Exploring the relationship between LST and land cover of Bengaluru by concentric ring approach

Assessment of land use/land cover changes and its effect on land surface temperature using remote sensing techniques in Southern Punjab, Pakistan

Land surface temperature (LST) is defined as a phenomenon which shows that microclimate of an urban system gets heated much faster than its surrounding rural climates. The expansion of buildings has a noteworthy influence on land use/land cover (LULC) due to conversion of vegetation land into commercial and residential areas and their associated infrastructure by which LST is accelerated. The objective of the research was to study the impact of changes in LULC on LST of Southern Punjab (Pakistan) through remote sensing (RS) data. Landsat images of 30-year duration (1987, 1997, 2007 and 2017) were employed for identifying vegetation indices and LST in the study region. These images also helped to work out normalized difference water index (NDWI) and normalized difference built-up index (NDBI) maps. There was an increase from 29620 (3.63 %) to 88038 ha (10.8 %) in built-up area over the 30 years. LST values were found in the range 12-42 °C, 11-44 °C, 11-45 °C and 11-47 °C in the years 1987, 1997, 2007 and 2017, respectively. Regression coefficients (R2) 0.81, 0.78, 0.84 and 0.76 were observed between NDVI and LST in the corresponding years respectively. Our study showed that NDVI and NDWI were negatively correlated with less LST; however, NDBI showed positive correlation with high LST. Our study gives critical information of LULC and LST and will be a helpful tool for policy makers for developing effective policies in managing land resources.

The effect of different area uses and topography on surface temperature and climate parameters

Global warming and the ecological burden it causes affect people and the environment negatively and make adaptation difficult. For people to adapt to the environment and vice versa, they need to do extensive research and planning. Planning, on the other hand, is taking an easy form with the technology that has recently developed. GIS infrastructures and supporting satellite images along with software help with provincial-scale planning. This study has been handled on a scale covering the provincial borders of Nevsehir. The thermal data of the 10-year-old Landsat 7 satellite was analyzed and mapped in the Arc-GIS 10.2 package program. In the same way, maps of wind, air temperature, topography, and land use were created and the relationship between them was revealed by "Spearman's correlation" method. According to the results obtained, the average surface temperature in the study area was determined as 34.4 °C. When evaluated in terms of land use, natural grasslands have the highest surface temperature of 40.6 °C, while city structures have the highest average surface temperature of 33.3 °C. At the same time, the lowest surface temperature measured in the study area, 13.8 °C, is also found in natural grassland areas. A significant positive correlation was measured between the wind speed and the land use pattern, while a significant negative correlation emerged between the wind speed and the air temperature. In addition, there is another significant negative correlation between height and land surface temperature (LST). Furthermore, a high degree of positive significance was determined between altitude and wind speed. Finally, between air temperature and LST, a positive significance was observed.

Effect of landscape pattern changes and environmental indices on land surface temperature in a fragile ecosystem in southeastern Iran

Climate change and urbanization along with uncontrolled development in less developed countries have led to an increased ecosystems' thermal environment. Some factors such as environmental indices and landscape pattern changes can alter Land Surface Temperature (LST). Thus, the accurate evaluation of the relationship between these factors and LST is considered important for managing ecosystems, especially fragile ones under high stress. The southeast of Iran has witnessed many destructions in the environmental dimension in the past years. Moreover, this region has a low socio-economic situation, which increases the need to study in this region. In the present study, we used Landsat TM5 satellite images (1989), Landsat 8 OLI/TIRS ones (2019), and Google Earth Engine (GEE) system to prepare the maps of temporal-spatial LST changes, Land Use/Land Cover (LULC), and selected environmental indices including Normalized Difference Vegetation (NDVI), Built-up (NDBI), Water Indices (NDWI), Land Surface Moisture (LSM) and albedo. Then, the correlation levels of LST with the aforementioned indices were assessed by using Geographically Weighted Regression (GWR), as well as assessing LST variation following LULC change. In addition, the Moran index was used to analyze global and local spatial autocorrelation. The results represented an 8.67-degree increase in the mean LST during 1989-2019. Urban and built-up areas had a significant effect on increasing the temperature of the region. Additionally, water bodies and vegetation cover in the region were the most crucial parameters in LST reduction. All of the applied indices were strongly related to LST (>0.70), while some exhibited more correlation in each year. Further, the highest correlation of LST was observed with LSM and NDBI in 1989, as well as with NDVI and NDWI during 2019. In addition, the Moran index value reduced from 1989 to 2019 (from 0.93 to 0.89). Finally, the region rehabilitation based on sustainable development principles played an important role in the direct and indirect decrease in LST.

Combined Effects of Artificial Surface and Urban Blue-Green Space on Land Surface Temperature in 28 Major Cities in China

The two most common land cover types in urban areas, artificial surface (AS) and urban blue-green space (UBGS), interact with land surface temperature (LST) and exhibit competitive effects, namely, heating and cooling effects. Understanding the variation of these effects along the AS ratio gradient is highly important for the healthy development of cities. In this study, we aimed to find the critical point of the joint competitive effects of UBGS and AS on LST, and to explore the variability in different climate zones and cities at different development levels. An urban land cover map and LST distribution map were produced using Sentinel-2 images and Landsat-8 LST data, respectively, covering 28 major cities in China. On this basis, the characteristics of water, vegetation, and LST in these cities were analyzed. Moreover, the UBGS (water or vegetation)–AS–LST relationship of each city was quantitatively explored. The results showed that UBGS and AS have a competitive relationship and jointly affect LST; this competition has a critical point (threshold). When the proportion of UBGS exceeds this value, UBGS replaces AS as the dominant variable for LST, bringing about a cooling effect. In contrast, when AS dominates LST, it causes a warming effect. The critical points between AS and water and between AS and vegetation in 28 major cities in China were 80% and 70%, respectively. The critical point showed an obvious zonal difference. Compared with cities in subtropical and temperate climate regions, the critical point of arid cities is higher, and UBGS exhibited better performance at alleviating the urban thermal environment. The critical point of cities with higher development levels is lower than that of cities with lower development levels. Even areas with relatively low AS coverage are prone to high temperatures, and more attention should be paid to improving the coverage of UBGS. Our research results provide a reference for the more reasonable handling of the relationship between urban construction, landscape layout, and temperature control.

Short-term forecast model of taxi demand based on time and space heterogeneity

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

Developing an SDSS for optimal sustainable roof covering planning based on UHI variation at neighborhood scale

Nowadays, academics have paid special attention to global warming, because it has several issues such as urban heat island (UHI) related to the quality of life. For this purpose, a spatial decision support system (SDSS) has been developed to investigate the effect of parcels' roof covering type on surface heat island (SHI) values and its variation at the neighborhood scale in Tehran, Iran. This SDSS, as the innovation of the present research, consists of two main steps including estimating the UHI value in the study area and adopting the optimum set of parcels to change their roofs' cover with three types of vegetation, high-albedo material, and flagstone. The first step is accomplished by aggregating various indices related to land cover obtained from Landsat 8 images. The aggregation is done by linear regression method (LRM) with an RMSE and R² equal to 0.942 and 0.897, respectively. Then, the genetic algorithm was used to select the optimal subset, including 10% of the parcels in the area, to change their roof covering type based on minimizing the UHI's variation. The standard deviation obtained after the changes improved from 13.222 to 10.781 °C. The results indicate that to control UHI in the center of the region, it is necessary to inhibit UHI effects at the boundary of the study area with vegetation roof covering since flagstone and high-albedo materials have local effects on controlling the UHI effects.

Characterizing Urban Expansion Combining Concentric-Ring and Grid-Based Analysis for Latin American Cities

Spatio-temporal characterization of urban expansion is the first step towards understanding how cities grow in space. We summarize two approaches used in urban expansion measurement, namely, concentric-ring analysis and grid-based analysis. Concentric-ring analysis divides urban areas into a series of rings, which is used to quantify the distance decay of urban elements from city centers. Grid-based analysis partitions a city into regular grids that are used to interpret local dynamics of urban growth. We combined these two approaches to characterize the urban expansion between 2000–2014 for five large Latin American cities (São Paulo, Brazil; Mexico City, Mexico; Buenos Aires, Argentina; Bogotá, Columbia; Santiago, Chile). Results show that the urban land (built-up area) density in concentric rings decreases from city centers to urban fringe, which can be well fitted by an inverse S curve. Parameters of fitting curves reflect disparities of urban extents and urban form among these five cities over time. Grid-based analysis presents the transformation of population from central to suburban areas, where new urban land mostly expands. In the global context, urban expansion in Latin America is far less rapid than countries or regions that are experiencing fast urbanization, such as Asia and Africa. Urban form of Latin American cities is particularly compact because of their rugged topographies with natural limitations.

Cooling Effect of Different Land Cover Types: A Case Study in Xi’an and Xianyang, China

One of the climate problems caused by rapid urbanization is the urban heat island effect, which directly threatens the human survival environment. In general, some land cover types, such as vegetation and water, are generally considered to alleviate the urban heat island effect, because these landscapes can significantly reduce the temperature of the surrounding environment, known as the cold island effect. However, this phenomenon varies over different geographical locations, climates, and other environmental factors. Therefore, how to reasonably configure these land cover types with the cooling effect from the perspective of urban planning is a great challenge, and it is necessary to find the regularity of this effect by designing experiments in more cities. In this study, land cover (LC) classification and land surface temperature (LST) of Xi’an, Xianyang and its surrounding areas were obtained by Landsat-8 images. The land types with cooling effect were identified and their ideal configuration was discussed through grid analysis, distance analysis, landscape index analysis and correlation analysis. The results showed that an obvious cooling effect occurred in both woodland and water at different spatial scales. The cooling distance of woodland is 330 m, much more than that of water (180 m), but the land surface temperature around water decreased more than that around the woodland within the cooling distance. In the specific urban planning cases, woodland can be designed with a complex shape, high tree planting density and large planting areas while water bodies with large patch areas to cool the densely built-up areas. The results of this study have utility for researchers, urban planners and urban designers seeking how to efficiently and reasonably rearrange landscapes with cooling effect and in urban land design, which is of great significance to improve urban heat island problem.