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During the last decades, synthetic aperture radar (SAR) image exploitation has matured with the launch of different satellite missions and the development of different techniques, which allow exploiting the capabilities of the radar images. Among these techniques, persistent scatterer interferometry (PSI) has proven to be a powerful tool to derive terrain deformations over urban areas. It is based on the use of a large number of images over wide areas in order to obtain terrain displacements time series. The imagery from the different SAR missions has led to an archive with data that covers up to 30 years in the past. Moreover, different methods and algorithms have been proposed in order to perform this complex task. In this line, this work aims at identifying if data from different missions and processed by different techniques can be combined in order to study the evolution of urban monitoring. Three different PSI techniques are used in order to process data from four SAR missions: European Remote Sensing (ERS)-1/2, Environmental Satellite, COSMO-SkyMed, and the recent Sentinel-1 A/B. The rapidly evolving urban area of Madrid, where numerous undergrounding works have been carried out in the last decade, has been chosen as the testing environment. The density of persistent scatterers, the deformation accuracy validated with GPS displacements and deformation trends are used as the key performance items for the assessment. [ABSTRACT FROM PUBLISHER]

In recent years, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) has ascertained great potential for the three-dimensional (3-D) reconstruction of observed scenes, especially in urban areas. However, the number of proceed snapshots (observations) is usually less than that of slant height samples (unknowns) in TomoSAR inversion processes. This impairs the quality of the reconstructed vertical information. To cope with this issue and improve the reliability of reconstructed vertical information, this paper investigates the possible potential of a deterministic descriptive regularization-based method. Deterministic descriptive regularization is a well-conditioned optimization framework based on the descriptive idea of a regularization solution. This strategy can help to mitigate the effect of the ill-posed problem. Thus, it can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. For this purpose, the result of the proposed strategy is compared with the outcomes from the standard TomoSAR techniques, including Beamforming, Capon, and Minimum Norm. The proposed method for reconstruction of the reflectivity function of the observed scene has been performed on a dataset acquired by the Sentinel-1 sensor in 2022 over Tehran City, Iran. The experimental results indicate that the proposed algorithm can estimate building heights with a vertical accuracy of better than 91%. These results demonstrate the great potential of the proposed method for reconstructing the full 3-D images of urban areas. [ABSTRACT FROM AUTHOR]

It is well known that multi-temporal series of synthetic aperture radar (SAR) data are able to provide interesting clues about temporal evolution of some land covers. In this work, a per-segment approach based on the use of ancillary geographic information system data is proposed. The results of a test case show the effectiveness of the technique and its usefulness for quickly detecting changes from rural to urban land uses and discriminating to some extent different urban dynamics. [ABSTRACT FROM AUTHOR]

Urban areas are continuously expanding, against the background of accelerating urbanization, while their correct detection might be useful in a wide range of applications in urban planning and environmental studies. The paper proposes the development of a new method for the detection of the built-up areas based on SAR (Synthetic Aperture Radar) data, combining two images on ascendant and descendant orbits, while keeping both polarizations. In the pre-processing operation, we have obtained 4 backscattering bands, 4 primary texture bands, and 12 secondary texture bands which were subsequently used for classification purposes. The method combines a classification of the SAR images with a texture analysis (Iso-Tex) by using spectral signatures and a separation threshold. The results were compared to those achieved in a supervised classification for two cities in Central and Eastern Europe: Cluj-Napoca, in Romania and Wroclaw, in Poland. The potential of the new Iso-Tex method has been proven by the transition to a higher accuracy level compared to the supervised classification. The main advantage of the method is the separation of classes generating detection problems to both radar and optical systems (bare soil, excessively humid soil areas, and the upper part of the slopes). [ABSTRACT FROM AUTHOR]

Accurate mapping of impervious surface in urban areas is of great demand in environmental and socio-economic studies since impervious surface growth is recognized as an indicator of urbanization. To demonstrate the potential of full polarimetric Synthetic Aperture Radar (SAR) in impervious surface detection in different urban areas, this study focused on the exploitation of only SAR data. Three cities with different levels of urbanization – Tehran, Kordkuy, and Arak – have been selected to reduce the effect of input data on achieved results. Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) images have been classified by support vector machine (SVM) with the help of training data from high-resolution satellite images. Quantitative assessment of classification accuracy revealed that Kordkuy, a not fully developed city (i.e. 84.2%) has the lowest accuracy and Arak, a medium urbanized city, has the highest accuracy (i.e. 90.0%). To further explore the efficiency of full polarimetric SAR, grey level co-occurrence matrix (GLCM) texture of polarized bands has been extracted and put into the classification procedure. The texture information of SAR data provided positive contribution to the impervious surface estimation in three study cases. The improvement is especially noted in dark impervious surface class. All three study areas show an increase of about 6–8% in classification accuracy. The results prove that single use of full polarimetric SAR images holds high potential in identifying impervious surfaces in urban areas. The findings are of great importance in frequent urban impervious surface mapping and monitoring especially in cloud-prone area, where the use of optical data as well as the fusion of optic and SAR data are limited. [ABSTRACT FROM AUTHOR]

With the advancement of the space-based imaging technology, large amounts of images of different modality and spatial, spectral, and temporal resolutions are available. However, it is clearly demonstrated that combining images from different sources improves results in remote-sensing applications, it still remains a challenge to make full use of their benefits. These challenges become complicate, extending to multitemporal analysis and very high resolution (VHR) images, which includes different acquisition conditions, and consequently radiometric and geometric differences between images. To design methods that are robust to data set shifts, recent remote-sensing literature has considered solutions based on domain adaptation (DA) approaches. In this research, we aim to propose a method based on autoencoder which is a DA method to achieve fused features of synthetic aperture radar (SAR) and optical to benefit from their complementary information, and simultaneously aligning multitemporal features by reduction in spectral and radiometric differences and make multitemporal features more similar for better accuracy in change detection (CD). In continuation, to cope with the problem of detecting changes using VHR images, we introduce a framework to obtain the change map, in which, the differences caused by the registration error as well as the geometric differences of VHR images will be minimized, by maximum use of spatial information of images. For these purposes, a multitemporal pair of co-registered images, each composed of optical multispectral channels and a SAR amplitude channel acquired over the same urban area before and after changes, is assumed. Finally, the strategy of detecting changes resulted in improvement compared to common and simpler methods and also individual sensor CD method, which demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This study uses the ray tracing method to simulate synthetic aperture radar (SAR) images of urban areas. The images are constructed for polarisations: horizontal-horizontal (HH) and vertical-vertical (VV), and different types of buildings, vegetation, and streets. Simulated images of a given area are compared with real SAR images of the same area acquired by the TerraSAR-X satellite. The simulations use the measured backscatter coefficient for HH and VV polarisations and for five different classes of terrain: houses, trees, shrubs, grass, and ground. For multiple reflections, we apply the generalized bistatic Lambertian model. The results show that, despite the limits of the ray tracing method and the approximations involved in modelling three-dimensional objects in the simulated scene, the simulated SAR images correspond well with the actual scene. All features present in the real image are reproduced in the simulated image; in particular, the double reflections of buildings and the surrounding ground appear clearly. However, discrepancies exist, and these are also discussed. [ABSTRACT FROM PUBLISHER]

QuikSCAT backscatter is generally higher over urban areas than surrounding vegetated areas. Azimuthal anisotropy has been observed over some urban areas, but the strength of the azimuthal anisotropy in the urban backscatter signal has not been well quantified. This study investigates radar azimuthal anisotropy in urban areas. QuikSCAT L1Bσ0observations are compared for urban, non-urban, and uninhabited regions to identify the magnitude and possible causes of anisotropic responses. The possible cause of azimuthal variations (AVs) in the data is the presence of corner reflectors, resulting from urban infrastructure and land use, including buildings, roads, and road structure. Backscatter characteristics for each urban area are shown to be closely related to road orientation and organization. Each region is found to have a unique backscatter signal and azimuthal response. [ABSTRACT FROM AUTHOR]

In the capitalist rush to attract more visitors, cities are committing significant resources to heritage conservation, driven by the substantial economic benefits generated by the tourism industry. However, less famous or less well-resourced cities, often with smaller populations, also known as intermediary cities, find it difficult to allocate funds to protect their most significant heritage sites. In this conservation context, intermediary cities, often on the periphery or 'at the margins', can fill the gaps and needs of urbanism through a better strategic understanding of the challenges of global touristification, thus this research provides urban planning tools for local governments with limited resources to preserve their architectural heritage through remote sensing, for its advantages in terms of lower economic cost, as a valuable monitoring tool to effectively identify high-vulnerability sites that require priority attention in the conservation of architectural heritage. In other words, it allows for a reduction in the territory of those areas located 'at the margins' in terms of urban planning and management, by approaching the territorial, urban, architectural and tourism problems from a transdisciplinary perspective in the preservation of the architectural heritage. This study explores the application of optical (Sentinel-2) using neural networks for classifying the land cover and radar (Sentinel-1 and PAZ) satellite images to obtain the ground motion as a geotechnical risk study, together with geospatial data, for the monitoring of architectural heritage in intermediate cities. Focusing on the districts of Bragança and Guarda in Portugal, the approach allows the direct identification of vulnerable architectural heritage, identifying 9 highly-vulnerable areas using PAZ data and 7 areas using Sentinel-1 data. Furthermore, this work provides an understanding of the potential and limitations of these technologies in heritage preservation because compares the processing results of freely accessible medium-resolution Sentinel-1 radar imagery with the high-resolution radar images from the innovative PAZ satellite. [ABSTRACT FROM AUTHOR]

In connection with the detection of various spatial- and temporal-scale ground settlements, an integrated persistent scatterer interferometry (PSI) approach is discussed using multi-source, multi-temporal, and multi-resolution synthetic aperture radar (SAR) data. Based on the comprehensive analysis of characteristics of available radar sensors, two remote-sensing SAR data sets were selected: 1 m resolution X-band TerraSAR-X and 10 m resolution L-band Advanced Land Observing Satellite (ALOS) phased array L-band SAR. ‘Tianjin Binhai New Area’ has become one of the most important economic centres in China, and one of its fast-developing urban areas, Tanggu, was selected as the study area. PSI processing was conducted on both data sets. Substantial validation was performed for PSI results from both data sources using levelling measurement. The overall good agreement confirmed the ground deformation maps derived from both data sets. Integration of PSI results appears to be a potentially significant contribution to solving the problems related to common spatial and temporal gaps when using single-type data sets. Application of both data sets revealed the capability of integrated PSIs to measure ground deformation with strong temporal and spatial variation, thereby improving the interpretation of ground deformation characteristics which increases the confidence of hazard assessment and provides some insight into complex underlying mechanisms. [ABSTRACT FROM PUBLISHER]

Synthetic aperture radar (SAR) has often been used in earthquake damage assessment due to its extreme versatility and almost all-weather, day-and-night capability. In this article, we demonstrate the potential to use only post-event, high-resolution airborne polarimetric SAR (PolSAR) imagery to estimate the damage level at the block scale. Intact buildings with large orientation angles have a similar scattering mechanism  to collapsed buildings; they are all volume-scattering dominant and reflection asymmetric, which seriously hampers the process of damage assessment. In this article, we propose a new damage assessment method combining polarimetric and spatial texture information to eliminate this deficiency. In the proposed method, the normalized circular-pol correlation coefficient is used first to identify intact buildings aligned parallel with the flight direction of the radar. The ‘homogeneity’ feature of the grey-level co-occurrence matrix (GLCM) is then introduced to distinguish building patches with large orientation angles from the severely damaged class. Furthermore, a new damage assessment index is also introduced to handle the assessment at the level of the block scale. To demonstrate the effectiveness of the proposed approach, the high-resolution airborne PolSAR imagery acquired after the earthquake that hit Yushu County, Qinghai Province of China, is investigated. By comparison with the damage validation map, the results confirm the validity of the proposed method and the advantage of further improving the assessment accuracy without external ancillary optical or SAR data. [ABSTRACT FROM PUBLISHER]

Differential Interferometric Synthetic Aperture Radar (DIFSAR) data have been integrated in a Geographic Information System (GIS) for investigating deformations occurring in urban areas. The proposed approach is based on an extension of the Small Baseline Subset (SBAS) method that allows a proper combination of a large number of DIFSAR data. The obtained deformation measurements are accurately geocoded to achieve an easy merging of DIFSAR products relative to different acquisition geometries and the integration of such products into a GIS. This allows the detection and analysis of displacements of single structures and buildings in the investigated zone. The effectiveness of the approach has been tested on the SAR data acquired by the European Remote Sensing (ERS) satellites relative to the Vomero hill, a district of the city of Naples, Italy. [ABSTRACT FROM AUTHOR]

In this paper we analyse a multiple sensor dataset corresponding to three-dimensional data coming from interferometric radar (InSAR) or laser ranging (LIDAR) measurements. We consider digital elevation models (DEMs) extracted from a single LIDAR scan plus multiple SAR scans of the same area, downtown Denver, CO, USA. Horizontal resolution for both datasets is 2.5 m, a value allowing good characterization of sparse tall buildings. Fusion of DEMs extracted from InSAR data originated during flights orthogonal to each other allows reduction of layover and shadowing. A novel strategy combining advantages of existing fusion techniques is proposed. Results from individual and combined techniques are presented, compared and discussed. The ability of characterizing buildings allows us to raise the fusion strategy scope from the pixel-level up to the feature level, once 3D features are extracted. Related results are presented and discussed. Finally, fusion of InSAR and LIDAR data is considered. LIDAR can reliably determine building footprints, thus relieving the problem of multiple bouncing radar pulses. Some results show that analysis of combined InSAR and LIDAR data on a small area can provide an improvement in DEMs extracted from a much larger area where only InSAR data are available. [ABSTRACT FROM AUTHOR]

In this study, the adverse effects of the Marmara earthquake of 17 August 1999 on the land use/land cover (LULC) of Kocaeli province in Turkey were analysed by integration of ERS-2, LANDSAT TM, SPOT-4 and IRS-1C data of varying spectral and spatial resolutions with ground data such as geological maps, soil maps and elevation data. The study was carried out in three main stages. The first stage consisted of studies on the deformation of the Earth's crust, damage in the urban texture and changes to the coastline through analysis of satellite data before and after the earthquake. Earth crust deformations resulting after the earthquake were mapped by synthetic aperture radar (SAR) data using a SAR interferometer. Damage in urban centres and coastline changes were assessed by analysis of SPOT and LANDSAT imageries. IRS data after the earthquake were used for the current LULC map of the entire province. The second stage involved settlement suitability analysis by multicriteria evaluation (MCE) methodology taking into account the geological formations, land cover, soil classes, slope, main transportation network, and urbanization status of Kocaeli province. In the third stage, the results obtained through the settlement suitability analysis were interpreted by the information generated through satellite imageries. [ABSTRACT FROM AUTHOR]

Urban areas are increasing since several years as a result of development of built-up areas, network infrastructure, industrial areas or other built-up areas. This urban sprawl has a considerable impact on natural areas by changing the functioning of ecosystems. Mapping and monitoring Urban Fabrics (UF) is therefore relevant for urban planning and management, risk analysis, human health or biodiversity. For this research, Sentinel-2 (level 2A) single-date images of the East of France, with a high spatial resolution (10 m), are used to assess two semantic segmentation networks (U-Net) that we combined using feature fusion between a from scratch network and a pre-trained network on ImageNet. Moreover three spectral or textural indices have been added to the both networks in order to improve the classification results. The results showed a performance gain for the fusion methods in classifying several UF. However, there is a difference in performance depending on the urbanization gradient; highly urbanized areas provide a better distinction of some UF's classes than rural areas. [ABSTRACT FROM AUTHOR]

The DInSAR technique with a multibaseline is becoming popular nowadays to investigate slow urban deformation. In this paper, we focus on deformation retrieval in large areas, including urban and suburban areas. Based on the multibaseline DInSAR algorithm proposed by Mora, three extensions are derived. First, least-squares adjustment and error-controlling methods are used to obtain stable deformation velocity and height error estimations. The least-squares QR factorizaiton algorithm is emphasized to solve large, linear, and sparse functions. Second, a new complex network is presented to limit noise effects on the Delaunay triangular network. Third, by combining complex and Delaunay networks, large-area deformation is investigated, from centre urban areas to suburban areas. The enhanced algorithm is performed to investigate the subsidence of Cangzhou, Hebei province (northern China) during 1993-1997 by using 9 ERS SLC data. The experimental results show serious subsidence in the region and are validated by levelling data and groundwater wells data. Compared with levelling data, the estimation errors of linear deformation velocity in urban areas are in the range of (-2, 2) mm year-1, and in suburban areas, the errors are in the range of (-26, 15) mm year-1, which is sufficiently feasible to determine the status of subsidence relative to the maximum deformation velocity of about -100 mm year-1. The subsidence centres in urban areas are consistent with the spatial distribution of groundwater wells, which provides evidence that groundwater overexploitation is the main cause of subsidence in Cangzhou. The closure of wells will be a good way to control subsidence in the future. [ABSTRACT FROM AUTHOR]

The recent global urban expansion has seen a massive decline and loss in the connectivity of urban vegetation. In urban areas, some vegetation patches have increasingly become isolated and less connected by a matrix composed of impervious surfaces and transportation networks like roads. Vegetation fragmentation is a global threat to the remaining urban green spaces and has an impact on biodiversity conservation, environmental quality and urban microclimates. So far, a lot of work has been done on mapping and monitoring urban green spaces, some using conventional methods and of late using remotely sensed data. However, not much is known or well documented on the new developments in the remote sensing of vegetation fragmentation in urban areas over the last two decades. Thus, the objective of this research work is to present a detailed and comprehensive synthesis of the progress of remote sensing in assessing and monitoring landscape structure of urban green spaces and vegetation fragmentation. Specifically, scientific literature from the year 2000 to 2020 was reviewed to provide a state-of-the-art progress on the remote sensing of vegetation fragmentation in urban areas. Results indicate that between 2000 and 2020, there was a considerable increase in the number of scientific publications on vegetation fragmentation in urban landscapes. The discrete landscape pattern indices are the most widely used method. Comparatively, Land Remote Sensing Satellite System (Landsat) data was widely used due to its suitable spatial and temporal resolution, free availability and the presence of historical archival data that spans over a period of 40 years. The most commonly used scale was local (a city and/or municipality) followed by regional (more than one municipality in one continent) and then global (several selected cities and urban areas across continents). Only two studies were conducted at the global level. Further, geographic bias was observed in most of the accessed vegetation fragmentation studies. The review showed that vegetation fragmentation studies were done mostly for cities in China, North America and Europe while cities in most parts of Africa, Asia, Eurasia, Oceania and South America have not been comprehensively studied. The study also showed that the majority of cities across the globe have experienced severe vegetation fragmentation over the years. This review underscores the relevance of scientific findings in urban and spatial planning to minimize the loss of urban green spaces and to conserve and restore affected areas. [ABSTRACT FROM AUTHOR]

Traffic estimation from very-high-resolution remote-sensing imagery has received increasing interest during the last few years. In this article, we propose an automatic system for estimation of the annual average daily traffic (AADT) using very-high-resolution optical remote-sensing imagery of urban areas in combination with high-quality, but very spatially limited, ground-based measurements. The main part of the system is the vehicle detection, which is based on the deep learning object detection architecture mask region-based convolutional neural network (Mask R-CNN), modified with an image normalization strategy to make it more robust for test images of various conditions and the use of a precise road mask to assist the filtering of driving vehicles from parked ones. Furthermore, to include the high-quality ground-based measurements and to make the traffic estimates more consistent across neighbouring road links, we propose a graph smoothing strategy that utilizes the road network. The fully automatic processing chain has been validated on a set of aerial images covering the city of Narvik, Norway. The precision and recall rate of detecting driving vehicles was 0.74 and 0.66, respectively, and the AADT was estimated with a root mean squared error (RMSE) of 2279 and bias of −383. We conclude that separating driving vehicles from parked ones may be challenging if vehicles are parked along the roads and that for urban environment with short road links several remote-sensing images covering the road links at different time instances are necessary in order to benefit from the remote-sensing images. [ABSTRACT FROM AUTHOR]

The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical in efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal settlement areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorized according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a processing chain approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels. [ABSTRACT FROM AUTHOR]

Change detection (CD) from remote sensing data is a very challenging research problem, especially when we analyse an urban scene. Urban scenes are composed of many different types of objects, both natural and man-made. The building class is one of the important and most complex classes to analyse, important because it is useful for so many applications and complex because it exhibits many changes due to human activity and natural catastrophes. For these reasons, we focus our study on building change detection (BCD). In this paper we propose a classification scheme for BCD research according to several important dimensions including objective, input data, temporal resolution, analysis unit, target output unit, building features, processing technique, change categories, and assessment of results. This classification scheme can guide practitioners in choosing appropriate change detection methods to achieve their goals as well as informing new research efforts. Based on this multidimensional characterisation of BCD, we offer a number of suggestions for further work to be done in this field. [ABSTRACT FROM AUTHOR]

Urbanization proceeds currently at a rapid pace and the impact on natural ecosystems cannot be neglected. Consequently, it is important to be able to monitor the expansion of urban areas. Yet the process of extracting them from satellite imagery is not trivial. Urban is a non-uniform class with spectral proximity to barren land. In this article, a method for extracting urban areas from medium-resolution Earth observation data is presented. The information source is simulated data of the PROBA-V sensor. Visual and near-infrared bands are classified by the adaptive neuro-fuzzy inference system (ANFIS) neuro-fuzzy classifier into urban and non-urban classes. The method can overcome the main difficulty in similar efforts, i.e. the extensive commission errors of barren to the class urban. The main novelty relies on exploiting annual spectral variability of each land-use class at the pixel level. The basic assumption is that urban and barren areas may have similar spectral values but they have different phenological cycles. The overall accuracy obtained by the classification is 91.57% with a Cohen’s kappa coefficient (khat) of 0.84. Sufficient correlation at the city level is also achieved. Change detection is also possible in terms of hot-spot identification, however marginally suitable for medium-sized cities. [ABSTRACT FROM PUBLISHER]

This article presents a new approach to derive fine-scale socioeconomic information of urban areas using very high resolution satellite data. The rationale behind the method is to use high resolution satellite data, capable of resolving urban morphology details, to derive a classification of the image. Thus, it is assumed that there is a relationship between the socioeconomic profile and the urban morphology of an area in terms of availability of green areas, sport facilities, private swimming pools or pavement conditions. The method is tested using a case study of Lima, Peru. Using a sample of ground data, a neural network classifier was applied to a pre-classified image in which entropy had been used to mask extensive, non-built up areas that would otherwise have inserted spurious information into the classifier. The result shows a high correlation (0.70 R 2) when compared with validation data. The good performances also show that a physiographic satellite view of the city reflects the socioeconomic layout of their inhabitants, thus making remote sensing a complementary tool for social research and urban planning. While the parameterization of the problem may differ from one area to another, it is shown that an a priori choice of a few parameters may help to automatically characterize large areas in social terms, thus allowing social inequality and its evolution to be mapped in those areas with limited availability of data. In order to make the method widely applicable, the possibilities and limitations of applying the procedure to other large cities are discussed. [ABSTRACT FROM AUTHOR]

This article presents original stepwise image processing providing spatial delimitation of urban areas from Google Earth images in West Africa. In such images, converted to grey level images, urban areas are distinguished from their surroundings thanks to a specific textural aspect that may be retrieved by means of relevant morphological filters. Extraction of urban patterns consists of image analysis based on methods of mathematical morphology. The morphological filters used in the stepwise processing are chosen according to the spatial resolution, the visual quality of the original images and the environmental characteristics of the towns under study. Results are then compared with spatial data provided by a public geographical database. [ABSTRACT FROM AUTHOR]

Urbanization in most developing countries is gradually increasing. Quantitative estimation of urbanization to a standardized level is crucial for analysing the relation between urbanization and other factors, since urban growth affects diverse and complex natural and socio-economic phenomena. Spatially and spectrally, remotely sensed imageries covering urban areas display different characteristics from those of rural or mountainous areas. A three-dimensional discrete wavelet transform (3D DWT) scheme is regarded as useful for analysing both spatial and spectral information. In addition, the energy parameter of wavelet coefficient by 3D DWT indicates channels regarding dominant spatial and spectral frequency, extracted from the original image sets. Based on these spatial and spectral features and the energy parameter of 3D wavelet coefficients, three kinds of urban complexity index are proposed. Of the three indices, the third index is the most useful to quantitatively interpret urban complexity. These indices can be used for a basis to analyse the relationship between feature groups within urban environments. [ABSTRACT FROM AUTHOR]

Remote-sensing change detection based on multitemporal, multispectral, and multisensor imagery has been developed over several decades and provided timely and comprehensive information for planning and decision-making. In practice, however, it is still difficult to select a suitable change-detection method, especially in urban areas, because of the impacts of complex factors. This paper presents a new method using multitemporal and multisensor data (SPOT-5 and Landsat data) to detect land-use changes in an urban environment based on principal-component analysis (PCA) and hybrid classification methods. After geometric correction and radiometric normalization, PCA was used to enhance the change information from stacked multisensor data. Then, a hybrid classifier combining unsupervised and supervised classification was performed to identify and quantify land-use changes. Finally, stratified random and user-defined plots sampling methods were synthetically used to obtain total 966 reference points for accuracy assessment. Although errors and confusion exist, this method shows satisfying results with an overall accuracy to be 89.54% and 0.88 for the kappa coefficient. When compared with the post-classification method, PCA-based change detection also showed a better accuracy in terms of overall, producer's, and user's accuracy and kappa index. The results suggested that significant land-use changes have occurred in Hangzhou City from 2000 to 2003, which may be related to rapid economy development and urban expansion. It is further indicated that most changes occurred in cropland areas due to urban encroachment. [ABSTRACT FROM AUTHOR]

An object-based approach was utilized in the development of two urban land-cover classification schemes on high-resolution (0.6 m), true-colour aerial photography of the Phoenix metropolitan area, USA. An initial classification scheme was heavily weighted by standard nearest-neighbour (SNN) functions generated by samples from each of the classes, which produced an enhanced accuracy (84%). A second classification was developed from the initial classification scheme in which SNN functions were transformed into a fuzzy-rule set, creating a product transportable to different areas of the same imagery, or for land-cover change detection with similar imagery. A comprehensive accuracy assessment revealed a slightly lower overall accuracy (79%) for the rule-based classification. We conclude that the transportable classification scheme is satisfactory for general land-cover analyses; yet classification accuracy can be enhanced at site-specific venues with the incorporation of nearest-neighbour functions using class samples. [ABSTRACT FROM AUTHOR]

Results obtained during two campaigns (summer 2004 and autumn 2005) of observation of the planetary boundary layer dynamics over the Sofia city urban area are presented. An EARLINET scanning aerosol lidar, an ozone analyser and a ground meteorological station were used during the observations. The stable boundary layer height varied from 200 m to 600 m during the different seasons. The residual layer was found to be at 700-1200 m, being destroyed between 10:30 and 12:30 LST. The mixing layer developed up to heights of about 800-1300 m. The ground level ozone concentration was measured to be from 10 µg/m3 to 90 µg/m3. The convective boundary layer formation in three case studies (a clear sunny day, a partial solar eclipse, and in the presence of internal atmospheric gravity waves) are presented. In particular, the mixing layer development and the residual layer destruction are studied and considered, along with the relevant ground level ozone concentration variation. [ABSTRACT FROM AUTHOR]

It is usually quite difficult to extract and recover shadow information in the urban environment from remote sensing imagery. This paper describes the study of precisely detected shadow in satellite images and recovering information from the surface covered in shadow from very high resolution (VHR) satellite imagery. [ABSTRACT FROM AUTHOR]

On 17 August 1999 at 3:02 a.m. local time the Izmit earthquake occurred on the North Anatolian Fault Zone (NAFZ) in north-west Turkey, This earthquake caused considerable damage in the urban areas of Izmit, Adapazari (Sakarya), Golcuk and Yalova. This study used three different data sources to estimate the proportion of Adapazari that contained collapsed buildings: (i) government statistics on the number of collapsed buildings: (ii) the difference between pre- and post-earthquake land cover estimated from classified SPOT HRVIR XI images; and (iii) land cover estimated from density-sliced SPOT HRVIR Panchromatic image recorded after the earthquake. The results were similar at 16%, 16.1% and 15.5%, respectively. These were all slight overestimates; however, the remotely sensed estimates provided the spatial context of building collapse and in doing so highlighted areas of previously uncontrolled building. [ABSTRACT FROM AUTHOR]

A study is presented in which several representations of polarimetric SAR data were evaluated for the purpose of obtaining land use classification. Two methods comprising visual interpretation and an automatic procedure were used. For the study, fully polarimetric SAR data with a resolution of 3 m were obtained with the Dutch PHARUS sensor from a test area in the Netherlands showing various classes of land use. The land use classes consisted of bare soil, water, grass, urban areas, and forest. The visual inspection was performed by different groups of non-expert interpreters for each representation. It was found that people are quite successful by visual interpretation in performing land use classification using SAR data. Multi-polarized data are required for this purpose, although these data need not be fully polarimetric, since the best results were obtained with the hh - and hv -polarization combinations displayed in the red and green colour channels. The results show that land use classification by visual inspection is more effective than the automatic classification procedure. [ABSTRACT FROM AUTHOR]

Mapping urban areas at the regional and global scales has been used in ecology, environment, sociology, and other subjects. Recently, it has become increasingly popular to extract urban areas from night-time light remote-sensing data. In this article, we reported an alternative method to extract information of urban areas from VIIRS Day/Night Band (DNB) and MODIS normalized differential vegetation index (NDVI) data based on the adaptive mutation particle swarm optimization (AMPSO) algorithm and the Support Vector Machine (SVM) classification algorithm. This method was validated using the urban areas of nine Chinese cities classified from Landsat Enhanced Thematic Mapper (ETM+) images by object-oriented image classification technology. We demonstrated that this new method for urban area extraction had a good classification coherency with the Landsat8 OLI result. In addition, it is more robust than other classification methods, and can be used to characterize the inter-urban texture as well. [ABSTRACT FROM AUTHOR]