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Automated landform classification using DEMs

Automated landform classification using DEMs. Automated classification of geomorphic/ hydrologic spatial entities to support predictive ecosystem mapping (PEM). R. A. (Bob) MacMillan LandMapper Environmental Solutions. Outline. Introduction and background

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Automated landform classification using DEMs

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  1. Automated landform classification using DEMs Automated classification of geomorphic/ hydrologic spatial entities to support predictive ecosystem mapping (PEM) R. A. (Bob) MacMillan LandMapper Environmental Solutions

  2. Outline • Introduction and background • Automated landform classification from DEMs • Capturing and applying expert knowledge • Significance with respect to PEM • Closing thoughts

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

  4. What is automated landform classification? What does it require? How does it work? What can it produce? What can’t it produce? DEM LANDFORM CLASSIFICATION Introduction

  5. Automated landform classification A work in progress Previous efforts: classify farm fields for precision agriculture classify and describe landforms for soil survey LandMapRProgram Forestry sector interest potential to classify forested areas Background 800 m 800 m AGRICULTURE FORESTRY

  6. Not a paradigm shift! Merge long established concepts and procedures for manual delineation of spatial entities using API 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 Background 800 m 800 m MANUAL PROCEDURES 800 m 800 m NEW DATA SOURCES

  7. Natural Resource managers are facing increasing challenges: Growing globalization Increased competition Need for cost-effective operations Demands for sustainability Compliance with standards Expanding obligations for monitoring & certification More accurate forecasting Natural Resource Inventories are: At heart of virtually all natural resource issues Provide the basis for: responsible management and planning applying and extending knowledge & experience applying spatial decision support models ...over space and time Situation analysis

  8. Natural Resource Inventories undergoing significant change: Need a new generation of classification and mapping systems These need to draw upon existing classification & mapping approaches New systems must be: more dynamic, adaptive cheaper, faster, higher resolution able to model processes Expectations for Natural Resource Inventories: Digital from start to finish Provide framework for multi-scale, nested modeling of processes Ecosystem landscape watershed Have known accuracy Support management re - policy, regulations, planning, operations Situation analysis

  9. 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 Objective

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

  11. Evolution not revolution 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 Conceptual design

  12. 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 typically used as basis for operational planning and management DEM Resolution and Source 9 x 9 km (ETOPO5) 1 x 1 km (GTOPO30) 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 Appropriate Scale 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 Conceptual design • A multi-level, multi-scale hierarchy

  13. Lowest level in hierarchy expected to exhibit restricted range of morphological attributes equally restricted range of internal characteristics moisture status soil type hydrology/lithology considered landform facets differ in shape landform position hydrology KLM Series FMN Series COR Series DYD Series EOR Series 15 40 60 OBL HULG SZBL BLSS SZHG HULG OHG EOR COR DYD KLM FMN COR HGT High water level Low water level CHER GLEY CHER SOLZ SALINE GLEY GLEY Landformelements 700 m 800 m

  14. Classified using LandMapR originally 15 classes Identified deficiencies Improved recognition of depressions is required Additional elements to identify: stream channel and riparian entities — active channels, channel banks, flood plains Landform elements: Implementation 800 m 800 m

  15. Second level in hierarchy Characteristic pattern and scale of repetition Equated to: toposequences catenas associations Most commonly mapped physical entity in forestry tentative definitions proposed 34 classes Source: Kocaoglu (1975) Landformtypes Source: S. Nolan HUMMOCKY LANDFORM TYPE 3D SCHEMATIC

  16. Landform types: Conceptualization • Repeating patterns of landform elements Source: Dumanski et al., (1972)

  17. ExtendingLandMapRprogram: Recognize and classify 34 landform types Recognition based on: Relative size and shape in 3 dimensions height (relief) length (longest X) width (shortest X) Measures of morphology gradient, slope length drainage integration Landform types: Implementation 6 km 7 km 3D view illustrating hummocky landform type 25 m DEM 6 km 7 km 3D view illustrating rolling landform type (25 m DEM)

  18. Significant challenge involving: Pattern analysis Contextual classification Object recognition Key issue is to define: Appropriate search window to compute attributes for patches or regions to assign to classes Classifying areas as landform types 6 km 7 km 3D view illustrating hummocky landform type 25 m DEM 6 km 7 km 3D view illustrating rolling landform type (25 m DEM)

  19. Key to success Depressional catchments act as basic entities to class using attributes of: size and shape length, width, relief statistical distributions of: gradient slope lengths landform classes aspect classes channels and divides Classifying areas as landform types 800 m 800 m 3D view illustrating rolling landform type (25 m DEM) 800 m 400 m 3D view illustrating hummocky landform type 25 m DEM

  20. Depressional catchments Treated as objects attributes recorded in table data stored in table include: statistical summaries of: catchment morphology Classifying areas as landform types 800 m 400 m 3D view illustrating hummocky landform type (25 m DEM)

  21. > 9% HIGH > 5% MEDIUM < 5% RELIEF (Z) < 2% BASIN POTHOLE LOW RIDGED INCLINED INCLINED RIBBED PITTED CLIFF DUNED MOUNTAIN HILL RIDGED HUMMOCKY HUMMOCKY FLOOD PLAIN LEVEL PLAIN LEVEL TO DEP Classifying areas as landform types SHORT LENGTH (X) LONG <200 M 500 M > 1000 M • Process table to: • classify catchment entities HIGH > 50 M MEDIUM ROLLING GRADIENT (%) < 50 M LOW UNDULATING LEVEL WIDE > 1000 M < 10 M WIDTH (Y) 500 M < 5 M <200 M NARROW

  22. Not yet implemented Expect success given recent work & analysis of 100 5m DEMs Classifying areas as landform types M1h: high relief rolling H1m: moderate hummocky 800 m 800 m 800 m 400 m

  23. Bottom-up agglomeration Use finer resolution DEM 25 m to 100 m grid spacing Run LandMapR on DEM define landform types Physiographic Systems • Top-down sub-division and bottom-up agglomeration 120 km 75 k m 6 km 7 km 25 m DEM 500 m DEM • Top-down sub-division • Use coarse resolution DEM • 250 to 500 m grid spacing • Run LandMapR on DEM • define large regions

  24. Physiographic Regions 710 k m 1270 km 710 k m 1270 km 1270 km 1270 km 710 k m 5 km DEM 5 km DEM

  25. Better to define manually Classify 500 - 1000 m DEM Use simple 4 unit LandMapR classification to help assign boundaries manually Too few spatial entities to warrant effort of automated classification Incorporate additional data Consider bedrock & climate 710 k m PhysiographicRegions 1270 km 1270 km 710 k m

  26. Some useful technical details 700 m 800 m

  27. Cell to cell flow paths Conventional D8 flow Custom treatment of: flow over flat cells depressions in DEM Define depressional catchments, attributes: volume, area, depth depressions not artifacts, not “spurious” pits 1 2 3 3 9 2 1 8 5 6 7 4 3 2 1 1 ELEVATION 2 3 4 7 5 6 8 9 North-South 1 2 East-West 2 1 5 Role of hydrological topology

  28. Depressions are considered: Real landscape features define local top & bottom where: water slows down water ponds sediments deposited Establish local context Procedures need to: Recognize depressions selectively remove retain all information about elevation of all cells below pour point raised to pour elevation new “reversed” flow directions initial local direction of flow Divide 5 5 5 5 Pit Center Significance of depressions 800 m 400 m

  29. Depressional watersheds For each watershed record Shed Number & Shed Area Shed# of the Shed(s) it drains to For each depression record Pit Location (row, col) Pit Elevation (m) Pit Area (m2) & Volume (m3) For each pour point record Pour Elevation (m) Pour Point Location (row,col) Neighbour Location (row,col) C A B Pour Elev 2 Pour Elev 1 C A B A B SHED NO SHED AREA PIT LOC ROW COL PIT ELEV PIT AREA PIT VOL NEXT SHED POUR POINT ROW COL ELEV NEIGHBOUR ROW COL ELEV 58 171 93 131 726.8 40 6.1 70 96 129 727.1 97 129 727.1 59 134 95 4 722..9 17 1.7 59 95 1 723.0 95 1 723.0 60 30 96 10 722.8 3 0.4 56 94 11 723.0 93 10 722.9 61 108 96 38 722.8 2 0.2 54 94 16 722.9 93 16 722.8 62 8 98 7 726.4 1 0.1 53 95 38 726.5 96 37 726.5 63 870 98 61 722.8 3 0.4 59 96 6 723.0 95 6 723.0 64 1389 99 138 722.9 70 10.4 55 92 63 723.1 91 63 723.1 Computing pit characteristics

  30. C A B elevation of all cells below pour point raised to pour elevation new “reversed” flow directions Pour Elev 2 initial local direction of flow Divide Pour Elev 1 C A B 5 A 5 B 5 5 Pit Center Intelligent pit removal • Remove pits in sequence based on computed pit geometry: • Remove from lowest to highest • Remove into lowest neighbor (A>B) • Define new pit C = A+B+C • Compute attributes of new pit C • Intelligent pit removal process based on reversing flow directions • 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

  31. Depressional catchments Define local window within which to evaluate landform context establish landform position Define 1 repeat cycle ridge to ridge trough to trough wavelength of landscape Establishing landform context 800 m 400 m 800 m 400 m

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

  33. Establish interactions & flows Feature that is lacking in solely geomorphic classifications Essential for modeling ecological and hydrological processes — flows of energy, matter, water; in response to gravitational gradients Important framework for nesting and agglomeration, rolling spatial entities up 3.5km 4 km Hydrological response units

  34. Importance of HRUs in establishing connectivity: 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 sub-catchments From cell to cell C A B Pour Elev 2 Pour Elev 1 C A B A B Hydrological response units 800 m 800 m

  35. Superimpose HRUs on geomorphic classifications Hydrological response units 3.5km 4 km

  36. Require DEMs of: 5 – 10 m horizontal 0.3 – 0.5 m vertical to adequately capture landform features of interest DEMs of : 25-100 m horizontal 1-10 m vertical generalize & abstract the landscape too much; fail to capture significant features of interest Discussion - DEM resolution 25 m DEM WITH 5 m DEM INSERT 900 m 800 m 5 m DEM 900 m 800 m 25 m DEM

  37. 5 M 25 m DEMs WITH 5 m DEMs AS INSERTS 100 M DTED 800 m 50 M 10 M 1:20 K 25 M Optimal resolution for most natural landscapes 1:20 K 25 m What we have! What we need!

  38. Smoothing is essential bring out signal reduce local noise We mainly use: successive mean filters — 7x7 & 5x5 Also have smoothed using: block kriging thin plate spline with tension Interested in: wavelets, Fourier transforms Discussion - abstraction & smoothing DEM NOT FILTERED DEM FILTERED

  39. Landform types 2nd lowest level in hierarchy human recognition is easy machine recognition is challenging Discussion – human vs. machine strengths • Classifying landform elements versus landform types 800 m 800 m Source: Kocaoglu (1975) • Landform elements • Lowest level in the hierarchy • machine recognition is easy • human recognition is often tedious and error prone

  40. Developing a tool kit Still in initial stages conceptualization proof of concept programming Intent to utilize new data LIDAR, Radar, SRTM Significant features are: multi-scale outputs multiple scales of DEM nested hierarchy Conclusions

  41. Data and observations Field Maps Evidence and hypotheses Experience and knowledge Formulae and evidence rules Beliefs and belief-based rules Place boundaries Classify entities Source: Searle and Baillie (2000) Capturing and applying expert knowledge

  42. Landform classification Expert knowledge & belief Captured using Fuzzy logic Association of mapped soils with landform position Tacit expert knowledge Captured using weighted belief matrices Prediction of salinity hazard Analysis of spatial evidence Captured using probabilities computed from evidence Spatial reasoning: My examples

  43. Compute a series of terrain derivatives We computed 22 Only used 12 Convert terrain derivatives into fuzzy landform attributes Change absolute values Into relative values Based on expert beliefs Landform classification

  44. Convert terrain derivatives into fuzzy landform attributes Landform classification TERRAIN DERIVATIVE FUZZY LANDFORM ATTRIBUTE Profile Curvature Likelihood of being concave in profile

  45. Fuzzy attributes computed from hard terrain derivatives Landform classification

  46. Develop a rule-base Based on expert beliefs Expressed in semantic terms Apply the rule base To convert fuzzy landform attributes to fuzzy landform classes Landform classification

  47. Convert fuzzy landform attributes into landform classes Landform classification Fuzzy landform class (DSH) Fuzzy landform attributes Final hardened landform classes

  48. Fuzzy landform classes from fuzzy landform attributes Landform classification

  49. Allocating soils to landform classes

  50. Allocating soils to landform classes: Background • Soil survey is a paradigm-based science • 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

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