Part 2 - Applying Map Analysis Techniques To Site-Specific Mapping

1
Applying Map Analysis Techniques To
Site-Specific Management
Part 2 Mapped Data Analysis and Spatial
Modeling
Joseph K. Berry
Berry Associates 2000 South College, Suite
300 Fort Collins, CO 80525 Email
jberry_at_innovativegis.com Web Site
www.innovativegis.com/basis
2
Utilizing Remote Sensing for PF
Demo of Video Mapping System

• Collecting Remote Sensing Data
• Proximal Sensing
• Film Cameras
• Video Mapping
• Aerial Remote Sensing
• Film Cameras
• Video Mapping
• Scanners
• Satellite Imaging
• Scanners

A video camera is a broadband scanner
Normalized Density Vegetation Index (NDVI) plant
vigor
(Berry)
3
Geo-Registration of Imagery
Geo-registration is facilitated by GPS mapping
ground features visible in the image
Dycam Image
(Preliminary study, Colorado State University,
Soil and Crop Sciences )
4
Geo-Registered Result
rubber-sheet corrections remove image
geometric distortions in effect, it is like
printing the image on a rubber sheet then
stretching the image to fit the GPS
features inside Cessna, M-VMS, Dycam, 35mm,
camcorder and battery
M-VMS
Dycam
Camcorder and battery
(Preliminary study, Colorado State University,
Soil and Crop Sciences )
5
Related Spatial Technologies (RS)
3
1
Electromagnetic Spectrum (Light)
4
incoming light is preferentially absorbed
(reflected) depending on plant physiology
Species Photosynthesis Water Content
2
(Berry)
6
Linking NDVI to Nitrogen Levels
Nitrogen Treatment
NDVI
(Preliminary study, Wright, Red Hen Systems,)
7
Delineating Zones
Visible differences in an aerial image can be
used to delineate portions of a field that have
consistent texture and color (Management Zones).
The zones are assumed to have consistent levels
for each of the fields driving variables
(uniform conditions)
(Wright Berry)
8
Mgt Zones vs. Map Surfaces the bottomline
both approaches carve a field into smaller
pieces to better represent the unique conditions
and patterns occurring in the field. Zones
pre-partitions it into relatively large,
irregular areas that are assumed to be
homogenousdiscrete polygons. Surfaces, on the
other hand, process field samples for an estimate
of each factor at grid cells throughout a uniform
analysis gridcontinuous gradient.
relationships among Surfaces (data layers) are
easily investigated
Air Photo (soil color)
No map analysis is possible with Management Zones
(Berry)
9
RS Imagery as GIS Data Layers
A RS image is just a shishkebab of
numbers like any other
grid map (raster)
Image
NIR (R)
52
148
46
Red (G)
26
(Beyond our sight) Color Infrared
34
Green (B)
44
Remote sensing images are composed of numbers,
just like any other map in a grid-based
GIS Mapematical Processing
P
57
43
K
312
257
7.5
ph
7.2
etc.
(Berry)
10
The Precision Farming Process
As a combine moves through a field 1) it uses GPS
to check its location then 2) checks the yield
at that location to 3) create a continuous map of
the yield
variation every few feet. This map 4) is
combined with
soil, terrain and other maps to derive a
5) Prescription Map
that is used to adjust
fertilization levels every few feet in the
field
Steps 1)3)
(Cyber-Farmer, Circa 1990)
Prescription Map
On-the-Fly Yield Map
Map Analysis
Step 5)
Step 4)
Farm dB
Zone 3
Zone 2
Zone 1
Variable Rate Application
(Berry)
11
Step 3 Data Analysis

• Map Insights (Univariate-- within a single map)
• Standard Normal Variable (SNV) Maps
• Coefficient of Variation (CoffVar) Maps
• Slope/Aspect (Spatial Derivative) Maps

• Relating Maps (Multivariate-- among maps)
• Map Comparison
• Difference
• Change
• Difference Tests
• Corresponding Areas
• Coincidence
• Map Similarity
• Clustering
• Prescriptive Statistics
• Regression
• Trend Surfaces
• Spatial Data Mining

(Berry)
12
Linking Data and Map Distributions
A histogram depicts the numerical distribution A
map depicts the geographical distribution
the data values link the two views Click
anywhere on the map and the histogram interval is
highlighted click on a histogram interval and
the map locations are highlighted
(Berry)
13
Preprocessing and Map Normalization
Preprocessing involves conversion of raw data
into consistent units that accurately represent
field conditions. Calibration - translates
signals into measurements of crop production
units, such as bushels per acre (measure of
volume) or tons per hectare (measure of mass).
Adjustments - tweaking the values sort of
like a slight turn on that bathroom scale to
alter the reading to what you know is your true
weight. Corrections - dramatically changes the
measurement values, such as after the mass
flow correction to GPS coordinates
Normalization involves standardization of a data
set, usually for comparison among different types
of data. Goal - Norm_GOAL (mapValue / 250 )
100 0-100 - Norm_0-100 ((mapValue min)
100) / (max min) SNV - Norm_SNV ((mapValue -
mean) / stdev) 100
(Berry)
14
Preprocessing and Map Normalization
Applying the MapCalc equation Norm_GOAL
(Yield_Vol / 250 ) 100 generates a
standardized map based on a yield goal of 250
bushels/acre. This map can be used in analysis
with other goal-normalized maps, even from
different crops
Since normalization involves scalar mathematics
(constants), the pattern of the numeric
distribution (histogram) and the spatial
distribution (map) doesnt change same relative
distributions
(Berry)
15
Assessing Localized Variation in Yield
Scan Yield_Volume Coffvar Within 2 For
Yield_Coffvar Where, Coffvar Stdev/mean 100
The Scan operation moves a window around the
yield map and calculates the Coefficient of
Variation with a 2-cell radius of each
location higher values indicate areas with more
localized variability
(Berry)
16
Assessing Rate of Change in Yield
Slope 1997_Yield_Volume Fitted For
Yield_Slope Where, Slope Rise/Run 100
The Slope operation moves a window around the
yield map and calculates the inclination (rate of
change) in yield of neighboring cells higher
values indicate areas with rapidly changing
productivity
(Berry)
17
AnalysisWithin A Surface
Univariate analysis investigates relationships
within a single map

• Slope rate of change (spatial derivative) of
  each surface element (grid cell)
• Aspect orientation (direction) of each
  surface element
• The slope and aspect of an elevation surface
  (altitude derived from a surveyed points or
  rectified orthophotos) identifies terrain
  steepness and orientation example uses include
  road-building and water runoff modeling
• The slope and aspect of a barometric surface (air
  pressure gradient derived from a set weather
  station data) estimates wind speed and direction
• The slope and aspect of a thermal gradient in a
  lake (generated from remote sensing data of
  surface temperature) identifies rate and
  direction of cooling from a thermal input
  (nuclear powerplant ponds)
• The slope and aspect of a total revenue surface
  (generated by summing the cash flow stream for
  each surface element) identifies a marginal
  revenue surface which shows the spatial
  distribution of relative cash flow
• The slope and aspect of a proximity surface
  determines the speed and direction of the optimal
  movement in traversing each surface element
• what would the slope of a slopemap show? the
  aspect of a slopemap?

(Berry)
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AnalysisWithin A Surface continued
Univariate analysis investigates relationships
within a map surface

• Aggregation sum of the values for all or a
  portion of the surface elements (spatial
  integral) example uses include cut/fill
  calculations in road building and total yield
  estimates in precision farming
• Coefficient of Variation localized variation
  surrounding each surface element (surface
  roughness)
• Mathematical Translations scalar arithmetic,
  logarithmic, trigonometric and logical
  operations example use of taking the cosine of
  the zenith angle formed between the suns
  position and each elevation surface element to
  calculate insolation (sun energy at each
  location)
• Statistical Operations describe and
  characterize a surface
• Descriptive statistics (min. max, range, median,
  mode, mean, skewness)
• Similarity assessment (spatial autocorrelation)
• Predictive statistics (map generalization and
  interpolation)
• Accuracy assessment (residual analysis of how
  well a surface fits a data set)
• Other Stuff standard Normal Variable
  Surface pattern recognition filters

(Berry)
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Data Analysis (Visual comparison)
Visual Analysis of 2D Maps
Top-soil Phosphorous
Bottom-soil Phosphorous
so what do these maps tell you (Data Analysis)?
what management actions should be taken and
where (Spatial Modeling)?
(Berry)
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Data Analysis (Map-ematical comparison)
Mapped Data Analysis of Map Surfaces
Top-soil Phosphorous
Phosphorous Difference
Bottom-soil Phosphorous
Top-Bottom values are subtracted for each
location (Map-ematics)?
(Berry)
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Data Analysis (Difference map)
Visualizing Difference Map (2D)
add more Phosphorous just where it is needed
(Spatial Modeling)?
(Berry)
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Data Analysis (visually comparing maps)
What differences do you see? where did yield
change significantly? where did it stay about
the same?
(Berry)
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Data Analysis (comparing discrete maps)
(Berry)
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Data Analysis (discrete maps vs. continuous
surfaces)
Discrete maps intervals Continuous surfaces
values
(Berry)
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Comparing Map Surfaces (Difference map)
green indicates areas of increased
production yellow indicates minimal
change red indicates decreased production
(Berry)
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Data Analysis (assessing spatial patterns)
What spatial relationships do you see? do
relatively high levels of P often occur with high
levels of K and N? how often? where?
(Berry)
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Data Analysis (assessing spatial patterns)
Data Clustering identifies of similar data
patterns Management Zones
the data shishkebab for each grid location is
sent to a statistical algorithm that divides the
data set into groups that are 1) as similar
within each group and 2) as different between
groups as possible
(Berry)
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Investigating Surface Correlation (predictive
model)
Histogram/Map View Data Space (magnitude of
values) are linked to Geographic Space
(position of values)
Histogram/Map View Data Space (joint magnitude
of values) are linked to Geographic
Space (position of values)
(Berry)
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Investigating Surface Correlation (error analysis)
a predicted surface is compared to actual data
( difference map) for an assessment of overall
performance and spatial pattern of errors. In
this instance, the model is a good predictor
within the partitioned area but poor along the
west and north edges.
(Berry)
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Data Analysis (establishing relationships)
On-Farming Testing Investigating the Effects of
Alternatives
(Berry)
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Step 4 Spatial Model
Spatial Data Mining new technology (CART) that
is based on large sample size, repetitive data
grouping and data driven to develop more accurate
prediction equations than traditional statistics

• Knowledge-Based Relationships evaluates spatial
  relationships given input map data
• Look-Up Table
• If-Then Rules
• Expert Systems
• Evaluating Functions
• Equations
• Optimization Techniques
• Linear Programming
• Induction Modeling
• Genetics Modeling
• Tessellation

(Berry)
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Precision Farmings Big Picture
a new application of the Spatial Technologies
that utilizes spatial relationships in a field
for site-specific management
(Berry)
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So Where Are We in Precision Farming?
(Berry)
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Underlying Issues In Precision Farming

• ...Gaps in Our Thinking
• Limited Approach Mapping vs. Data Analysis
  Tools vs. Science
• Science Link Scientific Method Doctrine, The
  Random Thing, Appropriate Driving Variables,
  Correlation vs. Causation
• Market Confusion Empirical Verification,
  Economic Validation, Rationalization
  (Productivity vs. Stewardship)

The Environmental Trump Card
(Berry)
35
Micro Terrain Analysis (Slope and Flow)
Characterizing Slope A digital terrain surface is
formed by assigning an elevation value to each
cell in an analysis grid. The slant of the
terrain at any location can be calculated
inclination of a plane fitted to the elevation
values of the immediate vicinity
Characterizing Surface Flow A map of surface
flow is simulated by aggregating the steepest
downhill paths from each cell confluence
Slope and Flow maps draped over vertically
exaggerated terrain surface
(Berry)
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Micro Terrain Analysis (Slope and Flow)
Calibrating Slope and Flow Classes Areas of
Gentle, Moderate, and Steep slopes are
identified areas of light, moderate and heavy
flows are identified
(Berry)
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Micro Terrain Analysis (a simple erosion model)
Determining Erosion Potential The slope and flow
classes are combined into a single map
identifying erosion potential
(Berry)
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Micro Terrain Analysis (extending the erosion
model)
Simple Buffer
Effectively far away, though right near a stream
how can that be? what about different
soils? what about roughness? or time of
year?
(Berry)
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Precision Farming an Oxymoron?

• What are your thoughts
• Are there spatial variations in agricultural
  fields?
• Is our technology able to precisely measure
  the spatial variations?
• Can we derive and validate the spatial
  relationships in the patterns?
• Can we develop and implement spatially-based
  management actions?

Are you burnt out yet?
(Berry)
40
More Information on PF Data Analysis
PF Case Study (uses MapCalc Learner software)
the MapCalc Learner CD contains a copy of the
Precision Farming Primer and the agriculture data
set used in the case Studywww.redhensystems.com
www.innovativegis.com/basis Online text and Case
Study
(Berry)
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Online PowerPoint Slide Set
www.innovativegis.com/basis select Precision
Farming Primer then click on Appendix E
tuned for Internet Explorer 4.0 and can have
problems with some Netscape versions View in
Medium Text mode size window to fit the slides
(Berry)