Articles | Volume 22, issue 3
https://doi.org/10.5194/nhess-22-753-2022
© Author(s) 2022. 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-22-753-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Mar 2022
Research article |
| 09 Mar 2022
| 09 Mar 2022
Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine
Alexander L. Handwerger
CORRESPONDING AUTHOR
Joint Institute for Regional Earth System Science and Engineering,
University of California, Los Angeles, Los Angeles, CA, USA
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Department of Geology, University of Maryland, College Park, MD, USA
Shannan Y. Jones
Department of Geology, University of Maryland, College Park, MD, USA
Pukar Amatya
University of Maryland, Baltimore County, Baltimore, MD, USA
Goddard Earth Sciences Technology and Research II, Baltimore, MD, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Hannah R. Kerner
Department of Geography, University of Maryland, College Park, MD, USA
Dalia B. Kirschbaum
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
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25 citations as recorded by crossref.
- Assessing Many Image Processing Products Retrieved from Sentinel-2 Data to Monitor Shallow Landslides in Agricultural Environments R. Cavalli et al. 10.3390/rs16132286
- Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery S. Peters et al. 10.3390/rs16101722
- Landslide causative factors evaluation using GIS in the tectonically active Glafkos River area, northwestern Peloponnese, Greece G. Bathrellos et al. 10.1016/j.geomorph.2024.109285
- Automatic detection of earthquake triggered landslides using Sentinel-1 SAR imagery based on deep learning L. Chen et al. 10.1080/17538947.2024.2393261
- Patagonian Andes Landslides Inventory: The Deep Learning’s Way to Their Automatic Detection B. Morales et al. 10.3390/rs14184622
- Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. 10.5194/nhess-22-2317-2022
- Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments A. Deijns et al. 10.5194/nhess-22-3679-2022
- Landslides Triggered by Medicane Ianos in Greece, September 2020: Rapid Satellite Mapping and Field Survey S. Valkaniotis et al. 10.3390/app122312443
- Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding K. Burrows et al. 10.5194/nhess-22-2637-2022
- Managing natural disasters: An analysis of technological advancements, opportunities, and challenges M. Krichen et al. 10.1016/j.iotcps.2023.09.002
- Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility mapping over the North-Moungo perimeter, Cameroon A. Mfondoum et al. 10.1186/s40677-023-00254-5
- 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
- Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi P. Niyokwiringirwa et al. 10.1007/s10346-023-02203-7
- CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection Y. Xu et al. 10.1038/s41597-023-02847-z
- Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique V. Satriano et al. 10.3390/rs15030683
- 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 and Pre- and Post-Failure Analyses of the April 2019 Kantutani Landslide in La Paz, Bolivia, Using Synthetic Aperture Radar Data M. Shan et al. 10.3390/rs15225311
- Mapping landslides from space: A review A. Novellino et al. 10.1007/s10346-024-02215-x
- Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions G. Giardina et al. 10.1007/s10518-023-01716-9
- Indicative Effect of Excess Topography on Potential Risk Location of Giant Ancient Landslides—A Case Study in Lengqu River Section X. Wang & S. Bai 10.3390/app13148085
- 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
- Geometrical Variation Analysis of Landslides in Different Geological Settings Using Satellite Images: Case Studies in Japan and Sri Lanka S. Neranjan et al. 10.3390/rs16101757
- CO2 emission heat map of Gulf Cooperation Council region using Python for Geographic Information Systems A. Deshmukh 10.1088/1742-6596/2830/1/012008
- A brief address of the causal factors, mechanisms, and the effects of a major landslide in Kangra valley, North-Western Himalaya, India A. Mahajan et al. 10.1007/s12517-022-10163-w
- Automatic detection of landslide impact areas using Google Earth Engine Y. Yang et al. 10.1007/s44195-024-00078-2
23 citations as recorded by crossref.
- Assessing Many Image Processing Products Retrieved from Sentinel-2 Data to Monitor Shallow Landslides in Agricultural Environments R. Cavalli et al. 10.3390/rs16132286
- Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery S. Peters et al. 10.3390/rs16101722
- Landslide causative factors evaluation using GIS in the tectonically active Glafkos River area, northwestern Peloponnese, Greece G. Bathrellos et al. 10.1016/j.geomorph.2024.109285
- Automatic detection of earthquake triggered landslides using Sentinel-1 SAR imagery based on deep learning L. Chen et al. 10.1080/17538947.2024.2393261
- Patagonian Andes Landslides Inventory: The Deep Learning’s Way to Their Automatic Detection B. Morales et al. 10.3390/rs14184622
- Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. 10.5194/nhess-22-2317-2022
- Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments A. Deijns et al. 10.5194/nhess-22-3679-2022
- Landslides Triggered by Medicane Ianos in Greece, September 2020: Rapid Satellite Mapping and Field Survey S. Valkaniotis et al. 10.3390/app122312443
- Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding K. Burrows et al. 10.5194/nhess-22-2637-2022
- Managing natural disasters: An analysis of technological advancements, opportunities, and challenges M. Krichen et al. 10.1016/j.iotcps.2023.09.002
- Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility mapping over the North-Moungo perimeter, Cameroon A. Mfondoum et al. 10.1186/s40677-023-00254-5
- 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
- Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi P. Niyokwiringirwa et al. 10.1007/s10346-023-02203-7
- CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection Y. Xu et al. 10.1038/s41597-023-02847-z
- Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique V. Satriano et al. 10.3390/rs15030683
- 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 and Pre- and Post-Failure Analyses of the April 2019 Kantutani Landslide in La Paz, Bolivia, Using Synthetic Aperture Radar Data M. Shan et al. 10.3390/rs15225311
- Mapping landslides from space: A review A. Novellino et al. 10.1007/s10346-024-02215-x
- Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions G. Giardina et al. 10.1007/s10518-023-01716-9
- Indicative Effect of Excess Topography on Potential Risk Location of Giant Ancient Landslides—A Case Study in Lengqu River Section X. Wang & S. Bai 10.3390/app13148085
- 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
- Geometrical Variation Analysis of Landslides in Different Geological Settings Using Satellite Images: Case Studies in Japan and Sri Lanka S. Neranjan et al. 10.3390/rs16101757
- CO2 emission heat map of Gulf Cooperation Council region using Python for Geographic Information Systems A. Deshmukh 10.1088/1742-6596/2830/1/012008
2 citations as recorded by crossref.
Latest update: 22 Nov 2024
Short summary
Rapid detection of landslides is critical for emergency response and disaster mitigation. Here we develop a global landslide detection tool in Google Earth Engine that uses satellite radar data to measure changes in the ground surface properties. We find that we can detect areas with high landslide density within days of a triggering event. Our approach allows the broader hazard community to utilize these state-of-the-art data for improved situational awareness of landslide hazards.
Rapid detection of landslides is critical for emergency response and disaster mitigation. Here...
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