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Watt M.S.,Scion Research | Meredith A.,Indufor Asia Pacific Ltd | Watt P.,Indufor Asia Pacific Ltd | Gunn A.,Blakely Pacific Ltd
New Zealand Journal of Forestry Science | Year: 2014

Conclusion: This study was undertaken in highly stocked unthinned Douglas-fir stands located in areas with complex topography. Consequently, the pulse density thresholds described here are likely to be conservative and could be used to guide acquisition of high-quality LiDAR datasets for this species.Background: LiDAR is an established technology that is increasingly being used to characterise spatial variation in stand metrics used in forest inventory. As the cost of LiDAR acquisition markedly declines with LiDAR pulse density, it is useful to identify how far pulse density can be reduced without compromising the precision of relationships between LiDAR and stand metrics. Using plot measurements and LiDAR data obtained from highly stocked and unthinned Douglas-fir plantations (Pseudotsuga menziesii [Mirb.] Franco), the objective of this research was to characterise the precision of regressions between LiDAR metrics and stand metrics (mean top height, Hm, volume, V and mean diameter, D) under a range of pulse densities using Digital Terrain Models (DTMs) representing two common scenarios. Under the first scenario, which represents an initial acquisition, the point cloud was sequentially culled and used for creation of a DTM and corresponding LiDAR cloud metrics. In the second scenario, which represents a subsequent acquisition, a DTM generated at high pulse density (10 pulses m−2) was used for the creation of the corresponding LIDAR cloud metrics.Methods: Models describing the precision of regressions between LiDAR metrics and stand metrics were developed at 10 pulses m−2. LiDAR data were culled to pulse densities ranging from 10 to 0.01 pulses m−2and the impact of culling on the precision of these regressions was examined under the two scenarios.Results: For the scenario with the culled DTM, precision of the three models remained stable until densities of 2 – 3 pulses m−2were reached. Below this threshold, there was a gradual decline in precision to pulse densities of 0.7 – 1 pulses m−2at which point the R2 was 95% of the maximum values. Further culling of the data resulted in a sharp decline in model precision for all three regressions. For the scenario where the DTM was held at a high pulse density, little change in the precision of the regressions was found until pulse densities of 0.04 to 0.2 pulses m−2were reached. There was a sharp decline in precision below pulse densities of 0.04 pulses m−2for all three models. © 2014, Watt et al.. Source

Watt P.,Indufor Asia Pacific Ltd | Watt M.S.,Scion Research
New Zealand Journal of Forestry | Year: 2012

Cost effective options for providing up-to-date information on the status of forest resources to improve planning are important. Recent advances in satellite technology have made it possible to acquire imagery on a regular basis at a spatial resolution that is likely to be useful to forest managers. This paper describes the use of RapidEye satellite imagery to identify new harvest areas. Processes are also demonstrated that use imagery to delineate stands, and areas within stands, with growth that differs from expected values. Source

Watt P.,Indufor Asia Pacific Ltd | Meredith A.,Indufor Asia Pacific Ltd | Yang C.,Indufor Asia Pacific Ltd | Watt M.S.,Scion Research
New Zealand Journal of Forestry Science | Year: 2013

Background: A number of data sources currently exist that can provide information on forest plantations at a range of scales over an entire rotation cycle. In particular, LiDAR is quickly becoming the technology of choice for harvest planning and providing local-scale estimates of forest structure. Its application is still limited as repeat annual acquisition at this scale is generally cost prohibitive. Development of temporally updateable models that can accurately project important metrics such as tree height between LiDAR acquisitions would be of considerable use to resource managers. The objective of this research was to develop models of Pinus radiata height using GIS spatial data supplemented with RapidEye satellite imagery. Methods: Multiple regression models were constructed to describe maximum canopy height (Hm) derived from LiDAR at two relatively distant study sites located in Kaingaroa and Tairua forests. A randomised selection of 300 m2 circular plots was made at both sites and average values of Hm within these plots were used for the modelling. Sources of information used for predicting Hm included stand age and spatial information describing environmental variables and stand productivity. This information was supplemented with spectra and vegetation ratios derived from high resolution RapidEye satellite imagery. Results: The most robust models of Hm that were developed for both sites included a combination of the crop age obtained from the stand GIS, Site Index (obtained from a GIS surface) and the red-edge vegetation ratio (REVI) The final models of Hm had respective R2 of 0.99 and 0.94 for the Kaingaroa and Tairua sites. At both sites, stand age was the strongest predictor of Hm. However, the inclusion of REVI from high resolution imagery did add an updatable temporal dimension to the model. Changes in REVI are sensitive to the impacts of abiotic and biotic factors that are not captured by stand age and Site Index. Conclusion: Applied operationally, this model can be used in a GIS environment to estimate tree height and identify areas of anomalous growth or disturbance caused by wind, snow, fire or disease. © 2013 Watt et al. Source

Watt M.S.,Scion Research | Adams T.,MetService | Watt P.,Indufor Asia Pacific Ltd | Marshall H.,Interpine
New Zealand Journal of Forestry Science | Year: 2013

Background: When aerial LiDAR data is used to construct Digital Elevation Models (DEMs) under vegetation, DEM quality will invariably suffer due to attenuation of the laser pulses by the land cover. Although the ratio of ground returns to outgoing pulses (GRper) is known to vary widely for forest applications, little research has quantified the influence of forest stand structure and site conditions on this ratio. An understanding of how these factors influence GRper is crucial for the development of accurate DEMs. Methods: Using an extensive national dataset obtained from New Zealand's plantation forests the objective of this research was to develop a multiple regression model of GRper that could be used to specify the necessary LiDAR pulse density for development of accurate DEMs. Results: Within the dataset GRper averaged 30.5% ranging from 0.73 to 92.2%. The final model of GRper included stand age, crop density, non-crop density and slope and accounted for 48% of the variance in GRper with root mean square error (RMSE) of 13.9%. The percentage of ground returns declined exponentially as stand age, crop and non-crop density increased and declined linearly with increases in slope. GRper was not substantially affected by either the pulse density, stand aspect or whether the stand comprised Pinus radiata or Pseudotsuga menziesii. Conclusion: The developed model highlights the sensitivity of GRper to stand and site conditions. This model is likely to be of considerable use in defining the optimal LiDAR pulse density across a range of forest environments. © 2013 Watt et al.; licensee Springer. Source

Watt M.S.,Scion Research | Meredith A.,Indufor Asia Pacific Ltd | Watt P.,Indufor Asia Pacific Ltd | Gunn A.,Blakely Pacific Ltd
New Zealand Journal of Forestry Science | Year: 2013

Background: Light Detection and Ranging (LiDAR) has been successfully used to describe a wide range of forest metrics at local, regional and national scales. However, little research has used this technology in young Douglas-fir stands to describe key stand characteristics used as criterion for operational thinning. The objective of this research was to develop models of Douglas-fir mean top height, basal area, volume, mean diameter (at breast height), green crown height and stand density from LiDAR and stand information. Methods: Data for this study were obtained from four widely separated young (age range of 9 to 17 years) Douglas-fir plantations in the South Island, New Zealand. LiDAR was acquired for the entire area and stand metrics were measured within 122 plots established across the study area. Spatially synchronous stand and LiDAR metrics were extracted from the plots. Using this dataset, multiple regression models were developed for each of the six stand metrics. Results: The final models constructed for mean top height, green crown height, total stem volume, mean diameter, basal area, and stand density had R2 values of 0.85, 0.79, 0.86, 0.86, 0.84 and 0.55, respectively, with root mean square errors of 1.02 m, 0.427 m, 20.2 m3 ha-1, 13.9 mm, 3.81 m2 ha-1 and 355 stems ha-1, respectively. With the exception of stand density, all relationships were relatively unbiased. Variables with the greatest contribution (with the partial R2 in brackets) to models of mean top height, green crown height, volume, mean diameter and basal area included the 75th (0.85), 1st (0.76), 10th (0.83), 95th (0.74), and 10th (0.72) LiDAR height percentiles. The LiDAR height interquartile distance was the most important contributor (partial R2 = 0.33) to the model of stand density. Conclusion: With the exception of stand density, the final models for stand metrics were sufficiently precise to be used for scheduling thinning operations. This study demonstrates the utility of LiDAR to accurately estimate key structural attributes of young Douglas-fir and to assist with forest management over a widely dispersed resource. © Watt et al.; licensee Springer. Source

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