Guest Editorial Introduction for the Special Issue on Remote Sensing ...

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 6, JUNE 2007

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Guest Editorial Introduction for the Special Issue on Remote Sensing for Major Disaster Prevention, Monitoring, and Assessment

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HE TSUNAMI generated by the great Sumatra-Andaman Earthquake of December 26, 2004 in the Indian Ocean caused extensive damage and resulted in a huge number of deaths. Hurricane Katrina struck the southern Gulf coast of the U.S., with far greater consequences than local and federal officials forecasted. The impact of earthquakes in Pakistan and Iran was exacerbated by inaccessibility of affected areas and inadequate infrastructure. All these major natural disasters caused problems that might have been alleviated by more substantive use of remotely sensed data. • One aspect of disaster prevention involves precise modeling of the natural phenomena underlying the hazards. The scale of these phenomena is often too large to depend solely on in situ measurements, and satellite or airborne observations provide the only means of acquiring data to develop and validate adequate models. • Even when observation of extreme events and short-term forecasting of their effects are reasonably advanced, management and interpretation of the huge amount of resulting information from remote sensing in near real time requires more advanced algorithms and improved computational capability. • Postevent crisis response and damage assessment in remote, inaccessible locations demand the development and effective delivery of timely products to support rescue teams. The remote sensing community is actively addressing these research topics, with a number of workshops [1], [2], working groups [3], [4], and EU- or UN-funded humanitarian projects [5]–[7] aimed at increasing cooperation and improving, knowledge, efficiency, and standardization of procedures and techniques for remote sensing data interpretation in major disaster management. We are quickly moving toward more advanced methodologies, linking remote sensing with ancillary data for more precise mapping, faster analysis, and more effective forecasting and data delivery to the final user. In the postdisaster phase, the availability of very high resolution optical satellite imagery improves the level of detail for damage assessment

Digital Object Identifier 10.1109/TGRS.2007.899144

and allows incorporation of remotely sensed information into geographic information systems, as once was possible only with aerial photos [8]. In the near future, TerraSAR-X and RADARSAT-2 will provide high-resolution SAR data, which will foster research on the use of weather-independent radar data for earthquake and similar disaster damage assessment [9], [10]. The fusion of data from different sources for pre- and postevent analysis may also improve damage mapping by a combined approach [11]. One example is a coastal zone integrated management system for hazard mapping in hurricane- or tsunami-prone areas, which would require coastal reef and vegetation mapping [12], settlement analysis [13], and 3-D characterization of the coastal landscape and ocean floor. In this special issue, we include 19 papers that address the use of remote sensing as a tool for major disaster prevention, monitoring, and assessment. The topics include earthquakes and landslides, tsunami, hurricanes and typhoons, floods and fires, as well as papers with a broader focus, highlighting innovative tools and procedures to exploit Earth observation data. Three papers describe general tools. The paper by Voigt et al. shows the procedures currently exploited at the German Space Agency to provide georeferenced and annotated images in a rather short time. These maps are distributed on the web for immediate use by local, national, and international agencies and organization. The paper is an excellent example of what wellprepared teams can provide currently using Earth Observation (EO) and ancillary data. The paper by Leprince et al. is a fine example of a preprocessing tool for precise coregistration, orthorectification, and correlation of optical images with a wide range of applicability to any kind of disasters. Finally, the paper by Riccio et al. presents an overall framework employing fractal based models, algorithms, and tools to support identification of natural area changes due to natural or man-made disasters. A second group of five papers is devoted to the use of EO data for earthquake applications. The papers by Arciniegas et al. and Gamba et al. show interesting results about SAR data usage for damage mapping after an earthquake. The important point stressed by both papers is the better accuracy obtained by analyzing integrated information at the block level. Exploiting ancillary information is important means for improving poor performances in this very active research field, especially in urban or populated areas, where

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this kind of information is available. The work by Sertel et al. adds further insight to this topic by analyzing the effect of spatial texture measures to characterize the effect of the same type of events. Finally, the use of SAR interferometry is the focus of the papers by Tralli et al. and Tupin et al. The first is devoted to analysis of the usefulness of interferometricderived information inside an open seismic hazard analysis (SHA) framework, mainly for loss estimation, while the second presents a methodological approach to reduce both global and local tropospheric contributions directly from differential interferograms. The three papers on typhoons and hurricanes show the diverse possibilities offered by EO data with respect to disaster management. The first paper by Pun et al. addresses satellite altimetry-derived upper ocean thermal structure in the western North Pacific Oocean, which has proved to be a critical information in typhoon’s intensity change. The paper by Barnes et al. provides an overview of the methods for data mining in large data sets. The application is the autonomous retrieval of areas of interest from very high spatial resolution images of areas struck by Hurricane Katrina. The methodology proves to be effective as a first tool for identifying areas to be further evaluated by in situ investigations. Finally, the paper by Rau et al. analyzes land cover changes due to typhoon events in Taiwan. Two papers analyze the usage of satellite data at coarser and finer spatial resolution for damage assessment in coastal areas affected by the December 2004 tsunami. Yamazaki and Kouchi describe how moderate spatial resolution images, but with a high spectral resolution, such as those provided by ASTER, can be used to provide adequately accurate damage maps. On a very similar subject, the paper by Bovolo and Bruzzone discusses a very clever methodology to detect changes in extended areas, such as those induced by the tsunami. To analyze the Indonesian coast, the authors subset the data into smaller patches and apply an adaptive change detection technique to improve the final damage pattern detection result. The remaining papers in this special issue address landslides, floods, and fires, all of which result in lose of life and have an economic cost associated with both the rescue and repair operations. The timely forecast of landslides using geospatial models with satellite-derived inputs is considered in the work by Hong et al. The paper introduces a framework for developing an experimental real-time prediction system to identify where rainfall-triggered landslides will occur by combining surface landslide susceptibility and a real-time satellite-observationbased rainfall analysis system. A different application of remotely sensed data is the use of extracted digital elevation models (DEMs) for landslide monitoring, as exemplified by Tsustui et al. In this work, aerial photography is used to reconstruct the terrain model and detect areas affected by landslides. Finally, landslide mapping by data fusion is performed and evaluated in the paper by Wang et al., where a DEM and multispectral data are combined and jointly considered to obtain land cover maps and subsequently landslide information. In the paper by Asante et al., remotely sensed precipitation data are ingested into a hydrologic model which is pa-

rameterized using spatially distributed global elevation, soil and land cover datasets developed from remote sensing, and in situ sources. The paper by Schumann et al. analyzes how the response of a flood model driven by remotely sensed data (flood extent and a high-resolution floodplain DEM) to different flood events. As a part of this work, a refined model is developed to match different river characteristics. The last paper in this issue, by Saatchi et al., utilize SAR data to estimate the distribution of forest biomass and canopy fuel loads, both critical inputs to fire forecast models. In turn, these data can be used to improve vulnerability analysis in forested areas prone to these disasters in hot summers. KUN-SHAN CHEN, Guest Editor Faculty of the Center for Space and Remote Sensing Research National Central University Chung-Li 32054, Taiwan, R.O.C. MELBA M. CRAWFORD, Guest Editor Director of the Laboratory for Applications of Remote Sensing Assistant Dean for Interdisciplinary Research in Agriculture and Engineering Purdue University West Lafayette, IN 47907 USA PAOLO GAMBA, Guest Editor Department of Electronics University of Pavia 1-127100 Pavia, Italy JAMES S. SMITH, Guest Editor Hydrospheric and Biospheric Sciences Laboratory NASA Goddard Space Flight Center Greenbelt, MD 20771 USA

R EFERENCES [1] Proc. 2nd Int. Workshop Remote Sens. Post-Disaster Response, Newport Beach, CA, Oct. 7/8, 2004. [Online]. Available: http://mceer.buffalo.edu/ publications/workshop/05-SP03/Default.asp [2] United Nations International and Regional Workshops on the Use of Space Technology for Disaster Management. [Online]. Available: http://www.oosa.unvienna.org/SAP/stdm/stdm_past.html [3] ISPRS WG III/2, Hazards, Disasters and Public Health. [Online]. Available: http://www2.polito.it/ricerca/TCVIII_WG2/ [4] International Charter, Space and Major Disasters. [Online]. Available: http://www.disasterscharter.org/main_e.html [5] UNOSAT. [Online]. Available: http://unosat.cern.web.ch [6] RESPOND, GMES Services Supporting Humanitarian Relief, Disaster Reduction & Reconstruction. [Online]. Available: http://www.respondint.org [7] ReliefWeb. [Online]. Available: http://www-reliefweb.int [8] P. Gamba and F. Casciati, “GIS and image understanding for near realtime earthquake damage assessment,” Photogramm. Eng. Remote Sens., vol. 64, no. 10, pp. 987–994, 1998. [9] A. Wiesmann, U. Wegmuller, Y. Haeberlin, A. Retiere, O. Senegas, T. Strozzi, and C. Werner, “SAR based products for the implementation of humanitarian aid and development assistance projects within the UNOSAT project,” in Proc. IGARSS, Anchorage, AK, Sep. 20–24, 2004, pp. 4803–4806. [10] M. Matsuoka and F. Yamazaki, “Building damage detection using satellite SAR intensity images for the 2003 Algeria and Iran earthquakes,” in Proc. IGARSS, Anchorage, AK, Sep. 20–24, 2004, pp. 1099–1102.


[11] F. Dell’Acqua, P. Lombardo, P. Gamba, and T. Macrì Pellizzeri, “Multisource urban classification: Joint processing of optical and SAR data for land cover mapping,” in Proc. IGARSS, Toulouse, France, Jul. 21–25, 2003, pp. 1044–1046. [12] B. J. Adams, C. Wabnitz, S. Ghosh, J. Alder, R. Chuenpagdee, S. E. Chang, P. R. Berke, and W. E. Rees, “Application of Landsat 5 & high-resolution optical satellite imagery to investigate urban tsunami

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damage in Thailand as a function of pre-tsunami environmental degradation,” in Proc. 3rd Int. Workshop Remote Sens. Post-Disaster Response, Chiba, Japan, 2005. unformatted CD ROM. [13] B. J. Adams, “Improved disaster management through postearthquake building damage assessment using multitemporal satellite imagery,” in Proc. ISPRS XXth Congr., Istanbul, Turkey, 2004, vol. VXXXV.

Kun-Shan Chen (S’86–M’92–SM’98–F’07) received the B.S.E.E. degree from the National Taiwan Institute of Technology, Taipei, Taiwan, R.O.C., and the M.S. and Ph.D. degrees from the University of Texas, Arlington, in 1987 and 1990, respectively, all in electrical engineering. Since 1992, he has been with the faculty of the Center for Space and Remote Sensing Research, National Central University, Jung-Li, Taiwan, where he served as Director from 2001 to 2004, is currently a Professor, and has joint appointments at the Institute of Space Sciences and Institute of Communication Engineering. He serves as a technical consultant at several national research agencies in areas of satellite remote sensing, radar, and radio techniques. He has published over 60 referred journal and 120 conference papers in addition to three book chapters. His research activities include microwave remote sensing, image processing and analysis for satellite and aircraft remote sensing data, radio and microwave propagation, and scattering from terrain and ocean with applications to remote sensing and wireless communications. Dr. Chen is a member of Electromagnetic Academy. He was a Technical Chairman of PIERS 1999 held in Taipei. In 2001, he was appointed as Chairman of Commission F, Taipei, China, of URSI. He has been the Editor-in-Chief of the Journal of Photogrammetry and Remote Sensing since 2001. He serves on the Editorial Board of the Journal of Electromagnetic Waves and Applications and the Transactions of the Aeronautical and Astronautical Society of the Republic of China. He is currently an Associate Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING.

Melba M. Crawford (M’89–SM’05–F’07) received the B.S. and M.S. degrees in civil engineering from the University of Illinois, Urbana, in 1970 and 1973, respectively, and the Ph.D. degree in systems engineering from The Ohio State University, Columbus, in 1981. She was a Faculty Member with the University of Texas at Austin from 1990 to 2005. She is currently with Purdue University, West Lafayette, IN, where she is the Director of the Laboratory for Applications of Remote Sensing and the Assistant Dean for Interdisciplinary Research in Agriculture and Engineering. She holds the Purdue Chair of Excellence in Earth Observation. In 2004–2005, she was a Jefferson Senior Science Fellow with the U.S. Department of State. She has served as a member of the NASA Earth System Science and Applications Advisory Committee (ESSAAC) and the NASA EO-1 Science Validation team for the Advanced Land Imager and Hyperion, which received a NASA Outstanding Service Award. She also serves on the Advisory Committee to the NASA Socioeconomic Applications and Data Center, Columbia University. Dr. Crawford is a member of the IEEE Geoscience and Remote Sensing Society, where she served as Education Director (1998–2000), Vice President for Professional Activities (1999–2001), and Vice President for Meetings and Symposia (2003–current). She is an Associate Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING and has been a Guest Editor for special issues on Hyperspectral Data, the Earth Observing One Mission, Advances in Methods for Analysis of Remotely Sensed Data, Landsat Missions, and Disaster Response.

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Paolo Gamba (M’93–SM’02) received the Laurea degree (cum laude) and the Ph.D. degree from the University of Pavia, Pavia, Italy, in 1989 and 1993, respectively, both in electronic engineering. In 1994, he joined the Department of Electronics, University of Pavia, where he is currently an Associate Professor. He has acted as Guest Editor for four special issues of international journals on the subject of urban remote sensing. He has published more than 50 papers in peer-review journals and presented nearly 150 papers at workshops and conferences. Dr. Gamba has been the 2002-2004 Chair of Technical Committee 7 “Pattern Recognition in Remote Sensing” of the International Association for Pattern Recognition and now chairs the GRSS Data Fusion Technical Committee. He is an Associate Editor of the IEEE GEOSCIENCE AND R EMOTE S ENSING L ETTERS . He is also the Organizer and Technical Chair of the GRSS/ISPRS Joint Workshops on “Remote Sensing and Data Fusion over Urban Areas,” held in Rome, Italy (2001); Berlin, Germany (2003); Tempe, AZ (2005); and Paris, France (2007). He was the recipient (First Place) of the 1999 ESRI Award for Best Scientific Paper in Geographic Information Systems.

James S. Smith (F’96) has academic background in mathematics, physics, and computer science. He is currently a Senior Scientist in the Hydrospheric and Biospheric Sciences Laboratory and a Goddard Senior Fellow at the NASA Goddard Space Flight Center, Greenbelt, MD. Before joining NASA, he was a Professor of natural resources at Colorado State University, Fort Collins. An early pioneer in the simulation of the bidirectional reflectance distributions of vegetation and thermal infrared canopy modeling, he and his collaborators have developed increasingly realistic remote sensing models at multiple scales over the past 30 years. He is currently applying biophysical ecology and satellite technology to understand the distribution and abundance of plant and animal organisms across the landscape and their response to biotic, abiotic, and anthropogenic influences. Dr. Smith was an Editor-in-Chief and is currently an Associate Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. He is a Fellow of the American Association for the Advancement of Science and The International Society of Optical Engineering.