Remote monitoring of forest insect defoliation. A review

Referencias bibliográficas

• Adelabu S, Mutanga O, Cho M, 2012. 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 Biotech 6 (1): 1-13.
• Alvarez Taboada MF, Lorenzo Cimadevila H, Wulder M, 2007. Monitorización del estado sanitario de las masas de Eucalyptus globulus en Galicia empleando modelos de proceso, SIG y teledetección. Proc 2º Simposio Iberoamericano de Eucalipto Globulus in Vigo (Spain), October 17-20, CIDEU 4, vol. II, pp.41-47.
• Babst F, Esper J, Parlow E, 2010. Landsat TM/ETM+ and tree-ring based assessment of spatiotemporal patterns of the autumnal moth (Epirrita autumnata) in northernmost Fennoscandia. Remote Sens Environ 114: 637-646. http://dx.doi.org/10.1016/j.rse.2009.11.005
• Battisti A, Stastny M, Netherer S, Robinet C, Schopf A, Roques A, et al., 2005. Expansion of geographic range in the pine processionary moth caused by increased winter temperatures. Ecol Appl 15 (6): 2084-2096. http://dx.doi.org/10.1890/04-1903
• B.C. Ministry of Forests, 2000. Forest health aerial overview survey standards for British Columbia, Version 2.0. Forest Practices Branch, and Canadian Forest Service. 48 pp.
• Cabello A, Frieyro J, Granado L, Hayas A, Méndez E, Montoya G, et al., 2011. Estudio de las afecciones por plagas y decaimiento en masas de coníferas mediante imágenes procedentes de sensores hiperespectrales. Proc XIV Congreso de la Asociación Espa-ola de Teledetección Int Conf on "Teledetección: Bosques y Cambio Climático", Asturias (Spain), September 21-23, pp. 81-84.
• Carter G, 1993. Responses of leaf spectral reflectance to plant stress. Am J Bot 80: 231-243. http://dx.doi.org/10.2307/2445346
• Carter G, Cibula W, Miller R, 1996. Narrow-band reflectance imagery compared with thermal imagery for early detection of plant stress. J Plant Physiol 148: 515-522. http://dx.doi.org/10.1016/S0176-1617(96)80070-8
• Casal G, Freire J, 2012. Síntesis de la evolución histórica de la teledetección en Espa-a (1889-2012). Revista de Teledetección AET 38: 109-120.
• Ciesla W, Dull C, Acciavatti R, 1989. Interpretation of SPOT- 1 color composites for mapping defoliation of hardwood forests by gypsy moth. Photogramm Eng Remote Sens 55: 1465-1470.
• Ciesla W, 2000. Remote monitoring in forest health protection. USDA Forest Service. 266 pp. PMCid:PMC1460913
• Ciesla W, Billings R, Compton J, Frament W, Mech R, Roberts M, 2008. Aerial sigantures of forest damage in the Eastern United States. The Forest Health Technology Enterprise Team (FHTET). USA. 121 pp.
• Cohen W, Spies T, Alig R, Oetter D, Maiersperger T, Fiorella M, 2003. Characterizing 23 years (1972-1995) of stand replacement disturbance in western Oregon forests with Landsat imagery. Ecosystems 5: 122-137. http://dx.doi.org/10.1007/s10021-001-0060-X
• Cohen W, Yang Z, Kennedy R, 2010. Detecting trends in forest disturbance and recovering using yearly Landsat time-series: 2.TimeSync-Tools for calibration and validation. Remote Sens Environ 114: 2911-2924. http://dx.doi.org/10.1016/j.rse.2010.07.010
• Coops N, Stanford M, Old K, Dudzinski M, Culvenor D, Stone C, 2003. Assessment of Dothistroma needle blight of Pinus radiata using airborne hyperspectral imagery. Phytopathology 93: 1524-1532. http://dx.doi.org/10.1094/PHYTO.2003.93.12.1524 PMid:18943616
• Coops N, Waring R, Wulder M, White J, 2009. Prediction and assessment of bark beetle-induced mortality of lodgepole pine using estimates of stand vigor derived from remotely sensed data. Remote Sens Environ 113: 1058-1066. http://dx.doi.org/10.1016/j.rse.2009.01.013
• De Beurs K, Townsend P, 2008. Estimating the effect of gypsy moth defoliation using MODIS. Remote Sens Environ 112: 3983-3990. http://dx.doi.org/10.1016/j.rse.2008.07.008
• Deel L, McNeil B, Curtis P, Serbin S, Singh A, Eshleman K, et al., 2012. Relationship of a Landsat cumulative disturbance index to canopy nitrogen and forest structure. Remote Sens Environ 118: 40-49. http://dx.doi.org/10.1016/j.rse.2011.10.026
• Dennison P, Nagler P, Hultine K, Glenn E, Ehleringer J, 2009. Remote monitoring of tamarisk defoliation and evapotranspiration following saltcedar leaf beetle attack. Remote Sens Environ 113: 1462-1472. http://dx.doi.org/10.1016/j.rse.2008.05.022
• Eklundh L, Johansson T, Solberg S, 2009. Mapping insect defoliation in Scots pine with MODIS time-series data. Remote Sens Environ 113: 1566-1573. http://dx.doi.org/10.1016/j.rse.2009.03.008
• Fleming R, Volney W, 1995 Effects of climate change on insect defoliator population processes in Canada's boreal forest: some plausible scenarios. Water Air Soil Pollut 8: 445-454. http://dx.doi.org/10.1007/BF01182854
• Fraser R, Latifovic R, 2005. Mapping insect-induced tree defoliation and mortality using coarse spatial resolution satellite imagery. Int J of Remote Sens 26: 193-200. http://dx.doi.org/10.1080/01431160410001716923
• García-López J, Allué-Camacho C, 2010. Effects of climate change on the distribution of Pinus sylvestris L. stands in Spain. A phytoclimatic approach to defining management alternatives. Forest System 19 (3): 329-339. http://dx.doi.org/10.5424/fs/2010193-8694
• Goodwin N, Coops N, Stone C, 2005. Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances. Int J Appl Earth Obs 7: 11-8. http://dx.doi.org/10.1016/j.jag.2004.10.003
• Hall R, Skakun R, Arsenault E, 2007. Remotely sensed data in the mapping of insect defoliation. Chap. 4, pp. 85-111 In Wulder M, Franklin S. 2007. Understanding forest disturbance and spatial pattern: remote sensing and GIS approaches. Taylor & Francis Group. FL, USA. 253 pp.
• Hayes D, Cohen W, Sader S, Irwin D, 2008. Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data. Remote Sens Environ 112: 735-749. http://dx.doi.org/10.1016/j.rse.2007.06.003
• Huang C, Goward S, Masek J, Thomas N, Zu Z, Vogelmann J, 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ 114: 183-198. http://dx.doi.org/10.1016/j.rse.2009.08.017
• Hunt G, 1977. Spectral signatures of particulate minerals in the visible and near infrared. Geophysics 42 (3): 501-513. http://dx.doi.org/10.1190/1.1440721
• Ilvesniemi S, 2009. Estimating Scots pine defoliation using aerial images and Landsat TM. Master thesis. University of Helsinki. 60 pp.
• Innes J, 1993. Forest health: its assessment and status. Cab International, Cambridge. 677 pp.
• Jensen J, 2005. Introductory digital image processing: a remote sensing perspective. 3rd Edition Pearson Education, Inc. 526 pp.
• Jensen J, 2007. Remote Sensing of the Environment: An earth resource perspective. 2nd Edition Pearson Education, Inc. 592 pp.
• Jepsen U, Hagen S, Ims R, Yoccoz N, 2008. Climate change and outbreaks of the geometrids Operophtera brumata and Epirrita autumnata in subarctic birch forest: Evidence of a recent outbreak range expansion. J Anim Ecol 77: 257-264. http://dx.doi.org/10.1111/j.1365-2656.2007.01339.x PMid:18070041
• Jepsen J, Hagen S, Hogda K, Ims R, Karlsen S, Tommervik H, et al., 2009. Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birch forest using MODIS-NDVI data. Remote Sens Environ 113: 1939-1947. http://dx.doi.org/10.1016/j.rse.2009.05.006
• Kantola T, Vastaranta M, Yu X, Lyytikäinen P, Holopainen M, Talvitie M, et al., 2010. Classification of defoliated tres using tree-level airborne laser scanning data combined with aerial images. Remote Sens 2: 2665-2679. http://dx.doi.org/10.3390/rs2122665
• Kantola T, Lyytikäinen P, Vastaranta M, Kankare V, Yu X, Holopainen M, et al., 2011. Using high density ALS data in plot level estimation of the defoliation by the Common pine sawfly. Proc 11th International SilviLaser Int Conf on "LIDAR applications for assessing forest ecosystems" Hobart (Australia), October 16-20. pp. 1-8.
• Karjalainen M, Kaasalainen S, Hyyppä J, Holopainen M, Lyytikäinen P, Krooks A, et al., 2010. SAR Satellite Images and Terrestrial Laser Scanning in Forest Damages Mapping in Finland. Proc ESA Symposium Int Conf on "Living Planet", Bergen (Norway), June 28-July 2.
• Kharuk V, Ranson K, Im S, 2009. Siberian silkmoth outbreak pattern analysis based on SPOT VEGETATION data. Int J Remote Sens 30 (9): 2377-2388. http://dx.doi.org/10.1080/01431160802549419
• Kennedy R, Yang Z, Cohen W, 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1 Land-Trend-Temporal segmentation algorithms. Remote Sens Environ 114 (12): 2897-2910. http://dx.doi.org/10.1016/j.rse.2010.07.008
• Lambin E, Linderman M, 2006. Time series of remote sensing data for land change science. IEEE Trans Geosci Remote Sens 44 (7): 1926-1928. http://dx.doi.org/10.1109/TGRS.2006.872932
• Lange H, Solberg S, 2008. Leaf area index estimation using LIDAR and forest reflectance modelling of airborne hyperspectral data. Proc IGARSS Int Conf on "The next generation", Boston (USA), July 6-11. pp. 475-478.
• Lu D, Mausel P, Brondizio E, Moran E, 2004. Change detection techniques. Int J Remote Sens 25: 2365-2407. http://dx.doi.org/10.1080/0143116031000139863
• MacLeod A, Evans H, Baker R, 2002. An analysis of pest risk from an Asian longhorn beetle (Anoplophora glabripennis) to hardwood trees in the European Community. Crop Protection 21(8): 635-645. http://dx.doi.org/10.1016/S0261-2194(02)00016-9
• Marceau D, Hay G, 1999. Remote sensing contributions to the scale issue. Can J Remote Sens 25 (4): 357-366.
• Melesse A, Weng Q, Thenkabail P, Senay G, 2007. Remote sensing sensors and applications in environmental resources mapping and modeling. Sensors 7: 3209-3241. http://dx.doi.org/10.3390/s7123209
• Mutanga O, van Aardt J, Kumar L, 2009. Imaging spectroscopy (hyperspectral remote sensing) in southern Africa: an overview. S Afr J Sci 105: 193-198.
• Pajares J, 2009. Los médicos del monte: una mirada a la sanidad forestal espa-ola desde sus inicios hasta los nuevos escenarios del siglo XXI. Proc. 5º Congreso Forestal Espa-ol on "Montes y Sociedad: Saber qué hacer" Ávila (Spain), September 21-25. pp. 2-17.
• Paritsis J, Veblen T, Smith J, Holz A, 2011. Spatial prediction of caterpillar (Ormiscodes) defoliation in Patagonian Nothofagus forests. Landscape Ecol 26: 791-803. http://dx.doi.org/10.1007/s10980-011-9608-0
• Peterson D, Resetar S, Brower J, Diver R, 1999. Forest monitoring and remote sensing: A survey accomplishments for the opportunities for the future. RAND Science and Technology Policy Institute, Washington DC, USA. Report MR-111.0-OSTP. 107 pp.
• Pontius J, Hallett R, Martin M, 2005. Using AVIRIS to asses hemlock abundance and early decline in the Catskills, New York. Rem Sens Environ 97: 163-173. http://dx.doi.org/10.1016/j.rse.2005.04.011
• Pontius J, Martin M, Plourde L, Hallet R, 2008. Ash decline assessment in emerald ash borer-infested regions: A test of tree-level hyperspectral technologies. Remote Sens Environ 112 (5): 2665-2676. http://dx.doi.org/10.1016/j.rse.2007.12.011
• Prados D, Ryan R, Ross K, 2006. Remote time series product tool 2006. Proc AGU Fall Meeting In Conf on "Computational Rapid Prototyping Capabilities for Advancing Science Toward Societal Benefits", San Francisco, CA (USA), December 11-15.
• Régnière J, Cooke B, Bergeron V, 1995. BioSIM: A computer-based desicion support tool for seasonal planning of pest management activities. Canadian Forest Service. 50 pp.
• Rouse J, Haas R, Schell J, Deering D, 1973. Monitoring vegetation systems in the Great Plains with ERTS. Proc Third ERTS Symposium Int Conf on "Earth Resources Technology Satellite-1", Washington DC (USA), December 10-14, pp 301-317.
• Santos M, Greenberg J, Ustina S, 2010. Using hyperspectral remote sensing to detect and quantify southeastern pine senescence affects in red-cockaded woodpecker (Picoides borealis) habitat. Remote Sens Environ 114 (6): 1242-1250. http://dx.doi.org/10.1016/j.rse.2010.01.009
• Seixas P, Oliveira I, Santos J, Leite S, 2011. Climate change and forest plagues: the case of the pine processionary moth in the Northeastern Portugal. Forest Systems 20(3): 508-515. http://dx.doi.org/10.5424/fs/20112003-11394
• Solberg S, Næsset E, Lange H, Bollandsas O, 2004. Remote sensing of forest health. Proc ISPRS Int Conf on "Laser- Scanners for forest and landscape assessment". Freiburg (Germany), October 3-6, pp. 161-166.
• Solberg S, Næsset E, Hanssen K, Christiansen E, 2006. Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sens Environ 102: 364-376. http://dx.doi.org/10.1016/j.rse.2006.03.001
• Solberg S, Eklundh L, Gjertsen A, Johansson T, Joyce S, Lange H, et al., 2007. Testing remote sensing techniques for monitoring large scale insect defoliation. Proc ForestSat 2007, Int Conf on Hyperspectral & Advanced sensors, Montpellier (France), November 5 pp.
• Solberg S, 2010. Mapping gap fraction, LAI and defoliation using various ALS penetration variables. Int. J. Remote Sens. 31: 1227-1244. http://dx.doi.org/10.1080/01431160903380672
• Somers B, Verbesselt J, Ampe E, Sims N, Verstraeten W, Coppin P, 2010. Spectral mixture analysis to monitor defoliation in mexed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data. Int J Appl Earth Obs 12: 270-277. http://dx.doi.org/10.1016/j.jag.2010.03.005
• Spruce J, Sader S, Ryan R, Smoot J, Kuper P, Ross K, et al., 2011. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens Environ 115: 427-437. http://dx.doi.org/10.1016/j.rse.2010.09.013
• Thomas S, Deschamps A, Landry R, van der Sanden J, Hall R, 2007. Mapping insect defoliation using multi-temporal Landsat data. Proc CRSS/ASPRS 2007 In Conf on "Our Common Borders- Safety, Security, and the Environment through Remote Sensing", Ottawa (Canada). October 28- November 1.
• Townsend P, Singh A, Foster J, Rehberg N, Kingdon C, Eshleman K, et al., 2012. A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sens Environ 119: 255-265. http://dx.doi.org/10.1016/j.rse.2011.12.023
• Townshend J, Justice C, Gurney C, McManus J, 1992. The impact of misregistration on change detection. IEEE Trans Geosci Remote Sens 30 (5): 1054-1060. http://dx.doi.org/10.1109/36.175340
• Vanhanen H, Veteli T, Päivinen S, Kellomäki, Niemelä P, 2007. Climate change and range shifts in two insect defoliators: Gypsy moth and Nun moth – a model study. Silva Fenn 41 (4): 621-638.
• Vogelmann J, 1990. Comparison between two vegetation indices for measuring different types of forest damage in the Northeastern United States. Int J Remote Sens 11: 2281-2297. http://dx.doi.org/10.1080/01431169008955175
• Vogelmann J, Rock B, Moss D, 1993. Red-edge spectral measurements from sugar maple leaves. Int J Remote Sens 14: 1563-1575. http://dx.doi.org/10.1080/01431169308953986
• Volney W, Fleming R, 2000. Climate change and impacts of boreal forest insects. Agr Ecosyst Environ 82: 283-294. http://dx.doi.org/10.1016/S0167-8809(00)00232-2
• Wang J, Sammis T, Gutschick V, Gebremichael M, Dennis S, Harrison R, 2010. Review of satellite remote sensing use in forest health studies. Open Geogr J 3: 28-42. http://dx.doi.org/10.2174/1874923201003010028
• Wang L, Huang H, Luo Y, 2010. Remote sensing of insect pests in larch forest based on physical model. Proc IGARSS Int Conf on "Remote Sensing: Global vision for local action", Honolulu, Hawaii (USA), July 25-30. pp. 3299-3302.
• Williams D, Liebhold A, 1995. Herbivorous insects and global change: potential changes in the spatial distribution of forest defoliator outbreaks. J Biogeogr 22: 665-671. http://dx.doi.org/10.2307/2845968
• Wulder M, Dymond C, Erickson B, 2004. Detection and monitoring of the mountain pine beetle. Report BC-X-398, Canadian Forest Service and Pacific Forestry Center. 32 pp.
• Wulder M., White J, Bentz, B, & Ebata T, 2006. Augmenting the existing survey hierarchy for mountain pine beetle red-attack damage with satellite remotely sensed data. The Forestry Chronicle 82: 187-202.
• Wulder M, Franklin S, 2007. Understanding forest disturbance and spatial pattern: remote sensing and GIS approaches. Taylor & Francis Group. FL, USA. 253 pp.
• Zhang T, Zhang X, Liu H, Pei X, 2010. Application of remote sensing technology in monitoring forest diseases and pests. Plant Diseases and Pests 1(3): 57-62.