Characterizing spectral-temporal patterns of defoliator and bark beetle disturbances using Landsat time series

Remote Sensing of Environment

Volumn 170, Issue , 2015, Pages 166-177

  Senf, Cornelius ^a   Pflugmacher, Dirk ^a   Wulder, Michael A ^b   Hostert, Patrick ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

^b PACIFIC FORESTRY CENTRE   (Canada)

Author keywords

Bark beetles; British Columbia; Defoliation; Insect disturbances; Landsat; LandTrendr; Mountain pine beetle (Dendroctonus ponderosae (Hopkins)); Time series; Western spruce budworm (Choristoneura freemani Razowski)

Indexed keywords

DECISION TREES; ECOLOGY; ECOSYSTEMS; LANDFORMS; MAPPING; PLANTS (BOTANY); TIME SERIES;

BARK BEETLE; BRITISH COLUMBIA; DEFOLIATION; DENDROCTONUS PONDEROSAE; INSECT DISTURBANCES; LANDSAT; LANDTRENDR; SPRUCE BUDWORM;

FORESTRY;

ADAPTIVE MANAGEMENT; ALGORITHM; BEETLE; DEFOLIATION; FOREST ECOSYSTEM; LANDSAT; PEST DAMAGE; REMOTE SENSING; TEMPORAL ANALYSIS; TIME SERIES;

BRITISH COLUMBIA; CANADA;

CHORISTONEURA; CHORISTONEURA OCCIDENTALIS; COLEOPTERA; DENDROCTONUS PONDEROSAE; HEXAPODA; PINUS MUGO; SCOLYTINAE;

EID: 84943391616     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.09.019     Document Type: Article

References (73)

1. Alfaro R.I., Berg J., Axelson J. Periodicity of western spruce budworm in southern British Columbia, Canada. Forest Ecology and Management 2014, 315:72-79.
2. Alfaro R.I., Thomson A.J., Van Sickle G.A. Quantification of Douglas-fir growth losses caused by western spruce budworm defoliation using stem analysis. Canadian Journal of Forest Research 1984, 15.
3. Alfaro R.I., Van Sickle A.J., Thomson A.J., Wegwitz E. Tree mortality and radial growth losses caused by the western spruce budworm in a Douglas-fir stand in British Columbia. Canadian Journal of Forest Research 1982, 12:780-787.
4. Assal T.J., Sibold J., Reich R. Modeling a historical mountain pine beetle outbreak using Landsat MSS and multiple lines of evidence. Remote Sensing of Environment 2014, 155:275-288.
5. Axelson J.N., Smith D.J., Daniels L.D., Alfaro R.I. Multicentury reconstruction of western spruce budworm outbreaks in central British Columbia, Canada. Forest Ecology and Management 2015, 335:235-248.
6. Breiman L. Random forests. Machine Learning 2001, 45:5-32.
7. Bright B.C., Hudak A.T., Kennedy R.E., Meddens A.J.H. Landsat time series and Lidar as predictors of live and dead basal area across five bark beetle-affected forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 7:3440-3452.
8. Campbell R., Smith D.J., Arsenault A. Multicentury history of western spruce budworm outbreaks in interior Douglas-fir forests near Kamloops, British Columbia. Canadian Journal of Forest Research 2006, 36:1758-1769.
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., Kennedy R. 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. Cooke B.J., Nealis V.G., Regniere J. Insect defoliators as periodic disturbances in northern forest ecosystems. Plant disturbance ecology: The process and the response 2007, 487-525. Elsevier, Burlington, MA. E.A. Johnson, K. Miyanishi (Eds.).
12. Coops N.C., Gillanders S.N., Wulder M.A., Gergel S.E., Nelson T., Goodwin N.R. Assessing changes in forest fragmentation following infestation using time series Landsat imagery. Forest Ecology and Management 2010, 259:2355-2365.
13. Coops N.C., Waring R.H., Wulder M.A., White J.C. Prediction and assessment of bark beetle-induced mortality of lodgepole pine using estimates of stand vigor derived from remotely sensed data. Remote Sensing of Environment 2009, 113:1058-1066.
14. Coops N.C., Wulder M.A., White J.C. Identifying and describing forest disturbance and spatial pattern: Data selection issues and methodological implications. Understanding forest disturbance and spatial pattern 2006, CRC Press, Boca Raton. M.A. Wulder, S.E. Franklin (Eds.).
15. 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.
16. Core Team R. R: A language and environment for statistical computing 2014, R Foundation for Statistical Computing, Vienna, Austria.
17. Crist E.P. A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sensing of Environment 1985, 17:301-306.
18. De Beurs K., Townsend P. Estimating the effect of gypsy moth defoliation using MODIS. Remote Sensing of Environment 2008, 112:3983-3990.
19. Flower A., Gavin D.G., Heyerdahl E.K., Parsons R.A., Cohn G.M. Drought-triggered western spruce budworm outbreaks in the interior Pacific northwest: A multi-century dendrochronological record. Forest Ecology and Management 2014, 324:16-27.
20. Franklin A., Waring R.H., McGreight 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.
21. Franklin S.E., Fan H., Guo X. Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation. International Journal of Remote Sensing 2008, 29:1215-1220.
22. Franklin S.E., Wulder M.A., Skakun R.S., Carroll A.L. Mountain pine beetle red-attack forest damage classification using stratified Landsat™ data in British Columbia, Canada. Photogrammetric Engineering & Remote Sensing 2003, 69:283-288.
23. Gillanders S.N., Coops N.C., Wulder M.A., Gergel S.E., Nelson T. Multitemporal remote sensing of landscape dynamics and pattern change: Describing natural and anthropogenic trends. Progress in Physical Geography 2008, 32:503-528.
24. 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.
25. 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.
26. Hicke J.A., Allen C.D., Desai A.R., Dietze M.C., Hall R.J., Ted Hogg E.H., Vogelmann J. Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Global Change Biology 2012, 18:7-34.
27. Hope G.D., Mitchell W.R., Lloyd D.A., Erickson W.R., Harper W.L., Wikeen B.M. Interior Douglas-fir zone. Ecosystems of British Columbia 1991, 153-166. British Columbia Ministry of Forests, Victory, British Columbia, Canada. D. Meidinger, J. Pojar (Eds.).
28. Huang C.Q., Coward S.N., Masek J.G., Thomas N., Zhu Z.L., 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.
29. Hummel S., Agee J.K. Western spruce budworm defoliation effects on forest structure and potential fire behavior. Northwest Science 2003, 77:159-169.
30. Kennedy R., Andréfouet S., Cohen W.B., Gómez C., Griffiths P., Hais M., Zhu Z. Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology and the Environment 2014, 12:339-346.
31. 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 Sensing of Environment 2010, 114:2897-2910.
32. Kennedy R.E., Yang Z., Cohen W.B., Pfaff E., Braaten J., Nelson P. Spatial and temporal patterns of forest disturbance and regrowth within the area of the northwest forest plan. Remote Sensing of Environment 2012, 122:117-133.
33. 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. FIREMON: Fire Effects Monitoring and Inventory System. In, USDA Forest Service General Technical Report RMRS-GTR-164-CD 2006, Fort Collins: USDA Forest Service Rocky Mountain Research Station.
34. Kurz W.A., Dymond C.C., Stinson G., Rampley G.J., Neilson E.T., Carroll A.L., Safranyik L. Mountain pine beetle and forest carbon feedback to climate change. Nature 2008, 452:987-990.
35. Kurz W.A., Stinson G., Rampley G.J., Dymond C.C., Neilson E.T. Risk of natural disturbances makes future contribution of Canada's forests to the global carbon cycle highly uncertain. Proceedings of the National Academy of Sciences of the United States of America 2008, 105:1551-1555.
36. Leckie D.G., Gillis M.D. Forest inventory in Canada with emphasis on map production. The Forestry Chronicle 1995, 71:74-88.
37. Liaw A., Wiener M. Classification and regression by randomForest. The R Journal 2002, 2:18-22.
38. Logan J.A., Régnière J., Powell J.A. Assessing the impacts of global warming on forest pest dynamics. Frontiers in Ecology and the Environment 2003, 1:130-137.
39. 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.
40. Maclauchlan L., Brooks J.E., Hodge J.C. Analysis of historic western spruce budworm defoliation in south central British Columbia. Forest Ecology and Management 2006, 226:351-356.
41. Manel S., Williams H.C., Ormerod S.J. Evaluating presence-absence models in ecology: The need to account for prevalence. Journal of Applied Ecology 2001, 38:921-931.
42. Masek J.G., Goward S.N., Kennedy R.E., Cohen W.B., Moisen G.G., Schleeweis K., Huang C. United States forest disturbance trends observed using Landsat time series. Ecosystems 2013, 16:1087-1104.
43. Masek J.G., Vermote E.F., Saleous N.E., Wolfe R., Hall F.G., Huemmrich K.F., Teng-Kui L. A Landsat surface reflectance dataset for North America, 1990-2000. Geoscience and Remote Sensing Letters, IEEE 2006, 3:68-72.
44. Meddens A.J.H., Hicke J.A. Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado 2014, Forest Ecology and Management, USA.
45. Meddens A.J.H., Hicke J.A., Ferguson C.A. Spatiotemporal patterns of observed bark beetle-caused tree mortality in British Columbia and the western United States. Ecological Applications 2012, 22:1876-1891.
46. Meddens A.J.H., Hicke J.A., Vierling L.A., Hudak A.T. Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery. Remote Sensing of Environment 2013, 132:49-58.
47. Meigs G.W., Kennedy R.E., Cohen W.B. 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 2011, 115:3707-3718.
48. Meigs G.W., Kennedy R.E., Gray A.N., Gregory M.J. Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific northwest region, USA. Forest Ecology and Management 2015, 339:71-86.
49. Nealis V. Spruce Budworms, Choristoneura Lederer (Lepidoptera: Tortricidae). Encyclopedia of Entomology 2008, 3524-3531. Springer, Netherlands. J. Capinera (Ed.).
50. Parker T.J., Clancy K.M., Mathiasen R.L. Interactions among fire, insects and pathogens in coniferous forests of the interior western United States and Canada. Agricultural and Forest Entomology 2006, 8:167-189.
51. Pflugmacher D., Cohen W.B., Kennedy R.E. Using Landsat-derived disturbance history (1972-2010) to predict current forest structure. Remote Sensing of Environment 2012, 122:146-165.
52. Pflugmacher D., Cohen W.B., Kennedy R.E., Yang Z. Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics. Remote Sensing of Environment 2014, 151:124-137.
53. Pojar J., Klinka K., Meidinger D.V. Biogeoclimatic ecosystem classification in British Columbia. Forest Ecology and Management 1987, 22:119-154.
54. Radeloff V.C., Mladenoff D.J., Boyce M.S. Detecting jack pine budworm defoliation using spectral mixture analysis: Separating effects from determinants. Remote Sensing of Environment 1999, 69:156-169.
55. 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.
56. Schoennagel T., Veblen T.T., Romme W.H. The interaction of fire, fuels and climate across Rocky Mountain forests. Bioscience 2004, 54:661-676.
57. Schwalm C.R., Williams C.A., Schaefer K., Baldocchi D., Black T.A., Goldstein A.H., Scott R.L. Reduction in carbon uptake during turn of the century drought in western North America. Nature Geoscience 2012, 5:551-556.
58. Shepherd R.F. Management strategies for forest insect defoliators in British Columbia. Forest Ecology and Management 1994, 68:303-324.
59. Skakun R.S., Wulder M.A., Franklin S.E. Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage. Remote Sensing of Environment 2003, 86:433-443.
60. Swetnam T.W., Lynch A.M. Multicentury, regional-scale patterns of western spruce budworm outbreaks. Ecological Monographs 1993, 63:399-424.
61. Thayn J.B. Using a remotely sensed optimized disturbance index to detect insect defoliation in the Apostle Islands, Wisconsin, USA. Remote Sensing of Environment 2013, 136:210-217.
62. 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 Sensing of Environment 2012, 119:255-265.
63. Vogelmann J.E., Tolk B., Zhu Z. Monitoring forest changes in the southwestern United States using multitemporal Landsat data. Remote Sensing of Environment 2009, 113:1739-1748.
64. Vogelmann J.E., Xian G., Homer C., Tolk B. Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems. Remote Sensing of Environment 2012, 122:92-105.
65. Volney W.J.A., Fleming R.A. Climate change and impacts of boreal forest insects. Agriculture Ecosystems and Environment 2000, 82:283-294.
66. Westfall J., Ebata T. 2012 summary of forest health conditions in British Columbia. In, Pest management report series: Ministry of Forests, Lands and Natural Resource operations 2012.
67. Woods A.J., Heppner D., Kope H.H., Burleigh J., Maclauchlan L. Forest health and climate change: A British Columbia perspective. The Forestry Chronicals 2010, 86:412-422.
68. 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.
69. Wulder M.A., Masek J.G., Cohen W.B., Loveland T.R., Woodcock C.E. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment 2012, 122:2-10.
70. 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.
71. Wulder M.A., White J.C., Goward S.N., Masek J.G., Irons J.R., Herold M., Woodcock C.E. Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment 2008, 112:955-969.
72. Wulder M.A., White J.C., Grills D., Nelson T., Coops N.C., Ebata T. Aerial overview survey of the mountain pine beetle epidemic in British Columbia: Communication of impacts. BC Journal of Ecosystems and Management 2009, 10:45-58.
73. Zhu Z., Woodcock C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 2012, 118:83-94.