Abstract
Mountain forests are more prone to environmental predispositions (EPs) than submountain ones. While remote sensing of mountain forests enables instantaneous damage mapping, the investigation of the causes requires field data. However, a local field or regionally modeled environmental characteristics influence remote data evaluation differently. This study focused on the evaluation of EPs effects damaging mountain forests between various spatial resolutions during environmental change. The evaluation was divided into managed and natural forests in the Hrubý Jeseník Mts. (Czech Republic; 240–1491 m a.s.l.; 50.082°N, 17.231°E). Damage was assessed through the discrimination analysis of the normalised difference vegetation index (NDVI) by MODIS VI during alternating drought and flood periods 2003–2014. The local environmental influence was assessed using the discrimination function (DF) separability of forest damage in the training sets. The regional influence was assessed through map algebra estimated via the DF and a forest decline spatial model based on EPs from differences between risk growth conditions and biomass fuzzy sets. Management, EPs and soil influenced forest NDVI at different levels. The management afflicted the NDVI more than the EPs. The EPs afflicted the NDVI more than the soil groups. Strong winters and droughts had a greater influence on the NDVI than the flood events, with the winter of 2005/2006 inverting the DF direction, and the 2003 drought increasing differences in managed forest biomass and decreasing differences in natural forest biomasses. More than 50% of declining managed forests in the training sets occurred on Leptosols, Podzols and Histosols. On a regional scale, the soil influence was eliminated by multiple predispositions. The EPs influenced 96% of natural forest and 65% of managed forest, though managed forest damage was more evident. The mountain forest NDVI decline was dependent on both management and risk predispositions.
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References
Allen CD, Macalady A, Chenchouni H, et al. (2010) Drought-induced forest mortality: a global overview reveals emerging climate change risks. For Ecol Manag 259(4): 660–684. https://doi.org/10.1016/j.foreco.2009.09.001
Anderson MC, Kustas WP, Norman JM (2003) Upscaling and Downscaling—A Regional View of the Soil-Plant-Atmosphere Continuum. Agron J 95(6): 1408–1423. https://doi.org/10.2134/agronj2003.1408
Bonan GB, Levis S, Kergoat L, Oleson KW (2002) Landscapes as patches of plant functional types: An intergrating concept for climate and ecosystem models. Global Biogeochem Cy 16(2): 1360–1384. https://doi.org/10.1029/2000GB001360
Brázdil R, Trnka M, Dobrovolný P, et al. (2009) Variability of droughts in the Czech Republic, 1881–2006. Theor Appl Climatol 97(3): 297–315. https://doi.org/10.1007/s00704-008-0065-x
Brovkina O, Cienciala E, Zemek F, et al. (2017) Composite indicator for monitoring of Norway spruce stand decline. Eur J Remote Sens 50 (1): 550–563. https://doi.org/10.1080/22797254.2017.1372697
Buček A, Maděra P, Čermák P, et al. (2004) The state and development dynamic evaluation of geobiocoenoses in the National nature reserve Praděd. Geobiocenologické spisy 10: 1–116. (In Czech)
Burian J, Peřina J, Zlatník V, Sotorník M (2001) Regional Plan of Forest Development (RPFD) - NFA 27 - Hrubý Jeseník. ÚHÚL Brandýs nad Labem. (In Czech)
Chappelka AH, Freer-Smith PH (1995) Predisposition of trees by air pollutants to low temperatures and moisture stress. Environ Pollut 87(1): 105–117. https://doi.org/10.1016/S0269-7491(99)80013-X
Chen X, Vierling L, Deering D, Conley A (2005) Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study. Int J Remote Sens 26 (24): 5433–5451. https://doi.org/10.1080/01431160500285142
Christensen M, Hahn K, Mountford EP, et al. (2005) Dead wood in European beech (Fagus sylvatica) forest reserves. For Ecol Manag 210(1–3): 267–282. https://doi.org/10.1016/j.foreco.2005.02.032
Cosby BJ, Wright RF, Hornberger GM, Galloway JN (1985) Modelling the effects of acid deposition: estimation of long-term water quality responses in a small forested catchment. Water Resour Res 21(11): 1591–1601. https://doi.org/10.1029/WR021i011p01591
Cramer W, Bondeau A, Woodward FI, et al. (2001) Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biol 7(4): 357–373. https://doi.org/10.1046/j.1365-2486.2001.00383.x
DeFries R, Hansen A, Newton AC, Hansen MC (2005) Increasing isolation of protected areas in tropical forests over the past twenty years. Ecol Appl 15(1): 19–26. https://doi.org/10.1890/03-5258
Fleischbein K, Wilcke W, Valarezo C, et al. (2006) Water budgets of three small catchments under montane forest in Ecuador: experimental and modelling approach. Hydrol Process 20(12): 2491–2507. https://doi.org/10.1002/hyp.6212
Frélichová J, Vačkář D, Pártl A, et al. (2014) Intergrated assessment of ecosystem services in the Czech Republic. Ecosyst Serv 8(1): 110–117. https://doi.org/10.1016/j.ecoser.2014.03.001
Galvão SL, Formaggio RA, Couto GE, Roberts DA (2008) Relationships between mineralogical and chemical composition of tropical soils and topography from hyperspectral remote sensing data. ISPRS J Photogramm 63(2): 259–271. https://doi.org/10.1016/j.isprsjprs.2007.09.006
Gorham E (1998) Acid deposition and its ecological effects: a brief history of research. Environ Sci Policy 1(3): 153–166. https://doi.org/10.1016/S1462-9011(98)00025-2
Guevara M, Vargas R (2019) Downscaling satellite soil moisture using geomorphometry and machine learning. PLoS ONE 14(9): e0219639. https://doi.org/10.1371/journal.pone.0219639
Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135(2–3): 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
Hájek F (2008) Process-based Approach to Automated Classification of Forest Structures Using Medium-format Digital Aerial Photos and Ancillary GIS Information. Eur J For Res 127(2): 115–124. https://doi.org/10.1007/s10342-007-0188-0
Halounová L, Hanzlová M, Horák J, et al. (2006) Remote sensing data and GIS tools for improvement of rainfall-runoff models in the Bělá River watershed in the northern Moravia. In: Second Goettingen GIS and Remote Sensing Days - Global Change Issues in Developing and Emerging Countries. Universita Gottingen: 303–312. https://doi.org/10.11894/5925
Hammer D, Kraft R, Wheeler D (2014) Alerts of forest disturbance from MODIS imagery. Int J Appl Earth Obs 33(1): 1–9. https://doi.org/10.1016/j.jag.2014.04.011
Hanewinkel M, Cullman DA, Schelhaas MJ, et al. (2013) Climate change may cause severe loss in the economic value of European forest land. Nat Clim Change 3(2): 203–207. https://doi.org/10.1038/nclimate1687
Hansen MC, Potapov PV, Moore R, et al. (2013) High-resolution Global Maps of 21st-Centrury Forest Cover Change. Science 342(6160): 850–853. https://doi.org/10.1126/science.1244693
Hawryło P, Bednarz B, Wężyk P, Szostak M (2018) Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2. Eur J Remote Sens 51(1): 194–204. https://doi.org/10.1080/22797254.2017.1417745
Hruška J, Cudlín P, Krám P (2001) Relationship between Norway spruce status and soil water base cations/aluminium rations in the Czech Republic. W Air Soil Pollut 130(3): 983–988. https://doi.org/10.1007/978-94-007-0810-5_11
Humphries HC, Burgeron PS, Reynolds KM (2010) Sensitivity Analysis od Land Unit Suitability for Conservation Using a Knowledge-Based System. Environ Manag 46(2): 225–236. https://doi.org/10.1007/s00267-010-9520-4
Jankovský L, Apltauer J, Lička D, et al. (2005) Dead wood and wood destroying fungi in mountain spruce stands. In: Reinprecht L, Hlaváč P, Tiralová Z (eds.), Drevoznehodnocujúce huby. Technická univerzita Zvolen: 21–28. (In Czech)
Knapp AK, Beier C, Briske DD, et al. (2008) Consequences of More Extreme Precipitation Regimes for Terrestrial Ecosystems. BioScience 58(9): 811–821. https://doi.org/10.1641/B580908
Lawton RO, Nair US, Pielke Sr. RA, Welch RM (2001) Climatic Impact of Tropical Lowland Deforestation on Nearby Montane Cloud Forests. Science 294(5542): 584–587. https://doi.org/10.1126/science.1062459
Lindgren M, Salemaa M, Tamminen P (2000) Forest Condition in Relation to Environmental Factors. In: Mälkönen E (eds.) Forest Condition in a Changing Environment. Forestry Sciences 65(1): 142–155. https://doi.org/10.1007/978-94-015-9373-1_16
Logsdon SD, Perfect E, Tarquis AM (2008) Multiscale Soil Investigations: Physical Concepts And Mathematical Techniques. Vadose Zone J 7(2): 453–455. https://doi.org/10.2136/vzj2007.0160
Lomský B, Šrámek V (2004) Different types of damage in mountain forest stands of the Czech Republic. J For Sci 50(11): 533–537. https://doi.org/10.17221/4652-JFS
McRoberts RE, Cohen WB, Næsset E, et al. (2010) Using remotely sensed data to construct and assess forest attribute maps and related spatial products. Scand J For Res 25(4): 340–367. https://doi.org/10.1080/02827581.2010.497496
Moselholm L, Andersen B, Johnsen I (1988) Acid deposition and novel forest decline in Central and Northern Europe. Assesment of Available Information and Appraisal of the Scandinavian Situation. Nordic Council of Ministers, Miljørapport 9(1): 1–121.
Paoletti E, Schaub M, Matyssek R, et al. (2010) Advances of air pollution science: From forest decline to multiple-stress effects on forest ecosystem services. Environ Pollut 158(6): 1986–1989. https://doi.org/10.1016/j.envpol.2009.11.023
Paterson S, Minasny B, McBratney A (2018) Spatial variability of Australian soil texture: A multiscale analysis. Geoderma 309(1): 60–74. https://doi.org/10.1016/j.geoderma.2017.09.005
Petoukhov V, Rahmstorf S, Petri S, Schellnhuber HJ (2013) Quasiresonant amplification of planetary waves and recent Northern Hemisphere weather extremes. PNAS 110(14): 5336–5341. https://doi.org/10.1073/pnas.1222000110
Puhe J, Ulrich B (2001) Global climate change and human impacts on forest ecosystems: postglacial development, present situation, and future trends in Central Europe. Springer-Verlag Berlin Heidelberg.
Reyer C, Lasch-Born P, Suckow F, et al. (2014) Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide. Ann For Sci 71(2): 211–225. https://doi.org/10.1007/s13595-013-0306-8
Roberts TM, Skeffington RA, Blank LW (1989) Causes of Type 1 Spruce Decline in Europe. Forestry 62(3): 179–222. https://doi.org/10.1093/forestry/62.3.179-a
Robinson VB (2007) Issues and chalenges of incorporating fuzzy sets in ecological modeling. In: Morris A, Kokhan S (eds.), Geographic Uncertainty in Environmental Security. Springer Verlag, Heidelberg: 33–52. https://doi.org/10.1007/978-1-4020-6438-8_3
Samec P, Caha J, Tuček P, et al. (2017) Discrimination between acute and chronic decline of Central European forests using map algebra of the growth condition and forest biomass fuzzy sets: A case study. Sci Total Environ 599–600(1): 899–909. https://doi.org/10.1016/j.scitotenv.2017.05.023
Samec P, Friedl M, Lipowski M, Žárník M (2008) Soil exploration in basic foreest stand types of Jeseníky Mts. and Beskids. In: Samec P (ed.), Metody zpracování dat v lesnickém monitoringu. Folia Forestalia Bohemica (Proceedings) 2(1): 63–77. (In Czech)
Samec P, Vavříček D, Šimková P, Pňáček J (2007) Multivariate Statistics Approach for Comparison of the Nutrient Status of Norway Spruce (Picea abies /L./ Karst.) and Top-soil Properties in Differently Managed Forest Stands. J For Sci 53(3): 101–112. https://doi.org/10.17221/2173-JFS
Sedláček J, Janderková J, Šefrna L (2009) Soil associations. 1:500 000. In: Hrčianová T, Mackovčin P, Zvara I (eds.), Landscape Atlas of the Czech Republic. Prague: Ministry of Environment, The Silva Tarouca Research Institute for Landscape and Ornamental Gardering: 134–135.
Tuček P, Caha J, Janoška Z, et al. (2014) Forest vulnerability zones in the Czech Republic. J Maps 10(1): 179–182. https://doi.org/10.1080/17445647.2013.866911
Vacek S, Bílek L, Schwarz O, et al. (2013) Effect of Air Pollution on the Health Status of Spruce Stands. A Case Study in the Krkonoše Mountains, Czech Republic. Mt Res Dev 33(1): 40–50. https://doi.org/10.1659/MRD-JOURNAL-D-12-00028.1
Vav’íček D, Kučera A (2016) Some risks associated with surface aviation liming of forest ecosystems not only in the Carpathians. In: Holušová K (ed.), Karpatské lesy: sborník příspěvků. Ústav pro hospodářskou úpravu lesů Brandýs nad Labem: 32–46. (In Czech)
Vavříček D, Pecháček J, Novák F (2011) The problematics of soil mapping. In: Sobocká J (ed.), Diagnostika, klasifikácia a mapovanie pôd. VÚPOP Bratislava: 277–284. (In Czech)
Vavříček D, Samec P, Šimková P (2005) Soil properties as a component of predisposition factors of Norway spruce forest decline in the Hanušovická highland mountain zone. J For Sci 51(12): 527–538. https://doi.org/10.17221/4585-JFS
Woo SW (2009) Forest decline of the world: A linkage with air pollution and global warming. Afr J Biotechnol 8(25): 7409–7414. www.ajol.info/index.php/ajb/article/view/77755
Xu Y (2019) Mapping Soil Moisture from Remotely Sensed and In-situ Data with Statistical Methods. LSU Doctoral Dissertations: #4961. https://digitalcommons.1su.edu/gradschool_dissertations/4961
Zapletal M, Chroust P, Kuňák D (2003) The relationship between defoliation of Norway spruce and atmospheric deposition of sulphur and nitrogen compounds in the Hrubý Jeseník Mts (the Czech Republic). Ekológia (Bratislava) 22(4): 337–347. www.sav.sk/journals/ekol/ek2003_04.htm
Zeng H, Talkkari A, Peltola H, Kellomäki S (2007) A GIS-based decision support system for risk assessment of wind damage in forest management. Environ Modell Softw 22(9): 1240–1249. https://doi.org/10.1016/j.envsoft.2006.07.002
Zeng C, Yang L, Zhu AX, et al. (2016) Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method. Geoderma 281(1): 69–82. https://doi.org/10.1016/j.geoderma.2016.06.033
Zhu J, Huang Z, Sun H, Wang G (2017) Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods. Remote Sens 9(3): 241–264. https://doi.org/10.3390/rs9030241
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The authors gratefully acknowledge the support by the Project LM2018123 CzeCOS of the Ministry of Education, Youth and Sports of the Czech Republic.
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Samec, P., Kudĕlková, R., Lukeš, P. et al. Influence of environmental predispositions on temperate mountain forest damage at different spatial scales during alternating drought and flood periods: case study in Hrubý Jeseník Mts. (Czech Republic). J. Mt. Sci. 19, 1931–1944 (2022). https://doi.org/10.1007/s11629-021-6671-0
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DOI: https://doi.org/10.1007/s11629-021-6671-0