Development of Height-Volume Relationships in Second Growth<i>Abies grandis</i>for Use with Aerial LiDAR

Tinkham, Wade T.; Smith, Alistair M. S.; Affleck, David L. R.; Saralecos, Jarred D.; Falkowski, Michael J.; Hoffman, Chad; Hudak, Andrew T.; Wulder, Michael A.

doi:10.1080/07038992.2016.1232587

Cited by 18 publications

(12 citation statements)

References 56 publications

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“…The slight differences between the field and ITD derived data can be somewhat attributed to the difference in acquisition dates (i.e., ALS data in 2019 and field data in 2020). Although we assumed that the field data represented the 'truth', studies comparing both ALS and laser rangefinder maximum tree height measurements with felled trees have demonstrated that ALS measurements were more accurate and exhibited less bias [14].…”

Section: Discussionmentioning

confidence: 99%

“…Given these compelling preliminary results of the ForestView ® algorithm, the next logical steps are to cross-compare this specific algorithm versus open-source and 'free closed source algorithms' in a similar manner to that conducted by [83]. Equally, further research should follow [14,47,83] to assess the utility of this algorithm to inform landscape scale assessments of forest stand metrics (e.g., basal area, trees per hectare) and projections of growth and yield. Further research could also explore the utility of this product to inform structural metrics of fire severity [48,84,85] and ecosystem vulnerability under a range of stressors [86].…”

Section: Discussionmentioning

confidence: 99%

“…Individual Tree Detection (ITD), species determination, and measurement of structural attributes using ALS approaches have been long sought-after goals in precision forestry [11,12]. In operational forestry the majority of allometric relations used to model aboveground biomass, carbon stocks, and merchantable yield rely on diameter at breast height (DBH) and maximum tree heights [13], with limited studies only using maximum tree heights [14]. Multiple ITD algorithms have been developed with varying degrees of success.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Sparks

Smith

2021

Forests

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Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Sparks

Smith

2021

Forests

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“…The regional tree size results used to be roughly quantified while using diameter-at-breast-height (DBH)-based allometry based on forest inventory data, which is undertaken every five years. Currently, the remote sensing technique has emerged as an important tool for forest inventory, providing continuous and up-to-date information on forest volume, which can help in forest resource management and forest growth observations [7][8][9]. Earlier studies involving the use of multi-source satellite techniques for forest volume prediction can be divided into two categories: using only Airborne Light Detection and Ranging (LiDAR) for accurate and convenient acquisition of small-scale forest volume distributions; and adopting wide-range Synthetic Aperture Radar (SAR) or multi-spectral satellite images, or coordinating multi-sensor data to estimate regional-wide forest volume mappings.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, forest volume, as one of the structure parameters, was often accurately estimated by LiDAR [25], for example, Clementel et al [26] have carried out statistical models combined with medium-resolution LiDAR to produce timber volume mapping, and Lo et al [19] demonstrated tree growth competition index (LCI) derived from LiDAR scanning while using a rasterized canopy height model (multilevel morphological active-contour algorithm) was a key factor for forest volume estimation. Additionally, the relationship between volume and height and the sensitivity of tree volume estimation to LiDAR trajectory error were implemented [9,27].…”

Section: Introductionmentioning

confidence: 99%

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Xie

Cao

et al. 2020

Remote Sensing

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Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

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Estimation of Forest Carbon from Aerial Photogrammetry

Pulido

Puettmann

Salas

2019

Lecture Notes in Computer Science

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Development of Height-Volume Relationships in Second GrowthAbies grandisfor Use with Aerial LiDAR

Cited by 18 publications

References 56 publications

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Estimation of Forest Carbon from Aerial Photogrammetry

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