A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Arctic soil hydraulic conductivity and soil type recognition based on aerial gamma-ray spectroscopy and topographical data
Tekijät: Jonne Pohjankukka, Paavo Nevalainen, Tapio Pahikkala, Pekka Hänninen, Eija Hyvönen, Raimo Sutinen, Jukka Heikkonen
Toimittaja: Magnus Borga, Anders Heyden, Denis Laurendeau, Michael Felsberg, Kim Boyer
Konferenssin vakiintunut nimi: International Conference on Pattern Recognition
Julkaisuvuosi: 2014
Kokoomateoksen nimi: 22nd International conference on pattern recognition
Aloitussivu: 1822
Lopetussivu: 1827
Sivujen määrä: 6
ISBN: 978-1-4799-5208-3
ISSN: 1051-4651
DOI: https://doi.org/10.1109/ICPR.2014.319
Tiivistelmä
A central characteristic of soil in the arctic is its load bearing capacity since that property influences forest harvester mobility, flooding dynamics and infrastructure potential. The hydraulic conductivity has the greatest dynamical influence to bearing capacity and hence is essential to measure or estimate. In addition, the arctic soil type information is needed in many
cases, e.g. in roads and railways building planning. In this paper we propose a method for hydraulic conductivity estimation via linear regression on aerial gamma-ray spectroscopy and
publicly available topographical data with derived elevation based features. The same data is also utilized for the arctic soil type recognition; both logistics regression and nearest neighbor
classification results are reported. The classification results for logistic regression resulted in 44.5 % prediction performance and 50.5 % for 8-nearest neighbor classifier respectively. Linear
regression results for estimating the hydraulic conductivity of the soil resulted in C-index value of 0.63. The hydraulic conductivity and soil type estimation results are promising and the proposed
topographic elevation features are apparently new for remote sensing community and should also have a wider general interest.
A central characteristic of soil in the arctic is its load bearing capacity since that property influences forest harvester mobility, flooding dynamics and infrastructure potential. The hydraulic conductivity has the greatest dynamical influence to bearing capacity and hence is essential to measure or estimate. In addition, the arctic soil type information is needed in many
cases, e.g. in roads and railways building planning. In this paper we propose a method for hydraulic conductivity estimation via linear regression on aerial gamma-ray spectroscopy and
publicly available topographical data with derived elevation based features. The same data is also utilized for the arctic soil type recognition; both logistics regression and nearest neighbor
classification results are reported. The classification results for logistic regression resulted in 44.5 % prediction performance and 50.5 % for 8-nearest neighbor classifier respectively. Linear
regression results for estimating the hydraulic conductivity of the soil resulted in C-index value of 0.63. The hydraulic conductivity and soil type estimation results are promising and the proposed
topographic elevation features are apparently new for remote sensing community and should also have a wider general interest.
Turun yliopiston Tutkimustietojärjestelmän portaali