The Use of Lidar and Volunteered Geographic Information to Map Flood Extents and Inundation

McDougall, Kevin; Temple-Watts, P.

doi:10.5194/isprsannals-i-4-251-2012

Cited by 44 publications

(26 citation statements)

References 13 publications

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“…In the area of flood management, Alfonso et al () tested a system in which citizens sent their reading of water level rulers by text messages. Since then, other studies have adopted similar approaches (Lowry & Fienen, ; McDougall, ; McDougall & Temple‐Watts, ) and have adapted them to new technologies, such as website upload (Degrossi et al, ; Starkey et al, ). Kutija et al () developed an approach in which images of floods are received, from which water levels are extracted.…”

Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

“…For example, Jongman et al () combined near‐real‐time satellite data with near‐real‐time Twitter data on the location, timing, and impacts of floods for case studies in Pakistan and the Philippines for improving humanitarian response. McDougall and Temple‐Watts () combined high quality aerial imagery, LiDAR data, and publically available volunteered geographic imagery (e.g., from Flickr) to reconstruct flood extents and obtain information on depth of inundation for the 2011 Brisbane floods in Australia.…”

Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

See 1 more Smart Citation

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

113

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Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

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Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

113

View full text Add to dashboard Cite

show abstract

“…Multi-source satellite and Twitter data were combined to delineate the flood extent on a daily basis [33]. Similar studies can also be found in [34][35][36][37][38]. These studies showed that social media data is valuable in providing disaster-related information at locations that are not being sensed by satellite sensors.…”

Section: Introductionmentioning

confidence: 54%

Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

Liu

et al. 2019

Remote Sensing

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The daily nighttime lights (NTL) and the amount of location-service requests (NLR) data have been widely used as a proxy for measures of disaster-induced power outages and geo-tagged human activity dynamics. However, the association between the two datasets is not well understood. In this study, we investigated how the NTL signals and geo-tagged human activities changed in response to Typhoon Mangkhut. The confusion matrix is constructed to quantify the changes of the NLR in response to Typhoon Mangkhut, as well as the changes of the NTL signals at the grid level. Geographically-weighted regression and quantile regression were used to examine the associations between the changes of the NTL and the NLR at both grid and county levels. The quantile regressions were also used to quantify the relationships between the dimmed NTL signals and the change of the NLR in disaster damage estimates at the county level. Results show that the percent of the grids with anomalous human activities is significantly correlated with the nearby air pressure and wind speed. Geo-tagged human activities varied in response to the evolution of Mangkhut with significant areal differentiation. Over 69.3% of the grids with significant human activity change is also characterized by declined NTL brightness, which is closely associated with abnormal human activities. Significant log-linear and moderate positive correlations were found between the changes of the NTL and NLR at both the grid and county levels, as well as between the county-level changes of NLR/NTL and the damage estimates. This study shows the geo-tagged human activities are closely associated with the changes of the daily NTL signals in response to Typhoon Mangkhut. The two datasets are complimentary in sensing the typhoon-induced losses and damages.

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“…Nedkov and Zlatanova performed a shortest-path calculation using crowdsourced data about infrastructure health [16]. Roche and Propeck-Zimmermann discussed the method and issues involved in using VGI to support crisis management [17], harnessing VGI to build and update SDI [18,19]. Mooney and Corcoran [20] described the potential for using VGI in health computing applications.…”

Section: Related Workmentioning

confidence: 99%

Dynamically Integrating OSM Data into a Borderland Database

Zhou

Zeng

et al. 2015

IJGI

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Spatial data are fundamental for borderland analyses of geography, natural resources, demography, politics, economy, and culture. As the spatial data used in borderland research usually cover the borderland regions of several neighboring countries, it is difficult for anyone research institution of government to collect them. Volunteered Geographic Information (VGI) is a highly successful method for acquiring timely and detailed global spatial data at a very low cost. Therefore, VGI is a reasonable source of borderland spatial data. OpenStreetMap (OSM) is known as the most successful VGI resource. However, OSM's data model is far different from the traditional geographic information model. Thus, the OSM data must be converted in the scientist's customized data model. Because the real world changes rapidly, the converted data must be updated incrementally. Therefore, this paper presents a method used to dynamically integrate OSM data into the borderland database. In this method, a basic transformation rule base is formed by comparing the OSM Map Feature description document and the destination model definitions. Using the basic rules, the main features can be automatically converted to the destination model. A human-computer interaction model transformation and a rule/automatic-remember mechanism are developed to interactively transfer the unusual features that cannot be transferred by the basic rules to the target model and to remember the reusable rules automatically. To keep the borderland database current, the global OsmChange daily diff file is used to extract the change-only information for the research region. To extract the changed objects in the region under study, the relationship between the changed object and the research region is analyzed considering the evolution of the OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 1708 involved objects. In addition, five rules are determined to select the objects and integrate the changed objects with multi-versions over time. The objects' change-type evolution is analyzed, and seven rules are used to determine the change-type of the changed objects. Based on these rules and algorithms, we programmed an automatic (or semi-automatic) integrating and updating prototype system for the borderland database. The developed system was intensively tested using OSM data for Vietnam and Pakistan as the experimental data.

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The Use of Lidar and Volunteered Geographic Information to Map Flood Extents and Inundation

Cited by 44 publications

References 13 publications

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

Dynamically Integrating OSM Data into a Borderland Database

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