The Need and Development for Dynamic Integrated GIS...
Transcript of The Need and Development for Dynamic Integrated GIS...
National Center for Geographic Information and Analysis (NCGIA), University at Buffalo (SUNY)
Buffalo, New York, USA
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
Landscape-based Environmental System Analysis & Modeling (LESAM) Laboratory
Research Leader: Chris S. Renschler
The LESAM Mission:The development and integration of appropriate and user-
friendly 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 km2
Data
GIS
Models
Graph.User
Interface
User scale of interest
Plot scale
Hillslope
Field
Farm
Watershed
Region
Single event
Short-term
Multiple events
Long-term
Historic dataScenario
GI Science and Environmental Models
Data
GIS
Models
Graph.User
Interface
question
answer
requirements
availabilit
y
preproces
singlocation
visualization
req
uir
emen
tsd
ata
exch
ange
User scale of interest
Plot scale
Hillslope
Field
Farm
Watershed
Region
Single event
Short-term
Multiple events
Long-term
Historic dataScenario
dataentry
GI Science and Environmental Models
Data
GIS
Models
Graph.User
Interface
question
answer
requirements
availabilit
y
preproces
singlocation
visualization
req
uir
emen
tsd
ata
exch
ange
User scale of interest
Plot scale
Hillslope
Field
Farm
Watershed
Region
Single event
Short-term
Multiple events
Long-term
Historic dataScenario
dataentry
GI Science and Environmental Models
User’s Expertise
Susquehanna River Basin, PA (modified from Osterkamp & Toy, 1997)
210-1-2
3
2
1
4
Log space km
Log sediment
yieldt km-2 yr-1
0
1 ha 1 km2100 m2 100 km2 10 000 km2
0.1
Sediment yield
t ha-1 yr-1
1
10
100
Scale of erosion processes and sediment yields
?
10 000 yrs
1000 yrs
1 yr
1 mon
1 day
1 hr
1 min
Log time years
Log space
km
10 000 km2
100 km2
1 km2
1 ha
100 m2
1 cm2
420-2-4-6
-6
-4
-2
0
2
4
watershed
front
thunderstorm
breeze
long waves El Niño climate change
Weather / Climate
1 dm2micro
plot
hill-slope
sub-
basin
Topographic Scale
1 m2
100 yrs
10 yrs
1Mio. km2
globe
-8
1 sec
Natural variability and role of scale
10 000 yrs
1000 yrs
1 yr
1 mon
1 day
1 hr
1 min
Log time years
Log space
km
10 000 km2
100 km2
1 km2
1 ha
100 m2
1 cm2
420-2-4-6
-6
-4
-2
0
2
4
watershed
front
thunderstorm
breeze
long waves El Niño climate change
Weather / Climate
1 dm2
Soil / Lithology
micro
plot
hill-slope
sub-
basin
infiltration / moisture structure texture
depth
landscape evolution
catena
Topographic Scale
1 m2
100 yrs
10 yrs
1Mio. km2
globe
-8
1 sec
Natural variability and role of scale
splash
10 000 yrs
1000 yrs
1 yr
1 mon
1 day
1 hr
1 min
Log time years
Log space
km
10 000 km2
100 km2
1 km2
1 ha
100 m2
1 cm2
420-2-4-6
-6
-4
-2
0
2
4
watershed
front
thunderstorm
breeze
long waves El Niño climate change
Weather / Climate
1 dm2
interrill
rill
gully
fluvial
Soil / Lithology Erosion Process
micro
plot
hill-slope
sub-
basin
infiltration / moisture structure texture
depth
landscape evolution
catena
Topographic Scale
1 m2
100 yrs
10 yrs
1Mio. km2
globe
-8
1 sec
Natural variability and role of scale
splash
10 000 yrs
1000 yrs
1 yr
1 mon
1 day
1 hr
1 min
Log time years
Log space
km
10 000 km2
100 km2
1 km2
1 ha
100 m2
1 cm2
420-2-4-6
-6
-4
-2
0
2
4
watershed
wood
stand
canopy
buffer
grass
front
thunderstorm
breeze
long waves El Niño climate change
Weather / Climate Vegetation
1 dm2
interrill
rill
gully
fluvial
Soil / Lithology Erosion Process
micro
plot
hill-slope
sub-
basin
infiltration / moisture structure texture
depth
landscape evolution
catena
Topographic Scale
1 m2
100 yrs
10 yrs
1Mio. km2
globe
-8
1 sec
Natural variability and role of scale
splash
10 000 yrs
1000 yrs
1 yr
1 mon
1 day
1 hr
1 min
Log time years
Log space
km
10 000 km2
100 km2
1 km2
1 ha
100 m2
1 cm2
420-2-4-6
-6
-4
-2
0
2
4
watershed
wood
stand
canopy
buffer
grass
front
thunderstorm
breeze
long waves El Niño climate change
Weather / Climate Vegetation
1 dm2
Management Unit
interrill
rill
gully
fluvial
Soil / Lithology Erosion Process
micro
plot
hill-slope
sub-
basin
infiltration / moisture structure texture
depthtillage ridgehorticulture
orchard
forest
landscape evolution
catena
Topographic Scale
1 m2
100 yrs
10 yrs
1Mio. km2
globe
-8
1 sec
crop
Natural variability and role of scale
fluvialsplash rill gullyinterrill
Susquehanna River Basin, PA (modified from Osterkamp & Toy, 1997)
210-1-2
3
2
1
4
Log space km
Log sediment
yieldt km-2 yr-1
- Net Storage -(transport
limited)
- Net Erosion -(supply limited)
0
1 ha 1 km2100 m2 100 km2 10 000 km2
0.1
Sediment yield
t ha-1 yr-1
1
10
100
Scale of erosion processes and sediment yields
fluvialsplash rill gullyinterrill
Susquehanna River Basin, PA (modified from Osterkamp & Toy, 1997)
210-1-2
3
2
1
4
Log space km
Log sediment
yieldt km-2 yr-1
- Net Storage -(transport
limited)
- Net Erosion -(supply limited)
by gully, channel and geomorphic processes
Sediment releasedmainly
0
1 ha 1 km2100 m2 100 km2 10 000 km2
0.1
Sediment yield
t ha-1 yr-1
1
10
100
by rill / interrill processes
Scale of erosion processes and sediment yields
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
- i+6
- i+5
- i+4
- i+3
- i+2
- i+1
- i-2
- i-3
- i-4
Degree of computation
complex
ity
mechanistic
empirical
quantit
ativ
e
qualita
tive
Use
r ex
pert
ise
Exp
ert k
now
ledg
e
Bla
ck b
ox
Com
preh
ensi
ve m
odel
s of
ent
ire
syst
em
Det
aile
d m
odel
of
part
ial s
yste
m
Scal
e hi
erar
chy
Water balanceat point scale
Model concepts to simulate landscape processes
Pedon 1-D vertical
Surface runoff & soil erosion
at flowpath scale
Flowpath / Catena1-D vert. + 1-D horiz.
Water balance& geomorphology at hillslope scale
Hillslope / sub-catchment1-D vert. + 2-D horiz.
Commonly Available U.S. Data Sources
Variuos platforms•NOAA•USDA-NRCS•USGSAvailable from
•GeoTIFF•Shapefile•GeoTIFF•Binary•ARCgrid
Format
•30 m•15 m•1 m
•1:24,000•1:100,000•1:250,000
•30 m
•3-arc-sec•1 arc-sec•1/3 arc-sec•1/9 arc-sec
Scale/Resolution
Historic/Actual Imagesvarious•1990, 2000/02•2000 (SRTM)Collection Date
•Landsat TM•Landsat ETM•National Aerial Photography Program (NAPP)
•TIGER database•FEMA Flood Data•Water Bodies•Hydrologic Units
•Classification of Landsat images
•Field survey•Photogrammetry•Interferometry•LIDAR•Topographic Maps
Source Data
RS ImageryHydrographyLand Use/CoverDEM/TopographyInformation
USGS DEM Vertical Resolution
• The hill shade view of several DEM quads allows you to quickly evaluate their accuracy
Level 2 in metersLevel 2 in feet
Level 1 in meters Level 2 in feet
USGS DigitalElevation
Data (1:24,000)
Topographical maps (DRG)
DLG - Digital Line Graph
Level 1 / 30m* Level 2 / 30m Level 2 / 10m
U.S. Geological Survey, 1999
DEM - Digital Elevation Models:
*High altitude Photogrammetry
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 ⇓ 1st 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 ⇓ 1st SCALING ⇓
Representation Database Scale Common Database Unit (data availability)
⇓ Discretization ⇓ 2nd 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 ⇓ 1st SCALING ⇓
Representation Database Scale Common Database Unit (data availability)
⇓ Discretization ⇓ 2nd SCALING ⇓
Representation Modeling Scale Modeling Unit (model requirements)
⇓ Modeling ⇓ 3rd 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 ⇓ 1st SCALING ⇓
Representation Database Scale Common Database Unit (data availability)
⇓ Discretization ⇓ 2nd SCALING ⇓
Representation Modeling Scale Modeling Unit (model requirements)
⇓ Modeling ⇓ 3rd SCALING ⇓
Representation Prediction Scale Prediction Unit (model design)
⇓ Post-processing ⇓ 4th 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 ⇓ 1st SCALING ⇓
Representation Database Scale Common Database Unit (data availability)
⇓ Discretization ⇓ 2nd SCALING ⇓
Representation Modeling Scale Modeling Unit (model requirements)
⇓ Modeling ⇓ 3rd SCALING ⇓
Representation Prediction Scale Prediction Unit (model design)
⇓ Post-processing ⇓ 4th SCALING ⇓
Representation Assessment Scale Scale of Interest (user requirements)
⇓ Evaluating ⇓ 5th 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 ⇓ 1st SCALING ⇓
Representation Database Scale Common Database Unit (data availability)
⇓ Discretization ⇓ 2nd SCALING ⇓
Representation Modeling Scale Modeling Unit (model requirements)
⇓ Modeling ⇓ 3rd SCALING ⇓
Representation Prediction Scale Prediction Unit (model design)
⇓ Post-processing ⇓ 4th SCALING ⇓
Representation Assessment Scale Scale of Interest (user requirements)
⇓ Evaluating ⇓ 5th SCALING ⇓
Representation Measurement Scale Observation Unit (measurement device)
⇑ Measuring ⇑ BASIC SCALING ⇑
Natural pattern Process Scale True Process Scale and Variance
Dig
ital
Dom
ain
Integrated Modeling System 1
Soil Erosion Assessment with the Geospatial Interface to the
Water Erosion Prediction Project (GeoWEPP)
Geo-spatial interface for theWater Erosion Prediction Project –
GeoWEPP
WEPP Watershed
Watershed outlet
OFE 1
OFE 2
Hill 1
Hill 2
Hill 4
Hill 5
Hill 3
Channel
Impoundment
• Hillslopes– Overland flow elements OFE’s
• Channels / Impoundments
• Watershed structure
• Soils
• Management
• Climate
Commonlyavailable
data
4.Scaling:Mappingof results
3.ScalingModel-
application
GraphicalUser
Interface
assessmentanswer
availabilitypre-processing
locationvisual-ization
require-ments
User’s scalesof interest
Plot scaleHillslope
Field
Farm
Watershed
Region
Single event
Short-term
Multiple events
Long-term
Historic dataScenario
additionaldata entry
2.Scaling:Discret-ization
1.Scaling:Common Database
5.Scaling:Quality
assessment
GIS
post-processing
assessmentquestion
uncertaintyassessment
Scaling theory to integrate environmental models and GI science
User’s Expertis
e
WEPP - GIS model integration
Arc View 3.1
TOPAZ
WEPP
Subcatchment, boundary, channel
network grids
CSA, MCL, Outlet point
DEM, Soil, Land use /
cover maps
Slope, land cover, soil,
projects, structures
Maps for Soil loss, Runoff
Sediment Yields
Plot Output,
main text outputs
GeoWEPP
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
Commonlyavailable
data
4.Scaling:Mappingof results
3.ScalingModel-
application
GraphicalUser
Interface
assessmentanswer
availabilitypre-processing
locationvisual-ization
require-ments
User’s scalesof interest
Plot scale
Hillslope
Field
Farm
Watershed
Region
Single event
Short-term
Multiple events
Long-term
Historic dataScenario
additionaldata entry
-Discret2.Scaling:
ization
1.Scaling:Common Database
5.Scaling:Quality
assessment
GIS
post-processing
assessmentquestion
uncertaintyassessment
Scaling theory to integrate environmental models and GI science
Commonlyavailable
data
Commonlyavailable
data
4.Scaling:Mappingof results
3.ScalingModel-
application
GraphicalUser
Interface
assessmentanswer
availabilitypre-processing
locationvisual -ization
require-ments
User’s scalesof interest
Plot scale
Hillslope
Field
Farm
Watershed
Region
Single event
Short-term
Multiple events
Long-term
Historic dataScenario
additionaldata entry
-Discret2.Scaling:
ization
1.Scaling:Common Database
5.Scaling:Quality
assessment
GIS
post-processing
assessmentquestion
uncertaintyassessment
Scaling theory to integrate environmental models and GI science
Commonlyavailable
data
DEM Slope Curvature
Flow PathComparison
InterpolationAggregation
TITAN2D
Field Survey
DerivativeDerivative
GIS
Data!
Uncertainty!
Scale!
Model + GIS !
Geo-spatialDynamic Response
Assessment Tool Data!
Uncertainty!
Scale!
Goals for GeoDRAT Platformwith 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-spatialDynamic 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