PREDICTION SPATIAL PATTERNS OF WINDTHROW PHENOMENON IN DECIDUOUS TEMPERATE FORESTS USING LOGISTIC REGRESSION AND RANDOM FOREST

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saeid shabani

Abstract

Forest management needs to evaluate various hazards where may cause economic or other losses to forest owners. The aim of this study was to prepare windthrow hazard maps based on logistic regression and random forest models in the Nowshahr Forests, Mazandaran Province, Iran. At first, 200 windthrow locations were identified from extensive field surveys and some reports. Out these, 140 (70%) locations were randomly selected as training data and the remaining 60 (30%) cases were used for the validation goals. In the next step, 10 predictive variables such as slope degree, slope aspect, altitude, Topographic Position Index (TPI), Topographic Wetness Index (TWI), distance to roads and skid trails, wind effect, soil texture, forest type and density were extracted from the spatial database. Subsequently, windthrow hazard maps were produced using logistic regression and RF models, and the results were plotted in ArcGIS. Finally, the area under the curves (AUC) and kappa coefficient were made for performance purposes. The validation of results presented that the area under the curve and kappa have a more accuracy for the random forest (97.5%, and 95%, respectively) than logistic regression (96.667%, and 93.333, respectively) model. Therefore, this technique has more potentiality to be applied in the evaluation of windthrow phenomenon in forest ecosystems. Additionally, both models indicate that the spatial distribution of windthrow incidence likelihood is highly variable in this region. In general, the mentioned findings can be used for management of future windthrow in favor of the economic benefits and environmental preservation.

Article Details

How to Cite
SHABANI, saeid. PREDICTION SPATIAL PATTERNS OF WINDTHROW PHENOMENON IN DECIDUOUS TEMPERATE FORESTS USING LOGISTIC REGRESSION AND RANDOM FOREST. CERNE, [S.l.], v. 23, n. 3, p. 387-394, oct. 2017. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/1589>. Date accessed: 18 dec. 2017.
Keywords
Disturbance, Risk Management; Tree Stability; Uprooting; Wind Damages
Section
Article

References

ANONYMOUS, Peer review report 1 on wind damage propagation in forests. Agricultural and Forest Meteorology, v. 217, p. 109–110, 2016.

BLENNOW, K.; SALLNÄS, O. WINDA—a system of models for assessing the probability of wind damage to forest stands within a landscape. Ecological Modelling, v. 175, p. 87–99, 2004.

BREIMAN, L. Random forests. Mach Learn, v. 45, p. 5–32, 2001.

CONSTANTINE, J. A.; SCHELHAAS, M. J.; GABET, E.; MUDD, S. M. Limits of windthrow-driven hillslope sediment flux due to varying storm frequency and intensity. Geomorphology, v. 175–176, p. 66–73, 2012.

COUTAND, C. Mechanical stress and wind damage. Encyclopedia of Applied Plant Sciences, v. 1, p. 20–26, 2017.

DUPONT, S. A simple wind–tree interaction model predicting the probability of wind damage at stand level. Agricultural and Forest Meteorology, v. 224, p. 49–63, 2016.

DUPUY, L.; FOURCAUD, T.; STOKES, A. A numerical investigation into the influence of soil type and root architecture on tree anchorage. Plant and Soil, v. 278, p. 119–134, 2005.

ERCANOGLU, M.; GOKCEOGLU, C. Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey) by fuzzy approach. Environ Geol, v. 41, p. 720–730, 2002.

HALE, S. E.; GARDINER, B.; PEACE, A.; NICOLL, B.; TAYLOR, P.; PIZZIRANI, S. Comparison and validation of three versions of a forest wind risk model. Environmental Modelling & Software, v. 68, p. 27–41, 2015.

KOOCH, Y.; ZACCONE, C.; LAMERSDORF, N. P.; TONON, G. Pit and mound influence on soil features in an Oriental Beech (Fagus orientalis Lipsky) forest. Eur J Forest Res, v. 133, p. 347–354, 2014.

LANDIS, J. R.; KOCH, G. G. The measurement of observer agreement for categorical data. Biometrics, v. 33, p. 159–174, 1977.

LOCATELLI, T.; TARANTOLA, S.; GARDINER, B.; PATENAUDE, G. Variance-based sensitivity analysis of a wind risk model - Model behaviour and lessons for forest modelling. Environmental Modelling & Software, v. 87, p. 84–109, 2017.

MOORE, I. D.; GRAYSON, R. B.; LADSON, A. Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrol Process, v. 5, p. 3–30, 1991.

OLIVEIRA, S.; OEHLER, F.; SAN-MIGUEL-AYANZ, J.; CAMIA, A.; PEREIRA, J. M. C. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management, v. 275, p. 117–129, 2012.

PELTOLA, H.; IKONEN, V. P.; GREGOW, H.; STRANDMAN, H.; KILPELAINEN, A.; VENALAINEN, A.; KELLOMAKI, S. Impacts of climate change on timber production and regional risks of wind-induced damage to forests in Finland. Forest Ecology and Management, v. 260, p. 833–845, 2010.

PETERS, J.; BAETS, B. D.; VERHOEST, N. E. C.; Samson, R.; Degroeve, S.; Becker, P. D.; Huybrechts, W. Random forests as a tool for ecohydrological distribution modelling. Ecological Modelling, v. 207, p. 304–318, 2007.

RUEL, J. C. Factors influencing windthrow in balsam fir forests: from landscape studies to individual tree studies. Forest Ecology and Management, v. 135, p. 169–178, 2000.
SCHELHAAS, M. J. The wind stability of different silvicultural systems for Douglas-fir in the Netherlands: a model-based approach. Forestry, v. 81, n. 3, p. 399–414, 2008.

SCHINDLER, D.; BAUHUS, J.; Mayer, H. Wind effects on trees. Eur J Forest Res, v. 131, p. 159–163, 2012.

STEPHENSON, C. M.; MACKENZIE, M.; EDWARDS, C.; TRAVIS, J. Modelling establishment probabilities of an exotic plant, Rhododendron ponticum, invading a heterogeneous, woodland landscape using logistic regression with spatial autocorrelation. Ecol. Model, v. 193, p. 747–758, 2006.

STOKES, A.; SALIN, F.; KOKUTSE, A. D.; BERTHIER, S.; JEANNIN, H.; MOCHAN, S.; KOKUTSE, N.; DORREN, L.; ABDGHANI, M.; FOURCAUD, T. Mechanical resistance of different tree species to rockfall in the French Alps. Plant Soil, v. 278, p. 107–117, 2005.

SUVANTO, S.; HENTTONEN, H. M.; NÖJD, P.; MÄKINEN, H. Forest susceptibility to storm damages is affected by similar factors regardless of storm type: Comparison of thunder storms and autumn extra-tropical cyclones in Finland. Forest Ecology and Management, v. 381, p. 17–28, 2016.

VACCHIANO, G.; GARBARINO, M.; LINGUA, E.; MOTTA, R. Forest dynamics and disturbance regimes in the Italian Apennines. Forest Ecology and Management, v. 388, p. 57–66, 2016.

VALINGER, E.; FRIDMAN, J. Factors affecting the probability of windthrow at stand level as a result of Gudrun winter storm in southern Sweden. Forest Ecology and Management, v. 262, p. 398–403, 2011.

YESILNACAR, E. K. The application of computational intelligence to landslide susceptibility mapping in Turkey. 2005. PhD Thesis. Department of Geomatics, University of Melbourne, 2005, 423p.