Mapping invasive species with geographic information systems and remote sensing. A presentation for a masters-level GIS course at Lehman College (New York, NY) Spring 2014.

1. Final project and presentation
Basic Mapping: Applications and Analysis
Lehman College - Spring 2014
Elia Machado, Ph.D.
Mapping invasive species with
remote sensing and GIS
Oliver C. Smith
13 May 2014
1
Ash leaves. From: The Guardian 2012. Photo: www.alamy.com

2. 1.0 About invasives
What’s an invasive?
Any organism—plant, insect, fish, bacteria and more
—that’s not native to an ecosystem and causes harm.
How many invasives are there?
An estimated 50,000 invasive species have been
introduced to the U.S.
(Ustin, DiPietro, Olmstead, Underwood, Scheer, 2002)
What are the impacts?
Biodiversity - Invasives are leading cause of
biodiversity loss and species extinction globally.
(Joshi, de Leeuw, van Duren 2004)
Economic - Invasives account for an estimated
$140B in annual costs in U.S.
(Ustin et. al., 2002)
Oliver C. Smith for GEP 204/504 20142
Japanese knotweed.
Photo: Birdlife International.
www.birdlife.org
Kudzu vine native to Japan.
Photo: National Geographic/Getty Images

3. EAB (agrilus planipennis)
Wood-boring beetle first seen in U.S. in
2002.
Native to Asia. Likely migrated to U.S.
in wood-packing material on ships.
Larval EAB attacks and kills ash trees by
disrupting vascular system.
(McCullough, Poland, Anulewicz, Cappaert, 2009)
Infestation is hard to detect in newly
infested trees. (Pontius, Martin, Plourde, Hallett,
2008)
EAB has killed tens of millions of ash trees
in Michigan, at least 12 additional states,
and two Canadian provinces.
(McCullough, et al., 2009)
2.0 Emerald ash borer (EAB)
3
Emerald ash borer. Photo: New York
invasive species info clearinghouse
EAB on penny.
Photo: Wisconsin’s EAB
information service
Emerald ash borer in U.S. and Canada (2014). Photo: USDA 2014
Oliver C. Smith for GEP 204/504 2014

4. Oliver C. Smith for GEP 204/504 2014
3.0 GIS and RS as applied to invasives research
4
Fig. 1. Publications applying RS and GIS techniques
in mapping invasive species. (Joshi, 2004)
Publications
UP
250%
1990-2000
Both widely used to map and predict distribution of
invasive species. (Joshi et al., 2004)
Scientific publications referencing GIS and RS for
invasives research up 250% from 1990 to 2000.
(Joshi et al., 2004)
RS used to detect invasives in several ways:
•Direct detection of the invasive, or habitat.
•Indirect detection of effect, e.g., dying trees.
GIS used to interpret geospatial data
e.g., mapping distribution patterns of invasives using
remotely sensed image data.

5. 4.0 Case study #1
Oliver C. Smith for GEP 204/504 20145
Title
Ash decline assessment in emerald ash borer-
infested regions: a test of tree-level,
hyperspectral technologies (Pontius et al., 2008)
Objective
Determine if commercially available RS can detect
early-stage stress in ash.
Methods
•Remotely sensed image data collection
Whiskbroom hyperspectral scanner, sub-orbital, 1-meter
resolution.
•Field level data collection
Evaluating ash tree health on-the-ground to quantify symptoms
of stress and EAB infestation.
•Spectral indexes
Selecting indexes with known sensitivity to plant stress indicators,
e.g., chlorophyl and water content in leaves.
Fig. 2. Areas (6) targeted for remote scanning.
(Pontius et al. 2008)
Areas targeted for remote scanning

6. 4.1 Field data collection
Methods
Health decline symptoms for 87 trees measured on
a range of factors:
•chlorophyll and water content in leaves
•canopy health
•direct evidence of EAB infestation
Ash crown vigor as health metric.
Photo: Alex Hyde. Alex Hyde Photography
http://alexhyde.photoshelter.com/
Assessing tree health.
Photo: MIT Technology Review (2013)
http://www.technologyreview.com/news/516411/
the-app-craze-branches-into-forestry/
Oliver C. Smith for GEP 204/504 20146
The result is a tree health decline rating
A 0-10 scale for interpreting remotely sensed
reflectance patterns.
Showing class
and cut-offs
Chlorophyll
factor A
Chlorophyll
factor B
Fig. 3. Summary health decline rating. (Pontius et al., 2008)

7. 4.2 Spectral indexes (predictive algorithms)
Six indexes sensitive to plant
stress are selected:
Indexes are applied to the
remotely sensed reflectance
patterns ...
Output:
Pixel-by-pixel tree health rating
for every forested pixel in the RS
imagery.
7
Healthy
Sick
Fig. 4. Key indexes and wavelengths. Source: (Pontius et. al, 2008)
Oliver C. Smith for GEP 204/504 2014
Each index focuses on a unique area of spectrum

8. 4.3 Results
Scanned region A:
High ash density and
prolonged EAB infestation.
Scanned region B:
High ash density and
no known EAB infestation.
Scanned region C:
High ash density, some
decline but no EAB reports.
Maybe this region is EAB infested!
3.9
8
2.1
Oliver C. Smith for GEP 204/504 2014
4.9
Decline
average
Decline
average
Decline
average
Fig. 5. Decline averages. (Pontius et. al, 2008)

9. 4.4 Conclusions
“The combination of traditional plot-level forest health assessment
techniques with commercially available hyperspectral remote sensing
imagery can produce accurate, detailed, large-scale maps of forest health.”
Oliver C. Smith for GEP 204/504 20149
(Pontius et al., 2008)

10. 5.0 Case study #2
Oliver C. Smith for GEP 204/504 201410
Title
Modeling local and long-distance dispersal of invasive emerald ash borer
in North America (Muirhead, Leung, Overdijk, Kelly, Nandakumar, Marchant, & MacIsaac, 2006)
Objective
Validate models for predicting EAB dispersal.
Methods
Two models are tested:
A. Short-range dispersal by flight.1
B. Long-range dispersal via human activity.
1Predicted areas of short-range dispersal mapped with ARCGIS and Albers-Equal Areas Conic
projection to maintain shape/distance between infested areas.

11. 5.1 Short-range dispersal by flight
Oliver C. Smith for GEP 204/504 201411
Epicenter
2002
2003
2004
2005
2004
infested
Dispersal
prediction
Fig. 6. Short-range dispersal 2002-2005. (Muirhead et al., 2006)
2005
infested
2002
infested
2003
infested

12. Oliver C. Smith for GEP 204/504 201412
Epicenter
Infested
area
Proximity to human population centers and epicenter
as factors influencing probability of infestation
5.2 Long-range dispersal via human activity
HighLow
Fig. 7. Probability of infestation. (Muirhead, 2006)
Infested
area
Results:
Probability of infestation:
•Decreases with distance from epicenter.
•Increases near human population centers.
•Model is accurate to 97.5%.
Table 1. Validating proximity to human population and
distance from epicenter as factors influencing probability
of infestation. (Muirhead, 2006)
population
centers???

13. 5.3 Conclusions
Oliver C. Smith for GEP 204/504 201413
•EAB has spread in North America through short-range flights and by
long-range dispersal linked to human activity.
•Probability of infestation decreases with distance from epicenters, but
increases in proximity to human population centers. (Muirhead et al., 2006)

14. Asner, G. P., Jones, M. O., Martin, R. E., Knapp, D. E., & Hughes, R. F. (2008). Remote sensing of native and
invasive species in Hawaiian forests. Remote Sensing of Environment, 112(5), 1912-1926.
BenDor, T. K., Metcalf, S. S., Fontenot, L. E., Sangunett, B., & Hannon, B. (2006).
Modeling the spread of the emerald ash borer. Ecological Modelling,197(1), 221-236.
Innes, J. L., & Koch, B. (1998). Forest biodiversity and its assessment by remote sensing.
Global Ecology & Biogeography Letters, 7(6), 397-419.
Jensen, J., & Jensen, R. (2012). Introductory geographic information systems. Englewood Cliffs, NJ: Prentice Hall.
Joshi, C., de Leeuw, J., & van Duren, I. C. (2004, July). Remote sensing and GIS applications for mapping
and spatial modelling of invasive species. Proceedings of ISPRS, 35, B7.
Kovacs, K. F., Haight, R. G., McCullough, D. G., Mercader, R. J., Siegert, N. W., & Liebhold, A. M. (2010).
Cost of potential emerald ash borer damage in US communities, 2009–2019. Ecological Economics, 69(3), 569-578.
McCullough, D., Poland, T., Anulewicz, A., & Cappaert, D. (2009). Emerald Ash Borer (Coleoptera: Buprestidae
Attraction to stressed or baited ash trees. Envion. Entomol. 38(6): 1668-1679 (2009).
Muirhead, J. R., Leung, B., Overdijk, C., Kelly, D. W., Nandakumar, K., Marchant, K. R., & MacIsaac, H. J. (2006).
Modelling local and long-distance dispersal of invasive emerald ash borer Agrilus planipennis (Coleoptera)
in North America. Diversity and Distributions, 12(1), 71-79.
Pontius, J., Martin, M., Plourde, L., & Hallett, R. (2008). Ash decline assessment in emerald ash borer-infested regions:
A test of tree-level, hyperspectral technologies. Remote Sensing of Environment, 112(5), 2665-2676.
Ustin, S. L., DiPietro, D., Olmstead, K., Underwood, E., & Scheer, G. J. (2002, June). Hyperspectral remote sensing
for invasive species detection and mapping. In Geoscience and Remote Sensing Symposium, 2002.
IGARSS'02. 2002 IEEE International (3, 1658-1660). IEEE.
6.0 References
14 Oliver C. Smith for GEP 204/504 2014