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 FORTLS: An R Package for Processing TLS Data and Estimating Stand Variables in Forest Inventories

2020 ◽  
Vol 3 (1) ◽  
pp. 38
Author(s):  
Juan Alberto Molina-Valero ◽  
Maria José Ginzo Villamayor ◽  
Manuel Antonio Novo Pérez ◽  
Juan Gabriel Álvarez-González ◽  
Fernando Montes ◽  
...  
Keyword(s):  
Data Processing ◽  
Laser Scanning ◽  
Basal Area ◽  
R Package ◽  
Efficient Manner ◽  
Cloud Data ◽  
Forest Inventories ◽  
Cost Efficient ◽  
Inference Techniques ◽  
Cloud Data Processing

Terrestrial Laser Scanning (TLS) enables rapid, automatic, and detailed 3D representation of surfaces with an easily handled scanner device. TLS, therefore, shows great potential for use in Forest Inventories (FIs). However, the lack of well-established algorithms for TLS data processing hampers operational use of the scanner for FI purposes. Here, we present FORTLS, which is an R package specifically developed to automate TLS point cloud data processing for forestry purposes. The FORTLS package enables (i) detection of trees and estimation of their diameter at breast height (dbh), (ii) estimation of some stand variables (e.g., density, basal area, mean, and dominant height), (iii) computation of metrics related to important tree attributes estimated in FIs at stand level, and (iv) optimization of plot design for combining TLS data and field measured data. FORTLS can be used with single-scan TLS data, thus, improving data acquisition and shortening the processing time as well as increasing sample size in a cost-efficient manner. The package also includes several features for correcting occlusion problems in order to produce improved estimates of stand variables. These features of the FORTLS package will enable the operational use of TLS in FIs, in combination with inference techniques derived from model-based and model-assisted approaches.

scholarly journals Evaluating the accuracy of ALS-based removal estimates against actual logging data

2020 ◽  
Vol 77 (3) ◽  
Author(s):  
Ville Vähä-Konka ◽  
Matti Maltamo ◽  
Timo Pukkala ◽  
Kalle Kärhä
Keyword(s):  
Model Prediction ◽  
Forest Inventory ◽  
Boreal Forests ◽  
Laser Scanning ◽  
Growth Models ◽  
Airborne Laser Scanning ◽  
Inventory Data ◽  
Attribute Data ◽  
Forest Inventory Data ◽  
Airborne Laser

Abstract Key message We examined the accuracy of the stand attribute data based on airborne laser scanning (ALS) provided by the Finnish Forest Centre. The precision of forest inventory data was compared for the first time with operative logging data measured by the harvester. Context Airborne laser scanning (ALS) is increasingly used together with models to predict the stand attributes of boreal forests. The information is updated by growth models. Information produced by remote sensing, model prediction, and growth simulation needs field verification. The data collected by harvesters on logging sites provide a means to evaluate and verify the accuracy of the ALS-based data. Aims This study investigated the accuracy of ALS-based forest inventory data provided by the Finnish Forest Centre at the stand level, using harvester data as the reference. Special interest was on timber assortment volumes where the quality reductions of sawlog are model predictions in ALS-based data and true realized reductions in the logging data. Methods We examined the accuracy of total volume and timber assortment volumes by comparing ALS-based data and operative logging data measured by a harvester. This was done both for clear cuttings and thinning sites. Accuracy of the identification of the dominant tree species of the stand was examined using the Kappa coefficient. Results In clear-felling sites, the total harvest removals based on ALS and model prediction had a RMSE% of 26.0%. In thinning, the corresponding difference in the total harvested removal was 42.4%. Compared to logged volume, ALS-based prediction overestimated sawlog removals in clear cuttings and underestimated pulpwood removals. Conclusion The study provided valuable information on the accuracy of ALS-based stand attribute data. Our results showed that ALS-based data need better methods to predict the technical quality of harvested trees, to avoid systematic overestimates of sawlog volume. We also found that the ALS-based estimates do not accurately predict the volume of trees removed in actual thinnings.

Economic losses in carbon forestry due to errors in inventory data

2020 ◽  
Author(s):  
Roope Ruotsalainen ◽  
Timo Pukkala ◽  
Annika Kangas ◽  
Mari Myllymäki ◽  
Petteri Packalen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Net Present Value ◽  
Nearest Neighbor ◽  
Carbon Price ◽  
Wood Products ◽  
Total Carbon ◽  
Economic Losses ◽  
Inventory Data ◽  
Forest Inventory Data

Forestry can help to mitigate climate change by storing carbon in trees, forest soils and wood products. Forest owners can be subsidized if forestry removes carbon from the atmosphere and taxed if forestry produces emissions. Errors in forest inventory data can lead to losses in net present value (NPV) if management prescriptions are selected based on erroneous data but not on correct data. This study assesses the effect of inventory errors on economic losses in forest management when the objective is to maximize the total NPV of timber production and carbon payments. Errors similar as in airborne laser scanning based forest inventory were simulated in stand attributes with a vine copula approach and nearest neighbor method. Carbon payments were based on the total carbon balance of forestry (incl. trees, soil and wood-based products) and calculations were carried out for 30 years using carbon prices of € 0, 50, 75, 100, 125 and 150 t-1. The results revealed that increasing the carbon price and decreasing the level of errors led to decreased losses in NPV. The inclusion of carbon payments for the maximization of the NPV decreased the effect of errors on the losses, which suggests that the value of collecting more accurate forest inventory data may decrease when the carbon price increases.

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.

scholarly journals Batch Extraction of Tree Stem From Natural Forest Based on Terrestrial Laser Scanning

2020 ◽  
Author(s):  
Jianpeng Zhang ◽  
Jinliang Wang ◽  
Weifeng Ma ◽  
Yicheng Liu ◽  
Qianwei Liu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Natural Forest ◽  
Point Cloud Data ◽  
Batch Extraction ◽  
Cloud Data ◽  
Pinus Densata ◽  
Picea Asperata ◽  
Tree Stem

Abstract Background: Aiming at the problems of low accuracy of tree stem extraction from point cloud data of natural forest and poor universality, a method for batch extraction of tree stem from natural forest point cloud data based on terrestrial laser scanning is proposed.Methods: First, the principal component analysis method is used to calculate the point cloud eigenvalues and eigenvectors, and the information entropy is minimized as a constraint to achieve the best neighborhood scale selection; Then combined with the spatial distribution features of the three-dimensional forest, using the Z-axis component of normal vector as the feature variable, the threshold method is used to filter out a large number of non-stem point clouds, and the 3D features are used for rough extraction of tree stem point cloud; Finally, density clustering is used to realize the precise extraction of tree stem point cloud. Results: Select the two typical representative natural forest sample plots of Pinus densata Mast. and Picea asperata Mast. in Shangri-La as the experimental data to extract stem. All the stem of the two natural forest sample plots were detected and extracted. Using the extracted individual tree stem point cloud and the true tree stem point cloud for correlation analysis, the R2 of the Pinus densata Mast. sample plot was 0.990, and the R2 of the Picea asperata Mast. sample plot with a more complex growth environment was 0.982. Conclusions: The results show that this method can well achieve batch extraction of tree stem point cloud from natural forest, and has the characteristics of high extraction accuracy and strong adaptability.

scholarly journals Application of terrestrial laser scanning in forest inventory – an overview of selected issues

2020 ◽  
Vol 81 (4) ◽  
pp. 175-194
Author(s):  
Grzegorz Krok ◽  
Bartłomiej Kraszewski ◽  
Krzysztof Stereńczak
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Sustainable Forest Management ◽  
Forest Policy ◽  
Terrestrial Laser Scanning ◽  
Remote Sensing Data ◽  
Temperate Climate ◽  
Accurate Information

Abstract Precise determination of forest resources is one of the most important tasks in conducting sustainable forest management. Accurate information about the forest’s resources allows for a better planning of current and future management as well as conservation activities. Such precise information is needed by both, individual forest managers and for developing the national forest policy. In recent years, interest in the use of remote sensing in forest inventory has significantly increased. Remote sensing allows for non-invasive measurements and the automation of data processing. The most accurate source of remote sensing data at the level of the sample plot is terrestrial laser scanning (TLS). Its use in forest inventory has been studied for about two decades. This paper aims to introduce studies on state of the art TLS technology as well as provide an overview of research conducted in stands within the temperate climate zone. This article furthermore discusses issues such as TLS data acquisition, data processing and presents results for the estimation of tree biometric features.

scholarly journals Evaluating the Potential of ALS Data to Increase the Efficiency of Aboveground Biomass Estimates in Tropical Peat–Swamp Forests

2018 ◽  
Vol 10 (9) ◽  
pp. 1344 ◽  
Author(s):  
Paul Magdon ◽  
Eduardo González-Ferreiro ◽  
César Pérez-Cruzado ◽  
Edwine Purnama ◽  
Damayanti Sarodja ◽  
...  
Keyword(s):  
Standard Error ◽  
Aboveground Biomass ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Inventory Data ◽  
Selection Scheme ◽  
Tropical Peat ◽  
Peat Swamp ◽  
Link Model ◽  
Tropical Peat Swamp

Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha − 1 with a standard error of 11.17 Mgha − 1 (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha − 1 with a slightly smaller standard error of 10.57 Mgha − 1 (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha − 1 . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precision.

scholarly journals A Tutorial on Model-Assisted Estimation with Application to Forest Inventory

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 244 ◽  
Author(s):  
Kelly S. McConville ◽  
Gretchen G. Moisen ◽  
Tracey S. Frescino
Keyword(s):  
Forest Inventory ◽  
R Package ◽  
Remotely Sensed ◽  
Variance Estimator ◽  
Inventory Data ◽  
Remotely Sensed Data ◽  
Forest Inventories ◽  
Regression Estimators ◽  
National Forest Inventories ◽  
The Many

National forest inventories in many countries combine expensive ground plot data with remotely-sensed information to improve precision in estimators of forest parameters. A simple post-stratified estimator is often the tool of choice because it has known statistical properties, is easy to implement, and is intuitive to the many users of inventory data. Because of the increased availability of remotely-sensed data with improved spatial, temporal, and thematic resolutions, there is a need to equip the inventory community with a more diverse array of statistical estimators. Focusing on generalized regression estimators, we step the reader through seven estimators including: Horvitz Thompson, ratio, post-stratification, regression, lasso, ridge, and elastic net. Using forest inventory data from Daggett county in Utah, USA as an example, we illustrate how to construct, as well as compare the relative performance of, these estimators. Augmented by simulations, we also show how the standard variance estimator suffers from greater negative bias than the bootstrap variance estimator, especially as the size of the assisting model grows. Each estimator is made readily accessible through the new R package, mase. We conclude with guidelines in the form of a decision tree on when to use which an estimator in forest inventory applications.

Sign in / Sign up

Export Citation Format

Share Document