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Soil Landscape Modelling – placing place in its place
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Orientador(es)
Resumo(s)
Landscape variables, which are also factors of soil formation, can be combined with existing soil map data to train Artificial Neural Networks (ANNs) in order to predict soil types in unmapped areas. In this study, the impact of location data and proximity of the training data on the performance of ANN models, for two catchments in northern Portugal, is evaluated. Results are largely concurrent between catchments, indicating that using latitude and longitude data produces more accurate models, whilst taking into account the spatial autocorrelative properties of input data makes ANN models converge for a better “local” rather than “global” solution. The conclusion is that hillslopes show some degree of connectivity which is passed onto soils, and conforms to the principles of the catena concept.
Descrição
Palavras-chave
Landscape Artificial Neural Networks Soil maps Geographical Information Systems
Contexto Educativo
Citação
Fonseca, I.L., Freire, S., Brasil, R., Rocha, J., & Tenedório, J. (2013). Soil Landscape Modelling – placing place in its place, In. Adélia Nunes, Lúcio Cunha, João Santos, Anabela Ramos, Rui Ferreira, Isabel Paiva, Luca Dimuccio (Eds.), Proceedings of the VI Congresso Nacional de Geomorfologia: Geomorfologia – Novos e Velhos Desafios, (pp. 151-155), APGEOM. ISBN 978-989-96462-4-7.