Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data

Næsset, Erik

doi:10.1016/s0034-4257(01)00290-5

Cited by 1,196 publications

(1,014 citation statements)

References 18 publications

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“…Tree heights can be computed from calculating the difference between the ground (last pulse returns) and top of canopy (first pulse returns) when the position and three-dimensional angle of the instrument is known (either from satellite Global Positioning Systems (GPS) and/or Inertial Navigation Systems (INS) measurements; Véga and St-Onge 2008). The error associated with LIDAR measurements of tree height are typically between 0.5 and 1.0 m (Persson et al 2002;Naesset 1997Naesset , 2002Magnussen and Boudewyn 1998;Magnussen et al 1999;Naesset and Økland 2002), and LIDAR is considered more accurate for height measurement than common field-based measurements (Naesset and Økland 2002).…”

Section: Lidarmentioning

confidence: 99%

Implications of differing input data sources and approaches upon forest carbon stock estimation

Wulder

White

Stinson

et al. 2009

Environ Monit Assess

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Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p < 0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare ( p < 0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant ( p = 0.288). Overall, the relationship between the two biomass estimates was strong (R 2 = 0.92, p < 0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.

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Section: Lidarmentioning

confidence: 99%

Implications of differing input data sources and approaches upon forest carbon stock estimation

Wulder

White

Stinson

et al. 2009

Environ Monit Assess

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“…The data from the five locations with destructive sampling were in the present study split in two, resulting in the three datasets used in the analysis (Table 1). 2 and 500 m 2 , laid out in the mature productive forest of the study area (Figure 1, right). Single-tree data used in the present study were from a subset of 11 plots.…”

Section: Field Datamentioning

confidence: 99%

“…An increasing number of forest inventories are based on data collected with airborne laser scanning (ALS) [1]. While commercial and operational ALS-based forest inventories most frequently are conducted according to the so-called area-based approach, as described by Naesset [2], methods targeting single trees have also been proposed [3][4][5][6]. The latter methods usually require ALS data with higher resolution, but intend to give information on a single-tree level, contrary to the area-based information provided by the former.…”

Section: Introductionmentioning

confidence: 99%

Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data

Hauglin

Gobakken

Astrup³

et al. 2014

Forests

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Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for a combined use of ALS and TLS in an inventory was tested and compared to a method without the use of TLS. Single-tree Norway spruce crown biomass was predicted using an ALS-model with training data obtained by TLS. ALS and TLS data were collected for sets of sample trees, including 68 trees with both ALS and TLS data. In total, 29 destructively sampled trees were used to fit a TLS crown biomass model, which then was used to predict crown biomass in a separate set of 68 trees. This dataset was subsequently used to fit an ALS crown biomass model. When validating the model, using a separate dataset with accurately measured crown biomass obtained through destructive sampling, the mean error was 32% of the observed mean biomass. Corresponding crown biomass predictions derived with ALS-predicted diameters and the use of conventional and existing allometric models resulted in a mean error of 35%. Thus, in the present study, a slight improvement, in terms of prediction accuracy, was found when using training data with ground reference values obtained by TLS. OPEN ACCESSForests 2014, 5 385

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“…For example, rmse of mean volume at the stand or plot level of area-based ALS inventory has been found to ranging between 10% and 27% (e.g., [4,5,7,41,51,52]). …”

Section: Forest Property Dmentioning

confidence: 99%

“…Estimation of forest characteristics will be performed, using the nonparametric k-nearest neighbor (k-NN) or k-most similar neighbor (k-MSN) method [3]. With respect to the estimation of stand mean characteristics (e.g., [4][5][6][7]) and tree species-or timber assortment-specific characteristics [8][9][10][11], it has become possible to achieve at least the same level of accuracy using low-pulse ALS data as that found in traditional standwise forest inventory (SWFI). Overviews on the use of ALS in forest inventory can be found in [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in Forest Net Present Value Estimations

Holopainen

Mäkinen

Rasinmäki³

et al. 2010

Forests

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Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV). The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS) inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%). Timber price fluctuation was portrayed with geometric mean-reverting (GMR) price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A) a property having an even development class distribution, (B) sapling stands, (C) young thinning stands, and (D) mature stands. Simulations resulted in predicted yield value (predicted NPV) distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1-7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate). ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant OPEN ACCESSForests 2010, 1 178 in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate). The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the output results with levels of confidence.

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Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data

Cited by 1,196 publications

References 18 publications

Implications of differing input data sources and approaches upon forest carbon stock estimation

Implications of differing input data sources and approaches upon forest carbon stock estimation

Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data

Uncertainty in Forest Net Present Value Estimations

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