Near-real-time monitoring of insect defoliation using Landsat time series

Forests

Volumn 8, Issue 8, 2017, Pages

  Pasquarella, Valerie J ^a   Bradley, Bethany A ^a   Woodcock, Curtis E ^b  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b BOSTON UNIVERSITY   (United States)

Author keywords

Continuous Change Detection and Classification (CCDC); Defoliation; Gypsy moth; Landsat; Lymantria dispar; Synthetic images; Time series

Indexed keywords

TIME SERIES;

CHANGE DETECTION; DEFOLIATION; GYPSY MOTHS; LANDSAT; LYMANTRIA DISPAR; SYNTHETIC IMAGES;

FORESTRY;

DEFOLIATION; INSECT; LANDSAT; MONITORING; MOTH; REAL TIME; SATELLITE IMAGERY; TIME SERIES ANALYSIS;

DEFOLIATION; FORESTS; INFESTATION; INSECTS; PATHOGENS; REMOTE SENSING; TIME SERIES ANALYSIS;

UNITED STATES;

HEXAPODA; LYMANTRIA; LYMANTRIA DISPAR;

EID: 85027454664     PISSN: None     EISSN: 19994907     Source Type: Journal    
DOI: 10.3390/f8080275     Document Type: Article

References (45)

1. Pimentel, D.; Lach, L.; Zuniga, R.; Morrison, D. Environmental and economic costs of nonindigenous species in the United States. BioScience 2000, 50, 53-65.
2. Aukema, J.E.; McCullough, D.G.; Von Holle, B.; Liebhold, A.M.; Britton, K.; Frankel, S.J. Historical accumulation of nonindigenous forest pests in the continental United States. BioScience 2010, 60, 886-897.
3. Hall, R.J.; Skakun, R.S.; Arsenault, E.J. Remotely sensed data in the mapping of insect defoliation. In Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches; Wulder, M.A., Franklin, S.E., Eds.; CRC Press, Taylor and Francis: Boca Raton, FL, USA, 2006; pp. 85-111.
4. Lovett, G.M.; Weiss, M.; Liebhold, A.M.; Holmes, T.P.; Leung, B.; Lambert, K.F.; Orwig, D.A.; Campbell, F.T.; Rosenthal, J.; McCullough, D.G.; et al. Nonnative forest insects and pathogens in the United States: Impacts and policy options. Ecol. Appl. 2016, 26, 1437-1455.
5. Liebhold, A.M.; Rossi, R.E.; Kemp, W.P. Geostatistics and geographic information systems in applied insect ecology. Annu. Rev. Entomol. 1993, 38, 303-327.
6. Senf, C.; Seidl, R.; Hostert, P. Remote sensing of forest insect disturbances: Current state and future directions. Int. J. Appl. Earth Obs. Geoinf. 2017, 60, 49-60.
7. Elkinton, J.S.; Liebhold, A.M. Population dynamics of gypsy moth in North America. Annu. Rev. Entomol. 1990, 35, 571-596.
8. Régnière, J.; Nealis, V.; Porter, K. Climate suitability and management of the gypsy moth invasion into Canada. Biol. Invasions 2009, 11, 135-148.
9. Liebhold, A.M.; Elmes, G.A.; Halverson, J.A.; Quimby, J. Landscape characterization of forest susceptibility to gypsy moth defoliation. For. Sci. 1994, 40, 18-29.
10. Gansner, D.A.; Herrick, O.W.; Mason, G.N.; Gottschalk, K.W. Coping with the gypsy moth on new frontiers of infestation. South. J. Appl. For. 1987, 11, 201-209.
11. Barron, E.S.; Patterson, W.A., III. Monitoring the effects of gypsy moth defoliation on forest stand dynamics on Cape Cod, Massachusetts: Sampling intervals and appropriate interpretations. For. Ecol. Manag. 2008, 256, 2092-2100.
12. Morin, R.S.; Liebhold, A.M. Invasive forest defoliator contributes to the impending downward trend of oak dominance in eastern North America. Forestry 2016, 89, 284-289.
13. Liebhold, A.M.; Halverson, J.A.; Elmes, G.A. Gypsy moth invasion in North America: A quantitative analysis. J. Biogeogr. 1992, 19, 513-520.
14. Gray, D.R. The gypsy moth life stage model: Landscape-wide estimates of gypsy moth establishment using a multi-generational phenology model. Ecol. Model. 2004, 176, 155-171.
15. Williams, D.L.; Ingram, K.J. Integration of digital elevation model data and Landsat MSS data to quantify the effects of slope orientation on the classification of forest canopy condition. In Proceedings of the Seventh International Symposium Machine Processing of Remotely Sensed Data with special emphasis on Range, Forest andWetlands Assessment, Purdue University,West Lafayette, IN, USA, 23-26 June 1981; Paper 445. Available online: http://docs.lib.purdue.edu/lars_symp/445/ (accessed on 26 Jul 2017).
16. Nelson, R.F. Detecting forest canopy change due to insect activity using Landsat MSS. Photogramm. Eng. Remote Sens. 1983, 49, 1303-1314.
17. Dottavio, C.L.; Williams, D.L. Satellite technology: An improved means for monitoring forest insect defoliation. J. For. 1983, 81, 30-34.
18. Williams, D.L.; Nelson, R.F. Use of remotely sensed data for assessing forest stand conditions in the eastern United States. IEEE Trans. Geosci. Remote Sens. 1986, 1, 130-138.
19. Rock, B.N.; Vogelmann, J.E.; Williams, D.L.; Vogelmann, A.F.; Hoshizaki, T. Remote detection of forest damage. BioScience 1986, 36, 439-445.
20. Rullan-Silva, C.D.; Olthoff, A.E.; de la Mata, J.D.; Pajares-Alonso, J.A. Remote monitoring of forest insect defoliation-A review. For. Syst. 2013, 22, 377.
21. Townsend, P.A.; Singh, A.; Foster, J.R.; Rehberg, N.J.; Kingdon, C.C.; Eshleman, K.N.; Seagle, S.W. A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sens. Environ. 2012, 119, 255-265.
22. Zhu, Z.;Woodcock, C.E.; Holden, C.; Yang, Z. Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time. Remote Sens. Environ. 2015, 162, 67-83.
23. Defoliators v0.1. Available online: https://github.com/valpasq/defoliators/releases/tag/v0.1 (accessed on 3 July 2017).
24. Pasquarella, V. 2016 Gypsy Moth Assessment-Southern New England [Data Set]. Zenodo. Available online: http://doi.org/10.5281/zenodo.801800 (accessed on 26 Jul 2017).
25. ESPA On-Demand Interface. Available online: https://espa.cr.usgs.gov/ (accessed on 31 May 2017).
26. Holden, C.E.; Arevelo, P.; Pasquarella, V. Yet Another Time Series Model (YATSM): V0.6.1. Available online: https://github.com/ceholden/yatsm/releases/tag/v0.6.1 (accessed on 31 May 2017).
27. Zhu, Z.; Woodcock, C.E.; Olofsson, P. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ. 2012, 122, 75-91.
28. Zhu, Z.;Woodcock, C.E. Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ. 2014, 144, 152-171.
29. De Beurs, K.; Townsend, P. Estimating the effect of gypsy moth defoliation usingMODIS. Remote Sens. Environ. 2008, 112, 3983-3990.
30. Pasquarella, V.J.; Holden, C.E.; Kaufman, L.; Woodcock, C.E. From imagery to ecology: Leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics. Remote Sens. Ecol. Conserv. 2016, 2, 152-170.
31. Crist, E.P. A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sens. Environ. 1985, 17, 301-306.
32. Kennedy, R.E.; Yang, Z.; Cohen, W.B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-Temporal segmentation algorithms. Remote Sens. Environ. 2010, 114, 2897-2910.
33. Holden, C.E.; Woodcock, C.E. An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations. Remote Sens. Environ. 2016, 185, 16-36.
34. Vogelmann, J.E.; Gallant, A.L.; Shi, H.; Zhu, Z. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sens. Environ. 2016, 185, 258-270.
35. Zhu, Z.; Gallant, A.L.;Woodcock, C.E.; Pengra, B.; Olofsson, P.; Loveland, T.R.; Jin, S.; Dahal, D.; Yang, L.; Auch, R.F. Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative. ISPRS J. Photogramm. Remote Sens. 2016, 122, 206-221.
36. Chastain, R.A.; Housman, I.W.; Clark, J. Using Google Earth Engine to Automate Forest Disturbance Detection in Near-Real Time: A Case Study that Increased Efficiencies by 80 Percent; RSAC-10091-TIP1; USDA Forest Service: Salt Lake City, UT, USA, 2015.
37. Spruce, J.P.; Sader, S.; Ryan, R.E.; Smoot, J.; Kuper, P.; Ross, K.; Prados, D.; Russell, J.; Gasser, G.; McKellip, R.; et al. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens. Environ. 2011, 115, 427-437.
38. Deel, L.N.; McNeil, B.E.; Curtis, P.G.; Serbin, S.P.; Singh, A.; Eshleman, K.N.; Townsend, P.A. Relationship of a Landsat cumulative disturbance index to canopy nitrogen and forest structure. Remote Sens. Environ. 2012, 118, 40-49.
39. Healey, S.; Cohen, W.; Zhiqiang, Y.; Krankina, O. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sens. Environ. 2005, 97, 301-310.
40. Foster, J.R.; Townsend, P.A.; Mladenoff, D.J. Spatial dynamics of a gypsy moth defoliation outbreak and dependence on habitat characteristics. Landsc. Ecol. 2013, 28, 1307-1320.
41. Johnson, E.W.; Ross, J. Quantifying error in aerial survey data. Aust. For. 2008, 71, 216-222.
42. Elkinton, J.; Boettner, G.; Liebhold, A.; Gwiazdowski, R. Biology, Spread, and Biological Control of Winter Moth in the Eastern United States; FHTET-2014-07; U.S. Department of Agriculture, Forest Service, Forest Health Technology Team: Morgantown, WV, USA, 2015; 22 p. Available online: https://www.fs.fed.us/nrs/pubs/ jrnl/2015/fhtet-2014-07_elkinton_2015_001.pdf (accessed on 1 June 2017).
43. Mandanici, E.; Bitelli, G. Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens. 2016, 8, 1014.
44. Yan, L.; Roy, D.P.; Zhang, H.; Li, J.; Huang, H. An automated approach for sub-pixel registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery. Remote Sens. 2016, 8, 520.
45. Flood, N. Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia. Remote Sens. 2017, 9, 659.