Extensive, Strategic Assessment of Southeast Alaska’s Vegetative Resources Willem van Hees, Bert...

Extensive, Strategic Assessment of Extensive, Strategic Assessment of Southeast Alaska’s Vegetative Southeast Alaska’s Vegetative

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Willem van Hees, Bert MeadWillem van Hees, Bert Mead

Pacific Northwest Research Station, Forest Inventory and Pacific Northwest Research Station, Forest Inventory and Analysis, Anchorage, AKAnalysis, Anchorage, AK

•FIA and R10 – Historic perspective

•Roles of FIA inventory

•Inventory description

•Analysis of inventory data

•FIA and R10 – Historic perspective

•Roles of FIA inventory

•Inventory description

•Analysis of inventory data

•1950’s - 60’s PNW – Alaska Forest survey Unit, Juneau, inventory, some remeasurement

•1970’s PNW, Forest Inventory & Analysis, Juneau, reinventory; FIA/R10 MOU

•1978 PNW FIA moves to Anchorage

•1980’s R10, reinventory

•1995 - 2001 PNW FIA, Anchorage, hex-based sample, annual 1/10 remeasurement

•Roles of FIA inventory

•FIA and R10 – Historic perspective

•Inventory description

•Analysis of inventory data

Strategic scale; spatial nature

• Information base for resource management in landscape/owner context

• Can serve NFS regional planning needs• Independent data set to validate and adjust allowable harvest ratesIndependent data set to validate and adjust allowable harvest rates

• Spatial data to model coverages of derived products unattainable elsewhere.Spatial data to model coverages of derived products unattainable elsewhere.

• Nationally consistent core data and analyses across political, administrative, and land ownerships boundaries. Internal Agency information needs include

• Resource Planning Act (RPA) assessmentsResource Planning Act (RPA) assessments

• Status reports on Criteria and IndicatorsStatus reports on Criteria and Indicators

• Special assessments such as the UN - FAO Forest Resource Assessment.Special assessments such as the UN - FAO Forest Resource Assessment.

•Roles of FIA inventory

•FIA and R10 – Historic perspective

•Inventory description

•Analysis of inventory data

Field Plot distributionField Plot distribution

44

22

33

11

Microplot:Microplot:6.8 ft. radius6.8 ft. radius

Subplot:Subplot:24.0 ft. radius24.0 ft. radius

120 ft. between120 ft. betweensubplot centerssubplot centers

Field Plot designField Plot design

Mapped PlotMapped Plot

44

22

33

11ForestForest

NonforestNonforest

Field Plot (re)measurement scheduleField Plot (re)measurement schedule

20032003 initiate annual remeasurement of 1/10 of plots with forested conditions

1995-2001 1995-2001 baseline measurement, all vegetated

Also in 2003: Initiate measurement for Forest Health Monitoring Also in 2003: Initiate measurement for Forest Health Monitoring

Forest Health MonitoringForest Health Monitoring

5 indicators of forest health:5 indicators of forest health:

• Ozone: Plant ozone injury used to adjust ozone emission standards

• Lichens: Sensitivity to pollutants used as an indicator of changing air quality

• Soil: Evaluation of soil physical and chemical properties, erosion and compaction.

• Vegetation: Assessment of abundance and spatial arrangement of all trees, shrubs, herbs, grasses, ferns for biodiversity changes

• Coarse woody debris: Estimates biomass of coarse woody debris, fine woody debris, duff, litter, slash, and fuelbed depths for carbon sequestration, fire models….

•Roles of FIA inventory

•FIA and R10 – Historic perspective

•Inventory description

•Analysis of inventory data

•Analysis of inventory data –

• Woodpile descriptors

• Nonforest vegetation characterization

• Spatial analyses

•Woodpile descriptors: Area of forest/nonforest 

0

1

2

3

4

5

6

7

8

9

10

Forest Nonforest

NFS

Other federal

State & local

Private

Owner Owner Total Total Forest NonforestForest Nonforest

NFS NFS 16.916.9 9.49.4 7.67.6

Other federalOther federal 4.4 4.4 0.60.6 3.83.8

State & localState & local 0.8 0.8 0.40.4 0.40.4

Private Private 0.7 0.7 0.60.6 0.10.1

AllAll 22.9 22.9 11.011.0 11.911.9

----------------------------------million acres------------------million acres------------------

•Woodpile descriptors: Area of timberland/other forest

Owner Owner Total Total Timberland Other forestTimberland Other forest

AllAll 10,995 10,995 4,096 4,096 6,898 6,898

------------------------------------------thousand acres-----------------------thousand acres-----------------------

Other federalOther federal 618 618 6 6 612 612

NFSNFS 9.3559.355 3,423 3,423 5,932 5,932

State & localState & local 390 390 272 272 118 118

PrivatePrivate 632 632 396 396 236 236

0

1,000

2,000

3,000

4,000

5,000

6,000

Timberland Other forest

NFS

Other Federal

State & Local

Private

• Woodpile descriptors: Forest type distribution

•Woodpile descriptors: volume/ac on timberland

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

Ft3

/acr

e

Alaskacedar -hemlock

Lodgepolepine

Mixedconifer

W.hemlock -Sitka spr.

W.redcedar -hemlock

W.hemlock

Sitka spr M.hemlock

Forest type

•Woodpile descriptors: growth and mortality on timberland

0

10000

20000

30000

40000

50000

60000

70000

Th

ou

san

d c

ub

ic f

eet

Alaskacedar -hemlock

Lodgepolepine

Mixedconifer

M. hemlock Sitka spr W. hemlock- sitka spr

W.redcedar -hemlock

W. hemlock

Forest type

GrowthMortality

• Woodpile descriptors: Mortality per acre

Cubic-foot mortality, per acre, Western redcedar/hemlock forest type

20-40

41-60

61-85

•Nonforest vegetation characterization

•Nonforest vegetation characterization: shrub & herbaceous types

0

10

20

30

40

50

60

70

80

thou

sand

acr

es

National Forest State and Local Private

Alder

Alder-w illow

Copperbush

Copperbush-blueberry

Copperbush-salmonberry

Ericaceous

Mixed shrub

Sagebrush-juniper

Salmonberry-blueberry

Sw eetgale

Unknow n

Willow

•Nonforest vegetation characterization: area of low shrub

Cooperative ResearchCooperative Research

Centre for Biodiversity & ConservationCentre for Biodiversity & Conservation

Schools of Biology & Geography, University of Schools of Biology & Geography, University of LeedsLeeds

William Kunin, Steve Carver, Jack Lennon, Simon Corne, Michael Pocock, William Kunin, Steve Carver, Jack Lennon, Simon Corne, Michael Pocock,

Naomi van der Velden & Crewenna DymondNaomi van der Velden & Crewenna Dymond

• Spatial analyses

Research ProgramResearch Program

Development

& testing of New Spatial Analytic Methods

Analysis and mapping of SEAK Forest Data

Basic research in

Macro-ecology

Research in

Forest Biology & Manage-ment

Research ProgramResearch Program

Development

& testing of New Spatial Analytic Methods

Analysis and mapping of SEAK Forest Data

Basic research in

Macro-ecology

Research in

Forest Biology & Manage-ment

Macroecology researchMacroecology research

• Cross-scale analyses of species distributions; extrapolating across scales

• Species-area relationships and their basis in individual species range structure

• Climate & tree spp effects on understory plant spp diversity

Research ProgramResearch Program

Development

& testing of New Spatial Analytic Methods

Analysis and mapping of SEAK Forest Data

Basic research in

Macro-ecology

Research in

Forest Biology & Manage-ment

Forest Biology & ManagementForest Biology & Management

• Biology of core & marginal tree populations

• Testing human impacts on vegetation

– Changes in forest composition after harvest

Core vs. marginal populationsCore vs. marginal populations

• Developed methods for determining the degree of “marginality” of SEAK tree populations

• Results suggest marginal populations become more

specialised in habitat tolerances (Global Ecology & Biogeography 11: 103-114)

Human impacts on vegetationHuman impacts on vegetation

• Analyses of clearcut sites suggest change in forest species composition

• Edge effects of clearcuts difficult to measure (due to position errors)

Research ProgramResearch Program

Development

& testing of New Spatial Analytic Methods

Analysis and mapping of SEAK Forest Data

Basic research in

Macro-ecology

Research in

Forest Biology & Manage-ment

Analysis & MappingAnalysis & Mapping

• Neural net analyses of SEAK data estimating forest characteristics

• Species maps including marginality index

• Biodiversity maps (of trees, understory spp)

Neural Net modelsNeural Net models

• Models trained with SEAK surveys

• Proven potential to outperform commercial LANDSAT image analysis in predicting:– Crown closure– Land cover type– Size/structure

• Application software being developed

Land Cover TypeLand Cover Type

Compared to commercially available post-processed images purchased from Pacific Meridian Resources

Summary comparison Summary comparison (PNN model)(PNN model)

0102030405060708090

100

Crown

cove

r

Crown

cove

r

Stand

size

Stand

size

Land

cove

r

Land

cove

r

PNN approx.

PNN correct

PMR approx.

PMR correct

Research ProgramResearch Program

Development

& testing of New Spatial Analytic Methods

Analysis and mapping of SEAK Forest Data

Basic research in

Macro-ecology

Research in

Forest Biology & Manage-ment

New methods in spatial analysisNew methods in spatial analysis

• Tests of relative effectiveness of various Neural Net models.

• Land cover fragmentation using fractal measures

• Novel methods for testing associations between autocorrelated variables

Future directionsFuture directions

• Neural net analyses could be applied easily to other regions or other issues (e.g. fire risk): a cost-efficient way to generalise from field survey data

• Species interactions at range margins: growth, competition, pathogens