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BOLETÍN DE LA SOCIEDAD GEOLÓGICA MEXICANA http://dx.doi.org/10.18268/BSGM2011v63n1a8 |
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Assessment of airborne LIDAR for snowpack depth modeling
Evaluación mediante LIDAR aerotransportado para el modelado de espesor del manto nivoso
Ignacio Moreno Baños1,*, Antoni Ruiz García2, Jordi Marturià i Alavedra1, Pere Oller i Figueras1, Jordi Piña Iglesias1, Pere Martínez i Figueras1, Julià Talaya López2
1 Institut Geològic de Catalunya, C/Balmes, 209–211, E08006, Barcelona.
2 Institut Cartogràfic de Catalunya, Parc de Montjuïc, 08038, Barcelona
* This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
The Institut Geològic de Catalunya (IGC) and the Institut Cartogràfic de Catalunya (ICC) have begun a joint project to model snowpack depth distribution in the Núria valley (a 38 km2 basin located in the Eastern Pyrenees) in order to evaluate water reserves in mountain watersheds . The evaluation was based on a remote sensing airborne LIDAR survey and validated with field–work calculations. Previous studies have applied geostatistical techniques to extrapolate sparse point data obtained from costly field–work campaigns. Despite being a recently developed technique, LIDAR has become a useful method in snow sciences as it produces dense point data and covers wide areas. The new methodology presented here combines LIDAR data with field–work, the use of geographical information systems (GIS) and the stepwise regression tree (SRT), as an extrapolation technique. These methods have allowed us to map snowpack depth distribution in high spatial resolution. Extrapolation was necessary because raw LIDAR data was only obtained from part of the study area in order to minimise costs. Promising results show high correlation between LIDAR data and field data, validating the use of airborne laser altimetry to estimate snow depth. Moreover, differences of total snow volume calculated from modeled snowpack distribution and total volume from LIDAR data differ by only 1%.
Keywords: Snowpack depth, stepwise regression tree, LIDAR, GIS, Pyrenees.
