Vegetation Impervious Soil Research Articles

Automated Surface Runoff Estimation with the Spectral Unmixing of Remotely Sensed Multispectral Imagery

This work presents a methodology for the hydrological characterization of natural and urban landscapes, focusing on accurate estimations of infiltration capacity and runoff characteristics. By combining existing methods from the literature, we created a systemic process that integrates satellite-based vegetation maps, topography, and soil permeability data. This process generates a detailed vegetation classification and slope-corrected composite curve number (CNcα) map using information at the subpixel level, which is crucial for estimating excess runoff during intense precipitation events. The algorithm designed with this methodology is automated and utilizes freely accessible multispectral imagery. Leveraging the vegetation–impervious–soil (V-I-S) model, it is assumed that land cover comprises V-I-S components at each pixel. Automated Music and spectral Separability-based Endmember Selection is employed on a generic spectral library to obtain the most relevant V-I-S endmember spectra for a particular image, which is then employed in multiple endmember spectral mixture analysis to obtain V-I-S fraction maps. The derived fractions are utilized in combination with the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index to adapt the CNcα map to different seasons and climatic conditions. The methodology was applied to Esch-sur-Alzette, Luxembourg, over a four-year period to validate the methodology and quantify the increase in the impervious surface area in the commune and the relationship with the runoff dynamics. This approach provides valuable insights into infiltration and runoff dynamics across diverse temporal and geographic ranges.

M-CORE: A Novel Approach for Land Cover Fraction Mapping Using Multisite Spectral Libraries

The concept of a generic spectral library (GSL), i.e., a structured collection of image spectra sampled from various optical remote sensing images covering different sites and points in time, has been suggested in previous studies to facilitate the tedious process of producing training data for remote sensing-based land cover (LC) mapping. When using multisource libraries collected over different sites, library pruning approaches are needed to extract an apt set of labeled spectra to perform mapping on a specific area. Library pruning mainly aims to discard image-irrelevant and redundant spectra, while limiting spectral confusion during the mapping process. Most library pruning approaches focus only on one of these aspects, which has been shown to negatively affect mapping accuracies. This article emphasizes the need for a multistep approach to optimize a GSL for site-specific mapping. We propose a new library pruning method called M-CORE, specifically designed to facilitate LC fraction mapping. The method extends multiple signal classification (MUSIC) with a confusion reduction (CORE) component. Vegetation-impervious-soil (VIS) fraction mapping experiments on a Sentinel-2 image of Brussels, using a GSL with and without local spectra, show the added value of M-CORE over previously proposed library pruning methods and demonstrate the feasibility of GSL-based mapping in an urban context.

From land cover to land use: Applying random forest classifier to Landsat imagery for urban land-use change mapping

The extensive record of Landsat imagery is commonly used to map urban land-cover and land-use change. Random forest (RF) classification was applied for mapping more detailed urban land-use and change categories than is typically attempted with Landsat data. Two dates of Landsat imagery (1990 and 2015) were utilized with surface reflectance, Vegetation-Impervious-Soil (V-I-S) fractions, grey-level cooccurrence matrix (GLCM) of V-I-S, and temporal variation of V-I-S inputs. GLCM V-I-S and temporal variation of Vegetation as input features of RF classifiers slightly improved accuracies of land use maps. A change map derived from an overlay analysis between the 2015 map and a Landsat-derived urban expansion map was more accurate than one from post-classification comparison of 1990 and 2015 maps. For the Taiwan study area, Transportation Corridor land use tended to lead conversion to Residential and Employment types in relatively undeveloped districts, and extensive urban land-use change occurred in peri-urban areas.

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.

Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities

Forthcoming spaceborne imaging spectrometers will provide novel opportunities for mapping urban composition globally. To move from case studies for single cities towards comparative and more operational analyses, generalized models that may be transferred throughout space are desired. In this study, we investigated how single regression models can be spatially generalized for vegetation-impervious-soil (VIS) mapping across multiple cities. The combination of support vector regression (SVR) with synthetically mixed training data generated from spectral libraries was used for fraction mapping. We developed three local models based on separate spectral libraries from Berlin (Germany), Brussels (Belgium), and Santa Barbara (U.S.), and a generalized model based on a combined multi-site spectral library. To examine the performance and transferability of the generalized model compared to local models, we first applied all model variants to simulated Environmental Mapping and Analysis Program (EnMAP) data from the three cities that were represented in the models, i.e., known sites. Next, we transferred the models to two unknown sites not represented in the models, San Francisco Bay Area (U.S.) and Munich (Germany). In the first mapping constellation, results demonstrated that the generalized model was capable of accurately mapping VIS fractions across all three known sites. Average mean absolute errors (AV-MAEs) were 8.5, 12.2, and 11.0% for Berlin, Brussels, and Santa Barbara. The performance of the generalized model was very similar to the local models, with ∆AV-MAEs falling within a range of ±0.7%. A detailed assessment of fraction maps and class-wise accuracies confirmed that modeling errors related to remaining limitations of urban mapping based on optical remote sensing data rather than to the choice between a local or generalized model. For the second mapping constellation, the generalized model proved to be useful for mapping vegetation and impervious fractions in the unknown sites. MAEs for both cover types were 5.4 and 10.9% for the San Francisco Bay Area, and 6.3 and 15.4% for Munich. In contrast, the three local models were only found to have similar accuracies as the generalized model for one of the two sites or for individual VIS categories. Despite the enhanced transferability of the generalized model to the unknown sites, deficiencies remained for accurate soil mapping. MAEs were 22.4 and 12.3%, and high over - and underestimations were observed at the low and high end of the fraction range. These shortcomings indicated possible limitations of the spectral libraries to account for the spectral characteristics of soils in the unknown sites. Overall, we conclude that the combination of SVR and synthetically mixed training data generated from multi-site libraries constitutes a flexible modeling approach for generalized urban mapping across multiple cities.

Income Driven Patterns of the Urban Environment

This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation–Impervious–Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1.263 million in 1991 to 1.409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics.

URBAN MORPHOLOGICAL DYNAMICS IN SANTIAGO (CHILE): PROPOSING SUSTAINABLE INDICATORS FROM REMOTE SENSING

Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (<i>end members</i>): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All <i>end members</i> were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.

URBAN MORPHOLOGICAL DYNAMICS IN SANTIAGO (CHILE): PROPOSING SUSTAINABLE INDICATORS FROM REMOTE SENSING

Abstract. Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (end members): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All end members were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.

An Algorithm Approach for the Analysis of Urban Land-Use/Cover: Logic Filters

Accurate classification of land-use/cover based on remotely sensed data is important for interpreters who analyze time or event-based change on certain areas. Any method that has user flexibility on area selection provides great simplicity during analysis, since the analyzer may need to work on a specific area of interest instead of dealing with the entire remotely sensed data. The objectives of the paper are to develop an automation algorithm using Matlab & Simulink on user selected areas, to filter V-I-S (Vegetation, Impervious, Soil) components using the algorithm, to analyze the components according to upper and lower threshold values based on each band histogram, and finally to obtain land-use/cover map combining the V-I-S components. LANDSAT 5TM satellite data covering Istanbul and Izmit regions are utilized, and 4, 3, 2 (RGB) band combination is selected to fulfill the aims of the study. These referred bands are normalized, and V-I-S components of each band are determined. This methodology that uses Matlab & Simulink program is equally successful like the unsupervised and supervised methods. Practices with these methods that lead to qualitative and quantitative assessments of selected urban areas will further provide important spatial information and data especially to the urban planners and decision-makers.

Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing

In this study, Landsat 5-TM data were used to map urban land classes and the changes that occurred within them over a period of six years. The land classes were identified by Landsat 5-TM scenes taken in the same season in 1988 and 1994. The phenomena of land class changes were evaluated by adopting two remote sensing approaches, namely mapping and modelling, in a case study of the Bangkok Metropolitan area of Thailand. The quantitative results of changes, which were computed from a post-classification method, were used to analyse the pattern of changes in the urban land classes. The change-detection analysis indicated that 2% of agricultural land was lost, and there was a 14% increase in the commercial areas. The results demonstrated that the pattern of change in the urban land classes in Bangkok was that of agriculture lands to open lands; open lands to residential, and residential to commercial. The highest commercial land growth was observed in the high-density residential areas along main roads and the railway line. Data were generated from the two dates of TM images for the vegetationimpervious-soil (V-I-S) composition model. The trends of changes in the urban land classes and the anatomy of the study area were presented quantitatively through the V-I-S model. Good agreement was obtained between the values of changes computed for the impervious surfaces from the V-I-S model (which showed 6% changes) and the change-detection map (which showed 5.6% changes). The results of changes in the spatial pattern of commercial and residential areas (high, medium and low) emphasize that remote sensing data can be used for V-I-S modelling and mapping of urban surface features.