Use of spatial information (with emphasis on optical remote sensing data) for landslide hazard and risk assessment

Transcription

1 Use of spatial information (with emphasis on optical remote sensing data) for landslide hazard and risk assessment Cees van Westen United Nations University ITC School for Disaster Geo-Information Management International Institute for Geo-Information Science and Earth Observation (ITC) Enschede, The Netherlands Associated Institute of the

2 Contents Introduction, landslide risk context Base data acquisition Landslide inventories Visual interpretation Automated landslide mapping Using spectral information Using altitude information Generation of landslide databases Spatial data for susceptibility assessment Spatial data for elements at risk Importance of landslide data for hazard, vulnerability and risk assessment

3 The most complicated simple formula RISK = HAZARD * VULNERABILITY * AMOUNT Hazard = Probability of event with a certain magnitude Vulnerability = Degree of damage. Amount = Quantification of the elements at risk US $ P=0.02 V = 1 1 P=0.5 V = 0.01 P=0.1 V = Risk curve

4 Environmental Factors Triggering Factors Landslide Inventor y Elements at Risk Geol ogy Soil Morpholog y Geomorpholog y Land cover (time 0) Land cover (time n) Hydr olog y (time 0) Hydr olog y (time n) Earthquake data Weather data (t= 0) Weather data (t= n) Time 0 Time n Buildings Roads Land use Essential facilities Cadastral data Cens us dat a A Spatial modeling of landslide initiation Magnitude - Frequency analysis Landslide runout assessment Scenario development Population (time 0) B Initiation hazar d (Type a) (Type n) Runout hazar d (T ype a) (Type n) Magnitude loss relationships Population (time n) C Hazard X Vulnerability X Am ount Specific Risk Loss (type) / ti me Total Risk Loss (type) / ti me All landslide types All landslide volumes All triggering events All elements at risk D

5 Scale Important aspects Small, medium, large, detailed Hazard assessment technique Inventory, heuristic, statistical, physical modeling Risk assessment technique Qualitative, (semi) quantitative Amount of data Available data, resources for data collection Characteristics of the area Homogeneity, spatial variability of factors, landslide type These are all interrelated JTC-1 Joint Technical Committee on Landslides: International Guidelines For Landslide Susceptibility, Hazard And Risk Zoning

6 Which data to collect? Objectives of the study Availability of existing data Available resources Complexity of the area Selection of analysis technique Selection Collection spatial and non spatial data

7 Main methods Inventory based Magnitude-frequency Activity mapping Data driven: Bivariate statistics Weights of evidence Information value Frequency ratio Multi-variate statistics Logistic regression Discriminant analysis Cluster analysis Artificial Neural Networks Knowledge driven: Boolean Logic Fuzzy logic Multiclass overlay Spatial multicriteria evaluation Probabilistic methods Parameter uncertainty Temporal prediction Deterministic methods Static methods Infinite slope based Profile based Dynamic methods

8 Relation scale and methods Qualitative methods Quantitative methods Scale Inventory Heuristic Analysis Statistical Analysis Processbased Analysis Probabilistic Analysis < 1:10,000 Yes Yes No Yes Yes 1:25,000 1:50,000 Yes Yes Yes No Yes > 1:100,000 Yes Yes Yes/No No No based on Soeters & van Westen (1996) and Aleotti & Chowdhury (1999)

9 International Institute for Geo-Information Science and Earth Observation (ITC)

10 EO data for landslide studies Landslide inventory Interpretation Image classification Landslide monitoring Environmental factors Rainfall monitoring Temporal resolution Spatial resolution Spectral resolution

11 Main assumption The past is the key to the future or Results obtained in the past do not give a guarantee for the future Landslide events that have happened in the past provide us the input to model them in the future. Keep in mind: Causal relations change through time. Triggering factors change through time Elements at risk change through time

12 Landslide inventory Important aspects scarp / transport / accumulation age (absolute /relative) Activity (is often confused with age ) Depth Volume Magnitude Type Speed of movement Causal mechanism Why did the landslide happen here?

13 Methods for landslide inventory Image interpretation (Semi) automatic classification Based on spectral information Based on altitude information Field investigation Archive studies Dating methods Monitoring networks

14 International Institute for Geo-Information Science and Earth Observation (ITC)

15 International Institute for Geo-Information Science and Earth Observation (ITC)

16 Image interpretation Stereo aerial photographs Analog format or digital image interpretation with single or multi-temporal data set. High Resolution satellite images With monoscopic or stereoscopic images, and single or multi-temporal data set LiDAR shaded relief maps Single or multi-temporal data set from bare earth model. Radar images Single data set landslides

17 Imagery at different resolution A - Aerial-photo, 1:62,000, 13-Jan-1956, 300 dpi (size 25%) B - Landsat TM, 30 m, 15-Jan-1985 C - Landsat ETM, 15 m, 08-Mar-2001 D - ASTER VNIR, 15 m, 14-Feb-2002 E - Spot PAN, 10 m, 28-Dec-1994 F - Aerial-photo, 1:37,000, 16-Feb-1972, 300 dpi (size 25%) G - Aerial-photo, 1:25,000, 02-Feb-2000, 300 dpi (size 25%) A B C D H - QuickBird, 70 cm, 16-Nov-2005 (size 25%) Notes: -Photo A is before the landslide. -Photo and labels are grouped. -In order to compare better all photos and QuickBird are reduced 25%. E ± 400 m 320 m F G H N Source: Castellanos and Van Westen

18 Visual interpretation of landslides Numerous landslides triggered by high rainfall events. E.g. Landslides due to Hurricane Stan in October 2005, near Lago Atitlan, Guatemala

19 Visual interpretation of landslides Pakistan, 8/10/ 2005 earthquake

20 Visual interpretation of landslides Varunavat Parvat landslide, Uttarkashi, India, which initiated in September 2003

21 Indian Cartosat Jammu and Kashmir pr ov., Uddam Cartosat-1 Landslide Source: Cartosat, Acquired: 09/10/2005 Copyright NRSA 2005 Image processing, map created 11/10/2005 by NRSA Cartosat 1: 2m resolution. Along track stereo Cartosat 2: 1 m resolution, panchromatic Much cheaper than IKONOS, QUICKBIRD

22 Example: Tegucigalpa, Honduras

23 North Landslide Saturday, October 31 Ponded water

24 Rotational Block Compression Block Toe Natural Barrier 18

25 Flood and landslide

26 Example: Berrinche landslide Airphoto 1:14,000 from 16-March-1975 Airphoto 1:20,000 from 9-February-1990 Airphoto 1:25,000 from 1998 Airphoto 1:10,000 photos from May 2001

27 Example: Berrinche landslide Aster image (15 m. spatial resolution) 2005 IRS-P6 (5.6 m. resolution) from 2006 Google Earth (Digital Globe image) 2007 Lidar hillshading image

28 LiDAR shaded relief GLENN, N.F., STREUTKER, D.R.,CHADWICK, D.J., THACKRAY,G.D. AND DO RSCH, S.J Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity. Geomorphology 73, Detection of landslides in heavily forested terrain. Interpretation of large and complex landslide features Reconstruction of landslide mechanism

29 Mirror stereoscope Screenscope Anaglyph Polarized light Stereo interpretation

30 Interpretation elements Tone is defined as the relative brightness in a black/white image Texture relates to the frequency of tonal change. It is the result of the composite appearance presented by an aggregate of unit features too small to be recognised individually. Shape or form refers to the geometric aspects of the object in the image Pattern refers to the spatial arrangement of features and implies a characteristic repetition of certain forms or relationships. Association refers to the occurrence of the object of study in combination with other objects that makes it possible to infer about its function or meaning

31 Landslide interpretation elements

32 International Institute for Geo-Information Science and Earth Observation (ITC)

33 Landslide mapping can be difficult

34 Image interpretation is subjective

35 Manizales, Caldas, Colombia International Institute for Geo-Information Science and Earth Observation (ITC)

36 Port de Vielha, Vall d Aran International Institute for Geo-Information Science and Earth Observation (ITC) Lateral spread, Rio Suarez, Colombia

37 Fiume d Acqua Fredda, Basilicata, Italy International Institute for Geo-Information Science and Earth Observation (ITC) Aliano area, Basilicata, Italy

38 International Institute for Geo-Information Science and Earth Observation (ITC)

39 View 3-D using analgyph image

40 Mapping landslides

41 Multitemporal analysis

42 Even if it is done by experts

43 (Semi) automated classification based on spectral characteristics Aerial photographs Image ratioing, thresholding Medium resolution multi spectral images Single data images, with pixel based image classification or image segmentation Multiple date images, with pixel based image classification or image segmentation Using combinations of optical and radar data Either use image fusion techniques or multi-sensor image classification, either pixel based or object based

44 Change detection

45 11,500 Mitch landslides in 10,000 sq. km area Susceptibility Study Area, 2,950 Mitch landslides in 980 sq. km

46 Landslides mapped after 1 event Pre-Mitch landslides Mitch landslides Post-Mitch landslides

47 Landslide Detection Using ASTER 10/01/05-10/13/05 Stan hurricane

48 Image segmentation Object oriented classification Landslides are objects with spectral and topographical characteristics E-cognition software Source: Barlow et al. (2006)

49 Applications of DInSAR Application Landslide Inventory Landslide Displacement Monitoring Description Landslide inventory maps obtained through an update of the existing knowledge of landslides location and activity, and the identification of new slope instabilities (using spaceborne and airborne remote sensing data, and geomorphological analysis). Detailed monographic report about status and activity of already identified landslides. Superficial deformation rates of landslide bodies derived from space-borne Remote Sensing observations are analysed, compared and integrated with ground information and geotechnical models to understand temporal and spatial evolution of each slope instability. Landslide Susceptibility Mapping (Landslide Hazard Mapping) Landslide vulnerability assessment Landslide susceptibility (or hazard) maps obtained through the integration of ground displacement observations from Space with thematic maps (e.g. land use, slope, geomorphology, lythology) using data integration techniques. Correlation of landslide displacements measured on persistent scatterers (houses) for which actual damage data is available. Correlation of movement velocity with damage degree. Source: Paganini (ESA)

50 Photogrammetry: : going back in time Source: Castellanos and Van Westen

51 Multitemporal change detection De Witte et al., 2007

52 Volume calculation International Institute for Geo-Information of landslide Science Earth Observation (ITC) using DEMs Before After

53 Multitemporal Lidar Airborne - airborne (multi temporal is often too expensive) Airborne helicopter Airborne terrestrial Terrestrial - terrestrial Source: Hsiao et al

54 (Semi) automated classification based on Altitude characteristics

55 Problems with landslide inventory Landslide frequency Time A B C D E

56 Landslide databases Available partly for many countries Often made as a project Continuity is often a problem Very difficult when landslides are multitemporal and reactivated Most are not kept up to date

57 When using landslide data Separate data set: Model data set & validation data set Landslide types & causal mechanism Landslide initiation & deposition Temporal: Period (e.g. decade) Event based (link with return period) Individual dates

58 Damage databases Landslide damage surveys and landslide inventory mapping is crucial for vulnerability assessment E.g. Territory of the National Basin Authority of the Liri-Garigliano and Volturno rivers (about 12,000 km2): landslides, 6450 damage surveys Landslide-induced damage to properties dataset Source: Cascine and Pisciotta

59 Environmental factors You must be able to explain what the relation is with landslide occurrence. Is the factor causing the landslide or resulting from the landslide? Has the factor changed since the landslide occurred? Can the factor be mapped for the whole study area? Does it have a spatial / temporal variability?

60 Environmental parameters Ercanoğlu (2003)

61 Geotechnical/hydrological parameters for dynamic modeling

62 Digital Elevation Data Photogrammetry Existing topomaps Satellite data InSAR LiDAR Many derivative maps from DEMs, but which ones are really useful?

63 DEM derivatives Altitude Hillshading Slope angles (degrees) Slope direction (in degrees)

64 DEM derivatives Flow accumulation Automatic drainage and catchment delineation Drainage direction

65 Dems from satellite images

66 Slope angle versus landslides SRTM DEM 1:50000 topomap 1:2000 topomap Lidar DEM

67 Use of DEM data

68 Geomorphological maps

69 Very detailed information Based on detailed field mapping

70 Geomorphological data

71 Difficult to store in GIS Store Geomorphological information in several data layers: A: Main Units B: Sub units C: Landslides D: Material types E: Hazard

72 Geological data Lithology: not the stratigraphic information, but engineering geological classification Geological structure: difficult to store in GIS. Actually 3-D GIS is required. Faults / lineaments: which ones are active? Why are faults related to landslides? They might actually move? They are degraded rocks.

73 Which classes to use? Do they have a relation with landslides? Different classes for different methods. E.g Deterministic - statistical Many existing landuse maps should be reclassified Image processing / classification Landuse is both a factor in the hazard assessment well as an element at risk Sometimes better to use landuse change map, than landuse map Landuse data After Coppin and Richards, (1990)

74 ELEMENTS AT RISK Buildings (type, floors, use) Population (age, gender, time of day ) Infrastructure ((rail)roads, utilities) Critical facilities (hospitals, police etc) The environment (forest, natural parks) High potential loss facilities Economic activities (urban, rural..)

75 Elements at risk

76 Elements at risk mapping Using existing data: Cadastral databases Building footprint maps Census data Land use maps Collecting them: Image interpretation (high res.imagery) Field mapping (mobile GIS)

77 Generating an elements at risk database High res image Ward DEM Lidar Contour map Input data Boundaries Mapping units DEM topo Population Landuse Nr Buildings Nr floors Attributes linked to mapping units

78 Different levels of aggregation

79 High res image Ward & census International Institute for Geo-Information Science and Earth Observation (ITC) DEM Lidar Flowchart Thematic layers: Flood discharges Seismic catalogs Soil and rock data Landslide information Technological information etc. Elements at risk Hazard maps Building Attributes: Urban land use Nr of buildings Height of buildings Nr. people (daytime) Nr. people (nighttime) Risk maps Lands lides Flooding Technological Earthquake Risk = Hazard * Vulnerability * Amount Risk curve Lands lides Flooding Technological Earthquake

80 Conclusions Landslide inventory mapping remains crucial for hazard and risk assessment Recent advances in Remote Sensing offer great opportunities in landslide inventory mapping It is time that image interpretation becomes the fashion again: new VHR imagery & Google Earth A landslide database should have info on: Temporal information Landslide types, volumes Damage More effort should be given to designing and maintaining landslide databases Not all data that can be easily obtained are the real useful ones for landslide hazard assessment

81 UNU-ITC DGIM