A bark beetle infestation predictive model based on satellite data in the frame of decision support system TANABBO

doi:10.3832/ifor3271-013

A bark beetle infestation predictive model based on satellite data in the frame of decision support system TANABBO

Renata Duračiová⁽¹⁾, Milan Muňko⁽¹⁾, Ivan Barka⁽²⁾, Milan Koreň⁽³⁾, Karolina Resnerová⁽⁴⁾, Jaroslav Holuša⁽⁴⁾, Miroslav Blaženec⁽⁵⁾, Mária Potterf⁽⁶⁾, Rastislav Jakuš^(4-5)

iForest - Biogeosciences and Forestry, Volume 13, Issue 3, Pages 215-223 (2020)
doi: https://doi.org/10.3832/ifor3271-013
Published: Jun 06, 2020 - Copyright © 2020 SISEF

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Geo-mapping

The European spruce bark beetle Ips typographus L. causes significant economic losses in managed coniferous forests in Central and Northern Europe. New infestations either occur in previously undisturbed forest stands (i.e., spot initiation) or depend on proximity to previous years’ infestations (i.e., spot spreading). Early identification of newly infested trees over the forested landscape limits the effective control measures. Accurate forecasting of the spread of bark beetle infestation is crucial to plan efficient sanitation felling of infested trees and prevent further propagation of beetle-induced tree mortality. We created a predictive model of subsequent year spot initiation and spot spreading within the TANABBO decision support system. The algorithm combines open-access Landsat-based vegetation change time-series data, a digital terrain model, and forest stand characteristics. We validated predicted susceptibility to bark beetle attack (separately for spot initiation and spot spreading) against beetle infestations in managed forests in the Bohemian Forest in the Czech Republic (Central Europe) in yearly time steps from 2007 to 2010. The predictive models of susceptibility to bark beetle attack had a high degree of reliability (area under the ROC curve - AUC: 0.75-0.82). We conclude that spot initiation and spot spreading prediction modules included within the TANABBO model have the potential to help forest managers to plan sanitation felling in managed forests under pressure of bark beetle outbreak.

Keywords

Spatial Predictive Model, Bark Beetle Infestation, GIS, ROC Curve, Norway Spruce

Authors’ address

(1)

0000-0002-9061-6567

Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Bratislava (Slovakia)

(2)

0000-0002-2364-8542
National Forest Centre, Forest Research Institute, Zvolen (Slovakia)

(3)

0000-0002-2956-944X
Faculty of Forestry, Technical University in Zvolen, Zvolen (Slovakia)

(4)

0000-0003-1459-0331
0000-0003-2280-1952
Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague (Czech Republic)

(5)

0000-0001-9743-614X
0000-0003-2280-1952
Institute of Forest Ecology, Slovak Academy of Sciences, Zvolen (Slovakia)

(6)

0000-0001-6763-1948
Department of Biological and Environmental Sciences, University of Jyvaskyla, Jyvaskyla, Finland

Corresponding author

Rastislav Jakuš
jakus@fld.czu.cz

Citation

Duračiová R, Muňko M, Barka I, Koreň M, Resnerová K, Holuša J, Blaženec M, Potterf M, Jakuš R (2020). A bark beetle infestation predictive model based on satellite data in the frame of decision support system TANABBO. iForest 13: 215-223. - doi: 10.3832/ifor3271-013

Academic Editor

Massimo Faccoli

Paper history

Received: Oct 17, 2019
Accepted: Apr 05, 2020

First online: Jun 06, 2020
Publication Date: Jun 30, 2020
Publication Time: 2.07 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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List of the papers citing this article based on CrossRef Cited-by.

(1)

Barka I, Lukeš P, Bucha T, Hlásny T, Strejček R, Mlčoušek M, Krístek S (2018)
Remote sensing-based forest health monitoring systems - case studies from Czechia and Slovakia. Central European Forestry Journal 64: 259-275.
Online | Gscholar

(2)

Bucha T, Barka I (2010)
Satellite-based regional system for observation of forest response to global environmental changes. In: “Advances in Geoinformation Technologies” (Horák J, Halounová L, Hlásny T, Kusendová D, Vozenílek V eds). Technical University of Ostrava, Ostrava, Czech Republic, pp. 1-14.
Gscholar

(3)

Caha J, Nevtípilová V, Dvorsky J (2014)
Constraint and preference modelling for spatial decision making with use of possibility theory. In: Proceedings of the “Hybrid Artificial Intelligence Systems” (Polycarpou M, De Carvalho ACPLF, Pan JS, Wozniak M, Quintian H, Corchado E eds). Salamanca (Spain) 11-13 June 2014. Springer International Publishing, Switzerland, pp. 145-155.
CrossRef | Gscholar

(4)

Culek M (1996)
Biogeografické členení Ceské republiky [Biogeographical division of the Czech Republic]. Enigma, Praha, Czech Republic. [in Czech]
Gscholar

(5)

Eastman JR (1999)
Guide to GIS and image processing - Volume 2. IDRISI production Clark University, Worcester, UK, pp. 170.
Gscholar

(6)

Egan J (1975)
Signal detection theory and ROC analysis. Academic Press, New York, USA, pp. 277.
Gscholar

(7)

Fawcett T (2006)
An introduction to ROC analysis. Pattern Recognition Letters 27: 861-871.
CrossRef | Gscholar

(8)

Fink AH, Brücher T, Ermert V, Krüger A, Pinto JG (2009)
The European storm Kyrill in January 2007: synoptic evolution, meteorological impacts and some considerations with respect to climate change. Natural Hazards and Earth System Sciences 9: 405-423.
CrossRef | Gscholar

(9)

Hair JF, Black WC, Babin BJ, Anderson RE (2010)
Multivariate data analysis (7th edn). Pearson Education Limited, London, UK, pp. 734.
Gscholar

(10)

Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2014)
High-resolution global maps of 21st-century forest cover change. Science 324: 850-853.
CrossRef | Gscholar

(11)

Gini C (1912)
Variabilità e mutabilità [Variability and mutability]. Reprinted in: “Memorie di metodologica statistica”, 1955 (Pizetti E, Salvemini T eds). Libreria Eredi Virgilio Veschi, Rome, Italy, pp. 156. [in Italian]
Gscholar

(12)

Gomarasca MA (2009)
Basics of geomatics. Springer-Verlag, New York, USA, pp. 697.
Online | Gscholar

(13)

Havašová M, Bucha T, Ferenčík J, Jakuš R (2015)
Applicability of a vegetation indices-based method to map bark beetle outbreaks in the High Tatra Mountains. Annals of Forest Research 58: 295-310.
CrossRef | Gscholar

(14)

Havašová M, Ferenčík J, Jakuš R (2017)
Interactions between windthrow, bark beetles and forest management in the Tatra national parks. Forest Ecology and Management 391: 349-361.
CrossRef | Gscholar

(15)

Hofierka J, Šúri M (2002)
The solar radiation model for open source GIS: implementation and applications. In: Proceedings of the “Open source GIS - GRASS user’s conference 2002” (Ciolli M, Zatelli P eds). Trento (Italy) 11-13 Sept 2002. University of Trento, Trento, Italy, pp. 19.
Gscholar

(16)

Jakuš R, Grodzki W, Jezik M, Jachym M (2003)
Definition of spatial patterns of bark beetle Ips typographus (L.) outbreak spreading in Tatra Mountains (Central Europe), using GIS. In: Proceedings of the Conference “GTR NE-311 Survey and Management of Forest Insects” (Mc Manus M, Liebhold A eds). Kraków (Poland) 1-5 Sept 2002. USDA Forest Service, Newtown Square, PA, USA, pp. 25-32.
Online | Gscholar

(17)

Jakuš R, Jezík M, Blazenec M (2005)
Prognosis of bark beetle attacks in TANABBO model. In: “GIS and Databases in the Forest Protection in Central Europe” (Grodzki W eds). Centre of Exellence PROFEST at the Forest Reseach Institute, Warsaw, Poland, pp. 35-43.
Gscholar

(18)

Jakuš R, Blazenec M, Koren M, Barka I, Lukášová K, Lubojacký J, Holuša J (2017)
TANABBO II model pro hodnocení rizika napadení lesních porostu lýkozroutem smrkovým Ips typographus (L.) [TANABBO II model for risk of Ips typographus (L.) attack assessment]. Lesnický pruvodce 1/2017. Výzkumný ústav lesního hospodárství a myslivosti, v. v. i., Jílovište, Czech Republic, pp. 71. [in Czech]
Gscholar

(19)

Karell L, Munko M, Duračiová R (2017)
Applicability of support vector machines in landslide susceptibility mapping. In: “The Rise of Big Spatial Data, Lecture Notes in Geoinformation and Cartography” (Ivan I, Singleton A, Horák J, Inspektor T eds). Springer International Publishing AG, Cham, Switzerland, pp. 373-386.
CrossRef | Gscholar

(20)

Kautz M, Dworschak K, Gruppe A, Schopf R (2011)
Quantifying spatio-temporal dispersion of bark beetle infestations in epidemic and non-epidemic conditions. Forest Ecology and Management 262: 598-608.
CrossRef | Gscholar

(21)

Kärvemo S, Van Boeckel TP, Gilbert M, Grégoire JC, Schroeder M (2014)
Large-scale risk mapping of an eruptive bark beetle - Importance of forest susceptibility and beetle pressure. Forest Ecology and Management 318: 158-166.
CrossRef | Gscholar

(22)

Kissiyar O, Blazenec M, Jakuš R, Willekens A, Jezík M, Baláz P, Valckenborg JV, Celer S, Fleischer P (2005)
TANABBO model: a remote sensing based early warning system for forest decline and bark beetle outbreaks in Tatra Mts. - overview. In: “GIS and Databases in the Forest Protection in Central Europe” (Grodzki W eds). Centre of Exellence PROFEST at the Forest Research Institute, Warsaw, Poland, pp. 15-34.
Gscholar

(23)

Kuhn M, Johnson K (2013)
Applied predictive modeling. Springer Science + Business, New York, USA, pp. 600.
Gscholar

(24)

Lieskovsky T, Duračiová R, Karell L (2013)
Selected mathematical principles of archaeological predictive models creation and validation in the GIS environment. Interdisciplinaria Archaeologica - Natural Sciences in Archaeology IV (2): 33-46.
Online | Gscholar

(25)

Meddens AJH, Hicke JA (2014)
Meddens AJH, Hicke JA (2014) Spatial and temporal patterns of Landsat-based detection oftree mortality caused by a mountain pine beetleoutbreak in Colorado, USA. Forest Ecology and Management 322: 78-88.
CrossRef | Gscholar

(26)

Metz CE (1978)
Basic principles of ROC analysis. Seminars in Nuclear Medicine 8: 283-298.
CrossRef | Gscholar

(27)

Mezei P, Grodzki W, Blazenec M, Jakuš R (2014a)
Factors influencing the wind-bark beetles’ disturbance system in the course of an Ips typographus outbreak in the Tatra Mountains. Forest Ecology and Management 312: 67-77.
CrossRef | Gscholar

(28)

Mezei P, Grodzki W, Blazenec M, Skvarenina J, Brandysova V, Jakuš R (2014b)
Host and site factors affecting tree mortality caused by the spruce bark beetle (Ips typographus) in mountainous conditions. Forest Ecology and Management 331: 196-207.
CrossRef | Gscholar

(29)

Mezei P, Jakus R, Pennerstorfer J, Havasova M, Skvarenina J, Ferencik J, Slivinsky J, Bicarova S, Bilcik D, Blazenec M, Netherer S (2017)
Storms, temperature maxima and the Eurasian spruce bark beetle Ips typographus - An infernal trio in Norway spruce forests of the Central European High Tatra Mountains. Agricultural and Forest Meteorology 242: 85-95.
CrossRef | Gscholar

(30)

Ortiz SM, Breidenbach J, Kändler G (2013)
Early detection of bark beetle green attack using TerraSAR-X and RapidEye data. Remote Sensing 5: 1912-1931.
CrossRef | Gscholar

(31)

Økland B, Nikolov C, Krokene P, Vakula J (2016)
Transition from windfall- to patch-driven outbreak dynamics of the spruce bark beetle Ips typographus. Forest Ecology and Management 363: 63-73.
CrossRef | Gscholar

(32)

Pontius RG, Schneider LC (2001)
Land-cover change model validation by an ROC method for the Ipswich watershed. Agriculture, Ecosystems and Environment 85: 239-248.
CrossRef | Gscholar

(33)

Potterf M, Nikolov C, Kočická E, Ferenčík J, Mezei P, Jakuš R (2019)
Landscape-level spread of beetle infestations from windthrown- and beetle-killed trees in the non-intervention zone of the Tatra National Park, Slovakia (Central Europe). Forest Ecology and Management 432: 489-500.
CrossRef | Gscholar

(34)

Phillips S, Dudik M (2008)
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161-175.
CrossRef | Gscholar

(35)

Raffa KF, Gregoire J, Lindgren BS, Gre J (2015)
Natural history and ecology of bark beetles. In: “Bark Beetles: Biology and Ecology of Native and Invasive Species”. Academic Press, Elsevier, pp. 1-40.
CrossRef | Gscholar

(36)

Robertson C, Nelson T, Boots B (2007)
Mountain pine beetle dispersal: the spatial-temporal interaction of infestations. Forest Science 53: 395-405.
Online | Gscholar

(37)

Rossi F, Breidenbach J, Puliti S, Astrup R, Talbot B (2019)
Assessing harvested sites in a forested boreal mountain catchment through global forest watch. Remote Sensing 11: 543.
CrossRef | Gscholar

(38)

Rouault G, Candau JN, Lieutier F, Nageleisen LM, Warzee N, Martin JC, Warzee N (2006)
Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Annals of Forest Science 63: 613-624.
CrossRef | Gscholar

(39)

Senf C, Pflugmacher D, Wulder MA, Hostert P (2015)
Characterizing spectral-temporal patterns of defoliator and bark beetle disturbances using Landsat time series. Remote Sensing of Environment 170: 166-177.
CrossRef | Gscholar

(40)

Simard M, Powell EN, Raffa KF, Turner, MG (2012)
What explains landscape patterns of tree mortality caused by bark beetle outbreaks in Greater Yellowstone? Global Ecology and Biogeography 21: 556ÂÂ 567.
CrossRef | Gscholar

(41)

Stoklasa M (2003)
Monitoring zdravotního stavu lesu z druzicových snímku [Forest health monitoring with the use of satellite images]. Ochrana prírody 58: 228-232. [in Czech]
Gscholar

(42)

Thatcher RC, Searcy JL, Coster JE, Hertel GD (1980)
The southern pine beetle. Technical Bulletin 1631, Expanded Southern Pine Beetle Research and Application Program, USDA Forest Service, Science and Education Administration, Pineville, LA, USA, pp. 265.
Gscholar

(43)

Wackernagel H (2003)
Multivariate geostatistics. An introduction with applications. Springer-Verlag, Berlin, Heidelberg, Germany, pp. 257.
Gscholar

(44)

Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL (2006)
Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. Forest Ecology and Management 221: 27-41.
CrossRef | Gscholar

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