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Identification of Landslides with combined RS and GIS data

This study focuses on the identification of landslides using remote sensing (RS) and geographic information system (GIS) data. It explores the geologic and terrain characteristics in Taiwan, as well as the use of satellite images for landslide detection. The study also discusses the results of landslide interpretation and highlights the need for a monitoring and early warning system. The findings of the study suggest that the combination of RS and GIS data can provide reliable and near real-time disaster estimation.

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Identification of Landslides with combined RS and GIS data

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  1. Identification of Landslides with combined RS and GIS data Kuo-Hsin Hsiao, Jin-King Liu, Ming-Fong Yu Speaker : Kuo-Hsin Hsiao August 25, 2005

  2. Identification of Landslides with combined RS and GIS data List of Contents 1. Introduction 2. Landslide Detection 3. Results of landslide interpretation 4. Concluding Remarks

  3. 1. Introduction ◆ Geologic and terrain characteristics in TAIWAN ◆ Climate condition – typhoon, torrential rainfall ◆ R.S. data – spatial、temporal resolution resolution, data acquisition, time required for interpretation, combined information of GIS, etc. Lithology-conglomerates Highly fractured rock formations Variations of Geologic Conditions: A section across Taiwan

  4. Requirement of A Monitoring and Early Warning System Both for Emergency Response and for Mitigation Policy Landslide Detection Using Satellite Images High Frequency Periodic Observation and Measurements : Month~Year Data : SPOT-5 or Formosat-2 . . Objectives of the study: Periodic landslide Monitoring for Sustainable Management using high resolution data (for detecting small landslides) Prevention of Illegal Land Use and Deterioration Disaster Damage Estimation Background Introduction Sun-Synchronous Orbit Altitude = 891 km; Inclination = 99.10 deg; Period = 14 Rev/day Orbit of FORMOSAT-2

  5. Research Area: Shihmen Reservoir Introduction SPOT image flight path (CSRSR)

  6. Purpose of the Reservoir General water supply Irrigation High-tech industry Watershed Area:764 KM2 Capacity: 2.5 x 108 M3 Terrain Variation : 252M~3,500 M Average Rain Fall : 2,500 mm/yr Land-Use Type Coniferous Tree, Deciduous Tree Orchard, Rice, Village, Farming, Foresting Bare Soil, River, Mixed-Forest, Bamboo, Grass Land Mixed Coniferous-Deciduous Tree, Others. Shihmen Reservoir Introduction Land-Use Coverage

  7. Shihmen Reservoir Introduction DEM slope Soil map forest type

  8. Duration : Aug. 23 ~ Aug. 25, 2004 Maximum total accumulative rain fall -1,600 mm Maximum rain fall 146 mm / hr Disaster – Typhoon AERE Introduction CWB Contour of Total Accumulative Rain Fall 2004-08-24-09:23 2004-08-25-09:23

  9. Satellite Radar Water Quality after Typhoon Introduction 2004/8/26 FORMOSAT-2 2004/8/26 FORMOSAT-2 High turbidity NSPO CWB NSPO Satellite IR Typhoon Road of AERE

  10. 2. Landslides Detection Data Acquisition Formosa-II 2005/04/04 Resolution：2m & 8m SPOT-5 2004/08/16 Resolution：10m SPOT5 2005/03/16 Resolution：2.5m & 10m

  11. Disaster Estimation & Analysis Processes Landslides Detection Classification Day-2 Image Day-1 Image overlay NDVI/CVA NDVI/CVA Change Detection NO Landslide Coverage Change ? Stop YES DTM + Image (3D Visualization) Disaster Estimation On-Site Photography Disaster Areas Overlay Analysis

  12. Various Types of Landcover and Landuses Landslides Detection (a)River-bank landslides (b)Slope landslides (c)Upstream landslides (d) snow on tops (e)Grass lands (f)Excavated lands (g)Cultivated lands (h)Mountain village (i)Plain villages (j) Cemetery (k)Roads (l)Rivers

  13. 3. Results of landslide interpretation

  14. Landslide interpreted from SPOT5 10m & 2.5m Results Spatial resolution : 10m Spatial resolution : 2.5m

  15. Landslide interpreted from formosat2 8m & 2m Results Spatial resolution : 8m Spatial resolution : 2m

  16. Statistical analysis Results

  17. 3D Visualization of Detected Landslides Results Landslide induced by typhoon AERE CSRSR Red regions denote the detected landslides of Formosat-2 test data. CSRSR CSRSR SPOT-5(2004/08/16) SPOT-5(2005/03/16) SPOT-5(2004/08/16) SPOT-5(2005/03/16) On-Site Photography On-Site Photography

  18. Landslide induced by typhoon AERE Results CSRSR On-Site Photography SPOT-5(2004/08/16) SPOT-5(2005/03/16) CSRSR Helicopter Photography SPOT-5(2004/08/16) SPOT-5(2005/03/16)

  19. Flight Simulation after NERE Typhoon Formosat-2 image SPOT image 2003/11/14 Before typhoon NERE 2004/8/31 (NSPO) After typhoon NERE

  20. Comparison with Existing Landslides in GIS databasealso with Cadastral Informations

  21. Requirements of A Monitoring and Early Warning System Bottleneck--Data Acquisition High frequency data acquisition of remote sensing. Near Real-time Dynamic Monitoring… Conclusions • Typhoon AERE • Statistics • 477 & 473 places, total areas of 693.33 & 723.74 hectares of landslides were detected using SPOT-5 and Formosat-2 fusion data • The reliability of landslide detection is high when comparing with on-site photography.

  22. With Formosat-2, A Possibility of Near real-time disaster estimation Provide disaster information in a short time. Historical data collection is important. A comparison can be made by GIS database. Construction of GIS database infrastructure is critical for post-disaster analysis. Conclusions

  23. Thanks for your attention

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