Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters

Magnussen, Steen; Renaud, Jean-Pierre

doi:10.1007/s13595-016-0581-2

Cited by 13 publications

(15 citation statements)

References 59 publications

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“…One such approach involves summing predicted SSDs from each grid cell composing a delineated stand [54]. More complex approaches involve multidimensional scaling [55], in which an estimator can be used for extrapolation to larger units, or segmenting areas into smaller units such as microstands, which are areas grouped by similar ALS-predicted attributes such as volume and height [56]. If stand-level predictions are the desired result, a final aggregation step should be used to scale up from cell-level predictions; however, this was beyond the scope of the current study and SSD predictions remained at the cell level.…”

Section: Model Applicationmentioning

confidence: 99%

Enhancing the Estimation of Stem-Size Distributions for Unimodal and Bimodal Stands in a Boreal Mixedwood Forest with Airborne Laser Scanning Data

Mulverhill

Coops

White

et al. 2018

Forests

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Stem size distribution (SSD), which describes tree frequencies in diameter classes within an area, has a variety of direct and indirect applications that are critical for forest management. In this study, we evaluated which structural characteristics derived from Airborne Laser Scanning (ALS) data were best able to differentiate between unimodal and bimodal stands in a managed boreal mixedwood forest in Alberta, Canada. We then used wall-to-wall ALS data to predict (for 20 m-by-20 m grid cells) the parameters of a Weibull SSD in unimodal cells, and a Finite Mixture Model (FMM) in bimodal cells. The resulting SSDs were evaluated for their fit to ground plot-measured SSDs using an Error Index (EI). We found that the variance of ALS return heights was the best metric for differentiating between unimodal and bimodal stands, with a classification accuracy of 77%. Parameters of both the Weibull and FMM distributions were accurately predicted (r 2~0 .5, Root Mean Square Error (RMSE)~30%), and that differentiating for modality prior to estimating SSD improved the accuracy of estimates (EI of 49.13 with differentiation versus 51.31 without differentiation). Unique to our presented approach is the stratification by SSD modality prior to the modelling of distributions. To achieve this, we apply a threshold to an ALS metric that allows SSD modality to be distinguished for each cell at the landscape level, and this a priori information is then used to ensure that the appropriate distribution is modelled. Our approach is parsimonious and efficient, enabling improved accuracy in SSD estimation across diverse landscapes when ALS data is the sole data source.

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Section: Model Applicationmentioning

confidence: 99%

Enhancing the Estimation of Stem-Size Distributions for Unimodal and Bimodal Stands in a Boreal Mixedwood Forest with Airborne Laser Scanning Data

Mulverhill

Coops

White

et al. 2018

Forests

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“…Often, remote sensing observations have been analyzed by computing correlations with forest attributes (e.g., lidar metrics correlated with biomass or basal area) [23][24][25][26][27][28]. Such approaches have a high predictive power, but typically focus on predicting single forest attributes (like biomass) and require field-data-intensive calibration of site-specific parameters in the statistical relationships.…”

Section: Introductionmentioning

confidence: 99%

Deriving Tree Size Distributions of Tropical Forests from Lidar

et al. 2021

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Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

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“…A large body of research has focussed on the application of ALS to forestry. ALS is well suited for inventory [9][10][11][12][13][14], and for predicting metrics such as height [11,[15][16][17], diameter at breast height (DBH) [18][19][20], tree crown diameter and volume [21,22], leaf area index (LAI) [23][24][25] and stand density (for reviews, see Kaartinen et al. [26] and Eysn et al [27]).…”

Section: Introductionmentioning

confidence: 99%

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

et al. 2020

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The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost and time efficiencies. However, little research has examined the accuracies of these technologies for measuring young trees. This study compared the data captured by a UAV laser scanning system (ULS), and UAV structure from motion photogrammetry (SfM), with traditional field-measured heights in a series of forestry trials in the central North Island of New Zealand. Data were captured from UAVs, and then processed into point clouds, from which heights were derived and compared to field measurements. The results show that predictions from both ULS and SfM were very strongly correlated to tree heights (R2 = 0.99, RMSE = 5.91%, and R2 = 0.94, RMSE = 18.5%, respectively) but that the height underprediction was markedly lower for ULS than SfM (Mean Bias Error = 0.05 vs. 0.38 m). Integration of a ULS DTM to the SfM made a minor improvement in precision (R2 = 0.95, RMSE = 16.5%). Through plotting error against tree height, we identified a minimum threshold of 1 m, under which the accuracy of height measurements using ULS and SfM significantly declines. Our results show that SfM and ULS data collected from UAV remote sensing can be used to accurately measure height in young forestry trials. It is hoped that this study will give foresters and tree breeders the confidence to start to operationalise this technology for monitoring trials.

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Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters

Cited by 13 publications

References 59 publications

Enhancing the Estimation of Stem-Size Distributions for Unimodal and Bimodal Stands in a Boreal Mixedwood Forest with Airborne Laser Scanning Data

Enhancing the Estimation of Stem-Size Distributions for Unimodal and Bimodal Stands in a Boreal Mixedwood Forest with Airborne Laser Scanning Data

Deriving Tree Size Distributions of Tropical Forests from Lidar

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

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