scholarly journals Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

2020 ◽  
Vol 12 (4) ◽  
pp. 661 ◽  
Author(s):  
Peter Krzystek ◽  
Alla Serebryanyk ◽  
Claudius Schnörr ◽  
Jaroslav Červenka ◽  
Marco Heurich
Keyword(s):  
Tree Species ◽  
National Park ◽  
Large Scale ◽  
Dead Wood ◽  
Multispectral Imagery ◽  
Normalized Cut ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
Bavarian Forest

Knowledge of forest structures—and of dead wood in particular—is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species—subdivided into conifers and broadleaf trees—and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in Šumava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for Šumava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood—located in areas of pest infestation—is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.

scholarly journals Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest

2019 ◽  
Vol 11 (22) ◽  
pp. 2614 ◽  
Author(s):  
Nina Amiri ◽  
Peter Krzystek ◽  
Marco Heurich ◽  
Andrew Skidmore
Keyword(s):  
Tree Species ◽  
Feature Selection Method ◽  
Silver Fir ◽  
Maximum Point ◽  
Individual Tree ◽  
Dead Trees ◽  
Multi Wavelength ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
532 Nm

Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.

scholarly journals The structure of forest stands in the Tatra National Park: The results of 2016–2017 inventory

2019 ◽  
Vol 80 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Jan Bodziarczyk ◽  
Jerzy Szwagrzyk ◽  
Tomasz Zwijacz-Kozica ◽  
Antoni Zięba ◽  
Janusz Szewczyk ◽  
...  
Keyword(s):  
Picea Abies ◽  
Fagus Sylvatica ◽  
National Park ◽  
Forest Stands ◽  
Protection Zone ◽  
Dead Trees ◽  
Standing Dead ◽  
Elevation Zone ◽  
Standing Dead Trees ◽  
Tatra National Park

Abstract The composition and structure of forest stands in the Tatra National Park were examined using data gathered in 2016 and 2017 from 617 circular sample plots (0.05 ha each). The diameter at breast height of all living trees, standing dead trees, snags, and wind throws was measured along with diameters and lengths of fallen logs within the plot boundaries. Tree height was measured for all living trees within the core (0.01 ha) of the sample plots. Using the obtained data, height-diameter curves were calculated for all major tree species and in the case of spruce, the height-diameter relationships were also calculated separately for each of the three elevation zones (up to 1200 m, between 1200 and 1400 m, above 1400 m). For each elevation zone and park protection zone, we also determined the volumes of live and dead trees. The volume of living trees in the Tatra National Park amounted to 259 m3/ha, which was higher than the volume of dead trees (176 m3/ha). Snags constituted the largest part of the dead wood whilst over 97% of the standing dead trees were spruce Picea abies. Among living trees, the share of spruce ranged from 81% in the low elevation zone to 98% in the middle zone. Other significant species in the lower zone were Abies alba (11%) and Fagus sylvatica (4.5%), while in the middle and upper elevation zones only Sorbus aucuparia occurred in significant numbers. Furthermore, in the lower elevation zone, Fagus sylvatica was the only species displaying significantly higher volumes in the ‘strict protection’ zone compared to the other park areas. In the ‘landscape protection’ zone, Picea abies was the most dominant species and the share of other species in the lowest elevation zones calculated based on tree density was smaller than calculated based on tree volume, indicating problems with stand conversion from spruce monoculture to mixed forest.

scholarly journals CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++

2020 ◽  
Vol V-2-2020 ◽  
pp. 203-210
Author(s):  
S. Briechle ◽  
P. Krzystek ◽  
G. Vosselman
Keyword(s):  
Neural Network ◽  
Tree Species ◽  
Deep Neural Network ◽  
Lidar Data ◽  
Individual Tree ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
Multiple Tree

Abstract. Knowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice.

scholarly journals The Effects of a Western Spruce Budworm Outbreak on the Dead Wood Component in Relation to Ownership in Forests of Eastern Oregon

2010 ◽  
Vol 25 (4) ◽  
pp. 176-180 ◽  
Author(s):  
David Azuma
Keyword(s):  
Coarse Woody Debris ◽  
Dead Wood ◽  
Woody Debris ◽  
Spruce Budworm ◽  
Western Spruce Budworm ◽  
Wood Component ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
The Dead

Abstract Forest Inventory and Analysis data were used to investigate the effects of a severe western spruce budworm outbreak on the dead wood component of forests in 11 counties of eastern Oregon for two time periods. The ownership and the level of damage (as assessed by aerial surveys) affected the resulting down woody material and standing dead trees. The pattern of coarse woody debris with respect to ownership and management intensity remained consistent into the next 10-year period. Harvesting tended to lower the amount of coarse woody debris on private forests. Federally managed forests had more standing dead trees than private lands, with more in the reserved than nonreserved areas. There was a reduction in the number of standing dead trees between the two periods.

Wie viel Totholz braucht der Wald? | Dead wood in managed forests: how much is enough?

2004 ◽  
Vol 155 (2) ◽  
pp. 31-37 ◽  
Author(s):  
Rita Bütler ◽  
Rodolphe Schlaepfer
Keyword(s):  
Boreal Forests ◽  
Dead Wood ◽  
Sustainable Forest Management ◽  
Habitat Preferences ◽  
Bird Species ◽  
Threshold Level ◽  
Bioenergetic Model ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees

Dead wood is of paramount importance for forest biodiversity. For this reason it was adopted as an indicator for sustainable forest management by the Ministerial Conference on the protection of forests in Europe. This paper aims to answer the question of how much dead wood is necessary for the maintenance of biodiversity in sub-alpine spruce forest ecosystems. For this purpose we studied the habitat preferences of the three-toed woodpecker, a bird species that depends heavily on dead trees. Previous ecological studies had already demonstrated that this woodpecker is an indicator of spruce forests with a high degree of naturalness and biodiversity. Our field study in Swiss sub-Alpine spruce and Swedish boreal forests showed that, below a threshold level of about 20 m3 standing dead trees per ha, the probability of finding these woodpeckers drastically decreases. Similar results were obtained using a bioenergetic model, which calculated the energy requirements of this insectivorous woodpecker. Based on the results, our recommendation is to ensure a scattering of dead-wood rich areas in forest landscapes. Each area should cover about one square kilometre and have a mean of 5% of standing dead trees (≥ 18 m3 ha–1), and a total of approx. 9% of dead wood(≥ 33 m3 ha–1 standing and fallen).

Effect of minimum diameter at breast height and standing dead wood field measurements on the accuracy of ALS-based forest inventory

2015 ◽  
Vol 45 (10) ◽  
pp. 1280-1288 ◽  
Author(s):  
Juha Keränen ◽  
Jussi Peuhkurinen ◽  
Petteri Packalen ◽  
Matti Maltamo
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Dead Wood ◽  
Minimum Diameter ◽  
Living Tree ◽  
Breast Height ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
Young Forests

Where airborne laser scanning (ALS) measures the entire aboveground vegetation, the target of a stand-level forest inventory is usually the living tree stock above a given diameter but excluding standing dead trees. The aim here was to investigate the effects of varying field-measured minimum diameters (3–10 cm) and standing dead wood on ALS-based forest inventories. The characteristics considered in this case were volume, basal area, number of stems, mean diameter, and mean height for each species, as well as the total growing stock and the total aboveground biomass. The field data comprised measurements of all trees that were ≥3 cm at breast height (1.3 m) on 601 sample plots located in pine-dominated managed forests in eastern Finland. The results showed that the minimum diameter had a significant effect on the estimates obtained in young forests, for which the three smallest minimum diameter datasets (3, 4, and 5 cm) gave the most accurate estimates. Minimum diameter had no marked influence in the case of middle-aged or mature forests. The inclusion of standing dead trees did not have any effect on the estimates of living tree characteristics. The effect of minimum diameter is minor where large-area inventory applications are concerned; however, especially from a silvicultural point of a view, a minimum diameter of 3 cm should be employed in young forests, for which a large proportion of the tree stock usually consists of small trees, i.e., with diameters of <5 cm.

scholarly journals Features of coarse woody debris volume formation in fresh sudibrova conditions in Zmiini islands tract of Kaniv Nature Reserve

2021 ◽  
pp. 102-112
Author(s):  
O. Chornobrov
Keyword(s):  
Scots Pine ◽  
Tree Species ◽  
Dead Wood ◽  
Nature Reserve ◽  
Woody Debris ◽  
Wood Volume ◽  
Dead Trees ◽  
Norway Maple ◽  
Standing Dead ◽  
Common Oak

Dead wood (woody debris) is an important component of forest ecosystems. It performs a number of ecological and environmental functions. The article studies the peculiarities of the formation of coarse wood detritus volume and its qualitative structure in forests in the conditions of fresh sudibrova of the Zmiiini Islands tract of Kaniv Nature Reserve. The study of dead wood was carried out in 140-year-old pine-oak forests of natural origin on a permanent sample plot (0.24 ha) by identifying and measuring of standing and lying deadwood components. It was found that dead wood in the forest ecosystem was formed due to the dying of trees of five species: common oak (Quercus robur L.), Scots pine (Pinus sylvestris L.), Norway maple (Acer platanoides L.), small-leaved lime (Tilia cordata Mill.) and common hornbeam (Carpinus betulus L.), and has a volume 56.3 m3·ha–1. Dead wood volume is dominated by standing dead trees — 82.1%, and the share of lying dead wood, respectively, is 17.9%. The main part of dead wood volume is formed by two tree species — common oak and Scots pine, the share of which together is 94.3%. Common oak and Scots pine is characterized by a predominance of standing dead wood, while for other tree species — lying dead wood. In general, dead wood is formed by detritus of I–IV classes of destruction, at the same time detritus of class II decomposition has a significant advantage (70.5%), recently dead wood has a much smaller share (I class, 24.8%), and other classes of destruction have insignificant shares, which together do not exceed 5.0%. No woody detritus of the last (V) class of destruction was detected. Volume of standing dead wood is 46.2 m3·ha–1, and is formed by whole and broken dead trees. In terms of species composition, common oak has a significant advantage (74.5%), Scots pine has a much smaller share (25.1%), and the share of Norway maple is insignificant (0.4%). The total standing dead wood volume is dominated by wood of class II destruction (33.0 m3·ha–1, 71.4%) compared with class I (13.2 m3·ha–1, 28.6%). Lying dead wood is represented by four classes of destruction (I–IV), however, no woody debris was found at the late (last) stage of decomposition (class V). In terms of volume, the second class of destruction has an absolute advantage (6.7 m3·ha–1, 66.3%), much less class III detritus (2.3 m3·ha–1, 22.8%). Lying dead wood of common oak is represented by all four classes of destruction, among which III (40.5%) and I (33.3%) classes predominate. Lying dead wood of other tree species is characterized by the predominance of II or III classes of destruction. The main factors in the formation of woody detritus in the pine-oak forest in the Zmiiini Islands tract could be the impact of adverse climatic conditions (long periods without precipitation in summer), which led to the weakening of individual trees and their death, gusts of wind that broke individual tree trunks, low-intensity snow breaks, and the influence of biotic factors (insects, pathogens).

scholarly journals Inventory of dead wood in the Kněhyně-Čertův mlýn National Nature Reserve, the Moravian-Silesian Beskids

2012 ◽  
Vol 50 (No. 4) ◽  
pp. 171-180 ◽  
Author(s):  
L. Jankovský ◽  
D. Lička ◽  
K. Ježek
Keyword(s):  
Forest Ecosystems ◽  
Dead Wood ◽  
Nature Reserve ◽  
Mountain Forest ◽  
National Nature Reserve ◽  
Dead Trees ◽  
Decomposition Processes ◽  
Standing Dead ◽  
Recorded Data ◽  
Standing Dead Trees

In four permanent experimental plots, dead wood was inventory under conditions of mountain forest ecosystems of the Kněhyně-Čertův ml&yacute;n National Nature Reserve, the Moravian-Silesian Beskids. Down woody material, standing dead trees as well as living trees were recorded. Data obtained were used to determine partial and summarized volumes of dead wood and its proportion in a living stand. Each of the surveyed areas was described not only from the viewpoint of mensuration but also with respect to subsequently carried out studies of biodiversity of wood mycoflora, succession of decomposition processes, natural regeneration on the dead wood etc. Mean volume of dead wood and a share in the total standing volume reaches 132 m<sup>3</sup>/ha(40%), of this 86 m<sup>3</sup>/hais down woody material and 46 m<sup>3</sup>/havolume of standing dead trees. Mean total standing volume per ha amounted to 332 m<sup>3</sup>/ha in the region of the Kněhyně-Čertův ml&yacute;n NNR.

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