Scale!

ares.tu.chiba.u.jp

Scale!

The Need and Development for

Dynamic Integrated GIS Enhancement

and Support Tools (DIGEST) –

The Geospatial Project Management Tool

(GeoProMT)

Chris S. Renschler and Laercio M. Namikawa

National Center for Geographic Information and

Analysis (NCGIA), University at Buffalo (SUNY)

Buffalo, New York, USA

Landscape-based Environmental System Analysis &

Modeling (LESAM) Laboratory

Research Leader: Chris S. Renschler

The LESAM Mission:

The development and integration of appropriate and userfriendly

analysis and modeling techniques in

GIScience and Environmental Modeling Tools

with readily available data sets

to support a rapid, practical and effective decision-making

in natural resources and natural hazard management.


Risk Management

of Geophysical

Mass Flows

The GeoWEPP Project –

Preventive and Post-Wildfire Rehabilitation

Management of Soil Erosion in Forest & Rangelands


Hayman Fire,

Colorado

August 2002

~470 km 2


Plot scale

Hillslope

Field

Farm

Watershed

Region

User scale

of interest

Single event

Short-term

Multiple events

Long-term

Historic data

Scenario

GI Science and Environmental Models

Graph.

User

Interface

Models

GIS

Data

Plot scale

Hillslope

Field

Farm

Watershed

Region

User scale

of interest

Single event

Short-term

Multiple events

Long-term

Historic data

Scenario

GI Science and Environmental Models

Graph.

User

Interface

question

answer

location

visualization

data

entry

Models

requirements

data exchange

GIS

requirements

Data

availability

preprocessing


Plot scale

Hillslope

Field

Farm

Watershed

Region

User scale

of interest

Single event

Short-term

Multiple events

Long-term

Historic data

Scenario

GI Science and Environmental Models

User’s Expertise

Graph.

User

Interface

question

answer

location

visualization

data

entry

Models

requirements

data exchange

GIS

requirements

Data

availability

preprocessing


Scale of erosion processes and sediment yields

Log

sediment

yield

1 ha 1 km 2

Sediment

yield

t ha -1 yr -1

100 m 2 100 km 2 10 000 km 2 0.1

t km -2 yr -1 0

4

100

3

10

2

1


1

-2

-1

0

Susquehanna River Basin, PA (modified from Osterkamp & Toy, 1997)

1

2

Log space

km


Natural variability and role of scale

Log

space

km

1

sec

1

min

1

hr

1

day

1

mon

1

yr

10

yrs

100

yrs

1000

yrs

10 000

yrs

-6

-4

micro

plot

1 cm 2

1 dm 2

1 m 2

-2

breeze

hillslope

100 m 2

1 ha

0

sub-

1 km 2

2

4

thunder

storm

front

long

waves

El Niño

climate change

water

shed

basin

globe

100 km 2

10 000 km 2

1Mio. km 2

-8

-6

Weather / Climate

-4

-2

0

2

4

Log time

years

Topographic Scale

Natural variability and role of scale

Log

space

km

1

sec

1

min

1

hr

1

day

1

mon

1

yr

10

yrs

100

yrs

1000

yrs

10 000

yrs

-6

-4

-2

0

2

4

infiltration / moisture structure texture

breeze

thunder

storm

front

long

waves

El Niño

depth

catena

landscape

evolution

climate change

micro

water

shed

plot

hillslope

subbasin

globe

1 cm 2

1 dm 2

1 m 2

100 m 2

1 ha

1 km 2

100 km 2

10 000 km 2

1Mio. km 2

-8

-6

Weather / Climate

-4

-2

0

2

4

Log time

years

Soil / Lithology

Topographic Scale


Log

space

km

-6

-4

-2

0

2

4

1

sec

splash

Natural variability and role of scale

1

min

interrill

breeze

1

hr

rill

thunder

storm

1

day

front

gully

1

mon

long

waves

1

yr

fluvial

El Niño

10

yrs

100

yrs

1000

yrs

infiltration / moisture structure texture

depth

catena

landscape

evolution

climate change

10 000

yrs

micro

water

shed

1 cm 2

1 dm 2

1 m 2

100 m 2

1 ha

1 km 2

100 km 2

10 000 km 2

1Mio. km 2

-8

-6 -4

Weather / Climate

-2

0

2

4

Log time

years

Erosion Process

Soil / Lithology

Topographic Scale

Log

space

km

-6

-4

-2

0

2

4

1

sec

Natural variability and role of scale

1

min

1

hr

1

day

1

mon

1000

yrs

infiltration / moisture structure texture

splash

grass

interrill

canopy

depth

buffer

breeze rill

catena

stand

gully

wood

thunder

storm

front

long

waves

1

yr

fluvial

El Niño

10

yrs

100

yrs

landscape

evolution

climate change

10 000

yrs

micro

water

shed

plot

hillslope

subbasin

globe

plot

hillslope

subbasin

globe

1 cm 2

1 dm 2

1 m 2

100 m 2

1 ha

1 km 2

100 km 2

10 000 km 2

1Mio. km 2

-8

-6 -4

Weather / Climate

-2

Vegetation

0

2

4

Log time

years

Erosion Process

Soil / Lithology

Topographic Scale


Log

space

km

-6

-4

-2

0

2

4

1

sec

Natural variability and role of scale

1

min

1

hr

1

day

1

mon

1

yr

1000

yrs

infiltration / moisture structure texture

splash

grass

interrill

tillage

ridge

canopy

depth

breeze rill

horticulture buffer

catena

crop orchard

stand

gully

forest wood

thunder

storm

front

long

waves

fluvial

El Niño

10

yrs

100

yrs

landscape

evolution

climate change

10 000

yrs

micro

water

shed

plot

hillslope

subbasin

globe

1 cm 2

1 dm 2

1 m 2

100 m 2

1 ha

1 km 2

100 km 2

10 000 km 2

1Mio. km 2

-8

-6 -4

Weather / Climate

-2

Vegetation

0

2

4

Management Unit

Log time

years

Erosion Process

Soil / Lithology

Topographic Scale

Scale of erosion processes and sediment yields

Log

sediment

yield

100 m 2 100 km 2 10 000 km 2 0.1

1 ha 1 km 2

splash interrill rill gully

t km -2 yr -1 - Net Storage -

fluvial

Sediment

yield

t ha -1 yr -1

4

3

- Net Erosion -

(supply limited)

(transport

limited)

100

10

2

1

1

0

-2

-1

0

1

2

Susquehanna River Basin, PA (modified from Osterkamp & Toy, 1997)

Log space

km


Scale of erosion processes and sediment yields

Log

sediment

yield

1 ha 1 km 2

splash interrill rill gully

t km -2 yr -1 - Net Storage -

fluvial

Sediment

yield

t ha -1 yr -1

4

3

- Net Erosion -

(supply limited)

(transport

limited)

2

1

Sediment released

mainly

100 m 2 100 km 2 10 000 km 2 0.1

100

10

by gully, channel

and geomorphic processes

1

0

by rill / interrill processes

-2

-1

0

1

2

Susquehanna River Basin, PA (modified from Osterkamp & Toy, 1997)

Log space

km

Classification of model approaches (Hoosbeek & Bryant)

World

Continent / Basin

Region

Watershed / County

Catena / Farm

Polypedon / Field

Pedon / Plot

Soil Horizon

Soil Structure

Basic Structure

Molecular Interaction

User expertise

qualitative

Expert knowledge

Degree of

empirical

Scale hierarchy

complexity

-i+6

-i+5

-i+4

-i+3

-i+2

mechanistic

-i+1

computation

-i-2

-i-3

-i-4

Black box

Comprehensive models

of entire system

Detailed model of

partial system

quantitative


Model concepts to simulate landscape processes

Pedon

1-D vertical

Flowpath / Catena Hillslope / sub-catchment

1-D vert. + 1-D horiz. 1-D vert. + 2-D horiz.

Water balance

at point scale

Surface runoff &

soil erosion

at flowpath scale

Water balance

& geomorphology

at hillslope scale


Commonly Available U.S. Data Sources

Information

DEM/Topography

Land Use/Cover

Hydrography

RS Imagery

Source Data

•Field survey

•Photogrammetry

•Interferometry

•LIDAR

•Topographic

Maps

•Classification of

Landsat images

•TIGER

database

•FEMA Flood

Data

•Water Bodies

•Hydrologic

Units

•Landsat TM

•Landsat ETM

•National Aerial

Photography Program

(NAPP)

Collection Date

•2000 (SRTM)

•1990, 2000/02

various

Historic/Actual Images

Scale/

Resolution

•3-arc-sec

•1 arc-sec

•1/3 arc-sec

•1/9 arc-sec

•30 m

•1:24,000

•1:100,000

•1:250,000

•30 m

•15 m

•1 m

Format

•Binary

•ARCgrid

•GeoTIFF

•Shapefile

•GeoTIFF

Available from

•USGS

•USDA-NRCS

•NOAA

Variuos platforms

USGS DEM Vertical Resolution

Level 2 in feet

Level 2 in meters

Level 1 in meters

Level 2 in feet

• The hill shade view of several DEM quads allows

you to quickly evaluate their accuracy


USGS Digital

Elevation

Data (1:24,000)

DEM - Digital

Elevation Models:

DLG - Digital

Line Graph

Topographical

maps (DRG)

Level 1 / 30m* Level 2 / 30m Level 2 / 10m

*High altitude Photogrammetry

U.S. Geological Survey, 1999


Natural pattern Process Scale True Process Scale and Variance

⇓ Measuring ⇓ BASIC SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)

Natural pattern Process Scale True Process Scale and Variance

⇓ Measuring ⇓ BASIC SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)

⇓ Pre-processing ⇓ 1 st SCALING ⇓

Representation Database Scale Common Database Unit (data availability)


Natural pattern Process Scale True Process Scale and Variance

⇓ Measuring ⇓ BASIC SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)

⇓ Pre-processing ⇓ 1 st SCALING ⇓

Representation Database Scale Common Database Unit (data availability)

⇓ Discretization ⇓ 2 nd SCALING ⇓

Representation Modeling Scale Modeling Unit (model requirements)

Natural pattern Process Scale True Process Scale and Variance

⇓ Measuring ⇓ BASIC SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)

⇓ Pre-processing ⇓ 1 st SCALING ⇓

Representation Database Scale Common Database Unit (data availability)

⇓ Discretization ⇓ 2 nd SCALING ⇓

Representation Modeling Scale Modeling Unit (model requirements)

⇓ Modeling ⇓ 3 rd SCALING ⇓

Representation Prediction Scale Prediction Unit (model design)


Natural pattern Process Scale True Process Scale and Variance

⇓ Measuring ⇓ BASIC SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)

⇓ Pre-processing ⇓ 1 st SCALING ⇓

Representation Database Scale Common Database Unit (data availability)

⇓ Discretization ⇓ 2 nd SCALING ⇓

Representation Modeling Scale Modeling Unit (model requirements)

⇓ Modeling ⇓ 3 rd SCALING ⇓

Representation Prediction Scale Prediction Unit (model design)

⇓ Post-processing ⇓ 4 th SCALING ⇓

Representation Assessment Scale Scale of Interest (user requirements)

Natural pattern Process Scale True Process Scale and Variance

⇓ Measuring ⇓ BASIC SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)

⇓ Pre-processing ⇓ 1 st SCALING ⇓

Representation Database Scale Common Database Unit (data availability)

⇓ Discretization ⇓ 2 nd SCALING ⇓

Representation Modeling Scale Modeling Unit (model requirements)

⇓ Modeling ⇓ 3 rd SCALING ⇓

Representation Prediction Scale Prediction Unit (model design)

⇓ Post-processing ⇓ 4 th SCALING ⇓

Representation Assessment Scale Scale of Interest (user requirements)

⇓ Evaluating ⇓ 5 th SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)


Natural pattern Process Scale True Process Scale and Variance

⇓ Measuring ⇓ BASIC SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)

Digital Domain

⇓ Pre-processing ⇓ 1 st SCALING ⇓

Representation Database Scale Common Database Unit (data availability)

⇓ Discretization ⇓ 2 nd SCALING ⇓

Representation Modeling Scale Modeling Unit (model requirements)

⇓ Modeling ⇓ 3 rd SCALING ⇓

Representation Prediction Scale Prediction Unit (model design)

⇓ Post-processing ⇓ 4 th SCALING ⇓

Representation Assessment Scale Scale of Interest (user requirements)

⇓ Evaluating ⇓ 5 th SCALING ⇓

Representation Measurement Scale Observation Unit (measurement device)

⇑ Measuring ⇑ BASIC SCALING ⇑

Natural pattern Process Scale True Process Scale and Variance

Integrated Modeling System 1

Soil Erosion Assessment with

the Geospatial Interface to the

Water Erosion Prediction

Project (GeoWEPP)


Geo-spatial interface for the

Water Erosion Prediction Project –

GeoWEPP

WEPP Watershed

Hill 3

OFE 1

Hill 1

OFE 2

Hill 4

Hill 2

• Hillslopes

– Overland flow elements OFE’s

• Channels / Impoundments

• Watershed structure

• Soils

• Management

• Climate

Channel

Hill 5

Impoundment

Watershed outlet


Scaling theory to integrate environmental models and GI science

Region

Watershed

Farm

Field

Hillslope

Plot scale

User’s Expertise

uncertainty

assessment

5.Scaling:

Quality

assessment

postprocessing

assessment

answer

4.Scaling:

Mapping

of results

User’s scales

of interest

Single event

Short-term

Multiple events

Long-term

Historic data

Scenario

Graphical

User

Interface

additional

data entry

Commonly availability

available pre-processing

data

GIS

1.Scaling:

Common

Database

location

visualization

requirements

assessment

question

3.Scaling

Modelapplication

2.Scaling:

Discretization

WEPP - GIS model integration

Maps for

Soil loss,

Runoff

Sediment

Yields

Plot

Output,

main text

outputs

GeoWEPP

CSA, MCL,

Outlet point

Arc

View

3.1

WEPP

Slope, land

cover, soil,

projects,

structures

DEM, Soil,

Land use /

cover

maps

TOPAZ

Subcatchment,

boundary,

channel

network grids


Old Fire (Waterman Canyon, Dec ’03)

Integrated Modeling System 2

Volcanic Debris Flow

Assessment with TITAN2D


Flow Extents

• Colima, Mexico

Saucedo, R., J. L. Macias and M. Bursik (2004). "Pyroclastic flow deposits of the 1991 eruption of Volcan

de Colima, Mexico." Bulletin of Volcanology 66(4): 291-306.

TITAN2D

• Mathematical, deterministic, and dynamic

model of avalanches and debris flows.

– Flows triggered by volcanic and seismic activities,

and extreme precipitation events.

– Particles - centimeter to meter-sized.

– Flow travels at up to hundreds of meters per

second, over tens of kilometers.


Visualization of Flow and GIS Data

Scaling theory to integrate environmental models and GI science

Region

Watershed

Farm

Field

Hillslope

Plot scale

User’s scales

of interest

Single event

Short-term

Multiple events

Long-term

Historic data

Scenario

Commonly

available

data

Graphical

User

Interface

Commonly

available

data

uncertainty

assessment

additional

data entry

availability

pre-processing

5.Scaling:

Quality

assessment

GIS

1.Scaling:

Common

Database

postprocessing

assessment

answer

assessment

question

4.Scaling:

Mapping

of results

3.Scaling

Modelapplication

location

visualization

requirements

2.Scaling:

Discretization


Scaling theory to integrate environmental models and GI science

Region

Watershed

Farm

Field

Hillslope

Plot scale

User’s scales

of interest

Single event

Short-term

Multiple events

Long-term

Historic data

Scenario

Field Survey

Commonly

available

data

Graphical

User

Interface

uncertainty

assessment

Derivative

Commonly availability

available pre-processing

data

additional

data entry

Comparison

5.Scaling:

Quality

assessment

1.Scaling:

Common

Database

postprocessing

assessment

answer

assessment

question

Derivative

Flow Path

4.Scaling:

Mapping

of results

TITAN2D

3.Scaling

Modelapplication

GIS

location

Interpolation

visualization

GIS

Aggregation

requirements

2.Scaling:

Discretization

DEM Slope Curvature

Model + GIS !

Data!

Uncertainty!

Scale!


Geo-spatial

Dynamic Response

Assessment Tool

Data!

Uncertainty!

Scale!

Goals for GeoDRAT Platform

with Role-Based Access Control (RBAC)

Integration of tools in interdisciplinary projects involving

• spatial data processing,

• environmental process modeling,

• geo-visualization, and

• decision-making components.

Consideration of Data, Scales and Uncertainties through:

• effective and seamless spatial data processing/sharing, and

• minimizing the impact of data processing algorithms on

results.


GeoDRAT Services

• utilize network data processing capabilities

• access shared data sources

• share own data and processing capabilities

• streamline interaction among

interdisciplinary research groups.

Geo-spatial

Dynamic Response

Assessment Tool


Integrated Geospatial

Data Modeling System

+

Geospatial Project Management

Tool (GeoProMT/GeoDRAT)

=

Dynamic Integrated GIS

Enhancement and Support Tools

(DIGEST)


DIGEST system integrated in a

Geographic Data Server

Mapping Camera System for Real- time

Images and Algorithm Validation

Visible-Near (VN),

Short (SW),

Medium (MW), and

Long Wave (LW)

Infrared (IR)

Chester F. Carlson Center for

Imaging Science, Rochester

Institute of Technology (RIT)


Time Sequence of Fire Propagation in LWIR


Conclusions:

Integrating GI Science & Environmental Models

• Interdisciplinary development / implementation

• Process-based approaches for wide application

• Evaluation of data scaling effects are essential

• Utilizing commonly available data sources

• Include educational tools to understand processes

pattern, basic problems and practical solutions

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