Identification of Suitable Imagery (data) for Damage Assessment

Transcription

1 Identification of Suitable Imagery (data) for Damage Assessment Dr. Norman Kerle INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

2 Lecture overview: What do we mean by damage assessment? Workflow of the problem Questions to ask Relevant sensors properties The right sensor for the right feature 3-D analysis Data integration Cost Refresher course Uganda

3 Principal questions and considerations What are we really trying to do? Advise the government on the consequences of an event? Aid in immediate disaster response? (direct emergency services, etc.) Assess the potential for secondary disaster (further building collapse, etc.) Guide cleanup and reconstruction? Refresher course Uganda

4 The Problem Disaster type Disaster Suitability for disaster type Constraints Data Earthquake Hurricane etc Characteristics spatial spectral temporal availability cost software expertise Location & extent Distance from source Surface materials Scale of object damage Distribution Length of event Tone, Possible pattern repeats Delayed effects spatial spectral temporal Refresher course Uganda

5 What to do? a checklist Consider the type of event and its characteristics Define clearly what you need to map/ identify (including the MMU Minimum Mapping Unit) Based on that, define what data characteristics you require What data are available? How quickly? How quickly are results needed? Do pre-event images exist & are they useful? What are the cost constraints? Are the technological and expert requirements met? Refresher course Uganda

6 Further thoughts Inventory of potentially available spaceborne data Inventory of potentially available airborne data Identification of pros and cons of data types Data A = Data B Interchangeability? - Accuracy - Update frequency Models Pixel-based/spectral Object-based Models - Sufficient data acquisition and processing time? - what is Plan B? Products - Acquisition - Atmospheric correction (necessary?) - Geometric correction -Processing - Accuracy & quality control Refresher course Uganda

7 Many image types needed to extract all these data This is where we started Refresher course Uganda

8 Questions simplify in damage assessment Disaster Characteristics spatial spectral temporal Location & extent Distance from source Scale of object damage Surface materials, clouds Distribution Tone, pattern, roughness Length of event Possible repeats Delayed effects Remember: a given disaster can lead to a number of consequences with different spatial, spectral and temporal charactersistics Total size of affected area Airbase nearby for airborne survey? Orbit characteristics of suitable satellites, footprint, pointability Possible tasking of satellite? Spatial resolution vs. object size Possible materials: water, debris/ rubble, vegetation; are clouds an issue? Use spectral signature of affected area for damage assessment Does event extend over prolonged time (repeated image acquisition!) Repeated effects? aftershocks? Secondary effects? fires, explosions, landslides, etc. e.g. Earthquake building collapse fire landslides dam break & flooding Refresher course Uganda

9 Questions in damage assessment Cloud cover? Type and size of infrastructure? Resolution? Refresher course Uganda

10 Consider the signature of an object What spectral and spatial requirements must our data meet? Refresher course Uganda

11 Pattern & tone Area unaffected by earthquake Area affected Bhuj, India, 2001 Red lines are before earthquake, green after note how feature separation changes Refresher course Uganda

12 1:3,000 scale multispectral imagery, single & multiple collapse Assessment of actual damage level can be difficult Red = destroyed Yellow = extensive damage Refresher course Uganda

13 The scale factor Refresher course Uganda

14 Resolution: 0.5 m 5 m 25 m 50 m Refresher course Uganda

15 Are features really there? SPOT ERS-2 RADARSAT Surface roughness is critical for radar studies! Objects must also be different from their surroundings Refresher course Uganda

16 We can look closer... ERS composite of pre- and post-event and first principal component Still nothing Refresher course Uganda

17 Even if features are there consider their characteristics Radar signals are sensitive to an object s orientation, as well as to the incidence angle Refresher course Uganda

18 Clearcut mapping in Alberta, Canada Radarsat image, degrees; C-HH Radarsat ERS-1 image, degrees; C-VV C-HH A Clearcuts B Pipeline C Steep valley D - Road Radar-based object detection is dependent on a multitude of parameters, incl. wavelength, polarisation, incident angle, surface roughness and moisture! Refresher course Uganda

19 3D damage assessment Stereo coverage can be acquired with both airand spaceborne sensors (1) Overlapping images flown in a strip (2) 2 photos taken during different flights (scale!) (3) Satellite images from 2 orbits (pointing or overlap) (4) Simultaneous acquisition with 2 sensors (e.g. shuttle radar mapping mission) Refresher course Uganda

20 3D data analysis Qualitative with stereoscope Qualitative and quantitative as anaglyphs in PC (StereoAnalyst, etc.) DEMs of change 3D visualisation Refresher course Uganda

21 Refresher course Uganda

22 Integration of different data Same type e.g. ERS principal component analysis High and low spatial resolution pansharpening, to combine high spatial with high spectral resolution Substitution of higher resolution image after IHS or principal component (PC) transformation Integration of auxiliary data, such as maps, GIS data or DEMs Refresher course Uganda

23 Ikonos, multispectral, 4 m Ikonos pansharpened, 1 m Refresher course Uganda

24 Principal components of SPOT image Substitution of SPOT PC1 with aerial photo Intensity, hue, saturation of SPOT image Substitution of SPOT intensity after IHS transformation with aerial photo Refresher course Uganda

25 Integration of GIS and map data As we saw before, even with high spatial resolution images, object identification can be difficult In addition, smaller features such as power lines require very high resolution data, while virtual elements (administrative boundaries) are not visible at all Damage assessment can be achieved by comparing an existing reference GIS infrastructure database with one updated following an event (object- rather than pixelbased analysis) Refresher course Uganda

26 Low-resolution imagery Remember: the purpose of the analysis results determines data type and strategy Refresher course Uganda

27 High-resolution imagery Refresher course Uganda

28 Low-cost airborne video Satellite data are nice, but also expensive Furthermore, (i) they are often not available in time, (ii) have the wrong characteristics, (iii) and only give a vertical view (limiting for some disasters) After a disaster, video footage is often collected by the police or the media, also in less developed countries Video data are simple and also not too easy to analysise, but they are cheap and also contain useful information Refresher course Uganda

29 Low-cost airborne video Refresher course Uganda

30 Low-cost airborne video Analysis can be done manually/visually Can also be automated (though research is still being done) We can apply damage mapping based on training data Refresher course Uganda

31 Low-cost airborne video But we can also use the data to extract some 3D information That can help in change and damage detection Refresher course Uganda

32 Cost Post-disaster damage assessment based on geoinformatics comes at a price The overall cost depends on several aspects: (1) Type and extent of event (2) Availability of reference data (e.g. existing GIS databases) (3) Need for use of commercial image data (Landsat, Ikonos, Quickbird, etc.) (4) Need for rapid custom image acquisition (5) Desired use of ground crews for additional information (6) Need for outside special resources (experts, databases, etc.) Refresher course Uganda

33 Advantages and disadvantages of satellite remote sensing in damage assessment Disadvantages preceded by will diminish with future satellites; the ones preceded by are likely to lose relevance as well Refresher course Uganda