Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon
| Autor: | Andrew T. Hudak, David M. Bell, Bryce Frank, Francisco Mauro, T. Ryan McCarley, Patrick A. Fekety, Hailemariam Temesgen, Matthew J. Gregory |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Canopy
010504 meteorology & atmospheric sciences Laser scanning Science 0211 other engineering and technologies Point cloud 02 engineering and technology 01 natural sciences LIDAR Statistics Calibration mixed-effect models 021101 geological & geomatics engineering 0105 earth and related environmental sciences Parametric statistics raster calibration Field (geography) Lidar Parametric model point-cloud semiparametric models biomass forest fuels General Earth and Planetary Sciences Environmental science |
| Zdroj: | Remote Sensing, Vol 13, Iss 261, p 261 (2021) Remote Sensing; Volume 13; Issue 2; Pages: 261 |
| ISSN: | 2072-4292 |
| Popis: | Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed. |
| Databáze: | OpenAIRE |
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