daily mean surface temperature, and the0.20℃ (10yr)-1 and 0.03℃ (10 yr)-1 increases for the daily minimum and maximum surface temperatures, respectively. The urban heat island effect and the effects of other land-use changes may also play an important role in the diurnal temperature range change. The spatial pattern of the differences in trends shows a marked heterogeneity. The land surface degradation such as deforestation and desertification due to human activities over northern China, and rapidly-developed urbanization over southern China, may have mostly contributed to the increases at stations north of about 38°N and in Southeast China, respectively. Furthermore, the vegetation cover increase due to irrigation and fertilization may have contributed to the decreasing trend of surface temperature over the lower Yellow River Basin. The study illustrates the possible impacts of land use changes on surface temperature over China.

Analysis of relationships between land surface temperature and land use changes in the Yellow River Delta

This study analyzed land use and land coverchanges and their impact on land surface temperature usingLandsat 5 Thematic Mapper and Landsat 8 OperationalLand Imager and Thermal Infrared Sensor imagery of theYellow River Delta. Six Landsat images comprising twotime series were used to calculate the land surfacetemperature and correlated vegetation indices. The YellowRiver Delta area has expanded substantially because of thedeposited sediment carried from upstream reaches of theriver. Between 1986 and 2015, approximately 35% of theland use area of the Yellow River Delta has beentransformed into salterns and aquaculture ponds. Overall,land use conversion has occurred primarily from poorlyutilized land into highly utilized land. To analyze thevariation of land surface temperature, a mono-windowalgorithm was applied to retrieve the regional land surfacetemperature. The results showed bilinear correlationbetween land surface temperature and the vegetationindices （i.e., Normalized Difference Vegetation Index,Adjusted-Normalized Vegetation Index, Soil-AdjustedVegetation Index, and Modified Soil-Adjusted VegetationIndex）. Generally, values of the vegetation indices greaterthan the inflection point mean the land surface temperatureand the vegetation indices are correlated negatively, andvice versa. Land surface temperature in coastal areas isaffected considerably by local seawater temperature andweather conditions.

Dynamicity of Land Use/Land Cover(LULC):An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation(DMC)in India

The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city and fostering the growth of physical infrastructure.Using multi-temporal satellite images,the dynamics of Land Use/Land Cover(LULC)changes,the impact of urban growth on LULC changes,and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India.The study used different case studies to highlight the study area’s heterogeneity,as the phenomenon of change is not consistent.Landsat TM and OLI-TIRS satellite images in 1991,2001,2011,and 2021 were used to analyse the changes in LULC types.We used the relative deviation(RD),annual change intensity(ACI),uniform intensity(UI)to show the dynamicity of LULC types(agriculture land;built-up land;fallow land;vegetated land;mining area;and water bodies)during 1991-2021.This study also applied the Decision-Making Trial and Evaluation Laboratory(DEMATEL)to measure environmental sensitivity zones and find out the causes of LULC changes.According to LULC statistics,agriculture land,built-up land,and mining area increased by 51.7,95.46,and 24.79 km^(2),respectively,from 1991 to 2021.The results also suggested that built-up land and mining area had the greatest land surface temperature(LST),whereas water bodies and vegetated land showed the lowest LST.Moreover,this study looked at the relationships among LST,spectral indices(Normalized Differenced Built-up Index(NDBI),Normalized Difference Vegetation Index(NDVI),and Normalized Difference Water Index(NDWI)),and environmental sensitivity.The results showed that all of the spectral indices have the strongest association with LST,indicating that built-up land had a far stronger influence on the LST.The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km^(2)/a,respectively,during 1991-2021.In summary,this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land,thus making effective peri-urban planning decisions.

Spatiotemporal Effects of Land Use and River Morphological Change on the Microclimate of Rajshahi Metropolitan Area

Microclimate is a climatic phenomenon of a very local area that is affected by the variation of natural earth surfaces in urban area. The urban growth expedites to change land use and land cover pattern of a city that modify the built environment, alter the composition of the atmosphere and form a distinct local climate in the cities. The radical land use and land cover change also alters the meteorological condition of a city. The relationship between land use land cover and thermal environment has long been studied to identify the impacts of urban growth but for this study area no such effort is taken. This study focuses on detecting the changing pattern of the cities adjacent Padma River and land uses, finally the impacts of such changes on the microclimate are assessed. This study is conducted based on Landsat time series data of 1989, 1996, 2005 and 2015 using ENVI and ArcGIS 10.2 softwares. The result demonstrates that the city is experiencing continuous change in land cover pattern, where the city centre is more vulnerable to change than its fringe. Another significant change is observed in the morphology of river Padma;it shows continuous shift of the river channel and increase in the Char land. The interactions of land use land cover and LST depicts a continuous increase in the mean surface temperature over bare soil following impervious area. The results underscore that two factors are contributing to influence the microclimate of the study area: First, the areal extent of surface water particularly the river Padma is losing stream and generating more bare soil (Char Land);second, impervious area is increasing and natural earth surfaces are decreasing.

Feedback and contribution of vegetation, air temperature and precipitation to land surface temperature in the Yangtze River Basin considering statistical analysis

Land surface temperature(LST),especially day-night LST difference(LSTd-LSTn),is a key variable for the stability of terrestrial ecosystems,affected by vegetation and climate change.Quantifying the contribution and feedback of vegetation and climate to LST changes is critical to developing mitigation strategies.Based on LST,Normalized vegetation index(NDVI),land use(LU),air temperature(AT)and precipitation(Pre)from 2003 to 2021,partial correlation was used to analyze the response of LST to vegetation and climate.The feedback and contribution of both to LST were further quantifed by using spatial linear relationships and partial derivatives analysis.The results showed that both interannual LST(LSTy)and LSTd-LSTn responded negatively to vegetation,and vegetation had a negative feedback effect in areas with significantly altered.Vegetation was also a major contributor to the decline of LSTd-LSTn.With the advantage of positive partial correlation area of 94.99%,AT became the main driving factor and contributor to LSTy change trend.Pre contributed negatively to both LSTy and LSTd-LSTn,with contributions of-0.004℃/y and-0.022℃/y,respectively.AT played a decisive role in LST warming of YRB,which was partially mitigated by vegetation and Pre.The present research contributed'to,the,detection,of LST changes and improved understanding of the driving mechanism.

Urban Heat Island and Land Use/Cover Dynamics Evaluation in Enugu Urban, Nigeria

This study specifically estimated the effect of land use/cover change (LULC) processes on land surface temperature (LST) in Enugu urban and its suburbs. With Landsat images and supervised classification technique, four LULC classes comprising built-up areas, vegetation, rock outcrop, bare ground/farmland areas were delineated. The LST was extracted from the thermal bands of the images. The rate of change in land cover classes between 2009 and 2018 showed that from 2009 to 2013, built-up areas increased from 31.65% to about 47.5%, while vegetation cover decreased from 18.43% to 11.23%. Also, the periods witnessed about 8.69 km2 of vegetation being converted to other land surfaces. The trend in the LST in Enugu urban showed the highest mean temperatures of 34.5°C in 2018 and 32.26°C in 2015. However, in 2013 there was a slight decrease in mean LST to 31.65°C which further decreased to 31.26°C in 2009. This change in temperature suggests that urbanisation could have significant effect on the micro-climate of Enugu city. Result also revealed weak relationships between LULC classes and the LST throughout the years. The results of the surface heat intensity for the urban and rural areas showed general increase over the years and this suggests that rural areas are also experiencing high temperature which could be due to the loss of vegetation, increase in artificial surfaces and urban encroachment. Findings from this study could be useful for effective urban land-use planning, policy development and management in Nigeria, and elsewhere.

Sensitivity of surface air temperature change to land use/cover types in China

Using CRU high resolution grid observational temperature and ERA40 reanalysis surface air temperature data during 1960-1999, we investigated the sensitivity of surface air temperature change to land use/cover types in China by subtracting the reanalysis from the observed surface air temperature (observation minus reanalysis, OMR). The results show that there is a stable and systemic impact of land use/cover types on surface air temperature. The surface warming of each land use/cover type reacted differently to global warming. The OMR trends of unused land (≥0.17℃/decade), mainly comprised by sandy land, Gobi and bare rock gravel land, are obviously larger than those of the other land use/cover types. The OMR over grassland, farmland and construction land shows a moderate decadal warming about 0.12℃ /decade, 0.10℃/decade, 0.12 ℃ /decade, respectively. Woodland areas do not show a significant warming trend (0.06 ℃ /decade). The overall assessment indicates that the surface warming is larger for areas that are barren and anthropogenically developed. The better the vegetation cover, the smaller the OMR warming trend. Responses of surface air temperature to land use/cover types with similar physical and chemical properties and biological processes have no significant difference. The surface air temperature would not react significantly until the intensity of land cover changes reach a certain degree. Within the same land use/cover type, areas in eastern China with intensive human activities exhibit larger warming trend. The results provide observational evidence for modeling research on the impact of land use/cover change on regional climate. Thus, projecting further surface climate of China in regional scale should not only take greenhouse gas increase into account, but also consider the impact of land use/cover types and land cover change.

A novel AI approach for modeling land surface temperature of Freetown,Sierra Leone,based on land-cover changes

Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)models.The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST changes.This study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in Sierra-Leon.Artificial neural network(ANN)and gene expression programming(GEP)were employed to develop LST prediction models.Images of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed models.The extracted data of LULC indices,such as normal difference vegetation index(NDVI),normal difference built-up index(NDBI),urban index(UI),and normal difference water index(NDWI),were utilized as attributes to model LST.The results show that the root-mean-square error(RMSE)of the ANN and GEP models were 0.91oC and 1.08 oC,respectively.The GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation,which can be conveniently used to test new data regarding the thematic area.The sensitivity analysis revealed that UI is the most influential parameter followed by NDBI,NDVI,and NDWI towards contributing LST.

Land Characterization Analysis of Surface Temperature of Semi-Arid Mountainous City Abha, Saudi Arabia Using Remote Sensing and GIS

This knowledge of land surface temperature and its spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions. Few studies have examined the influence of land use and terrain on the surface temperature effects of semi-arid mountainous urban areas. This study investigates the urban environment characterization and its effects on surface temperature using remote sensing. The methodologies adapted for this study are geometric and radiometric corrections of satellite data, extraction of land use/land cover and digital elevation model, estimation of vegetation density using Normalized Difference Vegetation Index (NDVI), and estimation of surface temperature and emissivity using temperature emissivity separation (TES) algorithm. Finally geospatial model and statistical techniques are used for assessing the overall impact of urban environmental characterization on urban climate of semi-arid region of Abha, Kingdom of Saudi Arabia. Herein, results reveal that the spatial distribution of surface temperature was affected by land use/land cover (LULC) and topography. The high dense built-up and commercial/industrial areas display higher surface temperature in comparison with surrounding lands. There is gradual decrease of LULC classes’ surface temperature with the increase in altitude. The cooling effect towards the surrounding urban built-up area is found increasing at the hill located vegetated area, the downward slope and valley terrain inside the recreational park. Therefore the spatial variation in surface temperature also reflected the effects of topography on LULC classes. Suitable mountainous land use utilization would help to expand the cooling effect. In the future, the outcomes of this study could be used to build environmentally sustainable urban planning suitable to semi-arid regions and to create practices that consider the local weather environment in urban planning.

Spatial and temporal analysis of urban heat island effect over Tiruchirappalli city using geospatial techniques

Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island effect.Thermal remote sensors measure the radiation emitted by ground objects,which can be used to estimate the land surface temperature and are beneficial for studying urban heat island effects.The present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter seasons.The study also identifies hot spots and cold spots within the study area.In this study,a significant land surface temperature difference was observed between the urban and rural areas,predominantly at night,indicating the presence of urban heat island at night.These diurnal land surface temperature fluctuations are also detected seasonally,with a relatively higher temperature intensity during the summer.The trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than 0.01.By using the spatial autocorrelation method with the urban heat island index as the key parameter,hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area.A hot spot with 95 and 90 percent confidence level was identified outside the urban area.This spike in temperature for a particular region in the rural area is due to industry and the associated built-up area.The study also identified cold spots with a 90 percent confidence level within the rural area.However,cold spots with a 95 and 99 percent confidence level were not identified within the study area.

基于Landsat数据的城市热岛效应与LUCC关系研究

以Landsat-5和Landsat-8遥感影像数据为基础,通过地表温度反演和支持向量机分类法分别获得研究区城市热岛的空间分布和土地利用/覆盖变化数据,再利用ArcGIS对上述结果进行叠加分析。结果表明:城市热岛范围与强度随着时间变化呈上升趋势;土地利用/覆盖的变化在空间位置上会影响城市热岛效应;其中城市中建设用地与裸土地是导致热岛效应加剧的主要原因,而水系与植被对城市热岛效应有明显的抑制作用。

基于Landsat 8 TIRS地表温度数据反演的深圳城市热岛效应时空分布及土地利用的影响

基于2014~2021年Landsat 8 TIRS卫星遥感影像数据,分别采用劈窗算法(SWA)和大气校正法(ARC)反演了深圳市地表辐射温度,并利用地面站点监测数据对反演结果进行了验证,探讨了该期间深圳城市热岛效应的时空分布特征及其影响因素。研究结果表明,这两种算法反演的地表温度(TSWA、TARC)与地面站点监测气温(TM)都存在显著线性相关(TSWA=1.01×T_(M)+2.65,TARC=0.85×T_(M)+5.51,p<0.01),但劈窗算法更接近地面站点监测数据。在2014~2021年间深圳城市热岛面积(HI>0.01)未观察到显著增加的趋势(p=0.94)。深圳市城市热岛分布与城市发展格局关系密切,城市规划用地类型对城市热岛效应有显著影响。生态水域和生态绿地缓解了城市热岛效应,而交通道路和工业仓储等城市用地强化了城市热岛的形成,并且城市路网的分布和密度对城市热岛的形成有强显著性影响(p=0.003)。

Evaluating the thermal environment of urban land surfaces in Yakutsk,a city located in a region of continuous permafrost

Rapid urbanization has led to changes in the urban land surface thermal environment.However,there are still much unknown about the urban land surface thermal conditions in permafrost regions.Permafrost is a unique geological environment,changes in the urban land surface thermal environment may trigger geological disasters caused by permafrost degradation.This study utilized remote sensing data and geographic de-tectors to identify the dynamic changes in land surface temperature(LST)and land use/land cover(LU/LC)in Yakutsk,as well as the potential factors contributing to LST variations.Between 1992 and 2020,the built-up area in Yakutsk increased by 36%,and the annual average LST in Yakutsk has risen by 6.67℃,accompanied by an expansion of high-temperature areas.Despite ongoing greening efforts,rapid urbanization poses a threat to these green spaces.Changes in the normalized difference built-up index(NDBI)and land use transfer(LDT)were identified as the primary drivers of urban LST changes.By integrating geographic detector technology and artificial neural network models,we optimized the selection of input factors in the prediction model and used it to explore the future changes in LST in Yakutsk.The average LST in Yakutsk is expected to reach 23.4℃ and 25.1℃ in 2030 and 2040,respectively,with a further increase in high-temperature areas.This study provides a reference for ecological,hydrological,and geological assessments of cities in permafrost regions.