Xinwu Li

A New Approach to Collapsed Building Extraction Using RADARSAT-2 Polarimetric SAR Imagery

Large-scale earthquakes severely damage peoples lives and properties. Airborne and spaceborne remote sensing can be used accurately and effectively to monitor and assess earthquake disasters in near real time, providing an important scientific basis and decision-making support for government emergency command and postdisaster reconstruction. This letter proposes a new H-α-ρ method (H is the entropy, α is the average scattering mechanism, and ρ is the circular polarization correlation coefficient) for extracting the spatial distribution of collapsed buildings using RADARSAT-2 fine-mode polarimetric synthetic aperture radar (SAR) data. The method was tested on imagery from the Yushu earthquake in the Qinghai Province of China which occurred in 2010. When compared with high-resolution optical imagery, the result indicates that the proposed method can be effectively used to identify collapsed buildings and also demonstrates that polarimetric SAR data have potential for application in urban disaster monitoring and assessment.

Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine

In recent years, the urban impervious surface has been recognized as a key quantifiable indicator in assessing urbanization impacts on environmental and ecological conditions. A surge of research interests has resulted in the estimation of urban impervious surface using remote sensing studies. The objective of this paper is to examine and compare the effectiveness of two algorithms for extracting impervious surfaces from Landsat TM imagery; the multilayer perceptron neural network (MLPNN) and the support vector machine (SVM). An accuracy assessment was performed using the high-resolution WorldView images. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2) were calculated to validate the classification performance and accuracies of MLPNN and SVM. For the MLPNN model, the RMSE, MAE, and R2 were 17.18%, 11.10%, and 0.8474, respectively. The SVM yielded a result with an RMSE of 13.75%, an MAE of 8.92%, and an R2 of 0.9032. The results indicated that SVM performance was superior to that of MLPNN in impervious surface classification. To further evaluate the performance of MLPNN and SVM in handling the mixed-pixels, an accuracy assessment was also conducted for the selected test areas, including commercial, residential, and rural areas. Our results suggested that SVM had better capability in handling the mixed-pixel problem than MLPNN. The superior performance of SVM over MLPNN is mainly attributed to the SVMs capability of deriving the global optimum and handling the over-fitting problem by suitable parameter selection. Overall, SVM provides an efficient and useful method for estimating the impervious surface.

Application of the inundation area-lake level rating curves constructed from the SRTM DEM to retrieving lake levels from satellite measured inundation areas

Remote sensing technology has great potential for measuring lake inundation areas and lake levels, and providing important lake water quantity and quality information which can be used for improving our understanding of climate change impacts on the global water cycle, and assessing the influence of the projected future climate change on the global water resources. One remote sensing approach is to estimate lake level from satellite measured inundation area based on the inundation area-lake level rating (IALLR) curves. However, this approach is not easy to implement because of a lack of data for constructing the IALLR curves. In this study, an innovative and robust approach to construct the IALLR curves from the digital elevation model (DEM) data collected during the Shuttle Radar Topography Mission (SRTM) was developed and tested. It was shown that the IALLR curves derived from the SRTM DEM data could be used to retrieve lake level from satellite measured inundation area. Applying the constructed IALLR curve to the estimated inundation areas from 16 Landsat Thematic Mapper (TM) images, 16 lake levels of Lake Champlain in Vermont were obtained. The root mean square error (RMSE) of the estimated lake levels compared to the observed water levels at the U.S. Geological Survey (USGS) gauging station (04294500) at Burlington, Vermont is about 0.12m.

Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province

Earth Observation is a powerful tool for the detection of floods. Microwave sensors are typically favored as they deliver data enabling water detection independent of solar illumination or cloud cover conditions. However, scale issues play an important role in radar based flood mapping. Depending on the flood related phenomenon under investigation, some sensors might be more suitable than others. In this study, we elucidate flood mapping at different spatial scale investigating the capability of Envisat ASAR Wide Swath Mode data at 150 m spatial resolution, as well as TerraSAR-X Scansar and Stripmap data at 8.25 m and 2.5 m resolution to especially assess urban flooding. For this purpose, we evaluate the results of automated multi-temporal water extraction from data sources of different scale against other parameters, such as settlement density, also taking a highly accurate building layer digitized from Quickbird data into consideration. Results reveal that while Envisat ASAR WSM derived flood maps are suitable to support the understanding of general flood patterns in a larger region, high resolution data of sensors such as TerraSAR-X is needed to truly assess urban flooding. However, even radar data of high spatial resolution still shows limitations; mainly in regions with a dense accumulation of corner reflectors leading to effects of layover, foreshortening, and shadowing, and hence the “over radiation” of flood affected areas.

Movement estimate of the Dongkemadi Glacier on the Qinghai–Tibetan Plateau using L-band and C-band spaceborne SAR data

We measured the complex motion of the Dongkemadi Glacier on Tanggula Mountain, Qinghai–Tibetan Plateau, using two-pass differential synthetic aperture radar interferometry (InSAR) with satellite L-band and C-band SAR data. We derived detailed motion patterns of the Dongkemadi Glacier for the winter seasons of 1996, 2007 and 2008 using a European Remote sensing Satellite-1/2 (ERS-1/2) tandem InSAR pair acquired from descending orbit and a 46-day-separation Advanced Land Observing Satellite (ALOS) InSAR pair acquired from ascending orbit. In this article, we focus on an analysis of the glaciers surface motion features and a validation of the results from the InSAR using Global Positioning System (GPS) survey data. The experimental results show that the glacier flow distribution displays strong spatial variations depending on elevation. The glacier is divided into four clearly defined fast-flowing units in terms of spatial variability of the glacier speed, with evidence from both ERS and ALOS/PALSAR InSAR pairs (palsar – Phased Array type L-band Synthetic Aperture Radar). Among the four fast-flowing units, three are on the Dadongkemadi Glacier (DDG) and one on the Xiaodongkemadi Glacier (XDG). The flow patterns are generally characterized by terrain complexity for both glacier branches. The upper central area of the DDG shows slow movement, maybe due to the convergent and uptaking effect of ice from steep slope areas with opposite flow directions.

Study of detecting method with advanced airborne and spaceborne synthetic aperture radar data for collapsed urban buildings from the Wenchuan earthquake

The large 8.0 scale earthquake that occurred in Wenchuan, Sichuan province, China on May 12, 2008 caused huge damage to peoples lives and property. Airborne and spaceborne remote sensing can be used accurately and effectively in almost real-time to monitor and assess earthquake disasters, providing an important scientific basis and decision-making support for government emergency command and post-disaster reconstruction. The high resolution, multi-band, multi-polarization, and full-polarization synthetic aperture radar (SAR) system and theories developed in recent years provide important data resources and the basic methodology for post-earthquake monitoring and evaluation. In this paper, the cities of Beichuan and Dujiangyan in the Wenchuan earthquake region are chosen as study sites. Using advanced high-resolution, multi-band, multi-polarization, and full-polarization SAR data, and applying urban building backscattering models and target backscattering and polarimetric target decomposition theory, the backscattering characteristics, polarimetric characteristics and texture features between collapsed and intact buildings post-earthquake are extensively compared and analyzed. Subsequently, a new SAR detection method for collapsed urban buildings is proposed from these characterizations. Preliminary results from comparisons between this method and high-resolution optical data show that the proposed method is effective and powerful in detecting collapsed urban buildings devastated by an earthquake.

Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China

Abstract The objective of this paper is to present a case study to derive land use/land cover (LULC) maps and investigate the long-term effects of LULC change on surface runoff in the fast urbanizing Beijing city. The LULC maps were derived from Landsat TM/ETM+ imagery (acquired in 1992, 1999, 2006, and 2009) using support vector machine method. A long-term hydrologic impact assessment model was applied to assess the impact of LULC change on surface runoff. Results indicated that the selected study area experienced rapid urbanization from 1992 to 2009. Because of urbanization, from 1992 to 2009, modeled runoff increased 30% for the whole area and 35% for the urban portion. Our results also indicated that the runoff increase was highly correlated with urban expansion. A strong relationship ( R 2 = 0.849 ) was observed between the impervious surface percent and the modeled runoff depth in the study area. In addition, a strong positive relationship was observed between runoff increase and percentage of urban areas ( R 2 = 0.997 for the whole area and R 2 = 0.930 for the urban portion). This research can provide a simple method for policy makers to assess potential hydrological impacts of future urban planning and development activities.

A Robust Approach for Object-Based Detection and Radiometric Characterization of Cloud Shadow Using Haze Optimized Transformation

Cloud shadows in satellite imagery hinder understanding of ground surface conditions due to reduced illumination and the potential for confusion with illuminated low-reflectance objects such as water bodies. This paper extends the application of the haze optimized transform (HOT) from haze mapping to include object-oriented detection of clouds and cloud shadows. An integrated processing chain encompassing these tasks has been implemented and successfully applied to Landsat Enhanced Thematic Mapper Plus and Multispectral Scanner imagery covering a variety of land covers and landscapes. The results confirm that the HOT-based method for cloud shadow detection is robust and effective. Cloud shadows have been identified and extracted with overall accuracy of about 95.3%. Clear-sky dark pixels (e.g., small lakes) are well separated from cumulus cloud shadow pixels. The spatial distribution of HOT response in a given cloud patch can be used to estimate the extent and variation of incoming visible radiation reduction in its corresponding shadow patch. This information, in turn, has been used to apply a radiometric gain to compensate for the shadowing effect on the land. The HOT response has been tested for radiometric characterization of cloud shadows and subsequent shadow illumination compensation.

Urban Land Cover Classification with High Resolution Polarimetric SAR Interferometric Data

The application potential and validity of high-resolution airborne E-SAR polarimetric interferometric SAR (POLinSAR) data for urban land cover extraction and classification are investigated in this paper. The methodology is based on unsupervised maximum likelihood classification of polarimetric, coherence, and phase differentials of POLinSAR data. In particular, the roles of phase differential (polarimetric phase difference (PPD), interferometric phase differential (IPD) across different interferometric combinations, and differential interferometric phase (DIP)) in the identification of urban land covers with vertical structures are addressed. The preliminary results are promising, and, in general, the inclusion of phase differential significantly improves the accuracy of the classification of urban land covers with vertical structures.

Urban Area SAR Image Man-Made Target Extraction Based on the Product Model and the Time–Frequency Analysis

This paper proposed an innovative framework to almost automatically extract man-made target from a high-resolution (HR) polarimetric SAR (PolSAR) image of an urban area. The core part of this framework is a new PolSAR image feature extraction method, which is developed by combining the spherically invariant random vector (SIRV) product model with the time-frequency (TF) analysis technology. The SIRV product model can better characterize HR SAR images, and the TF analysis will assist the classification by taking advantages of the anisotropic property to avoid the confusion of natural and man-made targets. Therefore, using this kind of extracted features, man-made targets can be easily discriminated with a simple unsupervised K-means classifier. Experimental results demonstrate the effectiveness of the proposed framework, in which man-made targets are extracted with clear contours, and natural surfaces are very continuous and homogenous. In addition, plenty of interesting targets with special scattering performances are highlighted in several rare classes. Their features are worth studying. Above all, because of barely requiring prior knowledge, the framework should be promising in a wide spectrum of applications by providing the rapid man-made target information acquisition of urban areas.