Resilience Capacities and Liveability in Algiers: A Morphological Approach

Urban form plays a critical role in enhancing urban climate resilience amidst the challenges of escalating global climate change and recurrent high-temperature heatwaves. Therefore, it is crucial to study the correlation between urban spatial form factors and land surface temperature (LST). This study utilized Landsat 8 remote sensing data to estimate LST. Random forest nonlinear analysis was employed to investigate the interaction between the urban heat island (UHI) and six urban morphological factors: building density (BD), floor area ratio (FAR), building height (BH), fractional vegetation coverage (FVC), sky view factor (SVF), and impervious surface fraction (ISF), within the framework of local climate zones (LCZs). Key findings revealed that Xi’an exhibited a significant urban heat island effect, with over 10% of the study area experiencing temperatures exceeding 40 °C. Notably, the average LST of building-class LCZs (1-6) was 3.5 °C higher than that of land cover-class LCZs (A-C). Specifically, compact LCZs (1-3) had an average LST 3.02 °C higher than open LCZs (4-6). FVC contributed the most to the variation in LST, while FAR contributed the least. ISF and BD were found to have a positive impact on LST, while FVC and BH had a negative influence. Moreover, SVF was observed to positively influence LST in the compact classes (LCZ2-3) and open low-rise class (LCZ6). In the open mid-rise class (LCZ5), SVF and LST showed a U-shaped relationship. There is an inverted U-shaped relationship between FAR and LST, with the inflection point occurring at 1.5. The results of nonlinear analysis were beneficial in illustrating the complex relationships between LST and its driving factors. The study’s results highlight the effectiveness of utilizing LCZ as a detailed approach to explore the relationship between urban morphology and urban heat islands. Recommendations for enhancing urban climate resilience include strategies such as increasing vegetation coverage, regulating building heights, organizing buildings in compact LCZs in an “L” or “I” shape, and adopting an “O” or “C” configuration for buildings in open LCZs to aid planners in developing sustainable urban environments.

The urban heat island (UHI) effect not only impacts urban climates and residents’ quality of life but also poses challenges to energy consumption and sustainable development in cities. While many studies have explored the relative importance and marginal effects of two-dimensional (2D)/ three-dimensional (3D) urban morphology on land surface temperature (LST) to mitigate UHI, the interactive effects of these 2D/3D morphology metrics on daytime and nighttime LST at different grid scales have been largely overlooked. This study focuses on the area within the outer ring of Tianjin, and analyzes the relative importance, marginal effects, and particularly the interaction effects of 2D/3D urban morphology on LST. Our findings reveal the following: (1) The normalized difference vegetation index (NDVI) has the most significant cooling effect across all variables and at all three grid scales. (2) NDVI and bare land coverage have the greatest impact on daytime LST, while building height and tree height (TH) predominantly influence nighttime LST. (3) The relationships between key 2D/3D metrics and LST are nonlinear. Overall, NDVI is negatively correlated with LST across all three grid scales. (4) Interactions between 2D/3D metrics affect LST; LST decreases when TH exceeds 1.8 m and building density is below 62%, or when TH is below 1.8 m and building density exceeds 62%. These findings provide valuable insights and recommendations for sustainable urban development and effective heat adaptation strategies in specific locations.

Spatiotemporal heterogeneity of the relationship between urban morphology and land surface temperature at a block scale

Urban morphology is a crucial contributor to urban heat island (UHI) effects. However, few studies have explored the complex effect of 2D/3D urban morphology on UHIs from a multiscale perspective. In this study, we chose the central area of Jinan city, which is commonly known as the "furnace," as the case study area. The 2D/3D urban morphology indexes-building coverage ratio (BCR) (for assessing the 2D building density), building volume density (BVD) (for assessing the 3D building density), and frontal area index (FAI) (for assessing 3D ventilation conditions) were calculated and derived to investigate the complexity of the relationship between 2D/3D urban morphology and the land surface temperature (LST) at different scales using the maximum information coefficient (MIC) and geographically weighted regression (GWR). The results indicated that (1) these 2D/3D urban morphology indexes are essential factors that are responsible for LST variation, and BCR is the most important urban morphology index affecting LST, followed by BVD and FAI. Importantly, the relationship between the BCR, BVD, FAI, and LST was an inverse U-shaped curve. (2) The relationship between 2D/3D urban morphology and LST variation showed a significant scale effect. With increased grid size, the correlation between the BCR, BVD, and FAI and the LST strengthened, "inflection point" of inverse U-shaped curve significantly declined, and their explanation rate of the LST first increased and then decreased, with a maximum value at the 700 m scale. Additionally, the FAI exerted a stronger negative effect, while the BCR and BVD generally had stronger positive effects on the LST as the grid size increased. This study extends our scientific understanding of the complex effect of urban morphology on the LST and is of great practical significance for multiscale urban thermal environment regulation.

Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China

With the intensified urbanization, urban morphology has a significant impact on land surface temperature (LST). Although the influence of urban morphology on the thermal environment has been widely recognized, further investigation is required in terms of multi-scale grid-based analyses and seasonal comparisons. Taking the area within Beijing's Fourth Ring Road as the study area, we employed a random forest model to quantify the inpact of urban morphology factors on LST. We investigated the seasonal effects of different urban morphology factors on LST across multiple grid scales, and examined the spatial correlation between urban morphology factors and the spatial distribution of LST using bivariate spatial autocorrelation analysis. The results showed that within the grid scale range of 180-360 m, the goodness of fit between urban morphology factors and LST generally exhibited an increasing-decreasing trend with increasing grid size. Among these, the optimal model performance was achieved at the 300 m grid scale, suggesting that this scale could be applied in urban planning to mitigate the urban heat island effect. The effects of urban morphology on LST exhibited seasonal differences. The influence of building height was the strongest in spring, with a relative importance value of 3.36. The effect of the density of built-up land peaked in summer and autumn, with relative importance values of 4.21 and 4.39, respectively. The influence of urban vegetation cover was more pronounced in winter, with a relative importance value of 2.15. Among the urban morphology factors, normalized difference built-up index was positively correlated with LST, while normalized difference vegetation index, building height, building volume, and sky view factor were negatively correlated with LST. All urban morphology factors and LST exhibited a certain degree of local spatial correlation. By taking urban morphology as the analytical entry point, this study would advance the understanding of the multi-scale and seasonal variation patterns of LST, reveal its spatial heterogeneity, and provide scientific support for climate-adaptive urban planning and the development of differentiated thermal environment regulation strategies.

Optimizing the spatial distribution of urban functional zones (UFZs) effectively improves the thermal environment. This study utilized an enhanced regression tree model and relied on Ecosystem Spaceborne Thermal Radiometer Experiment data to analyze the relative contributions and marginal effects of 2-D/3-D urban morphological factors on the diurnal land surface temperature (LST) in Shenyang, China. The results showed that public and residential areas dominated Shenyang's UFZs. The temperature in industrial areas was the highest during the day, and residential and commercial functional areas are high-temperature concentration areas. Furthermore, the effects of the urban spatial morphology on the LST differed between diverse time points and UFZs. The digital elevation model and the normalized difference vegetation index contributed significantly to daytime and nighttime LSTs. Construction indicators, such as the normalized difference built-up index and the proportion of construction land, significantly impacted commercial services. Residential daytime LST had a large contribution value, and the sum of its contribution rates reached approximately 30%. Population greatly contributed to the nighttime LST of the industrial and residential zones, accounting for 16.77% and 22.06%, respectively. Vegetation contributed to the cooling effect on daytime LST in summer, especially in industrial areas, contributing 29.79%. In addition, 3-D indicators, such as building height and building density, contributed to diurnal LST. Finally, when the proportion of construction land reached approximately 45%, it negatively affected LST. In this study, the main factors affecting day and night LSTs were identified, and this work acts as a relevant strategic reference for alleviating the urban heat island effect.

Urban Heat Islands (UHIs) and Urban Cool Islands (UCIs) can be measured by means of in situ measurements and interpolation methods, which often require densely distributed networks of sensors and can be time-consuming, expensive and in many cases infeasible. The use of satellite data to estimate Land Surface Temperature (LST) and spectral indices such as the Normalized Difference Vegetation Index (NDVI) has emerged in the last decade as a promising technique to map Surface Urban Heat Islands (SUHIs), primarily at large geographical scales. Furthermore, thermal comfort, the subjective perception and experience of humans of micro-climates, is also an important component of UHIs. It remains unanswered whether LST can be used to predict thermal comfort. The objective of this study is to evaluate the accuracy of remotely sensed data, including a derived LST, at a small geographical scale, in the case study of King Abdulaziz University (KAU) campus (Jeddah, Saudi Arabia) and four surrounding neighborhoods. We evaluate the potential use of LST estimates as proxy for air temperature (Tair) and thermal comfort. We estimate LST based on Landsat-8 measurements, Tair and other climatological parameters by means of in situ measurements and subjective thermal comfort by means of a Physiological Equivalent Temperature (PET) model. We find a significant correlation (r = 0.45, p < 0.001) between LST and mean Tair and the compatibility of LST and Tair as equivalent measures using Bland-Altman analysis. We evaluate several models with LST, NDVI, and Normalized Difference Built-up Index (NDBI) as data inputs to proxy Tair and find that they achieve error rates across metrics that are two orders of magnitude below that of a comparison with LST and Tair alone. We also find that, using only remotely sensed data, including LST, NDVI, and NDBI, random forest classifiers can detect sites with “very hot” classification of thermal comfort nearly as effectively as estimates using in situ data, with one such model attaining an F1 score of 0.65. This study demonstrates the potential use of remotely sensed measurements to infer the Physiological Equivalent Temperature (PET) and subjective thermal comfort at small geographical scales as well as the impacts of land cover and land use characteristics on UHI and UCI. Such insights are fundamental for sustainable urban planning and would contribute enormously to urban planning that considers people’s well-being and comfort.

Assessing the effects of 2D/3D urban morphology on the 3D urban thermal environment by using multi-source remote sensing data and UAV measurements: A case study of the snow-climate city of Changchun, China

The escalation of greenhouse gas emissions has led to a continuous rise in land surface temperature (LST). Studies have highlighted the substantial influence of urban morphology on LST; however, the impact of different dimensional indicators and their gradient effects remain unexplored. Selecting the urban area of Shenyang as a case, we chose various indicators representing different dimensions. By employing XGBoost for regression analysis, we aimed to explore the effects of urban 2D and 3D morphology on seasonal LST and its gradient effect. The following results were obtained: (1) The spatial pattern of LST in spring and winter in Shenyang was higher in the suburbs than in the center. (2) The correlation patterns of the indicators in spring and winter were similar, except for the proportion of woodland and grass (PWG), digital elevation model (DEM), and sky view factor (SVF), which exhibited opposing trends in summer and autumn. (3) Vegetation and construction had the highest influence on LST in the 2D index, followed by building forms and natural landscapes in the 3D urban morphology. (4) The influence of each indicator varied significantly across different gradients. Among all the indicators, the landscape index, social development, building forms, and skyscape had the highest impacts on urban areas. Vegetation and built-up areas had a greater influence on suburban areas. The findings of this study can assist in adjusting urban morphology and provide valuable recommendations for targeted improvements in thermal environments, thereby contributing to urban sustainable development.

Spatio-temporal analysis of LST, NDVI and SUHI in a coastal temperate city using local climate zone

The urban heat island (UHI) poses a serious threat to public health. Land Surface Temperature (LST), is a critical indicator for quantifying UHI intensity. However, the study focusing on the relationship between urban morphology and LST in historical urban areas remains a challenge. Taking the historic urban area of Guangzhou as an example, this study selected seven indicators, combined with the Local Climate Zones (LCZs) theory, and used hierarchical clustering method to explore the classification of block morphology of historic urban area. Moreover, multiple linear regression (MLR), geographical detector, and multiscale geographically weighted regression (MGWR) methods were employed to reveal the impact of block morphology on LST. The main findings are as follows: 1) LST in the study area exhibited significant spatial clustering characteristics (Moran’s I = 0.39). 2) There were eight types of block morphology in the study area, with 96% dominated by building coverage. 3) High-density low-rise (LCZ3) showed the most substantial impact on LST, followed by medium-density low-rise (LCZ3-II) and high-density mid-rise (LCZ2). 4) Building density (BD) exhibited the greatest overall impact on LST. From an interactional perspective, building height (BH) showed a notably pronounced effect. Moreover, the influence of block morphology indicators on LST demonstrated notable spatial heterogeneity, with local spatial variations in the impacts of BD and BH being more pronounced.

Studying driving factors of the urban heat island phenomenon is vital for enhancing urban ecological environments. Urban functional zones (UFZs), key for planning and management, have a substantial impact on the urban thermal environment through their two-dimensional (2D)/three-dimensional (3D) morphology. Despite prior research on land use and landscape patterns, understanding the effects of 2D/3D urban morphology in different UFZs is lacking. This study employs Landsat-8 remote sensing data to retrieve the land surface temperature (LST). A method combining supervised and unsupervised classification is proposed for UFZ mapping, utilizing multi-source geospatial data. Subsequently, parameters defining the 2D/3D urban morphology of UFZs are established. Finally, the Pearson correlation analysis and GeoDetector are used to analyze the driving factors. The results indicate the following: (1) In the Fifth Ring Road area of Beijing, the residential zones exhibit the highest LST, followed by the industrial zones. (2) In 2D urban morphology, the percentage of built-up landscape (built-PLAND) and Shannon’s diversity index (SHDI) are the main factors influencing LST. In 3D urban morphology, building density, the sky view factor (SVF), and the area-weighted mean shape index (shape index) are the main factors influencing LST. Therefore, low-density buildings with simple and dispersed shapes contribute to mitigating LST, while fragmented distributions of trees, grasslands, and water bodies also play important roles in alleviating LST. (3) In the interactive detection results, all UFZs show the highest interaction detection results with the built-PLAND. (4) Spatial variations are observed in the impact of different UFZs on LST. For instance, in the residential zones, industrial zones, green space zones, and public service zones, the SVF is negatively correlated with LST, while in the commercial zones, the SVF exhibits a positive correlation with LST.

This study aims to evaluate the spatiotemporal change of land cover (LC) and surface temperature of the Jobai Beel area, an exclusive agriculture zone, situated in the far-flung area of northwest Bangladesh using satellite data. Multi-temporal Landsat series of data from 1989 to 2020 and geospatial techniques have been employed to evaluate the LC change and land surface temperature (LST) variation. Different spectral indices such as NDVI, MNDWI, NDBal have been used to retrieve individual LC. Corresponding LST has also been extracted using the thermal bands. Supervised Classification and the post-classification change detection technique were employed to determine the temporal changes and validate the individual LC. The results were employed to assess the LST variation associated with LC changes. The results reveal that the area had undergone a drastic and inconsistent heterogeneous LC transformation during the study period. Water and vegetation areas have expanded at a rate of 0.24km2/year and 0.45km2/year respectively, while bare lands have shrunk at a rate of 0.70km2/year. In general, Bare land exhibits a significant positive correlation, when Vegetation areas show a significant negative correlation with LST. However, the correlation between water areas and LST was found statistically insignificant. Agriculture in the form of vegetation has been found the most dominating land cover character throughout the study period, which has been regulating the LST variation across the area.

Effects of 2D/3D urban morphology on land surface temperature: Contribution, response, and interaction