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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.

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]

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]

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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]

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]

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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]

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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]

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. [ABSTRACT FROM AUTHOR]

Driven by global sustainability goals, understanding land surface temperature (LST) in urban thermal environments (UTE) is crucial. This study analyses remotely sensed LST data from Aqua and Terra satellites in a rapidly growing non-tier Indian city. It uniquely integrates remote sensing and soft computational analysis to predict daytime LST using artificial neural networks (ANN). The study is categorized based on transfer functions and hyper-parameters to enhance the comprehension of LST prediction and simulation scenarios by employing Back Propagation Neural Network (BPNN) model. Results indicate that the tansig transfer function yields the best performance overall in Aqua and Terra LST prediction, while purelin performs the least effectively. This study provides evidence that soft computing techniques effectively estimate LST data, offering a valuable solution for data scarcity issues. However, the findings are specific to the study area and may vary elsewhere. Exploring alternative methods and incorporating data from other areas could potentially improve accuracy and overall performance. Future research could focus on investigating the mechanisms behind different methods and their interactions with various factors. Additionally, integrating other variables like vegetation index, soil moisture, and others could contribute to a more holistic understanding of LST dynamics and enhance predictive capabilities. [ABSTRACT FROM AUTHOR]

The current study investigated the spatiotemporal distribution and detection of drought severity using ground indices in R-Studio software and applying indices on the images of Landsat 8 and Sentinel 2 satellites in the growing season and annually. The outcomes showed that typical months had the largest range in terrestrial indices results, and the Normalized Difference Vegetation Index (NDVI) index had the greatest compatibility with the Standard Precipitation Evaporation Index (SPEI). By comparing these two times, during the growing season, plant health was evident in the northwest and annual images in the northeast. Moreover, a negative correlation was found between Land Surface Temperature (LST) and NDVI in elevation, which was more moderate at northern heights. High mean annual temperatures during growing seasons were common in the east and south. The eastern side of the study area had high annual vegetation density, but soil moisture was higher in the southwest. Floods negatively affected plant health; with the 2019 flood, the canola yield was very low. In summary, Sentinel 2 images showed drought better in the growing season and Landsat 8 images showed drought better in the dry season or at the end of plant growth. [ABSTRACT FROM AUTHOR]

The low-lying islands of the Indian Sundarbans Delta (ISD) contain a unique ecosystem with rich biodiversity, which are at the forefront of the impacts of climate change and natural disasters, such as cyclones. Therefore, in this study, we have analyzed the impact of Cyclone Yaas at the local level in the ISD. We utilized various indices derived from MODIS satellite images to analyze the local-level impacts. The results of our study revealed widespread impacts from high storm surges of 9–16 feet. In general, the daytime land surface temperatures (LSTs) were higher before compared to during the storm, due to lower insolation associated with cloudy skies. However, higher values were observed during the storm for nighttime LSTs and the vegetation indices. More specifically, at the local level, the differences were more pronounced in the vegetated and low-lying coastal areas of the islands. The results of the image analyses were also corroborated with field observations in some of the islands, which showed saltwater encroachment in agricultural lands, collapsed embankments built for protection against storm surge, and food insecurity. The results of our study highlighted the vulnerability of these islands to extreme weather events, and long-lasting impacts on the local communities. [ABSTRACT FROM AUTHOR]

Copyright of Urban Structure & Function Studies is the property of University of Mazandaran 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.)

Copyright of Urban Structure & Function Studies is the property of University of Mazandaran 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.)

Compared to conventional tools, the cutting performance of microtextured tools is significantly better. Laser surface texturing (LST) is the most important technique for producing a microstructure. The modification of microstructure properties of tools by LST has not received much attention. In this study, the effects of LST on the microstructural properties of tools were investigated, and the specific causes of the improvement in wear resistance of tools by laser micro texturing and the effects of micro-texture on adhesive wear of tools were revealed. In this study, microtextured tools with different laser parameters were fabricated and the test was conducted for cutting titanium alloys. The changes in tool microstructural properties and tool wear morphology were compared and analyzed. LST effectively reduces the release of co-elements during cutting, solidifies WC particles, and reduces the adhesive and oxidative wear of the tool. In addition, the micro-texture and surrounding annular protrusions significantly reduce cutting heat, which in turn reduces diffusive and abrasive wear. In this context, the morphology of the micro-texture facilitates adhesive fracture and peeling of the adhesive wear, while the annular protrusions help reduce tool wear. Their interaction significantly improves the wear resistance of laser-machined microtextured tools. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) imagery, notably Sentinel-1A's C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using MAPscape-Rice software, which integrates a fully automated processing chain to convert the data into terrain-geocoded σ° values. This facilitated the generation of rice area maps through a rule-based classifier approach, with classification accuracies ranging from 88.5 to 91.5 and 87.5 percent in 2017, 2018, and 2022, respectively. To estimate methane emissions, IPCC (37.13 kg/ha/season, 42.10 kg/ha/season, 43.19 kg/ha/season) and LST (36.05 kg/ha/season, 41.44 kg/ha/season, 38.07 kg/ha/season) factors were utilized in 2017, 2018 and 2022. Total methane emissions were recorded as 19.813 Gg, 20.661 Gg, and 25.72 Gg using IPCC and 19.155 Gg, 20.373 Gg, and 22.76 Gg using LST factors in 2017, 2018 and 2022. Overall accuracy in methane emission estimation, assessed against field observations, ranged from (IPCC) 85.71, 91.32, and 80.25 percent to (LST) 83.69, 91.43, and 84.69 percent for the years 2017, 2018 and 2022, respectively, confirming the efficacy of remote sensing in greenhouse gas monitoring and its potential for evaluating the impact of large-scale water management strategies on methane emissions and carbon credit-based ecosystem services at regional or national levels. [ABSTRACT FROM AUTHOR]

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.

Changes in LULC triggered by urbanization lead to an increase in impermeable surface, which seriously deteriorates urban climatic conditions and thermal status (LST). Under this circumstance, we analyzed the spatiotemporal deviation of LST and LULC from 2008 to 2023 by using remote sensing Landsat satellite images of Bhairab, Kishoreganj. Major findings are the highly increasing trend of settlement area (7%–23% in summer and 8.5%–19.0% in winter) and waterbody whereas the decreasing trend of agriculture, vegetation and barren land. The thermal status observation indicates the extreme weather conditions, whereas the summer became more hotter (31 °C–34 °C max T), and the winter became more colder (28 °C–24 °C max T). The insights can assist local authorities in restructuring future development plans to mitigate the urban climate environment and foster a vibrant social life in Bhairab.

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.

One of the environmental problems today is the rising land surface temperature and the formation of heat islands in metropolitan areas, which have arisen due to the unplanned expansion of these cities. Satellite imagery is widely used in urban environmental studies to provide an integrated view and reduce costs and time. In this study, Landsat satellite imagery in TM, ETM+, and OLI sensors from 1984 to 2020, remote sensing techniques, and GIS is used to analyze the data, and SPSS software is employed to examine the correlation between the data. The results indicate that the land surface temperature in District 1 of Tehran has increased during the last 38 years. Moreover, land use in District 1 has changed significantly over this period, and urban land use increased from 16 % (1984) to 35 % (2020) while vegetation declined from 32 % to 14 %. The results of linear regression analysis show a significant correlation between satellite images and weather station data. The significance coefficient (Sig) in all stations is less than 0.05 with a 95 % confidence interval. Besides, the coefficient of variation (R) for all stations is above 80 %, and the coefficient R2 has a desirable value. The findings suggest that the trend of rising temperatures in District 1 of Tehran has become an environmental problem and the changes in land use such as declining vegetation and increasing the acceleration of urbanization are among the factors that affect it.

IntroductionEvapotranspiration is one of the key components of water balance and irrigation planning. Thus, the accurate estimation of this component and the water consumption of plants can improve the management of water use and increase the efficiency of water consumption. Due to the limitation of tools for measuring evaporation-transpiration, remote sensing methods can be used for this purpose. There are several remote sensing algorithms for actual evaporation estimation including SEBAL, SEBS, Metric, etc. In this study we used the triangle method which only was used by Salimifard et al. (2022) in Mashhad Plain. They evaluated the results for the agricultural products, i.e., wheat and maize. The aim of this study is to evaluate the triangle method for a horticultural crop, i.e., pistachio in Kerman Plain. MethodologyThe study area is Kerman Plain in which pistachio is one of the most important agricultural products. Due to water scarcity in this plain, determining the water requirement of the crops is crucial for agricultural activities. Accordingly, it is important to have an appropriate estimation of actual evapotranspiration in the plain. In this paper, the triangular algorithm was used to estimate actual evapotranspiration in the Kerman Plain in the growing seasons of 2020 (1399) and 2021 (1400). For this purpose, the Landsat 8 satellite images with less than 10% cloudiness were used. The variables such as NDVI, LST, etc., were calculated by using the JAVA programming language in the Google Earth Engine code (GEE) system environment. The required meteorological data of Kerman station were acquired from IRIMO. The triangular algorithm is based on the two-dimensional spatial plot of normalized LST and normalized NDVI, which were calculated using bands 10, 5, and 4 of the Landsat 8 in the GEE. Estimation of the wet and dry edges was conducted by MATLAB code. the actual evapotranspiration obtained using the triangular method for a pistachio orchard, which was under irrigation management, was compared to the values obtained by the FAO-56 method. The results were evaluated by correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Error (ME).Results and discussionThe results showed that the amount of evapotranspiration for pistachio was estimated with acceptable accuracy (r= 0.73 and RMSE=1.8, nRMSE=0.4, ME=-1.6). However, the NSE less than zero (-1.3) shows that the observed (FAO-56) mean is a better predictor than the Triangle algorithm. The values obtained from the triangular algorithm were lower than the values of FAO 56, which was in line with the results of the previous studies for both Agricultural and horticultural crops. This underestimation could be due to the uncertainty of the algorithm, uncertainty in the measured data, or due to the time difference between the date of the selected images and the date of irrigation. Moreover, inappropriate quality of water and soil in Kerman Plain and the uncertainty of plant coefficients used are among the factors that can underestimate evapotranspiration values by the algorithm. ConclusionsIn this study, the triangular algorithm was used to estimate actual evapotranspiration in Kerman plain using remote sensing data. Actual evapotranspiration values obtained from the triangular algorithm were lower than FAO 56 values, which might be due to the uncertainty of the algorithm, uncertainty in the measured data, uncertainty of plant coefficients, or due to the time difference between the date of the selected images and the date of irrigation. To have a better evaluation of the remote sensing algorithms, it can be suggested to develop and apply a micro lysimeter in the farms and orchards, or to use the soil water balance of the farms and orchards. These may help to choose the more appropriate algorithm for the given study area, leading to providing the more proper and applicable advices for the farmers for managing the shortage of the water resources. Furthermore, it may help to update the crop coefficients which may lead to better estimation of evapotranspiration.

تغییرات پوشش و کاربری زمین در اثر فعالیت های انسانی تاثیرات نامطلوبی بر محیط زیست بر جای گذاشته است. مناطق شرقی استان اردبیل نمونه بارز این پدیده به شمار می آید. هدف از این تحقیق تجزیه و تحلیل تغییرات مکانی و زمانی در پوشش و کاربری زمین و اثرات آن بر دمای سطح زمین در دریاچه نئور می باشد. برای برآورد کاربری و پوشش زمین از مدل های جنگل تصادفی (RTC)، مدل حداکثر احتمال (MLC) و ماشین بردار پشتیبانی(SVM) استفاده شده و کارایی هر کدام توسط ضریب کاپا برآورد گردیده و مشاهده شد که مدل SVM از بیشترین میزان ضریب کاپا ( 87/0) برخوردار است. برای استخراج شاخص LST نیز از باندهای 6 لندست 5 و 10 لندست 8 بهره گرفته شده و مشاهده شد که بخش غربی دریاچه با افزایش دمای سطح زمین مواجه گردیده است. در طول دوره زمانی 2002، 2013 و 2022 تغییرات قابل توجهی در پهنه آبی دریاچه نئور و پوشش های گیاهی مجاور آن مشاهده شد. زمین های بایر بیشترین وسعت را در تمام دوره های مورد مطالعه داشته است. پوشش گیاهی بر اساس مدل SVM حدود 04/1 کیلومتر مربع افزایش یافته است. مساحت سطح دریاچه بر اساس مدل MLC در سال 2002 معادل 19/3 کیلومتر مربع برآورد گردید. مساحت پهنه آبی در مدل MLC در بازه زمانی 2002 تا 2022 حدود 56/1 کیلومتر مربع کاهش یافته و این میزان کاهش برای مدل های RTC و SVM به ترتیب معادل 67/0 و 69/0 کیلومتر مربع می باشد.

LSPwC is an important development of Laser shock peening (LSP) technology, and surface texturing is an effective method to improve tribological properties. The combination of these is expected to innovate a new surface texturing technology with a strengthing effect, but no one has attempted it. In this paper, a new LSPTwC technology combining them is innovatively proposed and validated on AISI 9310 steel, which is commonly used in helicopter transmission components for surface texturing. The LSPTwC surface was studied using an optical microscope, electron microscope, energy dispersive spectrometer, and so on. The results proved that LSPTwC is an effective texturing method of AISI 9310 steel, which modulates the texture and improves the properties, such as the microhardness increased by more than 10%. A model for calculating the texture and process parameters is also constructed on a statistical basis, and a modeling method for textured surfaces is proposed. It is verified that the calculation results and the constructed model are highly consistent with the test, with a diameter deviation

Land surface temperature (LST) is important in a variety of applications, such as urban thermal environment monitoring and water resource management. In this paper, eleven candidate split-window (SW) algorithms were adapted to Thermal Infrared Sensor-2 (TIRS-2) data of the Landsat 9 satellite for estimating the LST. The simulated dataset produced by extensive radiative transfer modeling and five global atmospheric profile databases was used to determine the SW algorithm coefficients. Ground measurements gathered at Surface Radiation Budget Network sites were used to confirm the efficiency of the SW algorithms after their performance was initially examined using the independent simulation dataset. Five atmospheric profile databases perform similarly in training accuracy under various subranges of total water vapor. The candidate SW algorithms demonstrate superior performance compared to the radiative transfer equation algorithm, exhibiting a reduction in overall bias and RMSE by 1.30 K and 1.0 K, respectively. It is expected to provide guidance for the generation of the Landsat 9 LST using the SW algorithms.

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth’s surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking.

Land surface temperature (LST) is a crucial parameter influencing the thermal environment of urban and natural areas. Land use/land cover (LULC) patterns significantly impact LST, highlighting the need for a comprehensive understanding of their relationship. This study delves into the intricate interplay between LULC and LST in Pune city, India, employing a spatiotemporal analysis approach. The study utilizes satellite imagery from 2000, 2010, and 2022 to map LULC changes with Maximum likelihood, support vector machine and Random tree classification and their evaluation with accuracy and Kappa coefficient. Additionally, projected LULC for 2030 is prepared to assess future LST scenarios. Winter and summer LST data are analyzed to capture the seasonal dynamics of LST-LULC interactions. Pune has undergone significant LULC transformations, with the proportion of built-up areas increasing from 7% in 2000 to 47% in 2022, while barren land has decreased from 41% to 37%. Built-up areas exhibit consistently lower LST values during winter, while barren land tends to have the highest LST. In contrast, during summer, built-up and barren areas experience higher LST values, while water bodies and vegetation display relatively lower LST values due to their cooling effects. Mean LST in Pune has increased from 34.59°C in 2000 to 39.38℃ in 2022, reflecting the changing thermal dynamics associated with LULC alterations. The expansion of built-up areas has contributed to temperature increases of up to 5.5℃ in specific locations, highlighting the urban heat island effect. The study's findings emphasize the need for sustainable land use planning and development strategies to mitigate the adverse impacts of urbanization on LST. Policymakers, urban planners, and environmentalists can utilize these insights to develop informed land use policies, zoning regulations, and climate-smart urban development strategies. [ABSTRACT FROM AUTHOR]

This study explores the dynamic transformations in land use and land cover (LULC), land surface temperature (LST), and the spatial distribution of Urban Hot Spots (UHSs) in Pathumthani from 1997 to 2023. Landsat satellite imagery was utilized for the analysis, employing the Maximum Likelihood classifier for LULC classification. The research reveals a substantial surge in built-up areas, particularly notable with a twofold increase in 2023 compared to 1997, concentrated along the north-south axis. While vegetation remains the dominant land cover, it experienced an initial increase from 2004 to 2023, followed by a declining trend. Nevertheless, vegetation constitutes over 60% of the total area, concentrated in the eastern and western regions. In summary, the spatial distribution of UHS in 1997 exhibited dispersion across the study area. However, starting from 2004, there was a noticeable concentration of UHSs within built-up areas, particularly in the northern part, coinciding with the presence of the Navanakorn industrial zone. Urban development encroachment into Muang Pathumthani, Thanyaburi, and Lam Luk Ka districts led to the emergence of UHSs in the southern part, mirroring the trajectory of urban sprawl. This southern section, adjacent to Bangkok and in proximity to Don Muang airport, witnessed UHS emergence due to urban expansion, notably influenced by the development of the mass rapid transit (MRT) connecting Pathumtani and Bangkok. The study highlights the importance of studying Land Surface Temperature (LST) to identify areas with higher temperatures, contributing valuable insights for urban planning and strategies to mitigate heat-related challenges in the region. [ABSTRACT FROM AUTHOR]

The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, and NDBI) were determined to examine the effects of land cover changes. In addition, the land surface temperature was calculated to assess urbanization expansion's impact on Baghdad's climate warming. The results showed a drastic decrease in vegetative cover and green land, on the other hand, a significant expansion in urbanized areas. Hence, from 2004 to 2021, the urbanized areas and open land rose by 37% and 3%, respectively, while the vegetative cover decreased by 41%. The maximum land surface temperature has risen 4° C, and the minimum land surface temperature has risen 2.5°C. [ABSTRACT FROM AUTHOR]

With the continuous improvement of urbanization levels in the Lhasa area, the urban heat island effect (UHI) has seriously affected the ecological environment of the region. However, the satellite-based thermal infrared land surface temperature (LST), commonly used for UHI research, is affected by cloudy weather, resulting in a lack of continuous spatial and temporal information. In this study, focusing on the Lhasa region, we combine simulated LST data obtained by the Weather Research and Forecasting (WRF) model with remote sensing-based LST data to reconstruct the all-weather LST for March, June, September, and December of 2020 at a resolution of 0.01° while using the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST as a reference (in terms of accuracy). Subsequently, based on the reconstructed LST, an analysis of the UHI was conducted to obtain the spatiotemporal distribution of UHI in the Lhasa region under all-weather LST conditions. The results demonstrate that the reconstructed LST effectively captures the expected spatial distribution characteristics with high accuracy, with an average root mean square error of 2.20 K, an average mean absolute error of 1.51 K, and a correlation coefficient consistently higher than 0.9. Additionally, the heat island effect in the Lhasa region is primarily observed during the spring and winter seasons, with the heat island intensity remaining relatively stable in winter. The results of this study provide a new reference method for the reconstruction of all-weather LST, thereby improving the research accuracy of urban thermal environment from the perspective of foundational data. Additionally, it offers a theoretical basis for the governance of UHI in the Lhasa region. [ABSTRACT FROM AUTHOR]

The study area was classified into three categories (vegetation cover, water, and others)using four satellite images of the Landsat 8 satellite captured during March for the period 2019-2022 into. The results showed that there is a change in the climatic conditions (temperature and rainfall) for the years of the study. The average temperature increased from 12.29°C to 25.967°C from the year 2019 to 2022. The annual amount of precipitation was decreased from 469.43 mm for the year 2019 to 105.49 mm for the year 2022. this negatively changed affected the water and agricultural resources, as the amount of water storage for Lake Hamrin and Lake Al-Wand together reached to 2,314,584,000 m³ and 40,404,000 m³ for the years 2019 and 2022, respectively. This led to decrease in the vegetation area from 1587.29 km² to 356.17 for the year 2019 km² and 2022, respectively. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs), which are formed by biophysical landscape transformations, have significant adverse effects on environmental quality as well as human health, resources, and facilities. Variations in UHI intensity give rise to urban hotspots (UHSs) and cold spots in different parts of the city. This study identifies such hotspots and cold spots in Delhi by classifying the city into zones of different UHI intensities using biophysical landscapes. The data on the selected biophysical landscapes were obtained from satellite images and secondary sources. The impact of different biophysical landscapes on UHI intensity was calculated using the weighted overlay method performed using ArcGIS software. The city of Delhi was thus divided into four zones, based on UHI intensity. It was found that UHSs cover about 45% of the total area and are mostly located in eastern and central Delhi. While built-up areas form the major source landscape, vegetation cover is the major sink landscape as per land surface temperature (LST) and UHI intensity. The findings of this study will help urban planners and policymakers identify UHSs and adopt suitable policies and measures to mitigate UHIs based on the different intensity zones. [ABSTRACT FROM AUTHOR]

Experimental and linear stability theory (LST) investigation of boundary layer transition on a flat plate was conducted with a flow of Mach number 5. The temperature distributions and second-mode disturbances on the flat plate surface at different unit Reynolds number (Reunit) values were captured by infrared thermography and PCB technology, respectively, which revealed the transition location of the flat-plate boundary layer. The PCB sensors successfully captured the second-mode disturbances within the boundary layer initially at a frequency of about 100 kHz, with a gradually expanding frequency range as the distance travelled downstream increased. The evolution characteristics of the second-mode instabilities were also investigated by LST and obtained for the second mode, ranging from 100 to 250 kHz. The amplitude amplification factor (N-factor) of the second-mode instabilities was calculated by the eN method. The N-factor of the transition location in the wind tunnel experiment predicted by LST is about 0.98 and 1.25 for Reunit = 6.38 × 106 and 8.20 × 106, respectively. [ABSTRACT FROM AUTHOR]

With the development of urban built-up areas, accurately extracting the urban built-up area and spatiotemporal pattern evolution trends could be valuable for understanding urban sprawl and human activities. Considering the coarse spatial resolution of nighttime light (NTL) data and the inaccurate regional boundary reflection on point of interest (POI) data, land surface temperature (LST) data were introduced. A composite index method (LJ–POI–LST) was proposed based on the positive relationship for extracting the boundary and reflecting the spatial-temporal evolution of urban built-up areas involving the NTL, POIs, and LST data from 1993 to 2018 in this paper. This paper yielded the following results: (1) There was a spatial-temporal pattern evolution from north-east to south-west with a primary quadrant orientation of IV, V, and VI in the Hefei urban area from 1993–2018. The medium-speed expansion rate, with an average value of 14.3 km2/a, was much faster than the population growth rate. The elasticity expansion coefficient of urbanization of 1.93 indicated the incongruous growth rate between the urban area and population, leading to an incoordinate and unreasonable development trend in Hefei City. (2) The detailed extraction accuracy for urban and rural junctions, urban forest parks, and other error-prone areas was improved, and the landscape connectivity and fragmentation were optimized according to the LJ–POI–LST composite index based on a high-resolution remote sensing validation image in the internal spatial structure. (3) Compared to the conventional NTL data and the LJ–POI index, the LJ–POI–LST composite index method displayed an extraction accuracy greater than 85%, with a similar statistical and landscape pattern index result. This paper provides a suitable method for the positive relationship among these LST, NTL, and POI data for accurately extracting the boundary and reflecting the spatial-temporal evolution of urban built-up areas by the fusion data. [ABSTRACT FROM AUTHOR]

This paper presents a semi-supervised change detection optimization strategy as a means to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The benefits of the Class-balanced Self-training Framework (CBST) and Deeplab V3+ were exploited to enhance classification accuracy for further analysis of microsurface land surface temperature (LST), as indicated by the change detection difference map obtained using iteratively reweighted multivariate alteration detection (IR-MAD). The evaluation statistics revealed that the DE_CBST optimization scheme achieves superior change detection outcomes. In comparison to the results of Deeplab V3+, the precision indicator demonstrated a 2.5% improvement, while the commission indicator exhibited a reduction of 2.5%. Furthermore, when compared to those of the CBST framework, the F1 score showed a notable enhancement of 6.3%, and the omission indicator exhibited a decrease of 8.9%. Moreover, DE_CBST optimization improves the identification accuracy of water in unchanged areas on the basis of Deeplab V3+ classification results and significantly improves the classification effect on bare land in changed areas on the basis of CBST classification results. In addition, the following conclusions are drawn from the discussion on the correlation between ground object categories and LST on a fine-scale: (1) the correlation between land use categories and LST all have good results in GTWR model fitting, which shows that local LST has a high correlation with the corresponding range of the land use category; (2) the changes of the local LST were generally consistent with the changes of the overall LST, but the evolution of the LST in different regions still has a certain heterogeneity, which might be related to the size of the local LST region; and (3) the local LST and the land use category of the corresponding grid cells did not show a completely consistent correspondence relationship. When discussing the local LST, it is necessary to consider the change in the overall LST, the land use types around the region, and the degree of interaction between surface objects. Finally, future experiments will be further explored through more time series LST and land use data. [ABSTRACT FROM AUTHOR]

Converting green areas and agricultural land into built-up areas is one of the most significant effects of urbanization in Iraqi cities. Greenery spaces are a fundamental requirement for any city because they promote a healthy lifestyle and preserve urban areas' aesthetic and ecological beauty. The current study examines urbanization's effect on Baghdad city vegetation and land surface temperature. The Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST) over Baghdad were used to determine the relationship among urban areas, vegetation areas, water bodies, and land temperature. The Baghdadvector-data from the General Survey Authority was used along with Landsat Thematic Mapper for 2004 and 2008 and Landsat Operational Land Imager for 2013, 2017, and 2021. In order to understand the correlation between urban areas, water bodies, and green areas with LST, a correlation was carried out using ArcGIS software, and a scatter diagram was made to evaluate the relationship among the elements. The results showed that the temperature increased on Baghdad's land surface between 2004 and 2021. Moreover, built-up areas increased from 17% in 2004 to 53.2% in 2021; in contrast, the green areas drastically declined by 39.7%. [ABSTRACT FROM AUTHOR]

This research focuses on the significant influence of vegetation cover on the distribution of land surface temperature (LST) in the city of Constantine. It aims to study the correlation between the normalized difference vegetation index (NDVI) and land surface temperature (LST) using satellite images from the Landsat 8 and Landsat 9 satellites, specifically the LC08L2SP L02T1 product, as well as images processed on the Google earth engine platform. The main objectives of this study are to collect values of land surface temperature and to carry out an in-depth analysis of the relationship between LST and NDVI. The NDVI is an index for quantifying the presence of vegetation and understanding its impact on thermal conditions. It can therefore be used to establish a link between thermal behavior and the amount of vegetation cover. In this study, the statistical method of regression was used to obtain the correlation between LST and NDVI. The results show a negative correlation between NDVI and LST. The approach of combining LST satellite data with visual information offers an effective method for identifying local warming and cooling hotspots. This approach makes it possible to locate specific areas experiencing higher or lower than average temperatures, which is crucial for monitoring the effectiveness of municipal policies aimed at mitigating the effects of heat stress. In addition, these results can guide future policy decisions by providing valuable information on areas that require special attention. [ABSTRACT FROM AUTHOR]

The present study analyses the seasonal influence of error estimated in downscaled land surface temperatures (LSTs) in a humid subtropical city using Landsat 8 data of summer and winter seasons in 2021. Thermal sharpening (TsHARP) algorithm is one of the most frequently used downscaling techniques which is originally based on normalised difference vegetation index (NDVI). This study assesses the capability of the TsHARP technique with a separate combination of four selected spectral indices (modified normalised difference water index, normalised difference bareness index, normalised difference built-up index [NDBI], and NDVI), and by determining the root mean square error (RMSE) and mean error produced by the sharpened LST. Besides, sharpened LST has also been estimated by combining the four spectral indices. It is observed that NDBI provides the most effective output (RMSE is 1.11 [30 m], 1.05 [120 m], 1.02 [240 m], and 0.99 [480 m] in summer, whereas RMSE is 0.61 [30 m], 0.59 [120 m], 0.57 [240 m], and 0.56 [480 m] in winter). NDBI-based sharpened LST generates the best relationship (R = 0.565 in summer and R = 0.537 in winter) with surface features. Fallow land generates the best relationship (R = 0.512 in summer and R = 0.530 in winter) with sharpened LST. The summer season (R = 0.438) generates a better relationship between surface features and sharpened LST than the winter season (R = 0.409). [ABSTRACT FROM AUTHOR]

Climate factors like temperature, precipitation, humidity, and sunshine time exert a profound influence on vegetation. The intricate interplay between the two is crucial to understand in the face of changing climate to develop mitigation strategies. In the current exploration, we delve how climate variability (CV) has impacted the vegetation in the Peshawar Basin (PB) using remote sensing data tools. The trend of climatic variability was investigated using the modified Mann–Kendall test and Sen's slope statistics. The changing climatic parameters were regressed on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The NDVI was further analyzed for spatiotemporal variability under land surface temperature (LST) influence. Results revealed that among the climate factors, average annual temperature and solar radiation have a significant (p < 0.05) negative impact on vegetation while precipitation and relative humidity significantly (p < 0.05) influence NDVI positively. The overall positive trend shows that vegetation improved between 2001 and 2020 with time, however some years (2010, 2012, 2014, 2016, and 2017) with low NDVI. NDVI varied in space considerably due to climatic extremes brought on by CV and the urbanization of agricultural land. NDVI regressed on LST showed that there was no or very little vegetation in the grids with high LST. The study concluded that the region is significantly impacted by both CV-related extreme weather events and anthropogenic activities. The vegetation is improving, but it is in danger of being destroyed by deforestation due to CV and human activities that exacerbate the risk of future calamities. To protect vegetation and avoid disasters, there is an immense need for adaptation and mitigation measures to deal with the region's fast-changing environment. The study urges local authorities to create climate-resilient governmental policies and supports regional sustainable development and vegetation restoration. [ABSTRACT FROM AUTHOR]

The vegetation of a river basin is affected by various climate factors, such as precipitation and land surface temperature (LST). This study explores the best machine learning model for the prediction of normalized difference vegetation index (NDVI) with LST and precipitation as input parameters. The study also determines the correlation between NDVI, LST, and precipitation of the Mahanadi basin from 2003 to 2021. Monthly precipitation data was extracted from the Center for Hydrometeorology and Remote Sensing (CHRS) portal. The Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive the LST and NDVI using Google Earth Engine (GEE). Four different machine learning models were used to predict the NDVI of the Mahanadi basin: linear regression (LR), random forest (RF), support vector regression (SVR), and k-nearest neighbors (KNN). The coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and explained variance score (EVS) were calculated to evaluate the performance of the models. The results show that the RF model has the highest R2 value in both the training and testing sets among these models, indicating that it is the most optimal among these models for predicting NDVI. The SVR model has the lowest RMSE value in the training set, but the KNN model has the lowest RMSE value in the testing set. The results also show that there is a positive correlation between precipitation and NDVI, a negative correlation between precipitation and LST, and between NDVI and LST. This study provides insights into the relationship between NDVI, LST, and precipitation, and the best machine-learning model for predicting NDVI. The findings of this study can be used to improve the management of river basins and to predict the effects of climate change on vegetation. [ABSTRACT FROM AUTHOR]

The study area was classified into three categories (vegetation cover, water, and others)using four satellite images of the Landsat 8 satellite captured during March for the period 2019-2022 into. The results showed that there is a change in the climatic conditions (temperature and rainfall) for the years of the study. The average temperature increased from 12.29°C to 25.967°C from the year 2019 to 2022. The annual amount of precipitation was decreased from 469.43 mm for the year 2019 to 105.49 mm for the year 2022.this negatively changed affected the water and agricultural resources, as the amount of water storage for Lake Hamrin and Lake Al-Wand together reached to 2,314,584,000 m3 and 40,404,000 m3 for the years 2019 and 2022, respectively. This led to decrease in the vegetation area from 1587.29 km2 to 356.17 for the year 2019 km2 and 2022, respectively.

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.

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.