Geotechnology Not Your Grandfather’s Map

Joseph K. BerryJoseph K. BerryCSU Alumnus,CSU Alumnus, MS in Business Management ’72 and PhD emphasizing Remote Sensing ‘76MS in Business Management ’72 and PhD emphasizing Remote Sensing ‘76

W.M. Keck Scholar in Geosciences, University of DenverW.M. Keck Scholar in Geosciences, University of DenverPrincipal, Berry & Associates // Spatial Information SystemsPrincipal, Berry & Associates // Spatial Information Systems

1701 Lindenwood Drive, Fort Collins, CO 805241701 Lindenwood Drive, Fort Collins, CO 80524Phone: (970) 215-0825 Email: [email protected]: (970) 215-0825 Email: [email protected]

Website at Website at www.innovativegis.com/basiswww.innovativegis.com/basis

GeotechnologyGeotechnologyNot Your Grandfather’s MapNot Your Grandfather’s Map

(Nanotechnology)(Nanotechnology) Geotechnology Geotechnology (Biotechnology)(Biotechnology)

Global Positioning Global Positioning SystemSystem

Remote SensingRemote Sensing

Geographic Information Geographic Information SystemsSystems

WhereWhere isis WhatWhat

GPS/GIS/RSGPS/GIS/RS

AnalysisAnalysis involves involves investigation of investigation of

spatial spatial relationshipsrelationships

(numerical)(numerical)

Prescriptive Prescriptive ModelingModeling

Mapping Mapping involves involves precise placementprecise placement (delineation) of (delineation) of

physical featuresphysical features

(graphic)(graphic)

Descriptive Descriptive MappingMapping

GeotechnologyGeotechnology is one of the three "mega technologies" for the 21st century and is one of the three "mega technologies" for the 21st century and promises to promises to forever change how we conceptualize, utilize and visualize forever change how we conceptualize, utilize and visualize spatial spatial

relationships in scientific research and commercial applications relationships in scientific research and commercial applications

(Berry)(Berry)


Internet Mapping

““The Sizzle”The Sizzle”


GPS Navigation

Desktop Mapping Multimedia Mapping

… we created a multimedia map of Pingree Park

Last Year

(Berry)(Berry)


Surface Modeling maps the spatial distribution and pattern of point data… Map Generalization— characterizes spatial trends (tilted plane) Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig) Other— roving windows and facets (e.g., density surface; tessellation)

Spatial Data Mining investigates the “numerical” relationships in mapped data… Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering) Predictive— relationships among maps (e.g., regression) Prescription— appropriate actions (e.g., decision rules; optimization)

Spatial Analysis investigates the “contextual” relationships in mapped data… Reclassify— reassigns map values (position, value, size, shape, contiguity) Overlay— map coincidence (point-by-point; region-wide; map-wide) Distance— proximity and connection (movement; optimal paths; visibility) Neighbors— roving windows (slope; aspect; diversity; anomaly)

Grid-Based Map Analysis

““The Science”The Science”


SpatialStatistics

(Berry)(Berry)

Surface Modeling Surface Modeling (Density Surface — “counts”)(Density Surface — “counts”)

(Berry)(Berry)

Elevation

…continuous Map Surface (grid-based analysis frame)

A value is stored at each A value is stored at each grid cell location indicates grid cell location indicates ““whatwhat is is wherewhere”— ”— for for example, a set of elevation example, a set of elevation values form the familiar terrain values form the familiar terrain surface we hike on.surface we hike on.

A paradigm shift from traditional A paradigm shift from traditional discrete discrete Map FeaturesMap Features comprised of comprised of PointsPoints, , LinesLines, , PolygonsPolygons..

……Map SurfacesMap Surfaces are used to investigate are used to investigate relationships within and among map layersrelationships within and among map layers

Map StackMap Stack

Hugag Density SurfaceHugag Density Surface Hugag Activity draped over ElevationHugag Activity draped over Elevation

ContinuousMap Surface

Avg- 17.49 StDev= 14.99

Most of the activity is on the NE ridge in cover type 14 near steep slopes toward the river

HugagHugagCountsCounts

DiscreteMap Surface

2 Hugags every 30 min for 30 days

Hugag

Surface Modeling Surface Modeling (Mapping the Variance)(Mapping the Variance)

The “The “iterative smoothingiterative smoothing” process is similar to slapping a big chunk of ” process is similar to slapping a big chunk of modeler’s clay over the “data spikes,” then taking a knife and cutting away modeler’s clay over the “data spikes,” then taking a knife and cutting away

the excess to leave a the excess to leave a continuous surfacecontinuous surface that encapsulates the that encapsulates the Peaks and valleys implied in the field samples – Peaks and valleys implied in the field samples – Spatial DistributionSpatial Distribution

Continuous Surface — Geographic Distribution

Numeric Distribution — Average, Standard Deviation

(Berry)(Berry)

Spatial InterpolationSpatial Interpolation (soil nutrient levels)(soil nutrient levels)

Spatial InterpolationSpatial Interpolation maps the maps the geographic distributiongeographic distribution inherent in the data inherent in the data

IDW SurfaceData “Spikes”

Corn Field Phosphorous (P)

(Berry)(Berry)

Comparing Spatial Interpolation ResultsComparing Spatial Interpolation Results

Comparison of the Comparison of the IDWIDW interpolated surfaceinterpolated surface

to theto thewhole field averagewhole field average

shows shows large differenceslarge differences in localized estimatesin localized estimates

(-16.6 to 80.4 ppm)(-16.6 to 80.4 ppm)

Comparison of the Comparison of the IDW interpolated surfaceIDW interpolated surface

to the to the Krig interpolated surfaceKrig interpolated surface shows shows small differencessmall differences

in localized estimatesin localized estimates (-13.3 to 11.7 ppm)(-13.3 to 11.7 ppm)

(Berry)(Berry)

Spatial Data MiningSpatial Data Mining

What spatial relationships What spatial relationships do you see?do you see?

Interpolated Spatial DistributionInterpolated Spatial Distribution

Phosphorous (P)

……do relatively high levels do relatively high levels of P often occur with high of P often occur with high levels of K and N?levels of K and N?

… …how often? …where?how often? …where?

HUMANSHUMANS can “see” broad can “see” broad generalized patternsgeneralized patterns

in a in a singlesingle map variable map variable

(Berry)(Berry)

Clustering Maps for Data ZonesClustering Maps for Data Zones

……groups of “floating balls” in data space identify locations in the field groups of “floating balls” in data space identify locations in the field with similar data patterns– with similar data patterns– data zonesdata zones

COMPUTERS can “see” detailed patterns in multiple map variables

(Berry)(Berry)

The Precision Ag ProcessThe Precision Ag Process (Fertility example)(Fertility example)

As a combine moves through a field As a combine moves through a field 1)1) it uses GPS to it uses GPS to check its location then check its location then 2)2) checks the yield at that checks the yield at that

location to location to 3)3) create a continuous map of the yield create a continuous map of the yield variation every few feet (variation every few feet (dependent map variabledependent map variable). ).

On-the-Fly On-the-Fly Yield MapYield Map

Steps 1–3)Steps 1–3)

Derived Derived Nutrient MapsNutrient Maps

Step 4)Step 4)

Prescription MapPrescription Map

Zone 3

Zone 2

Zone 1

The yield map The yield map 4)4) is analyzed in combination with is analyzed in combination with soil, terrain and other maps (soil, terrain and other maps (independent map independent map variablesvariables) to derive a “Prescription Map” …) to derive a “Prescription Map” …

Variable Rate ApplicationVariable Rate Application

Step 5)Step 5)

5)5) …that is used to adjust fertilization levels every …that is used to adjust fertilization levels every few feet in the field (few feet in the field (actionaction).).

Intelligent ImplementsIntelligent Implements

““As-applied” mapsAs-applied” maps

……more generally termed the more generally termed the Spatial Data Mining ProcessSpatial Data Mining Process (e.g., Geo-Business application)(e.g., Geo-Business application)(Berry)(Berry)

ContinuousSpatial Distribution

(Detailed)

Map AnalysisMap Analysis

Spatially Spatially Interpolated data Interpolated data

((Geographic Space — Geographic Space — Spatial Statistics)Spatial Statistics)

Data Analysis Perspectives Data Analysis Perspectives (review)(review) (Data vs. Geographic Space)(Data vs. Geographic Space)

Identifies the Identifies the Central TendencyCentral Tendency Maps the Maps the VarianceVariance

Central TendencyCentral Tendency

Average = 22.0Average = 22.0

StDev = 18.7StDev = 18.7TypicalTypical

How TypicalHow Typical

DiscreteDiscreteSpatial ObjectSpatial Object

(Generalized)(Generalized)22.0 28.2

Traditional AnalysisTraditional Analysis

Field DataField DataStandard Normal Curve Standard Normal Curve

fit to the data fit to the data

((Data Space Data Space — Non-spatial Statistics)— Non-spatial Statistics)

(Berry)(Berry)

Precision Ag(Individual Field Focus)

Terrain

Soils

Yield

Potassium

CIR Image

Precision ConservationPrecision Conservation (compared to Precision Ag)(compared to Precision Ag)

Isolated Perspective2-dimensional

Precision Conservation(Farm, Watershed,… Focus)

Wind Erosion

Runoff

Leaching Leaching

Leaching

SoilErosion

Chemicals

(Stewardship Focus) (Production Focus)

Interconnected Perspective

3-dimensional

(Berry)(Berry)


Surface Modeling maps the spatial distribution and pattern of point data… Map Generalization— characterizes spatial trends (tilted plane) Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig) Other— roving windows and facets (e.g., density surface; tessellation)

Spatial Data Mining investigates the “numerical” relationships in mapped data… Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering) Predictive— relationships among maps (e.g., regression) Prescription— appropriate actions (e.g., decision rules; optimization)

Spatial Analysis investigates the “contextual” relationships in mapped data… Reclassify— reassigns map values (position, value, size, shape, contiguity) Overlay— map coincidence (point-by-point; region-wide; map-wide) Distance— proximity and connection (movement; optimal paths; visibility) Neighbors— roving windows (slope; aspect; diversity; anomaly)

Grid-Based Map Analysis

SpatialStatistics

Spatial Analysis

(Berry)(Berry)

““The Science”The Science”


Slopemap …relative terrain steepness

Flowmap …relative amount of water

Elevation

…continuous Map Surface (grid-based analysis frame)

Spatial Analysis Spatial Analysis (example procedures)(example procedures)

(Berry)(Berry)(Berry)(Berry)Simple Proximity to RoadsSimple Proximity to Roads

…far from Roads

RoadsRoads& Water& Water

Viewshed from RoadsViewshed from Roads

…not seen

Visual Exposure from RoadsVisual Exposure from Roads

…seen a lot

……whereas whereas Spatial StatisticsSpatial Statistics investigates investigates Numerical RelationshipsNumerical Relationships, ,

Spatial AnalysisSpatial Analysis investigates investigates Geographic ContextGeographic Context

Map StackMap Stack

Calculating Slope and FlowCalculating Slope and Flow (terrain analysis)(terrain analysis)

Elevation Surface

Inclination of a fitted plane to a location and its eight surrounding elevation values

Total number of the steepest Total number of the steepest downhill paths flowing into each downhill paths flowing into each location location

Slope (47,64) = 33.23%

Slope map draped on Elevation

Slopemap

Flow Flow (28,46)(28,46) = 451 Paths = 451 Paths

Flow map draped on Elevation

Flow map

Deriving Erosion PotentialDeriving Erosion Potential (terrain modeling)(terrain modeling)

Erosion_potentialFlow/SlopeSlope_classes

Flow_classes

But all buffer-feet are not the same…

Need to reach farther under some conditions and not as far under others— common sense?

Erosion PotentialErosion Potential

Simple Buffer – fixed geographic reach

Protect the stream

Flowmap

Slopemap

Individual MapAnalysis Operations

Calculating Effective DistanceCalculating Effective Distance (variable-width buffer)(variable-width buffer)

Erosion_potential

Streams

Erosion Buffers

Distance away from the streams is a function of Distance away from the streams is a function of the erosion potential (Flow/Slope Class) with the erosion potential (Flow/Slope Class) with intervening heavy flow and steep slopes intervening heavy flow and steep slopes computed as effectively closer than simple computed as effectively closer than simple distance— “as the crow walks”distance— “as the crow walks”

Effective Distance Effective Distance

Variable-width BuffersVariable-width Buffers

Effective Erosion DistanceEffective Erosion Distance

CloseClose FarFar

Heavy/Steep(far from stream)

Light/Gentle(close)

Simple BufferSimple Buffer

(Berry)(Berry)

ConclusionsConclusions

WhereWhere isis WhatWhat

Mapping Mapping involves involves precise placementprecise placement (delineation) of (delineation) of

physical featuresphysical features

(graphic)(graphic)


(Berry)(Berry)

Multimedia Multimedia MappingMapping

GIS GIS ModelingModeling

AnalysisAnalysis involves involves investigation of investigation of

spatial spatial relationshipsrelationships

(numerical)(numerical)


WhyWhy andand So WhatSo What

GeotechnologyGeotechnology promises to promises to forever change how we conceptualize, utilizeforever change how we conceptualize, utilizeand visualizeand visualize spatial relationships in scientific research and commercial applications spatial relationships in scientific research and commercial applications

Remote Sensing, GPS, Internet Mapping, Desktop Mapping, Multimedia Mapping,Remote Sensing, GPS, Internet Mapping, Desktop Mapping, Multimedia Mapping,Spatial Statistics and Spatial AnalysisSpatial Statistics and Spatial Analysis

Where to go from here…Where to go from here…

GPS – Google Earth — and BeyondGPS – Google Earth — and Beyond (# OSHR 1502 100 )(# OSHR 1502 100 )

Thursdays October 9, 16, 23, 30 from 5:00 p.m. to 7:00 p.m.Thursdays October 9, 16, 23, 30 from 5:00 p.m. to 7:00 p.m.

and Saturday field lab, October 25and Saturday field lab, October 25 from 9:00 a.m. to 1:00 p.m.

Osher Lifelong Learning Institute Osher Lifelong Learning Institute Colorado State University, Division of Continuing Colorado State University, Division of Continuing EducationEducationPhone: 303-376-2618 Phone: 303-376-2618 Web Site: Web Site: http://www.learn.colostate.edu/fortcollins/osher/

www.innovativegis.com

http://www.learn.colostate.edu/fortcollins/osher/



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Geotechnology Not Your Grandfather’s Map