Lidar measurement of snow depth: a review
نویسندگان
چکیده
Laser altimetry (lidar) is a remote-sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. Recently lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of both airborne and ground-based sensors. Modern sensors allow mapping of vegetation heights and snow or ground surface elevations below forest canopies. Typical vertical accuracies for airborne datasets are decimeter-scale with order 1m point spacings. Ground-based systems typically provide millimeter-scale range accuracy and sub-meter point spacing over 1m to several kilometers. Many system parameters, such as scan angle, pulse rate and shot geometry relative to terrain gradients, require specification to achieve specific point coverage densities in forested and/or complex terrain. Additionally, snow has a significant volumetric scattering component, requiring different considerations for error estimation than for other Earth surface materials. We use published estimates of light penetration depth by wavelength to estimate radiative transfer error contributions. This paper presents a review of lidar mapping procedures and error sources, potential errors unique to snow surface remote sensing in the near-infrared and visible wavelengths, and recommendations for projects using lidar for snow-depth mapping.
منابع مشابه
Lidar Measurement of Snow Depth: Accuracy and Error Sources
Airborne laser altimetry (lidar) is a remote sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. In recent years lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of sensors. Modern sensors allow recording of multiple pulse retur...
متن کاملDissertation Quantifying Scale Relationships in Snow Distributions
Spatial distributions of snow in mountain environments represent the time integration of accumulation and ablation processes, and are strongly and dynamically linked to mountain hydrologic, ecologic, and climatic systems. Accurate measurement and modeling of the spatial distribution and variability of the seasonal mountain snowpack at different scales are imperative for water supply and hydropo...
متن کاملLiDAR-derived snowpack data sets from mixed conifer forests across the Western United States
Airborne-based Light Detection and Ranging (LiDAR) offers the potential to measure snow depth and vegetation structure at high spatial resolution over large extents and thereby increase our ability to quantify snow water resources. Here we present airborne LiDAR data products at four Critical Zone Observatories (CZO) in the Western United States: Jemez River Basin, NM, Boulder Creek Watershed, ...
متن کاملMapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using groundpenetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aeri...
متن کاملIndependent Evaluation of the SNODAS Snow Depth Product Using Regional Scale Lidar-Derived Measurements
Repeated Light Detection and Ranging (LiDAR) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km LiDAR-derived dataset of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experi5 ment (CLPX-2), as a validation source for an operational hydrologic sno...
متن کامل