gis in geology miloš marjanović lesson 4 21.10.2010

GIS IN GEOLOGY

Miloš MarjanovićLesson 421.10.2010.

GIS in Landslide assessment (basic)

Introduction to landslides and landslide risk assessment

Case studies: Landslide susceptibility analysis on Fruška Gora Mountain, Serbia example of GIS application in Engineering Geology

Methods applied:− Raster modeling− Multi-criteria analysis− Entropy model− Statistical analisis

Data resources:− Topographic map 1: 50 000 (1: 5000 for the second case study)− Digital geological map 1: 50 000 (1: 5000 for the second case study)− Satellite imagery LANDSAT TM 7− Hydrometeorological data

Accent on analytical potential of GIS environment in basic landslide assessment

Contouring and surface modeling

Geostatistics

Desktop and Web

publishing

Desktop mapping

Artificial Intelligence(AI)

Database Management Systems

(DBMS)

General statistics

Spread- sheets

Image Processing

(IP)

Computer Aided

Drawing (CAD)

Geographic Information

System (GIS)


Geological Hazards

Climatic Hazards

Environmental Hazards

Special types

EarthquakesTsunamisVolcanic eruptionsLandslides

Tropical stormsFloodsDroughts

PollutionDeforestationDesertificationPest infestation

Epidemics

Industrial & chemical accidents


Natural “hazards”

Landslide phenomena – theoretical background Definition Typology

Slides Falls Topplings Flows Lateral spreads


Landslide phenomena – theoretical background

Classification by velocity: High Medium Low



Genesis and factors: Predisposition factors:

Geological – Geo-mechanical

weak & sensitive materials, sheared materials, weathered materials, fissured and jointed or inconvenient by other structural entity, contrast in permeability (heterogeneous materials), contrast in deformability…

Morphological

Triggering factors: Increase of shear stress: erosion/excavation at the toe, loading at the

crown, earthquake, rockfall Decrease in strength: rainfall/meltdown/leakage, cyclic loading, Phys-

Chemical changes Combination: earthquake + liquefaction, vegetation removal…



Stage of activity


Landslide Qualitative Risk Assessment terminology:

Susceptibility (S): intensity classification, volume/area and spatial

distribution of existing or potential landslides

Hazard (H): spatial (Ps) and temporal (Pt) probability of landslide

occurrence over an area in a given time sequence

Vulnerability (V): measure of exposure to adverse phenomena (0-100%)

Element at risk (ER): population and constructions (buildings,

infrastructural objects) measured in units (#, $)

Risk (R): probability and severity for adverse phenomena to take effect

ER

H=WPs·WPt·S R=H·V(ER)


Landslide risk framework – from analysis to management


Management has to deal with:

Scientific uncertainty

Acceptable and tolerable risk ($, # of casualties)

General risk (not only landslides)

Territory issue

Science vs. decision


models of the past uncertainty time-consuming terminology success problems

scientist

models of the future decision immediate action common or regulation

language limitations solutions

decision maker


Inventory (location, volume/area, travel distance) Susceptibility zoning

Heuristic (basic) Statistical: univariate, bivariate (weights of evidence, information

value, frequency ratio), multivariate (discriminant, regression, Likelihood ratio cluster analysis, AI)

Deterministic

Hazard – frequency analysis Probabilistic based on the historical data on landslides, data on

landslide triggers, dendrochronology, lichenometry Modeling the primary variable (the triggering variable)

Risk Appending the individual, societal, and economic risk analysis


Case study: Fruška Gora Mountain, Serbia


study areaacc. 40 km2

landslide occurrences

Case study: Fruška Gora Mountain, Serbia


Scale and level selection: mid-scale of1:50 000 (in accordance to resources), preliminary zonation

Classification selection: for landslides according to Varnes et al. only slide movements, for susceptibility classes own system is developed

Method selection: heuristic – multi-criteria analysis Input data type and region type selection: raster, pixel


Overlaying and spatial analysis are easily feasible over referenced layers

Classification could be adjusted in bigger detail

The exporting to ASCII format provides excellent communication with GIS-coupled engines (for different modules generation, as well as for the machine learning algorithms)

Pros for raster data type

Bulky and demanding in terms of memory capacity, and processing speed

Cons for raster data type


Multi-criteria analysis


Geo-parameters modeling

Elevation (Pe) suggesting the concept of Ep derived from DEM reclassified into 4 classes normalized

DNnorm=(DN – DNmin)/(DNmax – DNmin)



Slope angle (Ps) suggesting the physical relation derived from DEM reclassified into 4 classes

(5 degrees intervals) normalized



Aspect (Pa) suggesting the influence of

moisture content, soil thickness derived from DEM reclassified into 4 classes

(SE, SW, NE, NW) weighted and normalized



Distance from streams (Pds) suggesting the influence

of the linear erosion pattern buffered from drainage

network vector filtered for the erosional

preference reclassified into 4 intervals normalized



Vegetation cover (Pv) suggesting the influence of

root system on the slope stability mapped by NDVI by using

3,4 Landsat 7 TM chanel,

due to chlorophyll spectra

authenticy reclassified into 2 classes normalized



Lithological composition (Pl) suggesting different stability

conditions in different materials digitized and simplified after

geological map 1:50 000 weighted reclassified into 4 classis

a-alluvions

b-loess sediment

c-calcareous sediments

d-clayey soils

normalized



Rainfall (Pr) suggesting the moist distribution

governed by heavy rains interpolated from sample point

data set (tables from HMSS)

by normal kriging with fitted

parameters (sill/nugget) reclassified into 4 classes normalized


Computing the weights of influence of geo-parameters

Analytical Hierarchy Process Pair-wise matrix

Comparing relative weights of influence of geo-parameters against each other and summing the columns

Eigenvector matrix

Normalizing all members of the first matrix by the column sum and averaging the values by rows. The last column gives eigenvector – weights distribution function

GIS environment uses eigenvector to calculate the susceptibility raster map

GIS in Engineering Geology

Computing the weights of influence of geo-parametersS = 0,29⋅Pl +0,27⋅Ps +0,15⋅Pr + 0,14⋅Pds +0,08⋅Pv + 0,05⋅Pe +0,02⋅Pa


Calibration of classes

Entropy model

optimal increase of information

gain at 4 – 9 classes Calibration using

geomorphological reference map

optimal error at 4 class intervals


Final output map as an interpretation of landslide susceptibility

Susceptibility map

1. lowest zone

2. mild zone

3. moderate zone

4. highest zone


Purpose:

Regional planning Preliminary assessment for further detailed analysis Base for hazard and risk mapping


GIS IN GEOLOGY

Miloš MarjanovićExercise 44.11.2010.

Exercise 4 – AHP-GIS extension

gis in geology miloš marjanović lesson 4 21.10.2010

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