Using a remotely sensed optimized Disturbance Index to detect insect defoliation in the Apostle Islands, Wisconsin, USA

Remote Sensing of Environment

Volumn 136, Issue , 2013, Pages 210-217

  Thayn, Jonathan Boyd ^a  

^a ILLINOIS STATE UNIVERSITY   (United States)

Author keywords

Apostle Island National Lakeshore; Disturbance Index; Gypsy Moth; Nelder Mead optimization

Indexed keywords

APOSTLE ISLAND NATIONAL LAKESHORE; BACKGROUND VARIATION; DISTURBANCE INDEX; GYPSY MOTHS; INSECT DEFOLIATION; LOCAL CONDITIONS; NELDER MEADS; UNDISTURBED FORESTS;

MAPS;

OPTIMIZATION;

DEFOLIATION; ENVIRONMENTAL MONITORING; FOREST CANOPY; INDEX METHOD; MAPPING METHOD; MOTH; OPTIMIZATION; REMOTE SENSING;

APOSTLE ISLANDS; UNITED STATES; WISCONSIN;

HEXAPODA; LYMANTRIA; LYMANTRIA DISPAR;

EID: 84878745933     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2013.05.008     Document Type: Article

References (47)

1. Akella M.R., Delmelle E., Batta R., Rogerson P., Blatt A. Adaptive cell tower location using geostatistics. Geographical Analysis 2010, 42:227-244.
2. Beals E. Forest Bird Communities in the Apostle Islands of Wisconsin. The Wilson Bulletin 1960, 72:156-181.
3. Bélisle C.J.P. Convergence theorems for a class of simulated annealing algorithms on Rd. Journal of Applied Probability 1992, 29:885-895.
4. Bortolot Z.J., Wynne R.H. Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data. ISPRS Journal of Photogrammetry and Remote Sensing 2005, 59:342-360.
5. Burt J.E., Barber G.M., Rigby D.L. Elementary statistics for geographers 2009, Guilford Press.
6. Campbell R.W., Sloan R.J. Forest stand responses to defoliation by the gypsy moth. Forest Science 1977, 23. (a0001-z0001).
7. Chavez P.S. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 1988, 24:459-479.
8. Crist E.P., Cicone R.C. A physically-based transformation of thematic mapper data - The TM tasseled cap. Geoscience and Remote Sensing, IEEE Transactions 1984, GE-22:256-263.
9. de Beurs K.M., Townsend P.A. Estimating the effect of gypsy moth defoliation using MODIS. Remote Sensing of Environment 2008, 112:3983-3990.
10. Deel L.N., McNeil B.E., Curtis P.G., Serbin S.P., Singh A., Eshleman K.N., et al. Relationship of a Landsat cumulative disturbance index to canopy nitrogen and forest structure. Remote Sensing of Environment 2012, 118:40-49.
11. DeRose R.J., Long J.N., Ramsey R.D. Combining dendrochronological data and the disturbance index to assess Engelmann spruce mortality caused by a spruce beetle outbreak in southern Utah, USA. Remote Sensing of Environment 2011, 115:2342-2349.
12. Eisenbies M.H., Davidson C., Johnson J., Amateis R., Gottschalk K. Tree mortality in mixed pinehardwood stands defoliated by the European gypsy moth (Lymantria dispar L.). Forest Science 2007, 53:683-691.
13. Foss L.K., Rieske L.K. Species-specific differences in oak foliage affect preference and performance of gypsy moth caterpillars. Entomologia Experimentalis et Applicata 2003, 108:87-93.
14. Gede M. The use of the Nelder-Mead Method in estimating projection parameters for globe photographs. Acta Geodaetica et Geophysica Hungarica 2010, 45:17-23.
15. Hais M., Jonášová M., Langhammer J., Kučera T. Comparison of two types of forest disturbance using multitemporal Landsat TM/ETM+imagery and field vegetation data. Remote Sensing of Environment 2009, 113:835-845.
16. Hayes D.J., Cohen W.B., Sader S.A., Irwin D.E. Estimating proportional change in forest cover as a continous variable from multi-year data. Remote Sensing of Environment 2008, 112:735-749.
17. He L., Chen J.M., Zhang S., Gomez G., Pan Y., McCullough K., et al. Normalized algorithm for mapping and dating forest disturbances and regrowth for the United States. International Journal of Applied Earth Observation and Geoinformation 2011, 13:236-245.
18. Healey S.P., Cohen W.B., Zhiqiang Y., Krankina O.N. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment 2005, 97:301-310.
19. Healey S.P., Yang Z., Cohen W.B., Pierce D.J. Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sensing of Environment 2006, 101:115-126.
20. Hilker T., Wulder M.A., Coops N.C., Linke J., McDermid G., Masek J.G., et al. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment 2009, 113:1613-1627.
21. Huang C., Goward S.N., Masek J.G., Thomas N., Zhu Z., Vogelmann J.E. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment 2010, 114:183-198.
22. Hurley A., Watts D., Burke B., Richards C. Identifying Gypsy Moth defoliation in Ohio using Landsat data. Environment and Engineering Geoscience 2004, X:321-328.
23. Jensen J.R. Introductory Digital Image Processing 2005, Pearson Prentice Hall, Upper Saddle River, NJ. 3rd ed.
24. Jones C.G., Ostfeld R.S., Richard M.P., Schauber E.M., Wolff J.O. Chain reactions linking acorns to gypsy moth outbreaks and lyme disease risk. Science 1998, 279:1023-1026.
25. Joria P.E., Ahearn S.C. A comparison of the SPOT and Landsat thematic mapper satellite systems for detecting gypsy moth defoliation in Michigan. Photogrammetric Engineering and Remote Sensing 1991, 57.
26. Judziewicz E.J., Koch R.G. Flora and vegetation of the Apostle Islands National Lakeshore and Madeline Islands, Ashland and Bayfield Counties, Wisconsin. Michigan Botanist 1993, 32:43-193.
27. Kauth R.J., Thomas G.S. The Tasseled Cap - a graphical description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data 1976, Purdue University, West Lafayette, Indiana, (44B41-44B51).
28. Kuemmerle T., Hostert P., Radeloff V.C., Perzanowski K., Kruhlov I. Post-socialist forest disturbance in the carpathian border region of Poland, Slovakia, and Ukraine. Ecological Applications 2007, 17:1279-1295.
29. Lagarias J.C., Reeds J.A., Wright M.H., Wright P.E. Convergence properties of the Nelder-Mead simplex optimization method in low dimensions. SIAM Journal on Optimization 1998, 9:112-147.
30. Liebhold A.M., Elkinton J.S., Wiliams D., Muzika R.M. What causes outbreaks of the gypsy moth in North America?. Population Ecology 2000, 42:257-266.
31. Liebhold A., Mastro V., Schaefer P.W. Learning from the legacy of Léopold Trouvelot. Bulletin of the Entomological Society of America 1989, 35:20-22.
32. Masek J.G., Huang C., Wolfe R., Cohen W., Hall F., Kutler J., et al. North American forest disturbance mapped from a decadal Landsat record. Remote Sensing of Environment 2008, 112:2914-2926.
33. Nelder J.A., Mead R. A simplex method for function minimization. The Computer Journal 1965, 7:308-313.
34. Olofsson P., Foody G.M., Herold M., Stehan S.V., Woodcock C.E., Wulder M.A. Good practices for assessing accuracy and estimating area of land change 2013, (in review).
35. Olofsson P., Foody G.M., Stehman S.V., Woodcock C.E. Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment 2013, 129:122-131.
36. Pimentel D., Zuniga R., Morrison D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics 2005, 52:273-288.
37. Potere D., Felerabend N., Strahler A.H., Bright E.A. Wal-Mart from space: A new source for land cover change validation. Photogrammetric Engineering and Remote Sensing 2008, 74:913-919.
38. Pukkala T. Population-based methods in the optimization of stand management. Silva Fennica 2009, 43:261-274.
39. R Core Team R: A language and environment for statistical computing 2012, R Foundation for Statistical Computing, Vienna, Austria.
40. Song C., Woodcock C.E., Seto K.C., Lenney M.P., Macomber S.A. Classification and change detection using landsat TM Data: When and how to correct atmospheric effects?. Remote Sensing of Environment 2001, 75:230-244.
41. Stehman S.V., Foody G.M. Accuracy assessment. The SAGE handbook of remote sensing 2009, 288-309. SAGE Publications. T.A. Warner, M.D. Nellis, G.M. Foody (Eds.).
42. Strangeways H.J., Ioannides R.T. Rigorous calculation of ionospheric effects on GPS earth-satellite paths using a precise path determination method. Acta Geodaetica et Geophysica Hungarica 2002, 37:281-292.
43. Tobin P.C., Hajek A.E. Release, establishment, and intial spread of the fungal pathogen Entomophaga maimaiga in island populations of Lymantria dispar. Biological Control 2012, 63:31-39.
44. Tobin P.C., Van Stappen J., Blackburn L.M. Human visitation rates to the Apostle Islands National Lakeshore and the introduction of the non-native species Lymantria dispar (L.). Journal of Environmental Management 2010, 91:1991-1996.
45. Williams D.L. Computer analysis and mapping of gypsy moth defoliation levels in Pennsylvania using Landsat-1 digitial data. Proceedings of the NASA Earth Presentations 1975, 1A:167-181.
46. Wilson B.F., McComb B.C. Dynamics of dead wood over 20years in a New England oak forest. Canadian Journal of Forest Research 2005, 35:682-692.
47. Wulder M.A., White J.C., Bentz B., Alvarez M.F., Coops N.C. Estimating the probability of mountain pine beetle red-attack damage. Remote Sensing of Environment 2006, 101:150-166.