Evaluating the capacity of single photon lidar for terrain characterization under a range of forest conditions
| Autor: | I. Sinclair, Murray Woods, C. Papasodoro, T. Krahn, C. Onafrychuk, D. Bélanger, Joanne C. White |
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| Rok vydání: | 2021 |
| Předmět: |
Accuracy and precision
010504 meteorology & atmospheric sciences 0208 environmental biotechnology Point cloud Elevation Soil Science Geology Terrain 02 engineering and technology Vegetation 01 natural sciences 020801 environmental engineering Altitude Lidar Environmental science Computers in Earth Sciences Digital elevation model 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing of Environment. 252:112169 |
| ISSN: | 0034-4257 |
| DOI: | 10.1016/j.rse.2020.112169 |
| Popis: | Accurate digital elevation models are key data products used to inform forest management. Light detection and ranging (lidar) technologies have emerged as a useful tool for acquiring detailed terrain information, although the accuracy of this data is known to vary with topographic complexity and the density and characteristics of overlying vegetation. Single Photon Lidar (SPL) provides a high-density point cloud that can be acquired from a much higher altitude than discrete return, small-footprint lidar (hereafter, linear-mode lidar or LML), providing efficiencies and potential cost savings for operational mapping programs. Herein, we assess the absolute and relative accuracies of leaf-on and leaf-off SPL data acquired at different altitudes for characterizing terrain under varying vegetation types and densities and compare to results for LML data. Our assessment was forest-focused and primarily point based, using 299 Real-Time Kinematic survey checkpoints to quantify elevation errors (Δh); however, we also investigated and reported accuracy for linear transects, and conducted a wall-to-wall comparison of the SPL-derived 1-m digital elevation models (DEMs) against an LML-derived DEM. Point cloud characteristics for the leaf-on 2018 SPL data were markedly different, with 88% of returns as first returns, compared to 17% for the LML, and 59% and 46% for the leaf-off SPL data acquired at 3800 m and 2000 m, respectively. Of the datasets considered herein, the SPL data acquired under leaf-on conditions in 2018 had the lowest accuracy and precision for characterizing terrain underneath vegetation cover, with an RMSE of 10.97 cm and a 95th quantile of 24.03 cm; however these values are within commonly accepted error limits for elevation products. The leaf-off SPL data were most accurate overall; however, the differences between the leaf-off SPL data acquired at 3800 m versus 2000 m were often minor ( |
| Databáze: | OpenAIRE |
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