Search

This study reports on the tribological behaviour of sliding surfaces having cross-shaped micro-dimples on a surface. One wall is smooth and moving at a constant speed against the other fixed wall with dimples. The laser machine helps to create the desired dimples on the surface of the fixed wall. For the purpose of generating hydrodynamic pressure and tribological behaviour, the effects of cross-shaped dimples and oriented cross-shaped dimples have been compared with circular-shaped dimples. Additionally, the impact of sliding speed, dimple area density and depth on tribological behaviour was examined. The findings show that compared with a circular-shaped dimple, an unconventional cross-shaped and orientated cross-shaped dimple generates a higher net hydrodynamic pressure in the fluid domain and offers superior stability between the sliding surfaces. It has been demonstrated that geometrical factors like dimple depth and area density as well as operational factors like sliding speed have a substantial impact on the hydrodynamic average pressure and tribological behaviour of sliding surfaces. The experimental findings indicate that, for the same geometric and operating parameters, cross- and orientated cross-shaped dimples have a 20%–25% lower friction coefficient between the sliding surfaces than circular dimples. The results of the experiment support those of the analysis and CFD. [ABSTRACT FROM AUTHOR]

Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures. [ABSTRACT FROM AUTHOR]

This study takes a unique approach, using satellite remote sensing to analyze the land surface temperature (LST) in seven coastal cities located at latitudes between 18° and 22° and longitudes between 106° and 91°. The methodology consists of obtaining the LST on two selected days, one in July 2019 and one in July 2020, to determine how the temperature was modified by the atypical mobility caused by Coronavirus Disease 2019 (COVID-19) and social distancing. Using these data, we determine the representative surface temperature that tended to rise (RHST) and the representative surface temperature that tended to decrease (RLST), depending on the affected area. This approach allows us to comprehensively compare how mobility modified the four variables studied: territorial extension, population, types of soil (paving), and vegetation. We concluded that, among the factors analyzed, the types of paving and vegetation were those that generated a more significant decrease in temperature; the variables of territorial extension and the number of inhabitants had a smaller impact. This study paves the way for a discussion of the significant influence of mobility on the behavior of the LST. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems are vital to arid environments like Saudi Arabia, offering crucial ecological services and enhancing biodiversity. This study investigates the spatial distribution and temporal dynamics of mangrove coverage in the Al Wajh lagoon area, crucial for crafting effective conservation and management strategies. Employing high-resolution Landsat and Sentinel imagery, the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were analysed across 35 mangrove clusters. By 2023, mangroves covered approximately 24.5 km2. Kendall Tau-b Correlation Coefficients confirmed a significant positive trend. The study further explored the relationship between NDVI and surface temperature, revealing an inverse correlation with slope values ranging from -4.03 to -44.1, suggesting that increased mangrove coverage significantly contributes to local cooling, thereby underscoring the climate regulation benefits of mangrove conservation. This positive change in NDVI post-2020 is directly attributable to the conservation efforts embedded within the Red Sea Project, Vision 2030, development phases, demonstrating the critical role of designated conservation zones in mitigating the environmental impacts of large-scale development projects. The findings underscore the need for targeted policies to protect and expand mangrove habitats as part of broader environmental sustainability goals in the region. [ABSTRACT FROM AUTHOR]

Efficient drought monitoring in the Urmia Lake basin (ULB) is imperative to protect its ecosystem, agriculture, and the livelihoods of local communities relying on its water resources, considering the lake's susceptibility to changes in water availability. This study presents a novel approach to address the pressing issue of precise drought monitoring in regions with limited and unevenly distributed weather stations. By utilizing Tropical Rainfall Measuring Mission (TRMM) satellite-derived data and a Geographically Weighted Regression (GWR) model, we have significantly refined the spatial resolution of TRMM-3B43 precipitation (5 km) compared with the original TRMM data (about 25 km). This innovative methodology, which uses the globally available MODIS data of Normalized Difference Vegetation Index (NDVI) and day-night difference in land surface temperature (LSTdn) as independent variables, achieves spatial downscaling, enhancing the spatial resolution to 5 km for the period 2001–2019. The downscaled precipitation data from three models (M1: GWR_NDVI, M2: GWR_LSTdn, and M3: GWR_NDVI-LSTdn) were applied for drought assessment based on the Standardized Precipitation Index (SPI) and modified Rainfall Anomaly Index (mRAI). The findings demonstrate that the M2 model is more accurate than the other models for spatial downscaling of TRMM 3B43 data, with reductions in RMSE and MAE values by 6.04 and 4.16 mm, respectively. Additionally, this downscaling model significantly improves KGE values from 0.56 to 0.92 while achieving the lowest percentage bias. Monthly downscaled TRMM data based on LST across 12 synoptic stations in the basin reveals enhanced accuracy post-downscaling. Spatial analysis of average monthly precipitation maps illustrates a descending rainfall trend from June to September, followed by an ascending trend from October, with peak rainfall in November and December, notably in the western and southwestern regions. This analysis of precipitation trends offers valuable insights into the spatial distribution of rainfall within the basin, revealing variations across regions crucial for effective water resource management. The concentration of higher precipitation levels in the western and southwestern sectors underscores the significance of targeted water conservation and storage efforts. Drought severity analysis unveiled persistent and escalating drought conditions, causing significant impacts across the basin. A comparative assessment of severity classes using SPI and mRAI indices at 12 synoptic stations demonstrated strong agreement. Drought occurrences were a near-annual affair, attaining severe levels during specific years, notably 2008, 2011, and 2019. Spatial analysis revealed widespread drought events affecting nearly half of the basin area, with a noticeable worsening in severity during critical years. This highlights the practical need for adopting drought mitigation strategies and water resources management. [ABSTRACT FROM AUTHOR]

The NOAA-AVHRR (National Oceanographic and Atmospheric Administration–Advanced Very High-Resolution Radiometer) archive includes data from 1981 onwards, which allow for estimating land surface temperature (LST), a key parameter for the study of global warming as well as surface characterization. However, algorithms for LST retrieval were developed before the latest sensors and were based on more reduced atmospheric datasets. Here, we present 50 novel sets of coefficients for an LST retrieval algorithm from NOAA-AVHRR sensors, to which we added one historical methodology, which we validate against historical in situ as well as independent satellite data. This validation shows that the historical algorithm performs surprisingly well, with an in situ RMSE below 1.5 K and a quasi-null bias when compared with independent satellite data. A couple of the novel algorithms also perform within expectations (errors below 1.5 K), so any of these could be used for the complete processing of the AVHRR dataset. In our case, considering consistency with previous works, we opt for the use of the historical algorithm, now also tested for more recent periods. [ABSTRACT FROM AUTHOR]

Changing built-up area is the obvious reason for the fluctuation of land surface temperature phenomena that leads to a distressing urban environment. Urban vegetation has the potential to minimize the land surface temperature intensity. The present study investigated the relationship between land surface temperature (LST) and vegetation patterns in Asansol Municipal Corporation. Landsat 5 TM and Landsat 8 OLI satellite images for the years 1991 and 2021 were used for analysis of landscape metrics viz., class area, patch density, edge density, and mean shape index. Correlation techniques were applied to depict the association between the variables. The study concludes that vegetation configuration has no significant relationship with LST during the study period. However, the vegetation composition is slightly associated with LST which suggests that vegetation composition may play a crucial role in mitigating the LST phenomenon. However, the relationship is very complex and varies spatially and scale-wise. [ABSTRACT FROM AUTHOR]

This research presents a GIS-based framework used to detect urban heat islands and determine which urban settlement elements are most critical when heatwave risks exist. The proposed method uses the Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm applied to the satellite land surface temperature distribution recorded during heatwaves for the detection of urban heat islands. A pixel classification confidence level maximization approach, obtained by running a maximum likelihood classification algorithm, is performed to determine the optimal number of clusters. The areas labeled as hotspots constitute the detected urban heat islands (UHIs). This method was tested on an urban settlement set up by the municipality of Naples (Italy). Comparison tests were performed with other urban heat island detection methods such as standard deviation thresholding and Getis-Ord Gi* hotspot detection; indices measuring the density of buildings, the percentage of permeable open spaces, and vegetation cover are taken into consideration to evaluate the accuracy of the urban heat islands detected. These tests highlight that the proposed method provides the most accurate results. It could be an effective tool to support the decision maker in evaluating which urban areas are the most critical during heatwave scenarios. [ABSTRACT FROM AUTHOR]

The rapid expansion of urban land has altered land use/land cover (LULC) types, affecting land surface temperatures (LSTs) and intensifying the urban heat island (UHI) effect, a prominent consequence of urbanization. This study, which focuses on Harbin, a representative city in a cold region, employs the patch-generating land use simulation (PLUS) model to predict LULC changes and a Bidirectional Long Short-Term Memory (Bi-LSTM) model to predict LST. The PLUS model exhibits a high prediction accuracy, evidenced by its FoM coefficient of 0.15. And the Bi-LSTM model also achieved high accuracy, with an R 2 value of 0.995 and 0.950 and a root mean square error (RMSE) of 0.199 and 0.390 for predictions in winter and summer, respectively, surpassing existing methods. This study analyzed the trends in LULC, LST, and the urban thermal field variance index (UTFVI) to assess the relationships among LST, LULC, and UTFVI. The results show that urban land increased by 27.81%, and woodland and grassland decreased by 61.07% from 2005 to 2030. Areas with high temperatures increased by 40.86% in winter and 60.90% in summer. The proportion of the medium UTFVI zone (0.005–0.010) in urban land increased by 50.71%, and the proportion of areas with medium UTFVI values and above (>0.005) decreased at a rate of 84.70%. This finding suggests that the area affected by the UHI has decreased, while the UHI intensity in some regions has increased. This study provides a technical reference for future urban development and thermal environment management in cold regions. [ABSTRACT FROM AUTHOR]

Modeling the impacts of Land Use/Land Cover changes (LULCC) on Land Surface Temperature (LST) is crucial in understanding and managing urban heat islands, climate change, energy consumption, human health, and ecosystem dynamics. This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. A set of maps for land cover, LST, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and topography was integrated into the Cellular Automata-Artificial Neural Network (CA-ANN) and the Long-Short-Term Model (LSTM) models to predict the LULC and LST for 2030. The results showed an expansion of urban areas in GAM from 54.13 km2 (6.6%) in 1980 to 374.1 km2 (45.3%) in 2023. However, agricultural areas decreased from 152.13 km2 (18.5%) in 1980 to 140.38 km2 (17%) in 2023, while barren lands decreased from 54.44 km2 (6.6%) in 1980 to 34.71 km2 (4.22%) in 2023. Forested areas declined from 4.58 km2 (0.56%) in 1980 to 4.35 km2 (0.53%) in 2023. Rangelands/ sparsely vegetated areas declined from 557 km2 (67.7%) in 1980 to 270.71 km2 (32.9%) in 2023. The results of modeling LST showed an increase in average LST for all land cover types, with the most significant increases evident within urban areas and Rangelands/Sparsely vegetated areas. The slightest increase in LST was within forested areas as the average LST increased from 28.42 °C in 1980 to 34.16 °C in 2023. The forecasts for the future showed a continuous increase in LST values in all land cover types. These findings highlight the impact of land surface dynamics and their impact on increasing land surface temperature, which urges the adoption of more sustainable planning policies for more livable and thermally comfortable cities. [ABSTRACT FROM AUTHOR]

Droughts pose significant challenges to food security, affecting millions of people and hectares of land in India. Despite their widespread impact, assessing drought patterns at a high spatial resolution remains a challenge, particularly in regions with poor data availability. Standardised Precipitation Evapotranspiration Index (SPEI) is one of the most widely accepted drought indices. The publicly available SPEI reanalysis dataset has a poor spatial resolution for regional drought studies. Land Surface Temperature (LST) has the potential to act as an effective proxy for SPEI and can be used for high-resolution drought studies. The study considered six homogeneous drought regions (NWH, CPR, NEI, NCP, NWI, and SPR) over India, and the recent trend in LST and SPEI over these regions was estimated using modified Mann-Kendell (MMK) trend and Sen's slope estimator at monthly, seasonal, and annual time scales. The correlation between SPEI and LST was also checked at a 95% confidence level. Results indicate statistically significant decreasing annual LST trends in NWH (slope = −0.10), CPR (slope = −0.09), and NWI (slope = −0.11) regions, while increasing SPEI trends were observed in NWI (slope = 0.06) and NCP (slope = 0.01) regions during 2001–2019. Additionally, NCP (p = 0.025), CPR (p = 0.002), and NWI (p = 0.020) exhibited significantly decreasing LST trends during winter, with a notable post-monsoon decrease observed in NWI. Significant correlations between annual LST and SPEI were observed only in CPR (r =−0.527) and NWI (r = −0.601), with seasonal associations found predominantly in winter across CPR, NEI, NCP, and NWI regions. Notably, a significant correlation was observed in all months except July over NWI, with consistent correlations during most of the winter months in NWH, CPR, NCP, and NWI regions. The observed trends and correlations can provide valuable insights for policymakers and stakeholders in formulating effective drought mitigation strategies. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) and Water Vapor Pressure (WVP) contour maps can be produced using cameras aboard satellites, for instance, under the name "Remote Sensing (RS)". Satellite image observations should be verified before using based on a reliable data. Global Navigation Satellite System Radio Occultation (GNSS-RO) method is observing accurate Earth atmosphere parameters continuously. In the present research, LST and WVP differences between Landsat 8 (LC08), Sentinel-3 (S3), and MODIS (Terra and Aqua) images and GNSS-RO are assessed in Egypt depending on the satellites operating periods and data availability during the years from 2015 to 2020. Statistically, S3 and Terra have insignificant differences with RO temperature with an average bias of 3.48 °C and 1.47 °C, respectively, but LC08 and Aqua have significant differences with it. For WVP, Aqua and LC08 have insignificant differences with an average bias of 0.02 kg/m2 and 2.31 kg/m2, respectively, but S3 and Terra have significant differences with RO observations. When comparing LC08 LST data to other satellites, it was found that there were insignificant differences between LC08 and S3 as well as Terra. However, significant differences were observed when comparing LC08 LST data to Aqua. Additionally, significant differences were noted when comparing LC08 WVP data to other satellites. In response to these differences, improvement models have been developed to enhance the estimation of terrestrial data through remote sensing, particularly for satellites that exhibited significant disparities when compared to reference observations (RO). [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) increases and urban heat island (UHI) variability are the major urban climatology problems arising in urban development. This study attempts to assess the effects of urban land use and land cover change on microclimate dynamics in Addis Ababa city. Three different sets of remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005) and Landsat 8 OLI/TIRS (2021) were used for the study. LSTs were retrieved from Landsat5 TM and Landsat7 ETM+ using a mono window,and the thermal infrared band (TR-10) of Landsat–8 was used to retrieve LST. Regression and correlation analyses of the LST, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were performed in SPSS V23. The study also examined the different residential urban morphology types (UMTs) of the LST and NDVI. The selected built-up blocks of UMTs included apartments, villas and mud houses. These UMTs are extracted by digitizing them from the Google Earth explorer. The results from this study showed that the proportion of urban green space (UGS) to other LULC types decreased from 120.4 km2 in 1990 to 76.26 km2 in 2021. However, the built-up area increased at a rate of 216.5 km2 (39.03%) from 1990 to 2021. The rapid expansion of built-up land in the study area was the main factor influencing the increase in LST. The residential UMTs exhibited significant differences in mean LSTs and NDVIs. The results indicate that UMT inhibited by Villia had the highest mean NDVI value and that the highest mean LST was observed in Apartment. The results of multiple linear regression analysis clearly indicate that built-up and green vegetation contributed 92.2% of the LST variations with R2 = 0.92 and VIF ≤ 10 in Addis Ababa city. The results of the study indicate that strengthening public participation in urban greening and optimizing the NDVI and NDBI are important strategies for mitigating the effects of microclimate change and that sustaining urban development and providing better quality of life for the urban population are important. [ABSTRACT FROM AUTHOR]

Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat 8 OLI/TIRS imagery images over the years 2016–2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures. [ABSTRACT FROM AUTHOR]

The influence of landslides (LS) on forest structure, composition, and functionality has gained limited scientific attention compared to socioeconomic aspects. This study aims to fill this gap by investigating the dynamics of pre- and post-LS occurrences in and around the Kali Tiger Reserve (KTR), Western Ghats. Our approach integrates multi-source, multi-temporal earth observation data, vegetation indices, field observations, and machine learning techniques. This study identified 245-LS caused due to a catastrophic rainfall event in July 2021 the most severe over a century that impacted the tropical dense forests. The present study highlights the emergence of invasive alien species (IAS), particularly Chromolaena odorata, following these landslide incidents. Field observations revealed a significant loss of large trees, which corroborated with the Global Ecosystem Dynamics Investigation (GEDI) based Canopy Height Model (CHM) and very high-resolution (VHR) data. The affected areas witnessed a significant rise in land surface temperature (LST) and a decrease in vegetation moisture. A comparative analysis with operational tree loss monitoring using optical (30-m Landsat based Global Forest Watch (GFW), and microwave (L-band Synthetic Aperture Radar (SAR) JICA-JAXA (ALOS-2) Forest Early Warning System) revealed improved performance in mapping small landslides with current approach. These results emphasize the necessity of conducting local and large scale investigations of forest dynamics before and after landslides to meet environmental commitments at various levels. The landslide events will likely induce significant alterations in the forest's microclimate. Our research recommends an immediate action plan to restore affected sites, remove IAS, and encourage the planting of native vegetation for biodiversity conservation. [ABSTRACT FROM AUTHOR]

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming. The understanding of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited. In this study, we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats. We explored the impacts of wind farms on land surface temperature (LST) and vegetation net primary productivity (NPP) between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas. The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020. Spatially, wind farms are mainly clustered in three aggregation areas in the center. Further, wind farms increased nighttime LST, with a mean value of 0.23 °C, but had minor impacts on the daytime LST. Moreover, wind farms caused a decline in NPP, especially over forest areas, with an average reduction of 12.37 GC/m². Given the impact of wind farms on LST and NPP, we suggest that the development of wind farms should fully consider their direct and potential impacts. This study provides scientific guidance on the spatial pattern of future wind farms.

Abstract Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures.

Abstract Droughts pose significant challenges to food security, affecting millions of people and hectares of land in India. Despite their widespread impact, assessing drought patterns at a high spatial resolution remains a challenge, particularly in regions with poor data availability. Standardised Precipitation Evapotranspiration Index (SPEI) is one of the most widely accepted drought indices. The publicly available SPEI reanalysis dataset has a poor spatial resolution for regional drought studies. Land Surface Temperature (LST) has the potential to act as an effective proxy for SPEI and can be used for high-resolution drought studies. The study considered six homogeneous drought regions (NWH, CPR, NEI, NCP, NWI, and SPR) over India, and the recent trend in LST and SPEI over these regions was estimated using modified Mann-Kendell (MMK) trend and Sen’s slope estimator at monthly, seasonal, and annual time scales. The correlation between SPEI and LST was also checked at a 95% confidence level. Results indicate statistically significant decreasing annual LST trends in NWH (slope = −0.10), CPR (slope = −0.09), and NWI (slope = −0.11) regions, while increasing SPEI trends were observed in NWI (slope = 0.06) and NCP (slope = 0.01) regions during 2001–2019. Additionally, NCP (p = 0.025), CPR (p = 0.002), and NWI (p = 0.020) exhibited significantly decreasing LST trends during winter, with a notable post-monsoon decrease observed in NWI. Significant correlations between annual LST and SPEI were observed only in CPR (r =−0.527) and NWI (r = −0.601), with seasonal associations found predominantly in winter across CPR, NEI, NCP, and NWI regions. Notably, a significant correlation was observed in all months except July over NWI, with consistent correlations during most of the winter months in NWH, CPR, NCP, and NWI regions. The observed trends and correlations can provide valuable insights for policymakers and stakeholders in formulating effective drought mitigation strategies.

Abstract Land surface temperature (LST) increases and urban heat island (UHI) variability are the major urban climatology problems arising in urban development. This study attempts to assess the effects of urban land use and land cover change on microclimate dynamics in Addis Ababa city. Three different sets of remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005) and Landsat 8 OLI/TIRS (2021) were used for the study. LSTs were retrieved from Landsat5 TM and Landsat7 ETM+ using a mono window,and the thermal infrared band (TR-10) of Landsat–8 was used to retrieve LST. Regression and correlation analyses of the LST, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were performed in SPSS V23. The study also examined the different residential urban morphology types (UMTs) of the LST and NDVI. The selected built-up blocks of UMTs included apartments, villas and mud houses. These UMTs are extracted by digitizing them from the Google Earth explorer. The results from this study showed that the proportion of urban green space (UGS) to other LULC types decreased from 120.4 km2 in 1990 to 76.26 km2 in 2021. However, the built-up area increased at a rate of 216.5 km2 (39.03%) from 1990 to 2021. The rapid expansion of built-up land in the study area was the main factor influencing the increase in LST. The residential UMTs exhibited significant differences in mean LSTs and NDVIs. The results indicate that UMT inhibited by Villia had the highest mean NDVI value and that the highest mean LST was observed in Apartment. The results of multiple linear regression analysis clearly indicate that built-up and green vegetation contributed 92.2% of the LST variations with R2 = 0.92 and VIF ≤ 10 in Addis Ababa city. The results of the study indicate that strengthening public participation in urban greening and optimizing the NDVI and NDBI are important strategies for mitigating the effects of microclimate change and that sustaining urban development and providing better quality of life for the urban population are important.

The study explores the intricate interplay between land use land cover (LULC), normalized difference vegetation index (NDVI), and land surface temperature (LST) within the Lower Son River Basin in India from 1991 to 2020. The region’s ecological balance has been increasingly strained due to rapid urbanization and changing land use patterns. Through a combination of Landsat TM & OLI/TIRS satellite imageries and geospatial analysis techniques, this study unveils the intricate connection between land use and land cover changes, vegetation, and land surface temperature variations. The study area is classified into three altitudinal zones (Zone I: 39–300 m, Zone II: 301–600 m and Zone III: 601–1,247 m) to examine the changes in depth. The area has seen significant changes in LULC, vegetation and LST in all the three altitudinal zones. The findings hold significant implications for sustainable land management and environmental conservation strategies in the Lower Son River Basin. As per the result, 103,438 ha of vegetation was converted into agriculture land and 82,572 ha of agricultural land was transformed into settlements from 1991 to 2020. This trend shows human pressure on the land resource in the study area. Minor increase in water body is seen which is attributed to commissioning of Bansagar dam. Zone I has seen highest settlement growth while Zone III experienced severe deforestation of around 15%. Zone II and III needs attention for holistic sustenance. Analysis of LST shows that it has increased by 0.82 °C from 1991 to 2020 which is a red flag. The study underscores the critical importance of balanced land use practices to preserve ecological integrity and mitigate the adverse effects of urbanization and climate change.

The interactions between the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-Up Index (NDBI), and Land Surface Temperature (LST) are complex. The assessment of Land Use/Land Cover (LULC) changes in the North-western region of Algeria between 1995 and 2021 confirms the direct influence of these factors on surface thermal processes (LST). The use of new information technologies, particularly remote sensing coupled with GIS, favourably contributes to the processing of a large volume of data and to the use of specific methods aimed at confirming and/or disproving the hypotheses put forward. The application of LULC classification methods clearly highlights the magnitude of transformations, predominantly in favor of intensified urbanization over the past two decades. Indeed, agricultural lands have experienced a reduction of 17.45%, while urbanized areas have nearly doubled. This phenomenon can, in part, be attributed to the mass migration of populations from inland areas to the coast due to climate change not only: secondary for political problems between 1990 and 2001. Similarly, barren lands have increased by 10.45%. These changes have real implications for ecosystems (mainly loss of biodiversity) and the climate (pollution, GHG emissions, and rising ambient temperatures), often leading to the creation of in certain places. The estimation of average LST from multiple satellite scenes reveals an increasing trend, rising from 36.61°C in 1995 to 40.35°C in 2021. The direct relationship between LST and NDVI and between LST and NDBI confirms the close association between land use change and increasing surface temperatures. The Pearson coefficient between LST and NDVI showed a negative correlation, ranging between -0.52 and -0.47, while it was positively correlated between LST and NDBI, with values around 0.66. The emergence of hotspots in the region, confirmed by analysis results employing the Getis-Ord G* method, is marked by clearly increasing spatial envelopes. This phenomenon is associated with a distinct reduction in vegetation cover density, coupled with an increased vulnerability to drought conditions. These initial results argue in favor of preserving green and blue networks and, more largely, ecosystems.

Introduction: Soil moisture can be considered in the control of desertification, agricultural activities, watershed management and optimal management of water resources. Since the country of Iran is facing many problems in these fields, the expansion of studies in the field of accurate estimation of soil moisture becomes important (Mehrabi et al., 2019). The GLDAS system may have a high error compared to the measured data in some areas. Therefore, it is necessary before the data and results of this product are used as a decision-making tool in the region. The quality of these data should be evaluated locally using ground-measured data (Polo et al, 2016 & Sanchez-Lorenzo et al, 2013 & Zhang, 2019). In this study, Terra MODIS data was used to estimate soil moisture due to higher spatial resolution (1 km). Due to the fact that it is difficult to estimate the humidity time series in the field, and radar remote sensing methods produce humidity maps with low spatial resolution. Therefore, in this study, a new method was introduced to prepare a soil moisture map with higher spatial resolution based on NDVI and LST MODIS products. The purpose of this study is to estimate soil moisture in Jiroft city using the products of the Morris sensor and NDVI and LST indices. Considering that Jiroft plain is one of the agricultural poles of Iran. Estimating the time series of soil moisture and then providing a drought index based on soil moisture is a useful method for investigating agricultural drought in the study area. Results: LST and NDVI have high relative importance in arid regions (Park et al, 2016). In the study area, an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created using OLS method between soil moisture observations (2021 and 2022) and NDVI and LST time series data. Based on the results, both independent variables NDVI and LST were significant at the 99% level)p-value<0.01). VIF values were less than 10. Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. In general, R² = 0.78 and it shows the accuracy of soil moisture estimation method. R² was obtained as 0.74 and 0.8 in the years 2021 and 2022, respectively, which indicates the accuracy of the predicted values. Discussion & Conclusions: Estimation of spatial and temporal changes of soil moisture is an important issue in low data areas such as Iran. We proposed a multiple regression model based on Modis NDVI and LST to obtain surface soil moisture at a regional scale. The results showed that the multivariate linear regression method can be used to estimate soil moisture products with high resolution in areas with little data in the surface layers of the soil. Park et al. (2016) stated that LST and NDVI have high relative importance in arid regions. In the studied area, according to previous studies (Bai et al, 2020 & Wang et al, 2007), an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series became. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created between soil moisture observations (2021 and 2022) and NDVI and LST time series data using OLS method. Based on the results of simulated soil moisture changes and the OLS method in estimating soil moisture (0-30 cm), both independent variables NDVI and LST were significant at the 99% level (p-value <0.01)). VIF values are less than 10 Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. Overall, R2 was 0.7 And it showed the accuracy of the soil moisture estimation method, which is consistent with the results of (Khanmohammadi et al, 2008 & Lin et al, 2015). [ABSTRACT FROM AUTHOR]

The interactions between the normalised difference vegetation index (NDVI), the normalised difference built-up index (NDBI), and land surface temperature (LST) are complex. The assessment of land use/land cover (LULC) changes in the North-western region of Algeria between 1995 and 2021 confirms the direct influence of these factors on surface thermal processes. The use of new information technologies, particularly remote sensing coupled with GIS, favourably contributes to the processing of a large volume of data and to the use of specific methods aimed at confirming and/or disproving the hypotheses put forward. The application of LULC classification methods clearly highlights the magnitude of transformations, predominantly in favour of intensified urbanisation over the past two decades. Indeed, agricultural lands have experienced a reduction of 17.45%, while urbanised areas have nearly doubled. This phenomenon can, in part, be attributed to the mass migration of populations from inland areas to the coast, not only due to climate change: secondary for political problems between 1990 and 2001. Similarly, barren lands have increased by 10.45%. These changes have real implications for ecosystems (mainly loss of biodiversity) and the climate (pollution, GHG emissions, and rising ambient temperatures). The estimation of average LST from multiple satellite scenes reveals an increasing trend, rising from 36.6 °C in 1995 to 40.35 °C in 2021. The direct relationship between LST and NDVI and between LST and NDBI confirms the close association between land use change and increasing surface temperatures. The Pearson coefficient between LST and NDVI showed a negative correlation, ranging between -0.52 and -0.47, while it was positively correlated between LST and NDBI, with values around 0.66. The emergence of hotspots in the region, confirmed by the results of analysis employing the Getis-Ord G* method, is marked by clearly increasing spatial envelopes. This phenomenon is associated with a distinct reduction in vegetation cover density, coupled with an increased vulnerability to drought conditions. These initial results argue in favour of preserving green and blue networks and, more largely, ecosystems. [ABSTRACT FROM AUTHOR]

This research analyses the long-term vegetation trends in Shada Mountain across six elevation zones, utilizing Landsat 5, 7, 8, and 9 imageries processed via Google Earth Engine and R. The study managed differences in images resolution through meticulous calibration and image processing techniques. The study is structured around two objectives: examining the relationship between vegetation and its proximity to streams and land surface temperature and analyzing trends in the Normalized Difference Vegetation Index (NDVI). Regression analysis revealed a negative correlation between vegetation and proximity to streams in lower zones (1-3), with no significant effect in higher zones (4-6). NDVI trend analysis indicated an overall increase in vegetation across most zones, with the exception of zone 5, which displayed a negative trend (slope -0.0025). The findings reveal that the decline is particularly pronounced among key tree species such as Ficus cordata subsp. salicifolia and Acacia asak, suggesting potential impacts from climate change and land use alterations. These zone-specific insights deepen our understanding of the dynamic ecological processes in semi-arid environments and guide targeted environmental management and conservation efforts. [ABSTRACT FROM AUTHOR]

We examined dam-induced land use and land cover change (LULCC) detection in the Amerti-Nashe hydroelectric power reservoirs. These dams were constructed for a variety of purposes, primarily for irrigation in the lower reaches and hydropower generation in the upper reaches. The main objective of the study was to investigate dam-induced LULCC in the study area over the last 41 years. Satellite images were downloaded from the United States Geological Survey (USGS). The data from remote sensing satellite images were used to reflect the spatial variation in LULCC, and land surface temperature (LST) in the study areas near hydropower plants in the years 1985, 2003, and 2021. The supervised classification method is adopted, and the maximum likelihood rule is used as the parameter rule for image classification. The land use classification results show that grazing land, woody grassland, and cultivated land gradually decreased, which could be associated with the establishment of dams. But commercial farming, waterbodies, and built-up areas increased during these study periods. LST variations based on its relationship with Land Use Land Cover (LULC) in the area decreased after the dam was constructed, particularly in the irrigated lowland and places inundated by water from the reservoir in the upper stream. Generally, the construction of the Amerti-Nashe hydroelectric dam projects in the study area has changed land use type and LST both at a lower and upper stream. Therefore, hydropower development in the area needs strategic planning on how to minimize land use land cover change impacts on the environment and the local community. [ABSTRACT FROM AUTHOR]

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5–3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research. [ABSTRACT FROM AUTHOR]

Extreme events related to climate change are increasing in intensity, frequency, and duration worldwide. Europe is identified as a heatwave hotspot, with trends three-to-four time faster than the northern mid-latitudes; effects of heatwaves are combined in urban contexts with the heat island phenomenon, making cities critical for climate risk prevention and management. Land surface temperature represents an essential parameter for assessing the intensity of thermal impact on urban ecosystems and on public health. This parameter is widely used to map and assess urban heat islands in light to support climate-resilient adaptation planning. The general aim of this study is to assess urban heat island intensity, during a significant heatwave, in a critical heat-related risk region in Southern Italy (Salento). Specific objectives are 1) assessing climate change trends for heat-related extremes (hot days and heatwaves), 2) calculating urban heat islands intensity at regional and urban scale, 3) assessing spatial relationships among thermal intensity and urban characteristics (soil sealing and surface albedo). Identification of heatwaves is based on climatological data and statistical analyses; spatial thermal analyses and correlations are based on Landsat-8 imagery while land cover data are derived from ortho-photos. Climate analyses show a notable increase of the maximum annual temperature of 0.5°C per decade, with an increase of eight hot days per decade. Spatial analyses on thermal impact highlight that urban heat island intensity is much lower within cities and towns than in rural areas, showing a "reverse effect" compared to the typical microclimatic characteristics of urban contexts. In fact, thermal intensity in the city of Lecce ranges from -11°C to 5.6°C. Also, by NDVI analyses, we found that permeable surfaces were 2°C higher than impermeable surfaces, with statistically significant differences. Results from albedo analysis suggest that the characteristics of building material in historical sectors of cities may play a crucial role in this "reverse effect" of urban heat islands. Further studies are required to better investigate the contribution of different factors in this context. [ABSTRACT FROM AUTHOR]

Urban green areas are considered significant contributors to environmental quality as they have been shown to reduce temperature and noise levels and improve air quality. Consequently, several studies have analyzed the relationship of surface temperature, vegetation cover, and building density using remote sensing indices. However, existing studies have limitations as they either do not explore this relationship in the context of semi-arid urban regions or do not monitor change over multiple years. To address these issues, this study analyzes the relationships among those indices and their changes over a five-year span (in both winter and summer) in the semi-arid urban region of Kuwait. Results showed that 9% of Kuwait's urban area became vegetated from 2013 to 2019, despite the increase in surface temperature, and 4% went the other way. Both surface temperature and vegetation density were correlated with building density. Yet, both were weakly anticorrelated with each other in the summer and uncorrelated in the winter, perhaps due to the lack of densely vegetated spaces in the study area and Kuwait’s arid climate. Nonetheless, selected areas with high vegetation cover showed a significant reduction in surface temperature in the summer, while in the winter there was no to very weak impact on temperature. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Arts & Social Sciences (JASS) is the property of Sultan Qaboos University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global warming has shown an “Arctic amplification effect” in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land–atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 °C, which is a 42.95–––53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 °C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land–atmosphere energy balance in pan-Arctic.

Understanding the land surface process in numerical models necessitates the consideration of Land Surface Temperature (LST) and soil moisture. LST plays a crucial role in regulating surface and sub-surface heat; thus, influencing water circulation within the atmosphere. This study utilized Landsat 8 Optical Land Imager (OLI) images with a 30 m resolution to analyze LST, Soil Moisture Index (SMI) and Vegetation Dryness Index (TVDI) for the Erer Watershed, which is in the Wabi Shebelle River basin in Ethiopia. The investigation spanned six years, from 2015 to 2021, and it involved determining the Normalized Difference Vegetation Index (NDVI) for each specified year to get LST, soil water stress, and TVDI. The thermal infrared band's digital values were converted into spectral radiance using the relation specified in the Landsat user's manual. The final LST was derived using surface emissivity based on NDVI classes. The findings revealed an increasing trend in LST, SMI, and TVDI over time, signifying decline in soil moisture. The north-eastern and north-western sections of the Erer Watershed exhibited the highest values of LST, SMI, and TVDI, with a negative correlation observed with soil moisture. The spatial distribution of LST, SMI, and TVDI can be used as a valuable reference for managing soil water stress and to understand ecosystem services. Additionally, LST may serve as a significant indicator in monitoring environmental changes, particularly in relation to drought, within the study area.

Mangrove ecosystems are vital to arid environments like Saudi Arabia, offering crucial ecological services and enhancing biodiversity. This study investigates the spatial distribution and temporal dynamics of mangrove coverage in the Al Wajh lagoon area, crucial for crafting effective conservation and management strategies. Employing high-resolution Landsat and Sentinel imagery, the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were analysed across 35 mangrove clusters. By 2023, mangroves covered approximately 24.5 km2. Kendall Tau-b Correlation Coefficients confirmed a significant positive trend. The study further explored the relationship between NDVI and surface temperature, revealing an inverse correlation with slope values ranging from −4.03 to −44.1, suggesting that increased mangrove coverage significantly contributes to local cooling, thereby underscoring the climate regulation benefits of mangrove conservation. This positive change in NDVI post-2020 is directly attributable to the conservation efforts embedded within the Red Sea Project, Vision 2030, development phases, demonstrating the critical role of designated conservation zones in mitigating the environmental impacts of large-scale development projects. The findings underscore the need for targeted policies to protect and expand mangrove habitats as part of broader environmental sustainability goals in the region.

Cellular automata (CA) models are employed for simulating geographical distributions, while Markov-Chain models are utilized for simulating temporal changes. This study aims to forecast the dynamics of land use and land cover (LULC) change and land surface temperature (LST) using a CA and Markov-Chain model for the period 1992–2022 in Islamabad, Pakistan. This research innovatively predicts future LULC and LST and examines their correlations with various vegetation indexes. LULC maps were generated from time series data of Landsat satellite images (Landsat 5, 7, and 8) for the years 1992, 2002, 2012, and 2022, utilizing the random forest algorithm. Five satellite indexes were employed: normalized difference vegetation index (NDVI), bare soil index (BSI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and soil adjusted vegetation index (SAVI). Through the CA-Markov-Chain analysis, the results showed significant new findings, such as a rise in the built-up area of Islamabad, which increased from 105.63 km2 (11.66%) in 1992 to 447.39 km2 (49.38%) in 2022. The study predicted that the built-up area would further increase to 531.82 km2 (55.38%) by 2042. The study analyzed the enhancement of LST, which was about 2.40 °C from 1992 to 2022, ultimately because of the expansion of uncontrolled urban areas. The correlation of LST with the vegetation indexes NDVI, BSI, NDWI, NDBI, and SAVI was also analyzed through regression analysis. Furthermore, the surface temperature was predicted well by the urban index (UI), a nonvegetation index, demonstrating the positive correlation of R2 = 0.87 with respect to retrieved surface temperature. Using the UI as a predictor of LST, our projections indicate that regions with temperatures ranging from 20 to 24 °C and from 25 to 28 °C will decrease in coverage from 4.63% to 3.85% and from 25.33% to 21.08%, respectively, between 2032 and 2042.

Remote sensing plays a vital role in analyzing urban changes. In this regard, various datasets collected from satellites today serve as a foundation for decision-makers and urban planners. This study compares the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) as indicators for the creation of surface heat islands. Using Landsat 8 OLI/TIRS C2 L2 images, spatial correlations between land surface temperature (LST) were examined for August 2013, 2019 and 2023. Urban heat islands (UHI) are a contemporary phenomenon and increasingly common in large urban areas compared to surrounding, less populated areas. With the advancement in remote sensing, it is possible to adequately determine the spatial differentiation and prevalence of urban heat islands (UHI). The study is based on Landsat 8 satellite image sets for the Sarajevo basin in August 2013, 2019 and 2023, which were used to analyze LST, NDVI, and NDBI indices. This work indicates a relationship between LST and NDVI but varies depending on the analyzed year. Normalized Difference Built-up Index (NDBI) serves as a suitable indicator for surface UHI effects and can be used as an indicator to assess its spatial distribution within a larger urban environment.

The study conducted in Jammu District, India, investigates land use and land cover (LULC) transformations over the past three decades using satellite data and remote sensing techniques. Analyzing data from 1990 to 2020, significant changes were observed. Agricultural land expanded by 157.76 km2 (6.71%), barren land by 151.69 km2 (6.45%), and settlements by 96.97 km2 (4.12%). However, vegetation decreased by 389.77 km2 (16.57%), while water bodies experienced minimal changes. Land Surface Temperature (LST) analysis, utilizing MODIS data (2000-2020), revealed warming trends, with temperatures ranging from 15.92°C to 42.77°C in 2010 and 14.04°C to 37.01°C in 2020. Notably, NDVI values peaked in 2020 (0.759) and were lowest in 1990 (−0.243), indicating healthier vegetation and lower surface temperatures. This inverse correlation highlights NDVI's potential as an indicator for assessing vegetation health and its impact on local temperature conditions. Man-Kendall Z statistics indicated negative trends for Tmax and Tmin, while rainfall data showed significant positive trend. Population growth, urbanization, climate change and agricultural intensification emerged as principal drivers of the LULC changes in the region. This study underscores the importance of geospatial tools in monitoring LULC changes, providing valuable insights for policymakers and planners to formulate sustainable land use planning and management strategies. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method. [ABSTRACT FROM AUTHOR]

Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data products are usually desired for many applications. Instead of developing and launching new high-resolution satellite sensors for LST observations, a more economical and practical way is to develop proper methodologies to derive high-resolution LSTs from available Low-Resolution (LR) datasets. This study explores different algorithms to downscale low-resolution LST data to a high resolution. The existing regression-based downscaling methods usually require simultaneous observations and ancillary data. The Super-Resolution Reconstruction (SRR) method developed for traditional image enhancement can be applicable to high-resolution LST generation. For the first time, we adapted the SRR method for LST data. We specifically built a unique database of LSTs for the example-based SRR method. After deriving the LST data from the coarse-resolution passive microwave observations, the AMSR-E at 25 km and/or AMSR-2 at 10 km, we developed an algorithm to downscale them to a 1 km spatial resolution with the SRR method. The SRR downscaling algorithm can be implemented to obtain high-resolution LSTs without auxiliary data or any concurrent observations. The high-resolution LSTs are validated and evaluated with the ground measurements from the Surface Radiation (SURFRAD) Budget Network. The results demonstrate that the downscaled microwave LSTs have a high correlation coefficient of over 0.92, a small bias of less than 0.5 K, but a large Root Mean Square Error (RMSE) of about 4 K, which is similar to the original microwave LST, so the errors in the downscaled LST could have been inherited from the original microwave LSTs. The validation results also indicate that the example-based method shows a better performance than the self-similarity-based algorithm. [ABSTRACT FROM AUTHOR]