DIGITALNA ARHIVA ŠUMARSKOG LISTA
prilagođeno pretraživanje po punom tekstu
ŠUMARSKI LIST 3-4/2009 str. 38 <-- 38 --> PDF |
D. Klobučar, R. Pernar: UMJETNE NEURONSKE MREŽE U PROCJENI SASTOJINSKIH OBRASTA...Šumarski list br. 3–4, CXXXIII (2009), 145-155 5. ZAKLJUČCI – Conclusions Istraživanje procjene i raspodjele sastojinskih obras ta postupkom umjetne neuronske mreže provedeno je na primjeru gospodarske jedinice “Jamaričko brdo”, šu marije Lipovljani. Na osnovi provedenih istraživanja i dobivenih rezultata izvedeni su sljedeći zaključci: U šumarstvu RH, svrsishodno primjenjivanje potvrđenih vrijednosti daljinskih istraživanja u praćenju stanja i inventarizaciji šumskih resursa zahtijeva raz vijen sustav periodičnog snimanja ili pridobivanja scena šumskih površina Višeslojni perceptron ima dobra generalizacijska svojstva u procjeni sastojinskih obrasta metodama daljinskih istraživanja s crno-bijelih cikličkih aerofotosnimaka Samoorganizirajuća neuronska mreža može se primi jeniti u kontroli raspodjele sastojinskih obrasta s cikličkih aerofotosnimaka Ovim istraživanjem naznačena je jedna od velikog broja mogućnosti primjene umjetnih neurons kih mreža u šumarskoj znanstvenoj i operativnoj dje latnosti. Stoga, istraživanja i primjenu treba nastaviti i na drugim područjima (iskorištavanje, zaštita, ekologija i dr.) kako bi se racionalizirali radovi u šumarstvu. 6. LITERATURA– References Atkinson,P.M.,A. R. L.Tatnall, 1997: Neural net works in remote sensing. International Journal of Remote Sensing, 18: 699–709. Ardö,J., P.Pilesjo, A.Skidmore,1997: Neural net works, multitemporal Landsat Thematic Ma pper data and topographic data to classify forest damage in the Czech Republic. Canadian Journal of Remote Sensing, 23, 217–219. Beamish, D.2001:AReview of Neural Networks in Remote Sensing, 1–45. Benediktsson, J.A., P.H.Swain, O. K.Evsoy, 1990: Neural network approach versus statistical methods in classification of multi-source remote sensing data. IEEETransactions on Geoscience and Remote Sensing, 28(4), 540–551. Bolduc,P., K.Lowell, G.Edwards,1999: Automated estimation of localized forest volumes from large-scale aerial photographs and ancillary cartographic information in a boreal forest. International Journal of Remote Sensing 20(18), pp. 3611–3624. Cetin,M., T.Kavzoglu, N. Musaoglu,2004: Cla ssification of multi-spectral, multi-temporal and multi-sensor images using principal components analysis and artificial neural networks: Bey kozcase. Civco, D. L.1993:Artificial neural networks for land cover classification and mapping. International Journal of Geographical Information Systems 7: 173–186. DalbeloBašić, B.2004: Sustavi koje uče. Knjiga “Informacijska tehnologija u poslovanju”, 191–209, Zagreb. Demuth,H., M.Beale, M.Hagan,2006: Neural Net work Toolbox for Use with Matlab® User’s Guide.Version 5.The Mathworks Inc., Natick, MA. Foody, G.M., P.J.Curran,1994: Estimation of tropical forest extent and regenerative stage using remotely sensed data. Journal of Biogeography, 21, 223–244. Foody,G.M. 2001:Thematic mapping from remotly sensed data with neural networks: MLP, RBF and PNN based approaches, Journal of Geographical Systems 3: pp. 217–232. Franco-Lopez,H.,A. R. Ek, M. E. Bauer, 2001: Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment, 77, 251–274. Gimblett, R.H., G. L.Ball,1995. Neural network architectures for monitoring and simulating chan ges in forest resources management. AIApplications 9: 103–123. Gonzales, R.C., R. E.Woods,S. L.Eddins,2004: Digital Image Proceessing using MATLAB. Haykin, S.1999: Neural Networks:AComprehensive Foundation. Prentice Hall, New Jersey. Hepner, G.F.,T.Logan, N.Ritter, N.Bryant, 1990: Artificial neural network classification using a minimal training set: Comparison to conventional supervised classification. Photogrammetric Engineering and Remote Sensing 56: 469–473. Holmström, H.2002: Estimation of single tree characteristics using the kNN method and plotwise aerial photograph interpretations. Forest Ecology and Management,Volume 167, Issues 1–3, 303–314. Hyyppä, H.J., J. M. Hyyppä,2001: Effects of Stand Size on theAccuracy of Remote Sensing – Based Forest Inventory. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 12, 2613–2621. |