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Automated Predictive Mapping:
Some thoughts based on my experiences
R. A. (Bob) MacMillanLandMapper Environmental Solutions Inc.
Presented to ABMI Workshop: Feb 28, 2014
Outline
Theoretical and Conceptual Considerations
Practical and Operational Considerations
Theoretical & Conceptual Considerations• The Importance of having a Theoretical Framework for
Predictive Mapping– Universal Model of Variation
– Z(x) = Z*(x) + ε’(x) + ε’
• What is Optimization and Why is it Important?– Why all the fuss about optimization?
– Implications of optimization:
• Conventional vs. Pedometric Mapping and Modelling– Why do I now favour pedometric mapping?
• What is Truth? – What do we want and need to map?
– How can we measure how well we mapped what we wanted?
– How “close” is what we predicted to what we observe?
Practical & Operational Considerations• Variable Selection – finding the most effective predictors
– Exploratory data analysis• Discovering structure in the predictor data layers
• Multi-scale analysis to find the most effective resolutions
• Harmonization of disparate site observations
• Model Selection – finding the most effective model(s)– Ensemble models and optimizing multiple models
• Polygon Disaggregation– Making the most of available conventional polygon maps
• Rigour and Structure in Predictive Mapping– Take time to do things right
– Reproducibility and automated work flows
• Open data, open platforms, open software, collaboration
The Importance of Having a Strong
Theoretical Foundation
Existing Empirical Models of Variation
Universal Model of Spatial Variation
A Unifying Framework for all Forms of
Spatial Prediction
Theoretical Basis for Current Manual or
“Conventional ” Soil or Ecological Mapping– Jenny (1941)
• CLORPT (Note no N=space)
– Simonson (1959)• Process Model of additions,
removals, translocations, transformations
– Ruhe (1975)• Erosional -Depositional
surfaces, open/closed basins
– Dalrymple et al., (1968)• Nine unit hill slope model
– Milne (1936a, 1936b)• Catena concept, toposequences
The Soil-Landscape Model as the
Fundamental Basis for Soil Mapping
Ecological Theory
• J.S. Rowe (1996)
– Landforms, with their vegetation, modify and
shape their coincident climates over all scales
• Earth surface energy-moisture regimes at all scales /sizes are
the dynamic driving variables of functional ecosystems at all
scales/sizes
• Climatic regimes are primarily interpreted from visible terrain
features known to be linked to the regimes of radiation and
moisture (viz. landform and vegetation)
• J.S. Rowe (1996)
– All fundamental variations in landscape ecosystems
can initially (in primary succession) be attributed to
variations in landforms as they modify climate
• Boundaries between potential ecosystems can be mapped
to coincide with changes in those landform characteristics
known to regulate the reception and retention of energy and
water
Ecological Theory
Universal Model of Spatial Variation:
A Unifying Scientific Theory– Waldo Tobler (1970)
• First law of geography
– Everything is related to everything else, but near things are more related than distant things
– Matheron (1971)• Theory of regionalized variables
– Webster and Cuanalo (1975)• clay, silt, pH, CaCO3, colour
value, and stoniness on transect
– Burgess and Webster (1980 ab)• Soil Property maps by kriging
• Universal kriging (drift) of EC
Universal Model of Spatial Variation:
A Unifying Scientific Theory– Applies to ALL spatial prediction
• Matheron (1971)
• Adapted to predict soil variation
– Burgess and Webster (1980 ab)
– Webster and Burrough (1980)
– Burrough (1986)
– Webster and McBratney (1987)
– Oliver (1989)
Source: Oliver, 1989
Universal Model of Soil Variation• A Unifying Framework for Predictive Mapping
Z(s) = Z*(s) + ε’(s) + ε’
Predicted soil type or
soil property value
Deterministic part of
the predictive model
Stochastic part of the
predictive model
Pure Noise part of
the predictive model
Predicted spatial
pattern of some soil
property or class
including uncertainty
of the estimate
part of the variation
that shows spatial
structure, can be
modelled with a
variogram
part of the variation
that is predictable by
means of some
statistical or heuristic
soil-landscape model
part of the variation
that can’t be predicted
at the current scale
with the available
data and models
Source: Burrough, 1986 eq. 8.14
Deterministic Part of Prediction Model:
Z*(s)
• Conceptual Models
– Conceptual or mental soil-landscape models
– Produce area-class maps
• Statistical Models
– Scorpan – relate soils/soil properties to covariates
– Explain spatial distribution of soils in terms of known soil forming factors as represented by covariates
EOR Series DYD Series KLM Series FMN Series
15
40
60
COR Series
In d iv id u a l sa lin ity h a za rd ra tin gsfor ea ch la ye r
10 0 x 10 0 m g rid
La nd sca pecu rva tu re
Veg e ta tio n
R a in fa ll
G e o lo g y
S o ils
L a n d su rfa ce
S a lin ity h az a rdm ap
L a ye r w e igh ting s
2 x
1 x
2 x
1 x
3 x
To ta l sa lin ityha za rd ra ting
Stochastic Part of Prediction Model: ε’(s)
Optimal Interpolation by Kriging
Fit Semi-variogram to lag data
6
6
7
6
6
7
7
5
8 5
x
y
Collect point sample observations
Irregular spatial distribution
(of observed point values)
Compute semi-variance
at different lag distances
Estimate values and error at fixed grid locations
6.1 5.7 5.3 5.8
7.0 6.5 6.0 5.2
7.6 6.0 5.77.0
7.2 7.0 6.2 5.5
Stochastic Part of Prediction Model: ε’(s)
• Geostatistical Estimation
– Predict soil properties• Point or block kriging
– Predict soil classes• Indicator kriging (0/1)
• Soil class likelihood values
– Predict error of estimate
• Correct Deterministic Part
– Error in deterministic part is computed (residuals)
– If structure exists in error then krige error & subtract
Pure Noise Part of Prediction Model: ε’
• Some Variation not Predictable
– Have to be honest about this• Should quantify and report it
• Deterministic Prediction
– Mental and Statistical Models• Not perfect – often lack suitable
covariates to predict target variable
• Lack covariates at finer resolution
• Geostatistical Prediction
– Insufficient point input data• Can’t predict at less than the
smallest spacing of input point datad1 d2 d3 d4
SemiVariance
Lag (distance)
Sill
Nugget
Range
What is Optimization and Why is it
Important?
Why all the fuss about optimization?
The Importance of Optimization in the
Universal Model of Variation• Minimize Variance
– Between observed and
predicted values or classes
Source: Zhu et al., 1997
• Maximize Accuracy
– Discrete polygon surfaces
– Continuous value surfaces
Conventional vs. Pedometric Mapping
Implications relative to optimization
Implications relative to other sciences
Conventional vs. Pedometric Mapping
• Conventional Mapping
– Subjective and empirical
• Only tries to implement the
deterministic past of UMSV
– Impossible to replicate
• Too many possible outcomes
– Impossible to automate
• Too many manual decisions
– Impossible to optimize
• Unlimited number of
possible realizations
• Can’t maximize fit to data
Conventional vs. Pedometric Mapping
• Pedometric Mapping
– Objective and systematic
• Implements both parts
Deterministic & Stochastic
– Completely reproducible
• Models can be re-run
– Can be automated
• Fully automated workflows
– Can be optimized
• Multiple models can be run
to identify best fit to data
• Objective ID of best fit
Rationale Against Conventional Mapping
• Essentially a Religion
– Based on belief and faith
• Subjective assessments
– Can only map static features
• Can’t handle time, management
– Can’t objectively optimize
• Impossible to guarantee fit of
predictions to observed values
– Very difficult to update
• Takes years to re-map an area
• Can’t reuse/recycle existing data
EOR Series DYD Series KLM Series FMN Series
15
40
60
COR Series
Rationale for Pedometric Mapping
• Time to Become a Science
– Use objective, evidence-based
input data and models
• Like meteorology, hydrology
– Use multiple-model runs
• To reduce & quantify error
– Quantify fit and error
• Best possible fit of predictions
to observed values (optimized)
– Allow for real-time update
• Continuous improvement
• Model dynamic properties
What is Truth?
What do we want/need to map?
How can we know if we have mapped it?
How “close” is what we predict to what
we observe?
What Do We Want/Need to Map?
• Conceptual Classes of Objects
– Ecological Class Objects
• Defined by attributes that describe
their environmental settings
• Often presented on maps as map unit
collections of classes within polygons
– Soil Class Objects (Series, MUs)
• Defined by observable profile
attributes which are taken to reflect
the controlling environment
• Often presented on maps as map unit
collections of classes within polygons
EOR Series DYD Series KLM Series FMN Series
15
40
60
COR Series
Environmental Gradients Are Used to
Define Ecological Classes
• Ecological Field Guides
– Record and present
knowledge using
• Text descriptions
• Ecological Keys
• Edatopic Grids
• Landscape Profiles
• Summary Tables
Source: Steen and Coupé (1997)
Implications of Mapping Classed Objects
• Key Assumptions
– Classes can be recognized
consistently and correctly
• Most sites are not proto-
typical of a single class,
show partial membership
– Class attributes and
definitions do not overlap
(mutually exclusive)
– All key properties of
interest co-vary spatially
with all classes
• Problems and Challenges
– Often impossible to map a
single uniform class
• Usually have map units with a
collection of 2 or more classes
– Classes do not change
abruptly at boundaries
• Often it is just the proportions
of classes that change
– Ambiguity and error
• Same class in different settings
• Different classes in same setting
How Do We Use Area-Class Maps?
• General Planning
– General planning and
decision making often at a
regional level
• Need information on
extent or area of given
classes and their attributes
• Estimates of proportions
of classes within defined
areas is usually “good
enough”
• Not adversely affected by
error if classes are “close”
• Operational Planning
– Usually applies to some
minimum sized area under
intensive management
• Ideally desire knowledge of
exact classes at exact locations
• But often can’t manage to
exact site level so proportions
of classes within a minimum
size management area is often
sufficient
• Not adversely affected by
error if classes are “close”
What Do We Really Need from Area-
Class Maps?
• Exact Class Match?
– Exact match of classes at
exact locations
• Observed class exactly
matches prototypical class
definition at all locations
• In a perfect world this is
the ideal
– Assumes classes change
abruptly at boundaries
• In real world we tend to
map assemblages of classes
• Approximate Match?
– Proportions of named
classes within a defined area
• May be “good enough” to
support decisions we need to
make for management
– Similarity of observed site
to definition of class
• If observed site conditions
are “close” to those defined
for prototypical class that
may also be “good enough”
What is Truth in Area-Class Maps?
• Exact Measurement Error
– Four ecologists traversed
the same 4 transects
– Exact agreement at exact
locations (congruency)
• Any 2 agreed with each other
– Average agreement 42%
– Minimum agreement 23%
– Maximum agreement 73%
• All 4 agreed with each other
– Average agreement 21%
– Minimum agreement 11%
– Maximum agreement 30%Source: Moon, 2008
A cautionary tale
Congruency = Exact Class Match at
Exact Spatial Locations
What is Truth in Area-Class Maps?
• Proportions in a Small Area
– Traverse area approximates
minimum size map polygon
– Compute proportions of each
class along total traverse
• Primary call agreement all 4
– Average agreement 64%
– Minimum agreement 37%
– Maximum agreement 86%
• Primary & alternate agreement
– Average agreement 71%
– Minimum agreement 43%
– Maximum agreement 95%Source: Moon, 2008
A cautionary tale
Overlap = Degree to which proportion
estimates along total traverse agree
How “Close” is What we Predict to
What we Observe?
• In Geographic Space?
– Displacement of field
observations from map
predictions
• Can easily be +/- 25 m
even with GPS locations
• So, if class is found
anywhere within 25 m is
that a correct prediction?
– If we know extent of class
within minimum area
• Is that “close” enough
• In Parameter Space?
– Point of predicting classes
is to predict attributes
• If attributes of predicted
class at predicted location are
similar to observed attributes
– Is that “close” enough
• Can assess similarity of
observed to predicted
attributes at each location
– From this can assess how
“close” predicted attributes
are to observed attributes
How “Close” is What we Predict to
What we Observe?
• In Parameter Space?• Can assess similarity of
observed to predicted
attributes at each location
– From this can assess how
“close” predicted class is to
observed class
How “Close” is What we Predict to
What we Observe?
• Measures of Class Similarity
– Taxonomic distance
• Difference in taxonomic space
between observed profile and
prototype reference profile
– Carré and Jacobson, 2009
– Minasny and McBratney, 2007
– Site attribute similarity
• Difference in attribute space
between observed site attributes
and attributes of reference site
– Zhu et al., Shi, SoLIM
Practical & Operational Considerations
Variable Selection
Model Selection
Polygon Disaggregation
Rigour and Structure in Predictive Mapping
Open data, open platforms, open software, collaboration
Variable SelectionFinding the most useful predictor variables
Discovering structure in the input variables
Multi-scale analysis to find the most
effective resolutions
Harmonization of disparate observations
Discovering Structure in Input Data Sets
• Inherent Spatial Structure
– Systematic analysis of
environmental covariates
• Detect distances and scales
over which each covariate
exhibits a strong relationship
with itself or with a soil or
property to be predicted.
– Analyse range of variation
inherent to each covariate
– Vary window sizes and grid
resolutions and compute
regressions on derivatives
» Functional relationships
are dependent on scale
Source: Park, 2004
Discovering Structure in Input Data Sets
• Relate correlations to scale
– Exploratory data analysis
• Systematic examination of
relationships of properties,
classes or processes to scale
• Multi-scale analysis
– varying window size and
grid resolution
• Assess strength of correlation
relative to resolution & scale
– New geomorphic measures
• Primarily contextual and
related to landform position
Source: Smith et al., 2006
Source: Schmidt and Andrew., 2005
Discovering Structure in Input Data Sets
• Terrain Attributes
– Multi-scale analysis
• Varying window size and
grid resolution
• Identifies that some
variables are more useful
when computed over larger
windows or coarser grids
– Finer resolution grids not
always needed or better
– Drop off in predictive
power of DEMs after
about 30-50 m grid
resolution
Source: Deng et al., 2007
Discovering Structure in Input Data Sets:
Smoothing vs. Coarsening of DEM Data
Source: Li et al., 2011
Multi-scale DEM Analysis: MrVBFSmooth and subsample
Original: 25 m Generalised: 75 m Generalised 675 mFlatness
Bottomness
Valley Bottom
Flatness
Valley Bottom
Flatness
Bottomness
Flatness
Source: Gallant, 2012
Multiple Resolution Landform Position
MrVBF Example Outputs
Source: Gallant, 2012
Broader Scale 9” DEM
MRVBF for 25 m DEM
Hyper-scale Analyses of Terrain Context
• ConMAP: Hyper-scale Contextual Analysis of Topographic Parameters
Source: Behrens et al., in press
– Neighborhood example
• Diameter
– 21 km
• Predictors
– 775
Hyper-scale Analyses of Terrain Context
• ConSTAT: Hyper-scale Contextual Analysis of Topographic Parameters
Source: Behrens et al., in press
ConStat (ConMap)- neighborhood reduction
a) Full neighborhoodb) Reduction of radiic) Reduction on radii d) Combination of b and c
Hyper-scale Analyses of Terrain Context
• Hyper-scale Terrain
Analysis in ConSTAT
– Systematic analysis of relative
importance of terrain
measures at different scales
• Compute statistics of terrain
measures at different scales
– Use data mining (Random
Forests) to identify
importance of different
statistics at different scales
and at each different location
Source: Berhens et al., in press
Hyper-scale Analyses of Terrain Context
• ConSTAT: Hyper-scale Contextual Analysis of Topographic Parameters
Source: Behrens et al., in press
Improved Measures of Landform Position
• SAGA-RHSP: relative
hydrologic slope position
• SAGA-ABC: altitude
above channel
Source: C. Bulmer, unpublishedCalculation based on: MacMillan, 2005
Source: C. Bulmer, unpublished
Improved Measures of Landform Position
• TOPHAT – Schmidt
and Hewitt (2004)
• Slope Position – Hatfield
(1996)
Source: Hatfield (1996)Source: Schmidt & Hewitt, (2004)
Improved Measures of Landform Position -
Scilands
Source: Rüdiger Köthe , 2012
Improved Classification of Landform Types
– Scilands/LandMapR
Source: MacMillan/Rüdiger Köthe , 2012
Measures of Relative Slope Length (L)
Computed by LandMapR• Percent L Pit to Peak • Percent L Channel to Divide
MEASURE OF LOCAL CONTEXTMEASURE OF REGIONAL CONTEXT
Image Data Copyright the Province of British Columbia, 2003
Source: MacMillan, 2005
Image Data Copyright the Province of British Columbia, 2003
Measures of Relative Slope Position
Computed by LandMapR• Percent Diffuse Upslope Area • Percent Z Channel to Divide
RELATIVE TO MAIN STREAM CHANNELSSENSITIVE TO HOLLOWS & DRAWS
Source: MacMillan, 2005
Harmonization of Input Data Sets
• Inconsistent class names
– Need to identify different
class codes/names that are
essentially the same entity
• Maybe assess each class in
terms of similarity to all others
• Harmonize several similar
class names to the same name
– Need to address how to use
maps of compound units as
evidence for training data
• Sample units by proportion
Source: Sun et al., 2010
Harmonization of Soil Profiles by Depth
Source: David Jacquier, 2010
Harmonization of soil profile depth data through spline fitting
Harmonization of Soil Profiles by DepthFrom discrete soil classes to continuous soil properties
‘Modal’
profile
Fit mass-
preserving
spline
Spline
averages
at
specified
depth
ranges
Estimate
averages for
spline at
standardised
depth
ranges, e.g.,
globalsoilmap
depth ranges
Fitted
Spline
Clearfield soil seriesWapello County, Iowa
Mukey: 411784Musym: 230C
Source: Sun et al., (2010)
Harmonization of soil profile
data through spline fitting
Model SelectionFinding the most useful model or models
Main types of modeling options
Arguments for adopting objective models
amenable to optimization
Arguments for adopting ensemble models
Approaches to Producing Predictive Area-
Class Maps
Knowledge-Based Classification In SoLIM
Source: Zhu, SoLIM Handbook
Knowledge-Based Classification In LandMapR
Source: Steen and Coupé, 1997
Source: MacMillan, 2005
Knowledge-Based Classification In LandMapR
Source: Global Forest Watch Canada, 2012
Approaches to Producing Predictive Area-
Class Maps
Supervised Prediction Models Based on
Analysis of Evidence• Regression Trees, Random Forests
– McKenzie & Ryan, 1998, Odeh et al., 1994
• Fuzzy Logic-Neural Networks
– Zhu, 1997
• Bayesian Expert Knowledge
– Skidmore et al., 1996
– Cook et al., 1996, Corner et al., 1997
• GLMs – General Linear Models
– McKenzie & Austin, 1993
– Gessler et al., 1995Source: McKenzie and Ryan, 1998
Supervised Classification Using Regression Trees
Note similarity of supervised rulesand classes to typical soil-landformconceptual classes
Note numeric estimate of likelihood of occurrence of classes
Source: Zhou et al., 2004,
JZUS
Uncertainty of prediction
Bui and Moran (2003)
Geoderma 111:21-44
Extrapolation
Source: Bui and Moran., 2003
Predicting Area-Class Soil Maps Using
Regression Trees/Random Forests
Approaches to Producing Predictive Area-
Class Maps
Unsuperivsed Fuzzy K-means Clustering
Credit: J. Balkovič & G. Čemanová
Source: Sobocká et al., 2003
Example of Application of Fuzzy K-means
Unsupervised Classification
From: Burrough et al., 2001, Landscsape Ecology
Note similarity of unsupervised
classes to conceptual classes
The Importance of Selecting Objective Models
Amenable to Optimization
Source: Heuvelink et al., 2004
Deterministic part of
the predictive model
Stochastic part of the
predictive model
Lots of things qualify
as regression!
Regression just
means minimizing
variance
Why is optimization
so important?
Z(s) = Z*(s) + ε(s) + ε
The Usefulness of Running Multiple
Models or Ensemble Models• All Models are wrong
– But some models are useful
• Some are more correct than
others
• Can assess error of each
model objectively
• Then weight each model to
average predictions
– Operates on the principal of
the wisdom of crowds
• Average out errors across
different models
Source: Hengl et al., in prep
Source: Sun et al., 2010
Predicting Area-Class Soil Maps Using
Multiple Regression Trees (100 x)
Prepare a database and tables of mapping units & soil series, and covariates
Select 1/n of the points systematically (n=100)
Sample soil series randomly from the multinomial distribution of mapping unit composites
Construct decision tree
Predict soil series at all pixels
Calculate the soil series statistics based on the n predictions for each pixel
Calculate the probability for each soil series
Generate soil series maps
Repeat n
times
Used See 5, (RuleQuest
Research, 2009
Polygon DisaggregationMaking the most of existing polygon class maps
Conventional manual maps are useful
Many manual maps have compound map
units with multiple component classes
Many ways to disaggregate compound maps
Knowledge-Based Polygon Disaggregation
Using Simple Expert Knowledge in USA
Gilpin
Pineville
Laidig
Guyandotte
Dekalb
Component Soils
Craigsville
Meckesville
Cateache
Shouns
Source: Thompson et al., 2010 WCSS
Predicting Area-Class Soil Maps
Source: Grinand et al., 2008
Clovis Grinand, Dominique Arrouays,
Bertrand Laroche, and Manuel Pascal Martin.
Extrapolating regional soil landscapes from an
existing soil map: Sampling intensity,
validation procedures, and integration of
spatial context. Geoderma 143, 180-190
Example of Application of Disaggregation of
a Soil Map by Clustering into Components
Source: Faine, 2001
Source: Sun et al., 2010
Polygon Disaggregation of Area-Class
Soil Map Using 100x Regression Trees
Prepare a database and tables of mapping units & soil series, and covariates
Select 1/n of the points systematically (n=100)
Sample soil series randomly from the multinomial distribution of mapping unit composites
Construct decision tree
Predict soil series at all pixels
Calculate the soil series statistics based on the n predictions for each pixel
Calculate the probability for each soil series
Generate soil series maps
Repeat n
times
Used See 5, (RuleQuest
Research, 2009
Source: Sun et al., 2010
Predicting Area-Class Soil Maps Using
Multiple Regression Trees (100 x)
A closer look at the junction point in the middle of 4 combined maps,
(a) the original map units, and
(b) the most likely soil series map and its associated probability.
The length of the image is approximately 14 km.
Legend
monr_comppct
Value
High : 100
Low : 7
(a)
(b)
Rigour and Structure in Predictive
Mapping
Take time to do things rightStrive for reproducibility through automated
work flows
Rigour and Structure in Predictive
Mapping
• Take Time to Get it Right
– Exploratory Data Analysis
• Find the best predictors at
the best resolution(s)
• Assess multiple inputs at
different resolutions & scales
• Find surrogates for parent
material type & texture
– Evaluate Multiple Models
• Apply and assess multiple
prediction models
• Assess model performance
quantitatively
• Strive for Reproducibility
– It is not Science if Everyone
Else Cannot Reproduce it
• All evidence, input layers and
models must be available
– Automated Workflows are
Highly Desirable
• Let you rerun the process if
any new data are obtained
• Let others rerun the process to
verify your results
– Grow and Reuse Input Data
Open FrameworkOpen data, Open platforms, Open software
Facilitate collective and collaborative
action through open systems
Continuously add to and reuse data
Reinvent conventional terrestrial mapping
An Example of an Open Platform:
Global Soil Information Facility
Source: Hengl et al., 2011
A Conceptual Framework for a Global Soil
Information Facility (GSIF)
Source: Hengl et al., 2011
Collaborative and
open modelling on
an inter-active, web-
based server-side
platform
Collaborative and
open production,
assembly and sharing
of covariate data
(World Grids)
Collaborative and
open collection,
input and sharing of
geo-registered field
evidence
(Open Soil Profiles)
Maps we can all contribute to, access, use, modify and
update, continuously and transparently
Everything is
accessible,
transparent and
repeatable
A Conceptual Framework for a Global Soil
Information Facility (GSIF)
Source: Hengl et al., 2011
Possibility of making
use of existing
legacy soil maps
(even new soil maps)
needed for soil
prediction anywhere
Possibility of
rescuing, sharing,
harmonizing and
archiving soil
profile point data
needed for soil
prediction anywhere
Possibility to
develop and use
global models (even
for local mapping)
Possibility to
develop and use
multi-scale and
multi-resolution
hierarchical models
Possibility to assess
error and correct for
it everywhere
Visit: www.worldsoilprofiles.org, www.worldgrids.org
The Future: Collaborative Global to Local,
Multi-Scale Mapping through Open Platforms
Source: Hengl et al., 2011
Possibility for combining
Top-Down and Bottom-up
mapping through weighted
averaging of 2 or more sets
of predictions
)
Possibility to
develop and use
global models (even
for local mapping)
The Future: Global to Local, Multi-Scale
Modeling of Soil Properties or Classes
Source: Hengl et al., 2011
Possibility to
develop and use
global models (even
for local mapping)
Possibility to
develop and use
multi-scale and
multi-resolution
hierarchical models
The Future: From Static Mapping to
Continuously Updated Modelling
Possibility to move from single snapshot
mapping of static soil properties or classes
to continuous update and improvement of
maps of both static and dynamic
properties within a structured and
consistent framework.
The Future: Collaborative Global to Local,
Multi-Scale On-line Mapping
Source: Hengl et al., 2011
A Global
Collaboratory!
Working together
we can map the
world one block at a
time!
The next generation
of soil surveyors is
everyone!
Thank You
Hope this was helpful