mapping and simulation of heterogeneous ecosystems larry band university of north carolina
Download
Skip this Video
Download Presentation
Mapping and simulation of heterogeneous ecosystems Larry Band, University of North Carolina

Loading in 2 Seconds...

play fullscreen
1 / 33

Mapping and simulation of heterogeneous ecosystems Larry Band, University of North Carolina - PowerPoint PPT Presentation


  • 120 Views
  • Uploaded on

Mapping and simulation of heterogeneous ecosystems Larry Band, University of North Carolina. Assessment of TEM/PEM Approaches: Mapping & Simulation of Heterogeneous Ecosystems Larry Band, University of North Carolina. Presentation Outline.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Mapping and simulation of heterogeneous ecosystems Larry Band, University of North Carolina' - keitha


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
mapping and simulation of heterogeneous ecosystems larry band university of north carolina
Mapping and simulation of heterogeneous ecosystemsLarry Band, University of North Carolina

Assessment of TEM/PEM Approaches: Mapping & Simulation of Heterogeneous EcosystemsLarry Band, University of North Carolina

mapping and simulation of heterogeneous ecosystems larry band university of north carolina1

Presentation Outline

Mapping and simulation of heterogeneous ecosystemsLarry Band, University of North Carolina
  • Overview and assessment of TEM/PEM initiative:
    • Business plan drivers
    • Current spatial data models
    • Need for integrated assessment tools
    • Comparison with similar/related initiatives
  • Presentation of Hierarchical GIS/Ecosystem Models
    • Terrain analytic and remote sensing components
    • Integrated ecosystem, hydrology, climate modeling approach
  • Knowledge based, continuous inference schemes
  • Summary statements, recommendations for extending, implementing PEM initiative
mapping and simulation of heterogeneous ecosystems larry band university of north carolina2

Propositions to Consider

Mapping and simulation of heterogeneous ecosystemsLarry Band, University of North Carolina
  • Drivers for PEM business case may be dynamic, at least in terms of priorities
    • paradigm shifts in natural resource management
  • Measurement and spatial analytical technologies are developing very rapidly
    • remote sensing, wireless monitoring, spatial information and modelling systems, knowledge acquisition and representation
  • Require flexible framework: extensible, interoperable with existing and developing IM technologies, needs
  • PEM is a model: subject to uncertainty of input data, processing - represent uncertainty in results
mapping and simulation of heterogeneous ecosystems larry band university of north carolina3

I. Overview and Assessment

Mapping and simulation of heterogeneous ecosystemsLarry Band, University of North Carolina

Process:

Ecosystem carbon, water, nutrient cycling

Watershed hydrology

Disturbance

Silvicultural operations

  • Drivers/Goals:
  • Timber Supply Inventory
  • Forest productivity
  • Carbon budgets
  • Biodiversity
  • Sustainable management
    • water supply, quality, flooding
    • fisheries
    • sedimentation
  • Silvicultural response

Pattern:

Geomorphic template

Soils/substrate

Topoclimate

slide5

Assessment of TEM Initiative

  • BC Biogeoclimatic classification system an excellent knowledge base for ecosystem mapping and ecoregionalization
    • most critical asset (ecosystem-landscape relations) well developed one of the best (possibly THE BEST) operational knowledge base available in the world
  • Data Model of TEM built on hierarchical cartographic framework of nested scale/minimum mapping area
    • Point-line-area (node-arc-polygon) model: discrete variation, hard boundaries
    • Manual TEM production time consuming, may lack reproducibility
slide6

Assessment of PEM Initiative

  • PEM provides reproducibility, greater production efficiency by automating/formalizing landscape model
    • opportunity tostandardize automated, self documenting BEC implementation
  • Currently designed to reproduce TEM data model
    • produces discrete regions as polygons or raster connected components
  • Ecosystem gradients represented as boundaries or sequence of polygons with minimum mappable areas
    • evaluate alternative data models (e.g. continuous) available with more recent developments in GIS, information capture, inference
current and potential pem goals functions
Current and potential PEM goals/functions
  • Paradigm shift in forest resource management
    • need to provide more integrated ecosystem basis for resource inventory
  • Opportunity to extend beyond inventory to incorporate integrated modelling of critical environmental processes
    • forest ecosystem productivity - including timber production
    • biodiversity, carbon, nutrient cycling and export
    • water supply, sediment delivery
  • Develop integrated assessment and modeling of alternative forest resource/watershed management schemes
issues to consider for pem tem alternatives
Issues to Consider for PEM/TEM Alternatives
  • Data Models: Continuous vs. discrete landscape paradigms
    • boolean or fuzzy logic/spatial representation
    • matching resolution to dramatically improved information base and storage/retrieval technology
    • Layer-based vs. entity (object) based
  • Top-down vs. bottom up approaches to ecosystem mapping, ecoregionalization, ecosystem modeling
    • Region splitting or region aggregation
    • Representation of internal (sub-entity) heterogeneity (in form and function)
ii hierarchical integration of gis ecosystem models top down and bottom up approaches
II. Hierarchical integration of GIS/ecosystem models: top-down and bottom-up approaches
  • Landscape level: Forest ecosystem patterns conditioned by slope, aspect, elevation, substrate, hydrologic flowpaths effects on carbon processes, disturbance regime, regeneration - amenable to bottom-up (aggregation) approach as well as incorporation of system dynamics
  • Regional level: Synoptic climate and physiographic province level controls on large scale forest ecosystem patterns - amenable to top-down (disaggregation) approach
slide10

Example of a top down ecoregionalization:

Satellite driven simulation of Provincial patterns of forest productivity (gm.C.m-2 .yr-1) - calibrated with landscape level estimates:• Use as ecosystem metric (along with drivers - climate, substrate, terrain) to regionalize ecosystem function

• Regression tree model (RT) summarizes nested effects of climate, substrate, terrain drivers on productivity - RT nodes become ecosystem units with pop. statistics (e.g. means, variances, covariances of NPP, T, pcp, elev., slope, … known)

• Pathfinder resolution (1-8km) adequate down to level of ecodistrict - inadequate below

slide11

• Satellite driven simulation of forest productivity at the provincial level guides recursive decomposition of the landscape (productivity field) into nested, functional ecoregions with minimum w/n unit variation in productivity, and productivity drivers (terrain, climate, …)

• Each region has consistent documented relations between productivity and abiotic drivers

• Aggregation/disaggregation methods fully documented and reproducible

slide12

Alternative or Complementary Bottom-Up Approach

Regional HydroEcological Simulation System:Formal geomorphic hierarchy for landscape representation Surface attributes and processes bound at specific class levels in nested landscape model

slide13
Data model and data flow resembles PEM framework: GIS based hierarchical landscape ecosystem representation
slide14

Landscape partitioned successively into watersheds (basins) and hillslopes arranged and organized by drainage network. Hillslopes partitioned into functional patches (can be grid cells as special case) containing soil, vegetation attributes and processes

Ability to associate dynamic (process) behaviour with specific class levels

e.g. radiation, carbon, nutrient cycling, runoff, soil moisture, ...

slide15

Explicit and index distribution approaches for hillslope hydrology and ecosystem characteristics and processes Higher order units (subwatersheds, basins, landscapes aggregated from lower order units)

slide16

3-D Perspective of HJ Andrews Terrain

1. Extract/synthesize geomorphic hierarchy

2. Map/infer canopy, soils information

slide17

Hierarchical partitioning of Lookout Ck into geomorphic units - coarse stream network version

slide18

Simulate/map any model variable (input/output, storage) at aggregated time steps (days-decades), and scales (patches-landscape)

Distribution of August ET over HJ Andrews landscape simulated and mapped at the Patch Level

slide20

Knowledge Documentation

Soil-Landscape Model Building

S <= f ( E )

Inference Engine

3. Knowledge based, continuous inference schemes

  • Soils typically unavailable at similar resolution to terrain, canopyand do not occur as “pure” entities
  • Soil Inference Model (SoLIM) developed for estimation of soil fields using GIS/soil-landscape model
  • Methods extensible to ecosystem classes as PEM framework

Hardened polygon maps

Similarity maps

Soil attributes

GIS/RS information

slide21

resembles

resembles

at Sijk

resembles

resembles

at Sij2

at Sijn

at Sij1

as

expressed

SoLIM inference of fuzzy membership in multiple soils at the same location: can handle soil/ecosystem mixtures

Category 1, Category 2, …, Category k, …, Category n

...

Soil at point (i,j)

Similarity Vector (S)

(Sij1, Sij2, …, Sijk, …, Sijn)

(Zhu and Band, 1994, Can. J. Rem. Sensing)

slide22

Case-Based Reasoning

Data Mining

Local Experts’ Expertise

Artificial Neural Network

Spatial Distribution

Perceived

as

Cl, Pm, Og, Tp

Inference

(under fuzzy logic)

Similarity Maps

G.I.S.

SoLIM KB approaches need to be flexible to take advantage of differing data availability/land characteristics/ecosystem models and driving questions for different biomes

Soil landscape model:

currently uses info from GIS, RS, extend to include model predictions

S <= f ( E )

(Zhu and Band, 1994, CJRS)

slide23

GIS/RS/Model Techniques

Knowledge Acquisition

Fuzzy Inference Engine

Sij(Sij1, Sij2, …, Sijk, …, Sijn)

Methods can relate both primary environmental data (slope, aspect, soil type) and model derived data (soil water stress indices) to infer soil/ecosystem classes or properties

(Knowledgebase)

(GIS Database)

(Similarity Representation)

(Zhu, 2000)

slide24

Catenary Sequence:

Soil series or phases closely associated and intermixed as inclusions, complexes or intermediate forms

(Zhu, 2000)

slide25

Grey scale mapping of membership functions indicates gradational variation of continuous fields and uncertainty of location and attribute

Can use membership functions to estimate attribute values intermediate to soil central concepts

(Zhu, 2000)

slide26

Inferred soil patterns with conventional boundaries

Elevation

Planform Curvature

Geology

Slope Gradient

Field validation of inferred soil properties shows comparable to better results to those derived from standard soil maps in areas with moderate to high relief

Up Stream Drainage

Profile Curvature

(Zhu, 2000)

slide28

Overview of the Analytical Framework and Research Components

Input

Methods

Variables

Analysis

Results

Evaluation

Vegetation & Soil

Parameters

Resource

&

Temperature

Gradients

Predictive

Accuracy

Hydro-Ecological

Modeling

Climate Data

Digital Terrain

Data

Predictive

Vegetation

Maps

Statistical

Modeling

Residual

Analyses

Field Sampling

Species

Composition

Species

Response

&

Life-History

Models

Substrate Rockiness

Geologic Structure

Measurements

Substrate

&

Geological

Factors

Ecophysiological

Literature

GIS Modeling

& Map

Digitization

Lithologic

Formations

Satellite Imagery

Meetenmeyer and Moody, 2001

slide29

Bonferroni Multiple Comparisons

Days of Drought

Stress (1987-90)

slide30

IV. Comments and Recommendations

  • British Columbia’s Biogeoclimatic Ecosystem Classification provides a state-of-the-art knowledge base for supporting TEM/PEM initiatives
  • Initial work in different regions is impressive and reflects driving questions and technology available - both of which change very rapidly
  • Forest management paradigm shift requires more integrated, flexible tools that can address ‘multiple use-multiple impact’
  • Development of continuous classification systems and integrated landscape ecohydrological models provide opportunities for building onand extending PEM/TEM initiative
slide31

• Similar to PEM: accuracy and robustness of SoLIM-like approach highly dependent on knowledge base and implementation - • A limitation of relying on primary GIS variables (e.g. elevation, slope, aspect) is the production of multiple instances of the same entity (same soil series or forest ecosystem on south and north facing slope, different elevations) - concept of ecological equivalence• More direct variables (temperature, pcp, radiation load), or direct physiologic limitations on NPP, NEP could be derived from distributed hydroecological model

slide32

Need to develop menu of standardized techniques for knowledge capture, representation and inference methods to deal with range of ecosystems, available data, practitioners and driving questions

  • Choice of KB development dependent on availability of local data and expertise
  • Methods of assessing and representing data and inference method uncertainty
  • Software system needs to be extensible and built for interoperability to incorporate existing and developing models appropriate for range of ecosystems and issues
slide33

• Fuzzy inference methodology should be easily extensible to ecosystem entities:• Fuzzy PEM (or similar approach) would support representation of spatial and attribute uncertainty, take advantage of existing state-of-the-art forest ecosystems knowledge base to produce continuous classifications

• Make use of derived direct biophysical (e.g. climate, soil water) and physiological fields (for reference canopy) to gain more insight into what are the direct controls on ecosystem form and function

• Use enhanced attribute fields of forest ecosystem patterns to couple with dynamic models of critical environmental processes - extend ecosystem modelling to include greater treatment of ecosystem dynamics

ad