scholarly journals Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

2020 ◽  
Vol 12 (6) ◽  
pp. 918 ◽  
Author(s):  
Diogo N. Cosenza ◽  
Luísa Gomes Pereira ◽  
Juan Guerra-Hernández ◽  
Adrián Pascual ◽  
Paula Soares ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Volume Estimation ◽  
Estimation Accuracy ◽  
Filtering Algorithms ◽  
Growing Stock ◽  
Forest Inventories ◽  
Dominant Height ◽  
Ground Point

Ground point filtering of the airborne laser scanning (ALS) returns is crucial to derive digital terrain models (DTMs) and to perform ALS-based forest inventories. However, the filtering calibration requires considerable knowledge from users, who normally perform it by trial and error without knowing the impacts of the calibration on the produced DTM and the forest attribute estimation. Therefore, this work aims at calibrating four popular filtering algorithms and assessing their impact on the quality of the DTM and the estimation of forest attributes through the area-based approach. The analyzed filters were the progressive triangulated irregular network (PTIN), weighted linear least-squares interpolation (WLS) multiscale curvature classification (MCC), and the progressive morphological filter (PMF). The calibration was established by the vertical DTM accuracy, the root mean squared error (RMSE) using 3240 high-accuracy ground control points. The calibrated parameter sets were compared to the default ones regarding the quality of the estimation of the plot growing stock volume and the dominant height through multiple linear regression. The calibrated parameters allowed for producing DTM with RMSE varying from 0.25 to 0.26 m, against a variation from 0.26 to 0.30 m for the default parameters. The PTIN was the least affected by the calibration, while the WLS was the most affected. Compared to the default parameter sets, the calibrated sets resulted in dominant height equations with comparable accuracies for the PTIN, while WLS, MCC, and PFM reduced the models’ RMSE by 6.5% to 10.6%. The calibration of PTIN and MCC did not affect the volume estimation accuracy, whereas calibrated WLS and PMF reduced the RMSE by 3.4% to 7.9%. The filter calibration improved the DTM quality for all filters and, excepting PTIN, the filters increased the quality of forest attribute estimation, especially in the case of dominant height.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

scholarly journals An Automated Approach for Extracting Forest Inventory Data from Individual Trees Using a Handheld Mobile Laser Scanner

2021 ◽  
Vol 42 (3) ◽  
Author(s):  
Mustafa Zeybek ◽  
Can Vatandaşlar
Keyword(s):  
Data Processing ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Natural Forest ◽  
Estimation Accuracy ◽  
Future Research ◽  
Inventory Data ◽  
Pilot Studies ◽  
Cloud Data ◽  
Forest Inventories

Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near)natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83–0.99, p<0.05). The average RMSE for tree DBHs was 1.8 cm at the forest landscape level. As for tree detection, 92.5% of 292 trunks were correctly classified on point cloud data. In general, estimation accuracy was sufficient for operational forest inventory needs. However, they could markedly decrease in »hard plots« located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in »easy plots«. By improving the algorithmic performances, the accuracy levels can be further increased by future research.

scholarly journals THE OPPORTUNITIES AND CHALLENGES OF USING AIRBORNE LASER SCANNING FOR FOREST INVENTORIES IN LITHUANIA

2018 ◽  
Author(s):  
Gintautas MOZGERIS ◽  
Ina BIKUVIENĖ ◽  
Donatas JONIKAVICIUS
Keyword(s):  
Root Mean Square ◽  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Volume Estimation ◽  
Target Point ◽  
Mean Square ◽  
Forest Inventories ◽  
Airborne Laser ◽  
Mean Square Errors

The aim of this study was to test the usability of airborne laser scanning (ALS) data for stand-wise forest inventories in Lithuania based on operational approaches from Nordic countries, taking into account Lithuanian forest conditions and requirements for stand-wise inventories, such as more complex forests, unified requirements for inventory of all forests, i.e. no matter the ownership, availability of supporting material from previous inventories and high accuracy requirements for total volume estimation. Test area in central part of Lithuania (area 2674 ha) was scanned using target point density 1 m-2 followed by measurements of 440 circular field plots (area 100–500 m2). Detailed information on 22 final felling areas with all trees callipered (total area 42.7 ha) was made available to represent forest at mature age. Updated information from conventional stand-wise inventory was made available for the whole study area, too. A two phase sampling with nonparametric Most Similar Neighbor estimator was used to predict point-wise forest characteristics. Total volume of the stand per 1 ha was predicted with an root mean square error of 18.6 %, basal area – 17.7 %, mean diameter – 13.6 %, mean height – 7.9 % and number of tree – 42.8 % at plot-level with practically no significant bias. However, the relative root mean square errors increased 2–4 times when trying to predict forest characteristics by three major groups of tree species – pine, spruce and all deciduous trees taken together. Main conclusion of the study was that accuracy of predicting volume using ALS data decreased notably when targeting forest characteristics by three major groups of tree species.

scholarly journals Improved Cellular Automaton for Stand Delineation

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 37
Author(s):  
Weiwei Jia ◽  
Yusen Sun ◽  
Timo Pukkala ◽  
Xingji Jin
Keyword(s):  
Cellular Automaton ◽  
Laser Scanning ◽  
Laser Pulses ◽  
Canopy Height ◽  
Growing Stock ◽  
Forest Inventories ◽  
Airborne Laser ◽  
Canopy Height Model ◽  
Stand Attributes ◽  
Height Model

Airborne laser scanning (ALS) is becoming common in forest inventories. The data obtained by laser scanning contain the locations of the echoes of laser pulses. If these data are used in forest management, they need to be segmented into spatially continuous stands that are homogeneous in terms of stand attributes. Prior to segmentation, the laser pulse data can be processed into canopy height model, which shows the distance of canopy surface from the ground. This study used a cellular automaton with a canopy height model for the delineation of tree stands, considering three criteria: homogeneity of the stand in terms of growing stock attributes, stand area, and stand shape. A new method to consider stand shape in cellular automaton was presented. This method had a clear beneficial effect on the stand delineation result. Increasing weight of the shape criterion led to more roundish and less irregular stand shapes. Also, increasing weight of the stand area improved the shape of the stands. The cellular automaton led to average stand areas of 1–1.7 ha, depending on cell size and the parameters of the automaton. The cellular automaton explained 84.7–94.2% of the variation in maximum canopy height when 5 m × 5 m cells were used. Cell sizes of 5–10 m were found to result in the best stand delineation results.

scholarly journals Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation

2020 ◽  
Vol 12 (5) ◽  
pp. 808 ◽  
Author(s):  
Jialong Duanmu ◽  
Yanqiu Xing
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
High Efficiency ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Estimation Accuracy ◽  
Distribution Analysis ◽  
Cloud Data ◽  
The Impact

Personal laser scanning (PLS) has significant potential for estimating the in-situ diameter of breast height (DBH) with high efficiency and precision, which improves the understanding of forest structure and aids in building carbon cycle models in the big data era. PLS collects more complete stem point cloud data compared with the present laser scanning technology. However, there is still no significant advantage of DBH estimation accuracy. Because the error caused by merging different point cloud fragments has not yet been eliminated, overlapping and inaccurate co-registered point cloud fragments are often inevitable, which are usually the leading error sources of PLS-based DBH estimation. In this study, a novel pre-processing algorithm named annular neighboring points distribution analysis (ANPDA) was developed to improve PLS-based DBH estimation accuracy. To reduce the impact of inaccurately co-registered point cloud fragments, ANPDA identified outliers through iterative removal of outermost points and analyzing the distribution of annular neighboring points. Six plots containing 247 trees under different forest conditions were selected to evaluate the ANPDA. Results showed that in the six plots, error reductions of 53.80–87.13% for bias, 38.82–57.30% for mean absolute error (MAE), and 27.17–56.02% for root mean squared error (RMSE) were achieved after applying ANPDA. These results confirmed that ANPDA was generally effective for improving PLS-based DBH estimation accuracy. It appeared that ANPDA could be conveniently fused with an automatic PLS-based DBH estimation process as a preprocessing algorithm. Furthermore, it has the potential to predict and warn operators of potential large errors during hierarchical semi-automatic DBH estimation.

scholarly journals The Comparison of Stem Curve Accuracy Determined from Point Clouds Acquired by Different Terrestrial Remote Sensing Methods

2020 ◽  
Vol 12 (17) ◽  
pp. 2739 ◽  
Author(s):  
Milan Hunčaga ◽  
Juliána Chudá ◽  
Julián Tomaštík ◽  
Martina Slámová ◽  
Milan Koreň ◽  
...  
Keyword(s):  
Cross Sections ◽  
Point Cloud ◽  
Laser Scanning ◽  
Mean Squared Error ◽  
Point Clouds ◽  
Volume Estimation ◽  
Environment Modeling ◽  
Accuracy Of Measurements ◽  
Wide Range ◽  
Standing Trees

The knowledge of tree characteristics, especially the shape of standing trees, is important for living tree volume estimation, the computation of a wide range of forest stand features, and the evaluation of stand stability. Nowadays, nondestructive and accurate approaches to data collection in the forest environment are required. Therefore, the implementation of accurate point cloud-based information in the field of forest inventory has become increasingly required. We evaluated the stem curves of the lower part of standing trees (diameters at heights of 0.3 m to 8 m). The experimental data were acquired from three point cloud datasets, which were created through different approaches to three-dimensional (3D) environment modeling (varying in terms of data acquisition and processing time, acquisition costs, and processing complexity): terrestrial laser scanning (TLS), close-range photogrammetry (CRP), and handheld mobile laser scanning (HMLS) with a simultaneous localization and mapping algorithm (SLAM). Diameter estimation errors varied across heights of cross sections and methods. The average root mean squared error (RMSE) of all cross sections for the specific methods was 1.03 cm (TLS), 1.26 cm (HMLS), and 1.90 cm (CRP). TLS and CRP reached the lowest RMSE at a height of 1.3 m, while for HMLS, it was at the height of 8 m. Our findings demonstrated that the accuracy of measurements of the standing tree stem curve was comparable for the usability of all three devices in forestry practices.

scholarly journals Accuracy of Photo-Optical Measurement of Wood Piles

2020 ◽  
Vol 3 (1) ◽  
pp. 90
Author(s):  
Tobias Cremer ◽  
Ferreol Berendt ◽  
Felipe de Miguel Diez ◽  
Felix Wolfgramm ◽  
Lubomir Blasko
Keyword(s):  
Optical Measurement ◽  
Volume Estimation ◽  
Optical Systems ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Mean Width ◽  
Broadleaved Tree Species ◽  
Broadleaved Tree

The accurate estimation of timber volume is of the utmost importance. For industrial timber, the volume is often estimated as stacked cubic meters. In addition to manual measurements, volume estimation is possible with photo-optical systems. Over 100 piles of industrial timber of broadleaved tree species were analyzed. In the study, a standard manual measurement method for the estimation of wood pile volumes was compared with a smartphone based photo-optical application for the determination of woodpile volume. Mean gross volume of the piles was approximately 56 m3 and mean width of piles was 9.43 m. A strong correlation was found between the manual measurements and the photo-optical measurement. However, volume estimation of large volume piles seems to perform better in comparison to piles with small volumes. Further research is needed to determine the effects of variables such as log quality or quality of pile on volume estimation accuracy.

scholarly journals Comparison of photogrammetric canopy models from archived and made-to-order aerial imagery in forest inventory

Silva Fennica ◽  
2020 ◽  
Vol 54 (5) ◽  
Author(s):  
Sakari Tuominen ◽  
Andras Balazs ◽  
Annika Kangas
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Species Recognition ◽  
Aerial Imagery ◽  
Large Area ◽  
Growing Stock ◽  
Forest Inventories ◽  
Optical Imagery ◽  
Canopy Model

In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0–20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.

scholarly journals Analysis of Taper Functions for Larix olgensis Using Mixed Models and TLS

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 196
Author(s):  
Dandan Li ◽  
Haotian Guo ◽  
Weiwei Jia ◽  
Fan Wang
Keyword(s):  
Laser Scanning ◽  
Mixed Model ◽  
Volume Estimation ◽  
Stem Diameter ◽  
Estimation Accuracy ◽  
Larix Olgensis ◽  
Stem Analysis ◽  
Basic Model ◽  
Tree Effect ◽  
Taper Function

Terrestrial laser scanning (TLS) plays a significant role in forest resource investigation, forest parameter inversion and tree 3D model reconstruction. TLS can accurately, quickly and nondestructively obtain 3D structural information of standing trees. TLS data, rather than felled wood data, were used to construct a mixed model of the taper function based on the tree effect, and the TLS data extraction and model prediction effects were evaluated to derive the stem diameter and volume. TLS was applied to a total of 580 trees in the nine larch (Larix olgensis) forest plots, and another 30 were applied to a stem analysis in Mengjiagang. First, the diameter accuracies at different heights of the stem analysis were analyzed from the TLS data. Then, the stem analysis data and TLS data were used to establish the stem taper function and select the optimal basic model to determine a mixed model based on the tree effect. Six basic models were fitted, and the taper equation was comprehensively evaluated by various statistical metrics. Finally, the optimal mixed model of the plot was used to derive stem diameters and trunk volumes. The stem diameter accuracy obtained by TLS was >98%. The taper function fitting results of these data were approximately the same, and the optimal basic model was Kozak (2002)-II. For the tree effect, a6 and a9 were used as the mixed parameters, the mixed model showed the best fit, and the accuracy of the optimal mixed model reached 99.72%.The mixed model accuracy for predicting the tree diameter was between 74.22% and 97.68%, with a volume estimation accuracy of 96.38%. Relative height 70 (RH70) was the optimum height for extraction, and the fitting accuracy of the mixed model was higher than that of the basic model.

scholarly journals Assessment of Tree Diameter Estimation Methods from Mobile Laser Scanning in a Historic Garden

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1013
Author(s):  
Enrique Pérez-Martín ◽  
Serafín López-Cuervo Medina ◽  
Tomás Herrero-Tejedor ◽  
Miguel Angel Pérez-Souza ◽  
Julian Aguirre de Mata ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Laser Scanning ◽  
Mean Squared Error ◽  
Reference Sample ◽  
3D Models ◽  
World Heritage Site ◽  
Estimation Methods ◽  
Forest Inventories ◽  
3D Data ◽  
Unesco World Heritage Site

Geo-referenced 3D models are currently in demand as an initial knowledge base for cultural heritage projects and forest inventories. The mobile laser scanning (MLS) used for geo-referenced 3D models offers ever greater efficiency in the acquisition of 3D data and their subsequent application in the fields of forestry. In this study, we have analysed the performance of an MLS with simultaneous localisation and mapping technology (SLAM) for compiling a tree inventory in a historic garden, and we assessed the accuracy of the estimates of diameter at breast height (DBH, a height of 1.30 m) calculated from three fitting algorithms: RANSAC, Monte Carlo, and Optimal Circle. The reference sample used was 378 trees from the Island Garden, a historic garden and UNESCO World Heritage site in Aranjuez, Spain. The time taken to acquire the data by MLS was 27 min 37 s, in an area of 2.38 ha. The best results were obtained with the Monte Carlo fitting algorithm, which was able to estimate the DBH of 77% of the 378 trees in the study, with a root mean squared error (RMSE) of 5.31 cm and a bias of 1.23 cm. The proposed methodology enabled a supervised detection of the trees and automatically estimated the DBH of most trees in the study, making this a useful tool for the management and conservation of a historic garden.

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