Application of topography and logistic regression in forest type spatial prediction

Document Type : Research article

Authors

1 M.Sc. of Forestry, University of Agricultural Sciences and Natural Resources of Gorgan

2 Associate Prof., University of Agricultural Sciences and Natural Resources of Gorgan

3 Assistant Prof., Department of Statistics, Golestan University

4 Assistant Prof., University of Agricultural Sciences and Natural Resources of Gorgan

Abstract

 
This research was carried out for predicting probability of forest type's presence using topographic attributes in the Educational and Research Forest of Doctor Bahramnia, district 1, with 1714 ha area. Field sampling was performed, based on cluster random sampling and systematic random sampling and 249 plots were sampled with 0.1 hectare area (without plantation area). Diameter of trees larger than   12.5 cm, species information and geographic position were registered in each plot with GPS. Since, thick trees are dominated in the canopy cover, 10 thick trees in each plot were selected to determine the forest type. Using computing of frequency percent of species, forest type in each plot was determined and four types of Fagus-Carpinus, Quercus-Carpinus, Carpinus-Parrotia and Parrotia-Acer (mixed with Carpinus betulus) were determined in the study area. The topographic attribute maps were derived from a digital terrain model and these attributes were extracted in location of plots. Logistic regression was implemented to analysis of forest type’s correlation with attributes and to construct a predictive model. The analysis was performed using 70% of the plots and each forest type map was resulted by extrapolation of model in GIS. Validation of results was performed by 30% of the remained plots and total accuracy computed for each model. Validation result of accuracy showed that spatial prediction models of forest types for Fagus-Carpinus and Quercus-Carpinus have narrow amplitude than Carpinus-Parrotia and Parrotia-Acer (mixed with Carpinus betulus) types that spread in large extent region. This result showed that spatial predictive models of forest types, which have narrow amplitude, are more acceptable. The results also showed that consideration of altitude, solar radiation potential and aspect in the model were the main factors which are controlling forest types in the study area.
 

Keywords


- بی‌نام، 1386. طرح جنگل‌داری تجدیدنظر دوم سری یک دکتر بهرام‌نیا. گروه جنگل‌داری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، 478 صفحه.
- رفیعیان، ا.، درویش‌صفت، ع.ا. و نمیرانیان، م.، 1385. تعیین تغییرات گستره جنگلهای شمال کشور بین سالهای 1373 تا 1380 با استفاده از تصاویر سنجنده ETM+ (مطالعه موردی در جنگلهای بابل). علوم و فنون کشاورزی و منابع طبیعی، 10 (3): 285-277.
- زارع چاهوکی، م.ع.، جعفری، م.، آذرینوند، ح. و شفیع‌زاده، م.، 1386. مقایسه روشهای مدل‌سازی برای پیش‌بینی احتمال حضور گو‌نه‌های گیاهی در مراتع مناطق خشک و نیمه‌خشک (مطالعه موردی: پشتکوه استان یزد). مجله علمی-پژوهشی مرتع، 1 (4): 356-342.
- گرجی ‌بحری، ی.، 1379. بررسی طبقه‌بندی تیپولوژی جنگل و برنامه‌ریزی جنگل‌شناسی جنگل تحقیقاتی واز. رساله دکتری، دانشکده منابع طبیعی، دانشگاه تهران،          139 صفحه.
- محمدی، ج.، 1385. پدومتری (سیستم‌های اطلاعات مکانی). انتشارات پلک، 637 صفحه.
- Brito, C., Crespo, L.G. and Paulo, O.S., 1999. Modeling wildlife distributions: logistic multiple regression vs overlap analysis. Ecography,          22: 251-260.
- Buckland, S.T. and Elston, D.A., 1993. Empirical models for the spatial distribution of wildlife. Journal of Applied Ecology, 30: 478-495.
- Canton, Y., Barrio, G.D., Benet, A.S. and Lazaro, R., 2004. Topographic controls on the spatial distribution of ground cover in the Tabernas badlands of SE Spain. Catena, 55: 341-365.
- Casasnovas, J.A.M., Ramosa, M.C. and Poesen, J., 2004. Assessment of sidewall erosion in large gullies using multi-temporal DEMs and logistic regression analysis. Geomorphology, 58: 305-321.
- Claessens, L., Verburg, P.H., Schoorl, J.M. and Veldkamp, A., 2006. Contribution of topographically based landslide hazard modeling to the analysis of the spatial distribution and ecology of Kauri (Agathis australis). Landscape Ecology, 21: 63-76.
- Clark, W.A. and Hosking, P.L., 1986. Statistical Methods for Geographers. Wiley, New York,     528 p.
- Franklin, J., McCullough, P. and Gray, C., 2000. Terrain variables used for predictive mapping of vegetation communities in southern California. In: Wilson, J.P. and Gallant, J.C., (eds.), Terrain Analysis, Principles and Applications. John Wiley and Sons Inc: 331-353.
- Gorsevski, P.V. and Gessler, P.E., 2006. Spatial Prediction of Landslide Hazard using Logistic Regression and ROC Analysis. Blackwell Publishing Ltd Transactions in GIS, 10 (3):      395-415.
- Guisan, A., Weiss, S.B. and Weiss, A.D., 1999. GLM versus CCA spatial modeling of plant species distribution (abstract- GEOBASE). Plant Ecology, 143 (1): 107-122.
- Hanewinkel, M., Zhou, W. and Schill, Ch., 2004. A neural network approach to identify forest stands susceptible to wind damage. Forest Ecology and Management, 196: 227-243.
- Hettricj, H. and Rosenweig, S., 2003. Multivariate statistics as a tool for model-based prediction of floodplain vegetation and fauna. Ecological Modeling, 169: 73-87.
- Hidalgo, P.J., Marin, J.M., Quijada, J. and Moreira, J.M., 2008. A Spatial distribution model of cork oak (Quercus suber) in southwestern Spain: A suitable tool for reforestation. Forest Ecology and Management, 255: 25-34.
- Moore, I.D., Grayson, R.B. and Landson, A.R., 1991. Digital terrain modeling: a review of hydrological, geomorphologic, and biological applications. Hydrological Processes, 5: 3-30.
- O΄Callaghan, J.F. and Mark, D.M., 1984. The extraction of drainage network from digital elevation data. Computer vision. Graphics and Image Processing, 28: 44-323.
- Ohmann, J.L. and Gregory, M.J., 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon. U.S.A. Can. J. For. Res., 32: 725-741.
- Peffer, K., Pebesma, E.J. and Burrough, P.A., 2003. Mapping alpine vegetation using vegetation observations and topographic attributes. Landscape Ecology, 18: 759-776.
- Vogiatzakis, I.N. and Griffiths, G.H., 2006. A GIS-based empirical model for vegetation prediction in Lefka Ori, Crete. Plant Ecology, 184: 311-323.
- Walker, P.A., 1990. Modeling wildlife distributions using a geographic information system: kangaroos in relation to climate. Journal of Biogeography, 17: 279-289.
- Wiser, S.K., Robert, K.P. and Peter, S.W., 1998. Prediction of rare-plant occurrence: A southern Appalachian example. Ecological Applications,     8 (4): 909-920.