Recent Applications of Artificial Neural Networks in Forest Resource Management: An Overview

Making good decisions for adaptive forest management has become increasingly difficult. New artificial intelligence (AI) technology allows knowledge processing to be included in decision‐support tool. The application of Artificial Neural Networks (ANN), known as Parallel Distributed Processing (PDP), to predict the behaviours of nonlinear systems has become an attractive alternative to traditional statistical methods. This paper aims to provide an up‐to‐date synthesis of the use of ANN in forest resource management. Current ANN applications include: (1) forest land mapping and classification, (2) forest growth and dynamics modeling (3) spatial data analysis and modeling (4) plant disease dynamics modeling, and (5) climate change research. The advantages and disadvantages of using ANNs are discussed. Although the ANN applications are at an early stage, they have demonstrated potential as a useful tool for forest resource management.

[1]  Daniel Z. Sui,et al.  Recent Applications of Neural Networks for Spatial Data Handling , 1994 .

[2]  Rattan Lal,et al.  Predicting soil carbon in Mollisols using neural networks. , 1998 .

[3]  P. Atkinson,et al.  Introduction Neural networks in remote sensing , 1997 .

[4]  Duc Truong Pham Neural Networks In Engineering , 1970 .

[5]  Odile Peyron,et al.  Climatic Reconstruction in Europe for 18,000 YR B.P. from Pollen Data , 1998, Quaternary Research.

[6]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[7]  Jon Atli Benediktsson,et al.  Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[8]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[9]  George F. Hepner,et al.  Application of an artificial neural network to landcover classification of thematic mapper imagery , 1990 .

[10]  William D. Batchelor,et al.  Development of a neural network for soybean rust epidemics , 1997 .

[11]  G. Gertner,et al.  Modeling red pine tree survival with an artificial neural network , 1991 .

[12]  Holger R. Maier,et al.  Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia , 1998 .

[13]  F. Recknagel,et al.  Artificial neural network approach for modelling and prediction of algal blooms , 1997 .

[14]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[15]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[16]  P. D. Heermann,et al.  Classification of multispectral remote sensing data using a back-propagation neural network , 1992, IEEE Trans. Geosci. Remote. Sens..

[17]  Bobby R. Hunt,et al.  Extraction of shoreline features by neural nets and image processing , 1991 .

[18]  Robert J. Marks,et al.  Inversion Of Snow Parameters From Passive Microwave Remote Sensing Measurements By A Neural Network Trained With A Multiple Scattering Model , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[19]  V. Prybutok,et al.  A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. , 1996, Environmental pollution.

[20]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[21]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[22]  Robert F. Cromp,et al.  Automatic labeling and characterization of objects using artificial neural networks , 1989 .

[23]  P. Gong,et al.  Mapping Ecological Land Systems and Classification Uncertainties from Digital Elevation and Forest-Cover Data Using Neural Networks , 1996 .

[24]  F. Tangang,et al.  Forecasting ENSO Events: A Neural Network–Extended EOF Approach. , 1998 .

[25]  Yaqiu Jin,et al.  Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks , 1997 .

[26]  A Comparison Of Neural Network And Expert System Methods For Analysis Of Remotely-sensed Imagery , 1992, [Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium.

[27]  G. Z. Gertner,et al.  Modeling individual tree survival probability with a random optimization procedure: an artificial neural network approach , 1995 .

[28]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[29]  G. O. Moe,et al.  Multispectral image-processing with a three-layer backpropagation network , 1989, International 1989 Joint Conference on Neural Networks.

[30]  George Z. Gertner,et al.  Using a Parallel Distributed Processing System to Model Individual Tree Mortality , 1991 .

[31]  J. Zhou,et al.  Using Genetic Learning Neural Networks for Spatial Decision Making in GIs , 1996 .

[32]  Robert M. Pap,et al.  Handbook of neural computing applications , 1990 .

[33]  H. R. Gimblett,et al.  Applying neural networks to vegetation management plan development , 1997 .

[34]  Scott E. Decatur,et al.  Application of neural networks to terrain classification , 1989, International 1989 Joint Conference on Neural Networks.

[35]  H. M. Rauscher,et al.  Enhancing the Scientific Process with Artificial Intelligence: Forest Science Applications , 1991 .

[36]  George L. Ball,et al.  Neural network architectures for monitoring and stimulating changes in forest resource management , 1995 .

[37]  V. E. Derr,et al.  Prediction of El Nino events in the Pacific by means of neural networks , 1995 .

[38]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[39]  Peng Gong Integrated Analysis of Spatial Data from Multiple Sources: An Overview , 1994 .

[40]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[41]  Kamal Sarabandi,et al.  Application of an Artificial Neural Network in Canopy Scattering Inversion , 1992, [Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium.

[42]  Geoffrey E. Hinton,et al.  Learning representations of back-propagation errors , 1986 .

[43]  George Z. Gertner,et al.  A framework for uncertainty assessment of mechanistic forest growth models: a neural network example , 1997 .

[44]  J.H.M. Wösten,et al.  Testing an Artificial Neural Network for Predicting Soil Hydraulic Conductivity , 1996 .

[45]  R N Coulson,et al.  Artificial intelligence and natural resource management. , 1987, Science.

[46]  X. H. Zhang,et al.  Application of neural networks to identifying vegetation types from satellite images , 1997 .

[47]  F. Verdenius,et al.  Process models for neural network applications in agriculture , 1997 .

[48]  A. Ishimaru,et al.  Surface roughness determination using spectral correlations of scattered intensities and an artificial neural network technique , 1993 .

[49]  Harry T. Valentine,et al.  A Carbon-balance Model of Stand Growth: a Derivation Employing Pipe-model Theory and the Self-thinning Rule , 1988 .

[50]  George F. Hepner,et al.  Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification , 1990 .

[51]  Suranjan Panigrahi,et al.  Artificial neural network models of wheat leaf wetness , 1997 .

[52]  D. E. Rumelhart,et al.  chapter Parallel Distributed Processing, Exploration in the Microstructure of Cognition , 1986 .

[53]  William W. Hsieh,et al.  Forecasting the equatorial Pacific sea surface temperatures by neural network models , 1997 .

[54]  Daniel L. Civco,et al.  Artificial Neural Networks for Land-Cover Classification and Mapping , 1993, Int. J. Geogr. Inf. Sci..

[55]  I. Dimopoulos,et al.  Application of neural networks to modelling nonlinear relationships in ecology , 1996 .