Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

The diameter distribution of savanna tree populations is a valuable indicator of savanna health because changes in the number and size of trees can signal a shift from savanna to grassland or forest. Savanna diameter distributions have traditionally been monitored with forestry techniques, where stem diameter at breast height (DBH) is measured in the field within defined sub-hectare plots. However, because the spatial scale of these plots is often misaligned with the scale of variability in tree populations, there is a need for techniques that can scale-up diameter distribution surveys. Dense point clouds collected from uncrewed aerial vehicle laser scanners (UAV-LS), also known as drone-based LiDAR (Light Detection and Ranging), can be segmented into individual tree crowns then related to stem diameter with the application of allometric scaling equations. Here, we sought to test the potential of UAV-LS tree segmentation and allometric scaling to model the diameter distributions of savanna trees. We collected both UAV-LS and field-survey data from five one-hectare savanna woodland plots in northern Australia, which were divided into two calibration and three validation plots. Within the two calibration plots, allometric scaling equations were developed by linking field-surveyed DBH to the tree metrics of manually delineated tree crowns, where the best performing model had a bias of 1.8% and the relatively high RMSE of 39.2%. A segmentation algorithm was then applied to segment individual tree crowns from UAV-LS derived point clouds, and individual tree level segmentation accuracy was assessed against the manually delineated crowns. 47% of crowns were accurately segmented within the calibration plots and 68% within the validation plots. Using the site-specific allometry, DBH was modelled from crown metrics within all five plots, and these modelled results were compared to field-surveyed diameter distributions. In all plots, there were significant differences between field-surveyed and UAV-LS modelled diameter distributions, which became similar at two of the plots when smaller trees (<10 cm DBH) were excluded. Although the modelled diameter distributions followed the overall trend of field surveys, the non-significant result demonstrates a need for the adoption of remotely detectable proxies of tree size which could replace DBH, as well as more accurate tree detection and segmentation methods for savanna ecosystems.

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Deriving Tree Diameter Growth and Probability of Survival Equations from Successive Diameter Distributions

Forest Science ◽

10.1093/forestscience/54.1.31 ◽

2008 ◽

Vol 54 (1) ◽

pp. 31-35

Author(s):

Thomas G. Matney ◽

Emily B. Schultz

Keyword(s):

General Procedure ◽

Growth And Yield ◽

Diameter Distribution ◽

Diameter Growth ◽

Individual Tree ◽

Statistical Probability ◽

Growth And Survival ◽

Tree Diameter ◽

Growth And Yield Models ◽

Diameter Distributions

Abstract Many growth and yield models have used statistical probability distributions to estimate the diameter distribution of a stand at any age. Equations for approximating individual tree diameter growth and survival probabilities from dbh can be derived from these models. A general procedure for determining the functions is discussed and illustrated using a loblolly pine spacing study. The results from the spacing study show that it is possible to define tree diameter growth and survival probability functions from diameter distributions with an accuracy sufficient to obtain a link between the individual tree and diameter growth and yield models.

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Predicting Future Diameter Distributions Given Current Stand Attributes

Canadian Journal of Forest Research ◽

10.1139/cjfr-2021-0216 ◽

2022 ◽

Author(s):

Quang V. Cao

Keyword(s):

Prediction Method ◽

Diameter Distribution ◽

Tree Level ◽

Individual Tree ◽

Distribution Parameters ◽

Level Model ◽

Novel Method ◽

Quadratic Mean Diameter ◽

Stand Attributes ◽

Individual Trees

This study discussed four methods to project a diameter distribution from age A1 to age A2. Method 1 recovers parameters of the distribution at age A2 from stand attributes at that age. Method 2 uses a stand-level model to grow the quadratic mean diameter, and then recovers the distribution parameters from that prediction. Method 3 grows the diameter distribution by assuming tree-level survival and diameter growth functions. Method 4 first converts the diameter distribution at age A1 into a list of individual trees before growing these trees to age A2. In a numerical example employing the Weibull distribution, methods 3 and 4 produced better results based on two types of error indices and the relative predictive error for each diameter class. Method 4 is a novel method that converts a diameter distribution into a list of individual-trees, and in the process, successfully links together diameter distribution, individual-tree, and whole stand models.

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Estimating Canopy Parameters Based on the Stem Position in Apple Trees Using a 2D LiDAR

Agronomy ◽

10.3390/agronomy9110740 ◽

2019 ◽

Vol 9 (11) ◽

pp. 740 ◽

Cited By ~ 4

Author(s):

Nikos Tsoulias ◽

Dimitrios S. Paraforos ◽

Spyros Fountas ◽

Manuela Zude-Sasse

Keyword(s):

Incident Angle ◽

Structural Parameters ◽

Point Clouds ◽

Absolute Error ◽

Apple Orchard ◽

Stem Diameter ◽

Coefficient Of Determination ◽

Individual Tree ◽

Canopy Volume ◽

Stem Position

Data of canopy morphology are crucial for cultivation tasks within orchards. In this study, a 2D light detection and range (LiDAR) laser scanner system was mounted on a tractor, tested on a box with known dimensions (1.81 m × 0.6 m × 0.6 m), and applied in an apple orchard to obtain the 3D structural parameters of the trees (n = 224). The analysis of a metal box which considered the height of four sides resulted in a mean absolute error (MAE) of 8.18 mm with a bias (MBE) of 2.75 mm, representing a root mean square error (RMSE) of 1.63% due to gaps in the point cloud and increased incident angle with enhanced distance between laser aperture and the object. A methodology based on a bivariate point density histogram is proposed to estimate the stem position of each tree. The cylindrical boundary was projected around the estimated stem positions to segment each individual tree. Subsequently, height, stem diameter, and volume of the segmented tree point clouds were estimated and compared with manual measurements. The estimated stem position of each tree was defined using a real time kinematic global navigation satellite system, (RTK-GNSS) resulting in an MAE and MBE of 33.7 mm and 36.5 mm, respectively. The coefficient of determination (R2) considering manual measurements and estimated data from the segmented point clouds appeared high with, respectively, R2 and RMSE of 0.87 and 5.71% for height, 0.88 and 2.23% for stem diameter, as well as 0.77 and 4.64% for canopy volume. Since a certain error for the height and volume measured manually can be assumed, the LiDAR approach provides an alternative to manual readings with the advantage of getting tree individual data of the entire orchard.

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A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral Lightness

Forests ◽

10.3390/f11060605 ◽

2020 ◽

Vol 11 (6) ◽

pp. 605 ◽

Cited By ~ 1

Author(s):

Jianyu Gu ◽

Heather Grybas ◽

Russell G. Congalton

Keyword(s):

Spectral Data ◽

Spatial Resolution ◽

Forest Type ◽

Canopy Height ◽

Aerial Imagery ◽

Tree Level ◽

Tree Crown ◽

Remotely Sensed Data ◽

Individual Tree ◽

Tree Crowns

Improvements in computer vision combined with current structure-from-motion photogrammetric methods (SfM) have provided users with the ability to generate very high resolution structural (3D) and spectral data of the forest from imagery collected by unmanned aerial systems (UAS). The products derived by this process are capable of assessing and measuring forest structure at the individual tree level for a significantly lower cost compared to traditional sources such as LiDAR, satellite, or aerial imagery. Locating and delineating individual tree crowns is a common use of remotely sensed data and can be accomplished using either UAS-based structural or spectral data. However, no study has extensively compared these products for this purpose, nor have they been compared under varying spatial resolution, tree crown sizes, or general forest stand type. This research compared the accuracy of individual tree crown segmentation using two UAS-based products, canopy height models (CHM) and spectral lightness information obtained from natural color orthomosaics, using maker-controlled watershed segmentation. The results show that single tree crowns segmented using the spectral lightness were more accurate compared to a CHM approach. The optimal spatial resolution for using lightness information and CHM were found to be 30 and 75 cm, respectively. In addition, the size of tree crowns being segmented also had an impact on the optimal resolution. The density of the forest type, whether predominately deciduous or coniferous, was not found to have an impact on the accuracy of the segmentation.

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Influence of ULS data acquisition characteristics on the achievable stem reconstruction accuracies

10.5194/egusphere-egu2020-7378 ◽

2020 ◽

Author(s):

Moritz Bruggisser ◽

Johannes Otepka ◽

Norbert Pfeifer ◽

Markus Hollaus

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Laser Scanner ◽

Point Clouds ◽

Stem Diameter ◽

Forest Monitoring ◽

Individual Tree ◽

Detection Rates ◽

Point Count ◽

Tree Stem

<p>Unmanned aerial vehicles-borne laser scanning (ULS) allows time-efficient acquisition of high-resolution point clouds on regional extents at moderate costs. The quality of ULS-point clouds facilitates the 3D modelling of individual tree stems, what opens new possibilities in the context of forest monitoring and management. In our study, we developed and tested an algorithm which allows for i) the autonomous detection of potential stem locations within the point clouds, ii) the estimation of the diameter at breast height (DBH) and iii) the reconstruction of the tree stem. In our experiments on point clouds from both, a RIEGL miniVUX-1DL and a VUX-1UAV, respectively, we could detect 91.0 % and 77.6 % of the stems within our study area automatically. The DBH could be modelled with biases of 3.1 cm and 1.1 cm, respectively, from the two point cloud sets with respective detection rates of 80.6 % and 61.2 % of the trees present in the field inventory. The lowest 12 m of the tree stem could be reconstructed with absolute stem diameter differences below 5 cm and 2 cm, respectively, compared to stem diameters from a point cloud from terrestrial laser scanning. The accuracy of larger tree stems thereby was higher in general than the accuracy for smaller trees. Furthermore, we recognized a small influence only of the completeness with which a stem is covered with points, as long as half of the stem circumference was captured. Likewise, the absolute point count did not impact the accuracy, but, in contrast, was critical to the completeness with which a scene could be reconstructed. The precision of the laser scanner, on the other hand, was a key factor for the accuracy of the stem diameter estimation.&#160;<br>The findings of this study are highly relevant for the flight planning and the sensor selection of future ULS acquisition missions in the context of forest inventories.</p>

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Individual tree extraction from terrestrial laser scanning data via graph pathing

Forest Ecosystems ◽

10.1186/s40663-021-00340-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Di Wang ◽

Xinlian Liang ◽

Gislain II Mofack ◽

Olivier Martin-Ducup

Keyword(s):

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Tree Level ◽

Forest Types ◽

Computationally Efficient ◽

Large Area ◽

Individual Tree ◽

Single Crown ◽

Tree Extraction

Abstract Background Individual tree extraction from terrestrial laser scanning (TLS) data is a prerequisite for tree-scale estimations of forest biophysical properties. This task currently is undertaken through laborious and time-consuming manual assistance and quality control. This study presents a new fully automatic approach to extract single trees from large-area TLS data. This data-driven method operates exclusively on a point cloud graph by path finding, which makes our method computationally efficient and universally applicable to data from various forest types. Results We demonstrated the proposed method on two openly available datasets. First, we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots. Second, we successfully extracted 270 trees from one hectare temperate forest. Quantitative validation resulted in a mean Intersection over Union (mIoU) of 0.82 for single crown segmentation, which further led to a relative root mean square error (RMSE%) of 21.2% and 23.5% for crown area and tree volume estimations, respectively. Conclusions Our method allows automated access to individual tree level information from TLS point clouds. The proposed method is free from restricted assumptions of forest types. It is also computationally efficient with an average processing time of several seconds for one million points. It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications, ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.

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Calibrating predicted diameter distribution with additional information in growth and yield predictions

Canadian Journal of Forest Research ◽

10.1139/x02-121 ◽

2003 ◽

Vol 33 (3) ◽

pp. 430-434 ◽

Cited By ~ 4

Author(s):

Annika Kangas ◽

Matti Maltamo

Keyword(s):

Growth Models ◽

Basal Area ◽

Growth And Yield ◽

Diameter Distribution ◽

Individual Tree ◽

Forest Management Planning ◽

Growing Stock ◽

Additional Information ◽

Tree Models ◽

Diameter Distributions

Diameter distribution of the growing stock is essential in many forest management planning problems. The diameter distribution is the basis for predicting, for example, timber assortments of a stand. Usually the predicted diameter distribution is scaled so that the stem number (or basal area) corresponds to the measured value (or predicted future value), but it may be difficult to obtain a distribution that gives correct estimates for all known variables. Diameter distributions that are compatible with all available information can be obtained using an approach adopted from sampling theory, the calibration estimation. In calibration estimation, the original predicted frequencies are modified so that they respect a set of constraints, the calibration equations. In this paper, an example of utilizing diameter distributions in growth and yield predictions is presented. The example is based on individual tree growth models of Scots pine (Pinus sylvestris L.). Calibration estimation was utilized in predicting the diameter distribution at the beginning of the simulation period. Then, trees were picked from the distribution and their development was predicted with individual tree models. In predicting the current stand characteristics, calibrated diameter distributions proved to be efficient. However, in predicting future yields, calibration estimation did not significantly improve the accuracy of the results.

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Fusing diameter distributions predicted by an area-based approach and individual-tree detection in coniferous-dominated forests

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0102 ◽

2020 ◽

Vol 50 (2) ◽

pp. 113-125

Author(s):

Janne Räty ◽

Petteri Packalen ◽

Eetu Kotivuori ◽

Matti Maltamo

Keyword(s):

Boreal Forests ◽

Laser Scanning ◽

Predictive Performance ◽

Diameter Distribution ◽

Individual Tree ◽

Tree Detection ◽

Forest Inventories ◽

Error Index ◽

Two Alternatives ◽

Diameter Distributions

An area-based approach (ABA) is the most common method used to predict forest attributes with airborne laser scanning (ALS) data. Individual-tree detection (ITD) offers an alternative to ABA; however, few studies have examined the selection of these two alternatives for the prediction of diameter distributions. We predicted diameter distributions by applying ABA and ITD in coniferous-dominated boreal forests using ALS data and examined their predictive performance based on the shapes of the diameter distributions (Gaussian, bimodal, and reverse-J). We proposed an ABA–ITD fusion for diameter distribution prediction. Firstly, the fusion was optimized and its potential was evaluated using an error index. Secondly, we offer two alternatives to incorporate the fusion into ALS-based forest inventories. Our results indicate that ITD is more prone to errors than ABA and that the predictive performance of ITD is more sensitive than ABA to the shape of the diameter distribution. The results show that ITD outperforms ABA with Gaussian diameter distributions. In contrast, ABA was seen as preferable to ITD with bimodal- or reverse-J-shaped diameter distributions. The findings indicate that ABA–ITD fusion has potential for predicting diameter distributions, although the predictive capability of ITD is limited compared with that of ABA.

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Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle

Forests ◽

10.3390/f10050415 ◽

2019 ◽

Vol 10 (5) ◽

pp. 415 ◽

Cited By ~ 11

Author(s):

Mohammad Imangholiloo ◽

Ninni Saarinen ◽

Lauri Markelin ◽

Tomi Rosnell ◽

Roope Näsi ◽

...

Keyword(s):

Laser Scanning ◽

Hyperspectral Imagery ◽

Digital Terrain Model ◽

Tree Height ◽

Point Clouds ◽

Tree Density ◽

Tree Canopy ◽

Tree Level ◽

Species Classification ◽

Individual Tree

Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.

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Prediction Comparison of Stand Parameters and Two Ecosystem Services through New Growth and Yield Model System for Mixed Nothofagus Forests in Southern Chile

Forests ◽

10.3390/f12091236 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1236

Author(s):

Paulo Moreno-Meynard ◽

Sebastian Palmas ◽

Salvador A. Gezan

Keyword(s):

Ecosystem Services ◽

Basal Area ◽

Growth And Yield ◽

Diameter Distribution ◽

Tree Level ◽

Yield Model ◽

Individual Tree ◽

Nothofagus Forests ◽

Level Model ◽

Stand Parameters

Forest managers need tools to predict the behavior of forests not only for the main stand parameters, such as basal area and volume, but also for ecosystem services such as timber volume and carbon sequestration. Useful tools to predict these parameters are growth and yield model systems with several possible options for modeling, such as the whole stand-level model, with or without diameter distribution generation, individual tree-level model, and compatibility models. However, those tools are scarce or developed mainly for forest plantations that are mostly located in the northern hemisphere. Thus, this study focuses on analyzing predictions of several growth and yield models built for native mixed Nothofagus forests from southern Chile, using the simulator Nothopack. A dataset of 19 permanent plots with three measurements were used for comparing the different models. Individual tree-level simulation presented the best goodness-of-fit statistics for stand parameters and ecosystem services. For example, the basal area gave an R2emp of 0.97 and 0.87 at 6 and 12 years of projection. However, the stand-level simulations with a generation of diameter distribution and both compatibility models showed satisfactory performance, both in accuracy and bias control. The simulator Nothopack, which has the capability of obtaining detailed outputs, is a useful tool to support management plans for these forest ecosystems.

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