Skip to main content

Advertisement

SpringerLink
Log in

A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: a case study of national Nepal

  • Original Paper
  • Published:
Landslides Aims and scope Submit manuscript

Abstract

Landslides are frequent all around the world, causing tremendous loss to human beings. Rapid access to the locations where landslides occur is crucial for emergency response. Most researches in landslide detection from remotely sensed images focus on small regions, which are handpicked. That makes it easy to distinguish landslides from background objects, but hard to apply in practical cases. The complicated non-landslide background pixels increase the difficulty to accurately detect landslides. In this study, we propose a technique framework to remove non-landslide background pixels for national Nepal using 12 Landsat8 images and digital elevation model (DEM). DEM is useful in removing flat areas, where landslides are less likely to occur. The framework consists of three sections: image enhancement, landslide proposal extraction, and detection model setup. Bare land, including landslides, is enhanced using vegetation index after haze/cloud re-movement. Later, calculate connective contours and propose them as potential regions that may contain landslides. For each proposal, calculate texture feature and build detection model using one of the Landsat8 images, which is further applied on other images to check its applicability and robustness. The assessment shows that the method is able to remove 99% of the background pixels in the scale of national Nepal, taking over billions of pixels. Even there is still much to do to achieve high accurate landslide detection results from large-scale images, the experiment validates a strong potential applicability for the proposed method in large-scale landslide-related analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Amos J (2015) Why Nepal is so vulnerable to quakes. BBC News Services

  • Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2–16

    Article  Google Scholar 

  • Brardinoni F, Slaymaker O, Hassan MA (2003) Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data. Geomorphology 54:179–196

    Article  Google Scholar 

  • Bruzzone L, Bovolo F (2013) A novel framework for the design of change-detection systems for very-high-resolution remote sensing images. Proc IEEE 101:609–630

    Article  Google Scholar 

  • Cai J, Goshtasby A (1999) Detecting human faces in color images. Image Vis Comput 18:63–75

    Article  Google Scholar 

  • Chen C-Y, Martin G (2002) Soil–structure interaction for landslide stabilizing piles. Comput Geotech 29:363–386

    Article  Google Scholar 

  • Chen G, Fan Y, Li Y (2017) Hazard mapping for earthquake-induced geo-disaster chain. In: Geotechnical Hazards from Large Earthquakes and Heavy Rainfalls. Springer, pp 349–362

  • Chen R-F, Chang K-J, Angelier J, Chan Y-C, Deffontaines B, Lee C-T, Lin M-L (2006) Topographical changes revealed by high-resolution airborne LiDAR data: the 1999 Tsaoling landslide induced by the Chi–Chi earthquake. Eng Geol 88:160–172

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Yamanaka M, Dhakal S, Bhandary NP, Yatabe R (2009) Comparative analysis of contributing parameters for rainfall-triggered landslides in the Lesser Himalaya of Nepal. Environ Geol 58:567–586

    Article  Google Scholar 

  • Dai F, Lee C (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

    Article  Google Scholar 

  • Dale VH et al (2001) Climate change and forest disturbances: climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides. BioScience 51:723–734

    Article  Google Scholar 

  • Debella-Gilo M, Kääb A (2011) Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation. Remote Sens Environ 115:130–142. https://doi.org/10.1016/j.Rse.2010.08.012

    Article  Google Scholar 

  • Delacourt C, Allemand P, Casson B, Vadon H (2004) Velocity field of the “La Clapière” landslide measured by the correlation of aerial and QuickBird satellite images Geophysical Research Letters 31

  • Dingle R (1977) The anatomy of a large submarine slump on a sheared continental margin (SE Africa). Journal of the Geological Society 134:293–310

    Article  Google Scholar 

  • Erhan D, Bengio Y, Courville A, Manzagol P-A, Vincent P, Bengio S (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660

    Google Scholar 

  • Fischman J (2015) How the deadly Nepal earthquake happened [infographic]. SCIENTIFICAMERICAN

  • GeoHazards I (2004) GEOHAZARDS theme report: for the monitoring of our environment from space and from Earth European Space Agency publication

  • Gorum T, Fan X, van Westen CJ, Huang RQ, Xu Q, Tang C, Wang G (2011) Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake. Geomorphology 133:152–167

    Article  Google Scholar 

  • Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804

    Article  Google Scholar 

  • Harp EL, Jibson RW (1996) Landslides triggered by the 1994 Northridge, California, earthquake. Bulle Seismol Soc Am 86:S319–S332

    Google Scholar 

  • Heleno S, Matias M (2015) Pina P mapping landslide source and transport areas in VHR images with object-based analysis and support vector machines. In: EGU General Assembly Conference Abstracts. p 3447

  • Hervás J, Barredo JI, Rosin PL, Pasuto A, Mantovani F, Silvano S (2003) Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide, Italy. Geomorphology 54:63–75

    Article  Google Scholar 

  • Hungr O, Clague J, Morgenstern N, VanDine D, Stadel D (2016) A review of landslide risk acceptability practices in various countries

  • Igwe O, Onwuka S, Oha I, Nnebedum O (2016) WCoE/IPL projects in West Africa: application of Landsat ETM+ and ASTER GDEM data in evaluating factors associated with long runout landslides in Benue hills, North-central Nigeria Landslides:1–11

  • Jan W, Chandler JH, Neil D, Tom D (2007) Aerial photography and digital photogrammetry for landslide monitoring. Geol Soc Lond Spec Publ 283:53–63

    Article  Google Scholar 

  • Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe Version 4 available from the CGIAR-CSI SRTM 90m Database (http://srtm.csi.cgiar.org)

  • Kainthola A, Singh PK, Singh TN (2015) Stability investigation of road cut slope in basaltic rockmass, Mahabaleshwar, India Geoscience. Frontiers 6:837–845. https://doi.org/10.1016/j.gsf.2014.03.002

    Google Scholar 

  • Kapur JN, Sahoo PK, Wong AK (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comp Vision Graphics Image Process 29:273–285

    Article  Google Scholar 

  • Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an artificial neural network (ANN). Geomorphology 113:97–109

    Article  Google Scholar 

  • Keefer DK (2002) Investigating landslides caused by earthquakes—a historical review. Surv Geophys 23:473–510

    Article  Google Scholar 

  • Kerle N, Froger J-L, Oppenheimer C, Vries BVWD (2003) Remote sensing of the 1998 mudflow at Casita volcano, Nicaragua. Int J Remote Sens 24:4791–4816

    Article  Google Scholar 

  • Korfiatis VC, Tassani S, Matsopoulos GK (2017) An independent active contours segmentation framework for bone micro-CT images. Comput Biol Med 87:358–370. https://doi.org/10.1016/j.compbiomed.2017.06.016

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113

    Article  Google Scholar 

  • Leprince S, Berthier E, Ayoub F, Delacourt C, Avouac J-P (2008) Monitoring earth surface dynamics with optical imagery. EOS, Trans Am Geophys Union 89:1–2. https://doi.org/10.1029/2008EO010001

    Article  Google Scholar 

  • Li Z, Shi W, Myint SW, Lu P, Wang Q (2016) Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sens Environ 175:215–230

    Article  Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22

    Google Scholar 

  • Lu P, Stumpf A, Kerle N, Casagli N (2011) Object-oriented change detection for landslide rapid mapping. IEEE Geosci Remote Sens Lett 8:701–705. https://doi.org/10.1109/LGRS.2010.2101045

    Article  Google Scholar 

  • Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A (2011) Data mining methods in the prediction of dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes 4:299

    Article  Google Scholar 

  • Martha TR, Govindharaj KB, Kumar KV (2015) Damage and geological assessment of the 18 September 2011 M w 6.9 earthquake in Sikkim, India using very high resolution satellite data. Geosci Front 6:793–805

    Article  Google Scholar 

  • Mohammadi S, Taiebat H (2016) Finite element simulation of an excavation-triggered landslide using large deformation theory. Eng Geol 205:62–72. https://doi.org/10.1016/j.enggeo.2016.02.012

    Article  Google Scholar 

  • Moosavi V, Talebi A, Shirmohammadi B (2014) Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method. Geomorphology 204:646–656

    Article  Google Scholar 

  • Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote sensing of environment 115:1145–1161

    Article  Google Scholar 

  • Ogawa K, Ito Y, Nakano K (2010) Efficient Canny edge detection using a GPU. In: Networking and Computing (ICNC), 2010 First International Conference on, IEEE, pp 279–280

  • Peruccacci S, Brunetti MT, Gariano SL, Melillo M, Rossi M, Guzzetti F (2017) Rainfall thresholds for possible landslide occurrence in Italy. Geomorphology 290:39–57. https://doi.org/10.1016/j.geomorph.2017.03.031

    Article  Google Scholar 

  • Russ JC (2016) The image processing handbook. CRC press

  • Russakovsky O et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115:211–252

    Article  Google Scholar 

  • Sauchyn D, Trench N (1978) Landsat applied to landslide mapping

  • Selby MJ (1982) Hillslope materials and processes hillslope materials and processes

  • Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using random forests. Remote Sens Environ 115:2564–2577

    Article  Google Scholar 

  • Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300

    Article  Google Scholar 

  • Tianchi L, Kathmandu N (1987) Landslide management in the mountain areas of China FORESTRY

  • Uijlings JR, van de Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vis 104:154–171

    Article  Google Scholar 

  • University D (2015) Nepal earthquake landslide locations, 30 June 2015. The Humanitarian Data Exchange

  • USGS (2012) Landsat data continuity mission

  • USGS (2015) Frequently asked questions about the Landsat missions. U.S. Department of the Interior | U.S. Geological Survey

  • Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131

    Article  Google Scholar 

  • Xu C (2015) Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: principles and case studies. Geoscience Frontiers 6:825–836

    Article  Google Scholar 

  • Xu C, Xu X, Yao X, Dai F (2014) Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan M w 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides 11:441–461

    Article  Google Scholar 

  • Yang X, Chen L (2010) Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides. Int J Appl Earth Obs Geoinf 12:487–495

    Article  Google Scholar 

  • Yildirim G, Süsstrunk S (2014) FASA: fast, accurate, and size-aware salient object detection. In: Asian Conference on Computer Vision. Springer, pp 514–528

  • Yu B, Chen F (2017) A new technique for landslide mapping from a large-scale remote sensed image: a case study of central Nepal. Comput Geosci. https://doi.org/10.1016/j.cageo.2016.12.007

  • Zheng Z, Zeng Y, Li S, Huang W (2016) A new burn severity index based on land surface temperature and enhanced vegetation index. Int J Appl Earth Obs Geoinf 45:84–94. https://doi.org/10.1016/j.jag.2015.11.002

    Article  Google Scholar 

  • Zhou H, Yuan Y, Shi C (2009) Object tracking using SIFT features and mean shift. Comput Vis Image Underst 113:345–352. https://doi.org/10.1016/j.cviu.2008.08.006

    Article  Google Scholar 

  • Zitnick CL, Dollár P (2014) Edge boxes: Locating object proposals from edges. In: European Conference on Computer Vision. Springer, pp 391–405

Download references

Acknowledgements

This study has been done with the help of prior works from Bo Yu and Fang Chen (Yu and Chen 2017). It is supported by the National Natural Science Foundation of China (grant number 41601451), the International Partnership Program of the Chinese Academy of Sciences (grant number 131C11KYSB20160061), and the International Partnership Program of the Chinese Academy of Sciences (grant number 131551KYSB20160002). We are grateful for the NASA’s Earth observation program for providing the Landsat data from the website http://landsat.usgs.gov.

Author information

Authors and Affiliations

  1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing, 100094, China

    Fang Chen, Bo Yu & Bin Li

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Fang Chen

  3. Hainan Key Laboratory of Earth Observation, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Sanya, 572029, China

    Fang Chen

Authors
  1. Fang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Yu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, F., Yu, B. & Li, B. A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: a case study of national Nepal. Landslides 15, 453–464 (2018). https://doi.org/10.1007/s10346-017-0884-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-017-0884-x

Keywords

Search

Navigation