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Remote sensing has been widely used in disaster management. However, application of optical imageries in damage detection is not always feasible for immediate damage assessment. In the case of the Sichuan earthquake in 2008, the damaged areas were covered by cloud and fog for most of the time. The all weather SAR imageries could instead provide information of the damaged area. Therefore, more efforts are needed to explore the usability of SAR data. In regards to this purpose, this research focuses on studying the ability of using various SAR data in damage identification through image classification, and furthermore the effectiveness of fusion of various sensors in classification is evaluated.   Three different types of SAR imagery were acquired over the heavily damaged zone Qushan town in the Sichuan earthquake. The 3 types of SAR data are ALOS PALSAR L-band, RADARSAT-1 C-band and the TerraSAR-X X- band imageries.   Maximum likelihood classification method is applied on the imageries.  Four classes: Water, collapsed area, built-up area and landslide area are defined in the study area. The ability of each band in identifying these four classes is studied and the overall classification accuracy is analysed. Furthermore, fusion of these 3 types of imageries is performed and the effectiveness and accuracy of image fusion classification are evaluated.   The results show that classification accuracy from individual SAR imagery is not ideal. The overall accuracy which PALSAR gives is 30.383%, RADARSAT-1 is 31.268% while TerraSAR-X only achieves 37.168%. Accuracy statistics demonstrate that TerraSAR-X performs the best in classifying these four classes.   SAR image fusion shows a better classification result. Double image fusion of PALSAR and RADARSAT-1, PALSAR and TerraSAR-X, and RADARSAT-1 and TerraSAR-X give an overall classification accuracy of 41.88%, 42.478% and 37.758% respectively. The result from triple image fusion even reaches 52.507%. They are all higher than the result given by the individual images.   The study illustrates that the VHR TerraSAR X band SAR data has a higher ability in classification of damages, and fusion of different band can improve the classification accuracy.

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Indiana University-Purdue University Indianapolis (IUPUI) / In 2008, grassroots NGOs formed 13 alliances in response to the need for emergency relief and post-disaster recovery after the Sichuan Earthquake that occurred in West China and killed approximately 87,000 people. These alliances served to raise and deliver relief materials, train and supervise volunteers, promote information sharing, and assist victims with mental health and livelihood recovery. However, all alliances were terminated within less than four years. Although plenty of scholarship discusses how corporate alliances evolve or fail, few studies focus on interorganizational collaboration among nonprofits. To explore how NGOs developed collective actions in China’s adverse sociopolitical environment, the author performed three years of observation in four coalitions and interviewed 60 alliance leaders, employees, and volunteers. This paper identifies four types of termination these NGO alliances experienced: three of them failed at their very births, five self-disbanded shortly after the end of emergency aid, three dissolved due to failed institutionalization, and the remaining two evolved into independent organizations. Tracking their life cycles, this study finds four main factors accountable for their terminations: political pressure, funding shortage, short-term orientation, and leadership failure. In particular, the repressive NGO regulation regime and limited funding sources fundamentally restricted all alliances’ capacity and sustainability. Further, the transient nature of disaster relief efforts and the conflict between disaster management and planned work areas contributed to the short-term orientation among alliance members and, thus, led to the closure of some alliances shortly after they provided emergency relief. In addition, though generally exempt from internal rivalry that often undermines inter-firm partnerships, NGO alliances of all types were confronted with leadership challenges—partner misfits concerning resources, strategy, and mission; flawed governing structures, and undesired individual leadership. The four factors interplayed and led to alliance dissolution through different combinations. The paper points out that, in addition to environmental uncertainty, leadership failure has become a major challenge for nonprofit collaborations.