scholarly journals Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands

2020 ◽  
Author(s):  
Tuomas Yrttimaa ◽  
Ninni Saarinen ◽  
Ville Kankare ◽  
Niko Viljanen ◽  
Jari Hynynen ◽  
...  
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Forest Structure ◽  
Point Cloud ◽  
Forest Canopy ◽  
Basal Area ◽  
Weighted Mean ◽  
Range Sensing ◽  
Close Range

Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably the root mean square error (RMSE) in Hg improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

scholarly journals Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands

2020 ◽  
Vol 9 (5) ◽  
pp. 309 ◽  
Author(s):  
Tuomas Yrttimaa ◽  
Ninni Saarinen ◽  
Ville Kankare ◽  
Niko Viljanen ◽  
Jari Hynynen ◽  
...  
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Forest Canopy ◽  
Basal Area ◽  
Point Clouds ◽  
Stem Volume ◽  
Weighted Mean ◽  
Range Sensing ◽  
Close Range

Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests

2013 ◽  
Vol 43 (8) ◽  
pp. 731-741 ◽  
Author(s):  
Christoph Straub ◽  
Christoph Stepper ◽  
Rudolf Seitz ◽  
Lars T. Waser
Keyword(s):  
Point Cloud ◽  
Forest Canopy ◽  
Complex Structure ◽  
Mixed Forest ◽  
Basal Area ◽  
Deciduous Tree ◽  
Stereo Images ◽  
Timber Volume ◽  
European Forests ◽  
Forest Attributes

Current technical advances in the field of digital photogrammetry demonstrate the great potential of automatic image matching for deriving dense surface measurements of the forest canopy. In contrast to airborne laser scanning (ALS), aerial stereo images are updated more regularly by national or regional mapping agencies in several countries. Frequently, ALS-based terrain models (DTMs) are available, and thus photogrammetric canopy heights can be derived. However, currently, there is little knowledge as to how accurately forest attributes can be modeled using the aerial stereo images acquired by these official, regular aerial surveys, especially for mixed forests in central Europe. Thus, a photogrammetric point cloud derived from UltraCamX stereo images in combination with an ALS-DTM and a classification of coniferous and deciduous tree regions (based on orthoimages) was used to create a stratified estimation of timber volume and basal area in a mixed forest in Germany. Suitable models were derived at the plot level using explanatory variables from the photogrammetric point cloud (which was normalized using an ALS-DTM). The prior stratification of conifer- and deciduous-dominated field plots slightly improved the estimation accuracy. The results verify that stereo images can be an alternative to ALS data for modeling key forest attributes, even in mixed central European forests with complex structure.

scholarly journals Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

2020 ◽  
Author(s):  
Tuomas Yrttimaa ◽  
Ville Luoma ◽  
Ninni Saarinen ◽  
Ville Kankare ◽  
Samuli Junttila ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Point Cloud ◽  
Laser Scanning ◽  
Structural Changes ◽  
Terrestrial Laser Scanning ◽  
Basal Area ◽  
Forest Stands ◽  
Weighted Mean ◽  
Delta G ◽  
Forest Conditions

Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS in quantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindrical characteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and forest stands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99-1.22 cm for diameter at breast height (Δdbh), 44.14-55.49 cm2 for basal area (Δg), and 1.91-4.85 m for tree height (Δh). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60-1.13 cm was recorded for changes in basal area-weighted mean diameter (ΔDg), 0.81-2.26 m for changes in basal area-weighted mean height (ΔHg), 1.40-2.34 m2/ha for changes in mean basal area (ΔG), and 74-193 n/ha for changes in the number of trees per hectare (ΔTPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.

Mixtures of airborne lidar-based approaches improve predictions of forest structure

2021 ◽  
Author(s):  
Ryan Christopher Blackburn ◽  
Robert Buscaglia ◽  
Andrew Sanchez Meador
Keyword(s):  
Feature Selection ◽  
New Mexico ◽  
Random Forests ◽  
Forest Structure ◽  
Point Cloud ◽  
Basal Area ◽  
Airborne Lidar ◽  
Forest Attributes ◽  
Field Plots ◽  
Paired T Test

The most common method for modeling forest attributes with airborne lidar, the area-based approach, involves summarizing the point cloud of individual plots and relating this to attributes of interest. Tree- and voxel-based approaches have been considered as alternatives to the area-based approach but are rarely considered in an area-based context. We estimated three forest attributes: basal area, overstory biomass, and volume, across 1,680 field plots in Arizona and New Mexico. Variables from the three lidar approaches (area, tree, voxel) were created for each plot. Random forests were estimated using subsets of variables based on each individual lidar approach and mixtures of each approach. Boruta feature selection was performed on variable subsets, including the mixture of all lidar-approach predictors (KS-Boruta). A corrected paired t-test was utilized to compare six validated models (area-Boruta, tree-Boruta, voxel-Boruta, KS-Boruta, KS-all, ridge-all) for each forest attribute. Based on significant reductions in error (SMdAPE), basal area and biomass were best modeled with KS-Boruta while volume was best modeled with KS-all. Analysis of variable importance shows voxel-based predictors are critical for the prediction of the three forest attributes. This study highlights the importance of multi-resolution voxel-based variables for modeling forest attributes in an area-based context.

scholarly journals Characterization of a Lhcb5 cDNA from Scots Pine (Pinus sylvestris)

1994 ◽  
Vol 106 (4) ◽  
pp. 1695-1696 ◽  
Author(s):  
S. Jansson ◽  
P. Gustafsson
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine

Characterization of the Proteinases Present in Germinating Seeds of Scots Pine, Pinus sylvestris

1978 ◽  
Vol 42 (2) ◽  
pp. 252-256 ◽  
Author(s):  
M. Aulikki SALMIA ◽  
SEIJA A. NYMAN ◽  
JUHANI J. MIKOLA
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Germinating Seeds

scholarly journals Amazon Forest Structure from IKONOS Satellite Data and the Automated Characterization of Forest Canopy Properties

Biotropica ◽  
2007 ◽  
Vol 40 (2) ◽  
pp. 141-150 ◽  
Author(s):  
Michael Palace ◽  
Michael Keller ◽  
Gregory P. Asner ◽  
Stephen Hagen ◽  
Bobby Braswell
Keyword(s):  
Satellite Data ◽  
Forest Structure ◽  
Forest Canopy ◽  
Amazon Forest

scholarly journals Growth Differences in the Progeny Trials of the Scots Pine (Pinus sylvestris L.)

2009 ◽  
Vol 50 (1) ◽  
pp. 84-97
Author(s):  
Malle Kurm ◽  
Andres Kiviste ◽  
Ursula Kaur ◽  
Tiit Maaten
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Basal Area ◽  
Data Set ◽  
Mother Tree ◽  
Single Tree ◽  
Plus Tree ◽  
Growth Differences ◽  
Forest District ◽  
Area And Volume

Kasvuomaduste erinevused hariliku männi (Pinus sylvestrisL.) järglaskatsetesThe progeny trials of Scots pine founded in Järvselja Training and Experimental Forest District in 1967 and 1968 by E. Pihelgas were analysed to estimate the dependence of progeny growth variables on mother tree or mother stand origin and age. Our progeny data set consists of 4622 DBH and 486 height measurements of the 41- and 42-year-old progeny trials. In 2008 and in 1995 all trees of the progeny trial were callipered and height of nine sample trees was measured on each plot. The analysis was carried out with the SAS/GLM procedure. It was proved that height of inland-originated progeny stands was significantly higher than height of progeny stands originated from maritime regions. Plus-tree progeny stands exceeded single tree and mixture-of-seeds progeny stands in height, diameter, basal area and volume.

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