A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests

Remote Sensing of Environment

Volumn 115, Issue 12, 2011, Pages 3707-3718

  Meigs, Garrett W ^a   Kennedy, Robert E ^a   Cohen, Warren B ^b  

^a OREGON STATE UNIVERSITY   (United States)

^b PACIFIC NORTHWEST RESEARCH STATION   (United States)

Author keywords

Bark beetle; Change detection; Defoliator; Fire; Fuel; Insect disturbance; Landsat time series; Mountain pine beetle; Pacific Northwest; Spectral trajectory; Western spruce budworm

Indexed keywords

BARK BEETLE; CHANGE DETECTION; DEFOLIATOR; INSECT DISTURBANCE; LANDSAT; MOUNTAIN PINE BEETLE; PACIFIC NORTHWEST; SPRUCE BUDWORM;

FIRES; FUELS; LANDFORMS; PHOTOMAPPING; RECOVERY; REMOTE SENSING; SATELLITE IMAGERY; SIGNAL DETECTION; SPECTROSCOPY; SURVEYS; TIME SERIES; TRAJECTORIES;

FORESTRY;

ALGORITHM; BARK; BEETLE; CONIFEROUS FOREST; DISTURBANCE; DOMINANCE; FIRE; FOREST FLOOR; FUEL; LANDSAT; MAPPING METHOD; MEASUREMENT METHOD; MIXED FOREST; MORTALITY; REMOTE SENSING; SATELLITE IMAGERY; SPECTRAL ANALYSIS; STAND STRUCTURE; TIME SERIES; VEGETATION STRUCTURE;

BARK; FIRES; FORESTRY; FUELS; IMAGE ANALYSIS; INSECTS; PINUS MUGO; RECOVERY; REMOTE SENSING; SATELLITES; SPECTROSCOPY; SURVEYS;

CASCADE RANGE; PACIFIC OCEAN; PACIFIC OCEAN (NORTHWEST); UNITED STATES;

CHORISTONEURA OCCIDENTALIS; COLEOPTERA; CONIFEROPHYTA; HEXAPODA; PICEA; PINUS CONTORTA; PINUS MUGO; SCOLYTINAE;

EID: 81355146459     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2011.09.009     Document Type: Article

References (47)

1. Balshi M.S., McGuire A.D., Duffy P., Flannigan M., Walsh J., Melillo J.M. Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach. Global Change Biology 2009, 15:578-600.
2. Bentz B.J., Regniere J., Fettig C.J., Hansen E.M., Hayes J.L., Hicke J.A., Kelsey R.G., Negron J.F., Seybold S.J. Climate change and bark beetles of the Western United States and Canada: Direct and indirect effects. Bioscience 2010, 60:602-613.
3. Brown J.K. Handbook for inventorying downed woods material. USDA Forest Service General Technical Report INT-GTR-16 1974, USDA Forest Service Intermountain Forest and Range Experiment Station, Ogden, UT.
4. Brown J.K., Oberheu R.O., Johnston C.M. Handbook for inventorying surface fuels and biomass in the Interior West. USDA Forest Service General Technical Report INT-GTR-129 1982, USDA Forest Service Intermountain Forest and Range Experiment Station, Ogden, UT.
5. Canty M.J., Nielsen A.A., Schmidt M. Automatic radiometric normalization of multitemporal satellite imagery. Remote Sensing of Environment 2004, 91:441-451.
6. Carlson T.N., Ripley D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 1997, 62:241-252.
7. Chavez P.S. Image-based atmospheric corrections - revisited and improved. Photogrammetric Engineering & Remote Sensing 1996, 62:1025-1036.
8. Ciesla W.M. Aerial signatures of forest insect and disease damage in the western United States. Forest Health Technology Team, FHTET-01-06 2006, USDA Forest Service, Ft. Collins, CO.
9. Cohen W.B., Goward S.N. Landsat's role in ecological applications of remote sensing. Bioscience 2004, 54:535-545.
10. Cohen W.B., Yang Z.G., Kennedy R.E. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. Remote Sensing of Environment 2010, 114:2911-2924.
11. Coops N.C., Wulder M.A., White J.C. Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation. Remote Sensing of Environment 2006, 105:83-97.
12. Daly C., Gibson W.P., Taylor G.H., Johnson G.L., Pasteris P. A knowledge-based approach to the statistical mapping of climate. Climate Research 2002, 22:99-113.
13. Dennison P.E., Brunelle A.R., Carter V.A. Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data. Remote Sensing of Environment 2010, 114:2431-2435.
14. Eidenshink J., Schwind B., Brewer K., Zhu Z.L., Quayle B., Howard S. A project for monitoring trends in burn severity. Fire Ecology 2007, 3:3-21.
15. Fleming R.A., Candau J.N., McAlpine R.S. Landscape-scale analysis of interactions between insect defoliation and forest fire in Central Canada. Climatic Change 2002, 55:251-272.
16. Franklin S.E., Waring R.H., McCreight R.W., Cohen W.B., Fiorella M. Aerial and satellite sensor detection and classification of western spruce budworm defoliation in a subalpine forest. Canadian Journal of Remote Sensing 1995, 21:299-308.
17. Geiszler D.R., Gara R.I., Driver C.H., Gallucci V.F., Martin R.E. Fire, fungi, and beetle influences on a lodgepole pine ecosystem of south-central Oregon. Oecologia 1980, 46:239-243.
18. Goheen E.M., Willhite E.A. Field guide to common diseases and insect pests of Oregon and Washington conifers. R6-NR-FID-PR-01-06, USDA Forest Service Pacific Northwest Region, Portland, OR 2006, (327 pp.).
19. Goodwin N.R., Coops N.C., Wulder M.A., Gillanders S., Schroeder T.A., Nelson T. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment 2008, 112:3680-3689.
20. Goodwin N.R., Magnussen S., Coops N.C., Wulder M.A. Curve fitting of time-series Landsat imagery for characterizing a mountain pine beetle infestation. International Journal of Remote Sensing 2010, 31:3263-3271.
21. Harmon M.E., Sexton J. Guidelines for measurements of woody detritus in forest ecosystems 1996, US LTER Network Office, Publication no. 20. University of Washington, Seattle, WA, 73 p.
22. Hudiburg T., Law B.E., Turner D.P., Campbell J.L., Donato D.C., Duane M. Carbon dynamics of Oregon and Northern California forests and potential land-based carbon storage. Ecological Applications 2009, 19:163-180.
23. Hummel S., Agee J.K. Western spruce budworm defoliation effects on forest structure and potential fire behavior. Northwest Science 2003, 77:159-169.
24. Kennedy R.E., Yang Z.G., Cohen W.B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sensing of Environment 2010, 114:2897-2910.
25. Key C.H., Benson N.C. Landscape assessment: Ground measure of severity, the composite burn index; and remote sensing of severity, the Normalized Burn Ratio. USDA Forest Service General Technical Report RMRS-GTR-164-CD 2006, USDA Forest Service Rocky Mountain Research Station, Fort Collins, CO.
26. Klutsch J.G., Negron J.F., Costello S.L., Rhoades C.C., West D.R., Popp J., Caissie R. Stand characteristics and downed woody debris accumulations associated with a mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in Colorado. Forest Ecology and Management 2009, 258:641-649.
27. Kurz W.A., Dymond C.C., Stinson G., Rampley G.J., Neilson E.T., Carroll A.L., Ebata T., Safranyik L. Mountain pine beetle and forest carbon feedback to climate change. Nature 2008, 452:987-990.
28. Littell J.S., Oneil E.E., McKenzie D., Hicke J.A., Lutz J.A., Norheim R.A., Elsner M.M. Forest ecosystems, disturbance, and climatic change in Washington State, USA. Climatic Change 2010, 102:129-158.
29. Logan J.A., Regniere J., Powell J.A. Assessing the impacts of global warming on forest pest dynamics. Frontiers in Ecology and the Environment 2003, 1:130-137.
30. Lynch H.J., Moorcroft P.R. A spatiotemporal Ripley's K-function to analyze interactions between spruce budworm and fire in British Columbia, Canada. Canadian Journal of Forest Research 2008, 38:3112-3119.
31. Lynch H.J., Renkin R.A., Crabtree R.L., Moorcroft P.R. The influence of previous mountain pine beetle (Dendroctonus ponderosae) activity on the 1988 Yellowstone fires. Ecosystems 2006, 9:1318-1327.
32. Meigs G.W., Donato D.C., Campbell J.L., Martin J.G., Law B.E. Forest fire impacts on carbon uptake, storage, and emission: The role of burn severity in the Eastern Cascades, Oregon. Ecosystems 2009, 12:1246-1267.
33. Negron J.F., Bentz B.J., Fettig C.J., Gillette N., Hansen E.M., Hays J.L., Kelsey R.G., Lundquist J.E., Lynch A.M., Progar R.A., Seybold S.J. US Forest Service bark beetle research in the western United States: Looking toward the future. Journal of Forestry 2008, 106:325-331.
34. Nelson T., Boots B., Wulder M.A. Large-area mountain pine beetle infestations: Spatial data representation and accuracy. The Forestry Chronicle 2006, 82:243-252.
35. Page W., Jenkins M.J. Mountain pine beetle-induced changes to selected lodgepole pine fuel complexes within the intermountain region. Forest Science 2007, 53:507-518.
36. Page W., Jenkins M.J. Predicted fire behavior in selected mountain pine beetle infested lodgepole pine. Forest Science 2007, 53:662-674.
37. R Development Core Team R: A language and environment for statistical computing 2011, R Foundation for Statistical Computing, Vienna, Austria, http://R-project.org.
38. Raffa K.F., Aukema B.H., Bentz B.J., Carroll A.L., Hicke J.A., Turner M.G., Romme W.H. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: The dynamics of bark beetle eruptions. Bioscience 2008, 58:501-517.
39. Russell R.E., Saab V.A., Dudley J.G., Rotella J.J. Snag longevity in relation to wildfire and postfire salvage logging. Forest Ecology and Management 2006, 232:179-187.
40. Simard M., Romme W.H., Griffin J.M., Turner M.G. Do mountain pine beetle outbreaks change the probability of active crown fire in lodgepole pine forests?. Ecological Monographs 2011, 81:3-24.
41. Swetnam T.W., Lynch A.M. Multicentury, regional-scale patterns of western spruce budworm outbreaks. Ecological Monographs 1993, 63:399-424.
42. Teillet P.M., Staenz K., Williams D.J. Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sensing of Environment 1997, 61:139-149.
43. Verbesselt J., Robinson A., Stone C., Culvenor D. Forecasting tree mortality using change metrics derived from MODIS satellite data. Forest Ecology and Management 2009, 258:1166-1173.
44. Vogelmann J.E., Howard S.M., Yang L.M., Larson C.R., Wylie B.K., Van Driel N. Completion of the 1990s National Land Cover Data Set for the conterminous United States from Landsat thematic mapper data and ancillary data sources. Photogrammetric Engineering and Remote Sensing 2001, 67:650-662.
45. Vogelmann J.E., Tolk B., Zhu Z.L. Monitoring forest changes in the southwestern United States using multitemporal Landsat data. Remote Sensing of Environment 2009, 113:1739-1748.
46. Westerling A.L., Hidalgo H.G., Cayan D.R., Swetnam T.W. Warming and earlier spring increase western US forest wildfire activity. Science 2006, 313:940-943.
47. Wulder M.A., Dymond C.C., White J.C., Leckie D.G., Carroll A.L. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. Forest Ecology and Management 2006, 221:27-41.