# Presentation: 573756

## Assessment and Mapping of Vegetation Communities in Shenandoah National Park John YoungUSGS Leetown Science CenterGary Fleming and Karen PattersonVirginia Natural Heritage ProgramPhil Townsend and Jane FosterU. of Wisconsin, Dept. of Forest Ecology & Management

## A Research Project

- Classify vegetation communities using data collected at field plots
- Investigate emerging remote sensing methods for mapping to USNVC associations
- Research the use of ecological gradient models to assist mapping and assessment of vegetation communities

*Shenandoah National Park, Virginia*

- Main Goals:

- _Funding:_ USGS NRPP Program, NPS Rec. Fee Demo Project, Joint Fire Science Program

## Cooperators

- USGS – Leetown Science Center (John Young)
- Project development and coordination
- Locate plots and assist in field sampling
- Remote sensing/environmental gradient modeling
- Vegetation distribution mapping
- Univ. of Wisconsin (Phil Townsend, Jane Foster)
- Hyperspectral remote sensing investigations
- Vegetation distribution mapping
- Virginia Natural Heritage (Gary Fleming, Karen Patterson)
- Field sampling of plant communities (plots)
- Vegetation community classification
- USNVC coordination with Nature Serve (Leslie Sneddon)
- Shenandoah National Park (Wendy Cass, Alan Williams)
- Project development and coordination
- Fire fuels data collection
- GIS support/data sharing

## What we did:

- Stratified park into ELU classes
- Sampled vegetation at field plots
- Classified vegetation into USNVC classes
- Processed hyperspectral imagery and mapped topographic gradients
- Built statistical model to predict vegetation communities
- Conducted accuracy assessment

## Attempt to insure sampling the range of habitats in park
Plants respond to gradients in habitat conditions (niche theory)
Can use GIS to develop models of habitat niches 
Elevation + Geology + Landform = “Ecological Land Units” (TNC)
Apportion field effort to sample representative and unique habitats

- Plants respond to gradients in habitat conditions (niche theory)
- Can use GIS to develop models of habitat niches
- Elevation + Geology + Landform = “Ecological Land Units” (TNC)
- Apportion field effort to sample representative and unique habitats

- Why stratify?

## Landform

## TNC Landform Classification (F. Biasi)

## ELU Types

- * 168 potential types, 140 types present in SHEN

## ELU Types

## mid elev, basaltic, upper slope

- mid elev, siliclastic, steep slope N/NE

- high elev, basaltic upper slope

- mid elev, granitic, steep slope N/NE

## ELU examples

**1110: Low elevation/basaltic/steep slope N-NE**

## ELU examples

**2212: Mid elevation/siliciclastic/slope crest**

## ELU examples

**3313: High elevation/granitic/upper slope**

## Field Plots

- 500 target plots were located proportional to ELU area (strata)
- Plots were randomly located within ELU, after selecting for:
- Areas  > 100 but < 500 meters from road
- Areas > 20 meters from ELU edge
- Random plot locations were loaded into GPS, navigated to in field
- Plots may have been moved in field to sample intact community (i.e. moved away from disturbance).
- Plot center of actual sample location remapped in field using 3D averaging

-  208 plots sampled over 3 years (2001, 2002, 2003)
- _+103_ plots previously sampled by VA-DNH
- = **311** plots sampled using Natural Heritage protocols

## Sample plots

- North District

## Sample plots

- Central District

## Sample plots

- South District

## Field studies led by Va. Natural Heritage
Followed Natural Heritage field sampling protocols 
20 x 20 meter plot
Plant ID, composition, size, cover %
Overstory trees
Understory trees, shrubs, grasses, herbaceous plants
Site topography
Soil sample
GPS-enabled site photo

- Followed Natural Heritage field sampling protocols
- 20 x 20 meter plot
- Plant ID, composition, size, cover %
- Overstory trees
- Understory trees, shrubs, grasses, herbaceous plants
- Site topography
- Soil sample
- GPS-enabled site photo

- Field Sampling

## 1 – pine-oak/heath, oak/heath, and acidic boulderfield forests

- 2 – northern hardwood, northern red oak, and eastern hemlock-hardwood forests
- 3 – mesic and dry-mesic mixed forests

## Slide 20

| VA Natural Heritage Ecological Group | Associations |
| --- | --- |
| ACIDIC COVE FOREST | 1 |
| ACIDIC OAK - HICKORY FOREST | 1 |
| BASIC OAK - HICKORY FOREST | 2 |
| CENTRAL APPALACHIAN NORTHERN HARDWOOD FOREST | 1 |
| EARLY SUCCESSIONAL OR DISTURBED DRY FOREST | 1 |
| EARLY SUCCESSIONAL OR HEAVILY DISTURBED MESIC FOREST | 2 |
| EASTERN HEMLOCK FOREST | 1 |
| HIGH-ELEVATION BOULDERFIELD FOREST / WOODLAND | 1 |
| HIGH-ELEVATION OUTCROP BARREN | 3 |
| HIGH-ELEVATION SEEPAGE SWAMP | 1 |
| LOW-ELEVATION BASIC OUTCROP BARREN | 2 |
| LOW-ELEVATION BOULDERFIELD FOREST / WOODLAND | 2 |
| MAFIC FEN / SEEP | 1 |
| MESIC MIXED HARDWOOD FOREST | 1 |
| MONTANE BASIC SEEPAGE SWAMP | 1 |
| MONTANE MIXED OAK AND OAK - HICKORY FOREST | 3 |
| MONTANE WOODLAND SEEP | 1 |
| MOUNTAIN / PIEDMONT ACIDIC SEEPAGE SWAMP | 1 |
| MOUNTAIN / PIEDMONT BASIC WOODLAND | 1 |
| NORTHERN RED OAK FORESTS | 1 |
| OAK / HEATH FOREST | 2 |
| PIEDMONT / LOW MOUNTAIN ALLUVIAL FOREST | 1 |
| PINE - OAK / HEATH WOODLAND | 1 |
| RICH COVE AND SLOPE FOREST | 2 |
| SHENANDOAH VALLEY SINKHOLE POND | 1 |
| Grand Total | 35 |

## US National Vegetation Classification System

- Floristic

- Physiognomic

## VA-DNH Classification Results

-   _14_ Vegetation *Formations*
-  _19_ TNC* Ecological Systems*
-   _25_ Va. DNH *Ecological Groups*
-   _31_ Vegetation *Alliances*
-   _34_ (35) Vegetation A*ssociations*

## * 1 type intermediate between G2 and G3 listed as G2

- * 5 types intermediate between G3 and G4 listed as G3

## Inputs to Mapping:

- Used coordinate location of each vegetation plot to sample image and topographic map data
- Image and topographic attributes used as inputs to statistical prediction of vegetation class
- Vegetation plot coordinates assumed to be accurate at scale of image and topographic data (< 15m)

## Image Sources Used:

- Primary: (statistical models)
- 	_Hyper-spectral_ (224 narrow wavelength bands)
- AVIRIS (May 2000, July 2001)
- 17m pixel resolution
- Secondary: (statistical models, filling holes)
- 	_Multi-spectral_ (7 broad wavelength bands)
- Landsat TM 5 and 7 (various dates leaf-on, leaf-off)
- 30m pixel resolution
- Ancillary: (reference)
- Scanned color infra-red _aerial photography_
- 1:24,000 leaf-on (2001) – 2 meter
- 1:12,000 leaf-off (1994/1997) – 1 meter

## Potential Image Sources for Veg Mapping:

- Quick-
- bird

- Ikonos

- Ground Resolution/IFOV (m)

- Discrete Wavelengths (bands)

- 0

- 1

- 5

- 10

- 15

- 20

- 25

- 30

- 35 ...

- 0

- 1

- 5

- 10

- 15

- 20

- 200+

- Aerial
- Photos

- SPOT 5

- ASTER

- Landsat

- Object Discrimination

- Material Identification

- AVIRIS

- IRS-1C

- AISA

- $$$$

- $$$

- $

## Why hyper-spectral?

## AVIRIS Image

- May 2000

## Image Pre-Processing:

- Convert from delivered units (DN) to radiance units
- To restore radiometric calibration
- Atmospheric correction, conversion  from radiance to reflectance units
- To measure physical properties
- Georegister to common map coordinate system (“orthorectify”)
- For GIS data overlay and analysis
- Same native format as GPS system useful

- Multi/Hyperspectral Imagery only

## AVIRIS hyperspectral (July 2001) - 3 of 224 bands

- 17 meter pixel resolution, ~10 nm contiguous wavelength bands

## Spectral curves at plots

## Habitat modeling

**Spectral data (imagery)**
**Elevation**
**Aspect **
**Relative slope position**
**Slope**
**Geology (and etc.)**

**Known**** ****Location (point)**

_**Spatial Data Layers**_

***f(x) ************ statistical function****

***= Predicted distribution map***

- * e.g. Logistic regression, Classification Tree, Discriminant analysis

## Some theory

- Soils

- Temperature

- ET

- Radiation

**Geology**

**Climate**

**Lat/Lon**

**Topography**

- Nutrients

- Heat
- Sum

- Soil & Air
- Water

**Plant Growth**
**and**
**Distribution**

- Competition,
- Predation,
- and
- Succession

- Natural &
- Human
- Disturbance

- P.A.R.

- Indirect
- Gradients

- Direct
- Gradients

- Resource
- Gradients

- GIS Models

- After: Guisan and Zimmerman (2000)

- * Model the
- “fundamental niche” from  GIS/Statistics

- * Assess the
- “realized niche”
- from field data

## Topographic Gradients:

- A rich literature developing on modeling species distributions using GIS-derived topographic gradients (see Guisan and Zimmerman, 2000)
- Gradient models developed from 15-meter DEM for ShenVeg:
- Moisture (TRMI, TCI, CTI)
- Light (Apect, Beer’s aspect, Solar radiation)
- Topographic Shape (Slope Curvature, TSI, Slope position)
- Additional mineral/nutrient gradients from geology maps
- Bedrock geology (Gathright, 1976)
- Surficial geology (Morgan et al., 2004)

## Canonical Linear Discriminant Analysis (CLDA)

- Data pulled for each field plot location:
- Veg. comm. class assignment
- Band reflectance (200+)
- Topographic attributes
- CLDA (Proc CANDISC in SAS) finds linear combinations of variables that maximize separation between groups
- Canonical scores (loadings) are then used in an ENVI image processing routine to assign all other image pixels to closest matching class
- Output is posterior probability of class membership for each 17x17 meter pixel

## Results - unfiltered

## Results – filtered and clipped

## 	Most of the information content is in hyperspectral image data

- 	Most of the information content is in hyperspectral image data

## Slide 39

## Slide 40

## Accuracy Assessment:

- 1)  Internal cross validation accuracy assessed by resubstitution using model training data
- 89% overall accuracy, 60-100% by class acc.
- 2)  223 stratified random AA plots field sampled in 2004 by independent crews using a field key
- 67% overall accuracy, 40-100% by class acc.
- Models re-run, incorporating new boundary and new surficial geology map
- 88% overall accuracy from cross validation
- 68 stratified random AA plots collected in 2005, added to 2004 set
- 56% overall accuracy, 0-100% by class acc.

## Accuracy Assessment:

## Accuracy Assessment issues:

- Models fit training data well, but not AA data
- Training data not representative of actual variation in park?
- Low sample size in some classes
- No AA data collected for outcrop or wetland classes
- As many as 3 possible veg types assigned to some AA sample points
- Uncertainty in AA veg community assignment?
- Veg at AA plots different from “archetype”?
- Training data biased towards *natural* vegetation, much of Shenandoah disturbed or at various stages of succession
- Possibly more disturbance sampled in AA plots than in training plots?

## Accuracy Assessment Solutions:

- Collapse classes for similar canopy types
- Going up to alliance not enough (35 to 31 classes)
- Modify combination of probabilities for final map
- De-emphasize overmapped classes
- Add additional training points in disturbed classes and re-run
- Create new disturbance classes
- Assign AA data to class, use in classification, cross validate
- Augment CLDA method with unsupervised methodology (i.e. segmentation) to find unanticipated classes
- CLDA (and other statistical/supervised methods) have to assign every pixel to an *a priori* class, not robust to outliers

## Lessons Learned:

- This stuff ain’t easy or quick!
- Stratification helped to sample habitats but didn’t adequately account for disturbance
- Classifying natural communities and mapping heavily disturbed forests at cross-purposes
- USNVC Associations can be difficult to distinguish, even with hyperspectral imagery
- Associations defined on ground cover (bottom-up)
- Imagery sees primarily canopy (top-down)
- Getting to plots in steep, heavily vegetated terrain very difficult, disturbed areas even harder
- Limited sampling of training and AA plots

## Recommendations:

- Read “Assessing Accuracy of Remotely Sensed Data” (Congalton and Green, 1999) before sampling
- Think hard about image sources, balance resolution to requirements of map use
- Make sure classification scheme is
- _Mutually exclusive_:  each mapped area falls into only 1 class (collapse similar canopy classes)
- _Exhaustive_:  all areas on map are sampled and classified (sample disturbed areas)
- May want to collect AA and training data in same field campaign using random plots
- Make sure GPS coordinates are as accurate as possible
- Differential GPS > WAAS > Averaging > 3D > 2D

- X

## Questions?