Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data?

Puliti, Stefano; Breidenbach, Johannes; Astrup, Rasmus

doi:10.3390/rs12081245

Cited by 72 publications

(54 citation statements)

References 35 publications

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“…All the remaining surface points were then interpolated by triangulated irregular networks. Many studies have generated CHM from UAVLS data with various spatial resolutions, ranging 0.1 to 1 m [10,16,49,50]. Yin and Wang (2019) have recommended that spatial resolution should be finer than one fourth of the crown diameter to correctly delineate crown boundaries and characterize the crown shapes.…”

Section: Uavls Data Preprocessingmentioning

confidence: 99%

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

et al. 2020

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Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

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Section: Uavls Data Preprocessingmentioning

confidence: 99%

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

et al. 2020

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“…Ni et al (2015) reported exceptional levels of accuracy when calculating tree heights, and Alonzo et al (2018) achieved similar accuracies when modelling tree density, basal area, aboveground biomass, and species composition in boreal conditions. While UAV-based forest inventories are limited to small areas due to the high cost, they could be particularly effective, for example, in predicting the diameter distribution or in the determination of ℎ (Puliti et al 2020).…”

Section: Simplifying the Cross-bioregional Assessment Of Fst (Ii)mentioning

confidence: 99%

Improvements in forest structural type assessment using airborne laser scanning

Adnan¹

2020

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Accurate forest structural type (FST) assessment provides a valuable support tool to distinguish the different structures in forest stands, achieve sustainable forest management and formulate effective decisions. Data from four research sites within three biogeographical regions-Boreal, Mediterranean and Atlanticwere used in this study, and reliable methodologies were developed for FST assessment. First, the Gini coefficient () of tree size inequality was used for the structural characterisation, and the effects of plot size, stand density and point density of airborne laser scanning (ALS) on the ALS-assisted estimations were evaluated for the Boreal region. Second, four forest structural attributesquadratic mean diameter (), , basal area larger than the mean () and stand density ()from the three biogeographical regions were used to develop regionindependent methods for FST assessment. Lastly, a threshold value to represent maximum entropy was determined and was used to classify the various FST directly from ALS data using L-coefficient of variation and L-skewness of ALS echo heights. Aboveground biomass (AGB) was predicted for each FST and was compared with the AGB predictions without prestratification. The results showed that (a) plot size had a greater effect on the ALS-assisted estimation compared to stand size and point density, and that 250-450 m 2 plot size (radius 9-12 m for circular plots) is the optimal plot size for reliable ALS-assisted estimations, (b) and are the most reliable bivariate descriptors for FST assessment, and single storey, multi-storey and reversed-J type forest structures can be separated by lower, medium and upper and values, respectively, while and are relevant for the separation of young/mature and sparse/dense subtypes, and (c) based on the mathematical proofs, the threshold values calculated from ALS echo heights and tree basal areas to represent maximum entropy should be 0.33 and 0.50, respectively. Moderate improvements were observed in the AGB predictions from FST classified directly from ALS data compared to the full dataset but critical differences were identified in the selection of ALS metrics by the prediction models. For example, higher percentiles were more relevant in uneven-sized structures and open canopy areas, while cover metrics and average percentiles were important in the even-sized structures and closed canopy areas. Thus, these results are very useful in improving our understanding of the relationships that underpin the choice of ALS predictors in structurally complex forests.

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“…Besides the further integration of remotely sensed data (Solberg et al 2019 ; Puliti et al 2020a , b ), NFI researchers also work on improved tree-level assessments through terrestrial (Ducey and Astrup 2013 ; Astrup et al 2014 ) or drone-based scanning techniques (Puliti et al 2019 , 2020a , b ). We perceive the great potential of these relatively new measurement techniques in documenting the vegetation status, which enables new measurements at a later stage and provides additional information, for example on stem and crown shapes (e.g.…”

Section: Current Developments and The Foreseeable Futurementioning

confidence: 99%

A century of National Forest Inventory in Norway – informing past, present, and future decisions

et al. 2020

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Past In the early twentieth century, forestry was one of the most important sectors in Norway and an agitated discussion about the perceived decline of forest resources due to over-exploitation was ongoing. To base the discussion on facts, the young state of Norway established Landsskogtakseringen – the world’s first National Forest Inventory (NFI). Field work started in 1919 and was carried out by county. Trees were recorded on 10 m wide strips with 1–5 km interspaces. Site quality and land cover categories were recorded along each strip. Results for the first county were published in 1920, and by 1930 most forests below the coniferous tree line were inventoried. The 2nd to 5th inventories followed in the years 1937–1986. As of 1954, temporary sample plot clusters on a 3 km × 3 km grid were used as sampling units. Present The current NFI grid was implemented in the 6th NFI from 1986 to 1993, when permanent plots on a 3 km × 3 km grid were established below the coniferous tree line. As of the 7th inventory in 1994, the NFI is continuous, and 1/5 of the plots are measured annually. All trees with a diameter ≥ 5 cm are recorded on circular, 250 m 2 plots. The NFI grid was expanded in 2005 to cover alpine regions with 3 km × 9 km and 9 km × 9 km grids. In 2012, the NFI grid within forest reserves was doubled along the cardinal directions. Clustered temporary plots are used periodically to facilitate county-level estimates. As of today, more than 120 variables are recorded in the NFI including bilberry cover, drainage status, deadwood, and forest health. Land-use changes are monitored and trees outside forests are recorded. Future Considerable research efforts towards the integration of remote sensing technologies enable the publication of the Norwegian Forest Resource Map since 2015, which is also used for small area estimation at the municipality level. On the analysis side, capacity and software for long term growth and yield prognosis are being developed. Furthermore, we foresee the inclusion of further variables for monitoring ecosystem services, and an increasing demand for mapped information. The relatively simple NFI design has proven to be a robust choice for satisfying steadily increasing information needs and concurrently providing consistent time series.

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Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data?

Cited by 72 publications

References 35 publications

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Improvements in forest structural type assessment using airborne laser scanning

A century of National Forest Inventory in Norway – informing past, present, and future decisions

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