scholarly journals Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology

2020 ◽  
Vol 12 (9) ◽  
pp. 1509 ◽  
Author(s):  
Christoph Gollob ◽  
Tim Ritter ◽  
Arne Nothdurft
Keyword(s):  
Data Acquisition ◽  
Forest Inventory ◽  
High Speed ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Acquisition Time ◽  
Inventory Data ◽  
Detection Rates ◽  
Forest Inventory Data ◽  
Wide Range

The use of new and modern sensors in forest inventory has become increasingly efficient. Nevertheless, the majority of forest inventory data are still collected manually, as part of field surveys. The reason for this is the sometimes time-consuming and incomplete data acquisition with static terrestrial laser scanning (TLS). The use of personal laser scanning (PLS) can reduce these disadvantages. In this study, we assess a new personal laser scanner and compare it with a TLS approach for the estimation of tree position and diameter in a wide range of forest types and structures. Traditionally collected forest inventory data are used as reference. A new density-based algorithm for position finding and diameter estimation is developed. In addition, several methods for diameter fitting are compared. For circular sample plots with a maximum radius of 20 m and lower diameter at breast height (dbh) threshold of 5 cm, tree mapping showed a detection of 96% for PLS and 78.5% for TLS. Using plot radii of 20 m, 15 m, and 10 m, as well as a lower dbh threshold of 10 cm, the respective detection rates for PLS were 98.76%, 98.95%, and 99.48%, while those for TLS were considerably lower (86.32%, 93.81%, and 98.35%, respectively), especially for larger sample plots. The root mean square error (RMSE) of the best dbh measurement was 2.32 cm (12.01%) for PLS and 2.55 cm (13.19%) for TLS. The highest precision of PLS and TLS, in terms of bias, were 0.21 cm (1.09%) and −0.74 cm (−3.83%), respectively. The data acquisition time for PLS took approximately 10.96 min per sample plot, 4.7 times faster than that for TLS. We conclude that the proposed PLS method is capable of efficient data capture and can detect the largest number of trees with a sufficient dbh accuracy.

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 INDOOR MAPPING OF A COMPLEX CULTURAL HERITAGE SCENE USING TLS AND HMLS LASER SCANNING

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 605-612
Author(s):  
V. E. Oniga ◽  
A. I. Breaban ◽  
E. I. Alexe ◽  
C. Văsii
Keyword(s):  
Standard Deviation ◽  
Cultural Heritage ◽  
Long Range ◽  
Interior Design ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Acquisition Time ◽  
Wide Range

Abstract. Indoor mapping and modelling is an important research subject with application in a wide range of domains including interior design, real estate, cultural heritage conservation and restoration. There are multiple sensors applicable for 3D indoor modelling, but the laser scanning technique is frequently used because of the acquisition time, detailed information and accuracy. In this paper, the efficiency of the Maptek I-Site 8820 terrestrial scanner, which is a long-range laser scanner and the accuracy of a HMLS point cloud acquired with a mobile scanner, namely GeoSlam Zeb Horizon were tested for indoor mapping. Aula Magna “Carmen Silva” of the “Gheorghe Asachi” Technical University of Iasi is studied in the current paper since the auditorium interior creates a distinct environment that combines complex geometric structures with architectural lighting and for preserving its great cultural value, the monument has a national historical significance. The registration process of the TLS point clouds was done using two methods: a semi-automatic one with artificial targets and a completely automatic one, based on Iterative Closest Point (ICP) algorithm. The resulted TLS point cloud was analysed in relation to the HMLS point cloud by computing the M3C2 (Multiscale Model to Model Cloud Comparison), obtaining a standard deviation of 2.1 cm and by investigating the Hausdorff distances from which resulted a standard deviation (σ) of 1.6 cm. Cross-sections have been extracted from the HMLS and TLS point clouds and after comparing the sections, 80% of the sigma values are less or equal to 1 cm. The results show high potential of using HMLS and also a long-range laser scanner for 3D modelling of complex scenes, the occlusion effect in the case of TLS being only 5% of the scanned area.

scholarly journals Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology

2021 ◽  
Vol 13 (16) ◽  
pp. 3129
Author(s):  
Christoph Gollob ◽  
Tim Ritter ◽  
Ralf Kraßnitzer ◽  
Andreas Tockner ◽  
Arne Nothdurft
Keyword(s):  
Data Acquisition ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Point Clouds ◽  
Acquisition Time ◽  
Ecosystem Monitoring ◽  
Individual Tree ◽  
3D Point Clouds ◽  
Apple Ipad ◽  
Laser Scanners

The estimation of single tree and complete stand information is one of the central tasks of forest inventory. In recent years, automatic algorithms have been successfully developed for the detection and measurement of trees with laser scanning technology. Nevertheless, most of the forest inventories are nowadays carried out with manual tree measurements using traditional instruments. This is due to the high investment costs for modern laser scanner equipment and, in particular, the time-consuming and incomplete nature of data acquisition with stationary terrestrial laser scanners. Traditionally, forest inventory data are collected through manual surveys with calipers or tapes. Practically, this is both labor and time-consuming. In 2020, Apple implemented a Light Detection and Ranging (LiDAR) sensor in the new Apple iPad Pro (4th Gen) and iPhone Pro 12. Since then, access to LiDAR-generated 3D point clouds has become possible with consumer-level devices. In this study, an Apple iPad Pro was tested to produce 3D point clouds, and its performance was compared with a personal laser scanning (PLS) approach to estimate individual tree parameters in different forest types and structures. Reference data were obtained by traditional measurements on 21 circular forest inventory sample plots with a 7 m radius. The tree mapping with the iPad showed a detection rate of 97.3% compared to 99.5% with the PLS scans for trees with a lower diameter at a breast height (dbh) threshold of 10 cm. The root mean square error (RMSE) of the best dbh measurement out of five different dbh modeling approaches was 3.13 cm with the iPad and 1.59 cm with PLS. The data acquisition time with the iPad was approximately 7.51 min per sample plot; this is twice as long as that with PLS but 2.5 times shorter than that with traditional forest inventory equipment. In conclusion, the proposed forest inventory with the iPad is generally feasible and achieves accurate and precise stem counts and dbh measurements with efficient labor effort compared to traditional approaches. Along with future technological developments, it is expected that other consumer-level handheld devices with integrated laser scanners will also be developed beyond the iPad, which will serve as an accurate and cost-efficient alternative solution to the approved but relatively expensive TLS and PLS systems. Such a development would be mandatory to broadly establish digital technology and fully automated routines in forest inventory practice. Finally, high-level progress is generally expected for the broader scientific community in forest ecosystem monitoring, as the collection of highly precise 3D point cloud data is no longer hindered by financial burdens.

scholarly journals Applying quantitative structure models to plot-based terrestrial laser data to assess dendrometric parameters in dense mixed forests

2018 ◽  
Vol 27 (1) ◽  
pp. e004 ◽  
Author(s):  
Chiara Torresan ◽  
Ugo Chiavetta ◽  
Jan Hackenberg
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Tree Height ◽  
Buffer Zone ◽  
National Forest Inventory ◽  
Volume Estimation ◽  
Quantitative Structure ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Laser Data

Aim of study: To assess terrestrial laser scanning (TLS) accuracy in estimating biometrical forest parameters at plot-based level in order to replace manual survey for forest inventory purposes.Area of study: Monte Morello, Tuscany region, ItalyMaterial and methods: In 14 plots (10 m radius) in dense Mediterranean mixed conifer forests, diameter at breast height (DBH) and height were measured in Summer 2016. Tree volume was computed using the second Italian National Forest Inventory (INFC II) equations. TLS data were acquired in the same plots and quantitative structure models (QSMs) were applied to TLS data to compute dendrometric parameters. Tree parameters measured in field survey, i.e. DBH, height, and computed volume, were compared to those resulting from TLS data processing. The effect of distance from the plot boundary in the accuracy of DBH, height and volume estimation from TLS data was tested.Main results: TLS-derived DBH showed a good correlation with the traditional forest inventory data (R2=0.98, RRMSE=7.81%), while tree height was less correlated with the traditional forest inventory data (R2=0.60, RRMSE=16.99%). Poor agreement was observed when comparing the volume from TLS data with volume estimated from the INFC II prediction equations.Research highlights: The study demonstrated that the application of QSM to plot-based terrestrial laser data generates errors in plots with high density of coniferous trees. A buffer zone of 5 m would help reduce the error of 35% and 42% respectively in height estimation for all trees and in volume estimation for broadleaved trees.

Large-scale estimation of aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data

2016 ◽  
Vol 186 ◽  
pp. 626-636 ◽  
Author(s):  
Liviu Theodor Ene ◽  
Erik Næsset ◽  
Terje Gobakken ◽  
Ernest William Mauya ◽  
Ole Martin Bollandsås ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Large Scale ◽  
National Forest Inventory ◽  
National Forest ◽  
Miombo Woodlands ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Airborne Laser
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