Influence of Sampling Design Parameters on Biomass Predictions Derived from Airborne LiDAR Data

Bouvier, M.; Durrieu, S.; Fournier, Richard; Saint-Geours, Nathalie; Guyon, Dominique; Grau, Eloi; Boissieu, Florian de

doi:10.1080/07038992.2019.1669013

Cited by 10 publications

(7 citation statements)

References 61 publications

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“…As the investigated control stands generally exhibited a one-layer vertical structure, no substantial error was expected from this simplification. Moreover, Bouvier et al (2019) stressed that DBH/height measurement errors impose a minor to negligible effect upon LiDAR-based biomass estimation for even-aged pine stands. The stand boundaries were first delineated with a GNSS receiver and (if required) manually adjusted based on the canopy height model derived from the ALS data.…”

Section: Field Datamentioning

confidence: 99%

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

et al. 2020

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Background Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns, and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning (ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded. Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALS-enhanced stand-level inventory. Results Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/− 5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m2. Only a slight gain was observed when bigger plots were used. Conclusions ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.

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Section: Field Datamentioning

confidence: 99%

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

et al. 2020

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“…Consequently, the accuracy of ALS-based AGB models can vary by species and forest types [21]. Bouvier et al [22] demonstrated, that plot size significantly impacted AGB model performance within pine forest in southwestern France. The accuracy of ALS-based models can potentially vary with the ALS data point density.…”

Section: Introductionmentioning

confidence: 99%

Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

Novotný

Navrátilová

Janoutová

et al. 2020

Forests

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Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m2) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region.

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“…aerial and unmanned aerial vehicles (UAV)), studies have recommended further investigation of variation in acquisition parameters for forest parameter assessment (Cao et al, 2016;Korhonen et al, 2011;Tompalski et al, 2019). Some studies focussed on the effect of point density on the accuracy of stand attribute predictions (Bouvier et al, 2019;Naesset, 2009;Singh et al, 2016). Relevant lidar metrics selected to build predictive models were found to differ significantly with pulse density in Naesset, (2009) but in Bouvier et al, (2019) there was no change in the four metrics used.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies focussed on the effect of point density on the accuracy of stand attribute predictions (Bouvier et al, 2019;Naesset, 2009;Singh et al, 2016). Relevant lidar metrics selected to build predictive models were found to differ significantly with pulse density in Naesset, (2009) but in Bouvier et al, (2019) there was no change in the four metrics used. However, in the range of explored pulse densities, i.e.…”

Section: Introductionmentioning

confidence: 99%

Scan Angle Impact on Lidar-Derived Metrics Used in Aba Models for Prediction of Forest Stand Characteristics: A Grid Based Analysis

Dayal

Durrieu

Alleaume

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Lidar scan angle can affect estimation of lidar-derived forest metrics used in area-based approaches (ABAs). As commonly used first-order metrics and various user-developed metrics are computed in the form of a grid or a raster, their response to various scan angles needs to be investigated similarly. The objective of this study was to highlight the impact of scan angles on 11 metrics (9 height-based and 2 other commonly used metrics) at the level of the grid-cell. The study area was divided into a grid of cell size 30 m. In every grid-cell, the flight lines that sampled at least 90% area of the grid-cell were identified. The flight lines and the corresponding point clouds were then classified based on their mean scan angle into four classes 0°–10°, 10°–20°, 20°–30° and 30°–40°. Metrics were computed for one flight line per class for each grid-cell. This resulted in a maximum of four values for a metric in every grid-cell. Comparing these values revealed the evolving nature of the metrics with the scan angle. For the comparison we used a paired t-test and simple linear regression. We observed that most of the metrics were systematically under-estimated with increasing scan angle. Gap-fraction, rumple index were affected more than standard deviation of height while the maximum height was relatively stable. Among the height percentiles, the higher percentiles were relatively more stable compared to the lower percentiles. Scan angles can indeed have an impact on the estimation of lidar derived metrics. Although, many of the metrics studied showed statistically significant differences in their computation for different scan angles, their impact on the accuracies of ABA models needs to be studied further by accounting for the differences as shown in this study.

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Influence of Sampling Design Parameters on Biomass Predictions Derived from Airborne LiDAR Data

Cited by 10 publications

References 61 publications

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

Scan Angle Impact on Lidar-Derived Metrics Used in Aba Models for Prediction of Forest Stand Characteristics: A Grid Based Analysis

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