Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain

Forest Ecology and Management

Volumn 347, Issue , 2015, Pages 200-208

  Rullán Silva, Cristóbal ^a,b   Olthoff, Adriana E ^a   Pando, Valentín ^a   Pajares, Juan A ^a   Delgado, José A ^a  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b UNIVERSIDAD JUÁREZ AUTÓNOMA DE TABASCO   (Mexico)

Author keywords

Fagus sylvatica; Insect defoliator; Landsat images; Mixed effects model; Moisture stress index; Spectral vegetation indices

Indexed keywords

ANIMALS; CONSERVATION; COST EFFECTIVENESS; DAMAGE DETECTION; MAPPING; MOISTURE; POPULATION STATISTICS; REMOTE SENSING; VEGETATION;

FAGUS SYLVATICA; INSECT DEFOLIATOR; LANDSAT IMAGES; MIXED EFFECTS MODELS; SPECTRAL VEGETATION INDICES; STRESS INDICES;

FORESTRY;

ACCURACY ASSESSMENT; DECIDUOUS FOREST; DEFOLIATION; ERROR ANALYSIS; FOREST INVENTORY; FOREST MANAGEMENT; LANDSAT; REMOTE SENSING;

COEFFICIENTS; FAGUS SYLVATICA; IMAGES; MODELS; PLANTS;

CANTABRIAN MOUNTAINS; SPAIN;

AGROMYZIDAE; FAGUS; FAGUS SYLVATICA; HEXAPODA; RYNCHAENUS FAGI;

EID: 84961335408     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2015.03.005     Document Type: Article

References (71)

1. Adelabu S., Mutanga O., Cho M. A review of remote sensing of insect defoliation and its implications for the detection and mapping of Imbrasia belina defoliation of Mopane Woodland. African J. Plant Sci. Biotechnol. 2012, 6(1):1-13.
2. Agúndez Leal, D., Martín Albertos, S., De Miguel y Del Ángel, J., Galera Peral, R., Jiménez Sancho, M., Díaz-Fernández, P., (Eds), 1995. Las regiones de procedencia de Fagus sylvatica L. en España. ICONA, Madrid.
3. Arzani, H., King, G.W., 1997. Application of remote sensing (Landsat TM data) for vegetation parameters measurement in western division of NSW. In: Proceedings XVIII IGC 1997 Winnepeg, Manitoba. Accessed from: http://www.internationalgrasslands.org/publications/pdfs/1997/2_21_019.PDF.
4. Babst F., Esper J., Parlow E. Landsat TM/ETM+ and tree-ring based assessment of spatiotemporal patterns of the autumnal moth (Epirrita autumnata) in northernmost Fennoscandia. Remote Sens. Environ. 2010, 114:637-646.
5. 2. Basic Appl. Ecol. 2010, 11:251-256.
6. Blanco-Fontao B., Fernández-Gil A., Obeso J., Quevedo M. Diet and habitat selection in Cantabrian Capercaillie (Tetrao urogallus cantabricus): ecological differentiation of a rear-edge population. J. Ornithol. 2010, 151:269-277.
7. Burnham K.P., Anderson D.R. Model Selection and Multimodel Inference 2002, Springer-Verlag, New York.
8. Cárcamo, S., Garmendia, A., Schwendtner, O., 2001. Influencia de los tratamientos selvícolas en la capacidad de acogida de un hayedo atlántico para pito negro (Dryocopus martius) y pico dorsiblanco (Dendrocopos leucotos). In: Proceedings of III Congreso Forestal Español, SECF-EGMASA, Granada, Spain, September 25-28, 2001.
9. Chander G., Markham B., Helder D. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113:893-903.
10. Chavez P. Image-based atmospheric corrections. Revisited and improved. Photogramm. Eng. Remote Sens. 1996, 62(9):1025-1036.
11. Ciesla W. Remote Monitoring in Forest Health Protection 2000, USDA Forest Service.
12. Clevenger, A.P., Purroy, F.J., Pelton, M.R. 1992. Movement and activity patterns of a European brown bear in the Cantabrian Mountains, Spain. In: Eighth International Conference on Bear Research and Management, Victoria, British Columbia, Canada, February 1989.
13. Cohen W., Goward S. Landsat's role in ecological applications of remote sensing. Bioscience 2004, 54(6):535-545.
14. Cristóbal J., Pons X., Serra P. Sobre el uso operativo de Landsat-7 TM+ en Europa. Rev. Teledet. 2004, 21:55-59.
15. CSFC, 2010. Redes de seguimiento de daños en los bosques de Castilla y León. Informe fitosanitario regional. Resultados 2010. Centro de Sanidad Forestal de Calabazanos-Junta de Castilla y León, España. (accessed December 2012). http://www.jcyl.es/web/jcyl/MedioAmbiente/es/Plantilla100/1284277645355/_/_/.
16. Day K.R., Watt A.D. Population studies of the beech leaf mining weevil (Rhynchaenus fagi) in Ireland and Scotland. Ecol. Entomol. 1989, 14:23-30.
17. De Beurs K., Townsend P. Estimating the effect of gypsy moth defoliation using MODIS. Remote Sens. Environ. 2008, 112:3983-3990.
18. Efron B., Gong G. A leisurely look at the bootstrap, the jackknife, and cross-validation. Am. Stat. 1983, 37(1):36-48.
19. Elena Roselló R. Clasificación Biogeoclimática de España Peninsular y Balear 1997, MAPA, Madrid, España.
20. Elvidge C.D., Lyon R.J.P. Estimation of the vegetation contribution to the 1.65/2.22 mm ratio in air-borne thematic-mapper imagery of the Virginia Range. Nevada. Int. J. Remote Sens. 1985, 6:75-88.
21. Eschrich W., Burchardt R., Essiamah S. The induction of sun and shade leaves of the European beech (Fagus sylvatica L.): anatomical studies. Trees 1989, 3:1-10.
22. Ferretti M., Nicolás M., Bacaro G., Brunialti G., Calderisi M., Croisé L., Frati L., Lanier M., Maccherini S., Santi E., Ulrich E. Plot-scale modelling to detect size, extent, and correlates of changes in tree defoliation in French high forests. For. Ecol. Manage. 2014, 311:56-69.
23. Fleming R., Volney W. Effects of climate change on insect defoliator population processes in Canada's boreal forest: some plausible scenarios. Water Air Soil Pollut. 1995, 8:445-454.
24. Foody G., Cutler M., McMorrow J., Pelz D., Tangki H., Boyd D., Douglas I. Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecol. Biogeogr. 2001, 10:379-387.
25. Fraser R., Latifovic R. Mapping insect-induced tree defoliation and mortality using coarse spatial resolution satellite imagery. Int. J. Remote Sens. 2005, 26:193-200.
26. Gebler A., Keitel C., Kreuzwieser J., Matyssek R., Seiler W., Rennenberg H. Potential risks for European beech (Fagus sylvatica L.) in a changing climate. Trees 2007, 21:1-11.
27. Goodwin N., Magnussen S., Coops N., Wulder M. Curving fitting of time series Landsat imagery for characterizing a mountain pine beetle infestation disturbance. Int. J. Remote Sens. 2010, 31(12):3263-3271.
28. Gómez-Manzanedo M., Roig S., Reque J.A. Caracterización selvícola de los hayedos cantábricos: influencia de las condiciones de estación y los usos antrópicos. For. Syst. 2008, 17(2):155-167.
29. Hantson S., Chuvieco E., Pons X., Domingo C., Cea C., Moré G., Cristobal J., Peces J.J., Tejeiro J.A. Cadena de preprocesamiento estándar para las imágenes Landsat del Plan Nacional de Teledetección (España). Rev. Teledet. 2011, 36:51-61.
30. Hardisky M.A., Klemas V., Smart R.M. The influence of soil salinity, growth form, and leaf moisture on the spectral reflectances of Spartina alterniflora canopies. Photogramm. Eng. Remote Sens. 1983, 49:77-83.
31. Holben B.N., Justice C.O. An examination of spectral band ratioing to reduce the topographic effect on remotely sensed data. Int. J. Remote Sens. 1981, 2(2):115-133.
32. Huete A. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25:295-309.
33. Hunt E.R., Rock B.N. Detection of changes in leaf water content using near and middle-infrared reflectances. Remote Sens. Environ. 1989, 30:43-54.
34. ITACYL-AEMET, 2013. Atlas Agroclimático de Castilla y León. Instituto Tecnológico Agrario Junta de Castilla y León-Agencia Estatal de Meteorología. Spain. (accessed May 2014). http://atlas.itacyl.es.
35. Jordan C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology 1969, 50:663-666.
36. Jump A.S., Hunt J.M., Peñuelas J. Rapid climate change-related growth decline at the southern range edge of Fagus sylvatica. Glob. Change Biol. 2006, 12:2163-2174.
37. Kharuk V., Ranson K., Im S. Siberian silkmoth outbreak pattern analysis based on SPOT VEGETATION data. Int. J. Remote Sens. 2009, 30(9):2377-2388.
38. King D.J., Olthof I., Pellikka P.K.E., Seed E.D., Butson C. Modelling and mapping forest ice storm damage using remote sensing and environmental data. Nat. Hazards, Special Issue Remote Sens. 2005, 35:321-342.
39. Lambert N., Ardo J., Rock B., Vogelmann J. Spectral characterization and regression-based classification of forest damage in Norway spruce stands in the Czech-Republic using Landsat Thematic Mapper data. Int. J. Remote Sens. 1995, 16:1261-1287.
40. Lienert J. Habitat fragmentation effects on fitness of plant population - a review. J. Nat. Conserv. 2004, 12:53-72.
41. Madrigal A., Calama R., Madrigal G., Aunós A., Reque J.A. Selvicultura de Fagus sylvatica L. Compendio de Silvicultura Aplicada en España 2008, 155-185. INIA, Madrid. R. Serrada, G. Montero, J.A. Reque-Kilchenman (Eds.).
42. MAGRAMA, 2005-2010. Inventario UE-ECE de daños forestales (IDF) en España. Red Europea de Seguimiento de daños en los bosques. Nivel I. Resultados Anuales de los muestreos. Ministerio de Agricultura, Alimentación y Medio Ambiente de España. (accessed December 2012). http://www.magrama.gob.es/es/desarrollo-rural/temas/politica-forestal/inventario-cartografia/redes-europeas-seguimiento-bosques/red_nivel_l_resultados.aspx.
43. MAGRAMA, 2007. Manual de Campo de la red de seguimiento de daños en los montes. Nivel I. Ministerio de Agricultura, Alimentación y Medio Ambiente de España.
44. Meigs G., Kennedy R., Cohen W. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sens. Environ. 2011, 115:3707-3718.
45. Metzger M., Bunce R., Jongman R., Mücher C., Watkins J. A climatic stratification of the environment of Europe. Global Ecol. Biogeogr. 2005, 14:549-563.
46. Nagelkerke N.J.D. A note on a general definition of the coefficient of determination. Biometrika 1991, 78:691-692.
47. Newcomer, M., Bird, J., Sabatine, S., Sady, G., Stalzer, A., Wheeler, T., Schmidt, C., Skiles, J., 2010. Utilizing NASA satellite missions to identify Bark Beetle infestation in Sequoia National Park. In: ASPRS 2010 Annual Conference, San Diego-California, April 26-30.
48. Paritsis J., Veblen T., Smith J., Holz A. Spatial prediction of caterpillar (Ormiscodes) defoliation in Patagonian Nothofagus forests. Landscape Ecol. 2011, 26:791-803.
49. Pontius J., Martin M., Plourde L., Hallet R. Ash decline assessment in emerald ash borer-infested regions: a test of tree-level hyperspectral technologies. Remote Sens. Environ. 2008, 112(5):2665-2676.
50. Rock B.N., Vogelmann J.E., Williams D.L., Vogehnann A.F., Hoshizaki T. Remote detection of forest damage. Bioscience 1986, 36:439-445.
51. Royle D., Lathrop R. Discriminating Tsuga canadensis hemlock forest defoliation using remote sensed change detection. J. Nematol. 2002, 34(3):213-221.
52. Rozas V. Detecting the impact of climate and disturbances on tree-rings of Fagus sylvatica L. and Quercus robur L. in a lowland forest in Cantabria, Northern Spain. Ann. For. Sci. 2001, 58:237-251.
53. a ép. 9, 109-155.
54. Sangüesa-Barreda G., Julio Camarero J., García-Martín A., Hernández R., de la Riva J. Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. For. Ecol. Manage. 2014, 320:171-181.
55. Schindler U. Zum Massenwechsel des Buchenspringrüsslers. Rhynchaenus fagi L. Z. angew. Entomol 1966, 58:182-186.
56. Schwarz Gideon E. Estimating the dimension of a model. Ann. Stat. 1978, 6(2):461-464.
57. Schwerdtfeger, F., 1981. Die Waldkrankheiten. Ein Lehrbuch der Forstpathologie und des Forstschutzes. Hamburg (4th ed. rev). In: FAO 2009 Global review of forest pests and diseases. FAO Forestry Paper 156. Roma.
58. Solberg S. Mapping gap fraction, LAI and defoliation using various ALS penetration variables. Int. J. Remote Sens. 2010, 31(5):1227-1244.
59. Somers B., Verbesselt J., Ampe E., Sims N., Verstraeten W., Coppin P. Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data. Int. J. Appl. Earth Obs. 2010, 12:270-277.
60. Spruce J., Sader S., Ryan R., Smoot J., Kuper P., Ross K., Prados D., Russell J., Gasser G., McKellip R., Hargrove W. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens. Environ. 2011, 115:427-437.
61. Storch I., Bañuelos M.L., Fernández-Gil A., Obeso J.R., Quevedo M., Rodríguez-Muñoz R. Subspecies Cantabrian capercaillie Tetrao urogallus cantabricus endangered according to IUCN criteria. J. Ornithol. 2006, 147:653-655.
62. Sturrok R.N. Climate change and forest diseases: using today's knowledge to address future challenges. For. Syst. 2012, 21(2):329-336.
63. Townsend P., Eshleman K., Welcker C. Remote sensing of gypsy moth defoliation to assess variations in stream nitrogen concentrations. Ecol. Appl. 2004, 14:504-516.
64. Townsend P., Singh A., Foster J., Rehberg N., Kingdon C., Eshleman K., Seagle S. A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sens. Environ. 2012, 119:255-265.
65. Trotter, C., Brown, J., 1999. Monitory forest canopy condition by remote sensing. Dept. of Conservation, Technical series 14. Wellington, N.Z.
66. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8:127-150.
67. Verkaik, E., Moraal, L., Nabuurs, G., 2009. Potential impacts of climate change on Dutch forests, mapping the risks. Wageningen, Alterra-rapport. Netherlands.
68. Vitasse Y., Basler D. What role for photoperiod in the bud burst phenology of European beech. Eur. J. Forest Res. 2012, 132:1-8.
69. Wolter P.T., Townsend P.A., Kingdon C.C., Sturtevant B.R. Remote sensing of the distribution and abundance of host species for spruce budworm in northern Minnesota and Ontario. Remote Sens. Environ. 2008, 112:3971-3982.
70. Woodcock B., Vanbergen A. Parasitism of the beech leaf-miner weevil in a woodland: patch size, edge effects and parasitoid species identity. Insect. Conserv. Divers. 2008, 1(3):180-188.
71. Zhang T., Zhang X., Liu H., Pei X. Application of remote sensing technology in monitoring forest diseases and pests. Plant Dis. Pests 2010, 1(3):57-62.