An Evaluation of Vegetation Filtering Algorithms for Improved Snow Depth Estimation from Point Cloud Observations in Mountain Environments

Abstract:

High-resolution snow depth measurements are possible through bare-earth (BE) differencing of point cloud datasets obtained using LiDAR and photogrammetry during snow-free and snow-covered conditions. These accuracy and resolution of these snow depth measurements are desirable in mountain environments in which ground measurements are dangerous and difficult to perform, and other remote sensing techniques are often characterized by large errors and uncertainties due variable topography, vegetation, and snow properties.

BE ground filtering algorithms make different assumptions about ground characteristics to differentiate between ground and non-ground features. Because of this, ground surfaces may have unique characteristics that confound ground filters depending on the location and terrain conditions. These include low-lying shrubs (<1 m), areas with high topographic relief, and areas with high surface roughness. We evaluate several different algorithms, including lowest point, kriging, and more sophisticated splining techniques such as the Multiscale Curvature Classification (MCC) to resolve snow depths. Understanding how these factors affect BE surface models and thus snow depth measurements is a valuable contribution towards improving the processing protocols associated with these relatively new snow observation techniques.

We test the different BE filtering algorithms using LiDAR and photogrammetric measurements taken from an Unmanned Aerial Vehicle (UAV) in Southwest Tasmania, Australia during the winter and spring of 2013. The study area is characterized by sloping, uneven terrain, and different types of vegetation including eucalyptus and conifer trees, as well as dense shrubs varying in heights from 0.3-1.5 meters. Initial snow depth measurements using the unfiltered point cloud measurements are characterized by large errors (~20-90 cm) due to the dense vegetation. Using filtering techniques instead of raw differencing improves the estimation of snow depth in our study area, and reduces the RMSE to 10 cm, depending on the technique used.