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

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Presentation on theme: "Integration of a Large-Area Invasive Spread Network (LISN) into the ISFS (w/ climate models) Static or fixed variables Dynamic variables."— Presentation transcript:

1 Integration of a Large-Area Invasive Spread Network (LISN) into the ISFS (w/ climate models) Static or fixed variables Dynamic variables

2 General Outline 1.NASA Invasive Ecological Forecasting (include Jackie’s thesis and other work) 2.Review original GYCC project 3.Details of the NASA project and some initial results from last two summers a. preliminary summary of field data b. preliminary analysis of RS data 4. Approaches to mapping WBP blister rust and mountain pine beetle

3 Team, Partners/Collaborators Bob Crabtree, PI, Yellowstone Ecological Research Center Bob Crabtree, PI, Yellowstone Ecological Research Center John Schnase, co-I, NASA-Goddard Space Flight Center John Schnase, co-I, NASA-Goddard Space Flight Center Chris Potter, co-I, NASA-Ames Research Center Chris Potter, co-I, NASA-Ames Research Center Paul Moorcroft, co-I, Harvard University Paul Moorcroft, co-I, Harvard University Shengli Huang, co-I, Yellowstone Ecological Research Cntr. Shengli Huang, co-I, Yellowstone Ecological Research Cntr. Collaborators:Mary Maj, GYCC and GYCC Weeds Committee; Jeff Pettingill, Idaho Falls, Nancy Glenn, ISU; Jack Norland, NDSU; Craig McClure, YNP; Rob Mickelsen/Kyle Moore/Bryce Fowler/Walt Grows, Caribou-Targhee National Forest; and Bruce Maxwell/Lisa Rew, MSU; Diana Six, University of Montana; USDA-ARS; David Bubenheim, NASA-ARC; Jeff Morrisette… Josh Harmsen, Ph.D. student, U.C.- Berkeley & YERC Josh Harmsen, Ph.D. student, U.C.- Berkeley & YERC Jackie Hatala, Staff Scientist, Harvard University & YERC Jackie Hatala, Staff Scientist, Harvard University & YERC Randy Mullen, Ecological Statistician, YERC Randy Mullen, Ecological Statistician, YERC

4 NASA LISN OBJECTIVE(S) Develop Hierarchical Bayesian models for predicting invasive weed (& pathogen) spread over time (Ecological Forecasting program)Develop Hierarchical Bayesian models for predicting invasive weed (& pathogen) spread over time (Ecological Forecasting program) Investigate 4 species of biological and economic importance: leafy spurge, Canada thistle, blister rust, and cheatgrass at ecosystem scalesInvestigate 4 species of biological and economic importance: leafy spurge, Canada thistle, blister rust, and cheatgrass at ecosystem scales Incorporate dynamic climate data, carbon-climate model output, and 3-D vegetation structure as covariates for future predictionsIncorporate dynamic climate data, carbon-climate model output, and 3-D vegetation structure as covariates for future predictions Add a large regional ecosystem (GYE) with new agency partners to the existing NISFSAdd a large regional ecosystem (GYE) with new agency partners to the existing NISFS Validate, compare, disseminate (publish) and benchmark models and integrate with the NISFSValidate, compare, disseminate (publish) and benchmark models and integrate with the NISFS

5 Hyperspectral Data Analysis of Whitebark Pine (Pinus albicaulis) and White Pine Blister Rust (Cronartium ribicola) at 4 Sites in the GYE Don Despain, BRD, U.S. Geological Survey Chuck Schwartz, Leader, Interagency Grizzly Bear Chuck Schwartz, Leader, Interagency Grizzly Bear Study Team, Bozeman, MT Ward McCaughey, Director, USFS Northern Rockies Ward McCaughey, Director, USFS Northern Rockies Experiment Station, Bozeman, MT Kerry Halligan, Yellowstone Ecological Research Center Kerry Halligan, Yellowstone Ecological Research Center Bob Crabtree, Yellowstone Ecological Research Center Bob Crabtree, Yellowstone Ecological Research Center Collaborators: Roy Bergstrom, Shoshone National Forest Dan Reinhart, Yellowstone National Park Field and Research Assistants: Sarah Elmendorf, Justin Pidot, Anne Johnson and Dave Sebonich (Shoshone N.F. crew), Keith Van Etten, Colby Gardner, David Bopp, and Matt Jones (data mgmt)

6 Objectives Can analysis of hypersectral transects, combined with field validation: (1) provide a detailed map of WBP condition, including the observed symptoms of healthy green, incipient flagging or chlorotic stress, red needle flagging, and dead (branches and snags) (2) provide a detailed map of WBP distribution including the percent composition of WBP versus other conifer species, (3) provide recommendations with regard to field methods and approaches, accuracy, spatial scale, cost, and detection limits

7 GYE Study Sites N = 4 Name Symptom Level Dead IndianHIGH – Limber Red LodgeMEDIUM Tom MinorLOW Daisy PassCONTROL

8 Source: Aug 12, 2000 What is Hyperspectral Imagery?

9 Hyperspectral and High spatial resolution combined (“H2 imagery”) provides more information on variables at finer scales 16 16pixels 13 pixels True color 30 m LandSat image 6 bands 399 pixels 472pixels Hyperspectral 1 meter image MNF Bands 7 (invert), 6, 1 Lamar River, 8/03/99 Image by W.A. Marcus 128bands

10 Tree status DBH size class Branch size classes * 1 - Healthy (needles abundant and green) Seedling <1/2 m tall <1' * 2 - Stressed (needles sparse, often discolored) Seedling/Sapling >1/2 m tall 1-3' 3 - Dead (all or nearly all needles red) Sapling 2-10 cm DBH 3-5' 4 - Snag (needles gone) 4 - Snag (needles gone) Pole Timber cm >5' Mature Timber 26+ cm * Later added two intermediate categories, 1.5 and 2.5

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14 red/dead pixels total mapped mapped/definite mapped/possible 728 unmapped/possible 624 mapped/absent 00 unmapped/definite 00 producers accuracy 95.00% users accuracy 97.24% Field Validation of red/dead (pixel level) This became convincing for me during 2001 field validations

15 Conifer Disease Detection: Red/Dead Flagging Overall = 98 %; User’s = 97 %, Producer’s = 95% NOTE: the large patches of red pixels are confirmed MPB and the small groups of red pixels are mostly blister rust infestations (red and dead flagging).

16 Species Identification: Mapping Whitebark Pine

17 2 2 2

18 Conclusions: 1.HRHS can accurately determine forest health parameters (healthy and declining status categories of WBP) based on biophysical constituents. 2.HRHS can accurately map symptoms of blister rust (red and dead branches and trees). 3.The distribution and clumping of pixels were clearly indicative of bark beetle kill vs. blister rust. 4.HRHS has the potential of an early warning indicator. 5.HRHS has a very good potential to accurately map WBP. 6.Field sampling must accompany aerial surveys (can combine collection of training data and ground-truth). 7.Ecosystem-wide transect surveys are cost-effective when compared to ground efforts (although overall expensive).

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

20 OBJECTIVE(S) (1) Develop Hierarchical Bayesian models for predicting invasive spread over time... OR What can we learn from invasive time series data when we match it with a rich set of covariate data? example covariates = precipitation patterns, disturbance type, habitat type, soil moisture, soil type, slope, aspect, productivity, canopy coverage, elevation, etc….

21 Hierarchical-Bayesian Model Structure: (2 parts) (1) Data Model (detection, measurement, observation, uncertainty, etc.) (2) 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 the process-based modelPopulation Demography model chosen as the process-based model

22 dN/dT (x, t) = (1) dispersal (two stage) (2) fecundity (3) available space (DD) and competition (4) mortality Demographic-Process Model Structure (from time-series, repeat plot/transect data) * Predictive models require matching covariate data

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

24 From “Future Considerations For Monitoring Whitebark Pine: Moving toward Model-based Inference” [GRYN] The proposed [GRYN] objectives fall primarily under a “design- based” framework which… derive[s] inferences about the state variables and/or vital rates of interest. However, one disadvantage is that it is poorly suited for future predictions. Predictions of future system states require a model- based approach… and a greater number of simplifying assumptions… that may have considerable advantage for understanding the system… Monitoring vs. Modeling: making the most given the data and the design This is a good justification for a Hierarchical approach given the “mess” of the data… e.g., how do you get a spatial signal in the data?

25 Data SourcesData TypeYears RecordedNotes Montana State University10 m x 2000 m transects2001, 2003, 2006, 2007Good Change Detection / Time Series YNP Fire Plots50 x 20 cm Daubenmire plots – 50 per site Good Disturbance data Greater Yellowstone Coordinating Committee 27 Different sources of data1991, 1994 – 2005Different protocols and GPS units YNP – yearly datasetRoadside, Trails and Backcountry campsites Covers entire Park YERC – Northern Range10 m x 100 m transects Gopher / thistle interaction recorded YERC – Targhee National Forest 10 m x 500 m transects YERC field crew collected HymapRemote sensing – 126 bands 1999, 2000, 2003, 2006Thistle Classification / Ground Truthing TIME SERIES Data and Covariates – the GOLD Data SourcesData TypeYears RecordedNotes GYCC weed dataPoint, Line, Polygon leafy spurge points from 11 different agencies YNP Fire Plots50 x 20 cm Daubenmire plots – 50 per site Leafy Spurge examined for but not found* YNP – yearly datasetRoadside, Trails and Backcountry campsites Leafy Spurge Points YERC – Targhee National Forest 10 m x 500 m transects w/ covariate attribute data Transects (concentrated in FCIWD) *Fremont County Idaho Weed Patrol* Point/Line, polygon w/ Both road and systematic ) 2002 = best time series leafy spurge points 2002 “triangle” 410 points CDWMA USFS, Dubios Weed Patrol Point and polygon Road and off road Leafy Spurge Points w/ control method recorded Covariate data from two sources: RS and field data

26 Data SourcesData TypeYears Recorded Notes University of Montana -- Maria Newcomb's Master's Thesis (Dr. Diana Six) 5.47m wide wandering transects 2001, 2002, 2007Blister 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, 2007Blister rust and pine beetle presence/absence at 3 hyperspectral sites in the GYE National Park Service I&M10 x 50m transects 2004, 2007Blister 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 Rust

27 Data SourcesData TypeYears Recorded Notes YERC HyMap hyperspectral imagery Three 15 x 3 meter transects 2000, 2006Extensive 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 ServiceAerial 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 for WBP Blister Rust

28 Repeat flights in 2000 and 2006

29 Classes: 1 = healthy, all green2 = stressed, some red needles 1 = healthy, all green2 = stressed, some red needles 3 = all red needles 4 = dead/snag 3 = all red needles 4 = dead/snag Year 2000 Year 2007

30 Proportion of Infested Trees measured on plots DBH Classes: SE1/2 = seedling 1/2m, DBH 25cm DBH

31 Number of Infested Trees measured on plots DBH Classes: SE1/2 = seedling 1/2m, DBH 25cm DBH

32 Proportion of Infested Trees measured on plots DBH Classes: SE1/2 = seedling 1/2m, DBH 25cm DBH

33 Number of Infested Trees measured on plots DBH Classes: SE1/2 = seedling 1/2m, DBH 25cm DBH

34 Proportion of Infested Trees measured on plots DBH Classes: SE1/2 = seedling 1/2m, DBH 25cm DBH

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

36 Blister Rust x Mountain Pine Beetle: is there an interaction ? what about drought? Example of two forest plot data sets from Tom Minor Basin, Montana – 2000 vs = 1/3

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38 Tom Miner Basin Daisy Pass Sheridan Point HyMap Imagery (R-17, G-8, B-2) of Study Locations

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

40 AVIRIS 2006 coverageAVIRIS 1997coverage AVIRIS 2006 coverageAVIRIS 1997 coverage USGS IGBST predicted WBP map

41 Spatial Analysis approaches to discrimination of MPB mortality patterns from BR patterns  Stand-level geospatial statistics such as that in Jackie’s thesis: Ripley’s K, etc.  Pixel-level simple geospatial statistics, filtering, etc: concentric rings vs. salt-and- pepper patchiness  DTA or logistic regression approaches: uses additional covariates (e.g., stand density, co- registered aerial sketch maps) other than spectral information

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

45 Pattern typical in YNP: decreased sedge, increase in soil fraction along game trails and heavy use areas, thistle invasion, insect kill RED for > 25% decrease in HGV Insect kill

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

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48 Hyperspectral Data AVIRIS HyMap

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


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