Gep oliver smith_preso_0513_fin

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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.

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Gep oliver smith_preso_0513_fin

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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