Articles | Volume 21, issue 9
https://doi.org/10.5194/nhess-21-2753-2021
© 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-21-2753-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Sep 2021
Research article |
| 08 Sep 2021
| 08 Sep 2021
Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria
Landslide Research Group, Technical University of Munich, Munich, Germany
Markus Keuschnig
GEORESEARCH, Forschungsgesellschaft mbH, Puch, Austria
Ingo Hartmeyer
GEORESEARCH, Forschungsgesellschaft mbH, Puch, Austria
Robert Delleske
GEORESEARCH, Forschungsgesellschaft mbH, Puch, Austria
Michael Krautblatter
Landslide Research Group, Technical University of Munich, Munich, Germany
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Cited
21 citations as recorded by crossref.
- An approach for prospective forecasting of rock slope failure time J. Leinauer et al. 10.1038/s43247-023-00909-z
- Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring D. Hermle et al. 10.3390/geosciences13120371
- A debris-flow forecasting method with infrasound-based variational mode decomposition and ARIMA H. Dong et al. 10.1007/s11629-024-8901-8
- Unmanned Aerial Vehicle Surveying and Mapping Trajectory Scheduling and Autonomous Control for Landslide Monitoring S. Liao et al. 10.1155/2022/2365006
- Tracking slow-moving landslides with PlanetScope data: new perspectives on the satellite's perspective A. Mueting & B. Bookhagen 10.5194/esurf-12-1121-2024
- Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes I. Fustos-Toribio et al. 10.5194/nhess-22-2169-2022
- Using drone-based multispectral imaging for investigating gravelly debris flows and geomorphic characteristics H. Chen et al. 10.1007/s12665-024-11544-y
- Unsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil G. Moço et al. 10.1111/tgis.13256
- Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing K. Strząbała et al. 10.3390/rs16152781
- A quick method of early landslide identification based on dynamic susceptibility analysis using M-SVM method: a case study Y. Liu et al. 10.1007/s10064-023-03440-9
- Prediction method of loess landslides based on faster R-CNN and WACM Q. Chen & H. Ding 10.1088/2631-8695/ad7a54
- Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data H. Xiao et al. 10.1002/gj.4677
- The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects H. Bao et al. 10.1007/s12665-022-10575-7
- Integration of satellite SAR and optical acquisitions for the characterization of the Lake Sarez landslides in Tajikistan O. Nardini et al. 10.1007/s10346-024-02214-y
- Landslide monitoring and prediction system using geosensors and wireless sensor network S. Chaulya et al. 10.1007/s44288-024-00007-3
- Performance Testing of Optical Flow Time Series Analyses Based on a Fast, High-Alpine Landslide D. Hermle et al. 10.3390/rs14030455
- Deep learning approaches for landslide information recognition: Current scenario and opportunities N. Chandra & H. Vaidya 10.1007/s12040-024-02281-8
- Spatiotemporal distribution characteristics of post-fire debris flows during the first rainy season following Yajiang Fire, Sichuan, China X. Gong et al. 10.1007/s10346-025-02563-2
- Subpixel offset tracking for landslide deformation monitoring: optimization of input conditions and assessment of vegetation noise H. Xiao et al. 10.1007/s10064-025-04425-6
- Rare flood scenarios for a rapidly growing high-mountain city: Pokhara, Nepal M. Fischer et al. 10.5194/nhess-22-3105-2022
- Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response Y. Liu & J. Zhang 10.5194/nhess-22-227-2022
21 citations as recorded by crossref.
- An approach for prospective forecasting of rock slope failure time J. Leinauer et al. 10.1038/s43247-023-00909-z
- Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring D. Hermle et al. 10.3390/geosciences13120371
- A debris-flow forecasting method with infrasound-based variational mode decomposition and ARIMA H. Dong et al. 10.1007/s11629-024-8901-8
- Unmanned Aerial Vehicle Surveying and Mapping Trajectory Scheduling and Autonomous Control for Landslide Monitoring S. Liao et al. 10.1155/2022/2365006
- Tracking slow-moving landslides with PlanetScope data: new perspectives on the satellite's perspective A. Mueting & B. Bookhagen 10.5194/esurf-12-1121-2024
- Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes I. Fustos-Toribio et al. 10.5194/nhess-22-2169-2022
- Using drone-based multispectral imaging for investigating gravelly debris flows and geomorphic characteristics H. Chen et al. 10.1007/s12665-024-11544-y
- Unsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil G. Moço et al. 10.1111/tgis.13256
- Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing K. Strząbała et al. 10.3390/rs16152781
- A quick method of early landslide identification based on dynamic susceptibility analysis using M-SVM method: a case study Y. Liu et al. 10.1007/s10064-023-03440-9
- Prediction method of loess landslides based on faster R-CNN and WACM Q. Chen & H. Ding 10.1088/2631-8695/ad7a54
- Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data H. Xiao et al. 10.1002/gj.4677
- The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects H. Bao et al. 10.1007/s12665-022-10575-7
- Integration of satellite SAR and optical acquisitions for the characterization of the Lake Sarez landslides in Tajikistan O. Nardini et al. 10.1007/s10346-024-02214-y
- Landslide monitoring and prediction system using geosensors and wireless sensor network S. Chaulya et al. 10.1007/s44288-024-00007-3
- Performance Testing of Optical Flow Time Series Analyses Based on a Fast, High-Alpine Landslide D. Hermle et al. 10.3390/rs14030455
- Deep learning approaches for landslide information recognition: Current scenario and opportunities N. Chandra & H. Vaidya 10.1007/s12040-024-02281-8
- Spatiotemporal distribution characteristics of post-fire debris flows during the first rainy season following Yajiang Fire, Sichuan, China X. Gong et al. 10.1007/s10346-025-02563-2
- Subpixel offset tracking for landslide deformation monitoring: optimization of input conditions and assessment of vegetation noise H. Xiao et al. 10.1007/s10064-025-04425-6
- Rare flood scenarios for a rapidly growing high-mountain city: Pokhara, Nepal M. Fischer et al. 10.5194/nhess-22-3105-2022
- Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response Y. Liu & J. Zhang 10.5194/nhess-22-227-2022
Latest update: 24 Oct 2025
Short summary
Multispectral remote sensing imagery enables landslide detection and monitoring, but its applicability to time-critical early warning is rarely studied. We present a concept to operationalise its use for landslide early warning, aiming to extend lead time. We tested PlanetScope and unmanned aerial system images on a complex mass movement and compared processing times to historic benchmarks. Acquired data are within the forecasting window, indicating the feasibility for landslide early warning.
Multispectral remote sensing imagery enables landslide detection and monitoring, but its...
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