Temperature and Climate Dynamics in National Capital Region of India

Tourism activities are changing the global landscape pattern. This study attempted to estimate changes in Land Use Land Cover (LULC) and Land Surface Temperature (LST) in District Buner and Shangla, Khyber Pakhtunkhwa (KPK), Pakistan, and specifically its tourist spots. Using remote sensing data from satellites (1990–2020) and future projections (2035–2050), we applied Artificial Neural Network (ANN) and Cellular Automata Markov (CA-Markov) models to examine past and future LULC and LST dynamics across two districts including four major tourist spots (Shangla Top as tourist spot one (TS1), Bar Puran (TS2), Shahida Sar (TS3), and Daggar (TS4). The LULC classification for the whole study area (1990–2020) indicates that built-up and agricultural areas increased with a net change of +0.8% and +3.2% for the Shangla and Buner districts, respectively. The highest mean LST was found in the built-up areas. The simulation results indicate an expansion of 4.5% and 5.8% of the total built-up areas, and the LST above 31 °C will cover 76% and 88% of the total areas in 2035 and 2050, respectively. This conversion is driven by tourism activities, causing urban heat island effects (UHIs), and environmental degradation. The analysis of tourist spots (1990–2020) shows the highest change in built-up areas at Shangla Top (TS1), while the highest LST (28 °C) for the Daggar (TS4). The future simulation (2035–2050) results for tourist spots show that TS4 would have the highest LULC change in built-up areas (5.67%), and TS4 would have the highest LST (31 °C) from 65.23 to 82.20%. These findings provide an essential understandings for developing long-term tourism policies meant to moderate the environmental impact of tourism in the region.

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.

The urban planner´s role should be adapted to the current globalised and overspecialised economic and environmental context, envisioning a balance at the regional scale, apprehending not only new technologies, but also new mapping principles, that allow obtaining multidisciplinary integral overviews since the preliminary stages of the design process. The urban heat Island (UHI) is one of the main phenomena affecting the urban climate. In the Netherlands, during the heat wave of 2006, more than 1,000 extra deaths were registered. UHI-related parameters are an example of new elements that should be taken into consideration since the early phases of the design process.\n
\nProblem statement
\n\nThus, the development of urban design guidelines to reduce the heat islands in Dutch cities and regions requires first an overall reflection on the heat island phenomenom (relevance of the large scale assessment, existing tools, instruments) and proposal of integrative and catalysing mapping strategies and then a specific assessment of the phenomenom at the selected locations in The Netherlands (testing those principles).\n
\nMain research question
\n\nCould the use of satellite imagery help analyse the UHI in the Netherlands and contribute to suggest catalysing mitigation acions actions implementable in the existing urban context of the cities, regions and provinces assessed?\n
\nMethod
\n\nThe development of urban design principles that aim at reaching a physical balance at the regional scale is critical to ensure a reduction of the UHI effect. Landsat and Modis satellite imagery can be analysed and processed using ATCOR 2/3, ENVI 4.7 and GIS, allowing not only a neighbourhood, city and regional scale assessment, but also generating holistic catalysing mapping typologies: game-board, rhizome, layering and drift, which are critical to ensure the integration of all parameters. The scientific inputs need to be combined not only with other disciplines but often also with existing urban plans. The connection between scientific research and existing agreed visions is critical to ensure the integration of new aspects into the plans.\n
\nResults
\n\nAt the neighbourhood level the areas that have a greater heat concentration in the cities of Delft, Leiden, Gouda, Utrecht and Den Bosch are the city centres characterised by their red ceramic roof tiles, brick street paving, and canals. Several mitigation strategies could be implemented to improve the UHI effect in those areas; however, since the city centres are consolidated and listed urban areas, the mitigation measures that would be easier to implement would consist in improving the roof albedo. A consistent implementation of albedo improvement measures (improving the thermal behaviour not only of flat roofs, but also of tiled pitched roofs) of all roofs included in the identified hotspots (with an average storage heat flux greater than 90 W/m2) would help reduce the temperatures between 1.4°C and 3°C. Pre-war and post-war compact and ground-based neighbourhoods present similar thermal behaviour of the surface cover, and green neighbourhoods and small urban centres also present similar thermal behaviour.\n
\n\nAt the city scale the analysis of 21 medium-size cities in the province of North Brabant, which belongs to the South region of the county -in relative terms the most affected by the UHI phenomenon during the heat wave of 2006-, reveals that albedo and normalised difference vegetation index (NDVI) are the most relevant parameters influencing the average nightime land surface temperature (LST). Thus, imperviousness, distance to the nearest town and the area of the cities do not seem to play a significant role in the LST night values for the medium-size cities analysed in the region of North Brabant, which do not exceed 7,700 ha in any case. The future growth of most medium-size cities of the regions will not per se aggravate the UHI phenomenon; in turn it will be the design of the new neighbourhoods that will impact the formation of urban heat in the province.\n
\n\nThe average day LST of provincial parks in South Holland varies depending on the land use. The analysis of the average night LST varies depending of the land use of the patches. The following surfaces are arranged from the lowest to the highest temperatures: water surfaces, forests, cropland, and greenhouse areas. For each of these land uses, NDVI, imperviousness and landscape shape index (LSI) shape index influence the thermal behaviour of the patches differently. NDVI is inversely correlated to day LST for all categories, imperviousness is correlated to day LST for all areas which do not comprise a significant presence of greenhouses (grassland and built patches) and inversely correlated to LST for areas with a high presence of greenhouses (cropland and warehouses). Greenhouse surfaces have highly reflective roofs, which contribute to the reduction of day LST. Finally, landscape shape index varies depending on the nature of the surrounding patches, especially for small patches (built areas, forests and greenhouse areas). When the patches analysed are surrounded by warmer land uses, slender and scattered patches are warmer, more compact and large ones are cooler. In turn, when they are surrounded by cooler patches it is the opposite: slenderer and scattered patches are cooler and more compact and larger ones are warmer. In Midden-Delfland (1 of the 6 South Holland provincial parks), most of the hotspots surrounding the park are adjacent to grassland patches. The measure to increase the cooling capacity of those patches would consist in a change of land use and/or an increase of NDVI of the existing grassland patches.\n
\nConclusions
\n\nSatellite imagery can be used not only to analyse the heat island phenomenom in Dutch neighbourhoods, cities and regions (identify neighbourhoods with highest surface temperature, identify impact of city size and morphology in surface temperature, calcuate average surface temperature for different land uses…), but also to suggest mitigation actions for the areas assessed. Moreover, satellite imagery is here used to generate catalysing mapping typologies: game-board, rhizome, layering and drift, ensuring that the measures proposed remain accurate enough to actualy be efficient and open enough to be compatible with the rest of urban planning priorities.

Urban vegetation has a decisive role in sustaining homogeneous Land Surface Temperature (LST) in a built-up environment. However, urban areas are facing rapid changes in land use/land cover (LULC) over the last few decades as green cover is being replaced by built-up structures. Consequently, LST is increasing and urban heat island (UHI) effects are expanding. In this context, this study was organized to assess urban green cover changes in Lahore and their impact on LST and UHI effects. For this, climate data was collected from the Pakistan Meteorological Department and Landsat images were acquired from Earth Explorer. LULC and LST maps were generated for 1990, 2000, 2010, and 2020 in ArcGIS 10.8. Also, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were computed to analyze the effects of vegetation and built-up areas on LST and UHI. The study found that over the last three decades, built-up area increased 113.85% by removing 392.78 km2 of green cover in the study area. Similarly, a rapid expansion of the high LST range and UHI effects was found towards the eastern and southern parts of the study area. Moreover, a negative correlation was found between LST and NDVI, whereas the correlation between LST and NDBI was found to be positive. Therefore, it was concluded that the continuation of green cover reduction is highly damaging because this might render the city more fragile ecologically. So, the study calls the attention of the responsible authorities for suitable measures against continuous green cover loss in the study area.

Urbanization leads to massive land use/land cover (LULC) change and contributes significantly to increases in urban land surface temperature (LST). The rapid, unplanned transformation of urban LULC significantly affects the functions of the associated ecosystem and biodiversity. This study aims to identify the variation in LULC, and its influences on the urban thermal environment, such as LST, urban heat island (UHI), and urban field variance index (UTFVI), for Chattogram city, Bangladesh. Multi-temporal Landsat Thematic Mapper/OLI: Operational Land Imager (TM/OLI) satellite images were used for the years 1999, 2009, and 2019, to classify LULC classes, identify the transition between different LULCs and to quantify variation in LST, UHI and UFVI distribution in the study area. Using the support vector machine algorithm, the LULC was classified, and the accuracy of the categorized maps was more than 85%. The spectral radiance model was used to extract urban thermal information from satellite images. The analysis of LULC estimation suggests a significant net increase in urban built-up areas (+3.51%) and a reduction in vegetation cover (−6.81%). The mean LST distribution shows that the high-temperature zones were associated with built-up areas, with the lowest temperatures being associated with vegetated cover and water bodies. The UHI and UTFVI estimation also indicate a trend toward increased surface heat stress in the study area. The study demonstrates that a rise in the non-evaporative surfaces (built-up area) and a reduction in the green cover has significantly increased the LST, UHI and UTFVI effects in the study area. Remote-sensing techniques were found to be very useful, particularly in minimizing the time required to evaluate urban expansion and to assess the influence of urbanization on LST.

Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait

Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh

The urbanization of landscapes and the increase in impervious land cover materials is known to cause significant changes in the landscape's thermal properties, typically leading to urban heat island (UHI). Although previous studies have investigated the impacts of land cover land use (LULC) and other factors on the urban land surface temperature (LST), the results of such studies are mixed. For instance, it is not yet clear what factors affect the spatial and temporal variation of the relationship between LULC and the LST, as well as the exact trajectory through which identified factors affect the thermal character of the urban landscape. In the current study, we examined the relationship between LULC, elevation and LST in Ilorin from the period of 2002–2020. The overriding aim was to understand the degree of association between LULC, elevation and LST, and the drivers of the spatial and temporal variation in the observed relationship. LST and NDVI were derived from Landsat data products. LST was derived using a mono-window algorithm and NDVI was used as proxy for LULC. The spatial pattern of LULC was analyzed using Moran's I spatial autocorrelation statistics. To investigate the relationship between LULC, elevation and LST, we adopted both ordinary least square (OLS) regression models and the geographically weighted regression (GWR) method to reveal both the linear and the geographically varying relationship between the LULC, elevation and LST. The results of the study show that the LULC pattern of Ilorin has been significantly altered during the 2000 to 2020 period. The urban proportion of the landscape has shown a significant increase, rising by more than 11% relative to 2002 figures, and the vegetation proportion, especially the forest component, has diminished within same temporal extent. LST values varied in both space and time, with high temperature clusters noticeable in the built-up areas and decreasing towards the urban periphery. The result of the autocorrelation analysis using Moran's I index reveals a significant clustering of LST in all the epochs investigated. Across the study area, minimum and maximum LST values of 0 °C and 41 °C were recorded in 2002 and 2020 respectively. Statistically significant relationships were observed between LULC, elevation and LST. However, the relationship between elevation and LST was very weak. Temporally, the strength of the relationship between the variables (as indicated in the variables' coefficient) as well as the overall model predictive performance (indicated by in the R2) fluctuated over the years. Spatially, the strength of relationship between LST and LULC and elevation varied significantly. LULC explained approximately between 26% and 64% of total LST variation in the city between 2002 and 2020. The study findings are relevant for efforts geared towards alleviating the degree of UHI or its impacts, generally, and in the city of Ilorin, in particular. An understanding of the spatial distribution of LULC and their impacts on the LST can be helpful in identifying areas needing attention. The observed relationship between relevant LULC classes can be incorporated into urban planning strategies to ensure a sustainable city development.

Examining the nexus between land surface temperature and urban growth in Chattogram Metropolitan Area of Bangladesh using long term Landsat series data

Land cover adjustment which includes urban landscaping, agriculture crops or hardscapes for constructing and parking lots promote variations in the energy and radiation balance. Due to the above listed modifications, the surface temperatures and near-surface air temperatures over urban patches are generally higher than those over rural areas, thus resulting in an Urban Heat Island (UHI). In this study, urban landscape and Land Surface Temperature (LST), net radiation, and Normalized Difference Vegetation Index (NDVI) data were extracted using remote sensing and GIS (Geographic Information System) were used to synthesize effect of urban modification and net radiation on the climate of Lagos metropolis. The urban landscape was estimated and a rapid increase was witness in the urban built-up area, 54.05 km2 between 1984–2000, 270.02 km2 between 2000–2013 With a reductions in rural built-up area, urbanized open land, rural open land and water body between 1984–2000 and 2000–2013 this reveals that a wide margin is observed to exist between urban built-up and rural built-up area which implies that Lagos metropolis is highly urbanized. LST was found to have a mean of 23.05°C for 1984, 25.99°C for 2000, and 26.66°C for 2013. Net radiation was found to have a mean of 449.43 W/m-2 for 1984, 376.80 W/m-2 for 2000, and 253.48 W/m-2 for 2013. In synthesizing the effects of factors of urban climate on Lagos metropolis factor analysis used and following factors were identified such: urbanized area, NDVI, LST, and net radiation. And a significant correlation exists between listed factors in Lagos metropolis. The result reveals that two factors were selected: (1) the effect of urbanization on LST and urban surface modification (using NDVI) and (2) the effect of LST on urban climate due to effect of net radiation. Based on the selected components, UHI areas were determined for Lagos metropolis using the urbanized area to delineate the urban built-up area from the rural built-up area, with reference to LST. Based on research findings, possible mitigation measures as means of controlling the menace of the UHI were highlighted for the study area.

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

Currently, more than half of the world’s population lives in cities, which leads to major changes in land use and land surface temperature (LST). The associated urban heat island (UHI) effects have multiple impacts on energy consumption and human health. A better understanding of how different land covers affect LST is necessary for mitigating adverse impacts, and supporting urban planning and public health management. This study explores a distance-based, a grid-based and a point-based analysis to investigate the influence of impervious surfaces, green area and waterbodies on LST, from large (distance and grid based analysis with 400 m grids) to smaller (point based analysis with 30 m grids) scale in the two mid-latitude cities of Paris and Geneva. The results at large scale confirm that the highest LST was observed in the city centers. A significantly positive correlation was observed between LST and impervious surface density. An anticorrelation between LST and green area density was observed in Paris. The spatial lag model was used to explore the spatial correlation among LST, NDBI, NDVI and MNDWI on a smaller scale. Inverse correlations between LST and NDVI and MNDWI, respectively, were observed. We conclude that waterbodies display the greatest mitigation on LST and UHI effects both on the large and smaller scale. Green areas play an important role in cooling effects on the smaller scale. An increase of evenly distributed green area and waterbodies in urban areas is suggested to lower LST and mitigate UHI effects.

Urban heat islands (UHIs) aggravate urban heat stress and, therefore, exacerbate heat-related morbidity and mortality as global warming continues. Numerous studies used surface urban heat island intensity (SUHII) to quantify the change in the UHI effect and its drivers for heat mitigation. However, whether the variations in SUHII among cities can demonstrate the physical difference and fluctuation of the urban thermal environment is poorly understood. Here, we present a comparison study on the temporal trends of SUHII and LST in urban and nonurban areas in 13 cities of the Beijing–Tianjin–Hebei (BTH) megaregion in China and further identify different types of changes in SUHII based on the temporal trends of land surface temperature (LST) in urban and nonurban areas from 2000 to 2020. We also measured the effect of the changes in four socioecological factors (i.e., population density, vegetation greenness (EVI), GDP, and built-up area) on the trends of SUHII to understand the dynamic interaction between the UHI effect and socioecological development. We found the following. (1) Nine out of thirteen cities showed a significant increasing trend in SUHII, indicating that the SUHI effects have been intensified in most of the cities in the BTH megaregion. (2) The spatial pattern of summer mean SUHII and LST in urban areas varied greatly. Among the 13 cities, Beijing had the highest mean SUHII, but Handan had the highest urban temperature, which suggests that a city with stronger SUHII does not necessarily have a higher urban temperature or hazardous urban thermal environment. (3) Four types of changes in SUHII were identified in the 13 cities, which resulted from different temporal trends of LST in urban areas and nonurban areas. In particular, one type of increasing trend of SUHII in seven cities resulted from a greater warming trend (increasing LST) in urban than nonurban areas (SUHII↑1), and another type of increasing trend of SUHII in Beijing and Chengde was attributed to the warming trends (increasing LST) in urban areas and the cooling trends (decreasing LST) in nonurban areas (SUHII↑2). Meanwhile, the third type of increasing trend of SUHII in Zhangjiakou was due to a greater cooling (decreasing LST) trend in nonurban areas than in urban areas (SUHII↑3). In contrast, three cities with a decreasing trend of SUHII were caused by the increase in LST in urban and nonurban areas, but the warming trend in nonurban areas was greater than in urban areas (SUHII↓1). (4) Among the relationship between the trend of SUHII (TrendSUHII) and the changes in socioecological factors (Trendpopulation density, TrendGDP per captica, TrendEVI, and Trendbuild-up area), a significantly positive correlation between TrendSUHII and TrendEVI indicated that the change in SUHII was significantly related to an increased rate of EVI. This is mainly because increased vegetation in nonurban areas would result in lower temperatures in nonurban areas.

Rapid and extensive urbanization has adversely impacted humans and ecological entities in the recent decades through a decrease in surface permeability and the emergence of Urban Heat Islands (UHI). While detailed and continuous assessments of surface permeability and UHI are crucial for urban planning and management of landuse zones, they mostly involve time consuming and expensive field studies and single sensor derived large scale aerial and satellite imageries. We demonstrated the advantage of fusing imageries from multiple sensors for landuse and landcover (LULC) change assessments as well as for assessing surface permeability and temperature and UHI emergence in a fast growing city, i.e. Tirunelveli, Tamilnadu, India. IRS-LISSIII and Landsat-7 ETM+ imageries were fused for 2007 and 2017, and classified using a Rotation Forest (RF) algorithm. Surface permeability and temperature were then quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Finally, we assessed the relationship between SAVI and LST for entire Tirunelveli as well as for each LULC zone, and also detected UHI emergence hot spots using a SAVI-LST combined metric. Our fused images exhibited higher classification accuracies, i.e. overall kappa coefficient values, than non-fused images. We observed an overall increase in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall and also for almost all LULC zones. The LST values showed an overall increase of surface temperature in Tirunelveli with the highest increase for urban built-up areas between 2007 and 2017. LST also exhibited a strong negative association with SAVI. Southeastern built-up areas in Tirunelveli were depicted as a potential UHI hotspot, with a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability, temperature and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion.

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