Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus)

Zielewska-Büttner, Katarzyna; Heurich, Marco; Müller, Jörg; Braunisch, Veronika

doi:10.3390/rs10121972

Cited by 26 publications

(26 citation statements)

References 80 publications

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“…It is known that gaps are related to forest biodiversity [63] and the size influences forest dwelling species, for instance ants [64] which are in turn related to TreMs. In addition remote sensing techniques have been used to detect habitat thresholds for indicator species as the three-toed woodpecker (Picoides tridactylus) based on canopy properties such as the amount of deadwood crown size [19], which will eventually result in canopy gaps and points in a similar direction as our results. Therefore, we could suggest to focus selection activities of habitat trees to forest areas with a certain degree of gaps in the canopy.…”

Section: Discussionsupporting

confidence: 56%

“…The remote sensing (RS) discipline mainly describes forest structure by summarizing variables that can 2 of 20 be determined from sensor data. These include maximum height, quantiles of height from surface models or point clouds, point densities, or structural complexity indices, tree counts, biomass estimates and many more [3,[13][14][15][16][17][18][19][20][21][22][23][24][25][26]. If the aim is to provide a broader perspective of the forest structure, metrics such as the Stand Structural Complexity Index (SSCI) [4] are for instance applicable.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

2020

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The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown deadwood, are an established tool to quantify the suitability of habitat trees for biodiversity conservation. The aim to predict TreMs based on remote sensing (RS) parameters is supposed to assist a more objective and efficient selection of retention elements. The RS parameters were collected by the use of terrestrial laser scanning as well as unmanned aerial vehicles structure from motion point cloud generation to provide a 3D distribution of plant tissue. Data was recorded on 135 1-ha plots in Germany. Statistical models were used to test the influence of 28 RS predictors, which described TreM richness (R 2 : 0.31) and abundance (R 2 : 0.31) in moderate precision and described a deviance of 44% for the abundance and 38% for richness of TreMs. Our results indicate that multiple RS techniques can achieve moderate predictions of TreM occurrence. This method allows a more efficient and objective selection of retention elements such as habitat trees that are keystone features for biodiversity conservation, even if it cannot be considered a full replacement of TreM inventories due to the moderate statistical relationship at this stage.3 of 20 regeneration, promotion of mixed and uneven-aged stands, and retention of habitat trees [46]. The plots were selected so that water bodies, roads, power lines etc. were excluded but smaller man-made objects like raised hides for hunting, skid tracks, hiking paths etc. could be included. The plots cover a range of altitudes between 434 m and 1334 m a.s.l. and the variance of the slopes is between 1 and 34 • . Eighty-one of the plots are located in formally protected areas of different categories from water protection areas to strict reserves with different levels of restriction on active forest management.

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Section: Discussionsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

2020

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“…The main objectives and the novelties of the current study were (i) to optimise tree segmentation based on normalized cut using two different stopping criteria for conifers and broadleaf trees and (ii) to map conifers, broadleaf trees, standing dead trees, and snags in a large temperate forest 924 km 2 in size situated in Šumava National Park and Bavarian Forest National Park. Thereby, we verified the statistical performance of the methods in a large-scale experiment and provided spatial information about the distribution of trees usable for area-wide information on the abundance and distribution of key habitat structures [28].…”

Section: Introductionmentioning

confidence: 82%

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

Krzystek

Serebryanyk²,

Schnörr

et al. 2020

Remote Sensing

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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.

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“…Tree deaths are significant in a circular ecology, since they provide resources to many organisms [1] and habitat to many mammals and birds [2]. Zielewska-Büttner et al [3] showed that the abundance of dead standing trees (snags) was the most important predictor of woodpecker habitat sections, stressing this way the importance of dead wood. At the same time, decaying wood is decreased at managed forests and this is a thread for organisms whose lives depends on dead wood [4].…”

Section: Introductionmentioning

confidence: 99%

Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations

Miltiadou

Αγαπίου

Aracil

et al. 2020

Forests

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In southern Australia, many native mammals and birds rely on hollows for sheltering, while hollows are more likely to exist on dead trees. Therefore, detection of dead trees could be useful in managing biodiversity. Detecting dead standing (snags) versus dead fallen trees (Coarse Woody Debris—CWD) is a very different task from a classification perspective. This study focuses on improving detection of dead standing eucalypt trees from full-waveform LiDAR. Eucalypt trees have irregular shapes making delineation of them challenging. Additionally, since the study area is a native forest, trees significantly vary in terms of height, density and size. Therefore, we need methods that will be resistant to those challenges. Previous study showed that detection of dead standing trees without tree delineation is possible. This was achieved by using single size 3D-windows to extract structural features from voxelised full-waveform LiDAR and characterise dead (positive samples) and live (negative samples) trees for training a classifier. This paper adds on by proposing the usage of multi-scale 3D-windows for tackling height and size variations of trees. Both the single 3D-windows approach and the new multi-scale 3D-windows approach were implemented for comparison purposes. The accuracy of the results was calculated using the precision and recall parameters and it was proven that the multi-scale 3D-windows approach performs better than the single size 3D-windows approach. This open ups possibilities for applying the proposed approach on other native forest related applications.

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Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus)

Cited by 26 publications

References 80 publications

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

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

Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations

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