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- Boudreault, Louis-Etienne, et al.
(författare)
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A LiDAR method of canopy structure retrieval for wind modeling of heterogeneous forests
- 2015
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 1873-2240 .- 0168-1923. ; 201, s. 86-97
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Tidskriftsartikel (refereegranskat)abstract
- The difficulty of obtaining accurate information about the canopy structure is a current limitation towards higher accuracy in numerical predictions of the wind field in forested terrain. The canopy structure in computational fluid dynamics is specified through the frontal area density and this information is required for each grid point in the three-dimensional computational domain. By using raw data from aerial LiDAR scans together with the Beer-Lambert law, we propose and test a method to calculate and grid highly variable and realistic frontal area density input. An extensive comparison with ground-based measurements of the vertically summed frontal area density (or plant area index) and tree height was used to optimize the method, both in terms of plant area index magnitude and spatial variability. The resolution of the scans was in general low (<2.5 reflections m(-2)). A decrease of the resolution produced an increasing systematic underestimation of the spatially averaged tree height, whereas the mean plant area index remained insensitive. The gridded frontal area density and terrain elevation were used at the lower boundary of wind simulations in a 5 km x 5 km area of a forested site. The results of the flow simulations were compared to wind measurements using a vertical array of sonic anemometers. A good correlation was found for the mean wind speed of two contrasting wind directions with different influences from the upstream forest. The results also predicted a high variability on the horizontal and vertical mean wind speed, in close correlation with the canopy structure. The method is a promising tool for several computational fluid dynamics applications requiring accurate predictions of the near-surface wind field. (C) 2014 The Authors. Published by Elsevier B.V.
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| 2. |
- Shendryk, Iurii, et al.
(författare)
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Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden
- 2014
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Ingår i: Forests. - : MDPI AG. - 1999-4907. ; 5:5, s. 992-1010
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Tidskriftsartikel (refereegranskat)abstract
- Knowledge of the forest biomass and its change in time is crucial to understanding the carbon cycle and its interactions with climate change. LiDAR (Light Detection and Ranging) technology, in this respect, has proven to be a valuable tool, providing reliable estimates of aboveground biomass (AGB). The overall goal of this study was to develop a method for assessing AGB using a synergy of low point density LiDAR-derived point cloud data and multi-spectral imagery in conifer-dominated forest in the southwest of Sweden. Different treetop detection algorithms were applied for forest inventory parameter extraction from a LiDAR-derived canopy height model. Estimation of AGB was based on the power functions derived from tree parameters measured in the field, while vegetation classification of a multi-spectral image (SPOT-5) was performed in order to account for dependences of AGB estimates on vegetation types. Linear regression confirmed good performance of a newly developed grid-based approach for biomass estimation (R-2 = 0.80). Results showed AGB to vary from below 1 kg/m(2) in very young forests to 94 kg/m(2) in mature spruce forests, with RMSE of 4.7 kg/m(2). These AGB estimates build a basis for further studies on carbon stocks as well as for monitoring this forest ecosystem in respect of disturbance and change in time. The methodology developed in this study can be easily adopted for assessing biomass of other conifer-dominated forests on the basis of low-density LiDAR and multispectral imagery. This methodology is hence of much wider applicability than biomass derivation based on expensive and currently still scarce high-density LiDAR data.
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