Normalized Difference Impervious Surface Index Research Articles

Coupling the Calibrated GlobalLand30 Data and Modified PLUS Model for Multi-Scenario Land Use Simulation and Landscape Ecological Risk Assessment

Rapid economic growth and urbanization have significantly changed the land use distribution and landscape ecological structure, which has a profound impact on the natural environment. A scientific grasp of the characteristics of land use distribution and its impact on landscape ecological risk is a prerequisite for sustainable urban development. This study aimed to calibrate GlobalLand30 data using the normalized difference impervious surface index (NDISI) obtained from Landsat images, thereby providing a more precise foundation for land simulation. Additionally, it sought to improve the accuracy of the patch-generating land use simulation (PLUS) through parameter sensitivity analysis. Building upon this, the research also simulates future land use in Beijing. Lastly, this study introduced an LER index to assess ecological risk in the current and future urban landscapes. The results showed that the GlobalLand30 data were calibrated and PLUS model accuracy was improved to more than 86%. The accuracy of the modified PLUS model based on a Morris sensitivity analysis was increased, and the kappa coefficients were increased by approximately 3%. The results of the multi-scenario simulation showed that under the SSP126-EP scenario, future land use in Beijing could balance urban development and ecological protection, and thus would be more suitable for sustainable development. In the other two scenarios, ecological land will be encroached by urban development. From 2000 to 2020, the degree of LER was generally lower, moderate, or higher, and the overall level of LER showed a downward trend continuing until 2100 in the SSP126-EG scenario. Future land use simulations and LER assessment under multi-scenarios could help decision makers develop multi-scale landscape protection strategies.

Impact of built environment on urban surface temperature based on multi-source data at the community level in Beilin District, Xi'an, China.

With the global warming and rapid urbanization in China, the urban built environment has undergone rapid changes, and the land surface temperatures (LSTs) of urban communities have obvious spatial heterogeneity. To explore the key driving factors of community LSTs, the multi-source data and spatial statistical methods being jointly used to analyze the spatial characteristics and main influencing factors of LST at the community level in the Beilin District of Xi'an City, China. The results are as follows: (1) Compared with communities dominated by construction land, communities with large area of green space and water bodies have lower LST. (2) According to the Akaike's information criterion (AICc) and maximum of adjusted R2, and other parameters, the No.1236 model was selected as the optimal model to analyze the influencing factors of community LST by exploratory data analysis, including building density (BD), building height standard deviation (BHS), percentage of public administration and public services land (PASL), percentage of green space and square land (PGSL), population density (POPD), normalized difference impervious surface index (NDISI), and perimeter-area fractal dimension (PAFRAC). (3) For each increase of one unit in NDISI and BHS when other factors remain unchanged, the LST will increase by 0.569°C and decrease by 0.478°C, respectively. (4) From the spatial stability and distribution of Local-R2, the warming factors of community LST are mainly NDISI, PAFRAC, BD, and PASL, while the cooling factors are BHS and PGSL. The spatial heterogeneity of community LST is not only related to the change of underlying surface properties but is also affected by intra-urban architectural morphology. Therefore, reasonable planning of urban built environment is of great significance for mitigating heat islands.

Spatiotemporal Dynamics of Ecological Condition in Qinghai-Tibet Plateau Based on Remotely Sensed Ecological Index

The eco-system in the Qinghai-Tibet Plateau (QTP) is extremely fragile, and highly vulnerable to climate change. Knowledge of the changes in the ecological conditions is vital to mitigate the impact of climate change. In this study, we investigated the trend of ecological conditions of the QTP using the remotely sensed ecological index (RSEI), which is the first PCA (principal component analysis) axis of the four indexes derived from the MODIS (Moderate resolution Imaging Spectroradiometer) images captured in the growing season of 2000–2020. The four indexes, i.e., NDVI (normalized difference vegetation index), heat (land surface temperature, LST), wetness (tasseled cap wetness index, WET) and dryness (normalized difference impervious surface index, NDBSI), were calculated on the Google Earth Engine platform. Using land use cover change (LUCC) data, long-term meteorological records and the supplementary annual livestock production, we explored the drivers of spatiotemporal changes in the RSEI. The results show the following points: (1) the ecological conditions of the QTP have remarkable spatiotemporal variations. There were two ecological degradation periods, one of them occurred in the central region during 2005–2010, mainly attributed to the rising temperatures and decreasing precipitation. The other occurred during 2015–2020, driven primarily by overgrazing in the southwest. From 2000 to 2005, it was a period of rapid ecological restoration in the ecologically fragile northeast region. (2) The contribution rate of pc1 was stable at 60%, while the contribution rate of pc2 remained below 40%, indicating that pc1 demonstrated most of the characteristics of the four indexes. The correlation coefficients between NDVI and WET with pc1 are both positive, while LST and NDBSI have negative correlation coefficients, i.e., negative effects. This is consistent with the actual situation. (3) Overgrazing caused grass degradation in the southwest area of the QTP, which might be the main reason for the poor ecological conditions (i.e., low RSEI value) during 2015–2020. (4) Temperature and precipitation showed an increasing trend during the study period. A warmer and wetter climate is expected to have profound impacts on the ecosystems in QTP and practices should be concentrated on identifying climate-sensitive ecosystem components and designating adaptative options.

GEOSPATIAL APPROACH FOR URBAN ENVIRONMENTAL QUALITY ASSESSMENT

Abstract. Growing population and change in gentrification patterns of urban areas affect the environment quality, especially in the developing nations. Most cities are thereby, facing serious environmental sustainability challenges. A resilient urban planning is required to solve these environmental problems, which in turn need information that is not available in the desired scales, hence, making it difficult to comprehend. In this study, an approach is proposed to comprehend the environmental quality of a city at community level. Landsat 8 images are used to compute the biophysical indicators, namely, land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI) and normalized difference impervious surface index (NDISI) in ArcGIS software. These indicators are then integrated with the indicators from the government census data to compute the community level index for urban environmental quality (UEQI). The results depicted that the highest value of our developed index occurred in the places where there is more of greenery and less built-up area. Based on the obtained UEQI values, different areas of the city are divided into five categories. The index values suggest that urban greening significantly contribute to enhance the urban environmental quality. Also, it highlights the critical zones where intervention should be made by the planners and policymakers for the sustainability of the city.

Spatiotemporal Evolution of the Urban Thermal Environment Effect and Its Influencing Factors: A Case Study of Beijing, China

Rapid urbanization has led to significant changes in land surface temperature (LST), which in turn affect the urban thermal environment effect and the health of residents. Exploring the causes of the urban thermal environment effect will provide guidance for promoting sustainable urban development. The spatiotemporal evolution of the urban thermal environment effect within the sixth ring road of Beijing was analyzed by inversion of remote sensing data to obtain the LST in 2004, 2009, 2014, and 2019. In addition, based on multivariate spatial data, we applied the standard deviation ellipse (SDE), spatial principal component analysis (PCA), and other methods to analyze and identify the relationships between the urban thermal environment effect and its influencing factors. The results show that from 2004 to 2019, the spatial distribution of urban development and LST within the sixth ring road of Beijing were closely related, the heat island area showed a small increasing trend, and differences in the thermal environment effect between different administrative regions in different periods were obvious. The main factors affecting the urban thermal environment effect were urban construction intensity, vegetation and water bodies, socioeconomic activities, and geomorphology. It is noteworthy that human factors had a greater impact than natural factors. Among them, the positive effect of the normalized difference impervious surface index (NDBBI) and the negative effect of the fractional vegetation cover (FVC) were the most prominent. This study provides theoretical support for mitigating the urban thermal environment effect and promoting sustainable urban development.

Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China

The urban heat island (UHI) phenomenon caused by rapid urbanization has become an important global ecological and environmental problem that cannot be ignored. In this study, the UHI effect was quantified using Landsat 8 image inversion land surface temperatures (LSTs). With the spatial scale of street units in Fuzhou City, China, using ordinary least squares (OLS) regression, geographically weighted regression (GWR) models, and multi-scale geographically weighted regression (MGWR), we explored the spatial heterogeneities of the influencing factors and LST. The results indicated that, compared with traditional OLS models, GWR improved the model fit by considering spatial heterogeneity, whereas MGWR outperformed OLS and GWR in terms of goodness of fit by considering the effects of different bandwidths on LST. Building density (BD), normalized difference impervious surface index (NDISI), and the sky view factor (SVF) were important influences on elevated LST, while building height (BH), forest land percentage (Forest_per), and waterbody percentage (Water_per) were negatively correlated with LST. In addition, built-up percentage (Built_per) and population density (Pop_Den) showed significant spatial non-stationary characteristics. These findings suggest the need to consider spatial heterogeneity in analyses of impact factors. This study can be used to provide guidance on mitigation strategies for UHIs in different regions.

Spatiotemporal Dynamics of Soil Impermeability and Its Impact on the Hydrology of An Urban Basin

The presence of impervious surfaces in catchments interferes with the natural process of infiltration, which has a marked influence on the hydrological cycle, affecting the base flow in rivers and increasing the surface runoff and the magnitude of flood flows. Like many Latin American cities, Loja (located in southern Ecuador) has experienced significant rates of urban growth in recent years, increasing the impervious surfaces in the catchment where it belongs. The aim of this study is to analyze the spatiotemporal dynamics of imperviousness in the study area for the period 1989–2020, using the Normalized Difference Impervious Surface Index (NDISI) and the supervised classification of Landsat images. The effect on flood flows was studied for each timestep using HEC-HMS hydrological model. Additionally, a future scenario of impervious surfaces was generated considering the observed spatiotemporal variability, possible explanatory variables, and logistic regression models. Between 1989 and 2020, there was an increase of 144.12% in impervious surfaces, which corresponds to the population growth of 282.56% that occurred in the same period. The period between 2001 and 2013 was the one that presented the most significant increase (1.06 km2/year). A direct relationship between the increase in impervious surfaces and the increase in flood flows was observed, reaching a significant variation towards the horizon year that could affect the population, for which measures to manage the surface runoff is necessary.

6+: A Novel Approach for Building Extraction from a Medium Resolution Multi-Spectral Satellite

For smart, sustainable cities and urban planning, building extraction through satellite images becomes a crucial activity. It is challenging in the medium spatial resolution. This work proposes a novel methodology named ‘6+’ for improving building extraction in 10 m medium spatial resolution multispectral satellite images. Data resources used are Sentinel-2A satellite images and OpenStreetMap (OSM). The proposed methodology merges the available high-resolution bands, super-resolved Short-Wave InfraRed (SWIR) bands, and an Enhanced Normalized Difference Impervious Surface Index (ENDISI) built-up index-based image to produce enhanced multispectral satellite images that contain additional information on impervious surfaces for improving building extraction results. The proposed methodology produces a novel building extraction dataset named ‘6+’. Another dataset named ‘6 band’ is also prepared for comparison by merging super-resolved bands 11 and 12 along with all the highest spatial resolution bands. The building ground truths are prepared using OSM shapefiles. The models specific for extracting buildings, i.e., BRRNet, JointNet, SegUnet, Dilated-ResUnet, and other Unet based encoder-decoder models with a backbone of various state-of-art image segmentation algorithms, are applied on both datasets. The comparative analyses of all models applied to the ‘6+’ dataset achieve a better performance in terms of F1-Score and Intersection over Union (IoU) than the ‘6 band’ dataset.

ASSESSMENT OF URBAN ENVIRONMENTAL QUALITY: A CASE STUDY OF CASABLANCA, MOROCCO

Abstract. By 2050, Most of the world’s population will live in cities, this demographic explosion will lead to significant urban development at the expanse of natural land which may harm the environmental quality. Consequently, assessing and modeling the urban environmental quality (UEQ) is requisite for efficient urban sprawl control and better city planning and management. The present study proposes a methodology to model and assess the environment of the urban system by developing the urban environmental quality index (UEQI) based on remote sensing data. Five environmental indicators were derived from the Landsat OLI image namely, Modified Normalized Difference Impervious Surface Index (MNDISI), Modified Normalized Difference, Water Index (MNDWI), Normalized difference vegetation Index (NDVI), Normalized difference built-up Index (NDBI) and Soil adjusted vegetation index (SAVI). Using the Principal Component Analysis (PCA) the urban environmental quality index was computed for the 17 communes of Casablanca city. The UEQI values were spatially mapped under three classes (good, moderate, and poor). The results obtained from the analysis showed a significant difference in the term of UEQI values among the communes. In addition, the environmental quality is inadequate in communes with fewer green spaces and more impervious surfaces. The outcomes of this work can serve as an efficient tool to determine the most critical interventions to be made by the authority for current and future urban planning and land/resource management.

Urban Impervious Surface Automatic Threshold Detection Model Derived From Multitemporal Landsat Images

Accurate mapping urban impervious surfaces (UIS) from satellite images is crucial for understanding urban land cover change directions and addressing UIS change-induced environmental issues. The UIS index is an effective way to reduce the computational cost of processing remote sensing imageries although continuous UIS detection from multitemporal Landsat images, repeated fine-tuning, and reselection for a proper threshold should be considered. In this article, we proposed a novel UIS index and an improved particle swarm optimization (PSO)-based UIS automatic threshold selection model (UISAT) to overcome those shortages when detecting UIS derived from multitemporal Landsat images. The proposed UIS index transformed different temporal Landsat images into index images with distinguishable pixel values for each gray pixel. In this way, the pixel gray values representing UIS were enhanced, and non-UIS was weakened with lower gray scales in the transformed index images. Next, an automatic threshold selection strategy was developed to enhance the applicability of the PSO model in terms of threshold fine-tuning and reselection. Then, we performed comprehensive assessments for the UISAT model, including in comparison to an improved convolutional deep belief network for relative accuracy verification, random points matching with Google Earth images, and comparing to other typical indices. The results demonstrated that the UISAT model provided the best performance in UIS detection from multitemporal Landsat images. The producer’s accuracies (PAs) of UISAT results in Beijing, Hefei, and Guangzhou, China, were 91.73%, 93.87%, and 92.84%, while the user’s accuracies (UA) were at 91.51%, 93.35%, and 92.24%. The proposed model outperformed the existing commonly used UIS indices [e.g., the Biophysical Composition Index (BCI), the Combinational Build-up Index (CBI), the Genetic Algorithm-based Urban Cluster Automatic Threshold (GA-UCAT), and the Gaussian-based Automatic Threshold Modified Normalized Difference Impervious Surface Index (G-MNDISI)] by an average increase of 6.51% in overall accuracy.

Synergetic interaction between spatial land cover dynamics and expanding urban heat islands.

Inherent statistics of the surface temperature pattern were used to categorize urban heat islands (UHIs) for a tropical mega city and its satellite towns. Application of flexible threshold values for UHI zoning made this procedure independent of seasonal or locational influences. UHI zones for the years 1999, 2009, and 2019 were mapped from Landsat thermal bands by applying the mono-window algorithm. The parameters affecting the UHI intensity were rigorously investigated. Dynamics of land use land cover patterns provided in-depth insight into the spatiotemporal variability of UHI. The abrupt rises in localized surface temperature for every decade were recognized and thoroughly explained with the fall in fractional vegetation cover index and increase in normalized difference impervious surface index. The temporal nature of urban agglomeration and fragmentation of vegetation cover was quantified through landscape metric algorithms. The vegetation pattern and associated surface temperature fall were further used to evaluate the weakening of UHI intensity around major recreational zones. Substantial cooling by 0.938°C was noted on daytime, from urban built-up at a 50-m distance to the green parks. Differential rates of urbanization and associated magnification of UHIs were looked into separately for central urban and satellite town areas. The characteristics of built-up density and proximity to green areas were employed to strategize mitigatory measures for the constantly growing UHI problem. Urgent needs for sustainable policies and green landscaping were highlighted through multi-criterion analysis.

GEOSPATIAL ANALYSIS OF IMPERVIOUS SURFACES AND THEIR EFFECT ON LAND SURFACE TEMPERATURE IN ABUJA, NIGERIA

Abstract. The rapid urban expansion in Abuja, Nigeria, has resulted in the replacement of land surface previously occupied by natural vegetation with various impermeable materials. This study examines the impact of the spatial distribution of impervious surfaces (IS) on land surface temperature (LST) in the study area using both graphical and quantitative approach. A Normalized Difference Impervious Surface Index (NDISI) was adopted to estimate IS and LST from Landsat ETM+ and OLI/TIRS satellite images (path: 189, row: 54) of Abuja for 4 distinct epochs of 2004, 2008, 2014 and 2018. In order to analyze the effect of IS on LST, the relationship between the normalized difference indices and LST, for each epoch, were determined using regression and correlation analyses. Results show the spatial patterns of impervious surfaces as distributed over Abuja, Nigeria and its impact on LST dynamics. It was observed that mean surface temperature increased by at least 2 °C every 4 years. Furthermore, results of the correlation analysis between NDISI and LST reveal that there exist varying positive correlations between the two variables in with correlation coefficients; R = 0.511, 0.166, 0.505, 0.785 in 2004,2008, 2014 and 2018 respectively, suggesting that impervious surfaces areas accelerate LST rise and Urban Heat Island (UHI) formation. This study gives great insight on the concept of impervious surfaces and its spatial pattern in Abuja city, Nigeria. The study recommends the widespread use of highly reflective or natural surfaces for rooftops, pavements and roads and that afforestation should be encouraged to increase green areas.

Integrated evaluation on multi-scale land surface temperature grading and bio-temperature suitability—a case study in Tianjin China

ABSTRACT The suitable thermal environment is a prerequisite for biological survival. In order to evaluate the change of land surface temperature (LST) and its effect on organisms, in this study, the Land satellite (Landsat) series data was used to invert the LST from 1988 to 2018 in Tianjin. The main surface features were extracted from the remotely sensed images using normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and normalized difference impervious surface index (NDISI). A multi-scale temperature grading method based on biological heat reaction was proposed to classify the LST. In terms of the suitability of the organism to temperature, the land surface temperature suitability (LSTS) model was established based on the niche suitability analysis method, and the suitability of organisms to temperature was evaluated based on the model. In the aspect of land area change, both the land surface temperature area (LSTA) model and the average relative contribution (ARC) were proposed to comprehensively evaluate the impact of the area change of surface features on LST. The results indicated that the temperature suitability of most organisms had decreased. The change of LST was related to the comprehensive change of surface features. In conclusion, the rise of LST had adverse effect on most organisms in Tianjin. Limiting the occupation of water area and cultivated land may inhibit the continuous rise in LST. Besides, the evaluation method novelty proposed in this study could provide new ideas to study the development trend of spatial objects in their environment thus would be applied widely.

Spatiotemporal Variation of Surface Urban Heat Islands in Relation to Land Cover Composition and Configuration: A Multi-Scale Case Study of Xi’an, China

Urban heat islands (UHI) can lead to multiple adverse impacts, including increased air pollution, morbidity, and energy consumption. The association between UHI effects and land cover characteristics has been extensively studied but is insufficiently understood in inland cities due to their unique urban environments. This study sought to investigate the spatiotemporal variations of the thermal environment and their relationships with land cover composition and configuration in Xi’an, the largest city in northwestern China. Land cover maps were classified and land surface temperature (LST) was estimated using Landsat imagery in six time periods from 1995 to 2020. The variations of surface heat island were captured using multi-temporal LST data and a surface urban heat island intensity (SUHII) indicator. The relationship between land cover features and land surface temperature was analyzed through multi-resolution grids and correlation analysis. The results showed that mean SUHII in the study area increased from 0.683 °C in 1995 to 2.759 °C in 2020. The densities of impervious surfaces had a stronger impact on LST than green space, with Pearson’s correlation coefficient r ranging from 0.59 to 0.97. The correlation between normalized difference impervious surface index and LST was enhanced with the enlargement of the grid cell size. The correlations between normalized difference vegetation index and LST reached maxima and stabilized at grid cell sizes of 210 and 240 m. Increasing the total area and aggregation level of urban green space alleviated the negative impacts of UHI in the study area. Our results also highlight the necessity of multi-scale analysis for examining the relationships between landscape configuration metrics and LST. These findings improved our understanding of the spatiotemporal variation of the surface urban heat island effect and its relationship with land cover features in a major inland city of China.

Mapping and Analyzing the Park Cooling Effect on Urban Heat Island in an Expanding City: A Case Study in Zhengzhou City, China

The Urban Heat Island (UHI) effect has been extensively studied as a global issue. The urbanization process has been proved to be the main reason for this phenomenon. Over the past 20 years, the built-up area of Zhengzhou city has grown five times larger, and the UHI effect has become increasingly pressing for the city’s inhabitants. Therefore, mitigating the UHI effect is an important research focus of the expanding capital city of the Henan province. In this study, the Landsat 8 image of July 2019 was selected from Landsat collection to obtain Land Surface Temperature (LST) by using Radiative Transfer Equation (RTE) method, and present land cover information by using spectral indices. Additionally, high-resolution Google Earth images were used to select 123 parks, grouped in five categories, to explore the impact factors on park cooling effect. Park Cooling Intensity (PCI) has been chosen as an indicator of the park cooling effect which will quantify its relation to park patch metrics. The results show that: (1) Among the five studied park types, the theme park category has the largest cooling effect while the linear park category has the lowest cooling effect; (2) The mean park LST and PCI of the samples are positively correlated with the Fractional Vegetation Cover (FVC) and with Normalized Difference Water Index (NDWI), but these are negatively correlated with the Normalized Difference Impervious Surface Index (NDISI). We can suppose that the increase of vegetation cover rate within water areas as well as the decrease of impervious surface in landscape planning and design will make future parks colder. (3) There is a correlation between the PCI and the park characteristics. The UHI effect could be mitigated by increasing of park size and reducing park fractal dimension (Frac_Dim) and perimeter-area ratio (Patario). (4) The PCI is influenced by the park itself and its surrounding area. These results will provide an important reference for future urban planning and urban park design to mitigate the urban heat island effect.

Enhanced normalized difference index for impervious surface area estimation at the plateau basin scale

Impervious surface (IS) area is an important indicator of ecological environment condition in the basin. We propose an index for IS extraction [i.e., enhanced normalized difference impervious surfaces index (ENDISI)] by integrating the spectrum character of Landsat-operational land imager (OLI) images, and an automatic threshold selection method using the generalized Gaussian model. Dianchi and Erhai Basin are employed as study areas to test the ENDISI method at the plateau basin scale. The results show that: (1) the ENDISI can reduce the impacts of arid land, bare rock, and bare soil on IS extraction effectively; (2) ENDISI had a much higher separability degree between ISs and pervious surfaces compared with normalized difference built-up index, modified normalized difference IS index, and combinational biophysical composition index; and (3) the overall accuracy and kappa coefficient values of IS extraction via automatic threshold selection exceed 93.9% and 82.4%, respectively. Therefore, the ENDISI can serve as an effective index algorithm for rapid and high-precision IS extraction at the plateau basin scale.

Temporal and spatial changes of urban impervious surface and its influence on urban ecolo-gical quality: A comparison between Shanghai and New York

The urban spatial expansion has led to the considerable substitution of natural vegetation-dominated land surfaces by impervious surfaces, especially in large cities, with great impacts on urban ecological quality. Two most heavily populated cities, Shanghai of China and New York of USA, were chosen as the study cases. Based on Landsat images obtained in 1989, 2002, 2015 in Shanghai and in 1991, 2001, 2015 in New York, normalized difference impervious surface index (NDISI) was used to extract impervious surface (IS) information. The remote sensing based ecolo-gical index (RSEI) was then applied to evaluate the changes of urban ecological quality caused by the increased impervious surface. Furthermore, landscape pattern indices were used to analyze the differences of spatial structure of impervious surface between Shanghai and New York and their influences on urban ecological quality. The results showed a significant difference in urban expansion rate and pattern between Shanghai and New York from the early 1990s to 2015. The IS expansion area in Shanghai was 17.4 times as much as that in New York. The annual IS increase rate of Shanghai was 62.2 times as much as that of New York. Shanghai had experienced an expansion from urban center to the surrounding countryside in a concentric ring pattern, whereas New York showed no much expansion but had IS increase mainly within the inner city through space filling pattern. These differences in IS change rate and spatial distribution pattern had resulted in the difference in urban ecological quality of the two cities. The mean RSEI in Shanghai dropped from 0.717 in 1989 to 0.453 in 2015, with a decrease of 36.8%. In contrast, the RSEI of New York had a decline of 6.9% from 0.552 in 1991 to 0.514 in 2015. The poor ecological condition urban area tended to have large IS patches that were well connected and aggregated.

Urban environmental quality assessment using remote sensing and census data

Urban planning to solve environmental problems in Latin American cities requires information that commonly does not exist or, if it does exist, is at different scales, which makes the information incomparable. Therefore, a quantitative approach is proposed to assess an urban environmental quality index (UEQI) for Cali, Colombia, integrating remote sensing and census data. Landsat TM images were used to extract biophysical indicators: land surface temperature (LST), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), leaf wetness content index (LWCI), normalized humid index (NHI), normalized difference built-up index (NDBI), and normalized difference impervious surface index (NDISI). These seven indicators were integrated with seven socioeconomic indicators obtained from census data using multivariate statistical analysis to determine the UEQ index at the commune level. The results showed consistent correlation among the indicators, and the highest values ​​of UEQI occurred in communes with less built-up areas and more green areas. There were statistically significant differences among the index at the commune level. Therefore, communes were categorized into five classes (very good, good, moderate, poor and very poor environmental quality) according to the index to determine the most critical interventions to be made by planning and decision-making institutions. This method could be applied to similar cities using large-scale administrative units.

Mapping seasonal impervious surface dynamics in Wuhan urban agglomeration, China from 2000 to 2016

Numerous methods have been successfully applied to estimate the regional impervious surface dynamics based on spectral or spatial information from remote sensing imagery. However, previous methods mainly focused on mapping impervious surfaces at annual or decadal time scales. Few studies have attempted to map impervious surface dynamics at finer time scales, such as on a seasonal time scale using temporal information. This study aims to map regional impervious surface dynamics on a seasonal time scale by using time series Landsat data. The semi-supervised support vector machine (SVM) algorithm was employed for classifying impervious surfaces based on temporal characteristics, which were derived from seasonal time series biophysical composition index (BCI) and seasonal time series modified normalized difference impervious surface index (NDISI). The proposed method was validated over the Wuhan urban agglomeration (WUA) in China from 2000 to 2016. The results showed that impervious surfaces in the Wuhan urban agglomeration increased from 903.24 km2 in 2000 to 3989.49 km2 in 2016, with an annual growth rate of 20.10%. Additionally, the proposed method yielded reasonable average overall classification accuracy (up to 88%). Our results demonstrated that the proposed method could accurately map seasonal impervious surface dynamics based on temporal characteristics. This study could enable the monitoring of time-intensive impervious surfaces at a regional scale using remote sensing data.

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales.