Towards a universal lidar canopy height indicator

Hopkinson, Chris; Chasmer, L.; Lim, Kevin; Treitz, Paul; Creed, Irena F.

doi:10.5589/m06-006

Cited by 81 publications

(45 citation statements)

References 17 publications

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“…For example, tree average height was related to the standard deviation of all returns, however dominant height (Lorey's height) was most strongly correlated to the maximum return height [35]. Two LiDAR metrics (the 30th percentile canopy height and vegetation cover) explained approximately 80% of the variation in biomass carbon estimated to occur in radiata pine plantations covering a range of stand ages and stocking levels in New Zealand [36].…”

Section: Discussionmentioning

confidence: 99%

Leaf Area Index, Biomass Carbon and Growth Rate of Radiata Pine Genetic Types and Relationships with LiDAR

Beets

Reutebuch

Kimberley

et al. 2011

Forests

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Abstract:Relationships between discrete-return light detection and ranging (LiDAR) data and radiata pine leaf area index (LAI), stem volume, above ground carbon, and carbon sequestration were developed using 10 plots with directly measured biomass and leaf area data, and 36 plots with modelled carbon data. The plots included a range of genetic types established on north-and south-facing aspects. Modelled carbon was highly correlated with directly measured crown, stem, and above ground biomass data, with r = 0.92, 0.97 and 0.98, respectively. LiDAR canopy percentile height (P30) and cover, based on all returns above 0.5 m, explained 81, 88, and 93% of the variation in directly measured crown, stem, and above ground live carbon and 75, 89 and 88% of the modelled carbon, respectively. LAI (all surfaces) ranged between 8.8-19.1 in the 10 plots measured at age 9 years. The difference in canopy percentile heights (P95-P30) and cover based on first returns explained 80% of the variation in total LAI. Periodic mean annual increments in stem volume, above ground live carbon, and total carbon between ages 9 and 13 years were significantly related to (P95-P30), with regression models explaining 56, 58, and 55%, respectively, of the variation in growth rate per plot. When plot aspect and genetic type were included with (P95-P30), the R 2 of the regression models for stem volume, OPEN ACCESSForests 2011, 2 638 above ground live carbon, and total carbon increment increased to 90, 88, and 88%, respectively, which indicates that LiDAR regression equations for estimating stock changes can be substantially improved by incorporating supplementary site and crop data.

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Section: Discussionmentioning

confidence: 99%

Leaf Area Index, Biomass Carbon and Growth Rate of Radiata Pine Genetic Types and Relationships with LiDAR

Beets

Reutebuch

Kimberley

et al. 2011

Forests

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“…To learn more about eFRI, the CEC-FRP engaged inventory specialists from across Canada to identify ways to improve the accuracy of their forest inventory and initiated projects to investigate and evaluate technology that integrates both LiDAR and high-resolution digital photography. As a result, the CEC-FRP committed to supporting research (Lim and Treitz 2004;Hopkinson et al 2005Hopkinson et al , 2006Chasmer et al 2006a, b;Thomas et al 2006) and the acquisition of new eFRI across all of Tembec's SFLs to facilitate comparisons and analyses within and among management units. In addition, all the silviculture treatments on 2 sustainable forests (i.e., Gordon Cosens and Nipissing; McPherson et al 2008, this issue) were compiled into a spatial database that could be used to assess future fibre production and undertake economic analyses.…”

Section: Improving Forest and Soil Inventoriesmentioning

confidence: 99%

The Canadian Ecology Centre – Forestry Research Partnership: Implementing a research strategy based on an active adaptive management approach

Bell¹,

Baker²,

Bruemmer³

et al. 2008

The Forestry Chronicle

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Between April 2000 and March 2007, the Canadian Ecology Centre -Forestry Research Partnership funded, directed, or catalyzed approximately 145 projects. Most of these focused on knowledge and data acquisition, providing a solid foundation for a series of sensitivity and gap analyses to determine whether a long-term goal of enhancing productivity on 6 forest management units in Ontario was achievable, and more importantly, sustainable. A research strategy provided the focus for knowledge and data acquisition and the partnership facilitated integrated research, development, transfer, and implementation. Here we provide an overview of this effort, which is expected to position forest managers of the 6 forests to apply an adaptive management process to increase understanding of the response of their forests to various forest management policies and practices in the future. The strategy and approach described could be useful to other jurisdictions aiming to more closely integrate forest research and operations as well as those interested in implementing adaptive management.

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“…Sexton, Bax, Siqueira, Swenson, and Hensley (2009) compare using ALS, SRTM, and airborne Xand P-band SAR interferometry (GeoSAR), for forest canopy height estimation in pine and hardwood forests in North Carolina, USA. The ALS data has 0.5-1.0 m diameter footprints spaced at 4-6 m, which is much coarser than in the ALS studies of Kwak et al (2007) and Hopkinson et al (2006). In pine forest, ALS performs much better (R 2 = 0.83, RMSE = 7.9%) than GeoSAR (R 2 = 0.70, RMSE = 28%), which again performs much better than SRTM (R 2 = 0.54, RMSE = 48%).…”

Section: Introductionmentioning

confidence: 99%

“…Kwak, Lee, Lee, Biging, and Gong (2007) use ALS to detect individual trees and estimate their heights, with R 2 = 0.77 and RMSE = 8.2%. Hopkinson, Chasmer, Lim, Treitz, and Creed (2006) use an indirect method to estimate canopy height from ALS data: a regression on the standard deviation of the difference between first and last returns. The R 2 performance is very good (0.95) but the root mean square error exceeds 10% of the measured mean height.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor forest vegetation height mapping methods for Tanzania

Trier

Salberg

Haarpaintner

et al. 2018

European Journal of Remote Sensing

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This paper proposes a new method for mapping of forest cover in Tanzania, in the form of yearly estimates of average vegetation height from time-series of Landsat and ALOS PALSAR satellite images. By using airborne laser scanning data and Landsat-8 data from 2014, a regression between average vegetation height and the specific leaf area vegetation index is established. By using all available Landsat acquisitions of the same area within 1 year, and producing a yearly estimate of vegetation height, the estimation error variance is reduced. The variance is further reduced by Kalman filtering the sequence of yearly estimates. A multisensor version of the method comprises application of the radar backscatter when L-band SAR data is available. To conclude, we have demonstrated that estimation of mean vegetation height is possible from dense time series of optical and SAR satellite data. Change detection was able to detect areas with total loss of biomass. ARTICLE HISTORY

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Towards a universal lidar canopy height indicator

Cited by 81 publications

References 17 publications

Leaf Area Index, Biomass Carbon and Growth Rate of Radiata Pine Genetic Types and Relationships with LiDAR

Leaf Area Index, Biomass Carbon and Growth Rate of Radiata Pine Genetic Types and Relationships with LiDAR

The Canadian Ecology Centre – Forestry Research Partnership: Implementing a research strategy based on an active adaptive management approach

Multi-sensor forest vegetation height mapping methods for Tanzania

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