Increase In Land Surface Temperature Research Articles

Analyzing the Urban Heat Island in Ho Chi Minh City in Relation to Urban Planning Processes from 2010 to 2020

This study aims to analyze the variation of land surface temperature (LST) and its effect on the urban heat island (UHI) in relation to the urban planning processes of Ho Chi Minh City (HCMC) from the years 2010 to 2020. These changes have impacted the urban environmental conditions and the city development. The study used the time series of Landsat 5 TM and Landsat 8 OLI acquired for the years 2010, 2015, and 2020, and used the algorithms for calculating the land surface temperature (LST), the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) to investigate the relationships between these phenomena in the city planning processes. The results highlight the increase in land surface temperature was related to the dense of impervious surface area and the decrease of vegetation area. The UHI zones occurred in the areas with high LST and NDBI values, and low NDVI values (concentrated in the central districts and the planned areas) compared to the surrounding areas with lower LST and NDBI and higher NDVI. The recommend planning solutions for the new urban development areas should focus on increasing the amount green areas to combat the UHI phenomenon and environmental problems against the development.

Spatio-temporal analysis of changes occurring in land use and its impact on land surface temperature.

This study shows how remote sensing and Geographic Information System (GIS) can extract land surface temperature (LST) from the Landsat 5, 7, and 8 datasets. In this research, LST over Kharun's lower catchment, located in Chhattisgarh, India, has been estimated. LST data from 2000, 2006, 2011, 2016, and 2021 were analyzed to see how the LULC pattern changed and how that changed LST. In 2000, the average temperature of the study region was 27.73 °C, whereas in 2021, it reached 33.47 °C. When the average temperature values for each class were determined, it was discovered that forest and adjacent waterbodies had the lowest values, with about 24.15 °C in 2000 and 27.65 °C in 2021, whereas urban regions had more variation in values, ranging from 30.15 °C in 2000 to 38.95 °C in 2021. There could be an increase in LST over time because cities are replacing the green cover. For example, there was a notable increase of 5.74 °C in the mean LST over the research area. The findings revealed that places with extensive urban sprawl had LST between 26 and 45°, which was greater than other natural land cover types, such as vegetation and waterbodies, which was between 24 and 35°. These findings support the suggested method's effectiveness for retrieving LST from the Landsat 5, 7, and 8 thermal bands when combined with integrated GIS approaches. So, the goal of this study is to look at Land Use Change (LUC) and changes in LST using Landsat data and figure out how they are related to LST, the Normalized Difference Vegetation Index (NDVI), and the Normalized Built-up Index (NDBI), which are used as major components.

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature–compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan–one of the top cities in Pakistan–using multi-temporal Landsat data over two decades (2002–2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (−6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5° C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (> 31.5°C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.

Evolution of seasonal land surface temperature trend in pond-breeding newt (Neurergus derjugini) in western Iran and eastern Iraq

BackgroundTemperature, as one of the effective environmental stimuli in many aspects of species life and ecosystems, can affect amphibians in many ways. Knowing and predicting temperature change and its possible effects on the habitat suitability and movements of amphibians have led many researchers to use climate change scenarios and species distribution models (SDMs). One of the important remote-sensing products that received less attention of conservation biologists is the land surface temperature (LST). Due to the small difference between LST and air temperature, this component can be used to investigate and monitor the daily and seasonal changes of habitats. This study aims to investigate the seasonal trend of LST in the habitat suitability and connectivity of the critically endangered newt (Neurergus derjugini) in its small distribution range, using the MODIS LST time series (2003 to 2021) and with the help of SDMs, Mann–Kendall (MK) and Pettitt non-parametric tests.ResultsIn the last decade, the increasing trend of LST versus its decreasing trends is obvious. Based on MK and Pettitt tests, in the winter and spring, with the decrease in latitude of 35.45° and increase in longitude of 46.14°, the core populations which are located in the southeast have experienced an increase in temperature. Considering the period time of breeding and overwintering, the continuity of winter and spring can be effective on the survival of adult newts as well as larvae in the microclimate. Linkages with the highest current flow between core populations in the winter and summer are the most likely to be vulnerable. At the level of habitat, the increase in LST is proportional to the trend of thermal landscape changes, and all seasons have had an increase in LST, but in winter and summer, the largest area of the habitat has been involved. By continuing the current trend, many high-altitude southern habitats in Iran will be endangered, and the species will be at risk of local extinction.ConclusionThe increasing trend of temperature in all seasons such as winter will affect many adaptations of the species and these effects are mostly evident in the southern parts of its distribution range therefore, captive breeding and reintroduction are recommended for the populations of these areas.

Surface Urban Heat Island and Thermal Profiles Using Digital Image Analysis of Cities in the El Bajío Industrial Corridor, Mexico, in 2020

The Surface Urban Heat Island (SUHI) effect refers to the difference in Land Surface Temperature (LST) between an urban area and its surrounding non-urban area. LST can provide detailed information on the variations in different types of land cover. This study, therefore, analyzes the behavior of LST and SUHIs in fourteen cities in the El Bajío Industrial Corridor, Mexico, using Landsat satellite images from 2020, with QGIS software. It utilizes thermal profiles to identify the land uses that intensify LST, which are essentially those that are anthropologically altered. The results show that the increases in LST and SUHI are more pronounced in cities with greater urban conglomeration, as well as those where there are few green areas and a sizeable industrial or mixed area, with few or no bodies of water. In addition, the increase in temperature in the SUHI is due to certain crops such as vegetables, red fruits, and basic grains such as corn, wheat, and sorghum that use fallow as part of agricultural practices, located around urban areas, which minimizes natural areas with arboreal vegetation.

Assessing the link between land use/land cover changes and land surface temperature in Himalayan urban center, Srinagar.

The rapidly growing urbanization and the consequent land use/land cover (LULC) changes have resulted in unsustainable growth of cities in Indian subcontinent especially in Himalayan region which are highly sensitivity to condition like climate change. Using multi-temporal and multi-spectral satellite datasets, this study analyzes the impact of LULC changes on land surface temperature (LST) in the Himalayan city of Srinagar from 1992 to 2020. For LULC classification, the maximum likelihood classifier technique was utilized, and to extract LST from Landsat 5 (TM) and Landsat 8 (TM) (OLI), spectral radiance was employed. The results show that, among various LULC classes, built-up area has seen a maximum increase of 14% while agriculture has decreased by about 21%. On the whole, Srinagar city has witnessed an increase in LST by 4.5°C with maximum increase of 5.35°C especially over marshes and a minimum increase of 4°C on agriculture landscape. Other LULC categories of built-up, water bodies, and plantation saw an increase in LST by 4.19°C, 4.47°C, and 5.07°C, respectively. The transformation of marshes into built-up saw a maximum increase in LST by 7.18°C followed by water body to built-up (6.96°C) and water body to agriculture (6.18°C) while minimum increase was seen in the conversion of agriculture to marshes by about 2.42°C followed by agriculture to plantation (3.84°C) and plantation to marshes (3.86°C). The findings may be useful to urban planners and policymakers in terms of land use planning and city thermal environment control.

UTILIZATION OF REMOTE SENSING FOR LAND SURFACE TEMPERATURE (LST) DISTRIBUTION MAPPING IN SOLOK CITY IN 2021

Solok City is one of the cities in West Sumatra which has a fairly rapid population growth, this has led to an increase in development and a decrease in green open land or vegetation land. This affects the ground surface which absorbs and reflects more of the sun's heat. These conditions have an impact on rising surface temperatures. This research was conducted to analyze changes in vegetation land, built-up land and changes in surface temperature in Solok City using Landsat-8 Imagery of Solok City in 2015 and 2021 using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDVI) algorithm models. (NDBI) and Land Surface Temperature (LST). The results of the study explain that the normalized difference vegetation index (NDVI) in Solok City has decreased, in 2015 the area of ​​vegetation density was 2344 Ha and in 2021 it was reduced to 1888 Ha. This is in line with the increase in building area / Normalized Difference Built-up Index (NDBI) in 2015, namely 1 from 921 Ha to 2295 Ha in 2021. Reduced vegetation area and increased built-up area increased Land Surface Temperature (LST) in the area. research, the temperature in 2015 was around 32.9° C and in 2021 there was an increase in surface temperature to 33.6° C. Pearson product-moment correlation was carried out to see the level of relationship between LST and NDVI and NDBI.

The Influence of Elevation, Land Cover and Vegetation Index on LST Increase in Taiwan from 2000 to 2021

Land Surface Temperature (LST) is an important factor in ground surface energy balance and in universal climatology studies. Elevation, Land Cover (LC), and vegetation index are three factors that influence ground surface variation, and their influences vary depending on geography. This study aimed to: (i) investigate the seasonal patterns and trends of daytime LST, and (ii) examine the influence of elevation, LC, and vegetation index on daytime LST increase in Taiwan from 2000 to 2021. LST, vegetation, and LC data were downloaded from the Moderate Resolution Imaging Spectroradiometer (MODIS) website, and elevation data were downloaded from the United States Geological Survey (USGS) website. The natural cubic spline method was applied to investigate annual seasonal patterns and trends in daytime LST. Linear regression modeling was applied to investigate the influence of elevation, LC, and vegetation index on daytime LST increases. The results showed that the average increase in daytime LST per decade in Taiwan was 0.021 °C. Elevation, LC, and vegetation had significantly affected the daytime LST increase, with R2 of 32.5% and 28.1% for the North and South parts of the country, respectively. The daytime LST increase in the North at elevations higher than 1000 m had an increasing trend, while in the South the increasing trend was found at elevations higher than 350 m above sea level. All types of forest and urban areas in the North had a higher daytime LST increase than the average, while in the South, the areas with water, closed shrubland, and urban parts had a higher daytime LST increase than the average.

Seasonal Variations of the Relationship between Spectral Indexes and Land Surface Temperature Based on Local Climate Zones: A Study in Three Yangtze River Megacities

Urban heat islands are representative problems in urban environments. The impact of spectral indexes on land-surface temperature (LST) under different urban forms, climates, and functions is not fully understood. Local climate zones (LCZs) are used to characterize heterogeneous cities. In this study, we quantified the contribution of three cities to high-temperature zones and surface urban heat island intensity (SUHII) across LCZs and seasons, used Welch and Games–Howell tests to analyze the difference in LST, then described the spatial pattern characteristics of LST, and used a geographically weighted regression model to analyze the relationship between spectral indexes and LST. The results showed that compact midrise, compact low-rise (LCZ 3), large low-rise (LCZ 8), heavy industry (LCZ 10), and bare rock or paved (LCZ E) contributed greatly to high-temperature zones and had strong SUHII. There were 92–98% significant differences between different LCZs. The spatial aggregation of LST gradually weakened with a decrease in temperature. The modified normalized difference water index (MNDWI) in most LCZs of all seasons for Wuhan could reduce LST well, while MNDWI only had cooling effects in winter for Nanjing and Shanghai. Normalized difference vegetation index (NDVI) in most LCZs performed a cooling role during summer and transition seasons (spring and autumn), while it showed a warming effect in winter. The cooling effect of NDVI in open building types was stronger than that of compact building types, while the cooling effect of MNDWI was better in compact building types than in open building types. With the increase of normalized difference built-up index (NDBI), all LCZs showed warming effects, and the magnitude of LST increase varied in different cities and seasons. These results contribute further insight into thermal environment in heterogeneous urban areas.

Impacts of Green Fraction Changes on Surface Temperature and Carbon Emissions: Comparison under Forestation and Urbanization Reshaping Scenarios

Global land cover dynamics alter energy, water, and greenhouse gas exchange between land and atmosphere, affecting local to global weather and climate change. Although reforestation can provide localized cooling, ongoing land use land cover (LULC) shifts are expected to exacerbate urban heat island impacts. In this study, we monitored spatiotemporal changes in green cover in response to land use transformation associated with the Khyber Pakhtunkhwa (KPK) provincial government’s Billion Tree Tsunami Project (BTTP) and the Ravi Urban Development Plan (RUDP) initiated by the provincial government of Punjab, both in Pakistan. The land change modeler (LCM) was used to assess the land cover changes and transformations between 2000 and 2020 across Punjab and KPK. Furthermore, a curve fit linear regression model (CFLRM) and sensitivity analysis were employed to analyze the impacts of land cover dynamics on land surface temperature (LST) and carbon emissions (CE). Results indicated a significant increase in green fraction of +5.35% under the BTTP, achieved by utilizing the bare land with an effective transition of 4375.87 km2. However, across the Punjab province, an alarming reduction in green fraction cover by −1.77% and increase in artificial surfaces by +1.26% was noted. A significant decrease in mean monthly LST by −4.3 °C was noted in response to the BTTP policy, while an increase of 5.3 °C was observed associated with the RUDP. A substantial increase in LST by 0.17 °C was observed associated with transformation of vegetation to artificial surfaces. An effective decrease in LST by −0.21 °C was observed over the opposite transition. Furthermore, sensitivity analysis suggested that LST fluctuations are affecting the % of CO2 emission. The current findings can assist policymakers in revisiting their policies to promote ecological conservation and sustainability in urban planning.

An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security

The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002–2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of − 0.80, − 0.58, − 0.43, and − 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of − 0.68, − 0.23, and − 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA–Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods.

Characterizing urban growth and land surface temperature in the western himalayan cities of India using remote sensing and spatial metrics

Urban heat islands (UHI) are developing due to increasing urbanization and loss of vegetation in major cities in India. Increased urbanization modifies the urban microclimate that leads to significant land-use changes resulting in surface conversion and heat release, which poses serious risks to human health, environment and the ecosystem of the Himalayan ecosystem. Hence, mitigating UHI becomes important and requires a better understanding of underlying associated biophysical processes. In the study an attempt has been made to demonstrate the impact of urbanization on land surface temperature (LST) in Shimla and Dehradun, capitals of the Western Himalayan states, India using satellite data and spatial metrics. The process was analyzed using urban coverage patterns obtained from Landsat 5, 7, and 8 and corresponding sensors from TM, ETM+, and OLI. The Built-up and Non-Built-up areas were extracted and the biophysical parameters NDVI, NDBI, NDWI and LST were calculated to capture different features of urban growth. The result indicated, that the built-up area increased from 32.19 km2 (2000) to 68.37 km2 (2016) in Dehradun and from 12.38 km2 (2000) to 29.47 km2 (2016) in Shimla during the study period, resulting in an increase in NDBI and LST and Reduction and NDVI and NDWI. Results showed that temperature hotspots were largest in urban areas, followed by vegetation and water bodies. A significant correlation (p < 0.05) was observed between LST and biophysical parameters -NDVI, NDBI, NDWI. Spatial metrics at the class and landscape levels show that increased urban growth from 2000 to 2016 has made the landscape fragmented and more heterogeneous. The Identified trends and changes in landscape patterns and their impact on heterogeneous urban areas suggest that the study is feasible to estimate LST, NDVI, NDBI and NDWI with reasonable accuracy that will likely have influence on policy interventions.

The Growth of Urban Heat Island Effect Monitored in a Rapidly Developing City of Lombok Island

The study aims to monitor urban development around Mataram City and the correlation between Urban Heat Island. Also, analyze the vegetation and built-up changes surrounding Mataram City with Land Surface Temperature (LST) growth. The boundary of the study location is a 25-kilometer radius from the Mataram City centroid. The data used in this research is Landsat 8 Surface Reflectance for the period June-September in 2013, 2019 and 2023 obtained from Google Earth Engine. The Random Forest method is used to obtain land use classification with urban, rice fields, dry land, water bodies, and vegetation. NDBI values are processed from Shortwave infrared (SWIR) and Near-infrared (NIR), NDVI from Near-infrared (NIR) and Red, and LST from thermal bands. The data processing results indicate that Mataram City and its neighboring areas are showing growth, marked by rising NDBI and LST values. This growth is characterized by an expansion of built-up areas to the north, east, and south especially rings 4 and 5, simultaneously by higher NDBI values. Additionally, there is a decrease in vegetation cover, particularly in lowland areas within these rings, leading to a decline in NDVI values. Areas with increased NDBI values also show an increase in LST.

秦巴山地植被绿度特征及其对地表水热的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 秦巴山地植被绿度特征及其对地表水热的响应 DOI: 10.5846/stxb202107272040 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 科技基础资源调查专项-中国南北过渡带综合科学考察（2017FY100902） Vegetation greenness characteristics in Qinling-Daba Mountains and its response to surface hydrothermal Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:地表水热通过影响土壤和大气间的水分交换以及植被生长用水对植被格局起关键作用。使用2000-2020年MODIS叶面积指数（Leaf area index，LAI）作为秦巴山地植被绿度的评价指标，基于遥感技术获取同时段地表温度（Land surface temperature，LST）和温度植被干旱指数（Temperature vegetation drought index，TVDI），用于指示地表水热状况。结合坡向和海拔等地形因素，使用均值和趋势法分析了秦巴山地植被绿度的时空特征；采用时滞相关性分析、主导因素分析探究了植被绿度与地表水热的相关关系，以及影响植被绿度变化的主导因素；借助广义可加模型，结合植被类型探讨了植被绿度与地表水热的非线性关系。结果表明：（1）秦巴山地LAI集中在1-5之间，LAI均值随海拔先增后减，不同坡向LAI差异较大，表现为大巴山北坡 > 大巴山南坡 > 秦岭南坡 > 秦岭北坡 > 西秦岭。秦巴山地LAI高值区与地表水热较好的地区在空间分布上具有一致性，LAI低值区与LST低值区或TVDI高值区较为一致。（2）秦巴山地LAI以由增变减趋势为主，变化趋势的海拔分异大于坡向分异。在LAI由增变减区，地表水热以由增变减为主，其次是持续增加。（3）秦巴山地LAI与LST主要为显著正相关，与TVDI主要为显著负相关。LAI对地表水热的时滞响应不明显，LAI受TVDI的影响较受LST的影响面积广。（4）除LST增加导致落叶阔叶林和混交林LAI单调递增外，地表水热对各植被类型LAI均为非线性影响，即存在一定阈值，阈值两侧LAI的响应规律不同，不同植被类型对地表水热响应的阈值差异较大。 Abstract:Surface hydrothermal plays a key role in vegetation pattern by affecting soil-atmosphere water exchange and vegetation growth water use. MODIS Leaf area index (LAI) from 2000 to 2020 was used as the evaluation index of vegetation greenness in Qinling-Daba Mountains. Land surface temperature (LST) and temperature vegetation drought index (TVDI) in the same period were obtained based on remote sensing technology to indicate surface hydrothermal conditions. Combined with terrain factors such as aspect and altitude, the temporal and spatial characteristics of vegetation greenness in Qinling-Daba Mountains were analyzed by mean and trend methods. The correlation between vegetation greenness and surface hydrothermal, as well as the leading factors affecting the change of vegetation greenness were explored by time-delay correlation analysis and dominant factor analysis. The nonlinear relationship between vegetation greenness and surface hydrothermal was discussed with the help of the generalized additive model and vegetation types. The results showed that:(1) the LAI of Qinling-Daba Mountains was concentrated in the range of 1-5. The average value of LAI increased first and then decreased with altitude, while the LAI varied greatly in different aspects, which was shown as the north slope of Daba Mountains > the south slope of Daba Mountains > the south slope of Qinling Mountains > the north slope of Qinling Mountains > the west Qinling Mountains. The LAI high value area in the Qinling-Daba Mountains was consistent with the area with good surface hydrothermal in terms of spatial distribution. The LAI low value area was consistent with LST low value area or TVDI high value area. (2) The LAI of the Qinling-Daba Mountains mainly changed from increase to decrease, and the change trend of altitude differentiation was larger than aspect differentiation. In the area of LAI from increase to decrease, surface hydrothermal was mainly from increase to decrease, followed by continuous increase. (3) The LAI of Qinling-Daba mountains was significantly positive correlation with LST, but significantly negative correlation with TVDI. The time lag effect of surface hydrothermal on LAI was not obvious, and LAI was more affected by TVDI than LST. (4) Except for the increase of LST which led to monotonous increase in LAI of deciduous broad-leaved forest and mixed forest, the surface hydrothermal had nonlinear effects on the LAI of various vegetation types. There was a certain threshold, and the response law of LAI on both sides of the threshold was different, and the thresholds of different vegetation's response to surface hydrothermal were quite different. 参考文献 相似文献 引证文献

Underestimation of the impact of land cover change on the biophysical environment of the Arctic and boreal region of North America

The Arctic and Boreal Region (ABR) is subject to extensive land cover change (LCC) due to elements such as wildfire, permafrost thaw, and shrubification. The natural and anthropogenic ecosystem transitions (i.e. LCC) alter key ecosystem characteristics including land surface temperature (LST), albedo, and evapotranspiration (ET). These biophysical variables are important in controlling surface energy balance, water exchange, and carbon uptake which are important factors influencing the warming trend over the ABR. However, to what extent these variables are sensitive to various LCC in heterogeneous systems such as ABR is still an open question. In this study, we use a novel data-driven approach based on high-resolution land cover data (2003 and 2013) over four million km2 to estimate the impact of multiple types of ecosystem transitions on LST, albedo, and ET. We also disentangle the contribution of LCC vs. natural variability of the system in changes in biophysical variables. Our results indicate that from 2003 to 2013 about 46% (∼2 million km2) of the region experienced LCC, which drove measurable changes to the biophysical environment across ABR over the study period. In almost half of the cases, LCC imposes a change in biophysical variables against the natural variability of the system. For example, in ∼35% of cases, natural variability led to −1.4 ± 0.9 K annual LST reduction, while LCC resulted in a 0.9 ± 0.6 K LST increase, which dampened the decrease in LST due to natural variability. In some cases, the impact of LCC was strong enough to reverse the sign of the overall change. Our results further demonstrate the contrasting sensitivity of biophysical variables to specific LCC. For instance, conversion of sparsely vegetated land to a shrub (i.e. shrubification) significantly decreased annual LST (−2.2 ± 0.1 K); whereas sparsely vegetated land to bare ground increased annual LST (1.6 ± 0.06 K). We additionally highlight the interplay between albedo and ET in driving changes in annual and seasonal LST. Whether our findings are generalizable to the spatial and temporal domain outside of our data used here is unknown, but merits future research due to the importance of the interactions between LCC and biophysical variables.

Modeling the impact of the future state of land use land cover change patterns on land surface temperatures beyond the frontiers of greater Kumasi: A coupled cellular automaton (CA) and Markov chains approaches

There is ample evidence of the impact of land use/land cover (LULC) on land surface temperatures (LST) in Kumasi. However, the situation in the future is largely unknown. This study predicts the future state of LULC and LST in and beyond the frontiers of Greater Kumasi. The study used coupled Artificial Neural Networks (ANNs), Cellular-Automaton-Markov chains, and multiple regression to simulate the future state of LULC and LST for 2032 and 2042. Landsat imageries from 1986 to 2022 were acquired from USGS. Road networks, bus stops, and social centers were obtained from OpenStreetMap, and population density from the socioeconomic data and application center (SEDAC). Support vector machine (SVM) was used for image classification. Between 1986 and 2022, built-up areas increased by 18.63%; high-density forests decreased by 42.13%, whereas low-density forests increased by 20.21%. Between 1986 and 2042, the built-up area is expected to expand by 27%, high-density forests are likely to decline by 45%, whereas low-density forest are likely to increase by 19.097%. In contrast, water bodies and bare grounds are expected to decrease by 0.61% and 0.134%, respectively. Very high LSTs increased by 8.23% between 1986 and 2022. Between 1986 and 2042, LSTs are expected to increase spatially by 16.145%. Mean LSTs are expected to increase by 7.43 °C (36.03%) between 1986 and 2042. Urban expansion clearly drives the increase in LSTs, and this is more likely to persist in the future. These research findings therefore call for institutional and sector-based coordination between governmental institutions.

Land use change impact on urban land surface temperatures: A GIS-supported satellite-based case study

This paper illustrates the use of GIS techniques and satellite data in order to analyze the impact of land use change on the local urban microclimate. Specifically, a case study is presented that concerns the city of Vienna. Thereby, satellite-based images were used to classify the city of Vienna into four zones toward the computation of land surface temperatures in two reference years. The classified maps were then statistically projected into the future, resulting in predicted land surface temperatures. The findings highlight the relationship between urbanization and temperature rise in the urban context. The study used data from Landsat 8 satellite in 2013 and 2020. Land cover maps were generated with QGIS for past and current conditions and future land cover maps were projected and corresponding land surface temperatures were predicted. The analysis of satellite data highlighted land surface temperature increase in the city of Vienna. This rise in land surface temperatures correlates with urbanization-driven change in land use and land cover.

Effect of Deforestation on Land Surface Temperature in the Chiquitania Region, Bolivia

Neotropical forests offer alternatives to surface cooling and their conservation is an effective solution for mitigating the effects of climate change. Little is known about the importance of tropical dry forests for temperature regulation in Chiquitania, a region with increasing deforestation rates. The impact that deforestation processes are having on the surface temperature in Chiquitania remains an open question. This study evaluated trends in forest cover loss based on land surface temperatures (°C) in forested and deforested areas in Chiquitania. We hypothesized a positive relationship between higher deforestation and a temperature increase, which would decrease the resilience of highly disturbed Chiquitano forests. We evaluated ten sampling sites (10 × 10 km), including five in forested areas with some type of protection and the other five in areas with populated centers and accelerated forest loss. We developed scripts on the Google Earth Engine (GEE) platform using information from the Normalized Difference Vegetation Index (NDVI, MOD13A2) and the daytime and nighttime Land Surface Temperature (LST, MYD11A1) from MODIS products for the period 2001–2021. The statistical significance of the trends of the time series averages of the MODIS products was analyzed using a nonparametric Mann–Kendall test and the degree of the relationship between the variables was determined using the Pearson statistic. Our results based on NDVI analysis showed consistent vegetation growth in forested areas across the study period, while the opposite occurred in deforested lands. Regarding surface temperature trends, the results for daytime LST showed a positive increase in the four deforested areas. Comparatively, daytime LST averages in deforested areas were warmer than those in forested areas, with a difference of 3.1 °C. Additionally, correlation analyses showed a significant relationship between low NDVI values due to deforestation in three sites and an increase in daytime LST, while for nighttime LST this phenomenon was registered in two deforested areas. Our results suggest a significant relationship between the loss of forest cover and the increase in land surface temperature in Chiquitania. This study could be the first step in designing and implementing an early climate–forest monitoring system in this region.

Spatiotemporal evolution patterns of urban heat island and its relationship with urbanization in Guangdong-Hong Kong-Macao greater bay area of China from 2000 to 2020

The unprecedented wave of urbanization has led to the development of the urban heat island (UHI) effect in the Guangdong-Hong Kong-Macao greater bay area (GBA) of China. In this study, the spatiotemporal evolution patterns of UHI and its relationship with the urban expansion of the GBA from 2000 to 2020 were explored. First, the space–time cube model and emerging hot spot analysis were applied to identify the spatiotemporal patterns of UHIs. Second, centroid movement analysis, spatial coupling analysis, and a geographical detector model were applied to quantify the relationship between UHIs and urban expansion. Results found that urban expansion was strongly influenced and highly coupled with the UHI change in the GBA. The main hot spot patterns, intensifying hot spot, persistent hot spot, and sporadic hot spot were distributed in the GBA center. Importantly, three intensifying hot spot areas were detected at the junctions of several major cities, where the increases in land surface temperature and urban expansion were spatially consistent. It is recommended that urban planning should consider the fragile thermal environment in highly urbanized areas. This study provides a novel spatiotemporal analytical framework for monitoring the long-time series of changes in the surface environment of cities.

Effect of landscape pattern changes and environmental indices on land surface temperature in a fragile ecosystem in southeastern Iran.

Climate change and urbanization along with uncontrolled development in less developed countries have led to an increased ecosystems' thermal environment. Some factors such as environmental indices and landscape pattern changes can alter Land Surface Temperature (LST). Thus, the accurate evaluation of the relationship between these factors and LST is considered important for managing ecosystems, especially fragile ones under high stress. The southeast of Iran has witnessed many destructions in the environmental dimension in the past years. Moreover, this region has a low socio-economic situation, which increases the need to study in this region. In the present study, we used Landsat TM5 satellite images (1989), Landsat 8 OLI/TIRS ones (2019), and Google Earth Engine (GEE) system to prepare the maps of temporal-spatial LST changes, Land Use/Land Cover (LULC), and selected environmental indices including Normalized Difference Vegetation (NDVI), Built-up (NDBI), Water Indices (NDWI), Land Surface Moisture (LSM) and albedo. Then, the correlation levels of LST with the aforementioned indices were assessed by using Geographically Weighted Regression (GWR), as well as assessing LST variation following LULC change. In addition, the Moran index was used to analyze global and local spatial autocorrelation. The results represented an 8.67-degree increase in the mean LST during 1989-2019. Urban and built-up areas had a significant effect on increasing the temperature of the region. Additionally, water bodies and vegetation cover in the region were the most crucial parameters in LST reduction. All of the applied indices were strongly related to LST (>0.70), while some exhibited more correlation in each year. Further, the highest correlation of LST was observed with LSM and NDBI in 1989, as well as with NDVI and NDWI during 2019. In addition, the Moran index value reduced from 1989 to 2019 (from 0.93 to 0.89). Finally, the region rehabilitation based on sustainable development principles played an important role in the direct and indirect decrease in LST.