role of geospatial technologies in...
TRANSCRIPT
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ROLE OF GEOSPATIAL TECHNOLOGIES IN
DISASTER MANAGEMENT
By :- Wan Mohd Naim Wan Mohd, PhD
Centre of Studies for Surveying Science and Geomatics Faculty of Architecture, Planning and Surveying
Universiti Teknologi MARA, Shah Alam
9 August 2016
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AIM OF PRESENTATION
• To highlight the use of geospatial technology in Disaster Management (Before, During, After)
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GEOSPATIAL TECHNOLOGY
GEOSPATIAL TECHNOLOGY
GIS
RS
GNSS
OTHERS
• refers to equipment used in visualization, measurement, and analysis of earth's features
Global Navigation Satellite System
Source : Havell, 2014
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Cycle of Disaster Management
Four (4) phases • Prevention and mitigation
(before) • Preparedness (before) • Response (during) • Recovery (after)
(Source : Sudheer, 2014)
Disaster Management ? - organisation and management of resources and responsibilities for dealing with all humanitarian aspects of emergencies, in particular preparedness, response and recovery in order to lessen the impact of disasters (International Federation of Red Cross and Red Crescent Societies)
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Current research projects related to Disaster Management
• Development of Landslide Hazard Zonation Mapping
• Evaluation of Various DEMs for flood inundation Modeling
• Development of Integrated Flood Management System
• GIS-based Flood Vulnerability Index
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LANDSLIDE HAZARD ZONATION MAPPING
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BACKGROUND • The increasing population and
expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide.
• Over the years, various techniques and models have been developed to predict landslide hazard zones.
• The development of these models are based on different landslide inducing factors such as:
Main Groups Factors
Ground Condition Geomorphology
Geology
Soil
Land use
Distance Related Roads
River
Drainage density
Faults
Geomorphometry DEM
Slope
Aspect
Elevation
Triggering Rainfall
Earth quakes
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BACKGROUND • Slope is one of the most important
factor in assessing landslide hazard areas – need high accuracy and high resolution DEM
• LiDAR technology and Geographical Information System (GIS) are important tools in assessing landslide hazards
• Multi-criteria Decision Making (MCDM) Multi-criteria decision making approach also play important role in determining relative importance of landslide factors
Source : Sight Power 8
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METHODOLOGY
DEM (LiDAR DTM) DEM (SRTM 30m)
SLOPE 1 LAND USE LITHOLOGY SOIL SLOPE 2
Phase 2 - Landslide Model Development • Expert opinion to rank factors • Modify previously developed models based on only
slope, land use, lithology and soil properties factors (Othman, W. Mohd. N. Surip, 2013)
LHZ Model 1
Phase 5 - Validation of Models
Phase 3 - Data Acquisition
Phase1 - Selection of Study Area Cheras and Kajang (5 x 5 km)
Phase 4 - Data Processing/analysis in GIS Rank Criteria
Calculate Weight and Standardize Score for the criteria used
Generate Landslide Hazard Zone Maps using different models
MODEL 1 MODEL 2 MODEL 3 MODEL 1 MODEL 2 MODEL 3
LHZ Model 3 LHZ Model 2 LHZ Model 1 LHZ Model 3 LHZ Model 2
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STUDY AREA – PART OF CHERAS AND KAJANG Area Coverage
• Size : 5 x 5 km
• From Cheras to Kajang
• Elevation Range : 20 – 321 m above MSL
• Mukim : Kajang, Semenyih and Cheras
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DATA COLLECTION
• Digital Terrain Model (DTM) – from LiDAR
• Digital Elevation Model from SRTM – from USGS website
• Soil Properties - derived from soil map
• Land use – Digitised from Orthoimage
• Lithology
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DATA ACQUISATION FROM LIDAR
EQUIPMENT DETAILS:
• LiDAR System is LiteMapper 6800-400(Riegl 680i-400kHz) • This Laser Scanner is Full Waveform which
has unlimited number of return echoes. • This System comes with high resolution RGB Camera System
60 Mega Pixel and automatic geo-correction system which is equipped with 512kHz Fiber Optic IMU.
DATA ACQUISITION: • Date: 19 December 2014, 30 December 2014 – 3 January 2015 • Requirement RSGIS & JMG for data acquisition: • Helicopter type : Eurocopter EC 120B • Helicopter Speed : 60 knot • Flying Altitude : 600 m AGL • Laser Scan Angle : 600 • PRR laser : 400 kHz (maximum range)
• Data acquisition - Hazard and Slope Risk Mapping Project at Cheras Selatan-Kajang-Bangi-Putrajaya, Selangor for RS & GIS Consultancy Sdn Bhd and Department of Mineral & Geoscience.
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LiDAR Project Area
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Digital Terrain Model
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LiDAR SRTM
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DEM – Shuttle Radar Topographic Mission (SRTM) – 30 x 30 m
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Orthoimage of Study Area
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Slope map derived from_LiDAR Slope map derived from_SRTM
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DEVELOPMENT OF LANDSLIDE HAZARD ZONATION MODELS
• Based on earlier studies by Ainon Nisa, Wan Mohd and Noraini Surip
• Study Areas - Ampang Jaya and Hulu Langat
• Technique used – GIS-based Multicriteria Decision Making (MCDM)
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Landslide Hazard Models Tested Model
No Technique/ Criteria Slp Lu Litho SP Geomor Asp Elev Rf Priv Prd Facc Drg
1 Ranking (Rank Sum) 0.333 0.133 0.267 0.2 0.067
2 Ranking (Rank Reciprocal) 0.438 0.109 0.219 0.146 0.088
3 Ranking (Rank Exponential) 0.454 0.073 0.291 0.164 0.018
4 Rating 0.335 0.168 0.252 0.211 0.034
5 AHP (Expert Opinion) 0.162 0.082 0.116 0.277 0.023 0.061 0.21 0.041 0.032
6
Pairwise Comparison (Expert
Opinion) 0.5 0.036 0.143 0.214 0.107
7
Pairwise Comparison (Expert
Opinion) 0.294 0.088 0.236 0.265 0.029 0.088
8 AHP (Expert Opinion) 0.361 0.113 0.091 0.199 0.141 0.051 0.044
9 AHP (Expert Opinion) 0.301 0.089 0.073 0.152 0.108 0.045 0.037 0.195
Slope (slp) Land use (Lu) Lithology (Litho) Soil Properties (SP)
Geomorphology (Geomor) Aspect (Asp) Elevation (Elev) Rainfall (Rf)
Proximity to river (Priv) Proximity to road (Prd) Flow Accumulation (Facc) Drainage Pattern (Drg)
FACTORS CONSIDERED :
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Developed Models Model
No Technique Formula
1 Rank Sum 0.333(s_slp) + 0.133(s_lu) + 0.267(s_lit) + 0.2(s_sp) +0.067(s_geomorf)
2 Rank Reciprocal 0.438(s_slp) + 0.109(s_lu) + 0.219(s_lit) + 0.146(s_sp) + 0.088(s_geomorf)
3 Rank Exponent 0.454(s_slp) + 0.073(s_lu) + 0.291(s_lit) + 0.164(s_sp) + 0.018(s_geomorf)
4 Rating 0.335(s_slp) + 0.168(s_lu) + 0.252(s_lit) + 0.211(s_sp) + 0.034(s_geomorf)
5 AHP 0.162(s_slp) + 0.082(s_lu) + 0.116(s_lit) + 0.277(s_sp) + 0.023(s_asp) + 0.061(s_elev) + 0.207(s_rfal) +
0.041 (s_priv) + 0.032(s_prd)
6 Pairwise Comparison 0.5(s_slp) + 0.036(s_lu) +0.143(s_lit) + 0.214(s_sp) + 0.107(s_asp)
7 Pairwise Comparison 0.294(s_slp) + 0.088(s_lu) + 0.029(s_geomorf) + 0.265(s_sp) + 0.236(s_lit) + 0.088(s_flowacc)
8 AHP 0.361(s_slp) + 0.141(s_asp) + 0.091(s_lit) + 0.113(s_lu) + 0.199(s_sp) + 0.051(s_priv)+0.044(s_prd)
9 AHP 0.301(s_slp) + 0.108(s_asp) + 0.073(s_lit) + 0.089(s_lu) +0.152(s_sp) +
0.045(s_priv) + 0.037(s_prd) + 0.195(s_drg)
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Landslide Hazard Zonation Maps Generated from Model 1, 2 and 3 21
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Landslide Hazard Maps Generated from Model 4, 5 and 6 22
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Landslide Hazard Maps Generated from Model 7, 8 and 9 23
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Comparison between landslide hazard class and landslide historical data – Area Hulu Kelang
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Models Used – For this study Criteria Considered • Slope • Lithology • Land use • Soil Properties
LHZ (Model 1) = (0.400 x s_slp) + (0.100 x s_lu) + (0.300 x s_litho) + (0.200 x s_sp) ------------(1)
LHZ (Model 2) = (0.347 x s_slp) + (0.219 x s_lu) + (0.218 x s_litho) + (0.174 x s_sp) -----------------------(2)
LHZ (Model 3) = (0.481 x s_slp) + (0.240 x s_lu) + (0.159 x s_litho) + (0.120 x s_sp) -----------------------------------(3)
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LHZ based on LiDAR data
RESULT – LHZ MAP BASED ON 3 DIFFERENT MODELS
LHZ based on SRTM data 26
Criteria Considered • Slope • Lithology • Land use • Soil Properties LHZ (Model 1) = (0.400 x s_slp) + (0.100 x s_lu) + (0.300 x s_litho) + (0.200 x s_sp) --------(1) LHZ (Model 2) = (0.347 x s_slp) + (0.219 x s_lu) + (0.218 x s_litho) + (0.174 x s_sp) ---(2) LHZ (Model 3) = (0.481 x s_slp) + (0.240 x s_lu) + (0.159 x s_litho)+ (0.120 x s_sp) ---(3)
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SITE 1
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SITE 4 SITE 3
SITE 2
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SITE 5
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SITE 6
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• Aim : to evaluate the accuracy of NextMap IFSAR DTM for flood inundation mapping
• Study Area : Padang Terap, Kedah
ACCURACY ASSESSMENT OF DEMS FOR FLOOD INUNDATION MAPPING
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STUDY AREA AND DATASETS
NextMap Airborne IFSAR
DTM Orthorectifies Radar Images (ORI) DSM
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Source : JPSurvey
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methodology
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RESULTS
GPS ASTER SRTM GPS ASTER SRTM
(m) DTM (m) DSM (m) (m) (m) (m) DTM (m) DSM (m) (m) (m)
1 2.982 2.469 2.897 11 6 22.328 22.193 24.84 26 31
2 4.223 5.297 5.438 10 4 22.419 22.391 23.593 26 21
3 3.14 3.047 3.495 7 5 20.366 20.638 19.951 21 26
4 1.691 2.092 6.206 8 6 19.717 19.61 24.366 18 21
5 1.821 2.298 3.928 7 7 18.656 19.556 20.24 24 27
6 3.298 3.837 3.667 7 2 28.041 25.134 27.972 29 31
7 3.517 3.819 2.78 8 8 19.3 20.025 19.735 17 26
8 3.551 3.822 3.425 6 5 19.982 19.349 20.069 23 26
9 1.657 2.528 1.03 8 3 22.897 22.599 27.066 28 27
10 2.81 3.689 3.183 14 10 20.19 20.219 22.118 23 26
11 2.482 3.039 3.65 7 7 21.145 21.194 20.893 18 24
12 2.263 2.345 1.738 6 6 19.14 17.361 19.872 18 19
13 2.742 4.378 3.438 9 6 41.192 41.088 41.631 40 42
14 3.012 3.061 3.475 8 10 21.809 21.74 21.94 17 25
15 1.886 3.167 2.97 8 6 21.927 20.09 21.481 17 33
KUALA NERANG
POINT NO
ALOR SETAR
NEXTMap IFSAR NEXTMap IFSAR
Descriptive statistics of the differences between various DEMs and reference DEM
ALOR SETAR (Flat area) KUALA NERANG (Terrain area)
RMSE (m)
Min (m)
Max (m)
RMSE (m)
Min (m)
Max (m)
IFSAR DTM - GPS 0.497 0.049 0.879 0.841 0.029 1.837
IFSAR DSM - GPS 1.529 0.085 4.515 2.092 0.069 4.649
ASTER - GPS 5.848 2.449 11.19 3.278 0.634 5.344
SRTM - GPS 4.268 1.298 7.190 5.300 0.14 8.672
Elevation points from GPS and manually observed from different DEMs
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Field Work
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Flood inundation map – different discharge values
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Flood Inundation Map based on different Cross-section interval
200 meter cross-section interval
Legend
4000 m3
ValueHigh : 7.48727
Low : 7.62939e-006
300 meter cross-section interval 400 meter cross-section interval 500 meter cross-section interval
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Accuracy of Flood Inundation Maps
Location Elevation
Water Depth from
Simulated Water
Simulated Water
Water Depth
Photograph (m) Level Depth (m) Differences
Point 1 19.108 1.2 19.702 0.592 -0.608
Point 2 16.805 0.3 16.992 0.186 -0.114
Point 3 14.813 1.65 17.074 2.26 0.61
Point 4 16.216 0.6 16.881 0.66 0.06
Point 5 16.771 2.1 17.665 0.89 -1.21
Point 6 16.297 2 19.315 2.83 0.83
Point 7 15.253 1.45 18.503 3.25 1.8
Point 8 19.000 1.2 20.461 1.456 0.256
Point 9 18.159 2.4 19.598 1.438 -0.962
Point 10 16.679 1.45 18.455 1.77 0.32
Point 11 17.374 1.8 19.675 2.3 0.5
Point 12 19.304 0.9 19.552 0.25 -0.65
Point 13 20.67 0.45 21.95 1.26 0.81
Point 14 15.847 0.6 17.115 1.269 0.669
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SYSTEM CAPABILITIES
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FLOOD DAMAGE INVENTORY USING
MOBILE DATA COLLECTION
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SAMPLE OF FLOOD DAMAGE INVENTORY
IN MOBILE APPS GEOJOT+ APP
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Online mode data collection using collector
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Features update in ArcGIS Online
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Offline mode data collection using collector
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DISPLAY DATA
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DATA DISPLAY IN GIS SOFTWARE
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MANAGEMENT OF EVACUATION CENTRE USING GIS
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CLOSEST FACILITIES AND SHORTEST PATH
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FLOOD VULNERABILITY INDEX ASSESSMENT USING
GIS-BASED MULTI CRITERIA DECISION MAKING
Factors Considered:
• Social Vulnerability
• Economic Vulnerability
• Infrastructure Vulnerability
• Physical Vulnerability
Location of study area (Google Earth, 2014)
FVI = (SocVul) + (EconVul) + (InfraVul)
+ ( PhyVul)
where,
SocVul – Social Vulnerability
EconVul – Economic Vulnerability
InfraVul – Infrastructure Vulnerability
PhyVul – Physical Vulnerability
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FVI = (SocVul) + (EconVul) + (InfraVul) +
( PhyVul)
where,
SocVul – Social Vulnerability
EconVul – Economic Vulnerability
InfraVul – Infrastructure Vulnerability
PhyVul – Physical Vulnerability
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FVA maps using Rank Sum method
(a) Social (b) Infrastructure (c) Economic and (d) Physical Vulnerabilities
(a) (b)
(c) (d)
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FVA maps using AHP method
(a) Social (b) Infrastructure (c) Economic and (d) Physical Vulnerabilities
(c) (d)
(a) (b)
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FVI map - using Rank Sum method
FVI using AHP method
Top 8 most vulnerable mukim based on AHP Method
Top 8 most vulnerable mukim based on Rank Sum Method
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SUMMARY
• Geospatial technologies (remote sensing, GIS and GPS) – widely used in all aspects of disaster management.