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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 23, 1997 - Issue 3
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Original Articles

Neural Networks, Multitemporal Landsat Thematic Mapper Data and Topographic Data to Classify Forest Damages in the Czech Republic

J. ArdöView further author information
,
P. PilesjöView further author information
&
A. SkidmoreView further author information
Pages 217-229 | Published online: 31 Jul 2014

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Abdulhakim Mohamed Abdi. (2020) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing 57:1, pages 1-20.
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Walaiporn Phonphan, Nitin K. Tripathi, Taravudh Tipdecho & Apisit Eiumnoh. (2014) Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network. Geocarto International 29:8, pages 842-859.
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J. Amini. (2010) A method for generating floodplain maps using IKONOS images and DEMs. International Journal of Remote Sensing 31:9, pages 2441-2456.
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J. F. Mas & J. J. Flores. (2008) The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing 29:3, pages 617-663.
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O. Mutanga & L. Kumar. (2007) Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data. International Journal of Remote Sensing 28:21, pages 4897-4911.
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T. Kavzoglu & P. M. Mather. (2003) The use of backpropagating artificial neural networks in land cover classification . International Journal of Remote Sensing 24:23, pages 4907-4938.
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X.-H. Liu, A. K Skidmore & H. van Oosten. (2003) An experimental study on spectral discrimination capability of a backpropagation neural network classifier. International Journal of Remote Sensing 24:4, pages 673-688.
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Ramanathan Sugumaran. (2001) Forest Land Cover Classification Using Statistical and Artificial Neural Network Approaches Applied to IRS LISS ‐ III Sensor. Geocarto International 16:2, pages 39-44.
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B Krishna Mohan. (2000) Classification of Remotely Sensed Images using Artificial Neural Networks. IETE Journal of Research 46:5, pages 401-410.
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Sarfaraz Masood, M. N. Doja & Pravin Chandra. (2019) Architectural Parameter-Independent Network Initialization Scheme for Sigmoidal Feedforward ANNs. Arabian Journal for Science and Engineering 45:4, pages 2901-2913.
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Jay D. Gatrell, Gregory D. Bierly, Ryan R. Jensen & Rajiv R. ThakurGenong Yu. 2020. Research Design and Proposal Writing in Spatial Science. Research Design and Proposal Writing in Spatial Science 139 158 .
L Kupková, M Potůčková, Z Lhotáková & J Albrechtová. (2018) Forest cover and disturbance changes, and their driving forces: A case study in the Ore Mountains, Czechia, heavily affected by anthropogenic acidic pollution in the second half of the 20th century. Environmental Research Letters 13:9, pages 095008.
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Shaun C. Cunningham, Peter Griffioen, Matt D. White & Ralph Mac Nally. (2017) Assessment of ecosystems: A system for rigorous and rapid mapping of floodplain forest condition for Australia's most important river. Land Degradation & Development 29:1, pages 127-137.
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Pradeep Kumar, Rajendra Prasad, Varun Narayan Mishra, Dileep Kumar Gupta, Arti Choudhary & Prashant K. Srivastava. (2015) Artificial neural network with different learning parameters for crop classification using multispectral datasets. Artificial neural network with different learning parameters for crop classification using multispectral datasets.
Alessandro Piscini & Stefania Amici. Fire detection from hyperspectral data using neural network approach. Fire detection from hyperspectral data using neural network approach.
Alessandro Piscini & Valerio Lombardo. (2014) Volcanic hot spot detection from optical multispectral remote sensing data using artificial neural networks. Geophysical Journal International 196:3, pages 1525-1535.
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V.L. Mulder, S. de Bruin, J. Weyermann, R.F. Kokaly & M.E. Schaepman. (2013) Characterizing regional soil mineral composition using spectroscopy and geostatistics. Remote Sensing of Environment 139, pages 415-429.
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A. Lausch, M. Heurich, D. Gordalla, H.-J. Dobner, S. Gwillym-Margianto & C. Salbach. (2013) Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales. Forest Ecology and Management 308, pages 76-89.
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T. Karthikeya Sharma, N. S. Sarvesh Babu & Y. N. Mamatha. 2012. Proceedings of International Conference on Advances in Computing. Proceedings of International Conference on Advances in Computing 101 106 .
Per-Ola Olsson, Anna Maria Jönsson & Lars Eklundh. (2012) A new invasive insect in Sweden – Physokermes inopinatus: Tracing forest damage with satellite based remote sensing. Forest Ecology and Management 285, pages 29-37.
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Jay D. Gatrell, Gregory D. Bierly & Ryan R. JensenJay D. Gatrell, Gregory D. Bierly & Ryan R. Jensen. 2012. Research Design and Proposal Writing in Spatial Science. Research Design and Proposal Writing in Spatial Science 125 143 .
Paul M. Mather & Magaly Koch. 2011. Computer Processing of Remotely‐Sensed Images. Computer Processing of Remotely‐Sensed Images 389 427 .
Taskin Kavzoglu. (2009) Increasing the accuracy of neural network classification using refined training data. Environmental Modelling & Software 24:7, pages 850-858.
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Brandt Tso & Paul Mather. 2009. Classification Methods for Remotely Sensed Data, Second Edition. Classification Methods for Remotely Sensed Data, Second Edition 317 347 .
Ryan R. Jensen, Perry J. Hardin & Genong Yu. (2009) Artificial Neural Networks and Remote Sensing. Geography Compass 3:2, pages 630-646.
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M.A. Wulder, J.C. White, B. Bentz, M.F. Alvarez & N.C. Coops. (2006) Estimating the probability of mountain pine beetle red-attack damage. Remote Sensing of Environment 101:2, pages 150-166.
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Motoaki Kishino, Akihiko Tanaka & Joji Ishizaka. (2005) Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data. Remote Sensing of Environment 99:1-2, pages 66-74.
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D. S. BoydF. M. Danson. (2016) Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography: Earth and Environment 29:1, pages 1-26.
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Jay D. Gatrell, Gregory D. Bierly & Ryan R. JensenGenong Yu. 2005. Research Design and Proposal Writing in Spatial Science. Research Design and Proposal Writing in Spatial Science 123 142 .
Scott M. Shupe & Stuart E. Marsh. (2004) Cover- and density-based vegetation classifications of the Sonoran Desert using Landsat TM and ERS-1 SAR imagery. Remote Sensing of Environment 93:1-2, pages 131-149.
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Andrew J. Tatem, Abdisalan M. Noor & Simon I. Hay. (2004) Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery. Remote Sensing of Environment 93:1-2, pages 42-52.
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O Mutanga & A.K Skidmore. (2004) Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sensing of Environment 90:1, pages 104-115.
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Ian Olthof, Douglas J King & R.A Lautenschlager. (2004) Mapping deciduous forest ice storm damage using Landsat and environmental data. Remote Sensing of Environment 89:4, pages 484-496.
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Peter Halls. 2003. Spatial Information and the Environment. Spatial Information and the Environment 91 102 .
Helén Falkenström & Sam Ekstrand. (2002) Evaluation of IRS-1c LISS-3 satellite data for defoliation assessment on Norway spruce and Scots pine. Remote Sensing of Environment 82:2-3, pages 208-223.
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Juho Heikkilä, Seppo Nevalainen & Timo Tokola. (2002) Estimating defoliation in boreal coniferous forests by combining Landsat TM, aerial photographs and field data. Forest Ecology and Management 158:1-3, pages 9-23.
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F. Mark Danson. 2007. Encyclopedia of Analytical Chemistry. Encyclopedia of Analytical Chemistry.
T. Kavzoglu & P.M. Mather. (2000) Using feature selection techniques to produce smaller neural networks with better generalisation capabilities. Using feature selection techniques to produce smaller neural networks with better generalisation capabilities.
Mike Wulder. (2016) Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography: Earth and Environment 22:4, pages 449-476.
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