Automated landform classification using DEMs Automated classification of

Automated landform classification using DEMs Automated classification of geomorphic/ hydrologic spatial entities to support predictive ecosystem mapping (PEM) R. A. (Bob) Mac. Millan Land. Mapper Environmental Solutions

Outline n Introduction and background n Automated landform classification from DEMs n Capturing and applying expert knowledge n Significance with respect to PEM n Closing thoughts Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Introduction 700 m n Who and what am I? l Soil scientist & mapper l Soil-landform modeller n What do I do? l Terrain analysis and classification from DEM EOR Series 800 m DYD Series KLM Series FMN Series COR Series 15 40 60 OBL HULG SZBL BLSS SZHG HULG OHG EOR COR DYD KLM FMN COR HGT n What can I contribute to this discussion of PEM? l High water level An outsider’s perspective Low water level CHER Land. Mapper Environmental Solutions © 2001 GLEY CHER SOLZ SALINE GLEY BC PEM Workshop, April 25 -27, 2001

DEM LANDFORM CLASSIFICATION Introduction n What is automated landform classification? l l What does it require? How does it work? What can it produce? What can’t it produce? Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Background n Automated landform classification l l A work in progress Previous efforts: 800 m AGRICULTURE • classify farm fields for precision agriculture • classify and describe landforms for soil survey • Land. Map. R Program l Forestry sector interest Land. Mapper • potential to classify forested areas Environmental Solutions © 2001 FORESTRY BC PEM Workshop, April 25 -27, 2001

Background n Not a paradigm shift! l Merge long established concepts and procedures for manual delineation of spatial entities using API l With improved data sources & new or emerging technologies for processing and classifying digital data • • high resolution DEMs (5 -10 m) applied machine vision fuzzy logic, expert systems, AI hydrologic & geomorphic modeling Land. Mapper Environmental Solutions © 2001 800 m MANUAL PROCEDURES 800 m NEW DATA SOURCES BC PEM Workshop, April 25 -27, 2001

Situation analysis n Natural Resource managers n Natural Resource are facing increasing challenges: l l l l Growing globalization Increased competition Need for cost-effective operations Demands for sustainability Compliance with standards Expanding obligations for monitoring & certification More accurate forecasting Land. Mapper Environmental Solutions © 2001 Inventories are: l l At heart of virtually all natural resource issues Provide the basis for: l responsible management and planning l applying and extending knowledge & experience l applying spatial decision support models l. . . over space and time BC PEM Workshop, April 25 -27, 2001

Situation analysis n Natural Resource Inventories undergoing significant change: l l l Need a new generation of classification and mapping systems These need to draw upon existing classification & mapping approaches New systems must be: Land. Mapper • more dynamic, adaptive • cheaper, faster, higher resolution • able to model processes Environmental Solutions © 2001 n Expectations for Natural Resource Inventories: l l Digital from start to finish Provide framework for multi -scale, nested modeling of processes – Ecosystem – landscape – watershed l l Have known accuracy Support management re policy, regulations, planning, operations BC PEM Workshop, April 25 -27, 2001

Objective l l Devise and implement new procedures & an operational toolkit for automatically defining… A multi-level hierarchy of nested hydrologically and geomorphologically oriented spatial entities • which act as a basic structural framework for different kinds of natural resource inventories and their interpretations — soil maps, terrestrial ecosystem, wildlife habitat, forest productivity • based on physical features that are: – – distinct & readily identifiable landform entities logical entities capable of supporting management & planning able to support definition of linkages & interactions able to support nesting & aggregation within a hierarchy Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Conceptual design Source: Band (1986 a) n Geomorphological-Hydrological spatial entities l Adopt, adapt & integrate previous successful approaches • Band, Fels and Matson, Graff and Usery, Irwin et al. , Pennock et al. , Pike, Skidmore, Wood, Franklin l Incorporate concepts of hydrological connectivity and hydrologic response units (HRUs) • Miller, Band & Wood, Band • ITC system of terrain mapping units — TMUs (Meijerink) l Embrace and evolve concepts from traditional forest inventory • multi-level hierarchies from Ecological Land Classification • landforms provide the basic spatial framework (Rowe) Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Conceptual design n Evolution not revolution l Based on capturing and applying expert understanding • heuristic, rule-based, classification approach • aim to have a machine replicate and apply human comprehension – – a form of applied machine vision/artificial intelligence teach machine to “see” and interpret images as a human might use fuzzy logic applied to dimensionless semantic constructs convert absolute terrain measures into relative concepts such as: » relatively steep, close to mid-slope, relatively convex, etc – define fuzzy definitions of landform classes (e. g. midslope, crest) » in terms of relative conceptual attributes (steepness, position) • finish with landform-based units that would be recognizable to: – expert human interpreters of air photos and topographic data Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Conceptual design n A multi-level, multi-scale hierarchy Appropriate Scale DEM Resolution and Source 1: 5 Million to 1: 10 Million 1: 1 Million to 1: 5 Million 1: 250, 000 to 1: 1 Million 1: 125, 000 to 1: 250, 000 1: 50, 000 to 1: 125, 000 1: 10, 000 to 1: 50, 000 1: 5, 000 to 1: 10, 000 1: 1, 000 to 1: 5, 000 9 x 9 km (ETOPO 5) 1 x 1 km (GTOPO 30) 500 x 500 m (DTED) 100 x 100 m (SRTM) 25 x 25 m 10 x 10 m 5 x 5 m 1 x 1 m Proposed Name Physiographic Province Physiographic Region Physiographic District Physiographic System Unnamed and undefined Landform Type Landform Element Unnamed and undefined • Widely accepted in the forestry and ecological sectors • Fundamental to Ecological Land Classification – Rowe, SBLC, Wiken, Boyacioglu • Primary interest is in lowest 1 or 2 levels in the hierarchy Land. Mapper – typically used as basis for operational planning and management Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Landform elements 700 m n Lowest level in hierarchy expected to exhibit l l restricted range of morphological attributes equally restricted range of internal characteristics • moisture status • soil type • hydrology/lithology l EOR Series 800 m DYD Series KLM Series Environmental Solutions © 2001 COR Series 15 40 60 OBL HULG SZBL BLSS SZHG HULG OHG EOR COR DYD KLM FMN COR HGT considered landform facets • differ in shape • landform position • hydrology Land. Mapper FMN Series High water level Low water level CHER GLEY CHER SOLZ SALINE GLEY BC PEM Workshop, April 25 -27, 2001

Landform elements: Implementation n Classified using Land. Map. R l originally 15 classes n Identified deficiencies l Improved recognition of depressions is required l Additional elements to identify: • stream channel and riparian entities — active channels, channel banks, flood plains 800 m Land. Mapper Environmental Solutions © 2001 800 m BC PEM Workshop, April 25 -27, 2001

Landform types n Second level in hierarchy l Characteristic pattern and scale of repetition l Equated to: • toposequences • catenas • associations l Source: S. Nolan HUMMOCKY LANDFORM TYPE Most commonly mapped physical entity in forestry • tentative definitions • proposed 34 classes Source: Kocaoglu (1975) 3 D SCHEMATIC Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Landform types: Conceptualization n Repeating patterns of landform elements Source: Dumanski et al. , (1972) Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Landform types: Implementation n Extending Land. Map. R program: l Recognize and classify 34 landform types n Recognition based on: l Relative size and shape in 3 dimensions 6 km 7 km 3 D view illustrating hummocky landform type 25 m DEM • height (relief) • length (longest X) • width (shortest X) l Measures of morphology • gradient, slope length • drainage integration Land. Mapper Environmental Solutions © 2001 6 km 7 km 3 D view illustrating rolling landform type (25 m DEM) BC PEM Workshop, April 25 -27, 2001

Classifying areas as landform types n Significant challenge involving: l l l Pattern analysis Contextual classification Object recognition n Key issue is to define: l Appropriate search window to compute attributes for patches or regions to assign to classes 6 km 7 km 3 D view illustrating hummocky landform type 25 m DEM 6 km 7 km 3 D view illustrating rolling landform type (25 m DEM) Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Classifying areas as landform types n Key to success l Depressional catchments act as basic entities to class l using attributes of: • size and shape • length, width, relief l 800 m 3 D view illustrating rolling landform type (25 m DEM) statistical distributions of: • • • gradient slope lengths landform classes aspect classes channels and divides 800 m 400 m 3 D view illustrating hummocky landform type 25 m DEM Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Classifying areas as landform types n Depressional catchments l Treated as objects • attributes recorded in table l data stored in table include: • statistical summaries of: – catchment morphology Land. Mapper Environmental Solutions © 2001 800 m 400 m 3 D view illustrating hummocky landform type (25 m DEM) BC PEM Workshop, April 25 -27, 2001

Classifying areas as landform types n Process table to: classify catchment entities HIGH LENGTH (X) 500 M LONG > 1000 M > 9% RIDGED MOUNTAIN CLIFF HUMMOCKY > 50 M > 5% INCLINED HILL HUMMOCKY ROLLING DUNED RIDGED MEDIUM < 50 M < 5% UNDULATING PITTED < 2% LEVEL > 1000 M WIDE LEVEL TO DEP <5 M Land. Mapper Environmental Solutions © 2001 FLOOD PLAIN (Y TH ID 500 M BASIN ) LEVEL PLAIN POTHOLE LOW INCLINED RIBBED < 10 M MEDIUM W RELIEF (Z) LOW HIGH GRADIENT (%) l SHORT <200 M NARROW BC PEM Workshop, April 25 -27, 2001

Classifying areas as landform types n Not yet implemented n Expect success given M 1 h: high relief rolling recent work & analysis of 100 5 m DEMs 800 m Land. Mapper Environmental Solutions © 2001 H 1 m: moderate hummocky 800 m 400 m BC PEM Workshop, April 25 -27, 2001

Physiographic Systems n Top-down sub-division and bottom-up agglomeration 120 km 75 k m 6 km 500 m DEM n Top-down sub-division • Use coarse resolution DEM – 250 to 500 m grid spacing • Run Land. Map. R on DEM – define large regions Land. Mapper Environmental Solutions © 2001 7 km 25 m DEM n Bottom-up agglomeration • Use finer resolution DEM – 25 m to 100 m grid spacing • Run Land. Map. R on DEM – define landform types BC PEM Workshop, April 25 -27, 2001

Physiographic Regions 710 k m 1270 km 710 k m 5 km DEM Land. Mapper Environmental Solutions © 2001 5 km DEM BC PEM Workshop, April 25 -27, 2001

Physiographic Regions n Better to define manually l Classify 500 - 1000 m DEM l Use simple 4 unit Land. Map. R classification to help assign boundaries manually 710 k m 1270 km n Too few spatial entities to warrant effort of automated classification n Incorporate additional data l Land. Mapper Consider bedrock & climate Environmental Solutions © 2001 1270 km 710 k m BC PEM Workshop, April 25 -27, 2001

Some useful technical details 700 m Land. Mapper Environmental Solutions © 2001 800 m BC PEM Workshop, April 25 -27, 2001

Role of hydrological topology 7 8 5 4 n Cell to cell flow paths l Conventional D 8 flow l Custom treatment of: 1 2 1 1 2 3 4 5 6 th ou -S Environmental Solutions © 2001 5 rth Land. Mapper 1 2 No • volume, area, depth • depressions not artifacts, not “spurious” pits 3 1 2 3 3 2 1 ELEVAT ION Define depressional catchments, attributes: 6 2 • flow over flat cells • depressions in DEM l 9 9 7 8 est East-W BC PEM Workshop, April 25 -27, 2001

Significance of depressions n Depressions are considered: l Real landscape features • define local top & bottom where: – water slows down – water ponds – sediments deposited • Establish local context l 800 m 400 m initial local direction of flow elevation of all cells below pour point raised to pour elevation new “reversed” flow directions Divide Procedures need to: • Recognize depressions – selectively remove – retain all information about Land. Mapper Environmental Solutions © 2001 5 5 Pit Center BC PEM Workshop, April 25 -27, 2001

Computing pit characteristics C n Depressional watersheds l For each watershed record l Pour Elev 1 For each pour point record • Pour Elevation (m) • Pour Point Location (row, col) • Neighbour Location (row, col) Land. Mapper Environmental Solutions © 2001 C A For each depression record • Pit Location (row, col) • Pit Elevation (m) • Pit Area (m 2) & Volume (m 3) B Pour Elev 2 • Shed Number & Shed Area • Shed# of the Shed(s) it drains to l A B SHED PIT LOC PIT PIT NEXT POUR POINT NEIGHBOUR NO AREA ROW COL ELEV AREA VOL SHED ROW COL ELEV 58 59 60 61 62 63 64 171 134 30 108 8 870 1389 93 131 726. 8 95 4 722. . 9 96 10 722. 8 96 38 722. 8 98 7 726. 4 98 61 722. 8 99 138 722. 9 40 17 3 2 1 3 70 6. 1 1. 7 0. 4 0. 2 0. 1 0. 4 10. 4 70 59 56 54 53 59 55 96 129 95 1 94 16 95 38 96 6 92 63 727. 1 723. 0 722. 9 726. 5 723. 0 723. 1 97 95 93 93 96 95 91 129 1 10 16 37 6 63 727. 1 723. 0 722. 9 722. 8 726. 5 723. 0 723. 1 BC PEM Workshop, April 25 -27, 2001

Intelligent pit removal n Remove pits in sequence n Intelligent pit removal process based on computed pit geometry: l l Remove from lowest to highest Remove into lowest neighbor (A>B) Define new pit C = A+B+C Compute attributes of new pit C based on reversing flow directions l l C A Find pour point for a given pit Trace down path from pour point Reverse flow directions of cells along path from pour point to pit Retain original elevations in pit area B initial local direction of flow Pour Elev 2 Pour Elev 1 A A elevation of all cells below pour point raised to pour elevation new “reversed” flow directions Divide C B B 5 5 Pit Center Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Establishing landform context n Depressional catchments l Define local window • within which to evaluate landform context • establish landform position l 800 m 400 m Define 1 repeat cycle • ridge to ridge • trough to trough • wavelength of landscape 800 m 400 m Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Establishing landform context n Flow paths establish: l Hydrological connectivity • follow flow down to pit or channel • follow flow up to peak or divide l Landform position — location of cell relative to: • pits and peaks • channels and divides • catchment max and min Land. Mapper Environmental Solutions © 2001 CELL DRAINAGE DIRECTION (LDD) DIVIDE RELATIVE SLOPE POSITION (Distance down slope from cell to pit Centre as % of maximum) MAXIMUM SLOPE LENGTH 63 DIVIDE CELL 4 5 8 7 6 5 PIT CENTRE 6 2 4 3 30 2 1 0 1 2 0 10 20 CELL DOWNSLOPE LENGTH (LDN) 80 100 88 75 63 50 38 25 12 CELL RELATIVE SLOPE POSITION (PUP) BC PEM Workshop, April 25 -27, 2001

Hydrological response units n Establish interactions & flows l Feature that is lacking in solely geomorphic classifications l Essential for modeling ecological and hydrological processes — flows of energy, matter, water; in response to gravitational gradients l Important framework for nesting and agglomeration, rolling spatial entities up Land. Mapper Environmental Solutions © 2001 3. 5 km 4 km BC PEM Workshop, April 25 -27, 2001

Hydrological response units n Importance of HRUs in establishing connectivity: l l l From catchment to catchment From channel segment to channel segment From sub-catchment entity to channel segment From upper to mid to lower to depressions within subcatchments From cell to cell Land. Mapper Environmental Solutions © 2001 800 m C 800 m A B Pour Elev 2 Pour Elev 1 C A A B B BC PEM Workshop, April 25 -27, 2001

Hydrological response units n Superimpose HRUs on geomorphic classifications 3. 5 km Land. Mapper Environmental Solutions © 2001 4 km BC PEM Workshop, April 25 -27, 2001

Discussion - DEM resolution n Require DEMs of: l 5 – 10 m horizontal l 0. 3 – 0. 5 m vertical to adequately capture landform features of interest n DEMs of : l 25 -100 m horizontal l 1 -10 m vertical generalize & abstract the landscape too much; fail to capture significant features of interest Land. Mapper Environmental Solutions © 2001 25 m DEM WITH 5 m DEM INSERT 900 m 800 m 5 m DEM 900 m 800 m 25 m DEM BC PEM Workshop, April 25 -27, 2001

25 m DEMs WITH 5 m DEMs AS INSERTS 100 M DTED 5 M 800 m 50 M 1: 20 K 25 m What we have! Land. Mapper Environmental Solutions © 2001 10 M Optimal resolution for most natural landscapes What we need! BC PEM Workshop, April 25 -27, 2001

Discussion - abstraction & smoothing n Smoothing is essential l bring out signal l reduce local noise n We mainly use: l successive mean filters — 7 x 7 & 5 x 5 n Also have smoothed DEM NOT FILTERED using: l l block kriging thin plate spline with tension n Interested in: l wavelets, Fourier Land. Mapper transforms Environmental Solutions © 2001 DEM FILTERED BC PEM Workshop, April 25 -27, 2001

Discussion – human vs. machine strengths n Classifying landform elements versus landform types 800 m Source: Kocaoglu (1975) n Landform elements n Landform types nd lowest level in hierarchy l Lowest level in the l 2 hierarchy l human recognition is easy l machine recognition is l human recognition is often challenging tedious and error prone BC PEM Workshop, Land. Mapper Environmental Solutions © 2001 April 25 -27, 2001

Conclusions n Developing a tool kit n Still in initial stages l conceptualization l proof of concept programming n Intent to utilize new data l LIDAR, Radar, SRTM n Significant features are: l multi-scale outputs l multiple scales of DEM l nested hierarchy Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Capturing and applying expert knowledge Data and observations Field Maps Experience and knowledge Evidence and hypotheses Beliefs and belief -based rules Formulae and evidence rules Place boundaries Classify entities Land. Mapper Environmental Solutions © 2001 Source: Searle and Baillie (2000) BC PEM Workshop, April 25 -27, 2001

Spatial reasoning: My examples n Landform classification l Expert knowledge & belief • Captured using Fuzzy logic n Association of mapped soils with landform position l Tacit expert knowledge • Captured using weighted belief matrices n Prediction of salinity hazard l Analysis of spatial evidence • Captured using probabilities Land. Mapper computed from evidence Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Landform classification n Compute a series of terrain derivatives l We computed 22 • Only used 12 n Convert terrain derivatives into fuzzy landform attributes l Change absolute values • Into relative values • Based on expert beliefs Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Landform classification n Convert terrain derivatives into fuzzy landform attributes TERRAIN DERIVATIVE Profile Curvature Land. Mapper Environmental Solutions © 2001 FUZZY LANDFORM ATTRIBUTE Likelihood of being concave in profile BC PEM Workshop, April 25 -27, 2001

Landform classification n Fuzzy attributes computed from hard terrain derivatives Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Landform classification n Develop a rule-base l Based on expert beliefs • Expressed in semantic terms n Apply the rule base l To convert • fuzzy landform attributes to • fuzzy landform classes Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Landform classification n Convert fuzzy landform attributes into landform classes Fuzzy landform attributes Fuzzy landform class (DSH) Final hardened landform classes Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Landform classification n Fuzzy landform classes from fuzzy landform attributes Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes: Background n Soil survey is a paradigm-based science l Most soil survey knowledge exists as informal tacit knowledge (Hudson, 1992) • Soil survey is deficient in not expressing scientific knowledge in a more formal way • Knowledge is not easily conveyed to others or used until it is expressed semantically and formally • A significant portion of the value of soil survey is lost if tacit knowledge acquired during mapping is not recorded Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes: Background n Fundamental assumption of soil survey l The distribution of soils in the landscape is predictable (Arnold, 1979, 1988) • Function of the 5 soil forming factors of Jenny (1941) • Topography plays a dominant role locally in influencing the distribution of soils at field scale • Research in Alberta has successfully defined landform segments with different soil regimes Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes: Objectives n To develop a generic procedure for capturing the tacit knowledge of expert soil surveyors l Relate the distribution of soil attributes and Soil Series to 4 landform positions • Upper, Mid-slope, Lower-slope, Depressions n To apply the procedure to the AGRASID database l Assign each soil in the Alberta SNF file a value for likelihood of occurring in each of the 4 landform positions l Link all soils in every AGRASID polygon to their most likely landform position or positions Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes: Methods n Used SNF as source of data l Selected 6 attributes from the SNF • Variant, drainage, calcareous, salinity, parent material & Subgroup classes Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes n Captured expert beliefs and knowledge l Experts assigned each class of each of the 6 attributes a likelihood value • Each class has a likelihood of occurring in each of the 4 landform positions l Experts assigned each attribute a weight Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes n Processed the rules against the SNF to: l Created a database relating soil names to landform position l New database is identical to the original Soil Names File • Except that each soil now has an associated value for likelihood of occurring in each of the 4 landform positions Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes n Processed the revised SNF against AGRASID data l Created a database relating soil names to landform position • • For every soil listed as occurring in every AGRASID polygon Processed over 25, 000 polygons Considered over 1800 different soil series Each soil also linked with morphological attributes of landform Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Allocating soils to landform classes: Results n Soil-landform models for every AGRASID polygon l Models place soils in landform positions • And relate each soil to its most likely associated landform attributes Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Predicting potential salinity hazard (PSH) Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Methods: typical application of Multi Criteria Evaluation n Usually based on expert belief l Estimates “suitability” of a site • Weighted linear combination • Factor scores * Factor weights l Where: • PSH = likelihood that a site will develop salinity • Wti = expert’s judgement of relative importance of a map • FSi = expert’s judgement of likelihood of salinity given a particular class on a map Land. Mapper Environmental Solutions © 2001 Source: Searle and Baillie (2000) BC PEM Workshop, April 25 -27, 2001

Methods: MCE requires 2 things n Estimate of FSi l l l Criteria scores for factor i Factor enhances or detracts from suitability of site for a result (i. e. becoming saline) Factors usually continuous numbers Scaled from 0 -100 or 0 -255 Example used here: • Shallow depth to bedrock is more likely to result in salinity Land. Mapper Environmental Solutions © 2001 n Estimate of Wti l l Weighting factor for map i Weighting factors sum to 1 Measure of the information content or usefulness of map i for predicting outcome S Usually computed from • Pairwise comparisons of relative weights • Relative weights assigned based on expert opinion BC PEM Workshop, April 25 -27, 2001

Methods: weight of evidence n Replace belief with evidence l Maps of visible salinity (1: 100, 000) Analyze spatial correspondence between: • a map depicting presence/absence of a phenomenon of interest (e. g. salinity) – Maps of visible soil salinity available • n maps depicting the spatial distribution of factors considered to influence the phenomenon of interest (e. g. salinity) – e. g. soils, surficial geology, bedrock geology, depth to bedrock, depth to water table, land use, hydro-geology, landform position, landform shape Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Methods: Computing factor scores n Analyze the evidence to: l Determine the likelihood of • Salinity of type k occurring • Given a specific environmental condition – e. g. shallow depth to bedrock l Compute the likelihood as: • FSk, i, j = P(Hk, i, j | Ei. j) where; Visible salinity over depth to bedrock – Hk, i, j is the absolute extent of salinity of type k found in areas mapped as j on i – Ei, j is the absolute extent of areas on map i belonging to class j » e. g. shallow to bedrock Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Methods: Computing weighting factors n Analyze the evidence to: l Determine relative utility of map i • How useful is map i in predicting – occurrence of salinity of type k l Compute the relative weight as: • Wtk, i = ( |P(Ek, i, j|Hk, i) - P(Hk, i, |Ei )| ) Land. Mapper Visible salinity over Land. Sat TM Band 3 where; – Ek, i, j is the absolute extent of areas on map i belonging to class j on that occur in areas mapped as salinity class k – Hk, i is the total absolute extent of salinity of type k that occurs on map i – Ei is the total absolute extent of map i Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Methods: Computing PSH by salinity type n Resolve MCE equation l PSHk = � (FSk, i, j*Wtk, i) • for each of k types of salinity (k= 8 here) l Every cell has a PSH value Mapped contact salinity over contact salinity PSH • represents relative likelihood that cell may exhibit that kind of salinity – White = high PSH (100) – Dark = low PSH (0) Land. Mapper Environmental Solutions © 2001 Coulee bottom salinity over coulee bottom PSH BC PEM Workshop, April 25 -27, 2001

Methods: Computing maximum PSH n Compare 8 PSH maps l Identify maximum PSH value for each cell • record maximum PSH value • record type of salinity associated with maximum PSH value l Compare maximum PSH Maximum PSH overlain with actual mapped salinity • to distribution of actual visible salinity of all types Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Discussion: PSH Slope gradient Land. Sat TM Slope position Bedrock type Depth to bedrock Soil type n Data mining l Making the most of data that are currently available • uses existing, widely available data sets • merges different types and scales of data l Using data to improve knowledge • systematic procedures uncover Land. Mapper – spatial inter-relationships – test assumptions/hypotheses – enhance the knowledge base Landform curvature Environmental Solutions © 2001 Surficial geology BC PEM Workshop, April 25 -27, 2001

How does all this relate to TEM and PEM? n Landform classification l Landform elements l Landform types l Hydrological response units n Predictive programs l belief based (Land. Map. R) l evidence based (PSH) n Allocation of soils to landform positions Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of landform elements to PEM? ƒ( Soil & = Vegetation Parent Material Climate Relief / Topography Organisms Time Ecological Map = Delineations ) ƒ( • species composition • density / stocking • height • age. . . forest /vegetation cover ) terrain / soil map units topographic features • texture Digital Base & Terrain Model • drainage Landfor • elevation • depth • hydrography m • minerology • slope, position, configuration • organic matter depth. . . • aspect. . . classes Land. Mapper Source: K. Jones personal communication Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance to PEM n TEM and PEM utilize l l Terrain Topography Landscape Soils n Could consolidate into l Landform units Land. Mapper Environmental Solutions © 2001 Source: EWG/RIC (1998) BC PEM Workshop, April 25 -27, 2001

Relevance to PEM n PEM uses vector overlay l BGC subzone l Elevation l Slope/Aspect l Forest cover (primary) l Terrain n Overlay produces l Spaghetti l Knowledge not used to define boundaries Land. Mapper Environmental Solutions © 2001 Source: Meidinger et al. , (2001) BC PEM Workshop, April 25 -27, 2001

Relevance to PEM n PEM vector overlay produces l l l Spaghetti Knowledge not used to define boundaries No protocols to reconcile boundary conflicts n Landform classes l Could be used to set primary boundaries Land. Mapper Environmental Solutions © 2001 Source: Meidinger et al. , (2001) BC PEM Workshop, April 25 -27, 2001

Relevance of landform types to PEM n Mapping entities/standards l Workshop: July, 1999 • Treatments often prescribed at the ecosite (site series) level • Often implemented at the landscape level (association) • Interpretive value of an association 6 km 7 km 3 D view illustrating hummocky landform type (25 m DEM) – Greater than the sum of its parts. l Landscape associations • a compound mapping unit entity whose definition includes a predictable pattern of member mapping entities Land. Mapper Environmental Solutions © 2001 6 km 7 km 3 D view illustrating rolling landform type (25 m DEM) BC PEM Workshop, April 25 -27, 2001

Relevance of hydrological connectivity (HRUs) to PEM n Hydrological framework l Increasingly important • Arc. GIS Hydro, WEPP, Band n Static versus dynamic l Current TEM/PEM approach • Focus is on “What is where” and “Where is what” Source: Maidment, 2000 – Static attributes of areas l Emerging hydrological entities • Includes “Why” & “What will be” Land. Mapper – “How do/will things change? ” – Dynamic - current status of areas Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of hydrological connectivity (HRUs) to PEM n Current expectations of PEM l Assign attributes to areas • current vegetation community • expected climax vegetation • environmental/edaphatic conditions Source: Flanagan et al. , 2000 – drainage, texture, slope, carbon n Emerging expectations l Predict and model change l Provide spatial framework for modeling • Support model operation Land. Mapper – Linkages and flows Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of hydrological connectivity (HRUs) to PEM n Arc. GIS Hydro l l Data model Dynamic modeling Off-site effects New standard? Source: Maidment, 2000 Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of predictive programs to PEM n Belief based l Land. Map. R landform classification • Captures and codifies expert beliefs about where and how to define landform boundaries and attributes n Evidence based (PSH) l Systematic analysis of evidence • Provides a method to both establish and test/evaluate/refine Land. Mapper – Beliefs regarding the importance of various input maps/variables (weights) – Beliefs regarding strength and direction of relationships between classes of input data and desired prediction. Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of landform classification to PEM n Landform classification rules l Formalize and systematize the rules for drawing boundaries • For recognizing and attributing fundamental spatial entities Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of analysis of evidence methods to PEM Table 1. Analysis of spatial correspondence between 8 kinds of visible salinity and 3 bedrock types for 82 P Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of predictive programs to PEM n Similarity and convergence l Predicted output (class) l Usually a function (F) of: • Expert belief about: • Or quantitative evidence about: – Importance of input variable in predicting output class (Weight) – Strength and direction of relationship between input variable value and each output class to be predicted l Do we need many custom programs? Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of predictive programs to PEM n Multi-purpose Predictive Calculator (MPC or UPC) l May be both possible and desirable l Many different processing options and possible outputs • Many different options for implementing calculations – Weighted means, Fuzzy JMF, Boolean, Bayesian, Cross products • Almost any possible combination of inputs & outputs – Continuous or classed input or output Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Relevance of allocation of soils to landforms to PEM n Parallels with TEM/PEM l Ecosystem map units & Site Series • Have expected relationships to landform l My landform elements • Could be associated with Site Series – Through similar belief matrices l My landform types • Could be associated with “landscape associations” – Allows component entities to be described and placed in landform positions – Without explicitly mapping them Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Some closing thoughts Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Back to Basics n J. S. Rowe (1996) l Earth surface energy-moisture regimes at all scales /sizes are the dynamic driving variables of functional ecosystems at all scales/sizes l Climatic regimes are primarily interpreted from visible terrain features known to be linked to the regimes of radiation and moisture (viz. landform and vegetation) l Thus, landforms, with their vegetation, modify and shape their coincident climates over all scales Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Back to Basics n J. S. Rowe (1996) l Fortunately, two of the enduring or slowly changing terrain features that are visible at the earth's surface landforms and the drainage patterns that help to reveal them - are also among the most important for understanding ecosystems and their sites l All fundamental variations in landscape ecosystems can initially (in primary succession) be attributed to variations in landforms as they modify climate Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Back to Basics n J. S. Rowe (1996) l Different kinds of landforms are climatically different, signifying important differences in the coincident ecosystems of which they are parts. l Boundaries are recognized by perceived changes in the ecological relationships of vegetation, landform, drainage and soil, from whose expression climate is inferred. Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Back to Basics n J. S. Rowe (1996) l 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 l For example, at the local scale, the change in contour from convex-upward to concave-upward, from the runoff to the run-in position on hill slopes, is always ecologically significant Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001

Thank you! Land. Mapper Environmental Solutions © 2001 BC PEM Workshop, April 25 -27, 2001