Articles | Volume 21, issue 5
https://doi.org/10.5194/nhess-21-1495-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-1495-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 May 2021
Research article |
| 12 May 2021
| 12 May 2021
HazMapper: a global open-source natural hazard mapping application in Google Earth Engine
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
North Carolina Geological Survey, Division of Energy, Mineral, and Land Resources, Department of Environmental Quality, Swannanoa, NC 28778, USA
Karl W. Wegmann
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
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- Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions G. Giardina et al. 10.1007/s10518-023-01716-9
- Spatial distribution characteristics of climate-induced landslides in the Eastern Himalayas D. Uwizeyimana et al. 10.1007/s11629-024-8869-4
- Ten simple rules for researchers who want to develop web apps S. Saia et al. 10.1371/journal.pcbi.1009663
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- A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images J. Li et al. 10.3390/rs14174252
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51 citations as recorded by crossref.
- Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine A. Handwerger et al. 10.5194/nhess-22-753-2022
- Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery M. Van Wyk de Vries et al. 10.1002/esp.5775
- Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery M. I. Volke et al. 10.1080/01431161.2023.2292015
- Managing natural disasters: An analysis of technological advancements, opportunities, and challenges M. Krichen et al. 10.1016/j.iotcps.2023.09.002
- Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine D. Notti et al. 10.5194/nhess-23-2625-2023
- Mapping landslides from space: A review A. Novellino et al. 10.1007/s10346-024-02215-x
- Canopy Height Mapper: a Google Earth Engine application for predicting global canopy heights combining GEDI with multi-source data. C. Alvites et al. 10.1016/j.envsoft.2024.106268
- Responses to Landslides and Landslide Mapping on the Blue Ridge Escarpment, Polk County, North Carolina, USA R. Wooten et al. 10.2113/EEG-D-21-00022
- Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya T. Chen et al. 10.1016/j.scitotenv.2024.171161
- Debris‐Flood Hazard Assessments in Steep Streams M. Jakob et al. 10.1029/2021WR030907
- A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics M. Abdelkader et al. 10.3390/rs16081368
- Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery S. Peters et al. 10.3390/rs16101722
- Evaluation of machine learning-based algorithms for landslide detection across satellite sensors for the 2019 Cyclone Idai event, Chimanimani District, Zimbabwe R. Das & K. Wegmann 10.1007/s10346-022-01912-9
- Enhancing FAIR Data Services in Agricultural Disaster: A Review L. Hu et al. 10.3390/rs15082024
- Modelling of large wood export at a watershed scale D. Komori et al. 10.1002/esp.5282
- Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape E. Lindsay et al. 10.3390/rs14102301
- A service-oriented collaborative approach to disaster decision support by integrating geospatial resources and task chain Z. Fang et al. 10.1016/j.jag.2023.103217
- Mapping land-use and land-cover changes through the integration of satellite and airborne remote sensing data M. Lin et al. 10.1007/s10661-024-12424-5
- Exploration of Multi-Decadal Landslide Frequency and Vegetation Recovery Conditions Using Remote-Sensing Big Data M. Aman et al. 10.1007/s41748-024-00432-x
- Evaluating root strength index as an indicator of landslide-prone slopes in eastern kentucky M. Swallom et al. 10.1007/s10346-024-02384-9
- 2021 Turkey mega forest Fires: Biodiversity measurements of the IUCN Red List wildlife mammals in Sentinel-2 based burned areas F. Aydin-Kandemir & N. Demir 10.1016/j.asr.2023.01.031
- On the emergence of geospatial cloud-based platforms for disaster risk management: A global scientometric review of google earth engine applications M. Waleed & M. Sajjad 10.1016/j.ijdrr.2023.104056
- The Relationship between Large Wood Export and the Long-Term Large Wood Budget on an Annual Scale in Japan, Using Storage Function with the Lumped Hydrological Method Y. Abe et al. 10.3390/w16070920
- Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques C. Luu et al. 10.1016/j.asr.2024.08.046
- WITHDRAWN: Inventory and distribution patterns of debris flow gullies in the Lhasa-Linzhi section of Sichuan-Tibet Railway G. Hu et al. 10.1016/j.nhres.2024.01.003
- Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi P. Niyokwiringirwa et al. 10.1007/s10346-023-02203-7
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- A semi-supervised multi-temporal landslide and flash flood event detection methodology for unexplored regions using massive satellite image time series A. Deijns et al. 10.1016/j.isprsjprs.2024.07.010
- Learnings from rapid response efforts to remotely detect landslides triggered by the August 2021 Nippes earthquake and Tropical Storm Grace in Haiti P. Amatya et al. 10.1007/s11069-023-06096-6
- Assessing torrentiality in catchments of the tropical Andes: A morphometric approach S. Machuca et al. 10.1016/j.jsames.2023.104775
- Geomorphometry and terrain analysis: data, methods, platforms and applications L. Xiong et al. 10.1016/j.earscirev.2022.104191
- A Survey of Emergencies Management Systems in Smart Cities D. Costa et al. 10.1109/ACCESS.2022.3180033
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- Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data T. Çinar & A. Aydin 10.15446/esrj.v27n4.110128
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- Earthquake-triggered landslides and Environmental Seismic Intensity: insights from the 2018 Papua New Guinea earthquake (M w 7.5) A. Sridharan et al. 10.1080/27669645.2023.2233140
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- Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy X. Wang et al. 10.1016/j.isprsjprs.2022.03.011
- Earthquake-induced soil landslides: volume estimates and uncertainties with the existing scaling exponents A. Yunus et al. 10.1038/s41598-023-35088-6
- Global Landslide Finder: Detecting the Time and Place of Landslides with Dense Earth Observation Time Series M. Aufaristama et al. 10.3390/geohazards5030039
- Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions G. Giardina et al. 10.1007/s10518-023-01716-9
- Spatial distribution characteristics of climate-induced landslides in the Eastern Himalayas D. Uwizeyimana et al. 10.1007/s11629-024-8869-4
- Ten simple rules for researchers who want to develop web apps S. Saia et al. 10.1371/journal.pcbi.1009663
- ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data N. Sharma & M. Saharia 10.1007/s10346-024-02360-3
- Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan M. Atefi & H. Miura 10.3390/rs14153647
- A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images J. Li et al. 10.3390/rs14174252
Latest update: 20 Nov 2024
Short summary
For many decades, natural disasters have been monitored by trained analysts using multiple satellite images to observe landscape change. This approach is incredibly useful, but our new tool, HazMapper, offers researchers and the scientifically curious public a web-accessible
cloud-based tool to perform similar analysis. We intend for the tool to both be used in scientific research and provide rapid response to global natural disasters like landslides, wildfires, and volcanic eruptions.
For many decades, natural disasters have been monitored by trained analysts using multiple...
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