Quantifying spatial distribution of snow depth errors from LiDAR using Random Forest

Wade T. Tinkham, Alistair M.S. Smith, Hans Peter Marshall, Timothy E. Link, Michael J. Falkowski, Adam H. Winstral

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

There is increasing need to characterize the distribution of snow in complex terrain using remote sensing approaches, especially in isolated mountainous regions that are often water-limited, the principal source of terrestrial freshwater, and sensitive to climatic shifts and variations. We apply intensive topographic surveys, multi-temporal LiDAR, and Random Forest modeling to quantify snow volume and characterize associated errors across seven land cover types in a semi-arid mountainous catchment at a 1 and 4. m spatial resolution. The LiDAR-based estimates of both snow-off surface topology and snow depths were validated against ground-based measurements across the catchment. LiDAR-derived snow depths estimates were most accurate in areas of low lying vegetation such as meadow and shrub vegetation (RMSE. = 0.14. m) as compared to areas consisting of tree cover (RMSE. = 0.20-0.35. m). The highest errors were found along the edge of conifer forests (RMSE. = 0.35. m), however a second conifer transect outside the catchment had much lower errors (RMSE. = 0.21. m). This difference is attributed to the wind exposure of the first site that led to highly variable snow depths at short spatial distances. The Random Forest modeled errors deviated from the field measured errors with a RMSE of 0.09-0.34. m across the different cover types. The modeling was used to calculate a theoretical lower and upper bound of catchment snow volume error of 21-30%. Results show that snow drifts, which are important for maintaining spring and summer stream flows and establishing and sustaining water-limited plant species, contained 30. ±. 5-6% of the snow volume while only occupying 10% of the catchment area similar to findings by prior physically-based modeling approaches. This study demonstrates the potential utility of combining multi-temporal LiDAR with Random Forest modeling to quantify the distribution of snow depth with a reasonable degree of accuracy.

Original languageEnglish (US)
Pages (from-to)105-115
Number of pages11
JournalRemote Sensing of Environment
Volume141
DOIs
StatePublished - Feb 5 2014

Bibliographical note

Funding Information:
We would like to thank Dr. Andrew Robinson for his guidance and review of the statistical methodologies utilized in this work. This project was funded by the UMAC and the Idaho Space Grant Consortium , which are both in turn funded by NASA . Additional funding and support was provided through the Agricultural Research Service Northwest Watershed Research Center . Funding support was provided by Idaho NSF EPSCoR and under awards NSF EPS-0814387 , NSF EPS-0701898 , NSF CBET-0854553 , and a NASA New Investigator Award NNX10AO02G .

Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.

Keywords

  • LiDAR
  • Random Forest
  • Snow
  • Snow depth
  • Snow volume

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