Abstract
This paper aims at modelling the air quality around a mine site, combining hard data — the field measurements of the number of epiphytic lichen species — and soft data — a remote sensing image of the region. The use of epiphytic lichens as bioindicators of air pollution is due to their identifiable reactions to different degrees of pollution. In a first step a calibration between the hard and soft data is carried out through a Probabilistic Neural Network classification algorithm. A new approach is proposed for the estimation of local conditional distribution functions with hard and soft derived data. An important tool for the air quality control due to mining activity is the stochastic simulation based on local pdfs providing a set of equiprobable images set.
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© 1997 Springer Science+Business Media Dordrecht
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Soares, A., Pereira, M.J., Branquinho, C., Catarino, F. (1997). Stochastic Simulation of Lichen Biodiversity Using Soft Information from Remote Sensing Data. In: Soares, A., Gómez-Hernandez, J., Froidevaux, R. (eds) geoENV I — Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1675-8_31
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DOI: https://doi.org/10.1007/978-94-017-1675-8_31
Publisher Name: Springer, Dordrecht
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