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 Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands

2020 ◽  
Vol 12 (20) ◽  
pp. 3328
Author(s):  
Mohammad Imangholiloo ◽  
Ninni Saarinen ◽  
Markus Holopainen ◽  
Xiaowei Yu ◽  
Juha Hyyppä ◽  
...  
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Sustainable Forest Management ◽  
Single Channel ◽  
Point Clouds ◽  
Tree Density ◽  
Airborne Laser Scanning ◽  
Tree Species Composition ◽  
Coniferous Trees ◽  
Airborne Laser

Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and tree species composition in seedling stands have created challenges for capturing this information using sensors providing sparse point densities that do not have the ability to penetrate canopy gaps or provide spectral information. Therefore, multispectral airborne laser scanning (mALS) systems providing dense point clouds coupled with multispectral intensity data theoretically offer advantages for the characterization of seedling stands. The aim of this study was to investigate the capability of Optech Titan mALS data to characterize seedling stands in leaf-off and leaf-on conditions, as well as to retrieve the most important forest inventory attributes, such as distinguishing deciduous from coniferous trees, and estimating tree density and height. First, single-tree detection approaches were used to derive crown boundaries and tree heights from which forest structural attributes were aggregated for sample plots. To predict tree species, a random forests classifier was trained using features from two single-channel intensities (SCIs) with wavelengths of 1550 (SCI-Ch1) and 1064 nm (SCI-Ch2), and multichannel intensity (MCI) data composed of three mALS channels. The most important and uncorrelated features were analyzed and selected from 208 features. The highest overall accuracies in classification of Norway spruce, birch, and nontree class in leaf-off and leaf-on conditions obtained using SCI-Ch1 and SCI-Ch2 were 87.36% and 69.47%, respectively. The use of MCI data improved classification by up to 96.55% and 92.54% in leaf-off and leaf-on conditions, respectively. Overall, leaf-off data were favorable for distinguishing deciduous from coniferous trees and tree density estimation with a relative root mean square error (RMSE) of 37.9%, whereas leaf-on data provided more accurate height estimations, with a relative RMSE of 10.76%. Determining the canopy threshold for separating ground returns from vegetation returns was found to be critical, as mapped trees might have a height below one meter. The results showed that mALS data provided benefits for characterizing seedling stands compared to single-channel ALS systems.

scholarly journals Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data

2018 ◽  
Vol 51 (1) ◽  
pp. 336-351 ◽  
Author(s):  
Øivind Due Trier ◽  
Arnt-Børre Salberg ◽  
Martin Kermit ◽  
Øystein Rudjord ◽  
Terje Gobakken ◽  
...  
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Airborne Laser

Effects of imaging conditions on crown diameter measurements from high-resolution aerial images

2006 ◽  
Vol 36 (5) ◽  
pp. 1206-1217 ◽  
Author(s):  
Antti Mäkinen ◽  
Ilkka Korpela ◽  
Timo Tokola ◽  
Annika Kangas
Keyword(s):  
Linear Regression ◽  
Root Mean Square ◽  
Tree Species ◽  
Linear Regression Analysis ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Mean Square ◽  
Crown Diameter ◽  
Calibration Models ◽  
Mean Square Errors

Imaging geometry, the structure of the forest, and certain tree properties can cause inaccuracy in image measurements of the crown dimensions of individual trees. Measurement error of the crown diameter was studied in relation to various factors to explain this error. A secondary aim was to generate calibration models for improving the accuracy of crown diameter image measurements. The crown diameters of a total of 715 sample trees in southern Finland were measured in the field and from aerial photographs at scales 1:6000, 1 : 12 000, and 1 : 16 000. The photo grammetric image measurement seemed to systematically underestimate the true crown diameter, and the major factor affecting the bias was tree species. The mean underestimation varied from 0.30 to 0.80 m, with root mean square errors of 0.95–1.10 m depending on the tree species. Linear regression analysis was employed to define the factors that had an effect on the image measurements, and calibration models in the form of linear regression models were generated. The calibration models worked reasonably well, and the root mean square error for the calibrated observations decreased by 22% for Scots pine (Pinus sylvestris L.), 53% for Norway spruce (Picea abies (L.) Karst), and 47% for silver birch (Betula pendula Roth).

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