Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain
نویسندگان
چکیده
The monitoring of Earth’s and planetary surface elevations at larger finer scales is rapidly progressing through the increasing availability resolution digital elevation models (DEMs). Surface observations are being used across an expanding range fields to study topographical attributes their changes over time, notably in glaciology, hydrology, volcanology, seismology, forestry geomorphology. However, DEMs frequently contain large-scale instrument noise varying vertical precision that lead complex patterns errors. Here, we present a validated statistical workflow estimate, model, propagate uncertainties DEMs. We review state-of-the-art DEM accuracy analyses, define conceptual framework consistently address those. show how characterize by quantifying heteroscedasticity measurements, i.e. with terrain- or sensor-dependent variables, spatial correlation errors can occur multiple scales. With high-precision observations, our based on independent data acquired stable terrain be applied almost anywhere Earth. illustrate for both pixel-scale derivatives, using slope glacier volume as examples. find largely underestimated literature, advocate new metrics essential ensure reliability future Earth assessments.
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ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
سال: 2022
ISSN: ['2151-1535', '1939-1404']
DOI: https://doi.org/10.1109/jstars.2022.3188922