Early detection of emerald ash borer infestation using multisourced data: A case study in the town of Oakville, Ontario, Canada

Journal of Applied Remote Sensing

Volumn 8, Issue 1, 2014, Pages

  Zhang, Kongwen ^a,b   Hu, Baoxin ^a   Robinson, Justin ^b  

^a YORK UNIVERSITY   (Canada)

^b Selkirk Geospatial Research Centre   (Canada)

Author keywords

data analysis; early detection; emerald ash borer; hyperspectral; remote sensing

Indexed keywords

AERIAL PHOTOGRAPHY; ANTENNAS; DATA REDUCTION; GEMS; REMOTE SENSING;

AIRBORNE HYPERSPECTRAL DATA; EMERALD ASH BORER; ENVIRONMENTAL THREATS; HIGH SPATIAL RESOLUTION; HYPERSPECTRAL; LEAF CHLOROPHYLL CONTENT; PREDICTION FUNCTION; REMOTELY SENSED DATA;

FORESTRY;

AIR CRAFT; DATA PROCESSING; FORESTRY; PHOTOGRAPHY; REMOTE SENSING;

EID: 84905728200     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.8.083602     Document Type: Article

References (37)

1. K. Maloney, J. Boughton, and N. Schneeberger, Emerald Ash Borer 2006 Brief, USDA Forest Service, Northeastern Area, State and Private Forestry, Newtown Square, Pennsylvania (2006).
2. T. D. Sydnor, M. Bumgardner, and A. Todd, "The potential economic impacts of emerald ash borer on Ohio US communities," Int. Soc. Arboriculture 1(33), 45-54 (2007).
3. J. S. Gould et al., Emerald Ash Borer, Agrilus Planipennis (Fairmarire), Biological Control Release and Recovery Guideline, United States Department of Agriculture (2013).
4. K. F. Kovacs, "Cost of potential emerald ash borer damage in U.S. communities," Ecol. Economic. 69(3), 569-578 (2010).
5. J. R. Rose, Landowners Guide for Woodlot Threaded by Emerald Ash Borer, Ontario Ministry of Natural Resource (2010).
6. A. C. Anulewicz, D. G. McCullough, and D. L. Cappaert, "Emerald ash borer (Agrilus planipennis) density and canopy dieback in three North American ash species," AUF 5(33), 338-349 (2007). (Pubitemid 47455627)
7. K. L. Ryall, J. G. Fidgen, and J. J. Turgeon, "Detectability of the emerald urban tree by using branch samples," Environ. Entomol. 40(3), 679-688 (2011).
8. D. G. McCullough and A. S. Katovich, Emerald Ash Borer. Pest Alert, Newtown Square, PA:USDA Forest Service NA-PR-02-04 (2004).
9. J. Pontius et al., "Ash decline assessment in emerald ash borer-infested regions: a test of tree-level, hyperspectral technologies," Remote Sens. Environ. 112, 2665-2676 (2008).
10. J. S. Souci, I. Hanou, and D. Puchalski, "High resolution remote sensing analysis for early detection and response planning for emerald ash borer," Photogramm. Eng. Remote Sens. 75(8), 905-911 (2009).
11. I. Hanou, "Town of Oakville hyperspectral EAB analysis," Town of Oakville government document, http://www.oakville.ca/assets/general%20- %20residents/eab-hyperspectral.pdf (2010).
12. D. Smitley, T. Davis, and E. Rebek, "Progression of ash canopy thinning and dieback outward from the initial infestation of emerald ash borer (Coleoptera: Buprestidae) in southeastern Michigan," J. Economic. Entormol. 101, 1643-1650 (2008).
13. P. Cosmopoulos and D. J. King, "Temporal analysis of forest structural condition at an acid mine site using multispectral digital camera imagery," Int. J. Remote Sens. 12(25), 2259-2275 (2008). (Pubitemid 38973490)
14. K. Omari et al., "Retrieval of forest canopy parameters by inversion of the proflair leaf canopy reflectance model using the LUT approach," IEEE J. Sel. Topics Appl. Earth Observ. 6(2), 715-723 (2013).
15. T. K. Ben Dor et al., "Modeling the spread of the emerald ash borer," Ecol. Model. 197(1), 222-236 (2006).
16. D. A. Herms et al., Insecticide Options For Protecting Ash Trees From Emerald Ash Borer, North Central IPM Center Bulletin, Canada (2009).
17. K. Zhang and B. Hu, "Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles," Remote Sens. 4, 1741-1757 (2012).
18. L. Jing et al., "Automated tree crown delineation from imagery based on morphological techniques," in Proc. 35th Int. Symposium on Remote Sensing of Environment, pp. 22-26, Beijing (2013).
19. G. le Maire, C. Francois, and E. Dufrene, "Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements," Remote Sens. Environ. 89, 1-28 (2004). (Pubitemid 38178919)
20. S. Thenkabail, J. G. Lyon, and A. Huete, Hyperspectral Remote Sensing of Vegetation, CRC Press, Taylor and Francis Group (2012).
21. Y. Zhang et al., "Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery," Remote Sens. Environ. 112, 3234-3247 (2008).
22. D. A. Sims and J. A. Gamon, "Relationships between leaf pigment content and spectral reflectance across a wide range of species leaf structures and development stages," Remote Sens. Environ. 81, 337-354 (2002). (Pubitemid 34853603)
23. J. D. Barnes et al., "A reappraisal of the use of DMSO for extraction and determination of chlorophylls a and b in lichens and higher plants," Environ. Exp. Bot. 32(2), 85-100 (1992).
24. G. A. Carter and R. L. Miller, "Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands," Remote Sens. Environ. 50, 295-302 (1994).
25. A. M. Smith et al., "Leaf area index from CHRIS satellite and application in plant yield estimation," in Proc. 26th Canadian Symposium on Remote Sensing, Wolfville, Nova Scotia (2005).
26. J. E. Vogelmann, B. N. Rock, and D. M. Moss, "Red edge spectral measurements from sugar maple leaves," Int. J. Remote Sens. 14, 1563-1575 (1993).
27. A. A. Gitelson and M. N. Merzlyak, "Quantitative estimation of chlorophyll-a using reflectance spectra experiments with autumn chestnut and maple leaves," J. Photochem. Phytobiol. 22, 247-252 (1994). (Pubitemid 24082925)
28. D. Haboudane et al., "Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture," Remote Sens. Environ. 81, 416-426 (2002). (Pubitemid 34853609)
29. M. N. Merzlyak et al., "Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening," Physiol. Plantarum 106, 135-141 (1999).
30. G. A. Carter, "Ratio of leaf reflectance in narrow wavebands as indicators of plants stress," J. Int. Remote Sens. 15, 697-703 (1994).
31. K. Zhang et al., "Improving the retrieval of the biophysical parameters of vegetation canopies by using the contribution index," Canadian J. Remote Sens. 37(6), 643-652 (2012).
32. S. Jacquemoud and F. Baret, "Prospect: a model of leaf optical properties," Remote Sens. Environ. 34(2), 75-91 (1990).
33. W. Verhoef, "Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model," Remote Sens. Environ. 16, 125-141 (1984).
34. J. A. Gamon, J. Penuelas, and C. B. Field, "A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency," Remote Sens. Environ. 41, 35-44 (1992).
35. P. Zarco-Tejada et al., "Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data," IEEE Trans. Geosci. Remote Sens. 39, 1491-1507 (2001). (Pubitemid 32732662)
36. D. Haboudane et al., "Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture," Remote Sens. Environ. 90, 337-352 (2004). (Pubitemid 38431251)
37. S. Jacquemoud et al., "PROSPECT + SAIL models: a review of use for vegetation characterization," Remote Sens. Environ. 113, S55-SS66 (2009).