Vegetation-impervious Surface-soil Research Articles

Evaluating eco-environment in urban agglomeration from a vegetation-impervious surface-soil-air framework: an example in Ningxia, China

Urban agglomerations (UA) are the fastest growing regional types during recent years, especially in developing countries. Monitoring and evaluating the eco-environment quality of UA is significant for sustainability. The previous remote sensing models of urban eco-environment are generally based on the vegetation-impervious surface-soil framework, which neglects the air quality in urban areas. We constructed a vegetation-impervious surface-soil-air (VISA) framework and derived a remote sensing model of UA eco-environment (RSUAE) based on the VISA. The RSUAE can integrate the greenness, dryness and imperviousness, moisture, heat, and air turbidity. The new model was evaluated by comparing with land cover types and the existing remote sensing-based ecological index and eco-environmental quality index models. Results demonstrated that RSUAE is valid to depict the difference of eco-environment quality among varied land cover types. The RSUAE model has general consistency with the existing models, while RSUAE takes into account well the air quality. The RSUAE was utilized to evaluate the eco-environment of UA along the Yellow River in Ningxia, China (NXUA) with Mann–Kendall trend test. Results identified 78.69% of the area has no significant change trend, 18.73% of the area has a significant increasing trend, and 2.58% of the area suffers significant decreasing trend in eco-environment quality from 2001 to 2019. The Yanchi county has the best improvement in eco-environment quality, whereas sensitivity analysis indicates that it is more vulnerable than other counties. The analysis tool and method presented in this study provide a reference for other UAs. The evaluation results in NXUA are noteworthy for local management.

Assessment of water infiltration of urban surface based on remote sensing: a case study of Wuhan, China

Assessment of water infiltration of the urban surface is the prerequisite and basis when we study the impact of urbanization on the urban ecological environment such as stormwater runoff and the heat island effect. However, few studies have been done on how to make such an assessment. In this paper, a detailed assessment of water infiltration of urban surface for Wuhan, China was conducted based on remote sensing. Classification of land-use types and their water infiltration criterion was proposed by the V-I-S (Vegetation-Impervious Surface-Soil) model. The Landsat-8 remote sensing data were used to classify various land-use types in Wuhan. The water infiltration capacity distribution in Wuhan was then obtained and classified into five levels. These results will also be helpful to provide a basis for establishing a comprehensive water infiltration model of the urban surface in the future.

Assessing impact of urbanisation on surface runoff using vegetation-impervious surface-soil (V-I-S) fraction and NRCS curve number (CN) model

The progressive decrease in natural surfaces to create space for urban dwellers with the consequential changes caused to hydrological regime of an area is taking a toll on environmental resources. The Natural Resources Conservation Service (NRCS) Curve Number (CN) model (earlier known as Soil Conservation Service Curve Number, SCS-CN) is used in the present study for assessing temporal variations in surface runoff in upper catchment of Rispana River, Dehradun city, India. Also, the composite CN is calculated using vegetation-impervious surface-soil (V-I-S) fraction derived from spectral normalisation and linear spectral mixture analysis method while assigning initial CN for hydrologic soil-cover complex for five years during 1991 to 2018. The results indicated a steady rise in impervious surface fraction and consequent surface runoff (8.25%) from 1991 to 2018, while urban green space fraction has shown depleting trends in north and central part of the city. The level of imperviousness highlighted an over 60% of central part of the city as impervious, which gradually decreases outwards from the city. A positive correlation is observed between surface runoff and ISA (R2 varies between 0.62 and 0.89, p < 0.05), whereas inverse relationship is observed with vegetative cover in all sub-basins (R2 varies between 0.61 and 0.91, p < 0.05). The study demonstrates the potential of NRCS-CN model with inputs drawn from V-I-S fraction for simulating surface runoff and understanding the urban flood risk. The work would be useful for the urban dwellers, administrators and planners for better planning and sustainable development of urban areas.

Quantifying urban expansion using Landsat images and landscape metrics: a case study of the Halton Region, Ontario

The Halton Region, as part of the Greater Toronto Area (GTA), is regarded as one of the fastest growing regions in Canada, generating 20% of national gross domestic product. It is also one of the most desirable places for living and for thriving businesses. This research attempts to assess the urban expansion in the Halton Region, Ontario, Canada from 1989 to 2019 using satellite images, analysis approaches, and landscape metrics. Multitemporal Landsat images and the supervised learning algorithms in GIS software were used to explore the dynamic changes and to classify the urban and nonurban areas. The temporal urban expansion in the Halton Region experienced a dramatic rise, and it mainly occurred from the centre of the area. The analysis of landscape metrics based on different methods including the Land Use in Central Indiana (LUCI) model, the vegetation-impervious surface-soil (V-I-S) model, and the census data of Canada was carried out to understand the transition mode of the urbanization in the Halton Region. Also, the population growth in the centre of the Halton Region was considered as one of the driving forces affecting urban expansion. The results showed that most of the landscape metrics rose between 1989 and 2019, indicating that leapfrog pattern of urbanization occurred over the entire period. The purpose of this research is to evaluate urbanization in the Halton Region and give the city managers data to make appropriate decisions in further urban planning.

A Method for Estimating Urban Flood-Carrying Capacity Using the VIS-W Underlying Surface Model: A Case Study from Wuhan, China

In 2016, 192 cities, including Wuhan, Nanjing, and Jiujiang, suffered from severe flooding, which raised social and government concerns in China. This paper proposes a method based on the underlying surface to estimate the urban flood-carrying capacity. First, water is extended into the underlying surface vegetation-impervious surface-soil (VIS) model to form the vegetation-impervious surface-soil and water (VIS-W) model. Second, the watershed is delimitated using a digital elevation model (DEM). Third, the natural water system’s storage capacity, including rivers, lakes, and puddles, is estimated. Using the rainfall–runoff hydrology model and the VIS-W model, the storing ability, the receiving ability, the discharge potential, and the emergency of pumping outward are assessed to derive the final flood-carrying capacity. Finally, the result is compared with data on the waterlogging points collected in July 2016 during a flood and waterlogging event in Wuhan. It is found that 84% of waterlogging points are located in the “weak” or “normal” areas, and 16% are located in the “strong” areas. Additionally, 99% of total waterlogging points are located upstream, based on the stream extracted by the DEM. This phenomenon indicates that Wuhan can mitigate flood disasters by fully utilizing the natural water system storage capacity to corporate the city’s drainage schedule.

Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

ABSTRACT This paper examines the application of remote sensing, based on the Vegetation-Impervious surface-Soil (V-I-S) model and spatial metrics, in an urban analysis for promoting sustainability and understanding urban growth theory. In order to improve the accuracy of land-cover classification, spectral angle mapping (SAM), spectral mixture analysis (SMA) and band ratioing were applied on satellite images for land-cover classification and comparison of the discrimination efficiency of these techniques. For the SMA, subsets of the Landsat (2, 3, 4, 5, and 7) and ASTER (1, 2, and 3N) images were selected. After endmember extraction and purification, the Bayesian probability of each component was computed and used for spectral unmixing. The classified images of different years were compared to analyze the changes in land-use and spatial pattern using V-I-S, a form of percentage of landscape (PLAND) and annualized urban sprawl index (AUSI). The result indicates that the performance of band ratioing (69% accuracy) is not asgood as that of SAM (75%) and SMA (86%) in discriminating between vegetationand agricultural land. It is concluded from the land-use analysis that the growth dynamics substantiate the urban theory of diffusion and coalescence and urban growth management strategies have not been completely successful.

A new scheme for urban impervious surface classification from SAR images

Urban impervious surfaces have been recognized as a significant indicator for various environmental and socio-economic studies. There is an increasingly urgent demand for timely and accurate monitoring of the impervious surfaces with satellite technology from local to global scales. In the past decades, optical remote sensing has been widely employed for this task with various techniques. However, there are still a range of challenges, e.g. handling cloud contamination on optical data. Therefore, the Synthetic Aperture Radar (SAR) was introduced for the challenging task because it is uniquely all-time- and all-weather-capable. Nevertheless, with an increasing number of SAR data applied, the methodology used for impervious surfaces classification remains unchanged from the methods used for optical datasets. This shortcoming has prevented the community from fully exploring the potential of using SAR data for impervious surfaces classification. We proposed a new scheme that is comparable to the well-known and fundamental Vegetation-Impervious surface-Soil (V-I-S) model for mapping urban impervious surfaces. Three scenes of fully polarimetric Radsarsat-2 data for the cities of Shenzhen, Hong Kong and Macau were employed to test and validate the proposed methodology. Experimental results indicated that the overall accuracy and Kappa coefficient were 96.00% and 0.8808 in Shenzhen, 93.87% and 0.8307 in Hong Kong and 97.48% and 0.9354 in Macau, indicating the applicability and great potential of the new scheme for impervious surfaces classification using polarimetric SAR data. Comparison with the traditional scheme indicated that this new scheme was able to improve the overall accuracy by up to 4.6% and Kappa coefficient by up to 0.18.

The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA

The relationship between urban land surface material fractions (ULSMFs) and brightness temperature has long attracted attention in research on urban environments. In this paper, a multiple endmember spectral mixture analysis (MESMA) method was applied to extract vegetation-impervious surface-soil (V-I-S) fractions in each pixel, and the surface brightness temperature was derived by using the radiation in the upper atmosphere, on the basis of Landsat 8 images. Then, a clustering analysis, ternary triangular chart (TTC), and a multivariate statistical analysis were applied to ascertain the relationship between the fractions in each pixel and the land surface brightness temperature (LSBT). The hypsometric TTC, as well as the geographical distribution features of the LSBT, revealed that the changes in LSBT were associated with the high fractions of impervious surfaces (or vegetation), in addition to the temperature distribution differences across locations with varying land-cover types. The data fitting results showed that the comprehensive endmember fractions of V-I-S explained 98.6% of fluctuating LSBT, and the impervious surface fraction had a positive impact on the LSBT, whereas the fraction of vegetation had a negative impact on the LSBT.

THE USAGE OF RUSBOOST BOOSTING METHOD FOR CLASSIFICATION OF IMPERVIOUS SURFACES

Abstract. Impervious surface areas are artificial structures covered by materials such as asphalt, stone, brick, rooftops and concrete. Buildings, parking lots, roads, driveways and sidewalks are shown as impervious surfaces. They increase depending on the population growth. The spatial development of impervious surface expansion is necessary for better understanding of the urbanization status and its effect on environment. There are different impervious surface determining approaches met in literature. In this paper, it is aimed to extract the impervious surface areas of Kayseri city, Turkey by using remote sensing techniques. It is possible to group these techniques under a few main topics as V-I-S (vegetation-impervious surface-soil) model, based on spectral mixture analysis or decision tree algorithms or impervious surface indices. According to these techniques, we proposed a new technique by using RUSBoost algorithm based on decision tree in this study. In this scope, Landsat 8 LDCM image belonging to July, 2013 was used. Determining of impervious surface areas accurately depends on accuracy of image classification methods. Therefore, satellite image was classified separately by using Classification Tree and RUSBoost boosting method which increases accuracy of the classification method based on decision tree. Classification accuracies of these supervised classification methods were compared and it was observed that the best overall accuracy was obtained with RUSBoost method. For this reason, RUSBoost method was preferred to determine impervious surface areas. The overall accuracies were obtained 95 % with Classification Tree and 97 % with RUSBoost boosting method.

大尺度不透水面遥感估算方法比较——以京津唐为例

PDF HTML阅读 XML下载 导出引用 引用提醒 大尺度不透水面遥感估算方法比较——以京津唐为例 DOI: 10.5846/stxb201309242350 作者: 作者单位: 中国科学院生态环境研究中心,中国科学院生态环境研究中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金面上项目(41371197);省部级项目(STSN-12-01) Remote sensing of impervious surface for the Beijing-Tianjin-Tangshan urban agglomeration: a comparison of different approaches Author: Affiliation: Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市不透水面既是常用的城市化程度指标,也是衡量环境质量的重要指标。采用遥感技术准确提取城市不透水面并分析其空间扩张过程,对生态城市建设具有重要意义。基于Landsat 5 TM影像,采用NDVI二元法和线性光谱分解法,分别提取北京、天津和唐山3个城市不透水面信息,并将不透水面估算结果与近同期的ALOS影像提取结果对比验证。结果表明,线性光谱分解法获取的不透水面结果较好,RMSE为20.6%,能有效提取大范围的不透水面信息。 Abstract:Impervious surfaces are mainly anthropogenic features such as paved roads, rooftops, driveways, sidewalks, and parking lots that are covered by impenetrable materials. With the urban expansion, vegetation and soils are replaced by impervious surfaces, which become a major ecological and environmental concern. This is because the increase of impervious surfaces generally leads to the decrease in vegetation, wetlands and agricultural lands, and consequently, to a series of environmental problems, such as the decease of groundwater recharge, the increase of surface runoffs and flood frequency and urban heat islands. The percent cover of impervious surfaces, as well as its spatial pattern, has been widely used as an indicator to quantify the urbanization level and urban environmental quality, and is essential to understand the interactions between human and the environment. Therefore, accurate mapping and estimating impervious surfaces is crucial for environmental and resources management. In this study, we compared and evaluated two methods: the Normalized Difference Vegetation Index (NDVI) based binary approach and the Linear Spectral Unmixing (LSU) method. These two approaches have been frequently used in mapping impervious surfaces. With the NDVI based binary approach, impervious surfaces are extracted based on information on vegetation distribution that can be well represented by NDVI,. Then vegetation fractional coverage was first estimated from a scaled NDVI, and then impervious surfaces were estimated as by subtracting the vegetation fraction from 1. This approach had the merit of simplicity. However, large errors may occur in impervious surface estimation. The LSU approach is based on the vegetation-impervious surface-soil (V-I-S) model proposed by Ridd in 1995, a novel conceptual model for remote sensing analysis of urban landscapes. The VIS model indicated that land cover in urban environment is a linear combination of these three components, that is, vegetation, impervious, and soils. The LSU approach has been widely used for remote sensing of impervious surfaces. This method provides a suitable technique to detect and map urban materials, and to address the mixed pixel problem in medium spatial resolution imagery. Taking the Beijing-Tianjin-Tangshan urban agglomeration as a case study, this research compared these two approaches on estimating impervious surfaces. The study area included Beijing City, Tianjin City, Tangshan City and Sanhe City, including a region with more than 40 000 square kilometers. Landsat 5 TM image data acquired in 2010 was used for mapping and estimating the impervious surfaces. A layer of impervious surfaces derived from ALOS images with spatial resolution of 2.5 m was used as a reference to evaluate the accuracies of the two methods. The results showed that the NDVI based binary approach had a root-mean-square error (RMSE) of 40.2%. The LSU approach was much better for impervious surfaces estimation than the NDVI based method, resulting in a RMSE of 20.0%. The residuals of the LSU approach ranged from -0.4 to 0.4. This accuracy was comparable to those from previous studies that were mostly conducted at a smaller geographical area, generally several thousands square kilometers. Our research expanded the knowledge of existing studies by proving that the LSU approach could be applied to a large study area for mapping impervious surfaces with acceptable accuracy. 参考文献 相似文献 引证文献

A tetrahedron-based endmember selection approach for urban impervious surface mapping.

The pixel purity index (PPI) and two-dimensional (2-D) scatter plots are two popular techniques for endmember extraction in remote sensing spectral mixture analysis, yet both suffer from one major drawback, that is, the selection of a final set of endmembers has to endure a cumbersome process of iterative visual inspection and human intervention, especially when a spectrally-complex urban scene is involved. Within the conceptual framework of a V-H-L-S (vegetation-high albedo-low albedo-soil) model, which is expanded from the classic V-I-S (vegetation-impervious surface-soil) model, a tetrahedron-based endmember selection approach combined with a multi-objective optimization genetic algorithm (MOGA) was designed to identify urban endmembers from multispectral imagery. The tetrahedron defining the enclosing volume of MNF-transformed pixels in a three-dimensional (3-D) space was algorithmically sought, so that the tetrahedral vertices can ideally match the four components of the adopted model. A case study with Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery in Shanghai, China was conducted to verify the validity of the method. The method performance was compared with those of the traditional PPI and 2-D scatter plots approaches. The results indicated that the tetrahedron-based endmember selection approach performed better in both accuracy and ease of identification for urban surface endmembers owing to the 3-D visualization analysis and use of the MOGA.

A brightness–darkness–greenness model for monitoring urban landscape evolution in a developing country – A case study of Shanghai

To monitor and model the evolution of urban landscapes, we develop a brightness–darkness–greenness (B–D–G) model. It is based on the vegetation–impervious surface–soil (V–I–S) model, proposed by Ridd (1995) to simplify urban environments to three basic ground components. The model integrates the knowledge of urban landscape composition and spectra of remote sensing. The B–D–G model is a fast and effective method to analyze urban landscape composition and its evolution based on remotely sensed images, by employing an explicit endmember evolution implication via the endmember spectrum dynamics. We verify this new method through in situ measurements of spectrum and high resolution images. Then, B–D–G model is used to detect the pattern and types of urban renewal. Despite some limitations, B–D–G model provides a new perspective of modeling urban dynamics and monitoring urban landscape evolution.

高原河谷城市植被时空变化及其影响因素-以青海省西宁市为例

高原型河谷城市具有特殊的自然地理与气候特征，生态环境脆弱，城市化引起的生态环境问题日益突出。植被作为其生态系统的载体，响应更加敏感。深入研究高原型河谷城市的植被变化及其影响因素，对促进西部大开发及城市化健康发展，建立良好的城市人居、生态环境具有重要的现实意义。西宁市作为典型的高原型河谷城市，植被覆盖在城市化与退耕还林（草）政策共同作用下变化明显。基于植被-不透水表面-土壤（V-I-S）模型，以西宁市城市规划区1995年与2009年两期landsat TM影像为数据源，利用线性光谱混合模型进行混合像元分解，获取研究区植被覆盖度的空间分布。通过整体分析、转移矩阵分析，格网分析等技术手段，研究植被时空变化特征并分别探讨川道与丘陵植被变化的影响因素。结果表明：研究期内，西宁城市规划区平均植被覆盖度维持在30%左右。2009年与1995年相比植被覆盖度出现下降，植被覆盖空间差异略有减小。在数量上，基本无覆盖、中覆盖、高覆盖等级呈增加趋势，低覆盖、全覆盖呈下降趋势。研究区西北部及西南部丘陵区植被覆盖整体趋于好转，主要由中低丰度植被等级变化而来，原因在于2000-2005年湟中县累计退耕还林（草）54.91km²，累计造林247.98km²，使研究区西北部植被覆盖等级提高，表明西宁市退耕还林（草）工程对于改善植被覆盖效果明显。同时丘陵区植被变化与气候影响趋势相同，表明其植被变化可能也受气候变化影响。城市扩展方向及强度对其周边植被覆盖的影响突出。市区快速扩张及农业退化使川道内中高丰度植被覆盖整体退化趋势明显。主要原因在于2000年后西宁进入快速发展期，城市用地规模迅速增大，川道内城市周边大量中高覆盖等级植被转变为基本无覆盖等级，造成植被退化。川道内城市区域植被变化与气候影响趋势相反，表明本文研究结果可能低估了城市化对川道内植被变化的影响幅度，相比气候影响，人为活动的影响更加强烈。研究区内植被覆盖等级的变化趋势为植被覆盖较差的等级（基本无覆盖和低覆盖）向高一级别发展，得益于退耕还林（草）工程；中等级别以上的覆盖等级出现一定程度的退化，尤其是位于川道中受到城市化干扰的区域植被退化问题尤为突出，需对这些区域采取植被保育措施，避免植被覆盖高等级区域受到城市化影响造成不可逆转的退化。;The geographical and climatic characteristics of Plateau Valley-City in the Western China are very typical and fragile. The urban ecosystems and environment have been damaged by urbanization recently years. Vegetation coverage plays a key role in the ecosystem of Plateau Valley-City, and it is very sensitive to urbanization. As a typical plateau Valley-City, Xining experienced rapid urbanization and implemented the policy of Converting Cultivated Land into Forest/Grass at the same time. Study on pattern dynamics of vegetation coverage of Xining urban plan zone and its causative factors is of great significance for promoting Western Development and urbanization, and contributes to creating a pleasant urban eco-environment in the Western China. Landsat TM data are the most economical and temporal continuous remote sensing images, however, vegetation pattern analysis on urban scale needs high resolution images. To resolve the problems and provide a more accurate result, Landsat TM data of 1995 and 2009 were unmixed by Linear Spectral Mixing Model (LSMM) in Xining plan zone to calculate proportion of vegetation cover, based on Vegetation-Impervious surface-Soil (V-I-S) model. Linear Spectral Mixture Model comprises five main processes: Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), end-members collection by n-D visualizer, linear spectral unmixing and accuracy test. End-members include vegetation, high albedo surface, low albedo surface and soil. By using statistical analysis, transfer matrix and grid analysis, we evaluated the pattern dynamic of vegetation coverage, and discussed the effect of urbanization, Converting Cultivated Land into Forest/Grass and climate change to vegetation changes in each Valley terrace area and hills area. The results indicate that: at overall level, the average vegetation coverage kept about 30% and showed a downtrend in the study period, meanwhile the regional differences had a little decrease. The areas of no vegetation coverage, moderate and high abundance vegetation coverage showed increasing trend, while the areas of low and full abundance vegetation coverage showed decreasing trend. The vegetation coverage in northwestern and southwestern area emerged in an increasing trend and it was mainly from low and moderate vegetation coverage. Huangzhong County is the main area affected by the policy, and finished Converting Cultivated Land into Forest/Grass and afforestation of 54.91km² and 247.98km² from 2000 to 2005. It suggested that the policy was very effective. Vegetation dynamics of hills areas may be affected by climate change because that they had same trend to the vegetation dynamics effects of climate change in Tibetan Plateau. Vegetation coverage around urban built-up area changed obviously along with urbanization direction and intensity. Areas of moderate and high abundance vegetation coverage presented a degenerate trend, especially in the urban built-up area influenced by urbanization. The main reason is that urban construction land rapid increased after 2000 and occupied moderate and high vegetation coverage. Effects of human activities on vegetation coverage may be more intense comparing to climate change because that their effect trends on vegetation are opposite. The changing trend of vegetation coverage grade can be summarized that low grades developed to high grades because of converting cultivated land into forest/grass and medium grades presented a degenerate trend because of urbanization. It is necessary to take measures to protect the vegetation around built-up area, and avoid irreversible degradation.

石羊河流域土地覆被空间演化及驱动机制分析

This article supported by remote sensing and GIS software.Using NDVI,DMSP-OLS and multi-temporal TM images covering the period 1998—2010 of Shiyang River Basin.We put forward the Land-Cover Index( LCI) based on the Vegetation-Impervious Surface-Soil( V-I-S) theory of urban land-cover system raised by Ridd and Calculate it.Then we use wavelet analysis,semivarigram and lacunarity analysis to Study the temporal and spatial scales of LCI.In order to find out the spatial distribution and evolvement rules of LCI in the last decades.Finally we attempt to utilize of the regression fit equation to identify the drive mechanism of influencing factors of land-cover change.The conclusions are obtained as follows: LCI distribution restricted by DEM in large area.The value of LCI high area( urban area) increasing,and the value of LCI low area( vegetation cover area) declining since 2002.That the level of regional urbanization significantly improved,vegetation cover slowly got better.And both of them gradually formed clusters.The level of regional urbanization greater impact by human factors,vegetation cover greater impact by natural factors simultaneously.

Investigation and comparison of land-cover change patterns in Xuzhou city, China, and Dortmund city region, Germany, using multitemporal Landsat images

Analyzing spatiotemporal characteristics of land-cover (LC) change is important for assessing environmental consequences of urban growth and supporting land management and planning. Less attention, however, has been given to the comparison between land-cover change patterns in developing and developed countries. In this study, Xuzhou city and Dortmund city region were selected as study areas. Multitemporal Landsat images were classified by using the integration method of maximum likelihood classifier, subpixel classifier, and multiple normalized difference vegetation index values based on Vegetation-Impervious Surface-Soil model. Urban growth patterns and processes of the two study areas were investigated and compared through land-cover change detection, buffer analysis, and jaggedness degree. The results indicated that the urban area in Xuzhou city increased more than threefold dramatically from 128.5 to 418.3 km 2 , and the increased sprawling development trend was observed during the study period, while Dortmund city region showed a slight increase from 498 to 715.5 km 2 in urban areas with an increasingly compact development trend. The results revealed a notable difference of spatiotemporal land-cover pattern dynamics between the two study areas as well as confirmed the effectiveness of the combined method of remote sensing and spatial analysis that can be used to support land management and policy decisions.

Spatiotemporal analysis of urban environment based on the vegetation–impervious surface–soil model

This study explores a spatiotemporal comparative analysis of urban agglomeration, comparing the Greater Toronto and Hamilton Area (GTHA) of Canada and the city of Tianjin in China. The vegetation–impervious surface–soil (V–I–S) model is used to quantify the ecological composition of urban/peri-urban environments with multitemporal Landsat images (3 stages, 18 scenes) and LULC data from 1985 to 2005. The support vector machine algorithm and several knowledge-based methods are applied to get the V–I–S component fractions at high accuracies. The statistical results show that the urban expansion in the GTHA occurred mainly between 1985 and 1999, and only two districts revealed increasing trends for impervious surfaces for the period from 1999 to 2005. In contrast, Tianjin has been experiencing rapid urban sprawl at all stages and this has been accelerating since 1999. The urban growth patterns in the GTHA evolved from a monocentric and dispersed pattern to a polycentric and aggregated pattern, while in Tianjin it changed from monocentric to polycentric. Central Tianjin has become more centralized, while most other municipal areas have developed dispersed patterns. The GTHA also has a higher level of greenery and a more balanced ecological environment than Tianjin. These differences in the two areas may play an important role in urban planning and decision-making in developing countries.

Urban land-use, land-cover classification through watershed segmentation in the V–I–S feature space

This article introduces an innovative approach using marker-controlled watershed segmentation (WS) in the Vegetation–Impervious Surface–Soil (V–I–S) feature space for urban land-use and land-cover (LULC) classification. The complement (e.g., the inverse) of the V–I–S feature space image shows depressions, which can be treated as topographic watersheds and they correspond to LULC classes. WS partitions the complement of V–I–S feature space image into LULC regions based on user-specified initial markers. These regions are then labelled with specific LULC classes, which lead to the subsequent LULC classification. The results showed that WS can achieve substantial classification accuracy. Compared to the traditional maximum likelihood classifier (MLC), our method requires less user input while improving the classification accuracy. A sensitivity test of the WS-based method on the location of the initial markers also is provided.

Towards a comprehensive evaluation of V-I-S sub-pixel fractions and land surface temperature for urban land-use classification in the USA

Remote-sensing image classification based on the vegetation–impervious surface–soil (V-I-S) model and land-surface temperature (LST) has proved to be more efficient in characterizing the urban landscape than conventional spectral-based classification. However, current literature emphasizes discussion of the classifier's accuracy improvement achieved by the input of V-I-S fractions and LST over conventional spectral-based classification while ignoring the stability evaluation. Hence, this study proposes an evaluation framework for exploring the superiority of the input features and the stability of classifiers by integrating statistical randomization techniques and a kappa-error diagram. The evaluation framework was applied to case studies for demonstrating the superiority of V-I-S fractions and LST in the context of urban land-use classification with five different types of classifiers, including the maximum likelihood classifier (MLC), the tree classifier, the Bagging classifier, the random forest (RF) and the support vector machine (SVM). It followed that the use of V-I-S fractions and LST (1) could alleviate the ‘salt and pepper’ effect; (2) is preferred by tree and tree-based ensembles for branch splitting; (3) could produce classification trees with less complexity; (4) could benefit the stability of classifiers in addition to the accuracy improvement; and (5) could allow histograms following nearly normal distribution in its feature space, which boosts the performance of MLC. It is shown that MLC becomes comparable with modern classifiers when trained with V-I-S fractions and LST combination. Because of its adequacy and simplicity, MLC is recommended for urban land-use classification when V-I-S fractions and LST are used as the only input features. However, replacing them with, or including, the band reflectance might degrade MLC. A direct use of spectral band reflectance is not recommended for any of the classification approaches being considered in this study, except for SVM, which is the most robust classifier as it has a consistently high performance for all the input feature combinations. We recommend using tree-based ensemble classifiers or SVM when V-I-S fractions and LST as well as the band reflectance are all used in the classification. The proposed evaluation framework can also be applied to the assessment of input features and classifiers in other remote-sensing classification endeavours.

Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince, Haiti

This research uses the most recent (2003) census data and a Landsat ETM+ image to build a population estimation model for Port-au-Prince, Haiti. The purpose of the study is to establish the linkage of population density with remotely sensed surface reflectance signals of an urban area, and use that to estimate population when census data are not available in a timely fashion. The research begins with deriving subpixel vegetation-impervious surface-soil (VIS) fractions derived from the Landsat ETM+ multispectral bands, and then uses the geographically weighted regression (GWR) model to examine how the variation of population density can be explained by the VIS variables and their derivatives. With comparison to the ordinary least square (OLS) model, the GWR model accounts for spatial non-stationarity in the relationship between population patterns and land characteristics in the study area. The study reveals that three VIS variables are significant in explaining population density: the mean value of houses fraction image, the mean value of vegetation fraction image, and the standard deviation of vegetation fraction image.

Extracting impervious surfaces from multi-source satellite imagery based on unified conceptual model by decision tree algorithm

Extraction of impervious surfaces is one of the necessary processes in urban change detection. This paper derived a unified conceptual model (UCM) from the vegetation-impervious surface-soil (VIS) model to make the extraction more effective and accurate. UCM uses the decision tree algorithm with indices of spectrum and texture, etc. In this model, we found both dependent and independent indices for multi-source satellite imagery according to their similarity and dissimilarity. The purpose of the indices is to remove the other land-use and land-cover types (e.g., vegetation and soil) from the imagery, and delineate the impervious surfaces as the result. UCM has the same steps conducted by decision tree algorithm. The Landsat-5 TM image (30 m) and the Satellite Probatoire d’Observation de la Terre (SPOT-4) image (20 m) from Chaoyang District (Beijing) in 2007 were used in this paper. The results show that the overall accuracy in Landsat-5 TM image is 88%, while 86.75% in SPOT-4 image. It is an appropriate method to meet the demand of urban change detection.