Land-use change and surface warming in Uganda’s oil-rich Albertine region (1995–2025): A geodetector analysis

The purpose of this study was to analyze the trend of change in land use land cover (LULC) and land surface temperature (LST) in Mirpur and its surrounding area over the last 30years using Landsat satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, 5-year intervals of Landsat 4-5 TM and Landsat 8 OLI images were utilized to track the relationship between LULC changes and LST. The modeled LST was validated with MODIS-derived LST within the study area. Cellular automata-based artificial neural network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were analyzed to determine their link with LST. The relation between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature was increasing steadily for the last 30years. For the year 2019, it was determined that roughly 86% of total land area has been converted to built-up area and that 89% of land area had an LST greater than 28°C. According to the study, if the current trend continues, 72% of the Mirpur area is predicted to see temperatures near 32°C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.

Population growth and population inflow from other regions has caused urbanization which altered land use land cover (LULC) in the lower Himalayan regions of Pakistan. This LULC change increased the land surface temperature (LST) in the region. LULC and LST changes were assessed for the period of 1990–2017 using Landsat data and the support vector machine (SVM) method. A combined cellular automata and artificial neural network (CA-ANN) prediction model was used for simulation of LULC changes for the period of 2032 and 2047 using transition potential matrix obtained from the data years of 2002 and 2017. The accuracy of the CA-ANN model was validated using simulated and classified images of 2017 with correctness value of 70% using validation modules in QGIS. The thermal bands of Landsat images from the years 1990, 2002 and 2017 were used for LST derivation. LST acquired for this period was then modeled for 2032 and 2047 using urban indices (UI) and linear regression analysis. The SVM land cover classification results showed a 5.75% and 4.22% increase in built-up area and bare soil respectively, while vegetation declined by 9.88% during 1990–2017. The results of LST for LULC classes showed that the built-up area had the highest mean LST as compared to other classes. The future projection of LULC and LST showed that the built-up area may increase by 12.48% and 14.65% in 2032 and 2047, respectively, of the total LULC area which was ~11% in 2017. Similarly, the area with temperature above 30 °C could be 44.01% and 58.02% in 2032 and 2047, respectively, of the total study area which was 18.64% in 2017. This study identified major challenges for urban planners to mitigate the urban heat island (UHI) phenomenon. In order to address the UHI in the study area, an urban planner might focus on urban plantation and decentralization of urban areas.

In developing countries, examining land use land cover (LULC) change pattern is crucial to understanding the land surface temperature (LST) effect as urban development lacks coherent policy planning. The variability in LST is often determined by continuously changing LULC patterns. In this study, LULC change effect analysis on LST has been carried out using geometric and radiometric corrected thermal bands of multi-spectral Landsat 7 ETM + and 8 TIRS/OLI satellite imagery over Gandhinagar, Gujarat, in the years 2001 and 2022, respectively. Maximum likelihood classification (MLC) was applied to assess LULC change while an NDVI-based single-channel algorithm was used to retrieve LST using Google Earth Engine (GEE). Results showed a substantial change in built-up (+ 347.08%), barren land (- 50.74%), and vegetation (- 31.66%). With the change in LULC and impervious surfaces, the mean LST has increased by 5.47 ℃. The impact of sparse built-up was seen on vegetation and agriculture as a maximum temperature of > 47 ℃ was noticed in all LULC classes except agriculture, where the temperature reached as high as > 49 ℃ in 2022. Since Gandhinagar is developing a twin-city plan with Ahmedabad, this study could be used as a scientific basis for sustainable urban planning to overcome dynamic LULC change and LST impacts.

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

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

In most of the developing countries, man-made developments in the environment have led to the growing demand to contextualize the land use land cover (LULC) changes and land surface temperature (LST) variations. Due to the modification in the surface properties of the cities, a difference in energy balance between the cities and its nonurban surroundings is observed. The aim of this study is to analyze the spatial and temporal patterns of LULC and LST and its interrelationship in Bengaluru urban district, India, during the period from 1989 to 2017 using remote sensing data. Intensity analysis was performed for the interval to analyze the LULC change and identify the driving forces. The impact of LULC change on LST was assessed using hot spot analysis (Getis-Ord Gi* statistics). The results of this study show that (a) dominant LULC change experienced is the increase in urban area (approximately 40%) and the rate of land use change was faster in the time period 1989-2001 than 2001-2017; (b) the major transition witnessed is from barren and agricultural land to urban; (c) over the period of 28years, LST patterns for different land use classes exhibit an increasing trend with an overall increase of approximately 6°C and the mean LST of urban area increased by about 8°C; (d) LST pattern change can be effectively analyzed using hot spot analysis; and (e) as the urban expansion occurs, the cold spots have increased, and it is mainly clustered in the urban area. It confirms the presence of an urban cool island effect in Bengaluru urban district. The findings of this work can be used as a scientific basis for the sustainable development and land use planning of the region in the future.

This study aims at assessing variations and changes in the intensity of urban land surface temperature (LST) over four major cities in different ecological zone. The study intends to examine the contributions of different land cover types and variation in ecological locations on the intensity of urban LST. Remote Sensing and GIS techniques were used to measure the extent of the LST intensity over different cities and implications of land use/land cover (LULC) changes, using the Landsat TM/ ETM from 1984 to 2012, and Landsat OLI/TIRS from 2015 to 2019. The contributions of different landscape types to urban LST intensity were examined, using contribution index (CI) and Landscape index (LI) methods while the relationship between urban LST, and changes in LULC was examined using zonal statistics. The results revealed that the spatial and temporal changes in the LULC have greatly influenced the LST in the cities, though this varies from identified LULC. Changes in estimated LST vary from 0.12 to 1 °C yearly, while the changes are much intensified in the core section of the cities. The contribution of each landscapes varies, − 0.25 < CI > − 1.17 for sink landscape and 0.24 < CI > 1.05 for source landscape. The results further reveal that as LI ≥ 1, the contribution of source landscape to intensity of LST is lesser than that of sink landscape, but LI ≤ 1 shows that source landscapes contribute more to intensity of LST than sink landscapes. This might be as a result of changes in the vegetation cover between 1984 and 2019 as revealed in LULC change. Loss in the vegetal cover is anthropogenically induced leading to an increase in built-up and impervious surfaces resulted in mean monthly and yearly temperature changes. It is observed that the core and densities areas of cities witnessed higher LST compared with the rural area. The study concludes that different types of land cover within an urban area can affect the spatial pattern of urban LST, though this varies from one ecological zone to another and distribution of LST intensity in the urban area depends on its changes LULC. Thus, as cities’ population is expected to keep expanding there is a need to establish more viable linkages between the ever-growing population and land use patterns. The major findings from this study are useful in informing policymakers of the need to promote more sustainable urban development in the cities.

Characterizing the relationship between land use land cover change and land surface temperature

Assessing the impact of land use land cover changes on land surface temperature over Pune city, India

Reliable and accurate environmental state prediction can help in long-term sustainable planning and management. Enormous land-use/ land-cover (LULC) transformation has been increasing the carbon emissions (CEs) and land surface temperature (LST) around the world. The study aimed to (i) examine the influences of land specific CEs on LST dynamics and (ii) simulate future potential LULC, CEs and LST pattern of Khulna City Corporation. Landsat satellite images of the year 2000, 2010 and 2020 were used to derive LULC, LST and CEs pattern and change. The correlation between land-use indices (NDBI, NDVI, NDWI) and LST was examined to explore the impacts of LULC change on LST. Unplanned urbanization has increased 11.79 Km2(26.10%) buildup areas and 25,268 tons of CEs during 2000–2020. The calculated R2 value indicates the strong positive correlation between CEs and LST. To simulate the future LULC, CEs and LST pattern for the year 2030 and 2040, multi-layer perceptron-Markov chain (MLP-MC)-based artificial neural network model was utilized with the accuracy rate of 94.12%, 99% and 98.48% for LULC, LST and CEs model, respectively. The simulation shows that by 2040, buildup area will increase to 87.33%, net CEs will increase by 19.82 × 104tons, and carbon absorptions will decrease by 23. 55 × 104tons and 69.54% of the total study area's LST will be above 390C. Such predictions signify the necessity of implementing a sustainable urban development plan immediately for the sustainable, habitable and sound urban environment.

This study investigated the spatiotemporal changes of land use land cover (LULC) and its impact on land surface temperature (LST) in the Galle Municipal Council area (GMCA), Sri Lanka. The same was achieved by employing the multi-temporal satellite data and geo-spatial techniques between 1996 and 2019. The post-classification change detection technique was employed to determine the temporal changes of LULC, and its results were utilized to assess the LST variation over the LULC changes. The results revealed that the area had undergone a drastic LULC transformation. It experienced 38% increase in the built-up area, while vegetation and non-built-up area declined by 26% and 12%, respectively. Rapid urban growth has had a significant effect on the LST, and the built-up area had the highest mean LST of 22.7 °C, 23.2 °C, and 26.3 °C for 1996, 2009, and 2019, correspondingly. The mean LST of the GMCA was 19.2 °C in 1996, 20.1 °C in 2009, and 22.4 °C in 2019. The land area with a temperature above 24 °C increased by 9% and 12% in 2009 and 2019, respectively. The highest LST variation (5.5 °C) was observed from newly added built-up area, which was also transferred from vegetation land. Meanwhile, the lowest mean LST difference was observed from newly added vegetation land. The results show that the mean annual LST increased by 3.2 °C in the last 22 years in GMCA. This study identified significant challenges for urban planners and respective administrative bodies to mitigate and control the negative effect of LST for the long livability of Galle City.

Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region

Urbanization is the main driver of expanding industry, transport, and buildings at the cost of green spaces in the world’s mega cities. Consequently, an immense land use change is related to local climate modification in urban areas. The process of urbanization in the city of Lahore is posing severe environmental issues such as recent smog events. The objective here is to determine and analyze the spatial variability of land surface temperature (LST) caused by the land use land cover (LULC) change between the year 1996 and 2016 in Lahore in the context of urbanization and, based on this, to predict the land use change and corresponding increase in LST in 2035. To quantify LULC change during the 1996–2016 period, we used supervised maximum likelihood method and ground observations from 400 locations to classify the satellite imagery from the Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) for the years 1996, 2010, and 2016. Moreover, to better understand spatial variability of surface urban heat island (UHI) with the process of urbanization, various indicators were derived from the remote sensing imagery, including the normalized difference vegetation and built-up indices (i.e., NDVI and NDBI, respectively). Moreover, we used an RS-integrated multi-layer perceptron-Markov chain analysis (MLP-MCA) model to predict the LULC change from the year 2016 to 2035 so that the predicted LULC change, as a driver of UHI, can be related to the future local climate of the city. Through relating LST with NDVI and NDBI, we reveal that rapid developments in the residential/infrastructural sectors are causing an immense degradation in the city vegetation areas. The LULC change analyses show that about 9% decrease in green areas during 1996–2016 caused an increase in 6∘C around the built-up areas and an overall difference of 4.8∘C between built-up and nearby sub-urban vegetation areas. Keeping this and MLP-MCA-based predictions for 2035 in view, we conclude that a future 3% decrease in vegetation-covered green areas will approximately cause an increase of 2∘C by 2035. The research output will help the city government and local developers to devise sustainable policies for land use planning and urban development.

Seasonal dynamics of land surface temperature and urban thermal comfort with land use land cover pattern in semi-arid Indian cities: Insights for sustainable Urban Management

Abstract. Urban Heat Island (UHI) refers to the phenomena of higher surface temperature occurring in urban areas as compared to the surrounding countryside attributable to urbanization. Spatio-temporal changes in UHI can be quantified through Land Surface Temperature (LST) derived from satellite imageries. Spatial variations in LST occur due to complexity of land surface – combination of impervious surface materials, vegetation, exposed soils as well as water surfaces. Jaipur city has observed rapid urbanization over the last decade. Due to rising population pressure the city has expanded considerably in areal extent and has also observed substantial land use/land cover (LULC) changes. The paper aims to determine changes in the LST and UHI phenomena for Jaipur city over the period from 2000 to 2011 and analyzes the spatial distribution and temporal variation of LST in context of changes in LULC. Landsat 7 ETM+ (2000) and Landsat 5 TM (2011) images of summer season have been used. Results reveal that Jaipur city has witnessed considerable growth in built up area at the cost of greener patches over the last decade, which has had clear impact on variation in LST. There has been an average rise of 2.99 °C in overall summer temperature. New suburbs of the city record 2° to 4 °C increase in LST. LST change is inversely related to change in vegetation cover and positively related to extent of built up area. The study concludes that UHI of Jaipur city has intensified and extended over new areas.