Description: Peri-urban areas are the extension of cities into contiguous areas, where households and farms coexist. Carbon stocks (CSs) assessment, a concept here extended to urban features, has not yet been studied in depth over peri-urban areas due to uncertainties in such CSs quantification, level of detail required about construction materials, and the high spatial variability of those stocks. Remote sensing (RS)-based techniques have been successfully utilized in urban areas for assessing phenomena such as soil sealing, sprawl patterns, and dynamics of surface imperviousness, especially focusing on land cover classification at high to medium spatial scales. Over the floodplain study area of Emilia-Romagna region (Italy), we compared mapping products derived from Landsat multiseasonal data with different CSs, in soils and impervious surfaces, such as buildings and roads. A multiscale correlation analysis and regression assessment between CSs layers and satellite products were run at different grid cell sizes (100, 250, 500, and 1000 m). Results show that RS products from processing of mid-resolution satellite data can effectively perform well enough to estimate CSs in peri-urban areas, especially at 500–1000 m scale. Urban Fraction Cover method, derived through weighting urban land cover classes (including dense, sparse, and industrial urban features) can represent a good proxy of the ratio of anthropogenic over natural CSs ( ${hbox{R!}^2}$ up to 0.75). Imperviousness Index (II) product scored high positive correlation with CSs over built-up areas ( ${hbox{R!}^2}$ up to 0.77), and strong negative correlation with organic carbon density in soil ( ${hbox{R!}^2}$ up to 0.73).

Description: An important condition for urban studies is quantitative acquisition of spatial information, e.g., three-dimensional (3-D) structural parameters of buildings. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation along azimuth, range, and elevation coordinates. For a small number of acquisitions and their irregular spacing, however, the common Fourier-based 3-D SAR-focusing approaches, even compressive sensing (CS)-based methods, bring about some imaging quality problems. This paper addresses the 3-D imaging of buildings based on the framework of CS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at C-band. In this paper, we propose a new distributed CS-based FP MB-InSAR tomography method (FP-DCS TomoSAR method), which is a new polarimetric spectral analysis method based on distributed compressive sensing (DCS), model order selection, and maximum-likelihood parameter estimation for 3-D structural parameter reconstruction. Compared to the CS method, the FP-DCS TomoSAR method takes advantage of the intersignal correlations between neighboring azimuth-range pixels as well as between polarimetric channels to attain higher superresolution imaging and elevation estimation accuracy. Numerical results on simulated and real data validate the effectiveness of this novel technique by using FP C-band data acquired by Radarset-2 and are compared with the FP noise subspace fitting estimator.

Description: Visual interpretation of built-up areas in high-resolution (HR) SAR images usually relies on the strong backscattering behavior and the heterogeneity. The bright pixels characterizing the built-up areas can be easily identified, whereas the built-up pixels appear in medium and low intensity may be missed. The coefficient of variation traditionally provides a way to discriminate heterogeneous and homogeneous regions. The analysis of the statistics of the coefficient of variation on different land cover classes allows us to construct two membership functions (MFs) representing the built-up class and non-built-up class, respectively. Then, each pixel is attached with a membership degree to either of the two classes computed by using the established MFs. At this level, the amplitude of the SAR image is transformed into the intuitionistic fuzzy sets (IFSs). The context texture information is specified by the labeled similarity matrix (LSM) computed using the IFSs operators. Since different IFSs operators are used, the proposed technique can be implemented in different ways. The proposed techniques were tested on a collection of 11 images selected from TerraSAR-X images acquired on Nanjing (China) and a COSMO-SkyMed image acquired on Hangzhou (China). By comparison with the existing techniques, the effectiveness of the proposed techniques in identifying and detecting built-up areas was confirmed.

Description: In China, rapid urbanization has increased the demand for urban land and intensified the conflict between limited land resources and urban development. In response, high urban density has been proposed to realize sustainable urban development. Achieving this goal requires an examination of the dynamics of urban density in China. Nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) are a good indicator of human activity. We applied NTL data to measure urban density in 70 major cities in China during 1992–2010. Based on temporal changes in NTL, we identified seven classes of urban density and clustered the distributions of urban density in 70 cities into six types. The dynamics of urban density were then obtained from the GDP density as an index of city development. The curves of urban density distribution gradually changed from a concave increase to W-shaped and S-shaped to a concave decrease, indicating that the current urban land use in China is unsustainable and that the shortage of land resources must be addressed. An examination of the distribution of urban density in Hong Kong revealed a different pattern and a potential solution for cities in mainland China.

Description: In this paper, we present a novel combination of object features to both match buildings from predisaster images to shapes in a postdisaster image and assess damage on those buildings. These features include scale profile ratios extracted from a tree of shapes representation of the original image as well as texture features. A supervised classifier is used to classify building damage into three representative classes tied to the European Macroseismic Scale (EMS-98). The method is compared to visual inspection results as well as other automated methods. Results clearly show the benefits of our method for fast crisis mapping applications with few human inputs required.

Description: Urban areas are often characterized by geometrically simple and repetitive patterns, in particular, in cases where settlements have been built-up from scratch in a well-planned manner, e.g., according to architectonic, economic, or sociopolitic constraints. This leads to preferred rectangular and regular alignment of objects like windows or balconies at façades for the majority of buildings in modern cities. In this paper, we show how this regularity can be exploited for the challenging task of synthetic aperture radar (SAR) scene description and demonstrate the applicability in case studies. We present a virtually parameter-free method to segment a persistent scatterer point cloud and fit optimal lattices to describe separate façades. Formulating the PS as nodes in a graph allows us to use spectral graph theory to distinguish lattices even when they are overlapping or disturbed due to layover. As a result, we obtain an object-based representation of the SAR data, which allows for many new applications in the field of building monitoring and change detection in urban areas.

Description: In this paper, a methodology which allows automated and efficient reconstruction of three-dimensional (3-D) geometric building models from an Airborne Laser Scanning (ALS) point cloud is introduced and its performance is analyzed and evaluated. The proposed method avoids abnormal and/or infinite solutions which are typically encountered in previously published methods that use the rooftop primitive adjacency matrix to solve the critical rooftop vertices. In particular, first, an improved random sample consensus (RANSAC) algorithm is proposed to segment the rooftop primitives, i.e., the planar patches that constitute rooftops, of each building or group of connected buildings. The algorithm successfully maintains topological consistency among primitives and avoids under- and over-segmentation with high efficiency. Second, a novel Voronoi-based primitive boundary extraction algorithm under constraints of outer and inner building boundaries is introduced in order to extract each primitive boundary. In this algorithm, the adjacent segmented primitive relationships among the various primitives are preserved by a subgraph of the Voronoi diagram so that the reconstructed neighbor primitives are seamlessly connected. Third, in order to refine the boundary shapes of primitives with irregular geometry, various criteria for making the boundary adjustments more effective are proposed. In this way, more regular 3-D buildings can be produced. Finally, the primitive boundary simplification criteria are formally introduced to generate compact 3-D building models. By using the simplification criteria, nonadjacency between neighbor primitives, intersection between boundaries, and self-intersections are, to a great extent, avoided. Numerous experimental results obtained using multiple data sets, including data from the cities of Toronto and Enschede as well as from the Niagara area, have shown that the proposed methodology has excellent performance and it can produce watertight 3-D po- yhedral building models.

Description: In this work, we present a new semi-supervised strategy for obtaining finer spatial resolution urban maps from coarser resolution satellite data. Our method first uses a coarse resolution map as a source of training data. Then, we use semi-supervised learning in order to refine the set of initial (labeled) training samples by the inclusion of additional (reliable) unlabeled samples at the finer resolution level, in fully automatic fashion. The new unlabeled samples are automatically generated by our proposed methodology, which only requires a limited number of initial labeled samples for initialization purposes. Then, we conduct land cover classification (at the finer spatial resolution level) using a probabilistic multinomial logistic regression (MLR) classifier—in both supervised and semi-supervised fashion—by considering different numbers of labeled and unlabeled samples. In order to exploit spatial information, we use a Markov random field (MRF)-based postprocessing strategy to refine the obtained classification results. In order to test our concept, we use a global dataset: the European Space Agency’s GlobCover product, as the coarser resolution map (300-m spatial resolution). Our experimental evaluation is further conducted using Landsat data (30-m spatial resolution) collected over three different locations in the city of Sao Paulo, Brazil, and over two different locations in the city of Guangzhou, China. We obtain promising results in the generation of finer resolution urban extent maps using very limited training samples, derived in all cases from the GlobCover product. These experiments suggest the potential of GlobCover to provide reliable training data in order to support mapping of urban areas at a global scale.

Description: In remote sensing change detection, Markov random field (MRF) has been used successfully to model the prior probability using class-labels dependencies. MRF has played an important role in the detection of complex urban changes using optical images. However, the preservation of details in urban change analysis turns out to be a highly complex task if multitemporal SAR images with their speckle are to be used. Here, the ability of MRF to preserve geometric details and to combat speckle effect at the same time becomes questionable. Blob-region phenomenon and fine structures removal are common consequences of the application of traditional MRF-based change detection algorithm. To overcome these limitations, the iterated conditional modes (ICM) framework for the optimization of the maximum a posteriori (MAP-MRF) criterion function is extended to include a nonlocal probability maximization step. This probability model, which characterizes the relationship between pixels’ class-labels in a nonlocal scale, has the potential to preserve spatial details and to reduce speckle effects. Two multitemporal SAR datasets were used to assess the proposed algorithm. Experimental results using three density functions [i.e., the log normal (LN), generalized Gaussian (GG), and normal distributions (ND)] have demonstrated the efficiency of the proposed approach in terms of detail preservation and noise suppression. Compared with the traditional MRF algorithm, the proposed approach proved to be less-sensitive to the value of the contextual parameter and the chosen density function. The proposed approach has also shown less sensitivity to the quality of the initial change map when compared with the ICM algorithm.