Urban Biophysical Composition Research Articles

Understanding the Relationship between Urban Biophysical Composition and Land Surface Temperature in a Hot Desert Megacity (Saudi Arabia).

The deteriorations of the thermal environment due to extreme land surface temperature (LST) has become one of the most serious environmental problems in urban areas. The spatial distribution of urban biophysical composition (UBC) has a significant impact on the LST. Therefore, it is essential to understand the relationship between LST and biophysical physical composition (BPC) to mitigate the effects of UHIs. In this study, an attempt was made to understand the relationship between LST and BPC in a hot desert coastal megacity (Jeddah megacity) in Saudi Arabia. Principal component analysis (PCA) was used to understand the factors affecting LST based on remote sensing indices. Correlation and regression analyses were carried out to understand the relationship between LST and BPC and the impact of BPC on LST. The results showed that, in Jeddah city from 2000 to 2021, there was a substantial increase in the built-up area, which increased from 3085 to 5557.98 hectares. Impervious surfaces had a significant impact on the LST, and green infrastructure (GI) was negatively correlated with LST. Based on the PCA results, we found that the GI was a significant factor affecting the LST in Jeddah megacity. The findings of this study, though not contributing to further understanding of the impact of BPC on LST, will provide planners and policy makers with a foundation for developing very effective strategies to improve the eco-environmental quality of Jeddah megacity.

Evaluation of spatially heterogeneous driving forces of the urban heat environment based on a regression tree model

The urban heat environment, represented by land surface temperature (LST), is highly heterogeneous. Previous studies have investigated the universal physical laws during LST modeling. However, spatial patterns of the driving forces and their temporal differences have not been well examined. In this study, spatially heterogeneous effects of two important urban biophysical compositions, Normalized Difference Vegetation Index (NDVI) and impervious surfaces, on LST are determined by a regression tree model. The results showed that LST patterns varied on different dates. Additionally, nonlinear relationships existed between LST, NDVI, and impervious surfaces. The regression tree model outperformed the multiple linear regression in modeling LST variations. Furthermore, the "rules" derived from regression tree indicated the forces that are driving LST locally. These results suggest that the "rules" can be used as urban planning indicators and provide insights into the variations in driving forces linked to the urban landscape. We found that the regression tree is an important approach to adopt for conducting studies on urban heat and provides insight into complex and hierarchical LST patterns and processes, thus facilitating urban heat island mitigation in terms of reducing energy use and air pollution in urban areas.

Relation between urban biophysical composition and dynamics of land surface temperature in the Kolkata metropolitan area: a GIS and statistical based analysis for sustainable planning

The major environmental impacts of urbanization have changed urban biophysical components which ultimately promoted land surface temperature (LST) as well as urban heat island (UHI). This study explores the upshot of land use land cover (LULC) and resultant effect on biophysical components to understand the heat island mechanism in the Kolkata Metropolitan Area (KMA) for four selected time period of 1991, 2001, 2011 and 2017. Six satellite-derived biophysical components were selected for the present analysis: NDBI, NDVI, NDWI, MNDWI, NDBaI and SAVI. Selected bands of Landsat-5 TM and OLI-8 were used for this purpose. The result shows that the built-up area has been increased from 322.68 km2 in 1991 to 982.86 km2 in 2017 and accordingly, LST also rises from 18.47 °C mean LST of 1991 to 23.30 °C mean LST of 2017. The correlation coefficient among the biophysical parameters and LST shows that the highest continuous increasing positive relationship between NDBI and LST (R = 0.71). Moreover, multiple linear regression model (MLR) is adapted to the prediction on LST with the variation of biophysical parameters. Finally, we produced hot spot maps using Getis-Ord-Gi* statistics for the selected year to highlights the hot spot and cold spot area in KMA. The methodology presented in this paper can be broadly applied for the planning purposes because LST monitoring is an important parameter of sustainable urban planning.

Combinational Biophysical Composition Index (CBCI) for Effective Mapping Biophysical Composition in Urban Areas

The current spectral indices for estimating urban biophysical compositions still have several limitations. First, most of those indices can highlight one urban biophysical composition, such as impervious surfaces, vegetation, or water. Furthermore, most of those indices depend on short-and mid-wave infrared and thermal infrared bands and thus have limited availability in high-resolution imagery. Finally, the separability between impervious surfaces and bare soil still needs to improve. Therefore, this paper proposed a combinational biophysical composition index (CBCI) for effective highlighting the four-major urban biophysical compositions. Correlation analyses were performed to estimate the relationship between the CBCI values and biophysical compositions, and the spectral discrimination index (SDI) was conducted to test the separability degree between impervious surfaces and bare soil. Moreover, normalized difference vegetation index (NDVI), normalized difference building index, BCI, and the combinational build-up index were utilized to perform the comparative analyses. Moreover, this paper investigated the applicability of the CBCI in different spatial resolution remote sensing images. Our results indicated that the CBCI had a much closer relationship with impervious surface fraction than the other four indices and had a stronger correlation with vegetation abundances when compared with the NDVI. In addition, the CBCI had much higher SDI values than the other indices. This paper proved that the CBCI was an effective index for highlighting the four-major urban biophysical compositions and had good performance for separating impervious surfaces and bare soil.

Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socio-economic factors: A case study of the Shanghai metropolitan region

Multi-scale assessment of urban thermal environment dynamics and its influencing factors have been considered an essential precondition for mitigation and regulation of urban heat island (UHI) effects. However, the annual cycle behavior of satellite-derived urban land surface temperature (LST) is still unclear due to the limitation of irregular and infrequent satellite LST data. This study investigated annual dynamics of LST and the UHI effects of Shanghai by using an annual temperature cycle (ATC) model to reconstruct the Moderate-resolution imaging spectroradiometer (MODIS) 8-day LST data with 1 km2 resolution in 2015. Ordinary least squares (OLS) and spatial regression models were further applied to investigate the relationships between day/night LST and urban biophysical composition and configuration and socio-economic characteristics in the metropolitan area of Shanghai, China. The results indicate that ATC model performed well with overall root mean square errors (RMSE) of 2.6 K. The performance of ATC is better in night-time than that in day-time. The thermal gradient analysis showed the urban center was hotter than outskirts by up to 2.0 K in day and 0.8 K in night. Spatial patterns of UHI intensity between day-time and night-time had significant differences in central urban areas. We also observed higher amplitude of the annual temperature cycle in central urban areas than that in rural. Furthermore, our statistical model showed biophysical indicators and land composition had a stronger influence on LST than the other explanatory variables. Nighttime light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting partnership (NPP) as a spatial proxy for socio-economic factors had significant positive relationships with mean LST. Compared with OLS and spatial lag model (SLM), spatial error model (SEM) is more appropriate to predict the urban LST. We conclude that distinct control strategies between day-time and night-time may improve efficiency of attenuating the UHI for a metropolitan region, and the spatial and temporal elements should be considered in landscape and urban planning.

Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating

The alteration of the environment at local and regional scale is often attributed to the rapid urbanization in the developing economies like India, China, etc. The land use and land cover (LULC) modification due to urbanization affect the thermal character and Surface Urban Heat Island Intensity (SUHII) of an urban area. The Greater Hyderabad Municipal Corporation (GHMC) has witnessed the drastic urbanization in the last decade and, is the sixth largest agglomeration of India. In the present study, the temporal dataset from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) are used to trace the LULC, and land surface temperature (LST) changes during 2002–2015 and demarcate the fluctuation of different biophysical surfaces. Subsequently, four different indices viz. Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Bareness Index (NDBaI) are used to document the individual and collective response of the different LULC classes/biophysical surfaces. The simple linear and step-wise multiple linear regression models determined the complex and linear behaviour of the LST with the various biophysical compositions. The GHMC exhibits a rapid urban expansion resulting into increased the LST clusters that corroborate to the changing LULC pattern. The spatiotemporal changes of LULC, LST, and UHI of GHMC from 2002 to 2015 are quantified to evaluate the effects of biophysical indices on moderating or exaggerating LST. The study outlines the urbanization in the GHMC and interaction of changing LULC pattern with the local climate and urban biophysical compositions.

Changing dynamics of urban biophysical composition and its impact on urban heat island intensity and thermal characteristics: the case of Hyderabad City, India

The biophysical composition; including the green surface cover and moisture dynamics substantially affects the thermal character and Surface Urban Heat Island intensity (SUHII) of an urban area. Therefore, biophysical indices are highly sensitive to the changing process in land use and land cover. Remote sensing based land surface temperature (LST) plays a significant role in analyzing the thermal behavior of urban areas at multiple scales to moderate the urban heat island. In the present study, Greater Hyderabad Municipal Corporation, is taken as a case study to assess biophysical controls on LST and UHI in an urban ecosystem by implementing biophysical indices. Therefore, the cluster of UHI and the proximity to the hotspots were created from spatial statistics. The areal coverage of urban land was increased from 31.2% in 1973 to 62.87% in 2015 with 5.03 sq km year−1 expansion rate. The LST hotspot (H–H) in 2002 observed in the central and the southeast portion of the region, ascribe to the presence of higher thermal anomalies, whereas, the mean LST (°C) of the neighboring region is below than the average. The highest negative correlation between the estimated LST (°C) and the biophysical indices was accounted over aquatic vegetation cover, followed by urban green spaces and built-up urban area, respectively. The simple linear and multiple regression models demonstrated the complex and nonlinear behavior of the UHI and LST with the biophysical components. Therefore, the spatial coherence among the biophysical indices with LST ensembles the necessity of urban greenery and parks within the urban counterpart to mitigate the outdoor thermal discomfort to a reasonable extent.

Dynamics of surface urban biophysical compositions and its impact on land surface thermal field

Urban heat effects over Abeokuta city, Nigeria have raised interests by relevant scientific communities owing to the recent infrastructural and economic growth of the area. In this paper, the surface temperatures and land surface biophysical types retrieved from Landsat TM, and ETM+images of Abeokuta city for 1984, 2003 and 2014 were analyzed. The images acquired were classified into appropriate land cover types using supervised classification schemes and a change detection analysis was carried out on the classified imageries using land change modeller (LCM) to evaluate the extent of modification of surface features. A quantitative approach was used to explore the relationships among temperature, surface biophysical components and spectral indices. Results showed that impervious surface and water areas were found to be correlated positively with high temperatures. Conversely, vegetated areas and bare surfaces correlated positively with mid temperature zones. The overall metrics error between the regression-modeled LST and the retrieved LST for the study area is quite low, between 2.63 °C for root mean square error (RMSE) and 2.17 °C for mean absolute error (MAE). This study concluded that, areas with increasing built-up surfaces and surface reflectivity will bring about LST rise, and consequently leads to urban heat island development.

Locally optimized separability enhancement indices for urban land cover mapping: Exploring thermal environmental consequences of rapid urbanization in Addis Ababa, Ethiopia

Abstract Landsat data were used to assess urbanization-induced dynamics in Land use/cover (LULC), surface thermal intensity, and its relationships with urban biophysical composition. The study was undertaken in Addis Ababa city, Ethiopia. Ground-based data and high resolution images were used as reference data in LULC classification. To more accurately quantify landscape patterns and their changes, we applied new locally optimized separability enhancement indices and decision rules (SEI–DR approach) to address commonly observed classification accuracy problems in urban environments. We tested the SEI–DR approach using eight Landsat images acquired between 1985 and 2010. Two approaches were applied to quantify surface heat intensity (SHIn) and to examine its spatial patterns over 25 years: thermal gradient analysis and hot spot analysis. A Simultaneous Autoregressive Spatial error model (SARerr) was used to explore relationships between surface temperature and biophysical variables describing urban surfaces. Compared to Maximum Likelihood (ML) and Support Vector Machine (SVM) classification, accuracy improvement achieved through use of the SEI–DR procedure was, respectively, 6% and 5% and the differences were statistically significant (P

Examining the impact of urban biophysical composition and neighboring environment on surface urban heat island effect

Due to atmospheric and surface modifications associated with urbanization, surface urban heat island (SUHI) effects have been considered essential in examining urban ecological environments. With remote sensing technologies, numerous land cover type related variables, including spectral indices and land cover fractions, have been applied to estimate land surface temperature (LST), thereby further examining SUHI. This study begins with the reexamination of the commonly used indicators of LST using Landsat Enhanced Thematic Mapper Plus (ETM+) and Landsat Thematic Mapper (TM) images which cover four counties of Wisconsin, United States. Origin of the large variation of LST found in urban areas is then investigated by discriminating soil and impervious surfaces. Except land cover types, neighboring environment is another key factor which may affect LST in urban areas. Thus, a neighboring effect considered method is proposed at the end of the study to better understand the relationship between impervious surfaces fraction (%ISA) and LST by taking the influence of neighboring environment into account. Results indicate that spectral indices have better performance in predicting LST than land cover fractions do within the study area. However, the result remains arguable due to the complexity and uncertainty of spectral mixture analysis. Impervious surfaces are found responsible for the large variation of LST in urban areas, which indicates that impervious surfaces should not be simply considered as a single land cover type has stable negative correlation with LST. Moreover, a better relationship is found between %ISA and LST when neighboring effect is considered, when compared to the traditional method which ignores the neighboring effect.

Impacts of urban biophysical composition on land surface temperature in urban heat island clusters

The spatio-temporal pattern of biophysical composition significantly affects land surface temperature (LST). Previous studies, however, mostly characterized urban heat island (UHI) clusters being spatially homogeneous. The landscape spatial heterogeneity in urban across UHI clusters challenges us to more accurately characterize the relationships between LST and corresponding urban biophysical composition. In this study, we introduced an innovative integrated approach that combined object-oriented image segmentation with local indicators of spatial autocorrelations (LISA) to extract UHI clusters from an LST image. We used a regression tree model to examine the nonlinear relationships between LST and each of three satellite-based indices within the UHI clusters: normalized differential vegetation index (NDVI), normalized differential build-up index (NDBI), and normalized difference bareness index (NDBaI). We found that both NDVI and NDBI are strongly correlated with the variations of LST whereas NDBaI has a weaker correlation with LST. We also found that the regression tree model built in this study enabled us to effectively detect the nonlinear relationship between LST and biophysical composition. Furthermore, based on a set of rules derived from a regression tree analysis, we found that urban landscapes strongly affect LST and its spatial heterogeneity within a UHI. These rules were used to detect the nonlinear impacts of complex urban biophysical composition on LST. The results of this study provided insights into how LST within UHI varies with urban surface characteristics at fine spatial scale and also a new method for investigating effects of land surface composition on LST in urbanized areas.

Examining the impacts of urban biophysical compositions on surface urban heat island: A spectral unmixing and thermal mixing approach

Land surface temperature (LST) is a central parameter for surface urban heat island (SUHI) studies, in which thermal remote sensing plays a key role. Traditionally, normalized difference vegetation index (NDVI), percent green vegetation (%GV), and percent impervious surface area (%ISA), have been widely applied to examine the impacts of land cover compositions on SUHI. Urban thermal pattern, however, is a complicated physical phenomenon involving a series of environmental factors, and it is insufficient to employ only one indicator for the explanation of the SUHI phenomenon. Therefore, considering different thermal properties of various land cover compositions, this study proposed a two-step physically based method, the spectral unmixing and thermal mixing (SUTM) model, to examine the impacts of typical land cover compositions on urban thermal pattern. The performance of SUTM was compared with those of linear and non-linear (quadratic) regression models with NDVI, %GV, and %ISA as individual independent variables. Results indicate that SUTM outperforms all regression models, with the lowest root mean square error (2.89K) and mean absolute error (2.11K). Moreover, when the accuracy was assessed at five interval levels of percent impervious surface area, it indicates that SUTM performs consistently well in both rural and urban areas. Comparatively, NDVI and %GV-based regression models perform well in rural areas, but poor in urban areas, whereas %ISA-based models perform well in urban areas, but relatively poor in rural areas. This study found that soil, including both moist and dry soil, has significant impacts on modeling SUHI.

BCI: A biophysical composition index for remote sensing of urban environments

Understanding urban environments and their spatio-temporal changes is essential for regional and local planning and environmental management. To facilitate monitoring and analyzing urban environments, remotely sensed data have been applied extensively because of its synoptic view and repeat coverage over large geographic areas. Compared with traditional per-pixel and sub-pixel image analyses, spectral indices have apparent advantages due to their easy implementation. However, most spectral indices are designed to highlight only one land cover, and confusion between other land cover types, in particular impervious surfaces and bare soil, has not been successfully addressed. This study proposes a biophysical composition index (BCI) for simple and convenient derivation of urban biophysical compositions for practical applications following Ridd's conceptual vegetation – impervious surface – soil triangle model by a reexamination of the Tasseled Cap (TC) transformation. Further, this research explores the applicability of BCI in various remotely sensed images at different spatial resolutions. Results indicate that, BCI has a closer relationship with impervious surface abundance than those of normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and normalized difference impervious surface index (NDISI), with correlation coefficients of approximately 0.8 at various resolutions. Also, the performances of BCI in quantifying vegetation abundance are comparable with NDVI at all three spatial scales. Additionally, with much higher values of separability metrics than any other index, the study confirms that BCI was shown to be the most effective index of the four evaluated for separating impervious surfaces and bare soil.

Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery

With rapid urban growth in recent years, understanding urban biophysical composition and dynamics becomes an important research topic. Remote sensing technologies introduce a potentially scientific basis for examining urban composition and monitoring its changes over time. The vegetation–impervious surface–soil (V–I–S) model, in particular, provides a foundation for describing urban/suburban environments and a basis for further urban analyses including urban growth modeling, environmental impact analysis, and socioeconomic factor estimation. This paper develops a normalized spectral mixture analysis (NSMA) method to examine urban composition in Columbus Ohio using Landsat ETM+ data. In particular, a brightness normalization method is applied to reduce brightness variation. Through this normalization, brightness variability within each V–I–S component is reduced or eliminated, thus allowing a single endmember representing each component. Further, with the normalized image, three endmembers, vegetation, impervious surface, and soil, are chosen to model heterogeneous urban composition using a constrained spectral mixture analysis (SMA) model. The accuracy of impervious surface estimation is assessed and compared with two other existing models. Results indicate that the proposed model is a better alternative to existing models, with a root mean square error (RMSE) of 10.1% for impervious surface estimation in the study area.