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a) Google earth image of global location of Iceland b) location of the study area on the Island c) False-color composite of the study area, Rejkjavik together with its surrounding and waters
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Observing the state and changes of land cover (LC) is critical for assessing the status and changes of ecosystems such as urban development. Remote sensing can extract useful data from a large region regularly and at the same time. The present study has used Landsat 5 and 8 images to derive LC classification and Land Surface Temperature (LST) maps...
Citations
... Research on the increase in land surface temperature due to land use change is also increasing with remote sensing technology (Tahooni et al., 2023). According to Mansourmoghaddam et al. (2023), surface temperature analysis can be done using GIS & remote sensing technology. The development of science and information technology in the geographical field which is increasingly sophisticated can be seen from the processing and analysis of geographic (spatial) data that is already based on cloud computing which has made it easier for users to carry out spatial temporal analysis of surface temperature (Kafy et al., 2020). ...
Land use change from vegetation to built-up land in Ambon City Center affects the increase of land surface temperature and creates the urban heat island phenomenon, which has the potential to affect local and global climate. The method used in this research is the analysis of Landsat 8 satellite images through Google Earth Engine (GEE), which enables efficient and accurate data processing. The analysis shows that the highest surface temperature in the region increases from 27.35°C in 2015 to 29.30°C in 2025, while the lowest temperature also increases. These findings confirm the need for attention to sustainable spatial management strategies to reduce the negative impacts of urbanization, maintain environmental quality, and improve the quality of life of people in Ambon City
... These methods provide valuable data for mapping, environmental monitoring, and long-term assessments with high spatial and temporal accuracy [5][6][7]. Its applications in geosciences are relatively diverse, including but not limited to shoreline changes, glacier retreats, vegetation cover, temperature variations, subsidence patterns, precipitation trends, and humidity fluctuations [8][9][10][11][12][13][14][15]. The growing prominence of image-based methods underscores their pivotal role in enhancing our understanding of dynamic Earth processes and climate-related transformations. ...
The study offers an in-depth investigation of the impact of climate conditions on various terrain features in the Kaffiøyra area, southwest Svalbard, focusing on the changes of shorelines, glacier termini, and outwash areas. We utilized cost-effective, consistent remote sensing data and GIS techniques to analyze historical long-term changes in these terrain features, evaluated the effects of temperature variations on glacier retreat, and developed a predictive model for future glacier area changes based on temperature data from climate change scenarios and the ground observation station near the site. The results demonstrated a range of responses in shoreline stability and changes across different zones in the study site. Based on the predefined baseline, the glacier-shorelines (tidewater glacier) exhibit significant erosion, with the averaged rates varying from -64.7 to + 9.2 m/yr. However, the land-shorelines show relatively stable, with changes ranging from -2.2 to +3.7 m/yr, and the outwash areas exhibited minor increases of less than 10% as compared to the data obtained in 1985. The analysis of seven glacier termini indicated a general reduction in the glacier area; notably, Aavatsmarkbreen, a tidewater glacier, retained only 32% of its original size by 2023. The land glaciers such as Waldemarbreen, Irenebreen, Elisebreen, Eivindbreen, Andreasbreen, and Oliverbreen, preserve 54.8%, 61.3%, 65.0%, 74.0%, 55.5%, and 43.7% of their areas, respectively. A marked negative linear correlation between temperature and remaining glacier areas was observed, enabling the prediction of future glacier variations under various climate conditions. The study outlines the evolution of terrain features in Kaffiøyra in nearly 40 years. Results show the relative stability of land-shorelines, significant glacier retreats in response to warming temperatures, and slight growth in outwash areas. The study highlights the critical need for robust land monitoring systems that enhance our understanding and enable effective decision-making through predictive modeling of climate impacts.
... In earlier studies, various machine learning models like support vector machine (SVM) (Khan et al., 2023), boosted regression tree (BRT), random forest (RF) (Han et al., 2023), and generative adversarial networks (GAN) (Li and Zheng, 2023) were likewise investigated and assessed and can be explored in later investigations. Regarding various approaches and methodologies, Markov chains and cellular automata (CA) are two digital-based methods for projecting the future in a range of fields, including ecology, land use estimation and urban planning, and climate change simulation (Ali et al., 2019;Mansourmoghaddam et al., 2021Mansourmoghaddam et al., , 2023cHussain et al., 2024;Luo et al., 2023). The results showed that the reduction of agricultural land in favor of the urban expansion and urban land has been an almost permanent and clear trend in Shanghai. ...
... Also, the area of this class is predicted to grow by 1.77% for the period of 2030. Due to Shanghai's location near high seas, the increase in the water class area may have occurred due to climate change and global warming and, as a result, an increase in the sea level, as previous research (Murali and Kumar, 2015;Mansourmoghaddam et al., 2023c;Pramanik, 2017;Meilianda et al., 2019;Alavipanah et al., 2022) has confirmed the effect of a sea level rise on the land cover of cities. These results are also consistent with the results obtained from the NDWI index. ...
... The analysis of scientific publications [7][8][9][10][11][12][13][14] showed the existing problem in predicting temperature. There are several methods used for temperature prediction, each with its own strengths, limitations, and applications. ...
... Most studies are devoted to identifying temperature patterns over a limited area or with limited methods. For example, analysis of climate change taking into account differences in data sources and disparate forecasts in Mexico City [9] or using remote sensing data from Reykjavik (Iceland) to study and predict temperatures for 1987-2030 [10]. The study of forecasting in specific territories is considered in article [11] using the example of long-term forecasting of average monthly temperatures for different types of climate in Iran using the SARIMA, SVR and SVR-FA models. ...
The paper presents the concept of an information system for forecasting the temperature regime of the Earth’s surface using machine learning. Forecasting is based on historical data for a specific area. In order to increase the accuracy of forecasting results, an analysis of the features of climatic zones was carried out to identify patterns. A comparison of the dependence of the average monthly temperatures of the earth’s surface in countries depending on their location in climate zones was carried out. The analysis of sources and scientific publications confirmed the relevance of the chosen research topic. Historical aspects of forecasting changes in climatic indicators are considered. Modern methods and approaches to temperature forecasting, their advantages and disadvantages are analyzed. An overview of the subject area was conducted and the regularities of temperature changes according to climate features were determined. A comparison of temperature regimes for countries located in different climate zones was made. For clarity, graphs of temperature changes were plotted and average indicators were calculated for each climate zone. The results of the study confirm the need to adjust the temperature forecast for certain areas, taking into account their location in a specific climate zone. The revealed regularities in the temperature regime of the countries indicate the need for an individual approach to forecasting and the use of such machine learning methods that are best adapted to the dependencies observed in the climate zone. The architecture of the information system for forecasting future temperatures depending on the climatic features of the studied territories is proposed. A concept has been formed for further research to find more accurate and effective approaches to predicting climate parameters and achieving the goals of sustainable development.
... These areas have spatial overlap with units where the largest area of UGS was decreased, suggesting that UGS patches within these units are isolated and fragmented [57]. It is interesting to note that previous studies have found higher LST in areas close to barren land and lower LST in areas close to vegetation and water bodies, which is known as the 'proximity effect' [58]. This study further found that the probability of this phenomenon is higher in the suburbs than in the central city. ...
Changes in land cover by rapid urbanization have diminished the cooling effect of urban green spaces (UGS), exacerbating the upward trend of land surface temperature (LST). A thorough and precise understanding of the spatio-temporal characteristics of UGS and LST is essential for mitigating localized high temperatures in cities. This study identified the spatio-temporal changes in UGS configuration and LST in Shanghai from 2003 to 2022. The correlation between UGS configuration and LST was explored using spatial autocorrelation analysis and causal inference. The results show that (1) the high-temperature space had grown from 721 km² in 2003 to 3059 km² in 2022; (2) in suburbs, the largest area of UGS tended to decrease, while the number of patches tended to increase, indicating a distinct feature of suburbanization; (3) changes in the largest area of UGS had more significant spatial correlation, indicating that urban sprawl primarily impacts large UGSs; and (4) compared to the number and shape of UGS, changes in the largest area are the key factor influencing regional LST. These findings enrich the knowledge of the spatio−temporal relationship between the UGS configuration and its cooling effect in urbanization, offering valuable insights for building cooler cities.
... West African countries have lost-and are still losing-large extents of their natural land cover classes, replaced by a heavily human-influenced landscape dominated by agriculture [53]. Some of the land cover changes in the Sota catchment are in accordance with the land cover changes revealed by the study of [54] in Reykjavik city, Iceland. This study mentions an increase in urban areas (21.5%) and waterbodies (3.4%) between 1987 and 2020. ...
Climate and land cover changes are key factors in river basins’ management. This study investigates on the one hand 60-year (1960 to 2019) rainfall and temperature variability using station data combined with gridded data, and on the other hand land cover changes for the years 1990, 2005, and 2020 in the Sota catchment (13,410 km², North Benin, West Africa). The climate period is different from the chosen land use change period due to the unavailability of satellite images. Standardized anomaly index, break points, trend analysis, and Thiessen’s polygon were applied. Satellite images were processed and ground truthing was carried out to assess land cover changes. The analyses revealed a wet period from 1960 to 1972, a dry period from 1973 to 1987, and another wet period from 1988 to 2019. The annual rainfall decreases from the south to the north of the catchment. In addition, rainfall showed a non-significant trend over the study period, and no significant changes were identified between the two normals (1960–1989 and 1990–2019) at catchment scale, although some individual stations exhibited significant trends. Temperatures, in contrast, showed a significant increasing trend over the study period at catchment scale, with significant break points in 1978, 1990, and 2004 for Tmax, and 1989 for Tmin. An increase of 0.4 °C and 1.2 °C is noted, respectively, for Tmax and Tmin between the two normals. The study also revealed increases in agricultural areas (212.1%), settlements (76.6%), waterbodies (2.9%), and baresoil (52%) against decreases in woodland (49.6%), dense forest (42.2%), gallery forest (21.2%), and savanna (31.9%) from 1990 to 2020. These changes in climate and land cover will have implications for the region. Appropriate adaptation measures, including Integrated Water Resources Management and afforestation, are required.
... Further, the study estimated a mean LST of 26.73 � C by 2023, considering future land use changes. Another study performed by Mansourmoghaddam et al. (2023) signified a 67.5% (7.7 � C) rise in mean LST, attributed to a significant land use change, including a 38.5% (8.2 km 2 ) rise in urban areas and 14.7% (3.8 km 2 ) rise in barren lands. Kafy et al. (2021), predicted LULC changes along with LST using a cellular-automata-based ANN algorithm for Dhaka Metropolitan area, anticipating a 13% increase in summer and a 20% increase in winter LST from 2020 to 2030. ...
... The outcomes align seamlessly with numerous studies indicating a consistent escalation in Land Surface Temperatures (LSTs) within urban areas. This phenomenon can be attributed to the growing diversification in city landscapes, as observed in the research conducted by Mansourmoghaddam et al. (2023), Tian et al. (2023), Kafy et al. (2021), Mustafa et al. (2020), and Ranjan et al. (2018). ...
The insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating urban climate, particularly to explain the intensity of urban heat islands and to define the health and welfare of the planet as well as the living beings. Urbanization-driven landscape changes severely disrupt comfortable living in almost every city, necessitating monitoring and modelling historical, current, and likely future LSTs. This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. These models have been widely accepted for the efficient prediction of climatic parameters, including LST, over an urban area. The landscape, elevation, and LST trend served as input to the models for an accurate prediction of LST. The analysis was performed over the Kolkata Metropolitan Area (KMA) with an additional 10 km buffer to understand urban growth and its effect on the LST of the entire region. The two developed models (MLR and ANN) effectively anticipated the LST over the KMA region. A continual increment in the surface temperatures ranging from 1 °C to 4 °C, over existing and likely-predicted urban areas was comprehended. It was anticipated that the regions near the urban areas will also experience severe discomfort and heat waves without proper mitigation measures. This scientific literature provides essential insights for decision-makers, stakeholders, and government officials to articulate new policies and modify the existing ones to create a sustainable and livable urban environment for the inhabitants.
... This results in the urban heat island effect, where temperatures in urban areas become much higher compared to the surrounding areas. This effect can result in extreme temperature increases and contribute to local climate change (Mansourmoghaddam et al., 2023). ...
This research presents a literature review on the analysis of land use change in urban areas and its impact on environmental degradation. Rapid and uncontrolled land use change in urban areas has resulted in the conversion of green land into residential, commercial, and industrial zones, with impacts such as urban heat island, air and water pollution, and loss of natural habitats. This research used a descriptive qualitative approach. The type of research used was a literature study. The results of this study show that it is important to have an in-depth understanding of land use change trends, their impacts on the environment, and their implications for urban sustainability. The results highlight the need for wise regional planning, sustainable natural resource management, and policies that consider environmental aspects to address the challenges of environmental degradation in urban areas.
... In this context, LCC monitoring showed that the fragility of the Hamoun Biosphere Reserve environment has driven the ecological system to its current state, with far-reaching impacts on human life. Other studies shows that there is a linear relationship between the drying up of water bodies and increasing land surface temperature (Mansourmoghaddam et al., 2023). this is the major point in the areas like Hamoun biosepher reserve that directly impacts on biodiversity ...
Change detection of lakes is important to monitor ecosystem health and wind erosion process in arid environments. The main purpose of this research is to evaluate unsupervised classification based on vegetation indices to monitor Land cover changes (LCCs). The Hamoun Biosphere Reserve is located in the east of Iran and is considered one of the most important wetlands in the center of the Iran Plateau. To detect land cover changes, using Landsat images from the 1990s, 2000s, 2010s and 2020s ground control points (GCP) and spectral profiles, four major land cover classes were obtained (sparse vegetation, dense vegetation, bare land, and water bodies). To create AOIs, the pure pixels were selected using obtained spectral profiles of the main land types by GCPs in 2020. The separability of representative AOIs by classes was examined by Jeffries-Matsushita distances and scattering ellipse parameters. A maximum likelihood classifier (MLC) was applied to Landsat images in 2020 with an overall accuracy of 93% and a Kappa statistic of 0.90. Subsequently, based on Soil Adjusted Vegetation Index (SAVI) maps, as additional input data, unsupervised classification was used to classify the same images in 2020. The observed accuracy and kappa statistic of the used classification technique was up to 0.91 and 0.89 respectively. The finding indicated that in 2000, the area of arid land increased (90% of all areas) and became a major land use type, whereas water bodies (74% of all areas in the 1990s) reached zero in this year. Yearly water body changes revealed a severe dryness condition in this wetland. After 2000, in most cases in subsequent years, the water body completely dried up and in the seasonally flooded years, it did not exceed 10% of the total wetland's area. On the other hand, before 2000, on average, 60% of the wetland's area was dominated by the water class. Our study showed that in the time series without GCP for monitoring past changes, an unsupervised SAVI-based technique could provide acceptable accuracy in this region.
... LST is measured using different technologies and sensors, such as remote sensing satellites, infrared radiometers, or ground thermometers (Gadekar et al., 2023). LST data provide important information on land surface temperature that can be used in a variety of applications, including environmental monitoring, climate research, and urban analysis (Mansourmoghaddam et al., 2023) Information on LST can help understand the temperature dynamics and thermal change patterns in a region (Wei & Blaschke, 2018). This is important in understanding climate change impacts, heat flow patterns, and temperature variability in urban and rural environments. ...
This research aims to understand the relationship between high settlement density and land surface temperature associated with the Urban Heat Island (UHI) phenomenon. The data processing method involves collecting settlement density data using UAVs equipped with thermal sensors, as well as using GeoAI, namely GEE, to analyze LST on Panggang Island. The results showed a positive relationship between settlement density and LST on Panggang Island, with high settlement density contributing to an increase in ground surface temperature. The benefits of the application of GeoAI and UAVs in this analysis include accurate mapping, understanding the impacts of urbanization, sustainable urban planning, and fact-based decision-making. It is hoped that this research can contribute to better urban management and reduction of environmental impacts in Pulau Panggang, DKI Jakarta.
