European Journal of Forest Research

, Volume 131, Issue 4, pp 989–1000 | Cite as

Evidences of drought stress as a predisposing factor to Scots pine decline in Valle d’Aosta (Italy)

  • Giorgio Vacchiano
  • Matteo Garbarino
  • Enrico Borgogno Mondino
  • Renzo Motta
Original Paper


Scots pine (Pinus sylvestris L.) forests of many inner Alpine valleys have recently displayed a quick loss of vitality. A decline disease has been suggested as the cause, with drought as the main predisposing factor and the additional contribution of biotic agents inciting tree dieback. This study is focused on Valle d’Aosta, a dry, inner-Alpine region in NW Italy. We inferred vitality changes between years 2000 and 2007 by computing reductions in enhanced vegetation index (EVI). Image differencing was carried out on pre-processed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery taken in late springtime and validated against ancillary ground truth. We: (1) tested whether EVI reductions in Scots pine forests were significantly higher than those of a control species and of a wetter region for the same species, (2) analyzed decline incidence as a function of site and topographic variables, and (3) assessed the relative influence of site and stand structure on decline probability by means of path analysis. Mean EVI in the study area increased due to an early onset of the 2007 growing season. Nevertheless, the incidence of decline was 6.3% and significantly greater for Scots pine than the control species and site. Low-elevation, northerly exposed sites exhibited the highest incidence of decline. Path analysis suggested that the most important determinants of decline probability were slope, solar radiation, and stand sparseness.


Pinus sylvestris Decline disease Drought Enhanced vegetation index MODIS 


  1. Allen CD, Breshears DD (1998) Drought-induced shift of a forest-woodland ecotone: rapid landscape response to climate variation. Proc Natl Acad Sci 95:14839–14842PubMedCrossRefGoogle Scholar
  2. Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-H, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259:660–684CrossRefGoogle Scholar
  3. Asner GP (1998) Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens Environ 64:234–253CrossRefGoogle Scholar
  4. Bannari A, Morin D, Bonn F, Huete AR (1995) A review of vegetation indices. Remote Sens Rev 13:95–120CrossRefGoogle Scholar
  5. Baumgartner A, Reichel E, Weber G (1983) Der Wasserhaushalt der Alpen: Niederschlag, Verdunstung, Abfluss und Gletscherspende im Gesamtgebiet der Alpen im Jahresdurchschnitt fur die Normalperiode 1931–1960. Verlag Oldenbourg, MünchenGoogle Scholar
  6. Biancotti A, Bellardone G, Bovo S, Cagnazzi B, Giacomelli L, Marchisio C (1998) Distribuzione regionale di piogge e temperature. Regione Piemonte, TorinoGoogle Scholar
  7. Bigler C, Bräker OU, Bugmann H, Dobbertin M, Rigling A (2006) Drought as an inciting mortality factor in Scots pine stands of the Valais, Switzerland. Ecosystems 9:330–343CrossRefGoogle Scholar
  8. Breda N, Huc R, Granier A, Dreyer E (2006) Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann For Sci 63:625–644CrossRefGoogle Scholar
  9. Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, Romme WH, Kastens JH, Floyd ML, Belnap J (2005) Regional vegetation die-off in response to global-change-type drought. Proc Natl Acad Sci 102:15144–15148PubMedCrossRefGoogle Scholar
  10. Burgess DW, Lewis P, Muller J (1995) Topographic effects in AVHRR NDVI data. Remote Sens Environ 54:223–232CrossRefGoogle Scholar
  11. Camerano P, Terzuolo PG, Varese P (2007) I tipi forestali della Valle d’Aosta. Compagnia delle Foreste, ArezzoGoogle Scholar
  12. Carter GA (1993) Responses of leaf spectral reflectance to plant stress. Am J Bot 80:239–243CrossRefGoogle Scholar
  13. Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88:677–684PubMedCrossRefGoogle Scholar
  14. Carter GA, Paliwal K, Pathre U, Green TH, Mitchell RJ, Gjerstad DH (1989) Effect of competition and leaf age on visible and infrared reflectance in pine foliage. Plant Cell Environ 12:309–315CrossRefGoogle Scholar
  15. Cech TL, Perny B (2000) Kiefernsterben in Tirol. Forstschutz Aktuell Wien 22:12–15Google Scholar
  16. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  17. Collins JB, Woodcock CE (1996) An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data. Remote Sens Environ 56:66–77CrossRefGoogle Scholar
  18. Colombo R, Busetto L, Migliavacca M, Cremonese E, Meroni M, Galvagno M, Rossini M, Siniscalco C, Morra di Cella U (2009) On the spatial and temporal variability of Larch phenological cycle in mountainous areas. Ital J Remote Sens 41:79–96CrossRefGoogle Scholar
  19. Coppin P, Jonckheere I, Nackaerts K, Muys B (2004) Digital change detection in environmental monitoring: a review. Int J Remote Sens 25:1565–1596CrossRefGoogle Scholar
  20. Costantini EAC, L’Abate G, Urbano F (2004) Soil regions of Italy. CRA-ISSDS, FirenzeGoogle Scholar
  21. Deshayes M, Guyon D, Jeanjean H, Stach N, Jolly A, Hagolle O (2006) The contribution of remote sensing to the assessment of drought effects in forest ecosystems. Ann For Sci 63:579–595CrossRefGoogle Scholar
  22. Dobbertin M, Rigling A (2006) Pine mistletoe (Viscum album ssp austriacum) contributes to Scots pine (Pinus sylvestris) mortality in the Rhone valley of Switzerland. For Pathol 36:309–322CrossRefGoogle Scholar
  23. Dobbertin M, Mayer P, Wohlgemuth T, Feldmeyer-Christe E, Graf U, Zimmermann NE, Rigling A (2005) The decline of Pinus sylvestris L. forests in the Swiss Rhone Valley—a result of drought stress? Phyton 45:153–156Google Scholar
  24. Eilmann B, Weber P, Rigling A, Eckstein D (2006) Growth reactions of Pinus sylvestris L. and Quercus pubescens Willd to drought years at a xeric site in Valais, Switzerland. Dendrochronologia 23:121–132CrossRefGoogle Scholar
  25. Falkenstrom H, Ekstrand S (2002) Evaluation of IRS-1c LISS-3 satellite data for defoliation assessment on Norway spruce and Scots pine. Remote Sens Environ 82:208–223CrossRefGoogle Scholar
  26. Fung T, LeDrew E (1988) The determination of optimal threshold levels for change detection using various accuracy indices. Photogramm Eng Remote Sens 54:1449–1454Google Scholar
  27. Giordano L, Gonthier P, Varese GC, Miserere L, Nicolotti G (2009) Mycobiota inhabiting sapwood of healthy and declining Scots pine (Pinus sylvestris L.) trees in the Alps. Fungal Divers 38:69–83Google Scholar
  28. Giuggiola A, Kuster TM, Saha S (2010) Drought-induced mortality of Scots pines at the southern limits of its distribution in Europe: causes and consequences. iForest 3:95–97CrossRefGoogle Scholar
  29. Gonthier P, Giordano L, Nicolotti G (2007) Sui disseccamenti acuti e generalizzati del pino silvestre nell’envers della media Valle d’Aosta. L’informatore Agricolo 23:41–45Google Scholar
  30. Gonthier P, Giordano L, Nicolotti G (2010) Further observations on sudden diebacks of Scots pine in the European Alps. For Chron 86:110–117Google Scholar
  31. Gottero F, Ebone A, Terzuolo P, Camerano P (2007) I Boschi del Piemonte: conoscenza e indirizzi gestionali. Regione Piemonte, Blu Edizioni, TorinoGoogle Scholar
  32. Guarín A, Taylor AH (2005) Drought triggered tree mortality in mixed conifer forests in Yosemite National Park, California, USA. For Ecol Manag 218:229–244CrossRefGoogle Scholar
  33. Guyot G, Guyon D, Riom J (1989) Factors affecting the spectral response of forest canopies: a review. Geocarto Int 4:3–18CrossRefGoogle Scholar
  34. Hasenauer H, Nemani RR, Schadauer K, Running SW (1999) Forest growth response to changing climate between 1961 and 1990 in Austria. For Ecol Manag 122:209–219CrossRefGoogle Scholar
  35. Heikkilä J, Nevalainen S, Tokola T (2002) Estimating defoliation in boreal coniferous forests by combining Landsat TM, aerial photographs and field data. For Ecol Manag 158:9–23CrossRefGoogle Scholar
  36. Holben B (1986) Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens 7:1417–1434CrossRefGoogle Scholar
  37. Hu L, Bentler P (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model 6:1–55CrossRefGoogle Scholar
  38. Huete AR, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213CrossRefGoogle Scholar
  39. Intergovermental Panel on Climate Change (2007) Climate change 2007: the scientific basis. IPCC Fourth assessment report. Cambridge University Press, CambridgeGoogle Scholar
  40. Jackson RD (1986) Remote sensing of biotic and abiotic plant stress. Annu Rev Phytopathol 24:265–287CrossRefGoogle Scholar
  41. Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-filled seamless SRTM data V4. International Centre for Tropical Agriculture (CIAT). Accessed 6 June 2009
  42. Jolly WM, Dobbertin M, Zimmermann NE, Reichstein M (2005) Divergent vegetation growth responses to the 2003 heat wave in the Swiss Alps. Geophys Res Lett 32:L18409CrossRefGoogle Scholar
  43. Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A (1998) The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans Geosci Remote Sens 36:1228–1249CrossRefGoogle Scholar
  44. Kleman J (1986) The spectral reflectance of stands of Norway spruce and Scotch pine, measured from a helicopter. Remote Sens Environ 20:253–265CrossRefGoogle Scholar
  45. Knutson KC, Pyke DA (2008) Western juniper and ponderosa pine ecotonal climate-growth relationships across landscape gradients in southern Oregon. Can J For Res 38:3021–3032CrossRefGoogle Scholar
  46. Letts MG, Nakonechny KN, Van Gaalen KE, Smith CM (2009) Physiological acclimation of Pinus flexilis to drought stress on contrasting slope aspects in Waterton Lakes National Park, Alberta, Canada. Can J For Res 39:629–641CrossRefGoogle Scholar
  47. Li HJ, Zheng L, Lei YP, Li CQ, Zhou K (2007) Comparison of NDVI and EVI based on EOS/MODIS data. Prog Geogr 26:26–32Google Scholar
  48. Liu WT, Kogan FN (1996) Monitoring regional drought using the vegetation condition index. Int J Remote Sens 17:2761–2782CrossRefGoogle Scholar
  49. Logan JA, Regniere J, Powell JA (2003) Assessing the impacts of global warming on forest pest dynamics. Front Ecol Environ 1:130–137CrossRefGoogle Scholar
  50. Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25:2365–2401CrossRefGoogle Scholar
  51. Lunetta RS, Knight JF, Ediriwickrema J, Lyon JG, Worthy LD (2006) Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens Environ 105:142–154CrossRefGoogle Scholar
  52. Manion PD (1991) Tree disease concepts. Prentice Hall, Englewood CliffsGoogle Scholar
  53. Matsushita B, Yang W, Chen J, Onda Y, Qiu G (2007) Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors 7:2636–2651CrossRefGoogle Scholar
  54. McCune B, Keon D (2002) Equations for potential annual direct incident radiation and heat load. J Veg Sci 13:603–606CrossRefGoogle Scholar
  55. McDowell N, Pockman WT, Allen CD, Breshears DD, Cobb N, Kolb T, Plaut J, Sperry J, West A, Williams DG (2008) Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol 178:719–739PubMedCrossRefGoogle Scholar
  56. McMurtrie R, Wolf L (1983) A model of competition between trees and grass for radiation, water and nutrients. Ann Bot 52:449–458Google Scholar
  57. Minerbi S, Cescatti A, Cherubini P, Hellrigl K, Markart G, Saurer M, Mutinelli C (2006) La siccità dell’estate 2003 causa di disseccamenti del pino silvestre in Val d’Isarco. For Obs 2(3):89–144Google Scholar
  58. Morisette JT, Khorram S (2000) Accuracy assessment curves for satellite-based change detection. Photogramm Eng Remote Sens 66:875–880Google Scholar
  59. Muchoney DM, Haack BN (1994) Change detection for monitoring forest defoliation. Photogramm Eng Remote Sens 60:1243–1251Google Scholar
  60. Myneni RB, Asrar G (1994) Atmospheric effects and spectral vegetation indices. Remote Sens Environ 47:390–402CrossRefGoogle Scholar
  61. Neale MC (1994) MxGui 3.2. Department of Psychiatry, Virginia Commonwealth University, Richmond. Accessed 1 Apr 2011
  62. Oberhuber W, Stumböck M, Kofler W (1998) Climate-tree-growth relationships of Scots pine stands (Pinus sylvestris L.) exposed to soil dryness. Trees 13:19–27Google Scholar
  63. Ozenda P (1985) La végétation de la chaîne alpine dans l'espace montagnard europeén. Masson, ParisGoogle Scholar
  64. Peñuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci 3:151–156CrossRefGoogle Scholar
  65. Peters AJ, Rundquist DC, Wilhite DA (1991) Satellite detection of the geographic core of the 1988 Nebraska drought. Agric For Meteorol 57:35–47CrossRefGoogle Scholar
  66. Polomski J, Schönfeld U, Braasch H, Dobbertin M, Burgermeister W, Rigling D (2006) Occurrence of Bursaphelenchus species in declining Pinus sylvestris in a dry Alpine valley in Switzerland. For Pathol 36:110–118CrossRefGoogle Scholar
  67. Quaglino A, Mondino GP, Nosenzo A, Borelli M, Motta R, Pividori M (1987) DEFOR86: Deperimento delle foreste in Valle d’Aosta—Possibili rapporti con l’inquinamento atmosferico. Università degli Studi di Torino e Regione Autonoma Valle d’Aosta, AostaGoogle Scholar
  68. Rebetez M, Dobbertin M (2004) Climate change may already threaten Scots pine stands in the Swiss Alps. Theor Appl Clim 79:1–9CrossRefGoogle Scholar
  69. Rebetez M, Dupont O, Giroud M (2009) An analysis of the July 2006 heatwave extent in Europe compared to the record year of 2003. Theor Appl Clim 95:1–7CrossRefGoogle Scholar
  70. Reineke LH (1933) Perfecting a stand-density index for even-aged forests. J Agric Res 46:627–638Google Scholar
  71. Rigling A, Dobbertin M, Bürgi M, Gimmi U, Pannatier E, Gugerli F, Heiniger U, Polomski J, Rebetez M, Rigling D (2006) Verdrängen Flaumeichen die Walliser Waldföhren? Merkblatt für die Praxis 41:1–16Google Scholar
  72. Rigling A, Eilmann B, Köchli R, Dobbertin M (2010) Mistletoe-induced crown degradation in Scots pine in a xeric environment. Tree Physiol 30:845–852PubMedCrossRefGoogle Scholar
  73. Rosenfield GH, Fitzpatrick-Lins K (1986) A coefficient of agreement as a measure of thematic classification accuracy in remote sensing. Photogramm Eng Remote Sens 52:223–227Google Scholar
  74. Rouault G, Candau JN, Lieutier F, Nageleisen LM, Martin JC, Warzée N (2006) Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Ann For Sci 63:613–624CrossRefGoogle Scholar
  75. Rutishauser T, Luterbacher J, Defila C, Frank D, Wanner H (2008) Swiss spring plant phenology 2007: extremes, a multi-century perspective, and changes in temperature sensitivity. Geophys Res Lett 35:L05703CrossRefGoogle Scholar
  76. Schär C, Vidale PL, Lüthi D, Frei C, Häberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427:332–336PubMedCrossRefGoogle Scholar
  77. Shaw JD (2000) Application of stand density index to irregularly structured stands. West J Appl For 15:40–42Google Scholar
  78. Shaw JD (2006) Forest Inventory and Analysis (FIA) annual inventory answers the question: what is happening to pinyon-juniper woodlands? J For 103:280–286Google Scholar
  79. Shipley B (2000) Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inference. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  80. Thabeet A, Vennetier M, Gadbin-Henry C, Denelle N, Roux M, Caraglio Y, Vila B (2009) Response of Pinus sylvestris L. to recent climatic events in the French Mediterranean region. Trees 23:843–853CrossRefGoogle Scholar
  81. Toscano S (2008) L’utilizzo di immagini satellitari per l’individuazione dei cambiamenti nella componente vegetazionale del territorio: aspetti procedurali critici e possibili soluzioni. Dissertation, Università degli Studi di Torino, TorinoGoogle Scholar
  82. Townshend JRG, Justice CO, Gurney C, McManus J (1992) The impact of misregistration on change detection. IEEE Trans Geosci Remote Sens 30:1054–1060CrossRefGoogle Scholar
  83. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150CrossRefGoogle Scholar
  84. Vacchiano G, Dobbertin M, Egli S, Giordano L, Gonthier P, Mazzoglio P, Motta R, Nola P, Nicolotti G, Patetta A, Polomski J, Rigling A, Rigling D (2008) Il deperimento del pino silvestre nelle Alpi occidentali: natura ed indirizzi di gestione. Compagnia delle Foreste, ArezzoGoogle Scholar
  85. Vallauri D (1998) Parasite dynamics of Viscum album L. in Austrian black pine stands in the Saignon watershed (southwestern Alps). Ann For Sci 55:823–835CrossRefGoogle Scholar
  86. van der Schrier G, Efthymiadis D, Briffa KR, Jones PD (2007) European Alpine moisture variability for 1800–2003. Int J Clim 27:415–427CrossRefGoogle Scholar
  87. van Leeuwen WJD, Huete AR, Laing TW (1999) MODIS vegetation index compositing approach: a prototype with AVHRR data. Remote Sens Environ 69:264–280CrossRefGoogle Scholar
  88. van Mantgem PJ, Stephenson NL, Byrne JC, Daniels LD, Franklin JF, Fule PZ, Harmon ME, Larson AJ, Smith JM, Taylor AH (2009) Widespread increases of tree mortality rates in the western United States. Science 323:521–524PubMedCrossRefGoogle Scholar
  89. Verbesselt J, Robinson A, Stone C, Culvenor D (2009) Forecasting tree mortality using change metrics derived from MODIS satellite data. For Ecol Manag 258:1166–1173CrossRefGoogle Scholar
  90. Vermote EF, El Saleous NZ, Justice CO (2002) Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sens Environ 83:97–111CrossRefGoogle Scholar
  91. Waring RH (1987) Characteristics of trees predisposed to die. Bioscience 37:569–574CrossRefGoogle Scholar
  92. Weber P, Bugmann H, Rigling A (2007) Radial growth responses to drought of Pinus sylvestris and Quercus pubescens in an inner-Alpine dry valley. J Veg Sci 18:777–792CrossRefGoogle Scholar
  93. Weber P, Bugmann H, Fonti P, Rigling A (2008) Using a retrospective dynamic competition index to reconstruct forest succession. For Ecol Manag 254:96–106CrossRefGoogle Scholar
  94. Wermelinger B, Rigling A, Schneider Mathis D, Dobbertin M (2008) Assessing the role of bark-and wood-boring insects in the decline of Scots pine (Pinus sylvestris) in the Swiss Rhone valley. Ecol Entomol 33:239–249CrossRefGoogle Scholar
  95. Wulff S (2002) The accuracy of forest damage assessments: experiences from Sweden. Environ Monit Assess 74:295–309PubMedCrossRefGoogle Scholar
  96. Xiao X, Hollinger D, Aber J, Goltz M, Davidson EA, Zhang Q, Moore B (2004) Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens Environ 89:519–534CrossRefGoogle Scholar
  97. Yuhas AN, Scuderi LA (2009) MODIS-derived NDVI characterisation of drought-induced evergreen dieoff in western North America. Geogr Res 47:34–45CrossRefGoogle Scholar
  98. Zarnoch SJ, Bechtold WA, Stolte KW (2004) Using crown condition variables as indicators of forest health. Can J For Res 34:1057–1070CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Giorgio Vacchiano
    • 1
  • Matteo Garbarino
    • 1
  • Enrico Borgogno Mondino
    • 2
  • Renzo Motta
    • 1
  1. 1.Department of Agriculture, Silviculture and Land ManagementUniversity of TorinoGrugliascoItaly
  2. 2.Department of Agricultural, Forestry and Environmental Economics and EngineeringUniversity of TorinoGrugliascoItaly

Personalised recommendations