Embed Size (px)
Citation preview
Spatial Forest Modeling
An overview of key concepts illustrated with case study
examples
Overview
• Why spatial modeling?• Overview of spatial modeling for forest planning
– Types of spatial models and their uses• Design issues and limitations of models for
forest management planning– What can the models do, and how do they do it
• Case studies– Management planning in Manitoba– Policy analysis in Northwest Ontario
Why use spatial models?
• Deal with landscape holistically• Use GIS-based technology to save time and
improve planning• Detailed modeling is complex and too difficult for
human planners. Use powerful computers and advanced algorithms to assist planners– Maximum efficiency of forest resource– Prevent foreclosure of future options– High quality solutions at a lower cost
Landscape ecology• Recognize the interplay between pattern and
process across the forest• Avoidance of forest fragmentation• Follow natural range of variation
– Structure, composition and spatial distribution
Treatments Values
Highly temporal problem
• Changes to landscape in one period will impact on subsequent periods
• Complexity exceeds capacity of human problem solving– Yet still needs to be
addressed
Period 1Period 2
Period 3…
Period n
Treatments now
Values derived from pattern in future landscapes
Operational planning issues
• Overcome unreasonable assumptions made in aspatial planning models– Cannot harvest ‘everywhere’ in all periods
• Forest access and road layout– Timing of access development
• Harvest openings– Harvest must be consistent with regulations
and economically reasonable
Hierarchical planning
Strategic planning
Aspatial wood supply model
Discontinuous hierarchy
Operational planning
GIS-based tools or automated layout
software
Strata-based harvest schedule
Feed
back
on
impa
cts
Strategic planning
Integrated hierarchy
Operational planning
Feed
back
on
impa
cts
Full spatial model
Reality is lack of adoption. • No explicit requirement• Shoot the messenger• Regulatory drag
• Common criticisms about spatial modeling technologies– Too complicated
• Solved by implementing wizards and other assistive technologies
– Too expensive• Translators to import models directly from aspatial world• Custom data prep tools: highly specific, highly efficient
– Datasets not accurate enough to support detailed spatial modeling
• Need to make decisions regardless
Types of spatial forest modelsAssessment Assist in the quantitative assessment of a
predefined landscape option with respect to specific values
Neptune, WRENSS, OLT
Landscape Simulating the development of very large natural landscapes over long time horizons, especially under the influence of natural disturbance dynamics
Simulating the implementation of harvest and silviculture on large forests over multiple rotations
Assist in generating map-based harvest sequences from results of aspatial strategic analysis
Assisting with engineering calculations related to short-term forest operations activities on individual sites
Forest planning
LANDIS, BFOLDS, SELES
Patchworks, FSOS
Stanley, ArcMap macros
Automated layout
Operation planning
Cable yarding layout
Design
Degree of spatial integration in forest management planning models
• Interactive, spatially-explicit strategic planning model
• Fine-grained resolution of management activities• Generates explicit, detailed harvest schedule for
long strategic time frames
Time scale
Spatial detail
Short termLess than one rotation
Long termMultiple rotations
Fully spatialstrategic model
Aspatial strategic model
Layout and operational
planning
CoarseAggregate
FineStand level
Semi-spatial strategic model
Computational
Complexity
Characteristics of a spatially explicit forest planning model
• Long-term strategic modeling tool incorporating high-resolution view of forest estate, with long time frame required to assess sustainability of actions.
• Extends a GIS-type database of forest conditions and road infrastructure– One record per polygon describing location,
topological relationship to neighbours, additional stand-level attributes
– Polygons generally homogeneous– Spatial road network data that describes existing and
potential connectivity from polygons to processing facilities
What is Patchworks?• Generic spatial modeling toolkit• GIS interface allows ‘live’ transparent access to simulation variables
in running model• Many wizards and visual tools to simplify the generation of modeling
scenarios, the interpretation of results, and the preparation ofinformation products
• ‘Point-n-click’ to adjust operating plan within strategic context, with dynamic real-time calculation of long-term cumulative effects
Modeling features• Interactive (GUI and wizards)
– Suitable for exploration• Many wizards to simplify complex tasks• Scriptable using a convenient high level language
– Use scripts to automate repetitive tasks• Highly customizable report writer generates web-ready
output– Easily develop custom summaries of simulation results as
charts, maps, tables• Generic data query tools
– Pivot table, relation join, import, export• Easy connection to other modeling tools using standard
file formats (shapefile, csv)
Fully temporal modeling database
• Shows time stream for all polygons through entire planning horizon
• Forest condition for all planning periods are concurrently held within model (virtual database)
• Instantaneous access to stand attributes in any planning period
Aggregate indicators• Typical of all wood supply models• Age dependent relationships describe stand characteristics and how
they change over time• Separate age-based curves for extant features and extractive
benefits• Subtotaled by geographic zones
Semi-spatial indicators
FeaturesExtant conditions
ProductsExtractive benefits and costs
Amount of conditionwithin polygon
(area, volume, equivalent habitat area, CWD, etc)
Amount of itemremoved
(area, volume)
Type and amountof treatment
(area)
Cost or benefitof treatment
(volume of product, $)
Management treatments• Management treatments can be applied to individual
polygons– Polygon is treated entirely or not at all– Post-treatment conditions reflect appropriate response to
treatment• Range of treatment types supported
– Final harvest (clearcut or variable retention)– Spacing, thinning– Selection and shelterwood systems
• Multiple treatments and mid-rotation treatments
Predefined spatial design
• Riparian reserves and other areas of concern are often predefined according to regulations– Pre-designated in
model as no-go zones• Deferred area plan
often pre-designed
Spatial design chosen by model
• Location of treatments, openings, disturbance patches, residual retention selected by model– Model selects location,
timing and types of treatments in order to achieve management objectives
Allocation unit• Operational block
– Pre-engineered harvest block
– Cannot harvest adjacent unit until after green-up
– All possible block shapes defined in advance
– More upfront work, will need to be redone if spatial policies change
• Inventory polygons– Use sub-division and
aggregations of inventory polygons
– Model may choose one or more adjacent polygons in order to form an opening
– Model keeps track of adjacent openings
– High degree of flexibility to design alternate landscape patterns using small building blocks
– Less upfront work
Neighbourhood influence
• How does the silvicultural state of neighbouring polygons influence model behaviour?
No impact Semi-spatial model
Treatment timing and intensity
The model will determine treatment eligibility based on the condition of neighbouring stands- Greenup delay, maximum opening size, interspersion of types
The model will determine the growth and development of a stand based on the condition of neighbouring stands- E.g., Success of natural regen based on proximity to seed source- Only implemented in small-scale research models
Stand dynamics and treatment response
Patch definition• Contiguous area of similar composition• Requires patch criteria (young forest, old forest, current harvest
openings) that identifies polygons that can join into patches• Requires explicit topological relationships defining neighbours• Define size classes of interest
• Multiple patch targets can be active simultaneously
• Variety of patch metrics indicators– Amount of area by size class– Percent area by size class– Frequency by size class– Percent frequency by size class– Shape
Patch definition• Patch membership criteria may be simple (absolute age based) or
complex (stand matches an ecological condition)
• ‘Proximal’ topology, includes more than simple touching relationships
• Recursive algorithm identifies all topologically connected polygons that meet patch criteria
Transportation definition• From landing to mill type
of formulation– Landings defined by
closest access point to a polygon
– Transportation network defined by well-connected mesh of road segments
– Mill locations connected to road network
– Mills can be specialized for the types of products that they utilize
Transportation model• When a stand is
harvested, the product is allocated to an eligible mill (connected by network, accepts the product type)– If harvest generated
multiple products, each will be sent to an eligible mill
• If products cannot be delivered to eligible mills, the stand will not be harvested
Edge target• Interspersion metric
– How are forest types located relative to each other?
• Length of edge within forest zone• Sum of edge between stands multiplied by contrast
weight edge matrix
Mature conifer Mature hardwood Immature conifer Immature hardwood
Young 1 1 0 0
Immature hardwood
1 0 0
Immature conifer 0 1
Mature hardwood 0
Setting targets• Any indicator can be set as a target• Targets can have minimum or maximum thresholds• Values can vary by planning period• ‘Weight’ values can be set to adjust the relative
importance of the target w.r.t. other targets
Current value of simulation
Desired minimum target level
Each bar represents one planning period
Goal programming formulation• Multiple factors considered simultaneously• All factors of interest expressed as terms in the
objective function• Solution never mathematically infeasible• Constraints are objectives having high relative weights• Weights are subjective!
1. Measure difference between goal and outcome
2. Multiply by weight3. Sum over all periods and
all targets4. Minimize result
Solution algorithms• Problems too large for OR techniques such as
LP/IP/Mixed-IP
• Stochastic meta-heuristics are the algorithms of choice– Simulated annealing, taboo search, genetic
algorithms– Adjust schedule of treatments
• Highest values/lowest cost for extractive benefits yielding desired current and future forest structure and landscape pattern
Transportation optimization• How to transport forest product
to mill destinations most efficiently?
• Scheduler will propose and assess impacts of various alternate road construction and transportation options– Select road transport options that
increase goal achievement (build, haul, maintain)
• Result is compact efficient road footprint
Patch optimization• Set targets on patch characteristics
– Area, frequency, shape
• Scheduler will form allocation that increases goal achievement
• Trade-off with other resource values depending on limits to resource and weight values
• Compliance with regulatory goals may need validation using assessment model
Case studies
• Long-term forest management plan on forest management unit
• Regional policy analysis of landscape management options
Case StudyLP Canada Duck Mountain Long Term Forest Management Plan
Manitoba
Duck MountainsForest summary• Aspen and mixedwood composition• Older ages due to past fire history• Surrounding agricultural land use
Model summary• 67,519 forest polygons• 200 year planning horizon,
40 5-year planning periods• 305 targets, 105 active• Harvest openings and
disturbance patches• Aspen delivered to Swan
River mill
Time since Fire map (Tardif, 2004) Management reserves
Available forest 234,882 haReserved forest 82,041 haNon forest 59,994 ha
NAT_MWD1_N
0
50
100
150
200
250
300
0 50 100 150 200 250 300 350 400
Age (years)
Mer
ch. V
ol (m
3/ha
)
HWD Vol m3/haSWD Vol m3/haTOT Vol m3/ha
Extrapolation – based on Kenkel’ssuccessional trends & SSIs
TSP dataRiding Mtn PSP
data
Long lived yield curves
Targets and indicators• Harvest levels• Growing stock• Carbon stocks• Seral stage stability• Forest unit stability (don’t unmix the mixedwoods)• Water yield• Forest zoning (Triad)• Patch distribution of disturbed forest (several distribution patterns)• Patch distribution of harvest openings (several distribution patterns)• Accelerate harvest in pest outbreak compartments• Cost efficient transportation modelling• Road density and roadless area analysis• Deferral of harvest in areas adjacent to ecologically rare sites
LP’s Scenario Planning FrameworkForest lands
inventoryDEM /
hydrologyGrowth and
yieldSongbird inventory
Community and public values
Net land base
Future forest conditions
Patchworks
Scenario 1Current practices
Scenario 2Biodiversity
emphasis
Scenario 3Natural
disturbance mgmt
Scenario 4Stewardship
zoning
Scenario 5Timber emphasis
Scenario 6Cohort Mgmt
Forecasting tools
Biodiversity effects
Water shedassessment
Climate change and productivity
Heritage resources
Spatial landscape assessment models
Water ResourceEvaluation Non-point
Silvicultural Source
Carbon Model Cultural ResourceInventory System
Assessment models
Output for evaluation, trade-off and risk assessment
Range of scenarios
Management control
Operations friendly
Ramp up harvest, even flow, no guidelines
Current Practices
Mimic status quo rules and regulations
Biodiversity emphasis
Broad range of variability, high retention, older canopy
Stewardship zone
Triad zoning (20-60-20)
Natural disturbance
mgmt
Natural disturbance pattern
Patch size distribution
n/a Narrow Broad Depends on zone
Mimic natural
Retention No retention 8-12 / ha 16-24 / ha Depends on zone
8 – 12 / ha
Opening size limit
n/a < 100 ha < 100 ha Depends > 100 ha
Road budget Lowest cost Moderate n/a n/a n/a
Cover type constraints
n/a Stability Stability Depends Stability
Silviculture Conventional Conventional Cohort mgt Depends Conventional
Etc...
Connections with external Connections with external assessment modelsassessment models
• Patchworks was a single component of a larger information system•• Spatial Landscape Assessment Spatial Landscape Assessment ModelingModeling (interspersion), (interspersion), Water
Resources Evaluation of Non-Point Silvicultural Sources (WRENSS)• PW developed forecasts of future forest conditions, to distribute to
other models in a standard format.
ForestSongbird
Point DataStratified Interpolation
FLI
PatchworksLandscape
ScriptingLanguage
Forest @Year 0
TSP, PSP,Bird Habitat
Plot Data
Resource SelectionFunctions
Logistic RegressionHabitat Yield
Curves
Interpolated BirdDensity Map(@ Year 0)
ForestSuccession
Predicted Patterns ofHabitat Occupancy
Snags, Height
Forest @Year t
• Sensitivity analysis to examine broad range of options• Output products to assist interpretation
Posters
Web-based reports
Map animations
Long-term planning on a forest management unit
• Patchworks was able to simultaneously integrate wood supply, biodiversity, water yield, and carbon into the modelling runs for multiple scenarios (indicators and targets).
• Long-term spatial layout factors can be integrated within the long-term forest management planning process. Explicit representation of these issues increases the confidence in the sustainability of the plan.
• Addressing multiple objectives and issues within a single consistent framework greatly assisted with scenario planning, public involvement, and conveying information about the trade-offs and choices .
• Explicit allocations greatly increased the transparency of modelling process (could show the what, where when of activities).
Next steps for this type of planning process
• ‘Operationalize’ the short-term schedule– Clean up draft allocation boundaries based on consultation with
operational forester– Substitute deferred blocks from previous plan– Ground truth using operational cruise
• Focus on operational efficiencies– Best allocations under reduced industrial demand– Develop economically efficient operating plan while still meeting long-
term sustainability goals – Test short-term operating plan within context of long-term strategic plan
• Provide framework for implementation monitoring– Close adaptive management loop
Case Study3W Ecoregional Landscape Guide Assessment Project
Northwestern Ontario
Project Background• The 3W ecoregional
model was built to evaluate implementation of different landscape guide objectives
• Compared achievement of guide direction with the impact on economic indicators and residual forest conditions
Model Background• The 3W model pushed
the boundaries of spatial forest modeling within one cohesive simulation:– 11 forest management
units (8,008,635ha)– 11 mill processing
destinations with a fully connected road network
– 1000 indicators, 100+ targets, 30 planning periods
– Multiple patch targets with large size ranges
We were able to create a reasonable approximation of the forest conditions and economic indicators in the region for a strategic level analysis of broad landscapetrends.
Objective BackgroundThree major categories of objectives:1. Common forest management
objectives (wood supply and costs)2. Landscape controls (opening sizes,
disturbance patches, protected areas)
3. Landscape objectives (pattern, old growth, caribou, young forest, etc.)
Landscape objectives developed from BFOLDS analysis.
• Raster-based stochastic landscape disturbance model.
• Monte-Carlo simulation used to estimate the simulated range of natural variation (SRNV).
• The SRNV was then used to establish objectives within the Patchworks model.
3W Analysis• Hundreds of scenarios
were tested, based on dozens of factors that could be individually adjusted to control hundreds of targets.
• Focused sensitivity analysis for FMU and regionally based Guide Test scenario variants.
Analysis TrendsRegional Wood Supply
Regional wood supply matrix based on mill product quotas supplied by multiple FMU operators.
Provided several different objective variations:
• Mill quotas from region• Mill quotas from FMU• Anywhere from anywhere
2006 NWR Wood Flow Matrix
SPF Supply in 1000 m3/yr Abitibi Fort Frances
Abitibi Thunder
Bay
Atikokan Forest
Products
Bowater Saw Ignace
Bowater Pulp
Bowater Saw
Thunder Bay
Domtar White River
Dubreuil Forest
Products
Great West Timber
Kenora Forest
ProductsLecours LKGH
Fort Frances
Thunder Bay Atikokan Ignace Thunder
BayThunder
Bay White River Dubreuil Thunder Bay Kenora Hearst Red Lake
Destination ID 7 14 5 2 13 19 28 17 10 30 12NWR Crown SupplyArmstrong (05-10 FMP) 354Black Sturgeon (01-06 FMP) 264Caribou (02-07 FMP) 261English River (04-09 FMP) 135 93 225 200Kenogami (05-10 FMP) 85Lake Nipigon (01-06 FMP) 176 252Ogoki (03-08 FMP)Pic River Ojibway (01-06 FMP) 126Spruce River (01-06 FMP) 250Total Crown Supply/MROL 0 250 135 0 354 489 176 126 806 0 85 0
Specific quota from FMUTo specific destination (restrictive)
Mill quota from anywhere in region (less restrictive)
Wood supply trends
• Scenario variations of regionally based or FMU based landscape objectives and variations in wood supply formulation.
• Each individual management unit had a unique set of spatial and temporal restrictions that the model was forced to work around.
• Removing political boundaries allowed the model to fill in more ‘gaps’ by looking elsewhere to satisfy a mill quota, a competing landscape guide objective or landscape control (patch size, etc.)
Regional LG objectives FMU based LG objectives
ObjectivesLandscape Guide SRNV
Taken from Monte Carlo simulation using BFOLD landscape model
Objectives included:– Area of young forest– Landscape classes– Old growth– Caribou habitat– Area of conifer– Distribution of young forest patches
Variation in spatial scale for SRNV objective sets:– Regional sum (entire 3W)– FMU sum (apply objectives for
individual management units)
SRNV represented in reports as a ‘box and whisker’indicator, with scenario outcome shown in red.
Several variations were used to test objective values for
scenarios (min& max, inter-quartile range, outliers
excluded).
Landscape guide trade-offs
• Some LG indicators had dependencies on preserving the amount of older conifer in the region or FMU
• The goal was to maintain or move toward the SRNV over time.
• These objectives were in direct competition to the achievement of conifer wood supply.
• Required an extensive analysis to balance and trade-off with the wood supply in the region.
Guide Test scenario example with management unit scale objectives
Spatial landscape patterns
• Creation and maintenance of SRNV landscape patterns.
• Spatial pattern controlled by young forest patch size, harvest patch size, caribou deferrals in recovery zones.
• Transportation expenditure objectives were a powerful tool to help control the resulting spatial pattern on disturbed and residual forest by controlling access over time.
• The model was sensitive to changes in transportation objectives (delivered wood costs) due to the large area and long travel distances. Lots of wood, but not necessarily affordable.
Guide test regional scenario landscape class objective and 50year pattern map.
Regional-scale operational economics
• Operational economics add realism to the analysis and are very important!• The project illustrated the idea that broad landscape level goals are
achievable… but significant impacts on industrial economics occurred with some implementation policy options.
• These impacts could not have been be estimated without an explicit spatial model that included operational economics simultaneously with wood supply and landscape goals.
Potential of a spatially explicit regional scale wood supply model:• This dataset (and similar regional models) have the potential to be used for
further studies.• There are many operators in the region, and a complex pattern of wood
supply allocation based on a legacy of ownership.• The industry is going through significant change in ownership, utilization
and consumption, and there is a tremendous opportunity to examine rationalization options.
