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date: 25 April 2019

Supporting Natural Hazards Management With Geospatial Technologies

Summary and Keywords

On a global scale, natural disasters continue to inflict a heavy toll on communities and to pose challenges that either persist or amplify in complexity and scale. There is a need for flexible and adaptive solutions that can bridge collaborative efforts among public agencies, private and nonprofit organizations, and communities. The ability to explore and analyze spatial data, solve problems, visualize, and communicate outcomes to support the collaborative efforts and decision-making processes of a broad range of stakeholders is critical in natural hazards and disaster management. The adoption of geospatial technologies has long been at the core of natural hazards risk assessment, linking existing technologies in GIS (geographic information system) with spatial analytical techniques and modeling. Practice and research have shown that though risk-reduction strategies and the mobilization of disaster-response resources depend on integrating governance into the process of building disaster resilience, the implementation of such strategies is best informed by accurate spatial data acquisition, fast processing, analysis, and integration with other informational resources. In recent years, new and accessible sources and types of data have greatly enhanced the ability of practitioners and researchers to develop approaches that support rapid and efficient disaster response, including forecasting, early warning systems, and damage assessments. Innovations in geospatial technologies, including remote sensing, real-time Web applications, and distributed Web-based GIS services, feature platforms for systematizing and sharing data, maps, applications, and analytics. Distributed GIS offers enormous opportunities to strengthen collaboration and improve communication and efficiency by enabling agencies and end users to connect and interact with remotely located information products, apps, and services. Newer developments in geospatial technologies include real-time data management and unmanned aircraft systems (UAS), which help organizations make rapid assessments and facilitate the decision-making process in disasters.

Keywords: geographic information systems, GIS, geospatial technologies, disaster management cycle, risk assessment, vulnerability, governance, distributed GIS, volunteered geographic information, infrastructure

The Role of Geospatial Technologies in Natural Hazards Risk Assessment and Disaster Management

Earthquakes, floods, forest fires, landslides, and tropical cyclones are among the costliest and most destructive natural hazards, often causing loss of life and property damage, breakdowns of infrastructure systems, and disruptions of vital sectors of the economy. When a disaster strikes, multiple organizations responsible for disaster response, restoration, recovery, and reconstruction are involved in processing massive amounts of information about the impacts on the affected populations, building stock, infrastructure systems, and community services (Tomaszewski, Judex, Szarzynski, Rodestock, & Wirkus, 2015). In disaster situations, collaborative efforts among public agencies and private and nonprofit organizations are critical to facilitate emergency-response operations across jurisdictional and organizational boundaries (Chen, Sharman, Rao, & Upadhyaya, 2008). Geospatial technologies provide capabilities for data collection, processing, analysis, and visualization that are essential in all phases of disaster management. These capabilities and tools are needed to guide hazard identification, emergency response, and disaster recovery. During the response phase, effective coordination of emergency operations relies on situational awareness and the ability to make rapid assessments in the face of uncertainty, when time and resource constraints are the norm and task flows depend on accurate spatial information to position logistical support (Chen et al., 2008; Cova, 1999; Cutter, 2003). Vulnerability assessments based on spatial analytical techniques guide the mitigation-planning needs of communities exposed to natural hazards and help them prepare for future extreme events (Cutter, 2003). In the process of recovery and reconstruction, spatial analysis facilitates damage assessment and provides decision support for planned future development in disaster areas.

Since the 1990s, geographic information systems (GIS) have provided core capabilities in integrating natural hazards assessment with disaster management. As Cova (1999, p. 845) has noted, “The value of GIS in emergency management arises directly from the benefits of integrating a technology designed to support spatial decision-making into a field with a strong need to address numerous critical spatial decisions.” Early applications of GIS included cartographic mapping of natural hazards and long-term risk assessment (Zerger & Smith, 2003). Premodeling storm surge, for example, provided local emergency managers with the ability to assess population exposure and designate evacuation routes. These early applications, however, rarely involved modeling and advanced decision support capabilities (Zerger & Smith, 2003). Since the late 1990s, geospatial technologies have evolved to address some of the key challenges and priorities in hazard mitigation and management, which has resulted in a comprehensive suite of tools and functionalities for mapping and visualization (including in 3D), spatial queries and quantitative spatial analyses, spatial statistics, hazard modeling in urban and natural environments, and simulating and predicting risk and change (Longley & Batty, 2003). Practice and research have shown that risk-reduction strategies and mobilization of disaster-response resources are best informed by accurate spatial-data acquisition, fast processing, analysis, and integration with other informational resources.

This article reviews the fundamental concepts of hazards and disaster management, GIS, geographical information science (GIScience), and geospatial technologies; it presents several geospatial applications used in natural hazards management, focusing on advancements and technologies that can best inform the actions on the ground, including (a) monitoring, prediction, and early warning systems and (b) damage assessments. A diverse set of applications is reviewed and discussed, highlighting the contributions of geospatial technologies to the analysis of (a) earthquakes, tsunamis, and volcanic activity; (b) floods, landslides, and coastal hazards; and (c) droughts and wildfires. Examples include GIS, global positioning systems (GPS), remote sensing, Web-based tools, citizen science, and participatory mapping. The use of emerging new technologies, particularly unmanned aircraft systems (UAS), or “drones,” and near real-time Web applications in response and disaster decision support, is also discussed. The growing contributions of volunteered mapping services and social networks in disaster response and relief operations are also examined. The article concludes with some takeaway points and proposes directions for future research.

Concepts and Definitions

Natural Hazards, Disasters, Risk, and Vulnerability

Natural hazards refer to geological, geophysical, or hydrometeorological phenomena (United Nations Office for Disaster Risk Reduction [UNISDR], 2016). In 2015, the United Nations General Assembly adopted the Sendai Framework for Disaster Risk Reduction (2015–2030), which formulates guiding principles and outlines a set of indicators intended to measure the progress being made toward reducing hazard exposure and vulnerability on a global scale (UNISDR, 2016). The Sendai Framework seeks to establish a unified terminology related to disaster risk reduction. According to UNISDR (2016, A/71/644, p. 13), a disaster is defined as “a serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts.” Although disasters are triggered by natural hazards, they often occur “where exposure . . . is exacerbated by poverty, lack of early warning systems, poor risk governance and an absence of civil protection mechanisms” (United Nations Office for Disaster Risk Reduction and Centre for Research on the Epidemiology of Disasters [UNISDR–CRED], 2016, p. 3). Subsequently, risk governance plays an important role in mitigating the effects of natural disasters and reducing vulnerability (Fekete et al., 2015). Vulnerability is “the degree to which someone or something can be affected by a particular hazard and depends on a number of factors and processes,” including physical exposure, economic and social characteristics, and personal well-being (UNISDR, 2010, p. 14). Risk is “the probability of harmful consequences or expected losses (deaths, injuries, property, livelihoods, economic activity disrupted or environment damaged) resulting from interactions between natural or human-induced hazards and vulnerable populations” (UNISDR, 2010, p. 16). Understanding the physical characteristics of a hazardous event and societal exposure are essential in all phases of disaster management, including disaster mitigation, preparedness, response, and recovery.

Earthquakes and volcanic eruptions are geophysical hazards whose destructive potential is often amplified by geological hazards such as landslides, mudslides, and debris flows (UNISDR, 2016). The United States Geological Survey (USGS) estimates that 500,000 detectable earthquakes occur worldwide annually (United States Geological Survey [USGS], 2017a). From 2000 to 2017, USGS has recorded a total of 31,750 earthquakes with a magnitude M5.0 or higher on the Richter scale. Of those, 22 earthquakes were of the most destructive magnitude M8.0 or higher; 253 were M7.0 to M7.9; and over 2,500 were M6.0 to M6.9 (USGS, 2017a). According to the UNISDR, the disaster mortality from earthquakes and tsunamis is the highest, followed by floods and other climate-related disasters (UNISDR–CRED, 2016). The USGS estimates that from 2000 to 2017 more than 800,000 people were killed by earthquakes and tsunamis worldwide. Of those, nearly 300,000 lives were lost in the 2004 Indian Ocean tsunami, and over 220,000 in the 2010 Haiti earthquake (USGS, 2017a). According to the Centre for Research on the Epidemiology of Disasters (CRED), which maintains the Emergency Events Database (EM-DAT), although the frequency of seismic activity and other geophysical hazards has not changed over the past 20 years, the frequency of weather-related extremes continues to increase (UNISDR–CRED, 2016).

On average, over 100 million people are affected every year by floods, and about 40 million by cyclones, hurricanes, and typhoons, and these numbers are expected to rise as more and more people are projected to settle in coastal areas (UNISDR, 2010). Cyclones and floods can have catastrophic impacts on coastal communities. The storm surge of Hurricane Katrina submerged almost 80% of the City of New Orleans, claimed the lives of over 1,800 people, and caused losses and damages in excess of $80 billion (Knabb, Rhome, & Brown, 2005). In 2017, Hurricane Harvey broke all records, releasing between 40 and 60 inches of rain in southeast Texas and southwest Louisiana (U.S. National Weather Service, 2017). The unusually active monsoon season of 2017 was also marked by widespread flooding in many parts of India, Bangladesh, and Nepal, which destroyed nearly 950,000 structures, including 18,000 schools (The Guardian, 2017). Overall, 40 million people suffered from the direct and indirect consequences of the floods—among them 1.8 million school-age children who were unable to return to school (The Guardian, 2017).

Droughts affect more people than any other natural disaster. The latest figures from EM-DAT suggest that in 2016, droughts affected 345 million people in Asia and over 40 million people in Africa (Guha-Sapir, Hoyois, Wallemacq, & Below, 2017). According to EM-DAT, the number of people in drought-stricken areas in Southeast Asia has increased sixfold compared to the annual average for 2006–2015, and in parts of Africa the number of people affected by droughts has almost doubled. Drought conditions were reported in nearly 70% of the conterminous United States in 2012–2013 (Guha-Sapir et al., 2017).

Every year, wildfires ravage millions of acres of forests, rangelands, and grasslands, destroying or causing damage to residences, infrastructure, and the environment, and costing billions of dollars in suppression costs and economic losses (National Interagency Fire Center [NIFC], 2017a, 2017b). From 1997 to 2017, there were 189 large-scale wildfires in the United States, and each burned more than 100,000 acres (NIFC, 2017b). According to the United States Forest Service and the National Interagency Fire Center, 2017 was the costliest year for wildfires in the U.S. history, with over $10 billion in damages and suppression costs. In 2017, wildfires scorched over 10 million acres, exceeding the 10-year annual average by more than 50% (NIFC, b). Significant fire activity (in percentage points above the 10-year annual average) was reported in the Northern Rockies (251% above normal), Rocky Mountains (110% above normal), Great Basin (107% above normal), Northern California (97% above normal), and Southern California (83% above normal; National Interagency Coordination Center [NICC], 2017, p. 7). The 2017 fires in Northern California destroyed nearly 8,000 residences and 180 commercial structures (NICC, 2017, p. 7).

Geographic Information Systems, GIScience, and Geospatial Technologies

The term geographic information refers to the collection and storage of georeferenced data that can be queried by both attribute and location. The digital representation of geographic information is based on locational characteristics (e.g., x and y coordinates or latitude and longitude) and attributes that describe a specific phenomenon or a geographic entity (Goodchild, Yuan, & Cova, 2007). The first digital representations of geographic objects and phenomena were introduced in the 1960s with the development of a prototype of the modern geographic information systems (Goodchild, 1992, 2009; Shuurman, 2013). The scholarly literature offers a variety of definitions of GIS (see Heywood, Cornelius, & Carver, 1998; Longley, Goodchild, Maguire, & Rhind, 2015). Broadly, a geographic information system is defined as encompassing four components: (a) computer hardware and software; (b) capabilities for collecting, storing, retrieving, and displaying spatial data; (c) toolsets and functions for data management, spatial analysis, and modeling; and (d) the GIS community of users and professionals, including institutional and managerial aspects of developing the GIS enterprise (Heywood et al., 1998; Longley et al., 2015).

GIScience1 is a rapidly developing scientific discipline focusing on fundamental questions and underlying theories that address the scientific and technological challenges in acquiring, handling, and representing geographic objects beyond technological advances in software development (Goodchild, 1992, 2009; Shuurman, 2013). In the United States, the development of the GIScience initiatives is guided by “ten key research priorities” put forward by University Consortium for Geographic Information Science, the USGIS, in 1996.2 Among the ten priorities (or “grand challenges”) are spatial-data acquisition and integration, distributed computing, geographic time representation, interoperability, cognition, scale, advancing GIS analytical capabilities, spatial-data infrastructures, uncertainty, and GIS and society (Goodchild, 2008, p. 6). The list has been expanded to include new priorities and contributions from the International Society for Photogrammetry and Remote Sensing (including topics related to the use of sensors, new sources of imagery, theory development, and education) and the National Research Council Computer Science and Telecommunications Board (with a focus on mobile sources of GIS data, geospatial ontologies, data mining, modeling, and making geospatial technologies more accessible; Goodchild, 2008). In 1998, similar initiatives led to the creation of AGILE, the Association of Geographic Information Laboratories for Europe (AGILE, 2016).

Critical aspects of natural hazards management are increasingly supported by remote-sensing data from both airborne and spaceborne platforms that have enabled near real-time monitoring of earthquakes, volcanic activity, wildfires, flooding, and landslides (Chen, Serpico, & Smith, 2012; Gillespie, Chu, Frankenberg, & Thomas, 2007; Joyce, Belliss, Samsonov, McNeil, & Glassey, 2009; Tralli et al., 2005). Remote-sensing applications are particularly useful when the emergency response requires planning for large and dispersed hazard areas (Liu & Hodson, 2016) and when damaged infrastructure prevents access to remote locations (Chen et al., 2012). Furthermore, remote-sensing techniques are employed in monitoring and damage assessments (Brakenridge et al., 2012; Chen, Liew, & Kwoh, 2005; Chini, Bignami, Stramondo, & Pierdicca, 2008; Kobayashi et al., 2011), deforestation (Ferreira & Huete, 2004; Omuto, 2011), glacier retreat (Pellikka & Rees, 2009), coastal hazards and erosion (Liu, Mason, Hilton, & Lee, 2004; McNairn, & Brisco, 2004), and climate dynamics (Foster & Rahmstorf, 2011).

In recent years, Global Positioning System (GPS) technologies have become ubiquitous and embedded in many applications and consumer products. GPS technologies provide a relatively high positioning accuracy, from a few meters to a submeter in more advanced applications. A GPS determines location through a constellation of 21 satellites “placed in circular 12-hour orbits . . . with a minimum of four satellites in good geometric position anywhere on the earth” (Hoffmann-Wellenhof, Lichtenegger, & Collins, 1997, p. 4). In hazards research, GPS technology has been used to monitor landslides (Hastaoglu & Sanli, 2011), earthquakes (Worden et al., 2018), and tsunamis (Fritz & Okal, 2008).

Geospatial technologies also include a wide variety of Web-based applications (Goodchild, 2009). Web resources such as Web atlases, geoportals, and geoweb services have facilitated access to geospatial resources for both GIS and non-GIS users and have enhanced the opportunities for collaborative decision-making and governance (Craglia et al., 2008; Goodchild & Glennon, 2010). Various platforms for user-generated content, known as volunteered geographic information (VGI; Goodchild, 2007a, 2007b) or Web 2.0 (Rinner, Kessler, & Andrulis, 2008) provided new distributed forms of data acquisition in which users can interact with both spatial data and user groups to create and disseminate geographic information (Scharl & Tochtermann, 2007).

Applications of Geospatial Technologies in Hazard Monitoring and Damage Assessments

GIS technology is commonly used for hazard identification and mapping. In recent years, satellite imagery from optical, thermal, and radar microwave sensors has enabled risk and damage assessments in near real time (Gillespie et al., 2007). High-resolution optical remote-sensing imagery available from EROS, IKONOS 2, SPOT 5, and QuickBird 2 has enhanced the capabilities for object-based image analysis (OBIA; Cheng & Han, 2016) and has been extensively used to map the extent of earthquake, flood, and wildfire damage (Brakenridge et al., 2012; Chen et al., 2005; Joyce et al., 2009; Sanyal & Lu, 2004; Tralli et al., 2005).

Thermal imaging sensors that provide essential information on “hotspots” of volcanic and fire activity are mounted on several satellite platforms. Most thermal scanners, including the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) are multispectral (i.e., they collect data across several wavelengths of the electromagnetic spectrum). Landsat 8 carries the Thermal Infrared Sensor (TIRS) with two thermal bands—Band 10 (10.60–11.19μ‎m) and Band 11 (11.50–12.51μ‎m; USGS, 2016). The main limitation of using optical sensors in natural hazards risk and damage assessments is the obstruction of imagery by cloud cover, smoke, or haze (Gillespie et al., 2007; Joyce et al., 2009).

A growing body of research has documented the use of the interferometric synthetic aperture radar (InSAR) for monitoring, detecting, and evaluating the consequences of natural disasters (Brunori et al., 2015; Kobayashi et al., 2011; Wang et al., 2017). Radar (radio detection and ranging) is an active remote-sensing technique involving the transmission and reception of microwave pulses with wavelengths of 1–1000 mm (Pritchard, 2006). In contrast to the data from passive optical sensors, data collected through radar instruments are not influenced by cloud cover, precipitation, or other environmental conditions (Pritchard, 2006). High-resolution InSAR data are available from ENVISAT, Sentinel-2, ALOS PALSAR, and TerraSAR. Radar remote-sensing techniques are increasingly used to examine ground deformation caused by earthquakes and volcanic activity, to develop high-resolution digital elevation models, and to monitor floods and wildfires (Brakenridge et al., 2012; Gillespie et al., 2007; Joyce et al., 2009; Sanyal & Lu, 2004; Tralli et al., 2005;). The next sections outline some applications of these techniques for the monitoring, risk assessment, and damage assessment of natural hazards.

Seismic and Volcanic Activity, Tsunamis, and Subsidence

The detailed and extensive spatial coverage provided by satellite imagery has found many applications in seismology, volcanology, and geology that support local emergency managers and local communities in their preparedness, mitigation, response, and recovery efforts. Integrating GIS, GPS, and remote-sensing data from both active and passive sensors has contributed to major advances in seismic risk assessment (Gillespie et al., 2007; Joyce et al., 2009; Tralli et al., 2005). Research has focused on exploring the predictive capabilities of remotely sensed data from passive (optical and thermal) and active (microwave) sensors. Remote-sensing data provide global coverage and critical scientific information about eruptive cycles, thermal anomalies, pyroclastic clouds, and the gaseous emissions of volcanoes (Gillespie et al., 2007; Joyce et al., 2009; Tralli et al., 2005). Since ground-based monitoring of seismic and volcanic activity is limited, particularly in less developed countries, remote sensing is often the most cost-effective and routinely available technology to support the monitoring and detection of hazardous events and to communicate risk to decision-makers and local communities (Sanyal & Lu, 2004).

Monitoring and Risk Assessment of Seismic and Volcanic Activity

Seismic hazard maps and site-specific geospatial data have long been used by emergency managers in risk assessments of earthquake-prone areas. Mapping the source area of seismic hazards requires knowledge of topography, geology, slope stability, and plate-boundary interactions (Allen & Wald, 2009; Tralli et al., 2005). The USGS Earthquake Hazards Program provides global estimates of the time-averaged shear-wave velocity to 30 m depth (VS30) based on high-resolution elevation data (Allen & Wald, 2009; Wald & Allen, 2007). The program also supports the Slope-based VS30 Map Viewer (Worden, Wald, Sanborn, & Thompson, 2015), which provides high-resolution topographic data as a proxy for seismic site conditions on a global scale. Another GIS-based platform, ShakeMap, is a software product developed by the USGS to generate earthquake-intensity maps based on field observations or user inputs for earthquake-prone areas in the United States and globally. ShakeMap estimates and interpolates ground motions based on observations recorded by GPS instrumentation (USGS, 2015). Accurate digital data of seismic activity acquired through the use of monitoring networks provide high-resolution temporal and spatial data for recording potentially destructive horizontal peak ground motions (Worden et al., 2018). Recorded data are interpolated using advanced GIS-based surface analysis methods to generate risk maps (Worden et al., 2018, 2010). The ShakeMap products also include atlases (both global and U.S.-based), probability maps, and scenario catalogs that are used for training purposes by emergency managers, utilities, and local governments (USGS, 2015). In addition, these products provide bases for loss estimations and structural design guidelines for buildings and transportation networks (Tralli et al., 2005). In developing countries, where instrumental records are limited, remote sensing is a reliable and efficient source of vital information used to support search and rescue operations, deployment of equipment, evacuations, and delivery of humanitarian assistance (Fekete et al., 2015; Kawasaki, Berman, & Guan, 2013; Sanyal & Lu, 2004).

Advancements in remote sensing have also assisted the development of enhanced predictive capabilities and early warning systems for the communities at risk. Analyzing 10,000 satellite thermal images for a period of ten years, Tronin (1996) found positive short-lived thermal anomalies prior to detected earthquakes in the Central Asian region. Tronin, Hayakawa, and Molchanov (2002) analyzed satellite imagery from the National Oceanographic and Atmospheric Administration’s (NOAA) Advanced Very High-Resolution Radiometer (AVHRR) scanner focusing on two study areas in China and Japan for a period of seven years. The results indicated a positive thermal anomaly of 3 °C to 6 °C six to 24 days prior to detected earthquakes of a magnitude of 4.7 or higher. These anomalies persisted in both study regions despite differences in spatial extent (Tronin et al., 2002). Similarly, Saraf and Choudhury (2004) detected a short-lived thermal anomaly of 5 °C before the 2003 M6.8 Algerian earthquake. Using MODIS imagery, Ouzounov and Freund (2004) found a thermal anomaly of 4 °C seven to ten days prior to the 2001 Bhuj earthquake in Gujarat, India. Avouac, Ayoub, Leprince, Konca, and Helmberger (2006) used ASTER images to analyze ground deformation of the 2005 M7.6 Kashmir earthquake. Joyce et al. (2009, p. 186) point out that even though “the detection of thermal anomalies has thus far been conducted retrospectively, refinement of this technique and routine investigation may hold information key to earthquake prediction and warnings.”

Thermal imagery is commonly used to study the eruptive cycles of volcanoes (Biggs et al., 2014). Studying the continuously erupting volcanoes Kilauea (in Hawaii) and Mt. Etna (in Sicily, Italy), Flynn, Harris, and Wright (2001) found that Landsat 7 ETM+ imagery provides spatially detailed information of active lava flows and volcanic deposits. Biggs et al. (2014) reported that InSAR techniques have been applied to mapping the eruptive deposits, volcanic deformation, and surface characteristics of over 620 volcanoes globally. Morales-Rivera, Amelung, and Mothes (2016) conducted a volcanic deformation survey over the Andes mountain range in Colombia, Ecuador, and Peru using ALOS-1 InSAR data. The study found previously undetected deformations in at least three of the seven volcanoes in the study area.

Despite a growing number of studies that focus on the monitoring and risk assessment of seismic activity, there is still weak integration of physical analysis with the analysis of disruptions of social and economic functions to improve community resilience. Further research is needed to incorporate advanced assessment methodologies with mitigation strategies that can reduce the long-term risk to communities from hazards.

Damage Assessment Following Earthquakes, Tsunamis, and Volcanic Eruptions

Chen et al. (2005) conducted a damage assessment of the Sumatra’s Aceh province devastated by the 2004 Indian Ocean tsunami using high-resolution SPOT 5 optical satellite imagery. The study evaluated the impacts of the tsunami on human settlements, local agricultural resources, and coastal habitats. Thematic maps reflecting the severity of damage developed by comparing pre- and postdisaster images provided the basis for organizing and distributing humanitarian assistance. Chini et al. (2008) used data from the European Remote Sensing Satellite Synthetic Aperture Radar (ERS‐SAR) and Environment Satellite Advanced SAR (ENVISAT‐ASAR) to analyze the impact of the 2004 M9.3 Indonesian earthquake and the subsequent Indian Ocean tsunami on the coastal regions of India, Thailand, Indonesia, and Sri Lanka. Pre‐ and postseismic SAR intensity images covering a period of ten days were analyzed using pixel correlations and visual examination. The tidal effects of the tsunami and the impact on coastal communities and local geomorphology were evaluated (Chini et al., 2008). Kobayashi et al. (2011) conducted mapping of ground displacement caused by the 2011 M9.0 Tohoku earthquake in Japan using ALOS/PALSAR InSAR data and GPS observations. The study demonstrated that incorporating ground measurements can efficiently remove atmospheric noise from the radar data and result in accurate mapping of the ground deformation. Wang et al. (2017) used InSAR imagery obtained from Sentinel-2 and ALOS-2 satellites to map large areas of ground deformation and measure the extent of the co-seismic slip of the 2016 Aketao earthquake in western China. Brunori et al. (2015) studied the 2012 ground fissures and land subsidence following the Jalisco, Mexico earthquake that had resulted in damages to the road network and adjacent buildings. They employed ENVISAT-ASAR and RADARSAT-2 SAR images, ground measurements, and modeling. The authors found that fault geology combined with water withdrawals from underlying aquifer systems contributed to the hazardous event, suggesting that future land development and hazard mitigation planning should consider environmental constraints. Damage assessment can play an important role in postdisaster recovery and reconstruction and in mitigation planning. However, in most of the reviewed studies, the link between damage assessments and planning for recovery and reconstruction has not been sufficiently explored.

Floods, Landslides, and Coastal Hazards

Floods occur as a consequence of the combined effect of multiple factors (Seneviratne et al., 2012). The extent of loss and damage caused by floods is influenced by climate extremes (e.g., heavy precipitation) as well as drainage basin characteristics (e.g., topography, geology, and soil storage capacity). Other factors related to the level of urbanization and patterns of development in the floodplain may also increase exposure and the vulnerability of people, infrastructure, and economic and cultural resources to flood events (Brody, Highfield, & Kang, 2011; Brody, Zahran, Highfield, Grover, & Vedlitz, 2008; Wilhelmi & Morss, 2013). High tides, storm surge, and sea-level rise in coastal areas are also contributing factors that can affect the magnitude and frequency of disastrous flood events and strain coping capacities (Seneviratne et al., 2012). Floods often cause structural damage to buildings, schools, roads, and bridges, extensive disruption of utility networks, sewage systems overflow, contamination of drinking water supplies, destruction of crops, and loss of agricultural productivity (Hallegatte & Przyluski, 2010). In mountainous areas, floods can trigger slope failure and landslides.

Flood Forecasting, Early Warning Systems, and Mitigation Planning

Given the extent and severity of flood impacts, the identification of flood-prone areas and flood forecasting have been key priorities in flood-risk assessment, management, and mitigation. Several types of spatial and hydrological data are needed to predict flood hazards. Most gauge stations in North America and Western Europe provide hydrometeorological time series on an hourly basis for use in hydrological modeling. Significant advances in flood forecasting and rainfall estimation and coverage have been achieved by incorporating atmospheric dynamics in numerical weather prediction models through data assimilation from meteorological satellites (Beven & Freer, 2001). Airborne LiDAR (Light Detection and Ranging) data and remote sensing have expanded the set of tools for flood hazard mapping, risk assessment, and damage prediction by providing high-resolution digital elevation models (DEMs) for use in hydrological modeling. Derivatives of LiDAR have also been used to estimate flow direction and accumulation and flood depth (Sanyal & Lu, 2004).

Green et al. (2017) evaluated the impact of pluvial (sheet flow) and fluvial (riverine) flooding on the response time of emergency vehicles (ambulance, police, and fire and rescue) operated by the City of Leicester, United Kingdom. The study modeled four design storm scenarios using a 6-hour hyetograph and a distributed hydrodynamic inundation model to quantify surface runoff water depths and identify the portions of the road network most vulnerable to pluvial flooding (Green et al., 2017). The flood-depth data were overlaid with the road network, and road restrictions were placed at locations where the water depth exceeded 25 cm. The reduction of service areas within 8- and 1-minute drive time was assessed using network and traffic analysis tools (Green et al., 2017). The study found that a 100-year pluvial and fluvial flooding will reduce the overall accessibility within the emergency-sector service areas by over 25%, while a 1000-year flood event will make over 60% of the service areas inaccessible (Green et al., 2017). Zelenakova et al. (2017) proposed a comprehensive framework to assess the regional impact of floods. The analysis accounts for property damage, adverse environmental impacts, and the loss of human life. The framework, applied to the southeastern part of the Slovak Republic, in Central Europe, incorporates GIS-based metrics to quantify flood risk in terms of economic loss and social and environmental consequences. The results provided a scientific basis for recommendation of flood-mitigation and flood-protection strategies (Zelenakova et al., 2017).

Geology, slope, soil type, lithology, land cover, vegetation, rainfall intensity, hydrological characteristics, and spatial variations in groundwater flow determine the landscape susceptibility to landslides (Chae, Park, Catani, Simoni, & Berti, 2017). Recent developments in landslide hazard assessment have focused on various approaches to integrating GIS data with in-situ measurements, hydrological models, and remote sensing. Integrating data from a variety of sources enables improved monitoring, establishment of indicators for developing early warning systems, and ability to rapidly evacuate residents from threatened areas (Chae et al., 2017). Sharma et al. (2017) integrated global rainfall data with precipitation forecasts and InSAR imagery to develop a GIS-based dynamic monitoring system of flood disasters. Weather forecast models such as Tropical Rainfall Measuring Mission (TRMM) and Global Ensemble Forecast System (GEFS) were combined with remotely sensed microwave data to identify areas potentially prone to flooding and develop an early warning system (Sharma et al., 2017). The methodology was validated using TRMM and GEFS data for the 2015 Chennai and the 2016 Assam floods in India (Sharma et al., 2017). Jiang et al. (2016) combined hydrologic modeling with SAR and InSAR techniques to evaluate the physical factors reactivating landslide hazards upstream of the Three Gorges Dam in China. Using high-resolution TerraSAR-X imagery, the authors applied the pixel offset tracking technique with the ensemble Kalman filter for successive data assimilation to assess the frequency and intensity of a periodic landslide activation zone extending from the Three Gorges Dam impoundment to the south bank of the Yangtze River. The methodology provided a comprehensive framework for enabling tactical decisions related to population relocation, development of early warning systems, and improvement of hazard mitigation planning (Jiang et al., 2016).

Predicting and Monitoring Coastal Flooding

Geospatial technologies are instrumental in developing hazards maps, estimating population at risk, and creating inventories of housing stock and infrastructure assets in vulnerable coastal areas (Brakenridge et al., 2012; Klemas, 2009). Rainfall events of low probability and high impact, such as those with a 1% chance of occurrence in any given year (a 100-year flood), provide the baseline for flood hazard mapping and insurance coverage. In the United States, estimates of the 100-year and 500-year flood events have been incorporated into the National Flood Insurance Program since 1968.3 Managed by the Federal Emergency Management Agency (FEMA), the Flood Insurance Program provides low-cost insurance to homeowners, renters, and businesses and at the same promotes the adoption and implementation of mitigation strategies and floodplain management regulations to reduce risk (Federal Emergency Management Agency, 2018). FEMA is also responsible for the Flood Insurance Rate Map (FIRM), which delineates “special hazard areas and the risk premium zones for insurance purposes” (FEMA, 2017).

Kermanshah and Derrible (2017) intersected FEMA’s 100-year floodplain with the road networks of the cities of Chicago and New York to quantify the potential impacts of flooding on travel demand and accessibility to major employment and commercial centers. Potential disruptions to the transportation system were measured in terms of the number of affected intersections and road segments (Kermanshah & Derrible, 2017). Travel demand before and after a flood event was estimated using the Origin-Destination Employment Statistics derived from the Longitudinal Employer-Household Dynamics data. To measure the impact of flooding on travel demand, the authors derived metrics from commuting patterns between jobs and households (Kermanshah & Derrible, 2017). The study found that commuters in the City of New York were more likely to experience delays and road closures due to flooding than were commuters in Chicago to experience delays due to network fragmentation (Kermanshah & Derrible, 2017).

Sea-level rise is likely to exacerbate the coastal flooding caused by tropical cyclones (Woodruff, Irish, & Camargo, 2013). Lu, Peng, and Zhang (2015) proposed an accessibility measure to prioritize the criticality of the transportation infrastructure exposed to the impacts of sea-level rise. Using travel data from the Florida Standard Urban and Transportation Model Structure (FSUTMS) and LiDAR data for Hillsborough County, Florida, the study evaluated 14 scenarios quantifying the accessibility reduction rate for each traffic analysis zone that was a result of the failure of a link or a link set (Lu et al., 2015). Potential capacity reduction or failure due to inundation was evaluated, and the most critical links, causing the largest overall accessibility reduction in a degraded network, were identified (Lu et al., 2015).

Landfalling tropical cyclones, as illustrated by Hurricane Katrina in 2005 and Cyclone Nargis in 2008, can produce a storm surge as high as several meters, with devastating impacts on coastal communities (Brakenridge et al., 2012). According to NOAA, a hurricane storm surge is primarily driven by the intensity and size of the wind field and its forward speed and direction, but other factors, such as the angle of approach, shoreline configuration and exposure, elevation, width, slope, and nearshore water depths determine the height and the battering force of the storm waves (National Oceanic and Atmospheric Administration–National Hurricane Center [NOAA-NHC], 2008). Shallow coastal environments can produce significantly higher water levels during a tropical cyclone compared to coastal environments with a steep and narrow continental shelf (NOAA-NHC, 2008). In the United States, the hydrodynamic Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model has been used for over 30 years to provide baseline GIS data for storm surge risk assessments and evacuation planning (Zachry, Booth, Rhome, & Sharon, 2015). The storm surge hazard maps released by the NOAA in 2014 depict the potential for storm surge inundation along the coastal plains of the United States from Maine to Florida and the Gulf Coast, providing essential information for preparedness, mitigation, and evacuation planning (Zachry et al., 2015).

Flood-Damage Assessments

During the response phase that follows an extreme event, postdisaster reconnaissance of the affected areas is instrumental in carrying out search and rescue operations, deploying emergency personnel and equipment, clearing waterways and roads, assessing damages to infrastructure and housing stock, and preventing a dangerous spreading of oil and chemical spills (Horney et al., 2018; Klemas, 2009; Tomaszewski et al., 2015; Womble, Ghosh, Adams, & Friedland, 2006).

In the aftermath of Hurricane Katrina, remotely sensed data based on optical and radar imagery helped FEMA and local emergency management teams identify the extent of the storm surge flooding and the condition of the levee system (Klemas, 2009; Womble et al., 2006). High-resolution optical satellite images from QuickBird, OrbView, IKONOS, and NOAA’s digital aerial photogrammetry taken immediately after Katrina’s landfall covered large areas along the flooded Mississippi and Louisiana coasts and provided the first comprehensive survey of the breached levee system. In optical imagery, the visibility of features under a heavy cloud cover or closed-canopy vegetation is often limited. Since the remnants of Katrina obscured parts of the impacted coastline, SAR scenes obtained from RADARSAT, ERS, and other sources were used to delineate flooded areas based on the distinctive backscatter signature of water (Womble et al., 2006, pp. 12–14). InSAR coherence analysis comparing backscatter signatures pre- and postflooding combined with GPS field data collection and DEM can increase the accuracy of the estimated flood area extent and water depths (Sanyal & Lu, 2004; Joyce et al., 2009).

In 2017, Hurricane Harvey produced record-breaking rainfall in southeastern Texas and southwestern Louisiana displacing thousands of residents and causing USD150 billion in damages (Emanuel, 2017). Images obtained from Landsat in the immediate aftermath of Harvey showed the extent of the flooding and shoreline retreat produced by the storm surge and intense rainfall (USGS, 2017b). During the peak period of flooding, nearly 80 streamflow-gaging stations measured water levels that were at or above flood stage (USGS, 2017b). A study conducted by USGS and FEMA estimated annual rainfall exceedance probability ranging from less than 0.2% to 14.0% in the affected region (Watson et al., 2018).). Nineteen inundation maps were produced to visualize the extent and maximum depth of the flood waters (Watson et al., 2018).

The onset of the monsoon season in India and Southeast Asia is often associated with increased cyclonic activity. Intense monsoonal rains and typhoons amplify flood risks and threaten the lives and livelihoods of millions of people residing in the low-lying coastal plains and flood-prone river basins. With a death toll of over 100,000 and damages amounting to USD10 billion, the 2008 category 4 tropical cyclone Nargis is considered one of the most destructive natural disasters in the Bay of Bengal and ranks as the second deadliest named cyclone in the recorded history (Brakenridge et al., 2012; Shi & Wang, 2008). Moving from the Bay of Bengal in a northeasterly direction, the cyclone made a landfall in the densely populated Ayeyarwady delta region, Myanmar (Burma), on May 2, 2008, causing a massive storm surge that flooded both coastal and inland areas (Brakenridge et al., 2012; Ozcelik, Gorokhovich, & Doocy, 2012; Shi & Wang, 2008). In the days after Nargis, response and recovery operations and the delivery of humanitarian assistance were hampered by a lack of scientific data and the reluctance of the military government to open the borders to relief efforts (Brakenridge et al., 2012; Ozcelik et al., 2012). Medium-resolution optical imagery from MODIS and population data from the Gridded Population of the World (SEDAC, 2005) provided an initial account of storm surge inundation and population exposure, demonstrating the value of geospatial technologies in conducting damage assessments in areas with sparse data (Brakenridge et al., 2012; Ozcelik et al,, 2012).

In Bangladesh, high-intensity monsoon rains often cause extensive flooding that affects both densely populated areas and agricultural fields. Kwak, Arifizzanman, and Iwami (2015) developed an advanced-water-detection algorithm to compile risk maps from MODIS imagery and estimate the damage to rice fields in the flooded areas. The results were validated using radar imagery from the Advanced Land Observing Satellite (ALOS) AVNIR-2 and ground measurements (Kwak et al., 2015). The results showed that satellite remote sensing can provide instant visualization of the affected areas and assist government institutions in making damage assessments and in response and recovery operations. Byun, Han, and Chae (2015) developed an unsupervised change-detection algorithm based on image fusion to delineate the flooded area in the city of N′djamena, Chad. Very high-resolution bi-temporal imagery from KOMPSAT-2 were processed using a probability mixture model for change detection. The proposed methodology enabled accurate differentiation of inundated areas from permanent waterbodies (Byun et al., 2015).

Remote sensing and GIS provide data and analytical tools to evaluate flood impacts on urban infrastructure. Muriel-Villegas, Alvarez-Uribe, Pariño-Rodriguez, and Villegas (2016) collected data on road infrastructure disruptions during the 2010–2011 rainy season in the state of Antioquia, Colombia, which indicated that 40% of the road closures were due to land sinking; 24%, to landslides; and 7%, to surface layer loss. The study proposed measures of link criticality, connectivity reliability, and network vulnerability that were applied to road closure analysis under extreme events (Muriel-Villegas et al., 2016). A GIS-based analysis provided further insight into the causes of the observed road failures and helped formulate adequate measures to reduce future adverse impacts (Muriel-Villegas et al., 2016). Renschler and Wang (2017) evaluated the flood extent caused by Hurricane Irene in a large rural area in New York State. Airborne LiDAR data collected two days after the flood event were overlaid with the FEMA flood zones and other GIS data layers to determine the return period of the flood. HEC-RAS hydraulic modeling using Hydrologic Engineering Center’s River Analysis System (HEC-RAS) and 3D visualization based on Google Sketchup provided further opportunities for data synthesis to better understand the spatial and temporal dynamics of a large-scale flood event and the impacts on vulnerable infrastructure assets (Renschler & Wang, 2017). Postdisaster damage assessments have implications for a re-evaluation of mitigation practices and the development of long-term resilience strategies (Womble et al., 2006).

Droughts and Wildfires

Droughts may increase susceptibility to wildfires, depending on the complex interactions among rainfall, biomass accumulation, and the availability of ignition sources. Increased precipitation during the growing season stimulates primary production and fuel accumulation (Scasta, Weir, & Strambaugh, 2016). Subsequently, below average rainfall can result in moisture deficiency in soil and vegetation, turning brush, tall grass, chaparral, and trees into fuels and increasing the risk of fire activity (Hayes, Wilhelmi, & Knutson, 2004; Scasta et al., 2016). Conversely, when these patterns are reversed (when there is less rain during the growing season and increased rainfall during the dormant season), the potential for fire activity is decreased (Scasta et al., 2016).

Monitoring, Rapid Response, and Early Warning Systems

Advances in GIS technologies and remote sensing have significantly improved our ability to assemble climatological data, monitor drought conditions, measure moisture deficiency, and estimate risk. Web-based geospatial applications have also proven to be effective in mobilizing governance institutions and resources on local, regional, and global scales. Droughts have a disastrous effect on millions of people in the developing world, particularly Africa and parts of Asia, causing widespread famine and loss of life. Two early warning systems—the Famine Early Warning Systems Network (FEWS NET), sponsored by the United States Agency for International Development (USAID), and Global Monitoring for Food Security (GMFS), funded by the European Space Agency—are prime examples of the use of geospatial technologies in support of the international community’s efforts to provide humanitarian assistance to the countries most affected by famine. FEWS NET and GMFS use remote-sensing data from MODIS, AVHRR, and other instruments to monitor rainfall, soil moisture, vegetation vitality, and other physical parameters and forecast drought conditions (Famine Early Warning Systems Network [FEWS NET], 2017). Remote-sensing data are often coupled with regional analysis of food availability and sociopolitical dynamics to estimate the number of people in need of humanitarian assistance. In 2017, FEWS NET determined that nearly 80 million people in Africa are threatened by acute levels of food shortages that can result in high mortality rates, particularly among children (FEWS NET, 2017). FEWS NET is also at the forefront of the efforts to mobilize the international community to support food security programs, which demonstrates the value of geospatial technologies in global risk governance.

Monitoring, early detection, and early fire suppression are instrumental in developing successful wildfire-management strategies (Allison, Johnston, Craig, & Jennings, 2016). While various types of aircraft have been used in fire monitoring for decades, fire reconnaissance has increasingly relied on satellite systems and unmanned aircraft systems (UAS), or drones, because of the cost associated with operating a fleet of aircraft and the limitations on how much spatial coverage the manned systems can afford (Allison et al., 2016). In 2001, in response to an abnormally active wildfire season, the U.S. Forest Service, in collaboration with the NASA’s Goddard Space Flight Center and the Department of Geographical Sciences at the University of Maryland created the first wildfire rapid response system using near real-time imagery from the MODIS (Sohlberg, Descloitres, & Bobbe, 2001). By implementing a distributed computing model with automated data processing and Web-based dissemination, the collaborative project provided the emergency management community with daily wildfire maps to facilitate the detection of fire activity and the deployment of resources (Sohlberg et al., 2001). Over the next few years, the geographic coverage of the rapid response fire imagery and data was expanded to serve a global audience (National Aeronautics and Space Administration [NASA], 2018).

Drought and Wildfire Damage Assessment

Band ratios and normalized indices derived from optical, thermal, and microwave imagery have been used to distinguish between fire-disturbed and undisturbed areas (Joyce et al., 2009), providing a starting point for wildfire damage assessments. Using Landsat-5 TM (Thematic Mapper) imagery, Chen, Moriya, Sakai, Koyama, and Cao (2014) evaluated the extent of the burned areas after the Greater Hinggan Mountains wildfire, one of the largest boreal forest fires in China in recent history. Based on the optical signature of fire-disturbed and undisturbed areas, several indices were computed, including the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the disturbance index (DI; Chen et al., 2014). The accuracy of the segmentation and classification was evaluated using statistical methods and official statistics. The study found that that normalized indices derived from optical data were effective in investigating fire damage (Chen et al., 2014). These assessments, however, can be limited when cloud cover, haze, or smoke are present. Recent studies have been successful in using SAR data to extract the extent of fire-scarred regions (Jenkins, Bourgeau-Chavez, French, Loboda, & Thelen, 2014; Rykhus & Zhong, 2011). Rykhus and Zhong (2011) documented fire disturbance progression and severity in the Yukon Flats National Wildlife Refuge in Alaska using InSAR data from Radarsat-1. Jenkins et al. (2014) measured the rate of postfire recovery in the Alaskan tundra and showed that SAR provides high-accuracy data for quantifying the changes caused by fire activity.

Recent studies have extended fire-damage analysis to include perturbations of the water cycle, precipitation patterns, surface albedo, lower tropospheric heating, and surface radiative cooling due to the aerosols emitted by fires (Ichoku et al., 2016). Using a combination of remote-sensing datasets, Ichoku et al. (2016) analyzed disruptions to the hydrological cycle, drought conditions, and fire activity related to biomass burning in the Northern sub-Saharan African region using MODIS (Collections 5 and 6) thermal anomalies products (MOD14/MYD14). Seasonal changes in fire activity were found to be inversely correlated with water cycle indicators, soil moisture, and vegetative cover, indicating that severe fire seasons can result in alterations of hydrologic conditions and exacerbate droughts (Ichoku et al., 2016).

The studies reviewed suggest that drought risk assessment is compounded by difficulties in predicting the time of onset and probability of occurrence, geographic extent (drought conditions can affect large regions and even continents), dependence on climatic variability and change, and significant challenges in designing mitigation strategies (Hayes et al., 2004; Scasta et al, 2016). Other factors, such as the El Niño-Southern Oscillation (ENSO; periodic warming of the waters of the central and east-central equatorial Pacific Ocean), are also known to affect precipitation patterns and drought conditions in some regions, including parts of South America, Australia, Indonesia, and the United States. Anthropogenic factors, such as biomass burning to clear land for agricultural production, combined with drier weather can also trigger widespread fire hazards, such as those observed in the Amazon rainforest (Juárez-Orozco, Siebe, & Fernández y Fernández, 2017) and the Northern Sub-Saharan African Sahel (Ichoku et al., 2016). Some studies suggest that the trends of increasing drought frequency and duration correlate positively with wildfire severity, geographic extent, size, time of onset, and duration (Dennison, Brewer, Arnold, & Moritz, 2014; Westerling, Gershunov, Brown, Cayan, & Dettinger, 2003; Westerling, Hidalgo, Cayan, & Swetnam, 2006). Despite the advances in geospatial technological development, further research is needed to bridge the analysis of environmental impacts with assessments of the disruptions of social and economic systems caused by droughts and wildfires.

Web Applications and Near Real-Time Mapping

Key new developments in the realm of geospatial technologies are the near real-time data acquisition from geo-sensors and airborne and spaceborne platforms and the use of remotely piloted aircraft and UAS. Both developments have the potential to improve emergency-management coordination and collaboration and enhance disaster-response capabilities.

Remotely Piloted and Unmanned Aircraft Systems

Disaster response following Hurricanes Harvey (August 25–29, 2017) and Irma (August 30–September 13, 2017) demonstrated how the extensive use of drone technology could facilitate and streamline search and rescue operations, expedite the evacuation of at-risk populations, improve situational awareness, and support the restoration of infrastructure services. After Hurricane Harvey, the Texas A&M Engineering Experiment Station Center for Robot-Assisted Search and Rescue coordinated 119 drone missions with the incident command system of the Emergency Operations Center in Fort Bend County, Texas (Murphy, 2017). Unmanned aircraft systems (UAS) flights helped guide search and rescue efforts and provided timely information on road access to neighborhoods whose residents were stranded by the flood waters (Murphy, 2017). The UAS missions also collected data on flood extent and tornado damage to guide federal disaster relief and individual assistance and, most importantly, provided early warning to residents and communities threatened by flash floods or other imminent hazards (Murphy, 2017). The use of UAS technology was expanded after Hurricane Irma. The Center for Disaster Risk Policy at Florida State University deployed drone technology in Collier County and completed 247 missions focused on doing damage assessments of critical infrastructure, particularly power transmission lines and communication towers. An additional 650 UAS flights to assess the infrastructure damage after Irma were completed in Miami, Fort Lauderdale, and the northern part of Florida (Werner, 2017).

Remotely piloted and unmanned aerial vehicles flying at moderate or low altitudes can also improve fire reconnaissance by overcoming the challenges of traditional “fly-over” techniques and fire-detection methods using spaceborne optical imagery (Allison et al., 2016). Dickinson et al. (2016) provide a detailed overview of the use of geospatial technologies in wetland fire monitoring and suppression. Werner (2015, p. 29) provides several examples of the use of drones in fire reconnaissance activities including the 2006–2009 fires in California, the 2013 Yosemite National Park Rim Fire, and in fire suppression efforts in Western Australia. Unmanned systems can complete long-endurance, long-range missions in the fire zone (depending on the vehicle specifications), address data needs, conduct multiple missions in the most affected areas, supplement manned aircraft missions, and provide tactical support for the fire-management agencies (Allison et al., 2016; Twidwell et al., 2016). Unmanned aerial vehicles have proven their efficiency in estimating the extent of the fire-disturbed areas and encroachment on urban settings and infrastructure. Videos and multispectral and thermal imagery sensors mounted on remotely piloted and unmanned aerial vehicles have supported timely risk communication by providing real-time continuous mapping of a wildfire’s spread, intensity, direction of movement, and proximity to urban and suburban areas (Allison et al., 2016; Twidwell et al., 2016).

Integrating drones in flood monitoring and firefighting operations requires careful consideration of the risks involved. In fire suppression operations, for example, unmanned and remotely piloted vehicles can pose a danger for piloted air tankers and the helicopters involved in physical fire suppression using retardants and other tactical means (Werner, 2015). There are several regulatory and institutional barriers to integrating UAS and Remote Piloted Systems (RPS) in hazard management. In the United States, any UAS operation requires prior approval by the Federal Aviation Administration via an emergency Certificate of Authorization (eCOA), Special Government Interests (authorizations given for significant and urgent government interests related to the national defense, homeland security, law enforcement, or emergency operations), or other exemptions (Twidwell et al., 2016; Werner, 2017). In Canada and Australia, efforts have been made to streamline and incorporate the process of UAS use in emergency management (Twidwell et al., 2016).

Web-Based GIS Applications for Near Real-Time Hazard Monitoring and Emergency Response

Real-time GIS applications have added a new dimension to disaster and crisis management. Emergency services have benefited from streaming live data via GIS-based big data platforms. Web applications designed to manage and display near real-time data of flood events, wildfires, earthquakes, and volcanic activity function as decision support tools for emergency management operations, but most importantly, they provide timely information to the public and increase the potential for the participative response and recovery process. The Flood Inundation Mapping and Alert Network (FIMAN) created to support the North Carolina Floodplain Mapping Program provides access to real-time flood information obtained from the readings of approximately 550 gauges (Dorman & Banerjee, 2016). In contrast to the traditional flood mapping based on historical return period, FIMAN enables emergency managers and local communities to make disaster response decisions based on dynamic updates of the flood stage. The information is updated every 15 to 30 minutes based on storm-specific rainfall (Dorman & Banerjee, 2016). During the flood event caused by Hurricane Joaquin in 2015, the North Carolina Emergency Operations Center made extensive use of FIMAN to monitor hazardous conditions, position emergency response assets, and inform elected officials and residents about imminent flood risks (Dorman & Banerjee, 2016).

Another example of real-time data management for reducing disaster risk is the Advanced Emergency Geographic Information System (AEGIS), developed by ESRI in collaboration with the Loma Linda University Medical Center. AEGIS demonstrates how a real-time Web-based application can serve emergency responders in a densely populated area prone to significant earthquake hazards (Fike, 2007). Accessible via the Internet or a cell phone, AEGIS provides timely information on emergencies, dispatch capabilities, traffic congestion, and accidents and uses rerouting services to reduce emergency responders’ travel time (Fike, 2007). Henriksen et al. (2018) introduced a framework for developing an interactive and participatory Web-based platform for flood-risk reduction. The platform, applied to selected Nordic countries, has been designed to expand access to hydrological data, modeling results, and near real-time flood forecasts. Combined with information extracted from social media and other digital data sources, the Web-based platform would provide further opportunities for enhancing the existing early warning and monitoring systems, improving risk communication, and engaging stakeholders in collaborative processes to develop effective solutions for adaptive and integrated flood-risk management (Henriksen et al., 2018).

Chung, Liu, Cheng, Lee, and Shieh (2015) explored the feasibility of using interferometric synthetic aperture radar (InSAR) to conduct a rapid assessment of the threat of flash floods in I-Lan County, Taiwan. The floods were precipitated by heavy rainfall caused by Typhoon Soulik in July 2013. The maps, derived from COSMO-SkyMed 1 radar satellite imagery, were made available to decision-makers and the public via Google Earth within 24 hours. Based on the methodology proposed by Chung et al. (2015), the Water Hazard Mitigation Center of the Taiwanese Water Resources Agency developed a standard operating procedure that was subsequently used during Typhoon Trami (August 2013) and Typhoon Soudelor (August 2015). Reflecting on participant observations and ethnographic interviews, Padawangi et al. (2016) explored the potential benefits of community-based and crowdsourced mapping in reducing the flood hazard risks faced by the informal settlements along the Ciliwung River in Jakarta, Indonesia. The participatory community mapping exercise, organized in 2012, revealed the complex nature of decision-making in the self-built river communities that are often excluded from the official planning processes, the scarcity of information, and the challenges in addressing persistent flood risks. The new crowdsourced flood-mapping system, operating since 2014, showed the potential of crowdsourced information to improve risk communication and raise flood-risk awareness in the most vulnerable communities in Southeast Asia (Padawangi et al., 2016).

The Fire Information for Resource Management System (FIRMS) and WorldView provide active fire hotspot data in a variety of formats, including Web services, shapefiles, KML files, and text (NASA, 2018). Launched in 2009, NASA’s Web application—Land, Atmosphere Near Real-Time Capability for EOS (LANCE; Giglio, Schroeder, & Justice, 2016; Schroeder, Oliva, Giglio, & Csiszar, 2014) offers a variety of near real-time active fire products derived from multiple satellite instruments, including MODIS, Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer 2 (AMSR2), Lightning Imaging Sensor (LIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). While earlier spaceborne platforms were able to detect medium- to large-scale fires, VIIRS and other recent advances in sensor technology make it possible to detect micro-scale fires, allowing for the early suppression and prevention of large-scale fire activity (Allison et al., 2016). Murphy, Souza-Filho, Sabatino, and Pabon (2016) developed hotmap, a Web-based monitoring system designed to stream frequently updated satellite images that will enable the end user to track volcanic activity and wildfires in near real-time, facilitating the dissemination of critical information to the communities at risk.

The Use of Citizen Science in Natural Hazards Management

Interactive Web-based GIS platforms that provide opportunities for spatial data exploration and simultaneously allow for user input, participation, and comparison of user-proposed solutions are a developing field of geospatial technologies (Goodchild, 2007a, 2007b; Goodchild & Glennon, 2010; De Longueville, Annoni, Schade, Ostlaender, & Whitmore, 2010; Kawasaki et al., 2013). Empowered by Web 2.0 technologies and the emerging culture of social networking, these new developments, often termed crowdsourcing (Howe, 2008), citizen science (Follett & Strezov, 2015), or volunteered geographic information (VGI; Goodchild, 2007a, 2007b) challenge the conventional centralized-expert-based approach to spatial data acquisition, quality control, and dissemination (Kawasaki et al., 2013). They also offer opportunities to expand disaster risk governance as a means of enhancing community resilience (Fekete et al., 2015).

Estellés-Arolas and González-Ladrón-de-Guevara (2012, p. 197) define crowdsourcing as “a type of participative online activity in which an individual, an institution, a nonprofit organization, or company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task.” According to Brabham (2008, p. 79), “Crowdsourcing is not merely a Web 2.0 buzzword, but is instead a strategic model to attract an interested, motivated crowd of individuals capable of providing solutions superior in quality and quantity to those that even traditional forms of business can.” Crowdsourcing provides the foundation of citizen science, broadly defined as the involvement of volunteers in scientific inquiry (Follett & Strezov, 2015). When applied to various platforms for georeferenced user-generated content, this distributed form of data acquisition becomes VGI (Goodchild, 2007a, 2007b). VGI draws upon Web 2.0 resources (Rinner et al., 2008) and provides opportunities for interactions between user groups who acquire, create, and disseminate spatial data (Craglia et al., 2008; Scharl & Tochtermann, 2007).

Kawasaki et al. (2013) provide several case studies in which disaster response and crisis management highlight the importance of user-generated geographic content for supporting relief efforts, emergency aid distribution, and collection of disaster-related information distributed through blogs and other Web-based platforms, such as Twitter. The authors note that the inefficient response by government agencies to the devastation caused by Hurricane Katrina led to a broad set of Web-based and on-site activities by volunteers, private companies, and nonprofit groups, transforming disaster response from a professional activity to a participative community process (Kawasaki et al., 2013). Other examples include Harvard University’s geospatial portal project led by the Center for Geographic Analysis, which provided a platform for organizing and sharing information, including user-generated content, with the global community following the 2008 Sichuan earthquake in China, the 2010 Haiti earthquake, the 2010 Chile earthquake, and the 2011 Japan Sendai earthquake.The Ushahidi platform based on volunteered information was instrumental in facilitating humanitarian assistance following the devastating 2010 Haiti earthquake (Fekete et al., 2015; Kawasaki et al., 2013).

Goodchild and Glennon (2010) describe several examples of VGI during the 2007–2009 fires in Santa Barbara, California. VGI played a major role in establishing volunteer map sites run by volunteer community groups, some of which received over 500,000 hits (Goodchild & Glennon, 2010). The Web-enabled VGI platforms provided “essential information about the location of the fire, evacuation orders, the location of emergency shelters, and much other useful information” (Goodchild & Glennon, 2010, p. 238), demonstrating the effectiveness of the community contributions and participatory mapping. Remarkably, volunteers were able to download satellite images from MODIS and other instruments and render them in a format suitable for display on Google Earth (Goodchild & Glennon, 2010). In many cases, this information was available to the public before the official updates.

The shortcomings of VGI are also worth noting. Among the most commonly cited drawbacks are the lack of formal mechanisms for data verification from crowdsourcing, the need for formal rules to ensure data quality, and the need to build trust in “digital empowerment” (Goodchild & Glennon, 2010, p. 240). Several approaches to overcoming these challenges have been proposed, which has lead to a broader understanding of how the further development of VGI will support spatial data infrastructures and sensor-based Web applications (De Longueville et al., 2010). Bordogna, Carrara, Criscuolo, Pepe, and Rampini (2016) reviewed a range of VGI applications in scientific projects and highlighted several strategies to improve data quality. Following a discussion of the extent to which the quality of the data acquired through VGI depends on the type of information and the project research objectives, the study evaluated several techniques to enhance VGI quality, including templates for automatic error checking and cross-referencing with well-established sources.

Addressing Current and Future Challenges for Natural Hazards Management

Geospatial technologies carry unmatched capabilities to address the critical issues that are inherent to the complexity and scope of natural hazards management (Cova, 1999; Cutter, 2003; Tomaszewski et al., 2015; van Westen, 2013). Observational, analytical, and predictive functionalities supported by accurate and timely spatial data acquisition are essential in sustaining the continuous monitoring of unfolding hazardous events and in making rapid assessments and decision-making during disaster response and recovery. This article highlighted several applications of geospatial technologies in hazard monitoring, identification, and mapping, as well as damage assessment and disaster response. It illustrated the advancement of recent geospatial technological development in support of disaster management. Newer accessible sources and types of data and numerical modeling within the GIS environment have greatly enhanced the ability of practitioners and researchers to develop decision support systems that facilitate rapid and efficient response to disasters through monitoring, forecasting, impact analysis, and near real-time decision-making. Satellite imagery from optical and thermal sensors and radar has enabled risk and damage assessments in areas affected by earthquakes, landslides, floods, and volcanic eruptions in near real time (Gillespie et al., 2007). GPS technologies combining geolocators with Web services and GIS databases have enabled new applications aimed at enhancing near real-time hazard monitoring, early warning systems, and rapid damage assessment. There is a growing interest in the applications of Unmanned Aircraft Systems (UAS) for post-earthquake research and relief operations, exploration of active volcanoes, flood risk assessments, wind damage, and disaster recovery (Gomez & Purdie, 2016).

Innovations in geospatial technologies also include distributed Web-based GIS services and mapping platforms for systematizing and sharing data, maps, applications, and analytics. Distributed GIS and VGI enable agencies and end users to connect and interact with remotely located information products, apps, and services; improve communication; and strengthen pre- and postevent collaboration (Goodchild, 2007a, 2007b; Goodchild & Glennon, 2010; De Longueville et al., 2010; Kawasaki et al., 2013; Zar, Jaboyedoff, Derron, & van Westen, 2015). Other new developments in geospatial technologies include integration of GIS with information technologies (IT). Distributed computing is likely to become critical in asset, power-outage, and transportation systems management. Embedding GIS in IT operations can secure rapid restoration of infrastructure services in the aftermath of a disaster, improve assistance delivery, and facilitate communication, coordination, and collaboration among the various stakeholders (Zhai, Yue, & Zhang, 2016).

Despite the progress that has been made, many critical research gaps and challenges remain. The use of geospatial data in natural hazards management is often constrained by the heterogeneity of data sources, access protocols, and metadata standards. Seeking to address the challenges associated with the use of heterogeneous data systems, Chen, Zhou, and Chen (2015) proposed a sharable metadata model (APEOPM) to support an Earth observation data system in evaluating hazard risk based on interoperability algorithms to reconcile different access protocols and archiving metadata models for retrieval of multiscale remote-sensing data. The validation procedure using observed data showed the efficiency of the enhanced data retrieval, encoding, and archiving protocols and models, and highlighted their utility in supporting the integration of heterogeneous data systems (Chen et al., 2015).

The increased reliance on remote-sensing data for monitoring, evaluation, developing early warning systems, rapid risk assessment, and damage analysis has raised questions about data quality and limitations. Yang et al. (2013, p. 881) highlighted three recurrent limitations of using satellite time series in studies associated with climate-related hazards: (a) biases associated with the short duration of satellite data time series that could distort the interpretation of long-term trends (e.g., interannual and decadal variability); (b) biases associated with the sensor’s spectral resolution and accuracy; and (c) the need for “better knowledge of errors in instruments and algorithms.” The authors suggested that these limitations could be addressed through intercomparison studies, quantification of uncertainty in common input data used in hazard risk analysis, and use of high-quality validation datasets (Yang et al., 2013).

Imagery acquired from optical sensors is often obscured by the presence of cloud cover, precipitation, or smoke aerosols from fires, which limits its usability (Zhang et al., 2013). Although studies of longer duration can eliminate imagery affected by obscuration, such approaches could be unpractical in rapid assessment and reconnaissance studies following a disaster when available data are sparse. Ticehurst, Guerschman, and Chen (2014) examined the suitability of the MODIS Open Water Likelihood for identifying the extent of flooded areas. The authors noted that though cloud cover may obstruct flood identification at the start and peak of a flood event, more cloud-free data are usually available at the time of flood recession (Ticehurst et al., 2014). The results from the computation were compared with streamflow and rainfall measurements at several sites during the wet season in northern Australia. The study identifies several approaches for minimizing errors in flood identification including consideration of view angle, and percentage water and cloud cover (Ticehurst et al., 2014). Progress has been made with the use of radar data that are not affected by weather conditions or aerosol thickness (Zhang et al., 2013).

Data fusion is a novel approach that integrates modeling with observational data to increase the accuracy of the remote-sensing data products (Zhang et al., 2013). Malinowski, Groom, Schwanghart, and Heckrath (2015) evaluated the accuracy of several flood extent maps derived from WorldView-2 imagery and a decision-tree algorithm with various combinations of input variables, including topographic data and selected spectral indices. The use of all available input data combined with object-based image analysis improved the overall mapping accuracy of the flood extent and captured complex patterns of localized flooding.

There are still gaps in knowledge and research needs related to integrating natural hazards assessments with better understanding of how disasters affect social and economic systems and what role governance institutions play in addressing these challenges. Reflecting on the critical dimensions of resilience to natural hazards, Koliou et al. (2018, p. 1) argued that “despite the broad interest engendered by recent hazard events and research funding initiatives, there has been little coordinated effort to address the complex interactions between physical, social, and economic infrastructure that enable community resilience.” Geospatial technologies provide various effective tools to integrate data from a variety of sources, conduct analyses, improve monitoring, and establish a broad range of indicators to track progress toward more resilient human and natural systems. Furthermore, geospatial technologies are a key component of decision-support systems and tools that can optimize emergency response actions and facilitate decision-making aimed at decreasing risk and vulnerability.

Finally, it should be noted that rural communities and small island nations around the world may not possess the means to afford advanced technologies such as computer-based GIS and digital image-processing software. However, these communities have explored opportunities to integrate community-based disaster risk management and participatory three-dimensional mapping (P3DM) using physical models into their hazard management and climate adaptation planning (Bobb-Prescott, 2014; DeGraff & Ramlal, 2015; Piccolella, 2013). Piccolella (2013) explored the benefits of P3DM for the risk assessment of future sea-level-rise scenarios in the small island community of Boe Boe, in the Choiseul province of the Solomon Islands. Gaillard et al. (2013) discussed the importance of the P3DM in fostering collaborative processes and a shared understanding of local vulnerabilities to natural hazards and climate change in a small village in the Terai plain, Nepal. Hohenthal, Minoia, and Pellikka (2017) examined the role of participatory mapping in enhancing water-management practices in Taita Hills, Kenya. The study revealed how the shared experience of creating participatory maps enabled discussions of the historical and political contexts undergirding water-related problems. The map products resulting from these discussions depicted areas that were in need of further management actions to address drought conditions, vulnerabilities and risks, pollution, failing infrastructure, water-use restrictions, poverty, and inadequate access to the water supply (Hohenthal et al., 2017).


This article is based on research supported by the U.S. National Science Foundation Grant CMMI#1541089. Any opinions, findings, conclusions, or recommendations expressed here are those of the author and do not necessarily reflect the views of the National Science Foundation.

Further Reading

Asch, T. V., Corominas, J., Greiving, S., Malet, J.‑P., & Sterlacchini, S. (Eds.). (2014). Mountain risks: From prediction to management and governance. Advances in Natural and Technological Hazards Research 34. Dordrecht, Germany: Springer Science.Find this resource:

Barrett, E. C., Brown, K. A., & Micallef, A. (1991). Remote sensing for hazard monitoring and disaster assessment: Marine and coastal applications in the Mediterranean region. Boca Raton, FL: CRC Press.Find this resource:

Glass, C. E. (2013). Interpreting aerial photographs to identify natural hazards. Waltham, MA: Elsevier Science.Find this resource:

Ismail-Zadeh, A., Urrutia Fucugauchi, J., Kijko, A., Takeuchi, K., & Zaliapin, I. (Eds.). (2014). Extreme natural hazards, disaster risks and societal implications. Cambridge, U.K.: Cambridge University Press.Find this resource:

Li, X. (Ed.). (2017). Hurricane monitoring with Spaceborne Synthetic Aperture Radar. Singapore: Springer Natural Hazards.Find this resource:

Pine, J. C. (Ed.). (2014). Hazards analysis: Reducing the impact of disasters (2nd ed.). Boca Raton, FL: CRC Press.Find this resource:

Pirasteh, S., & Li, J. (Eds.). (2017). Global changes and natural disaster management: Geo-information Technologies. Cham, Switzerland: Springer International.Find this resource:

Saha, S., & Mondal, A. (2012). GIS and remote sensing. Berlin, Germany: AV Akademikerverlag.Find this resource:

Scaioni, M. (Ed.). (2015). Modern technologies for landslide monitoring and prediction. Berlin, Germany: Springer Natural Hazards.Find this resource:

Singh, R., & Bartlett, D. (Eds.). (2018). Natural hazards: Earthquakes, volcanoes, and landslides. Boca Raton, FL: CRC Press.Find this resource:


Allen, T. I., & Wald, D. J. (2009). On the use of high-resolution topographic data as a proxy for seismic site conditions (Vs30). Bulletin of the Seismological Society of America, 99(2A), 935–943.Find this resource:

Allison, R. S., Johnston, J. M., Craig, G., & Jennings, S. (2016). Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors, 16, 1310.Find this resource:

Association of Geographic Information Laboratories for Europe (AGILE). (2016). History and Mission Statement.

Avouac, J.‑P., Ayoub, F., Leprince, S., Konca, O., & Helmberger, D. V. (2006). The 2005, Mw 7.6 Kashmir earthquake: Sub-pixel correlation of ASTER images and seismic waveforms analysis. Earth and Planetary Science Letters, 249, 514–528.Find this resource:

Beven, K., & Freer, J. (2001). Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology, 249, 11–29.Find this resource:

Biggs, J., Ebmeier, S. K., Aspinall, W. P., Lu, Z., Pritchard, M. E., Sparks, R. S. J., & Mather T. A. (2014). Global link between deformation and volcanic eruption quantified by satellite imagery. Nature Communications, 5, Article 3471.Find this resource:

Bobb-Prescott, N. (2014). Case study on the use of participatory three-dimensional modeling to facilitate effective contribution of civil society in the Caribbean islands in planning for action on climate change (CANARI Technical Report 401). Caribbean Natural Resources Institute. Laventille, Trinidad.Find this resource:

Bordogna, G., Carrara, P., Criscuolo, L., Pepe, M., & Rampini, A. (2016). On predicting and improving the quality of Volunteer Geographic Information projects. International Journal of Digital Earth, 9, 134–155.Find this resource:

Brabham, D. (2008). Crowdsourcing as a model for problem solving: An introduction and cases. Convergence: The International Journal of Research into New Media Technologies, 14, 75–90.Find this resource:

Brakenridge, G. R., Syvitsky, J. P. M., Overeem, L., Higgins, S. A., Kettner, A. J., Stewart-Moore, J. A., & Westerhoff, R. (2012). Global mapping of storm surges and the assessment of coastal vulnerability. Natural Hazards, 66, 1295–1312.Find this resource:

Brody, S., Highfield, W., & Kang, J. E. (2011). Rising waters: Causes and consequences of flooding in the United States. Cambridge, U.K.: Cambridge University Press.Find this resource:

Brody, S. D., Zahran, S., Highfield, W. E., Grover, H., & Vedlitz, A. (2008). Identifying the impact of the built environment on flood damage in Texas. Disasters, 32(1), 1–18.Find this resource:

Brunori, C. A., Bignami, C., Albano, M., Zucca, F., Samsonov, S., Gropelli, G., . . . Stramondo, S. (2015). Land subsidence, ground fissures, and buried faults: InSAR monitoring of Ciudad Guzman (Jalisco, Mexico). Remote Sensing, 7, 8610–8630.Find this resource:

Byun, Y., Han, Y., & Chae, T. (2015). Image fusion-based change detection for flood extent extraction using bi-temporal very high-resolution satellite images. Remote Sensing, 7, 10347-10363.Find this resource:

Chae, B‑G., Park, H.‑J., Catani, F., Simoni, A., & Berti, M. (2017). Landslide prediction, monitoring and early warning: A concise review of state-of-the art. Geosciences Journal, 21, 1033–1070.Find this resource:

Chen, K.‑S., Serpico, S. B., & Smith, J. A. (2012). Remote sensing of natural disasters. Proceedings of the Institute of Electrical and Electronics Engineers, 100, 2794–2797.Find this resource:

Chen, N., Zhou, L., & Chen, Z. (2015). A sharable and efficient metadata model for heterogeneous earth ebservation data retrieval in multi-scale flood mapping. Remote Sensing, 7, 9610–9631.Find this resource:

Chen, P., Liew, S. C., & Kwoh, L. K. (2005). Tsunami damage assessment using high resolution satellite imagery: A case study of Aceh, Indonesia. Proceedings 2005 Institute of Electrical and Electronics Engineers International, 2, 1405–1408. Presented at Geoscience and Remote Sensing Symposium, 2005. Seoul, South Korea.Find this resource:

Chen, R., Sharman, R., Rao, H. R., & Upadhyaya, S. J. (2008). Coordination in emergency response management. Communications of the ACM, 51(5), 66–71.Find this resource:

Chen, W., Moriya, K., Sakai, T., Koyama L., & Cao, C. X. (2014). Mapping a burned forest area from Landsat TM data by multiple methods. Geomatics, Natural Hazards and Risk, 7, 384–402.Find this resource:

Cheng, G., & Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11–28.Find this resource:

Chini, M., Bignami, C., Stramondo S., & Pierdicca, N. (2008). Uplift and subsidence due to the 26 December 2004 Indonesian earthquake detected by SAR data. International Journal of Remote Sensing, 29, 3891–3910.Find this resource:

Chung, H.‑W., Liu, C.‑C., Cheng, I.‑F., Lee, Y.‑R., & Shieh, M.‑C. (2015). Rapid response to a typhoon-induced flood with a SAR-derived map of inundated areas: Case study and validation. Remote Sensing, 7, 11954–11973.Find this resource:

Cova, T. J. (1999). GIS in emergency management. In P. A. Longley, M. F. Goodchild, D. J. Maguire, & D. W. Rhind (Eds.), Geographical information systems: Principles, techniques, applications, and management (pp. 845–858). New York, NY: John Wiley & Sons.Find this resource:

Craglia, M., Goodchild, M. F., Annoni, A., Camara, G., Gould, M., Kuhn, W., . . . Parsons, E. (2008). Next-generation digital earth: A position paper from the Vespucci initiative for the advancement of geographic information science. International Journal of Spatial Data Infrastructure Research, 3, 146–167.Find this resource:

Cutter, S. L. (2003). GI science, disasters, and emergency management. Transactions in GIS, 7, 439–446.Find this resource:

DeGraff, A., & Ramlal, B. (2015). Participatory mapping: Caribbean small island developing states. Paper presented at the Forum on the Future of the Caribbean. St. Augustine, Trinidad.Find this resource:

De Longueville, D., Annoni, A., Schade, S., Ostlaender, N., & Whitmore, C. (2010). Digital earth’s nervous system for crisis events: Real-time sensor web enablement of volunteered geographic information. International Journal of Digital Earth, 3, 242–259.Find this resource:

Dennison, P. E., Brewer, S. C., Arnold, J. D., & Moritz, M. A. (2014). Large wildfire trends in the western United States, 1984–2011. Geophysical Research Letters, 41, 2928–2933.Find this resource:

Dickinson, M. B., Hudak, A. T., Zajkowski, T., Loudermilk, E. L., Schroeder, W., Ellison, L., . . . Mauceri, J. (2016). Measuring radiant emissions from entire prescribed fires with ground, airborne and satellite sensors: RxCADRE 2012. International Journal of Wildland Fire, 25, 48–61.Find this resource:

Dorman, J. K., & Banerjee, N. (2016). A real-time flood warning system. ArcUser (Winter).Find this resource:

Emanuel, K. (2017). Assessing the present and future probability of Hurricane Harvey’s rainfall. Proceedings of the National Academy of Sciences of the United States of America, 114, 12681–12684.Find this resource:

Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38, 189–200.Find this resource:

Famine Early Warning Systems Network (FEWS NET). (2017). Already unprecedented food assistance needs grow further: Risk of famine persists. [FEWS NET global alert].

Federal Emergency Management Agency (FEMA). (2017). Flood Insurance Rate Map (FIRM).

Federal Emergency Management Agency (FEMA). (2018). The National Flood Insurance Program (NFIP).

Fekete, A., Tzavella, K., Armas, I., Binner, J., Garshagen, M., Giupponi, C., . . . Serre, D. (2015). Critical data source, tool or even infrastructure? Challenges of geographic information systems and remote sensing for disaster risk governance. ISPRS International Journal of Geo-Information, 4, 1848–1869.Find this resource:

Ferreira, L. G., & Huete, A. R. (2004). Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices. International Journal of Remote Sensing, 25, 1837–1860.Find this resource:

Fike, R. (2007). How GIS is changing Loma Linda University Medical Center’s view of the world. ArcNews Online (Fall).Find this resource:

Flower, V. J. B., Carn, S. A., & Wright, R. (2016). The impact of satellite sensor viewing geometry on time-series analysis of volcanic emissions. Remote Sensing of Environment, 182, 282–293.Find this resource:

Flynn, L. P., Harris, A. J. L., & Wright, R. (2001). Improved identification of volcanic features using Landsat 7 ETM+. Remote Sensing of Environment, 78, 180–193.Find this resource:

Follett, R., & Strezov, V. (2015). An analysis of citizen science–based research: Usage and publication patterns. PLoS ONE 10(11), e0143687.Find this resource:

Foster, G., & Rahmstorf, S. (2011). Global temperature evolution 1979–2010. Environmental Research Letters, 6(4), 044022.Find this resource:

Fritz, H. M., & Okal, E. A. (2008). Socotra Island, Yemen: Field survey of the 2004 Indian Ocean tsunami. Natural Hazards, 46, 107–117.Find this resource:

Gaillard, J. C., Monteil, C., Perrillat-Collomb, A., Chaudhary, S., Chaudhary, M., Chaudhary O., . . . Cadag, J. R. D. (2013). Participatory 3-dimension mapping: A tool for encouraging multi-caste collaboration to climate change adaptation and disaster risk reduction. Applied Geography, 45, 158–166.Find this resource:

Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178, 31–41.Find this resource:

Gillespie, T. W., Chu, J., Frankenberg, E., & Thomas, D. (2007). Assessment and prediction of natural hazards from satellite imagery. Progress in Physical Geography, 31, 459–470.Find this resource:

Goodchild, M. F. (1992). Geographical information science. International Journal of Geographical Information Systems, 6(1), 31–45.Find this resource:

Goodchild, M. F. (2007a). Citizens as voluntary sensors: Spatial data infrastructure in the world of Web 2.0. International Journal of Spatial Data Infrastructure Research, 2, 24–32.Find this resource:

Goodchild, M. F. (2007b). Citizens as sensors: The world of volunteered geography. GeoJournal, 69, 211–221.Find this resource:

Goodchild, M. F. (2008). Geographic information science: The grand challenges. In J. P. Wilson & A. S. Fotheringham (Eds.), The handbook of geographic information science (pp. 596–608). Wiley Online Library.Find this resource:

Goodchild, M. F. (2009). Geographic information systems and science: Today and tomorrow. Procedia Earth and Planetary Science, 1, 1037–1043.Find this resource:

Goodchild, M. F., & Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response: A research frontier. International Journal of Digital Earth, 3, 231–241.Find this resource:

Goodchild, M. F., Yuan, M., & Cova, T. (2007). Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, 21(3), 209–260.Find this resource:

Gomez, C., & Purdie, H. (2016). UAV-based photogrammetry and geocomputing for hazards and disaster risk monitoring: A review. Geoenvironmental Disasters, 3(23).Find this resource:

Green, D., Yu, D., Pattison, I., Wilby, R., Bosher, L., Patel, R., . . . Ryley, T. (2017). City-scale accessibility of emergency responders operating during flood events. Natural Hazards and Earth Systems Sciences, 17, 1–16.Find this resource:

The Guardian. (2017, August 31). South Asia floods kill 1,200 and shut 1.8 million children out of school.

Guha-Sapir, D., Hoyois, P., Wallemacq, P., & Below, R. (2017). Annual disaster statistical review 2016: The Numbers and Trends. Brussels, Belgium: Centre for Research on the Epidemiology of Disasters.Find this resource:

Hallegatte, S., & Przyluski, V. (2010). The economics of natural disasters: Concepts and methods (Policy Research Working Paper 5507). World Bank, Washington, DC.Find this resource:

Hastaoglu, K. O., & Sanli, D. U. (2011). Monitoring Koyulhisar landslide using rapid static GPS: A strategy to remove biases from vertical velocities. Natural Hazards, 58, 1275–1294.Find this resource:

Hayes, M. J., Wilhelmi, O. V., & Knutson, C. L. (2004). Reducing drought risk: Bridging theory and practice. Natural Hazards Review, 5(2), 106–113.Find this resource:

Henriksen, H. J., Roberts, M. J., van der Keur, P., Harjanne, A., Egilson, D., & Alfonso, L. (2018). Participatory early warning and monitoring systems: A Nordic framework for web-based flood risk management. International Journal of Disaster Risk Reduction, 31, 1295–1306.Find this resource:

Heywood, I., Cornelius, S., & Carver, S. (1998). An introduction to geographical information systems. Upper Saddle River, NJ: Prentice Hall.Find this resource:

Hoffmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (1997. Global positioning system: Theory and practice. Vienna, Austria: Springer-Verlag.Find this resource:

Horney, J. A., Casillas, G. A., Baker, E., Stone, K. W., Kirsch, K. R., Camargo, K., . . . McDonald, T. J. (2018). Comparing residential contamination in a Houston environmental justice neighborhood before and after Hurricane Harvey. PLoS ONE, 13(2), e0192660.Find this resource:

Hohenthal, J., Minoia, P., & Pellikka, P. (2017). Mapping meaning: Critical cartographies for participatory water management in Taita Hills, Kenya. Professional Geographer, 69(3), 382–395.Find this resource:

Howe, J. (2008). Crowdsourcing: Why the power of the crowd is driving the future of business. New York, NY: McGraw-Hill.Find this resource:

Ichoku, C., Ellison, L. T., Willmot, K. E., Matsui, T., Dezfuli, A. K., Gatebe, C. K., . . . Habib, S. (2016). Biomass burning, land-cover change, and the hydrological cycle in Northern sub-Saharan Africa. Environmental Research Letters, 11(9), 095005.Find this resource:

Jenkins, L. K., Bourgeau-Chavez, L. L., French, N. H. F., Loboda, T. V., & Thelen, B. J. (2014). Development of methods for detection and monitoring of fire disturbance in the Alaskan tundra using a two-decade long record of synthetic aperture radar satellite images. Remote Sensing, 6, 6347–6364.Find this resource:

Jiang, Y., Liao, M., Zhou, Z., Shi, X., Zhang, L., & Balz. T. (2016). Landslide deformation analysis by coupling deformation time series from SAR data with hydrological factors through data assimilation. Remote Sensing, 8, 1–22.Find this resource:

Joyce, K. E., Belliss, S. E., Samsonov, S. V., McNeil, S. J., & Glassey, P. J. (2009). A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Progress in Physical Geography, 33, 193–207.Find this resource:

Juárez-Orozco, S. M., Siebe, C., & Fernández y Fernández, D. (2017). Causes and effects of forest fires in tropical rainforests: A bibliometric approach. Tropical Conservation Science, 10, 1–14.Find this resource:

Kawasaki, A., Berman, M. L., & Guan, W. (2013). The growing role of web-based geospatial technology in disaster response and support. Disasters, 37(2), 201–221.Find this resource:

Kermanshah, A., & Derrible, S. (2017). Robustness of road systems to extreme flooding: Using elements of GIS. Travel demand, and network science. Natural Hazards, 86, 151–164.Find this resource:

Klemas, V. V. (2009). The role of remote sensing in predicting and determining coastal storm impacts. Journal of Coastal Research, 25, 1264–1275.Find this resource:

Knabb, R. D., Rhome, J. R., & Brown, D. P. (2005, August 23–30). Tropical cyclone report: Hurricane Katrina, National Hurricane Center, National Oceanographic and Atmospheric Administration, Miami, FL.

Kobayashi, T., Tobita, M., Nishimura, T., Suzuki, A., Noguchi, Y., & Yamanaka, M. (2011). Crustal deformation map for the 2011 off the Pacific coast of Tohoku earthquake, detected by InSAR analysis combined with GEONET data. Earth, Planets and Space, 63, 621–625.Find this resource:

Koliou, M., van de Lindt, J. W., McAllister, T. P., Ellingwood, B. R., Dillard, M., & Cutler, H. (2018). State of the research in community resilience: Progress and challenges. Online publication. Sustainable and Resilient Infrastructure.Find this resource:

Kwak, Y., Arifuzzanman, B., & Iwami, Y. (2015). Prompt proxy mapping of flood damaged rice fields using MODIS-derived indices. Remote Sensing, 7, 15969–15988.Find this resource:

Liu, S., & Hodgson, M. E. (2016). Satellite image collection modeling for large area hazard emergency response. ISPRS Journal of Photogrammetry and Remote Sensing, 118, 13–21.Find this resource:

Liu, J. G., Mason, P., Hilton, F., & Lee, H. (2004). Detection of rapid erosion in SE Spain: A GIS approach based on ERS SAR coherence imagery. Photogrammetric Engineering and Remote Sensing, 70, 1179–1185.Find this resource:

Longley, P. A., & Batty, M. (2003). Advanced spatial analysis: The CASA book of GIS. Redlands, CA: ESRI.Find this resource:

Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic information science and systems (4th ed.). Hoboken, NJ: Wiley.Find this resource:

Lu, Q‑C., Peng, Z‑R., & Zhang, J. (2015). Identification and prioritization of critical transportation infrastructure: A case study of coastal flooding. Journal of Transportation Engineering, 141(3), 04014082, 1–8.Find this resource:

Malinowski, R., Groom, G., Schwanghart, W., & Heckrath, G. (2015). Detection and delineation of localized flooding from WorldView-2 multispectral data. Remote Sensing, 7, 14853–14875.Find this resource:

McNairn, H., & Brisco, B. (2004). The application of C-band polarimetric SAR for agriculture: A review. Canadian Journal of Remote Sensing, 30, 525–542.Find this resource:

Morales-Rivera, A. M., Amelung, F., & Mothes, P. (2016). Volcano deformation survey over the Northern and Central Andes with ALOS InSAR time series. Geochemistry, Geophysics, Geosystems, 17, 2869–2883Find this resource:

Muriel-Villegas, J. E., Alvarez-Uribe, K. C., Pariño-Rodriguez, C. E., & Villegas, J. G. (2016). Analysis of transportation networks subject to natural hazards: Insights from a Colombian case. Reliability Engineering and System Safety, 152, 151–165.Find this resource:

Murphy, R. (2017). Update on Hurricane Irma unmanned aerial systems: A new record of 247 flights for public officials. Center for Robot-Assisted Search and Rescue, Corpus Christi, TX.Find this resource:

Murphy, S., Souza-Filho, C., Wright, R., Sabatino, G., & Pabon, R. C. (2016). HOTMAP: Global hot target detection at moderate spatial resolution. Remote Sensing of Environment, 177, 78–88.Find this resource:

National Aeronautics and Space Administration (NASA). (2018). EarthData—Rapid Response.

National Interagency Coordination Center (NICC). (2017). Wildland Fire Summary and Statistics Annual Report 2017.Find this resource:

National Interagency Fire Center (NIFC). (2017a). Federal firefighting costs (suppression only). The data covers 1985 to 2017.

National Interagency Fire Center (NIFC). (2017b). National report of wildland fires and acres burned by state figures from the fire and aviation management web applications program: Historical year-end fire statistics by state.

National Oceanic and Atmospheric Administration–National Hurricane Center (NOAA-NHC). (2008). Storm surge overview.

Omuto, C. T. (2011). A new approach for using time-series remote-sensing images to detect changes in vegetation cover and composition in drylands: A case study of eastern Kenya. International Journal of Remote Sensing, 32, 6025–6045.Find this resource:

Ouzounov D., & Freund, F. (2004). Mid-infrared emission prior to strong earthquakes analyzed by remote sensing data. Advances in Space Research, 33, 268–273.Find this resource:

Ozcelik, C., Gorokhovich, Y., & Doocy, S. (2012). Storm surge modelling with geographic information systems: Estimating areas and population affected by Cyclone Nargis. International Journal of Climatology, 32(1), 95–107.Find this resource:

Padawangi, R., Turpin, E., Herlily, Prescott, M. F., Lee, I., & Shepherd, A. (2016). Mapping an alternative community river: The case of the Ciliwung. Sustainable Cities and Society, 20, 147–157.Find this resource:

Pellikka, P., & Rees, W. G. (2009). Remote sensing of glaciers: Techniques for topographic, spatial and thematic mapping of glaciers. Boca Raton, FL: CRC Press.Find this resource:

Piccolella, A. (2013). Participatory mapping for adaptation to climate change: The case of Boe Boe, Solomon Islands. Knowledge Management for Development Journal, 9(1), 24–36.Find this resource:

Pritchard, M. E. (2006, July). InSAR, a tool for measuring Earth’s surface deformation. Physics Today, 59(7), 68–69.Find this resource:

Renschler, C. S., & Wang, Z. (2017). Multi-source data fusion and modeling to assess and communicate complex flood dynamics to support decision-making for downstream areas of dams: The 2011 Hurricane Irene and Schoharie Creek floods, NY. International Journal of Applied Earth Observation and Geoinformation, 62, 157–173.Find this resource:

Rinner, C., Kessler, C., & Andrulis, S. (2008). The use of Web 2.0 concepts to support deliberation in spatial decision-making. Computers, Environment, and Urban Systems, 32, 386–395.Find this resource:

Rykhus, R., & Zhong, L. (2011). Monitoring a boreal wildfire using multi-temporal Radarsat-1 intensity and coherence images. Geomatics, Natural Hazards and Risk, 2(1), 15–32.Find this resource:

Sanyal, J., & Lu, X. X. (2004). Application of remote sensing in flood management with special reference to monsoon Asia: A review. Natural Hazards, 33, 283–301.Find this resource:

Saraf, A. K., & Choudhury, S. (2004). Satellite detects surface thermal anomalies associated with the Algerian earthquakes of May 2003. International Journal of Remote Sensing, 26, 2705–2713.Find this resource:

Scasta, J. D., Weir, J. R., & Stambaugh, M. C. (2016). Droughts and wildfires in western U.S. rangelands. Rangelands, 38(4), 197–203.Find this resource:

Scharl, A., & Tochtermann, K. (2007). The geospatial web: How geobrowsers, social software and the Web 2.0 are shaping the network society. London, U.K.: Springer.Find this resource:

Socioeconomic Data and Applications Center (SEDAC). (2005). UN WPP-Adjusted Population Density, v4.10 (2000, 2005, 2010, 2015, 2020), Gridded Population of the World (GPW), v4, NASA’s Earth Observing System Data and Information System (EOSDIS) — CIESIN, Columbia University.Find this resource:

Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., Kossin, J., . . . Zhang, X. (2012). Changes in climate extremes and their impacts on the natural physical environment. In C. B. Field, V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, . . . P. M. Midgley (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation: A special report of Working Groups I and II of the Intergovernmental Panel on Climate Change (pp. 109–230). Cambridge, U.K.: Cambridge University Press.Find this resource:

Sharma, V. K., Mishra, N., Shukla, A. K., Yadav, A., Rao, G. S., & Bhanumurty, V. (2017). Satellite data planning for flood mapping activities based on high rainfall events generated using TRMM, GEFS and disaster news. Annals of GIS, 23, 131–140.Find this resource:

Shi, W., & Wang, M. (2008). Three-dimensional observations from MODIS and CALIPSO for ocean responses to Cyclone Nargis in the Gulf of Martaban. Geophysical Research Letters, 35, L21603, 1–5.Find this resource:

Shuurman, N. (2013). Geographic information science. Oxford this resource:

Schroeder, W., Oliva, P., Giglio, L., & Csiszar, I. A. (2014). The new VIIRS 375m active fire detection data product: Algorithm description and initial assessment. Remote Sensing of Environment, 143, 85–96.Find this resource:

Sohlberg, R., Descloitres, J., & Bobbe, T. (2001). MODIS Land rapid response: Operational use of terra data for USFS wildfire management. Earth Observer, 13, 8–10.Find this resource:

Ticehurst, C., Guerschman, J.‑P., & Chen, Y. (2014). The strengths and limitations in using the daily MODIS open water likelihood algorithm for identifying flood events. Remote Sensing, 6, 11791–11809,Find this resource:

Tomaszewski, B., Judex, M., Szarzynski, J., Rodenstock, C., & Wirkus, L. (2015). Geographic information systems for disaster response: A review. Journal of Homeland Security and Emergency Management, 12(3).Find this resource:

Tralli, D. M., Blom, R. G., Zlotnicki, V., Donnellan, A., & Evans, D. L. (2005). Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS Journal of Photogrammetry and Remote Sensing, 59, 195–198.Find this resource:

Tronin, A. A. (1996). Satellite thermal survey: A new tool for the studies of seismoactive regions. International Journal of Remote Sensing, 17, 1439–1455.Find this resource:

Tronin, A., Hayakawa, M., & Molchanov, O. A. (2002). Thermal IR satellite data application for earthquake research in Japan and China. Journal of Geodynamics, 33, 519–534.Find this resource:

Twidwell, D., Allen, C. R., Detweiler, C., Higgins, J., Laney, C., & Elbaum, S. (2016). Smokey comes of age: Unmanned aerial systems for fire management. Frontiers in Ecology and the Environment, 14(6), 333–339.Find this resource:

United Nations Office for Disaster Risk Reduction (UNISDR). (2010). Disaster through a different lens: Behind every effect, there is a cause; A guide for journalists covering disaster risk reduction.

United Nations Office for Disaster Risk Reduction (UNISDR). (2016). Report on the open-ended intergovernmental group on indicators and terminology relating to disaster risk reduction.

United Nations Office for Disaster Risk Reduction and Centre for Research on the Epidemiology of Disasters (UNISDR-CRED). (2016). Poverty and death: Disaster mortality 1996–2005.

United States Geological Survey (USGS). (2015). ShakeMap scenario catalogs.

United States Geological Survey (USGS). (2016). Landsat 8 (L8) data users’ handbook (LSDS-1574, version 2.0). Earth Resources Observation and Science Center, Sioux Falls, SD.Find this resource:

United States Geological Survey (USGS). (2017a). Earthquake statistics: Worldwide earthquakes (2000–2016).

United States Geological Survey (USGS). (2017b). Landsat images before and after Harvey illustrate flooding in Texas. USGS Eastern States Office of Communications.

U.S. National Weather Service. (2017). Major Hurricane Harvey: August 25–29, 2017.

van Westen, C. J. (2013). Remote sensing and GIS for natural hazards assessment and disaster risk management. In J. F. Shroder (Ed.), Treatise on geomorphology (pp. 259–298). Waltham, MA: Academic Press.Find this resource:

Wald, D. J., & Allen, T. I. (2007). Topographic slope as a proxy for seismic site conditions and amplification. Bulletin of the Seismological Society of America, 97, 1379–1395.Find this resource:

Wang, S., Xu, C., Wen, Y., Yin, Z., Jiang, G., & Fang, L. (2017). Slip model for the 25 November 2016 Mw 6.6 Aketao earthquake, Western China, revealed by Sentinel-1 and ALOS-2 observations. Remote Sensing, 9, 1–15.Find this resource:

Watson K. M., Harwell, G. R., Wallace, D. S., Welborn, T. L., Stengel, V. G., & McDowell, J. S. (2018). Characterization of peak streamflows and flood inundation of selected areas in southeastern Texas and southwestern Louisiana from the August and September 2017 flood resulting from Hurricane Harvey (Scientific Investigations Report 2018–5070, prepared in cooperation with the Federal Emergency Management Agency). U.S. Geological Survey, Reston, VA.Find this resource:

Werner, C. (2017, December 1). Fire technology: Drones soar following hurricanes. Firehouse, article 12375891.Find this resource:

Werner, D. (2015). Fire drones. Aerospace America, 53, 28–31.Find this resource:

Westerling, A. L., Gershunov, A., Brown, T. J., Cayan, D. R., & Dettinger, M. D. (2003). Climate and wildfire in the western United States. Bulletin of the American Meteorological Society, 84, 595–604.Find this resource:

Westerling, A. L., Hidalgo, H. G., Cayan, D. R., & Swetnam, T. W. (2006). Warming and earlier spring increase western US forest wildfire activity. Science, 313, 940–943.Find this resource:

Wilhelmi, O. V., & Morss, R. E. (2013). Integrated analysis of societal vulnerability in an extreme precipitation event: A Fort Collins case study. Environmental Science and Policy 26, 49–62.Find this resource:

Womble, J. A., Ghosh, S., Adams, B., & Friedland, C. (2006). Advanced damage detection for Hurricane Katrina: Integrating remote sensing and VIEWSTM field reconnaissance. (MCEER Special Report Series). Multidisciplinary Center for Earthquake Engineering Research, Buffalo, NY.Find this resource:

Woodruff, J. D., Irish, J. L., & Camargo, S. J. (2013). Coastal flooding by tropical cyclones and sea-level rise. Nature, 504, 44–52.Find this resource:

Worden, C. B., Thompson, E. M., Baker, J. W., Bradley, B. A., Luco, N., & Wald, D. J. (2018). Spatial and spectral interpolation of ground motion intensity measure observations. Bulletin of the Seismological Society of America, 108(2), 866–875.Find this resource:

Worden, C. B., Wald, D. J., Allen, T. I., Lin, K. W., Garcia, D., & Cua, G. (2010). A revised ground-motion and intensity interpolation scheme for ShakeMap. Bulletin of the Seismological Society of America, 100, 3083–3096.Find this resource:

Worden, C. B., Wald, D. J., Sanborn, J., & Thompson, E. M. (2015). Development of an open-source hybrid global Vs30 model. Seismological Society of America Annual Meeting, 21–23 April, Pasadena, CA.Find this resource:

Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., . . . Dickinson, R. (2013). The role of remote sensing in climate change studies. Nature Climate Change, 3, 875–883.Find this resource:

Zachry, B. C., Booth, W. J., Rhome, J. R., & Sharon, T. M. (2015). A national view of storm surge risk and inundation. Weather, Climate and Society, 7, 109–117.Find this resource:

Zar, C.‑A., Jaboyedoff, M., Derron, M.‑H., & van Westen, C. J. (2015). A prototype of a web-based decision support platform in natural hazards and risk management. ISPRS Journal of Photogrammetry and Remote Sensing, 4, 1201–1224.Find this resource:

Zelenakova, M., Ganova, L., Purcz, P., Horsky, M., Satrapa, L., Blistan, P., & Diaconu, D. C. (2017). Mitigation of the adverse consequences of floods for human life, infrastructure, and the environment. Natural Hazards Review, 18(4), 05017002, 1–15.Find this resource:

Zerger, A., & Smith, D. I. (2003). Impediments to using GIS for real-time disaster decision support. Computers, Environment and Urban Systems, 17, 123–141.Find this resource:

Zhai, X., Yue, P., & Zhang, M. (2016). A sensor web and web service-based approach for active hydrological disaster monitoring. ISPRS International Journal of Geo-Information, 5, 1–22.Find this resource:

Zhang, Y., Peng, C., Li, W., Zhang, T., Zhu, Q., Chen, H., & Zhao, P. (2013). Monitoring and estimating drought-induced impacts on forest structure, growth, function, and ecosystem services using remote-sensing data: Recent progress and future challenges. Environmental Reviews, 21, 103–115.Find this resource:


(1.) The concept of geographic information science (GIScience) was introduced in the early 1990s by Michael F. Goodchild, University of California, Santa Barbara, in two keynote addresses and an agenda-setting article for the International Journal of Geographic Information Systems, which outlined a program for a fundamental move toward substantive scientific inquiry in the field, beyond the technological advances in software development (Goodchild, 1992).

(2.) The University Consortium for Geographic Information Science (USGIS) was established in 1995 by a group of research institutions and national laboratories. It is nonprofit organization whose membership includes more than 80 institutions as of 2018.

(3.) A 500-year flood event is an event with a probability of occurrence or 0.2% in any given year.