Articles | Volume 16, issue 4
https://doi.org/10.5194/nhess-16-1035-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-16-1035-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Apr 2016
Research article |
| 26 Apr 2016
Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island
Sandra Heleno
CORRESPONDING AUTHOR
CERENA, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
Magda Matias
CERENA, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
Pedro Pina
CERENA, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
António Jorge Sousa
CERENA, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
Viewed
Total article views: 3,034 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Sep 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,390 | 1,504 | 140 | 3,034 | 113 | 113 |
- HTML: 1,390
- PDF: 1,504
- XML: 140
- Total: 3,034
- BibTeX: 113
- EndNote: 113
Total article views: 2,597 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 26 Apr 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,218 | 1,264 | 115 | 2,597 | 98 | 96 |
- HTML: 1,218
- PDF: 1,264
- XML: 115
- Total: 2,597
- BibTeX: 98
- EndNote: 96
Total article views: 437 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Sep 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 172 | 240 | 25 | 437 | 15 | 17 |
- HTML: 172
- PDF: 240
- XML: 25
- Total: 437
- BibTeX: 15
- EndNote: 17
Cited
32 citations as recorded by crossref.
- Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER N. Casagli et al. 10.1016/j.rsase.2016.07.001
- Machine learning and landslide studies: recent advances and applications F. Tehrani et al. 10.1007/s11069-022-05423-7
- Digital image processing and recognition technology for classification and recognition of hydrothorax cancer cells Y. Zhang & W. Zhang 10.3233/JIFS-179609
- Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: An Application to Brazil G. Xu et al. 10.1109/JSTARS.2022.3161383
- Texton-Based Ensemble Classification of Landslide Source and Transport Areas in VHR Imagery M. Silveira et al. 10.1109/LGRS.2016.2610003
- Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas S. Tavakkoli Piralilou et al. 10.3390/rs11212575
- Study of clay degradation in an earthslide combining OBIA and roughness analysis of UAV‐based optical images S. Fiolleau et al. 10.1002/esp.5468
- Dynamic Earthquake-Induced Landslide Susceptibility Assessment Model: Integrating Machine Learning and Remote Sensing Y. Yang et al. 10.3390/rs16214006
- Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster–Shafer M. Mezaal et al. 10.3390/rs10071029
- Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors D. Hölbling et al. 10.3390/geosciences7020037
- A repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France A. Cerbelaud et al. 10.1016/j.isprsjprs.2021.10.013
- Shallow landslides and vegetation at the catchment scale: A perspective C. Phillips et al. 10.1016/j.ecoleng.2021.106436
- Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India T. Sivasankar et al. 10.1007/s10661-021-09119-6
- Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography D. Hölbling et al. 10.3390/geosciences6040048
- Sensitivity study of multi-field information maps of typical landslides in mining areas based on transfer learning Y. Zhang et al. 10.3389/feart.2023.1105985
- Landslide mapping using object-based image analysis and open source tools P. Amatya et al. 10.1016/j.enggeo.2021.106000
- Application of oblique photogrammetry technique in geological hazard identification and decision management M. Tang et al. 10.1016/j.eqrea.2023.100269
- A prototype model for detection and classification of landslides using satellite data A. Sharma et al. 10.1088/1742-6596/2327/1/012029
- Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network W. Xia et al. 10.3390/rs13245116
- Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil H. Dias et al. 10.3390/rs15215137
- Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation F. Liu et al. 10.3390/land13020158
- Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method L. Zhao et al. 10.1515/geo-2022-0692
- Transferability of object-based image analysis approaches for landslide detection in the Himalaya Mountains of northern Pakistan A. Bacha et al. 10.1080/01431161.2019.1701725
- Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning H. Lu et al. 10.3390/rs12050752
- Landslides associated with recent road constructions in the Río Lucma catchment, eastern Cordillera Blanca, Peru A. EMMER et al. 10.1590/0001-3765202220211352
- Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms R. Ramos-Bernal et al. 10.3390/rs13224515
- Mapping Pluvial Flood-Induced Damages with Multi-Sensor Optical Remote Sensing: A Transferable Approach A. Cerbelaud et al. 10.3390/rs15092361
- Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal P. Amatya et al. 10.3390/rs11192284
- The Use of TERRA-ASTER Satellite for Landslide Detection F. Vecchiotti et al. 10.3390/geosciences11060258
- A New Method for Large-Scale Landslide Classification from Satellite Radar K. Burrows et al. 10.3390/rs11030237
- Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island S. Heleno et al. 10.5194/nhess-16-1035-2016
- Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island S. Heleno et al. 10.5194/nhessd-3-5633-2015
30 citations as recorded by crossref.
- Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER N. Casagli et al. 10.1016/j.rsase.2016.07.001
- Machine learning and landslide studies: recent advances and applications F. Tehrani et al. 10.1007/s11069-022-05423-7
- Digital image processing and recognition technology for classification and recognition of hydrothorax cancer cells Y. Zhang & W. Zhang 10.3233/JIFS-179609
- Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: An Application to Brazil G. Xu et al. 10.1109/JSTARS.2022.3161383
- Texton-Based Ensemble Classification of Landslide Source and Transport Areas in VHR Imagery M. Silveira et al. 10.1109/LGRS.2016.2610003
- Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas S. Tavakkoli Piralilou et al. 10.3390/rs11212575
- Study of clay degradation in an earthslide combining OBIA and roughness analysis of UAV‐based optical images S. Fiolleau et al. 10.1002/esp.5468
- Dynamic Earthquake-Induced Landslide Susceptibility Assessment Model: Integrating Machine Learning and Remote Sensing Y. Yang et al. 10.3390/rs16214006
- Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster–Shafer M. Mezaal et al. 10.3390/rs10071029
- Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors D. Hölbling et al. 10.3390/geosciences7020037
- A repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France A. Cerbelaud et al. 10.1016/j.isprsjprs.2021.10.013
- Shallow landslides and vegetation at the catchment scale: A perspective C. Phillips et al. 10.1016/j.ecoleng.2021.106436
- Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India T. Sivasankar et al. 10.1007/s10661-021-09119-6
- Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography D. Hölbling et al. 10.3390/geosciences6040048
- Sensitivity study of multi-field information maps of typical landslides in mining areas based on transfer learning Y. Zhang et al. 10.3389/feart.2023.1105985
- Landslide mapping using object-based image analysis and open source tools P. Amatya et al. 10.1016/j.enggeo.2021.106000
- Application of oblique photogrammetry technique in geological hazard identification and decision management M. Tang et al. 10.1016/j.eqrea.2023.100269
- A prototype model for detection and classification of landslides using satellite data A. Sharma et al. 10.1088/1742-6596/2327/1/012029
- Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network W. Xia et al. 10.3390/rs13245116
- Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil H. Dias et al. 10.3390/rs15215137
- Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation F. Liu et al. 10.3390/land13020158
- Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method L. Zhao et al. 10.1515/geo-2022-0692
- Transferability of object-based image analysis approaches for landslide detection in the Himalaya Mountains of northern Pakistan A. Bacha et al. 10.1080/01431161.2019.1701725
- Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning H. Lu et al. 10.3390/rs12050752
- Landslides associated with recent road constructions in the Río Lucma catchment, eastern Cordillera Blanca, Peru A. EMMER et al. 10.1590/0001-3765202220211352
- Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms R. Ramos-Bernal et al. 10.3390/rs13224515
- Mapping Pluvial Flood-Induced Damages with Multi-Sensor Optical Remote Sensing: A Transferable Approach A. Cerbelaud et al. 10.3390/rs15092361
- Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal P. Amatya et al. 10.3390/rs11192284
- The Use of TERRA-ASTER Satellite for Landslide Detection F. Vecchiotti et al. 10.3390/geosciences11060258
- A New Method for Large-Scale Landslide Classification from Satellite Radar K. Burrows et al. 10.3390/rs11030237
2 citations as recorded by crossref.
- Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island S. Heleno et al. 10.5194/nhess-16-1035-2016
- Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island S. Heleno et al. 10.5194/nhessd-3-5633-2015
Saved (preprint)
Latest update: 13 Dec 2024
Short summary
A method for semi-automatic landslide detection and mapping is presented and tested using a very high-resolution satellite image, sensed 3 days after a major damaging landslide event that occurred in Madeira Island (20 February 2010). The testing is developed in a 15 km2 wide area, where 95 % of the number of landslides scars is detected by this approach.
A method for semi-automatic landslide detection and mapping is presented and tested using a very...
Altmetrics
Final-revised paper
Preprint