Land Surface Temperature Trends Research Articles

The Impact of Urban Expansion on the Urban Thermal Environment: A Case Study in Nanchang, Jiangxi, China

Urban expansion has been changing the urban thermal environment. Understanding the spatial distribution and temporal trends in the urban thermal environment is important in guiding sustainable urbanization. In this study, we focused on the land use/land cover (LULC) changes and urban expansion in Nanchang city, Jiangxi province, China. The four elements in the remote sensing-based ecological index (RSEI) are heat, greenness, dryness, and wetness, which correspond to the land surface temperature (LST), NDVI, NDBSI, and WET, respectively. According to the synthetic images of the average indices, we conducted temporal trend analysis together with statistical significance test for these images. We conducted partial correlation analyses between LST and NDVI, NDVSI, as well as WET. In addition, we used the LULC maps to analyze the multi-year trends in urban expansion. Then, we superimposed the trends in daytime and nighttime LST in summer on urban expansion area to extract the LST trends at sample locations. The results showed that LULC in Nanchang has substantially changed during the study period. The areas with statistically significant trends in LST coincided with the urban expansion areas. Land cover change was the main reason for LST change in Nanchang. In particular, artificial surfaces showed the greatest increase in LST; for per 100 km2 expansion in artificial surfaces, the daytime and nighttime LST increased by 0.8 °C and 0.7 °C, respectively. Among all the study land cover types, water bodies showed the greatest differences in LST change between the daytime and nighttime. There were statistically significant correlations between increases in LST and increases in NDBSI as well as decreases in NDVI and WET. In view of the considerable impact of urban expansion on the urban thermal environment, we urge local authorities to emphasize on urban greening when carrying out urban planning and construction.

Assessment of Land Surface Temperature Trend and Its Spatial Correlation with Landscape Structural Elements in Rasht Watershed, Guilan Province

Assessment of Land Surface Temperature Trend and Its Spatial Correlation with Landscape Structural Elements in Rasht Watershed, Guilan Province

Spatial-Temporal Analysis of Physio-Climatic changes in Tawang- Chu River Basin, Eastern Himalaya in India

The Tawang-Chu River Basin of the eastern Himalayas is a heterogenous region that is highly vulnerable to climate change. The changing temperature patterns in this mountain river basin play a vital role in changing the ecological system and inter-ecological nexus. Landsat satellite images were used to analyze spatio-temporal changes in Land Use and Land Cover (LULC) patterns in the basin. The changing trends of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were analyzed (at intervals of five years) using Landsat data. Changes in rainfall trends were analyzed using the Mann-Kendall test. The results show that fluctuations in annual temperature and increasing rainfall uncertainties are affecting LULC vegetation health.

Spatial–temporal characteristics of surface thermal environment and its effect on Lake surface water temperature in Dianchi Lake basin

The surface thermal environment plays an important role in urban sustainable development and ecological environment protection. Existing researches mainly focus on the formation process and mechanism of the surface thermal environment and lack the analysis of its effect on the lake ecological environment under the influence of human activities. Therefore, based on the analysis of the variations in land surface temperature (LST) and lake surface water temperature (LSWT) of Dianchi Lake at multiple spatio-temporal scales, this study evaluated the response of LSWT by using the methods of spatial influence, the center of gravity migration trajectory, trend analysis, and correlation analysis. The results show that: (1) Urbanization has a greater warming effect on LSWT than on LST, and the warming effect at night is greater than that at daytime. From 2001 to 2018, the warming trend of LSWT in daytime and night was 0.01°C/a and 0.02°C/a, respectively, while the cooling trend of LST in daytime was −0.03°C/a and the warming trend of LST in night was 0.01°C/a. (2) Areas with high human activity are warming faster, both in the eastern and northern coastal areas of lake and the heavily urbanized sub-basins. (3) The spatial influence of LST and LSWT are highly correlated, and the response of the outer buffer in the range of 2 km is obvious, and the direction of gravity center migration trajectory is consistent. The results are of great significance for the control and improvement of urban heat island and ecological environment protection of Dianchi Lake in Kunming and can provide data support and decision support for urban planning, promoting the construction of the ecological civilization city in Kunming, and reducing the accumulation of urban surface heat.

Recent trends of land surface temperature in relation to the influencing factors using Google Earth Engine platform and time series products in megacities of India

Increasing land surface temperature (LST) is one of the significant anthropogenic issues that is threatening livelihoods in urban areas. Understanding the urban LST is essential for sustainable management of the urban landscape. Various studies focused on the spatial pattern and average trend of LST in relation to influencing factors, but little is known regarding the pixel-scale spatiotemporal trends and integration of multiple influencing factors in the different seasons using time series data. Therefore, we focused on the spatiotemporal trends of LST in relation to the influencing factors using the Google Earth Engine platform and time series products. MODIS land surface temperature data was used to estimate the annual and seasonal day-night LST from 2003 to 2020. Theil-Sen slope and Mann-Kendall statistics were used to examine the magnitude and significance of the spatiotemporal trend. All-subsets regression and hierarchical partitioning (HP) were applied to explore the driving factors of LST. Average LST and range varied between the cities, but were highest in Chennai and Delhi, respectively. Except for winter nighttime in Kolkata, all cities' annual and seasonal nighttime LST are increasing (0.027–0.075 °C/year) over a large part of the study area (33.934%–100% of areas), indicating that anthropogenic force is increasing in urban areas during nighttime. Annual and seasonal daytime LST is increasing in most parts (55.545%–94.604% of areas) of Chennai and Kolkata, but daytime LST is decreasing (33.602%–98.571% of areas) in Mumbai and Delhi due to the increasing concentration of absorbing aerosol over the city. Strength of independent factors and multiple influencing factors varied in the day-night time between the cities. But nighttime LST was well explained using the all-subsets regression compared to daytime LST, except for Mumbai. Also, nighttime light density and NDBI had consistent effects on nighttime, and daytime, respectively. These findings can help to take scientific adaptation and mitigation strategies for sustainable development.

Effects of climate change on vegetation and snow cover area in Gilgit Baltistan using MODIS data.

The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snowcover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC = - 0.74), and vegetation and snow cover (PCC = - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher 5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region.

Evaluation of earth observation datasets for LST trends over India and its implication in global warming

Land surface temperature (LST) is crucial in surface energy balance, urban climatology, intensifying global change, ecological and environmental concerns. The present study examined the LST trends and spatio-temporal variation over India from 2002 to 2022. This includes comparison of LST for the summer and winter seasons over two decades. Secondly, the present study examined the LULC category wise LST variability during the day and night-time using MODIS (The Terra Moderate Resolution Imaging Spectroradiometer) derived products. This study explored the feasibility of cloud computing for big data analysis for LST distribution in seven landuse categories over India, providing a conceptual response to global warming. Results showed the existence of spatial LST variation due to changes in land-use patterns and MODIS derived vegetation indices- NDVI. Daytime LST for the summer and winter seasons of 2002 was found to be 45.17 °C and 39.13 °C, respectively. Outcomes illustrate declining trends in range (LSTmin-LSTmax) for winter seasons, initially, it was observed as 56.29 °C for 2002 while later on it was observed to be 20.21 °C and 20.87 °C for 2021 and 2022 respectively. The LSTmin (summer) has shown an increasing trend towards upper LST values, from −3.01 °C to 12.21 °C from 2002 TO 2022. LSTmin_winter has shown a rising trend towards the upper LST values from −17.16 °C to 9.15 °C in 2022. The maximum LST for the DRs was observed to be 61.56 °C, followed by UR as 56.24 °C. The findings demonstrate that daytime LSTmin and LSTmax are found to be 19.50 °C to 56.24 °C for UR, 29.5 °C to 61.56 °C for DR, 19.24 °C to 54.08 °C for SAR, −21.1 °C to 0.05 °C for SCR and 15 °C to 32.18 °C for FHR. NDVI-LST (daytime, nighttime and diurnal temperature range) feature space generates an obtuse triangle and depicts a negative correlation of vegetation for a few LULC categories. The outcomes indicated that desert and snow regions have highest LSTmax followed by urban and semiarid regions during daytime. During the nighttime, desert and urban regions have the highest temperature followed by semi-arid and forest regions. The outcomes support the efficacy of earth observation datasets and help to facilitate a better understanding of LULC and its impact on regional climate.

Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments

Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan.

An Analysis of the Stability and Trends in the LST_cci Land Surface Temperature Datasets Over Europe

AbstractLong‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured.

Exploring the Variations and Influencing Factors of Land Surface Temperature in the Urban Agglomeration on the Northern Slope of the Tianshan Mountains

Changes in land surface temperature (LST) can have serious impacts on the water cycle and ecological environment evolution, which in turn threaten the sustainability of ecosystems. The urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) is located in the arid and semi-arid regions of northwest China, with an extremely fragile ecological environment and sensitive to climate change. However, studies on the LST of the UANSTM have not received much attention. Therefore, this study explored the spatial distribution pattern, fluctuation characteristics, and influencing factors of the LST of the UANSTM from 2005 to 2021 based on MODIS time series LST data and the geo-detector model with optimal parameters. The results show that the UANSTM is dominated by medium- and high-temperature classes, with high- and extremely high-temperature classes clustered in Turpan City. The daytime and nighttime LST patterns are significantly different, with a typical “daytime cold island and nighttime heat island” feature in the oasis region. During 2005–2021, LST fluctuated greatly in the northwestern part of the UANSTM, with LST showing an increasing trend during both daytime and nighttime, and the warming rate was more intense during daytime than nighttime. The increasing trend of LST in Urumqi, Changji Hui Autonomous Prefecture, Shihezi, and Wujiaqu is very significant and will remain consistent in the future. Precipitation, DEM, and AOD are the most important influencing factors of LST in the UANSTM, where precipitation and DEM are negatively correlated with LST, and AOD is positively correlated with LST. Land cover factors (LULC, NDVI,, and NDBSI) are the next most influential, and socioeconomic factors (NTL, GDP, and POP) are the least influential. The results of this study can provide a scientific reference for the conservation and sustainable development of the ecological environment of the UANSTM.

Temporal and Spatial Variation of Land Surface Temperature in Recent 20 Years and Analysis of the Effect of Land Use in Jiangxi Province, China

Under the background of global warming, it is of great significance to study the temporal and spatial evolution of land surface temperature (LST) on long-time scale and the impacts of land use in the fields of urban thermal environment and regional climate change. Based on MODIS LST long time series remote sensing data, the temporal and spatial evolution characteristics of pixel-wise LST in Jiangxi Province, the middle inland province of China from 2000 to 2020 were analyzed by using Theil-Sen + Mann-Kendall, coefficient of variation and Hurst index, and the response of LST to land use was identified by combining the contribution and diversity index. The results showed as follows: (1) LST was generally distributed as "high in Middle-East-West-South and low in North-northwest-southeast direction". LST showed an overall downward trend, indicating a weakening of the warming trend. The dynamic trend of LST was characterized by more descending than ascending tendency. The dynamic stability showed a coexistence of high and low fluctuation tendency, with a higher proportion of medium and low fluctuation areas having obvious spatial differences. The overall dynamic sustainability was characterized by uncertainty of future change trend. (2) The LST were strongly affected by land use in the past 20 years. Firstly, the areas of high LST were mostly located in construction land and unused land, while the areas of low LST were mostly in water area and forest land. However, forest land and water area of high temperature were gradually turned to construction land later on. Secondly, the land use structure and pattern had an strong effects on LST. With the increase of the area proportion of different land use, the LST showed significant differences. The more complex the spatial pattern of land use, the more obvious its impact on LST. The research results will provide some reference for the regions with the same characteristics as Jiangxi Province to deal with LST under the background of global climate change.

Assessment of urban thermal field variance index and thermal comfort level of Addis Ababa metropolitan city, Ethiopia

Land use land cover (LULC) conversion around urban areas is the root cause for the increasing trend of land surface temperature (LST) in many cities. The increase in LST is driven by the replacement of vegetation cover and other LULC by impervious surface. This study is aimed to assess the extent of urban thermal field variance index (UTFVI) and thermal comfort level of Addis Ababa city using geospatial techniques and linear regression model. Landsat image of 1990 TM, 2000 of ETM+ and 2020 of OLI/TIRS are used to analyze LST and Urban Heat Islands (UHI) for assessing UTFVI and urban thermal comfort level. The results showed that the UHI over Addis Ababa city is substantial increased over the past decades. The results reveled that LST has increased by 7.9 °C due to decline of vegetation cover and expansion of built-up area. Results show that about 225 km2 (42.7%) is excellent comfort for urban resident while about 241.4 km2 (45.8%) is categorized as worst ecological evaluation index, which results discomfort to the city dwellers. The key findings of from this study are crucial for informing city administrators and urban planners to reduce urban heat islands by investing on urban green areas and open spaces.

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

Rapid urbanization has induced land use and land cover change (LULC) that increases land surface temperature (LST). Analyzing seasonal variations of LULC and LST is a precondition for mitigating heat island effects and promoting a sustainable living environment. The objective of this study is to explore the association between the seasonal LST dynamics and LULC indices for the Dhaka district of Bangladesh. The LULC indices are comprised of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBAI), and Modified Normalized Difference Water Index (MNDWI). The results show that the LULC effect on LST in Dhaka is significant, with an increase in summer season LST from 34.58 °C to 37.66 °C and in winter season LST from 24.710C to 26.24 °C. Predictably, the highest and lowest LST values were observed in the built-up and vegetation-covered areas, respectively. Secondly, the correlation values indicate a significant inverse correlation (R2 > 0.50) between NDVI and LST, as well as MNDWI and LST. On the contrary, positive correlations were observed between NDBI and LST, and between NDBAI and LST for both the summer and winter seasons. Finally, subsequent vegetation decline (-69.34%) and increasing built-up area (+11.30%) between 2000 and 2020 in Dhaka district were found to be the most significant factors for the increasing trend and spatial heterogeneity of LST in Dhaka. The methodological approach of this study offers a low-cost efficient technique for monitoring LST hotspots, which can guide land use planners and urban managers for spatial intervention to ensure a livable environment.

Trends in land surface temperature and its drivers over the High Mountain Asia

The present study estimates spatial and temporal (seasonal/annual) trends in daytime land surface temperature (LST) and its drivers (elevation, cloud fraction (CF), atmospheric water vapor (AWV), normalized difference vegetation index (NDVI), and snow cover area (SCA)) over the High Mountain Asia (HMA) during 2001–2019 using MODIS remote sensing data. The findings of the study showed mixed trends in LST in the region with intra-regional variations. Overall, the warming in the region is attributed to increasing AWV and NDVI and declining CF and SCA. The study also found evidence of elevation-dependent warming (EDW) (4500–5500 m AMSL) in some regions. However, the same conclusion cannot be drawn in all the sub-regions of HMA, suggesting the need to explore the effects of additional natural and anthropogenic factors on LST with fine resolution data to improve the understanding of its spatio-temporal dynamics.

Spatio-temporal pattern of urban vegetation in the central business district of the Wa municipality of Ghana

Increasing physical infrastructure development influences the spatial distribution of vegetation and temperature in many cities around the globe. However, in emerging cities such as Wa Municipality in Ghana and elsewhere, research attention has focused on urban expansion or urban sprawl and the impacts thereof on the peri‑urban vegetation. Considering the importance of vegetation in regulating temperature, reducing windstorm, carbon sequestration and creating beauty, this paper concentrates on the threat posed by urban retrofitting activities on vegetation in open spaces in the Wa Central Business District (CBD). The study area was gridded into 1 ha plots using the Fishnet tool in ArcMap. One hundred and fifty-seven full plots and 75 plots of various sizes less than 1 ha were obtained from the fishnet gridding. One hundred and thirteen full plots were sampled for the counting of the trees. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) maps were prepared from 2014, 2017 and 2020 Landsat 8 satellite images. Point NDVI and LST values were extracted for 600 random points and used to analyze the spatial distribution using Global and Local Moran I tests. Two-sample t-test and Pearson correlation statistical tests were performed to assess the significance of any differences in the NDVI, LST and the tree density for 2014, 2017 and 2020. The results revealed that the average tree densities for 2017 and 2020 in the Wa CBD were 4 and 3 trees ha−1, respectively which were classified as very low. The spatial distribution of the NDVI values showed low, high and insignificant clusters. The NDVI values were within the range of 0.019–0.219. The clusters of high values were found around state institutional structures, where large open spaces existed while clusters of low values were found around the market, transport stations and densely populated areas where open spaces were very few. The land surface temperature trend revealed increasing minimum and maximum values. The minimum LST values were 32 °C, 33 °C and 36 °C in 2014, 2017 and 2020, respectively and the maximum LST values were 36 °C, 37 °C and 40 °C for 2014, 2017 and 2020, respectively. It is concluded that tree density and NDVI values in the CBD are low, signifying isolated trees and patches of vegetation in the area. Also, it was revealed that as greenness reduces LST increases. The Wa Municipal Assembly and the Regional Forestry Commission should intensify public education on tree planting and maintenance in the city.

Effect of land cover change and elevation on decadal trend of land surface temperature: a linear model with sum contrast analysis

Effect of land cover change and elevation on decadal trend of land surface temperature: a linear model with sum contrast analysis

Does urban climate follow urban form? Analysing intraurban LST trajectories versus urban form trends in 3 cities with different background climates

The current climate change trend urges the application of efficient spatial planning to mitigate the effects of urbanization on local urban warming. Nevertheless, how urban temperatures respond to urban form changes inside cities is still insufficiently understood. In this paper, we explored the relationship between urban form and diurnal space-time land surface temperature (LST) trends (2003–2019) in Beijing (continental climate), Cairo (arid) and Santiago (temperate). We analysed changes in land cover, white sky albedo (WSA), night-time lights (NL) and the enhanced vegetation index (EVI) inside areas representing clustered thermal performance (steady cold and hot spots and warming cold and hot spots). The structure of local climate zones (LCZs) was assessed for each LST trend. To test the relevance of other urban form dimensions, we analysed the hierarchical influence of the employed 2D metrics (i.e., built-up cover, WSA, NL and EVI) and additional 3D indicators (i.e., height and volume) on LST, applying machine learning classification and regression trees (CARTs) to Beijing's data. Despite diverse patterns of urban form change, cities in our sample present common LST trends, with thermal differences as a consequence of local climate. LCZs are composed of highly heterogeneous built-up areas inside LST trend categories. In the case of Beijing, LST is hierarchically driven by footprint, WSA and EVI. Moreover, by adding height and volume, urban form differences between LST trend classes that are not evident with 2D data were found. Our findings suggest that a compact green urban tissue is necessary to cope with the current trends of urban warming, taking into account city-specific measures based on the local background climate.

Systematic assessment of the warming trend in Madagascar's mainland daytime land surface temperature from 2000 to 2019

Land surface temperature (LST) is one of the main components in the physics of soil surface processes for confirmation of atmospheric temperature variation. Assessment on LST, regionally, is essential because it is highly vulnerable to environmental changes. The intense world's population growth, resulting in land changes for anthropic purposes. Madagascar's role as a biodiversity hotspot, has been experiencing a land change crisis resulting from significant deforestation since 2000. Vegetation cover is decreasing and being replaced by agricultural land and grazing, contributing to total greenhouse gasses (GHG) emissions. Therefore, this study evaluates the LST trend in Madagascar using LST data derived from MODIS Terra Satellite from February 2000–November 2019. This study estimated the 19-year linear trend of each four regions (9 seasonal-adjusted LST time series) and entire Madagascar using a multivariate regression model. In this study, Madagascar is divided into four regions; upper north, lower north, upper south, and lower south; and 36 sub-regions in order to minimize spatial autocorrelation. The results found: 1) There are seasonal variations in different sub-region, 2) The highest average LST increase per decade occurred in the lower south region of Madagascar (0.429 °C/decade), 3) LST has significantly increased in the eastern coast and south of Madagascar, the two deforestation hotspots 4) LST in Madagascar is warming by 0.338 °C/decade. Our study suggests that land use and land cover (LULC) is crucial in identifying LST.

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat-5 and Landsat-8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook’s distance, the effect of missing values in the analysis of the time series were examined. Results from the cross-correlation and cross-variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline.

Research on Retrieval of Surface Temperature Based on Landsat8 Data Atmospheric Correction Method

With the development of remote sensing technology, various land surface temperature retrieval algorithms have been proposed one after another. How to use remote sensing data to retrieve the changing trend of land surface temperature and then plan the development of future cities has become a research hotspot. This paper takes Jinan City, China, as an example, selects 2017 and 2018 Landsat8 OLI and TIRS remote sensing images, uses ENVI software, uses atmospheric correction method to perform temperature inversion, and combines the measured data of 10 measurement sites in Jinan City and the inversion results of this research Comparing the data. Using linear regression analysis, the correlation coefficients between the inverted temperature and the measured land temperature are 0.8815 and 0.8756, respectively, which means that the correlation is relatively high. The atmospheric correction method is used to invert the surface temperature of Ji Nan City. The rationality of this policy provides a reference for effectively responding to regional or global climate change.