A LiDAR method of canopy structure retrieval for wind modeling of heterogeneous forests

Boudreault, Louis-Étienne; Bechmann, Andreas; Tarvainen, Lasse; Klemedtsson, Leif; Shendryk, Yuri; Dellwik, Ebba

doi:10.1016/j.agrformet.2014.10.014

Cited by 50 publications

(42 citation statements)

References 36 publications

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“…The latter finding is rather significant as it implies that an underestimation of forest roughness lengths is safer than overestimating z 0 when using EWA-based methods for wind resource estimates (e.g., WAsP and similar methods). This is consistent with common practice: while recent evidence from direct lidar scans of forests suggests that z 0 should be at least several 200 M. Kelly and H. E. Jørgensen: Uncertainty in background-z 0 and AEP meters there (Boudreault et al, 2015), industrial practice has been to use z 0 of 1 m or less (e.g., Troen and Petersen, 1989;Mortensen et al, 2014).…”

Section: Uncertainty In Predicted Mean Wind Speedsmentioning

confidence: 61%

“…Some work on characterizing profileamenable roughness over forest (e.g., Bosveld, 1997;Tian et al, 2011;Boudreault et al, 2015) implies that z 0 over forest is larger than what has been typically assigned in wind resource assessment (i.e., z 0 > 1, not z 0 1), despite such underestimates being used for decades in the wind industry (Troen and Petersen, 1989;Mortensen et al, 2001;Emeis, 2013;Landberg, 2016). We now see an explanation for this looking at Fig.…”

Section: Applications and Implicationsmentioning

confidence: 99%

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Statistical characterization of roughness uncertainty and impact on wind resource estimation

Kelly¹,

Jørgensen²

2017

Wind Energ. Sci.

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Abstract. In this work we relate uncertainty in background roughness length (z 0 ) to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry-standard European Wind Atlas method, which is based on the geostrophic drag law. We statistically consider roughness and its corresponding uncertainty, in terms of both z 0 derived from measured wind speeds as well as that chosen in practice by wind engineers. We show the combined effect of roughness uncertainty arising from differing wind-observation and turbine-prediction sites; this is done for the case of roughness bias as well as for the general case. For estimation of uncertainty in annual energy production (AEP), we also develop a generalized analytical turbine power curve, from which we derive a relation between mean wind speed and AEP. Following our developments, we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and we also find that sites with larger background roughness incur relatively larger uncertainties.

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Section: Uncertainty In Predicted Mean Wind Speedsmentioning

confidence: 61%

Section: Applications and Implicationsmentioning

confidence: 99%

Statistical characterization of roughness uncertainty and impact on wind resource estimation

Kelly¹,

Jørgensen²

2017

Wind Energ. Sci.

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“…for plot-level wood volumes (Dassot et al, 2011) and for canopy structures (Boudreault et al, 2015). Although multiple solution plans such as forest wind modeling (Boudreault et al, 2015) have been proposed for overcoming the wind-caused problems, investigation of its influences and development of effective solutions are not easy tasks. Actually, this is the next-step work closely following this study.…”

Section: Discussionmentioning

confidence: 99%

Reflecting conifer phenology using mobile terrestrial LiDAR: A case study of Pinus sylvestris growing under the Mediterranean climate in Perth, Australia

Lin

West

2016

Ecological Indicators

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“…Since forest density in terms of Plant Area Index P AI can also be estimated from the airborne lidar data (Boudreault et al, 2015), it would be interesting to investigate a more refined roughness classification 25 based on this parameter. It was also demonstrated that raising the roughness value in the CORINE data reduced the mean prediction error compared to the original roughness conversion (Fig.…”

Section: Ideas For Further Improvementsmentioning

confidence: 99%

“…Since the lidar beams do not necessarily hit the tree top, this tree height estimate underestimates the maximum tree height. A relationship between scanning density and 5 maximum tree height was shown in Boudreault et al (2015). For the site considered here, the scanning density was between 0.25 and 1 points per m 2 , leading to an underestimation of the tree height of up to 2 m. Nilsson et al (2017) created maps of the mean tree height from Airborne Laser Scans using the the 95 percentile of points located above 1.5m above ground, leading to a somewhat lower tree height.…”

Section: Estimating Elevation and Forest Heightmentioning

confidence: 99%

From lidar scans to roughness maps for wind resource modeling in forested areas

Floors¹,

Enevoldsen²,

Davis³

et al. 2018

Preprint

Self Cite

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Abstract. Applying erroneous roughness lengths can have a large impact on the estimated performance of wind turbines, particularly in forested areas. In this study, a new method called the Objective Roughness Approach (ORA), which converts tree height maps created using airborne lidar scans to roughness maps suitable for wind modeling, is evaluated via crosspredictions between different anemometers at a complex forested site with seven tall meteorological masts using the Wind Atlas Analysis and Application program (WAsP). The cross-predictions were made using ORA maps created at four spatial 5 resolutions and from four freely available roughness maps based on land-use classifications. The validation showed that the use of ORA maps resulted in a closer agreement with observational data for all investigated resolutions compared to the land-use maps. Further, when using the ORA maps, the risk of making large errors (> 25 %) in predicted power density was reduced by 40-50 % compared to satellite based products with the same resolution. The results could be further improved for highresolution ORA maps by adding the displacement height. The improvement when using the ORA maps came down to two 10 factors, first they had a higher roughness length for forests, which was confirmed to by increasing the forest roughness value of the land-use based maps to the value of the ORA map, and second, due to the higher resolution of the ORA data, since the ORA maps with the highest resolution had the largest reduction in mean absolute errors.

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A LiDAR method of canopy structure retrieval for wind modeling of heterogeneous forests

Cited by 50 publications

References 36 publications

Statistical characterization of roughness uncertainty and impact on wind resource estimation

Statistical characterization of roughness uncertainty and impact on wind resource estimation

Reflecting conifer phenology using mobile terrestrial LiDAR: A case study of Pinus sylvestris growing under the Mediterranean climate in Perth, Australia

From lidar scans to roughness maps for wind resource modeling in forested areas

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