Integration of a Large-Area Invasive Spread Network (LISN) into the ISFS (w/ climate models) Static...

Integration of a Integration of a LLarge-Area arge-Area IInvasive nvasive SSpreadpread NNetwork (LISN) etwork (LISN) into the ISFS (w/ climate models)into the ISFS (w/ climate models)

Static or fixed variables

Dynamic variables

General OutlineGeneral Outline

1.1. NASA Invasive Ecological Forecasting NASA Invasive Ecological Forecasting (include Jackie’s thesis and other work)(include Jackie’s thesis and other work)

2.2. Review original GYCC projectReview original GYCC project3.3. Details of the NASA project and some Details of the NASA project and some

initial results from last two summersinitial results from last two summersa. preliminary summary of field dataa. preliminary summary of field data

b. preliminary analysis of RS datab. preliminary analysis of RS data

4. Approaches to mapping WBP blister 4. Approaches to mapping WBP blister rust and mountain pine beetlerust and mountain pine beetle

Team, Partners/CollaboratorsTeam, Partners/Collaborators

• Bob Crabtree, PI, Yellowstone Ecological Research CenterBob Crabtree, PI, Yellowstone Ecological Research Center• John Schnase, co-I, NASA-Goddard Space Flight CenterJohn Schnase, co-I, NASA-Goddard Space Flight Center• Chris Potter, co-I, NASA-Ames Research CenterChris Potter, co-I, NASA-Ames Research Center• Paul Moorcroft, co-I, Harvard UniversityPaul Moorcroft, co-I, Harvard University• Shengli Huang, co-I, Yellowstone Ecological Research Cntr.Shengli Huang, co-I, Yellowstone Ecological Research Cntr.

Collaborators:Collaborators: Mary Maj, GYCC and GYCC Weeds Committee; Mary Maj, GYCC and GYCC Weeds Committee; Jeff Pettingill, Idaho Falls, Nancy Glenn, ISU; Jack Norland, Jeff Pettingill, Idaho Falls, Nancy Glenn, ISU; Jack Norland, NDSU; Craig McClure, YNP; Rob Mickelsen/Kyle Moore/Bryce NDSU; Craig McClure, YNP; Rob Mickelsen/Kyle Moore/Bryce Fowler/Walt Grows, Caribou-Targhee National Forest; and Bruce Fowler/Walt Grows, Caribou-Targhee National Forest; and Bruce Maxwell/Lisa Rew, MSU; Diana Six, University of Montana; Maxwell/Lisa Rew, MSU; Diana Six, University of Montana; USDA-ARS; David Bubenheim, NASA-ARC; Jeff Morrisette…USDA-ARS; David Bubenheim, NASA-ARC; Jeff Morrisette…

• Josh Harmsen, Ph.D. student, U.C.- Berkeley & YERCJosh Harmsen, Ph.D. student, U.C.- Berkeley & YERC• Jackie Hatala, Staff Scientist, Harvard University & YERCJackie Hatala, Staff Scientist, Harvard University & YERC• Randy Mullen, Ecological Statistician, YERCRandy Mullen, Ecological Statistician, YERC

NASA LISN OBJECTIVE(S)NASA LISN OBJECTIVE(S)• Develop Hierarchical Bayesian models for Develop Hierarchical Bayesian models for

predicting invasive weed (& pathogen) spread predicting invasive weed (& pathogen) spread over time (Ecological Forecasting program)over time (Ecological Forecasting program)

• Investigate 4 species of biological and economic Investigate 4 species of biological and economic

importance: leafy spurge, Canada thistle, blister importance: leafy spurge, Canada thistle, blister rust, and cheatgrass at ecosystem scalesrust, and cheatgrass at ecosystem scales

• Incorporate dynamic climate data, carbon-climate Incorporate dynamic climate data, carbon-climate model output, and 3-D vegetation structure as model output, and 3-D vegetation structure as covariates for future predictionscovariates for future predictions

• Add a large regional ecosystem (GYE) with new Add a large regional ecosystem (GYE) with new agency partners to the existing NISFSagency partners to the existing NISFS

• Validate, compare, disseminate (publish) and Validate, compare, disseminate (publish) and benchmark models and integrate with the NISFSbenchmark models and integrate with the NISFS

Hyperspectral Data Analysis of Whitebark Pine Hyperspectral Data Analysis of Whitebark Pine ((Pinus albicaulisPinus albicaulis) and White Pine Blister Rust ) and White Pine Blister Rust

((Cronartium ribicolaCronartium ribicola) at 4 Sites in the GYE) at 4 Sites in the GYE

• Don Despain, BRD, U.S. Geological SurveyDon Despain, BRD, U.S. Geological Survey• Chuck Schwartz, Leader, Interagency Grizzly BearChuck Schwartz, Leader, Interagency Grizzly Bear

Study Team, Bozeman, MTStudy Team, Bozeman, MT• Ward McCaughey, Director, USFS Northern Rockies Ward McCaughey, Director, USFS Northern Rockies

Experiment Station, Bozeman, MTExperiment Station, Bozeman, MT• Kerry Halligan, Yellowstone Ecological Research CenterKerry Halligan, Yellowstone Ecological Research Center• Bob Crabtree, Yellowstone Ecological Research CenterBob Crabtree, Yellowstone Ecological Research Center

Collaborators:Collaborators: Roy Bergstrom, Shoshone National Forest Roy Bergstrom, Shoshone National ForestDan Reinhart, Yellowstone National ParkDan Reinhart, Yellowstone National Park

Field and Research Assistants:Field and Research Assistants: Sarah Elmendorf, Justin Pidot, Anne Sarah Elmendorf, Justin Pidot, Anne Johnson and Dave Sebonich (Shoshone N.F. crew), Keith Van Johnson and Dave Sebonich (Shoshone N.F. crew), Keith Van Etten, Colby Gardner, David Bopp, and Matt Jones (data mgmt)Etten, Colby Gardner, David Bopp, and Matt Jones (data mgmt)

ObjectivesObjectives Can analysis of hypersectral Can analysis of hypersectral transects, combined with field validation:transects, combined with field validation:

(1)(1) provide a detailed map of WBP condition, provide a detailed map of WBP condition, including the observed symptoms of healthy including the observed symptoms of healthy green, incipient flagging or chlorotic stress, red green, incipient flagging or chlorotic stress, red needle flagging, and dead (branches and snags)needle flagging, and dead (branches and snags)

(2)(2) provide a detailed map of WBP distribution provide a detailed map of WBP distribution including the percent composition of WBP versus including the percent composition of WBP versus other conifer species,other conifer species,

(3)(3) provide recommendations with regard to field provide recommendations with regard to field methods and approaches, accuracy, spatial scale, methods and approaches, accuracy, spatial scale, cost, and detection limitscost, and detection limits

GYE StudyGYE Study Sites N = 4 Sites N = 4

NameName Symptom Symptom LevelLevel

Dead IndianDead Indian HIGH – LimberHIGH – Limber

Red LodgeRed Lodge MEDIUMMEDIUM

Tom MinorTom Minor LOWLOW

Daisy PassDaisy Pass CONTROLCONTROL

Source: http://makalu.jpl.nasa.gov/html/img_spectroscopy.html, Aug 12, 2000

What is Hyperspectral Imagery?What is Hyperspectral Imagery?

HyperspectralHyperspectral and and High spatial resolutionHigh spatial resolution combined (“H2 imagery”) provides more combined (“H2 imagery”) provides more information on variables at finer scalesinformation on variables at finer scales

1616pixelspixels

13 pixels13 pixels

True color 30 m LandSat imageTrue color 30 m LandSat image

6 bands6 bands

399 pixels399 pixels

472472pixelspixels

Hyperspectral 1 meter imageHyperspectral 1 meter image

MNF Bands 7 (invert), 6, 1 MNF Bands 7 (invert), 6, 1 Lamar River, 8/03/99Lamar River, 8/03/99Image by W.A. MarcusImage by W.A. Marcus 128128

bandsbands

Tree statusTree status DBH size classDBH size class Branch size Branch size classesclasses

* 1 - Healthy (needles * 1 - Healthy (needles abundant and green)abundant and green)

Seedling <1/2 m tallSeedling <1/2 m tall <1'<1'

* 2 - * 2 - Stressed (needles Stressed (needles sparse, often sparse, often discolored)discolored)

Seedling/Sapling >1/2 Seedling/Sapling >1/2 m tallm tall

1-3'1-3'

3 - Dead (all or nearly 3 - Dead (all or nearly all needles red)all needles red)

Sapling 2-10 cm DBHSapling 2-10 cm DBH 3-5'3-5'

4 - Snag (needles gone)4 - Snag (needles gone)

Pole Timber 10-25 cmPole Timber 10-25 cm >5'>5'

Mature Timber 26+ cmMature Timber 26+ cm

* Later added two intermediate categories, 1.5 and 2.5* Later added two intermediate categories, 1.5 and 2.5

red/dead pixels red/dead pixels

total mapped 254 1016mapped/definite 247 988mapped/possible 7 28unmapped/possible 6 24mapped/absent 0 0unmapped/definite 0 0producers accuracy 95.00%users accuracy 97.24%

Field Validation of red/dead (pixel level)Field Validation of red/dead (pixel level)

This became convincing for me during 2001 field validationsThis became convincing for me during 2001 field validations

Conifer Disease Detection:Conifer Disease Detection: Red/Dead Flagging Red/Dead Flagging

Overall = 98 %;Overall = 98 %; User’s = 97 %, Producer’s = 95% User’s = 97 %, Producer’s = 95%

NOTE: the large NOTE: the large patches of red patches of red pixels are pixels are confirmed MPB confirmed MPB and the small and the small groups of red groups of red pixels are mostly pixels are mostly blister rust blister rust infestations (red infestations (red and dead and dead flagging).flagging).

Species Identification: Mapping Whitebark PineSpecies Identification: Mapping Whitebark Pine

2

2

2

Conclusions:Conclusions:

1.1. HRHS can accurately determine forest health parameters HRHS can accurately determine forest health parameters (healthy and declining status categories of WBP) based on (healthy and declining status categories of WBP) based on biophysical constituents.biophysical constituents.

2.2. HRHS can accurately map symptoms of blister rust (red HRHS can accurately map symptoms of blister rust (red and dead branches and trees).and dead branches and trees).

3.3. The distribution and clumping of pixels were clearly The distribution and clumping of pixels were clearly indicative of bark beetle kill vs. blister rust.indicative of bark beetle kill vs. blister rust.

4.4. HRHS has the potential of an early warning indicator.HRHS has the potential of an early warning indicator.

5.5. HRHS has a very good potential to accurately map WBP.HRHS has a very good potential to accurately map WBP.

6.6. Field sampling must accompany aerial surveys (can Field sampling must accompany aerial surveys (can combine collection of training data and ground-truth).combine collection of training data and ground-truth).

7.7. Ecosystem-wide transect surveys are cost-effective when Ecosystem-wide transect surveys are cost-effective when compared to ground efforts (although overall expensive).compared to ground efforts (although overall expensive).

Integration of a Integration of a LLarge-Area arge-Area IInvasive nvasive SSpreadpread NNetwork (LISN) etwork (LISN) into the ISFS (w/ climate models)into the ISFS (w/ climate models)

OBJECTIVE(S)OBJECTIVE(S)

(1) Develop Hierarchical Bayesian models for (1) Develop Hierarchical Bayesian models for predicting invasive spread over time . . . predicting invasive spread over time . . .

OROR

What can we learn from invasive time series What can we learn from invasive time series data when we match it with a rich set of data when we match it with a rich set of covariate data?covariate data?

example covariatesexample covariates = precipitation patterns, = precipitation patterns, disturbance type, habitat type, soil moisture, soil type, disturbance type, habitat type, soil moisture, soil type, slope, aspect, productivity, canopy coverage, slope, aspect, productivity, canopy coverage, elevation, etc….elevation, etc….

Hierarchical-Bayesian Model Structure: Hierarchical-Bayesian Model Structure: (2 parts)(2 parts)

(1)(1) Data Model (detection, measurement, Data Model (detection, measurement, observation, uncertainty, etc.)observation, uncertainty, etc.)

(2)(2) Process Model – that links to (1)Process Model – that links to (1)

• Allows uncertainty, expert opinionAllows uncertainty, expert opinion

• Mechanistic (dose-response relationships)Mechanistic (dose-response relationships)

• Probabilistic with dynamic covariatesProbabilistic with dynamic covariates

• Population Demography model chosen as Population Demography model chosen as the process-based modelthe process-based model

dN/dT (dN/dT (x, tx, t) = (1) dispersal (two stage)) = (1) dispersal (two stage)

(2) fecundity(2) fecundity

(3) available space (DD) (3) available space (DD) and competitionand competition

(4) mortality(4) mortality

Demographic-Process Model StructureDemographic-Process Model Structure(from time-series, repeat plot/transect data)(from time-series, repeat plot/transect data)

* Predictive models require matching covariate data* Predictive models require matching covariate data

Average year of HWA infestation computed from 100 Average year of HWA infestation computed from 100 stochastic runs of the demographic spread modelstochastic runs of the demographic spread model

From “Future Considerations For Monitoring Whitebark From “Future Considerations For Monitoring Whitebark Pine: Moving toward Model-based Inference” [GRYN]Pine: Moving toward Model-based Inference” [GRYN]

The proposed [GRYN] objectives fall primarily under a “design-The proposed [GRYN] objectives fall primarily under a “design-based” framework which… derive[s] inferences about the state based” framework which… derive[s] inferences about the state variables and/or vital rates of interest. variables and/or vital rates of interest.

However, one disadvantage is that it is poorly suited for future However, one disadvantage is that it is poorly suited for future predictions. Predictions of future system states require a model-predictions. Predictions of future system states require a model-based approach… and a greater number of simplifying based approach… and a greater number of simplifying assumptions… that may have considerable advantage for assumptions… that may have considerable advantage for understanding the system…understanding the system…

Monitoring vs. Modeling: making the Monitoring vs. Modeling: making the most given the data and the designmost given the data and the design

This is a good justification for a Hierarchical This is a good justification for a Hierarchical approach given the “mess” of the data… e.g., approach given the “mess” of the data… e.g., how do you get a spatial signal in the data?how do you get a spatial signal in the data?

Data Sources Data Type Years Recorded Notes

Montana State University 10 m x 2000 m transects 2001, 2003, 2006, 2007 Good Change Detection / Time Series

YNP Fire Plots 50 x 20 cm Daubenmire plots – 50 per site

1979 - 2005 Good Disturbance data

Greater Yellowstone Coordinating Committee

27 Different sources of data 1991, 1994 – 2005 Different protocols and GPS units

YNP – yearly dataset Roadside, Trails and Backcountry campsites

1996 - 2006 Covers entire Park

YERC – Northern Range 10 m x 100 m transects 2006 - 2007 Gopher / thistle interaction recorded

YERC – Targhee National Forest

10 m x 500 m transects 2006 - 2007 YERC field crew collected

Hymap Remote sensing –

126 bands

1999, 2000, 2003, 2006 Thistle Classification /

Ground Truthing

TIME SERIES Data and Covariates – the TIME SERIES Data and Covariates – the GOLDGOLD

Data Sources Data Type Years Recorded Notes

GYCC weed data Point, Line, Polygon 1994-2005 4000 leafy spurge points

from 11 different agencies

YNP Fire Plots 50 x 20 cm Daubenmire plots – 50 per site

1979 - 2005 Leafy Spurge examined for but not found*

YNP – yearly dataset Roadside, Trails and Backcountry campsites

2003-2005 11 Leafy Spurge Points

YERC – Targhee National Forest

10 m x 500 m transects

w/ covariate attribute data

2006 - 2007 56+ Transects (concentrated in FCIWD)

*Fremont County Idaho Weed Patrol*

Point/Line, polygon w/

Both road and systematic

2002-2007)

2002 = best time series

3000+ leafy spurge points

2002 “triangle” 410 points

CDWMA

USFS, Dubios Weed Patrol

Point and polygon

Road and off road

1999-2005 482 Leafy Spurge Points w/ control method recorded

Covariate data from two sources: RS and field dataCovariate data from two sources: RS and field data

Data Sources Data Type Years Recorded

Notes

University of Montana -- Maria Newcomb's Master's Thesis (Dr. Diana Six)

5.47m wide wandering transects

2001, 2002, 2007 Blister rust and pine beetle severity levels at 38 sites throughout the GYE

YERC Forest Plots – supported by GYCC funds

100 square-meter forest inventory plots

2000, 2007 Blister rust and pine beetle presence/absence at 3 hyperspectral sites in the GYE

National Park Service I&M 10 x 50m transects

2004, 2007 Blister rust presence and severity evaluated throughout GRYN+

U.S. Forest Service - Melissa Jenkins

standard Forest Service circular plots

1999, 2007? Blister rust severity levels evaluated at sites in the Caribou-Targhee NF

FIELD Time Series Data Sets for WBP Blister RustFIELD Time Series Data Sets for WBP Blister Rust

Data Sources Data Type Years Recorded

Notes

YERC HyMap hyperspectral imagery

Three 15 x 3 meter transects

2000, 2006 Extensive training and validation data at all three sites

YERC-NPS-NASA imagery from AVIRIS

Nearly wall-to-wall coverage over central YNP

1997, 2006 * Requires field validation and discrimination between MPB and BR; good use for Kendall’s field data

NAIP and other CIR or possibly color aerial photography

Wall-to-wall, hopefully digital

Various years; 2001, 2005 for Wyoming

* Requires field validation and discrimination between MPB and BR

U.S. Forest Service Aerial sketch maps of red/dead whitebark pine

1995-present * Requires field validation and discrimination between MPB and BR; messy data with poor comparability?

REMOTE SENSING Time Series Data Sets REMOTE SENSING Time Series Data Sets for WBP Blister Rustfor WBP Blister Rust

Repeat flights in 2000 and 2006

Classes:Classes: 1 = healthy, all green1 = healthy, all green 2 = stressed, some red needles2 = stressed, some red needles 3 = all red needles 3 = all red needles 4 = dead/snag4 = dead/snag

Year 2000 Year 2007

Proportion of Infested Trees measured on plotsProportion of Infested Trees measured on plots

DBH Classes: SE1/2 = seedling <1/2m; SE = seedling/sapling >1/2m, DBH <2cm; SA = sapling 2-10cm DBH; PT = pole timber 10-25cm DBH; MT = mature timber >25cm DBH

Number of Infested Trees measured on plotsNumber of Infested Trees measured on plots


Tom Minor Basin: Red map of Tom Minor Basin: Red map of red/dead from MPB (cluster) vs. red/dead from MPB (cluster) vs. Blister Rust (spatially patchy)Blister Rust (spatially patchy)

0

20

40

60

80

100

Num

ber

of T

rees

Total 2000BR

Total 2000MPB

2000 MPB+ BR

Total 2007BR

Total 2007MPB

2007 MPB+ BR

MPB x BR Interaction Effect

TMN

TMS

Blister Rust x Mountain Pine Beetle: is Blister Rust x Mountain Pine Beetle: is there an interaction ? what about drought?there an interaction ? what about drought?

Example of two forest plot data sets from Tom Example of two forest plot data sets from Tom Minor Basin, Montana – 2000 vs. 2007Minor Basin, Montana – 2000 vs. 2007

= 1/3

Tom Miner Basin

Daisy Pass Sheridan PointSheridan Point

HyMap Imagery (R-17, G-8, B-2) of Study LocationsHyMap Imagery (R-17, G-8, B-2) of Study Locations

MTMF analysis classifies red/dead whitebark MTMF analysis classifies red/dead whitebark pine flagging within the GYE to examine the pine flagging within the GYE to examine the

effects of blister rust and mountain pine beetleeffects of blister rust and mountain pine beetle

The red/dead classification results for the 2006 Red Lodge HyMap image show red/dead flagging locations by a red “+”

symbol.

AVIRIS 2006 coverageAVIRIS 2006 coverage AVIRIS 1997AVIRIS 1997 coveragecoverage

USGS IGBST predicted WBP map

Spatial Analysis approaches to Spatial Analysis approaches to discrimination of MPB mortality patterns discrimination of MPB mortality patterns

from BR patternsfrom BR patterns

Stand-level geospatial statistics such as that Stand-level geospatial statistics such as that in Jackie’s thesis: Ripley’s K, etc.in Jackie’s thesis: Ripley’s K, etc.

Pixel-level simple geospatial statistics, Pixel-level simple geospatial statistics, filtering, etc: concentric rings vs. salt-and-filtering, etc: concentric rings vs. salt-and-pepper patchinesspepper patchiness

DTA or logistic regression approaches: uses DTA or logistic regression approaches: uses additional covariates (e.g., stand density, co-additional covariates (e.g., stand density, co-registered aerial sketch maps) other than registered aerial sketch maps) other than spectral informationspectral information

HyMap RGB true color composite (left), NAIP true color HyMap RGB true color composite (left), NAIP true color composite (right). Middle image is a false color composite where composite (right). Middle image is a false color composite where residual red to green ratio, shade-normalized GV fraction and residual red to green ratio, shade-normalized GV fraction and shade-normalized NPV fraction are displayed as red, green and shade-normalized NPV fraction are displayed as red, green and blue. blue. CAN WE APPLY THIS TO THE 2006 AVIRIS collection of 5 CAN WE APPLY THIS TO THE 2006 AVIRIS collection of 5 million acres? (assume consistent time from infect to mortality)million acres? (assume consistent time from infect to mortality)

Combining NAIP and Hyperspectral for Combining NAIP and Hyperspectral for Mapping Dead and Red for Pattern AnalysisMapping Dead and Red for Pattern Analysis

Pattern typical in YNP: decreased sedge, increase in soil fraction Pattern typical in YNP: decreased sedge, increase in soil fraction along game trails and heavy use areas, thistle invasion, insect killalong game trails and heavy use areas, thistle invasion, insect kill

19991999 20032003RED for > 25% RED for > 25% decrease in HGVdecrease in HGV

19991999 20032003Insect Insect killkill

Validation of GV and NPV from HyMap SMA results using Validation of GV and NPV from HyMap SMA results using a single model composed of LIVE, BARK and shadea single model composed of LIVE, BARK and shade

Hyperspectral Hyperspectral DataData

AVIRISAVIRIS

HyMapHyMap

Integration of a Integration of a LLarge-Area arge-Area IInvasive nvasive SSpreadpread NNetwork etwork (LISN) into the ISFS (w/ (LISN) into the ISFS (w/

climate models)climate models)

Integration of a Large-Area Invasive Spread Network (LISN) into the ISFS (w/ climate models) Static...

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