A new invasive insect in Sweden - Physokermes inopinatus: Tracing forest damage with satellite based remote sensing

Forest Ecology and Management

Volumn 285, Issue , 2012, Pages 29-37

  Olsson, Per Ola ^a   Jönsson, Anna Maria ^a   Eklundh, Lars ^a  

^a LUND UNIVERSITY   (Sweden)

Author keywords

Forest damage; MODIS; Physokermes inopinatus; Remote sensing; Sooty mold; SPOT

Indexed keywords

CARBON BALANCE; DAMAGED AREA; EFFICIENT MONITORING; FIRST YEAR; FOREST DAMAGE; FORESTED AREA; IN-FIELD; INSECT ATTACKS; MIGRATION PATTERNS; MODIS; NORWAY SPRUCE; PHYSOKERMES INOPINATUS; RECREATIONAL ACTIVITIES; SATELLITE DATA; SPATIAL COVERAGE; SPOT;

CARBON FIBERS; FORESTRY; MOLDS; PLANTS (BOTANY); RADIOMETERS; REMOTE SENSING; SATELLITES; TIMBER;

DAMAGE DETECTION;

CARBON BALANCE; DEFOLIATION; INSECT; INVASIVE SPECIES; MODIS; NDVI; PEST DAMAGE; REMOTE SENSING; SATELLITE DATA; SPOT;

CARBON; FIBERS; FOREST PRODUCTS; INSECTS; RADIOMETRY; REMOTE SENSING; SATELLITES; SWEDEN;

SKANE; SWEDEN;

HEXAPODA; PICEA ABIES;

EID: 84865770023     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2012.08.003     Document Type: Article

References (62)

1. Ardö J., Pilesjö P., Skidmore A. Neural networks, multitemporal Landsat Thematic Mapper data and topographic data to classify forest damages in the Czech Republic. Canadian Journal of Remote Sensing 1997, 23:217-229.
2. Asner G.P., Hicke J.A., Lobell D.B. Per-pixel analysis of forest structure. Remote Sensing of Forest Environments. Concepts and Case Studies 2003, Kluwer Academic Publishers, USA. M.A. Wulder, S.E. Franklin (Eds.).
3. Battisti A. Forests and climate change-lessons from insects. IFOREST-Biogeosciences and forestry 2008, 1:1-5.
4. Carter W. Insects in relation to Plant disease 1973, John Wiley and Sons, New York. second ed.
5. Cihlar J., Latifovic R., Beaubien J., Trishchenko A., Chen J., Fedosejevs G. National scale forest information extraction from coarse resolution satellite data, part1. Remote Sensing of Forest Environments. Concepts and Case Studies 2003, Springer, New York. M.A. Wulder, S.E. Franklin (Eds.).
6. 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.
7. Coops N.C., Johnson M., Wulder M.A., White J.C. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sensing of Environment 2006, 103:67-80.
8. Coppin P., Jonckheere I., Nackaerts K., Muys B. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 2004, 10:1565-1596.
9. Danzig E., Kozár F. A new species of soft scale, Physokermes inopinatus (Homoptera, Coccoidea) from Hungary. Entomological review 1973, 52:532-533.
10. de Beurs K.M., Townsend P.A. Estimating the effect of gypsy moth defoliation using MODIS. Remote Sensing of Environment 2008, 112:3983-3990.
11. Eklundh L., Johansson T., Solberg S. Mapping insect defoliation in Scots pine with MODIS time-series data. Remote Sensing of Environment 2009, 113:1566-1573.
12. Ekstrand, S., 1989. The possibilities of assessing moderate damages on Norway spruce using landsat TM data and ancillary stand information in a Gis Environment. In: Geoscience and Remote Sensing Symposium, 1989. IGARSS'89. 12th Canadian Symposium on Remote Sensing, 1989 International, vol. 3. pp. 1553-1557.
13. Fetykó K., Kozár F., Daróczi K. Species list of the scale insects (Hemiptera, Coccoidea) of Romania, with new data. Acta Phytopathologica Hungarica 2010, (45):291-302.
14. Fletcher R.S. Evaluating high spatial resolution imagery for detecting citrus orchards affected by sooty mold. International Journal of Remote Sensing 2005, 26:495-502.
15. Franklin S., Wulder M., Skakun R., Carroll A. Mountain pine beetle red attack damage classification using stratified Landsat TM data in British Columbia, Canada. Photogrammetric Engineering and Remote Sensing 2003, 69:283-288.
16. 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.
17. Fraser R.H., Abuelgasim A., Latifovic R. A method for detecting large-scale forest cover change using coarse spatial resolution imagery. Remote Sensing of Environment 2005, 95:414-427.
18. Fraser R.H., Latifovic R. Mapping insect-induced tree defoliation and mortality using coarse spatial resolution satellite imagery. International Journal of Remote Sensing 2005, 26:193-200.
19. Gitelson A.A., Kaufman Y.J., Merzlyak M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 1996, 58:289-298.
20. 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.
21. Hicke J.A., Logan J. Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery. International Journal of Remote Sensing 2009, 30:4427-4441.
22. Huete, A., Liu, H.Q., Van Leeuwen, J.D., 1997. The use of vegetation indices in forested regions: issues of linearity and saturation. In: IEE Transactions on Geosciences and Remote Sensing in Proceedings of IGARSS '97 - International Geosciences and Remote Sensing Seminar, vol. 4. ESA Publications, Noordwijk, The Netherlands. pp. 1966-1968.
23. Isacsson, G., 2010. PM: Ungersk gransköldlus - sammanställning av kunskapsläget november 2010. Skogsstyrelsen, 2010-11-25. Uppdaterad 2010-12-01. <>. http://www.skogsstyrelsen.se/Global/aga-och-bruka/Skogsbruk/Skador%20p%C3%A5%20skog/%C3%96vriga%20insekter/Ungersk%20gransk%C3%B6ldlus%20PM%20dec%202011.pdf.
24. Jepsen J.U., Hagen S.B., Høgda K.A., Ims R.A., Karlsen S.R., Tømmervik H., Yoccoz N.G. Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birch forest using MODIS-NDVI data. Remote Sensing of Environment 2009, 113:1939-1947.
25. Jönsson P., Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data. In, 2002 PISCATAWAY. IEEE Transactions on Geoscience and Remote Sensing 2002, 40:1824-1832.
26. Jönsson P., Eklundh L. TIMESAT-a program for analyzing time-series of satellite sensor data. Computers and Geosciences 2004, 30:833-845.
27. Kharuk V., Ranson K.J., Fedotova E.V. Spatial pattern of Siberian silkmoth outbreak and taiga mortality. Scandinavian Journal of Forest Research 2007, 22:531-536.
28. Kharuk V.I., Ranson K.J., Kozuhovskaya A.G., Kondakov Y.P., Pestunov I.A. NOAA/AVHRR satellite detection of Siberian silkmoth outbreaks in eastern Siberia. International Journal of Remote Sensing 2004, 25:5543-5556.
29. Kosztarab M., Kozár F. Scale insects of Central Europe 1988, Akademiai Kiadό, Budapest, 456.
30. Lambert N., Ardö J., Rock B.N., Vogelmann J.E. Spectral characterization and regression-based classification of forest damage in Norway spruce stands in the Czech-Republic using Landsat thematic mapper data. International Journal of Remote Sensing 1995, 16:1261-1287.
31. Lefsky M.A., Cohen W.B. Selection of remotely sensed data. Remote Sensing of Forest Environments. Concepts and Case Studies 2003, Kluwer Academic Publishers, USA, ISBN 1-4020-7405-0. M.A. Wulder, S.E. Franklin (Eds.).
32. Lillesand T., Kiefer R., Chipman J. Remote Sensing and Image Interpretation 2004, John Wiley & Sons, USA. fifth ed.
33. Luther J.E., Franklin S.E., Hudak J., Meades J.P. Forecasting the susceptibility and vulnerability of balsam fir stands to insect defoliation with Landsat Thematic Mapper Data. Remote Sensing of Environment 1997, 59:77-91.
34. Marotta, S., 1997. General life history, in Soft Scale Insects their Biology, Natural Enemies and Control. vol. 7, Part A. Editors Ben-Dov, Y., Hodgson, C.J. Elsevier. pp. 3-452. ISBN: 978-0-444-89303-1.
35. McDonald A.J., Gemmell F.M., Lewis P.E. Investigation of the utility of spectral vegetation indices for determining information on coniferous forests - an overview. Remote Sensing of Environment 1998, 66:250-272.
36. Netherer S., Schopf A. Potential effects of climate change on insect herbivores in European forests-general aspects and the pine processionary moth as specific example. Forest Ecology and Management 2010, 259:831-838.
37. Price K.P., Jakubauskas M.E. Spectral retrogression and insect damage in lodgepole pine successional forests. International Journal of Remote Sensing 1998, 19:1627-1632.
38. Radeloff V.C., Mladenoff D.J., Boyce M.S. Detecting jack pine budworm defoliation using spectral mixture analysis - a regional measure of abundance from multispectral images. Remote Sensing of Environment 1999, 69:156-169.
39. Riley J.R. Remote sensing in entomology. Annual Review of Entomology 1989, 34:247-271.
40. Rouse, J.W., Haas, R.H., Shell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the Great Plains with ERTS-1. In: Third Earth Resources Technology Satellite, Symposium, pp. 309-317.
41. Running S.W., Justice C.O., Solomonson V., Hall D., Barker J., Kaufmann Y.J., Strahler A.H., Huete A.R., Muller J.P., Vanderbilt V., Wan Z.M., Teillet P., Carneggie D. Terrestial remote sensing science and algorithms planned for EOS_MODIS International. Journal of Remote Sensing 1994, 15:3587-3620.
42. Skakun, R., Franklin, S.E., Wulder, M.A., Carroll, A., 2002. Aerial, GIS, and field data calibration of a landsat ETM+ mountain pine beetle detection procedure in British Columbia, Canada. In: Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International, vol. 5. pp. 2887-2889.
43. 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.
44. Stathas G.J., Kozár F. First record of Physokermes inopinatus Danzig & Kozár1973 (Hemiptera: Coccidae) in Greece. Hellenic Plant protection Journal 2010, 3:7-8.
45. Summy K.R., Little C.R. Using color infrared imagery to detect sooty mold and fungal pathogens of glasshouse-propagated plants. Hortscience 2008, 43:1485-1491.
46. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 1979, 8:127-150.
47. Vogelmann J.E., Rock B.N. Assessing forest damage in high-elevation coniferous forests in vermont and new Hampshire using thematic mapper data. Remote Sensing of Environment 1988, 24:227-246.
48. Vogelmann J.E., Rock B.N. Use of thematic mapper data for the detection of forest damage caused by the pear thrips. Remote Sensing of Environment 1989, 30:217-225.
49. Vranjic, J.A., 1997. Effects on host plants, in: Editors Ben-Dov, Y., Hodgson, C.J., (Eds.), Soft Scale Insects their Biology, Natural Enemies and Control, vol. 7 (Part A). Elsevier, pp. 3-452. ISBN: 978-0-444-89303-1.
50. White J.C., Coops N.C., Hilker T. Detecting mountain pine beetle red attack damage with EO-1 Hyperion moisture indices. International Journal of Remote Sensing 2007, 28:2111-2122.
51. White J.C., Wulder M.A., Brooks D., Reich R., Wheate R.D. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Remote Sensing of Environment 2005, 96:340-351.
52. Williams D.L., Nelson R.F., Dottavio C.L. A georeferenced LANDSAT digital database for forest insect-damage assessment. International Journal of Remote Sensing 1985, 6:643-656.
53. Wulder M.A., Franklin S.E. Remote sensing of forest environments. Challenges and opportunities. Remote Sensing of Forest Environments Concepts and Case Studies 2003, Kluwer Academic Publishers, USA, ISBN 1-4020-7405-0. M.A. Wulder, S.E. Franklin (Eds.).
54. Wulder M.A., White J.C., Coops N.C., Butson C.R. Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sensing of Environment 2008, 112:2729-2740.
55. Ben-Dov, Y., Miller, D.R., Gibson. G.A.P. 2012. ScaleNet - Database of theScale Insects of the World. <> (accessed 24.02.12). http://www.sel.barc.usda.gov/scalenet/scalnet.htm.
56. ESA, 2012. <> (accessed 12.03.01). http://www.esa.int/esaLP/SEMM4T4KXMF_LPgmes_0.html.
57. MODIS, 2011. http://daac.ornl.gov/MODIS/MODIS-menu/modis_webservice.html.
58. Saccess, 2011. http://saccess.lantmateriet.se/map_viewer?map=31&maplevelindex=0.
59. SMHI, 2012. (accessed 07.06.12). http://www.smhi.se/klimatdata/meteorologi/nederbord.
60. Skogsstyrelsen, 2012. /. (accessed 07.10.12). http://www.skogsstyrelsen.se/Aga-och-bruka/Lokala-sidor/Distrikt/Skanes-distrikt/Om-distriktet.
61. Isacsson, Gunnar (Swedish Forest Agency). " Gransköldlus" 22nd September, 2011.
62. Kozár, Ferenc (Plant Protection Institute, Hungarian Academy of Sciences, Budapest, Hungary) " Physokermes inopinatus - forest damage" 9th July, 2012.