# Presentation: 679727

## Success of seabeach amaranth (Amaranthus pumilus Raf.) using habitat selection based on light detection and ranging (LIDAR) data 

**Jon D. Sellars****1,2**
**Claudia L. Jolls****1**
**Cass A. Wigent****1**

**1****East Carolina University**
**Department of Biology**
**Greenville, NC**

**2National Geodetic Survey**
**National Oceanic and Atmospheric Administration **

**Notes:**
Introduce us

## Amaranthus pumilus

**fugitive **
**intolerant of competition**
**requires disturbance**
**narrow elevation range on non-eroding beaches (B****ü****cher and Weakley 1990)**
**habitat is relatively homogenous**

***Amaranthus pumilus***

**Notes:**
Life history - reproduction

## LIght Detection And Ranging data  (LIDAR)

**30 Degree Scan Angle**

**Scan Width**
**(~300 m)**

**Overlapping Swaths**

**Scan Direction**
**(parallel to beach)**

**Laser**

- LIght Detection And Ranging data  (LIDAR)

**Twin Engine Aircraft  **
**Elevation ~700 m**

**Notes:**
The plane the plane!!!

## Discriminant Function Analysis

**Variables extracted from 2000 LIDAR were used to model suitable habitat in 2001 for Shackleford Banks and Cape Lookout Spit, Cape Lookout National Seashore, NC**
**Elevation**
**Elevation in the North American Vertical Datum of 1988 (m) **
**Distance from shore**
**Distance from the mean high water line (m) **
**Surface complexity**
**Standard deviation of the surface normal vectors in a 9 x 9 m****2**** neighborhood**
**Slope**
**Slope of the surface in degrees**
**Grey scale reflectance**
**Passive LIDAR data able to distinguish sand/water/vegetation**

**Notes:**
Discriminant function analysis is a multivariate technique that allows us to examine the contribution of individual variables to an overall model. We can then select the variables that best distinguish suitable habitat as based on the occurrence of seabeach amaranth. For this DFA, we analyzed 5 variables that were extracted from 2000 LIDAR. They were: elev, dist, surf complex and grey scale. 
Elevation = (corrected for MHW or absolute?) (meters?)
Distance from shore = shore determined how? (meters?)
Surface Complexity = (define)
Slope = (define, e.g. in what direction, pixel size, etc.)
Grey scale reflectance = passive LIDAR, define, index of vegetation cover?
The variables were square root transformed to help meet the assumptions of the DFA.  

Occurrence data collected by DGPS survey were compared against randomly generated points. 26 occurrence points (approximately 20 % of the DGPS points) were with held to act as a validation set. These points were systematically (every fifth DGPS point) culled from the set.
Why were only 26 of the 168 plant occurrences extracted?  How did you “extract” or decide which points to use?

## Discriminant Function Analysis

**Plant locations were captured with a Differential Global Positioning System (DGPS)**
**Estimated accuracy ****± 1.2 m**
**Plants (n = 168 represented by 126 unique DGPS locations) were compared to randomly generated points  (n = 426) with in the study area**
**3 x the number of random points were used to capture the greater background variation**
**Plant occurrences (n = 26) originally withheld from the model were used as a validation set**
**Every fifth point that represented a single plant location**
**All statistical analyses were performed in SPSS 11.5.0**
**Variables were square root transformed to keep the DFA robust despite outliers **

**Notes:**
Discriminant function analysis is a multivariate technique that allows us to examine the contribution of individual variables to an overall model. We can then select the variables that best distinguish suitable habitat as based on the occurrence of seabeach amaranth. For this DFA, we analyzed 5 variables that were extracted from 2000 LIDAR. They were: elev, dist, surf complex and grey scale. 
Elevation = (corrected for MHW or absolute?) (meters?)
Distance from shore = shore determined how? (meters?)
Surface Complexity = (define)
Slope = (define, e.g. in what direction, pixel size, etc.)
Grey scale reflectance = passive LIDAR, define, index of vegetation cover?
The variables were square root transformed to help meet the assumptions of the DFA.  

Occurrence data collected by DGPS survey were compared against randomly generated points. 26 occurrence points (approximately 20 % of the DGPS points) were with held to act as a validation set. These points were systematically (every fifth DGPS point) culled from the set.
Why were only 26 of the 168 plant occurrences extracted?  How did you “extract” or decide which points to use?

## Discriminant Function Analysis

| Group |  | Predicted Group Membership |  | Total |
| --- | --- | --- | --- | --- |
|   |   | Random | Occurrence |   |
| Count | Random | 355 | 65 | 420* |
|  | Occurrence | 26 | 116 | 142 |
| %  | Random | 84.5 | 15.5 | 100.0 |
|  | Occurrence | 18.3 | 81.7 | 100.0 |

| Contribution of Variable  |  |
| --- | --- |
| Variable | Correlation |
| Distance | 0.859 |
| Passive | -0.757 |
| Complexity | 0.414 |
| Slope | 0.038 |
| Elevation | -0.027 |

**Step 1.  We analyzed all five variables to identify the most important as based on correlation to the discriminant function. **

**Notes:**
Analysis of an initial DFA indicated that distance from mean high water and passive reflectance were best able to distinguish between Random and Occurrence Points.   

Explain the Original Group Membership column and the variables “Count” and “%”.

## Discriminant Function Analysis

| Group |  | Predicted Group Membership |  | Total |
| --- | --- | --- | --- | --- |
|   |   | Random | Occurrence |   |
| Count | Random | 364 | 62 | 426 |
|  | Occurrence | 8 | 134 | 142 |
| %  | Random | 85.4 | 14.6 | 100.0 |
|  | Occurrence | 5.6 | 94.4 | 100.0 |

| Contribution of Variable  |  |
| --- | --- |
| Variable | Correlation |
| Distance | 0.955 |
| Passive | -0.849 |

**Step 2.  We analyzed just Distance and Passive**

**Notes:**
Analysis of an initial DFA indicated that distance from mean high water and passive reflectance were best able to distinguish between Random and Occurrence Points.   

Explain the Original Group Membership column and the variables “Count” and “%”.

## Discriminant Function Analysis

**Distance and reflectance were best able to distinguish suitable habitat based on their ability to differentiate between Random and Occurrence Points**
**Variable coefficients from SPSS were used to model habitat in ArcView (3 m****2 ****cells)**
**Using Map Calculator function**
**Model correctly identified 24/26 (92 %) of validation points as occurring in suitable habitat**
**   ****Overlay analysis in ArcView**

**Notes:**
Analysis of an initial DFA indicated that distance from mean high water and passive reflectance were best able to distinguish between Random and Occurrence Points.   

Explain the Original Group Membership column and the variables “Count” and “%”.

## Probability a cell contains Suitable Habitat

**Area Enlarged**

**Probability a cell contains Suitable Habitat**

**0.50 - 0.75**

**> 0.75**

**      ****Plant Location**

## Probability a cell contains Suitable Habitat

**Area Enlarged**

**Probability a cell contains Suitable Habitat**

**0.50 - 0.75**

**> 0.75**

**Mean High Water (MHW)**

**MHW + 0.77 m**

**MHW + 2.00 m**

**      ****Plant Location**

## Conclusions

**Habitat variables can be extracted from remote sensing data**
**LIDAR data can be used to delineate and model suitable habitat for *****Amaranthus pumilus***
**Distance from shore and passive data efficiently model suitable habitat**
**Potentially distance from shore is a surrogate measure of disturbance**
**Passive data delineate areas free of vegetation**

## Applications of Remote Sensing Data to Rare Plant Conservation

**Covers large geographic areas**
**Identify critical environmental variables and habitat**
**Can aid rapid assessment of sites for species occurrence / re-introduction **

## ACKNOWLEDGMENTS

North Carolina Plant Conservation Program
National Park Service
East Carolina University
ALACE (NOAA, NASA, USGS)
Mike Aslaksen, NGS-NOAA 
Marj Boyer, NCDA-PCP
Jeff Colby, ECU
Karl Faser, ECU
Cecil Frost, NCDA-PCP
Michael Hearne, NOAA
Mark Jansen, NOAA
Marcia Lyons, NPS
Karen Trueblood, ECU
Keith Watson, USFWS
Jason Woolard, NGS-NOAA

**ACKNOWLEDGMENTS**
**North Carolina Plant Conservation Program**
**National Park Service**
**East Carolina University**
**ALACE (NOAA, NASA, USGS)**
**Mike Aslaksen, NGS-NOAA **
**Marj Boyer, NCDA-PCP**
**Jeff Colby, ECU**
**Karl Faser, ECU**
**Cecil Frost, NCDA-PCP**
**Michael Hearne, NOAA**
**Mark Jansen, NOAA**
**Marcia Lyons, NPS**
**Karen Trueblood, ECU**
**Keith Watson, USFWS**
**Jason Woolard, NGS-NOAA**