Detection of arboreal feeding signs by Asiatic black bears: effects of hard mast production at individual tree and regional scales

2018 ◽  
Vol 305 (4) ◽  
pp. 223-231 ◽  
Author(s):  
K. Tochigi ◽  
T. Masaki ◽  
A. Nakajima ◽  
K. Yamazaki ◽  
A. Inagaki ◽  
...  
Keyword(s):  
Black Bears ◽  
Individual Tree ◽  
Mast Production

scholarly journals Does hard mast production affect patterns of cementum annuli formation in premolar teeth of Asian black bears (Ursus thibetanus)?

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0211561
Author(s):  
Kahoko Tochigi ◽  
Yukino Aoki ◽  
Tetsuya Maruyama ◽  
Koji Yamazaki ◽  
Chinatsu Kozakai ◽  
...  
Keyword(s):  
Black Bears ◽  
Ursus Thibetanus ◽  
Mast Production

Autumn Food Habits and Home-Range Elevations of Japanese Black Bears in Relation to Hard Mast Production in the Beech Family in Toyama Prefecture

Mammal Study ◽  
2011 ◽  
Vol 36 (4) ◽  
pp. 199-208 ◽  
Author(s):  
Isao Arimoto ◽  
Yusuke Goto ◽  
Chika Nagai ◽  
Kengo Furubayashi
Keyword(s):  
Home Range ◽  
Food Habits ◽  
Black Bears ◽  
Mast Production

Effect of mast production on home range use of Japanese black bears

2011 ◽  
Vol 75 (4) ◽  
pp. 867-875 ◽  
Author(s):  
Chinatsu Kozakai ◽  
Koji Yamazaki ◽  
Yui Nemoto ◽  
Ami Nakajima ◽  
Shinsuke Koike ◽  
...  
Keyword(s):  
Home Range ◽  
Black Bears ◽  
Range Use ◽  
Home Range Use ◽  
Mast Production

scholarly journals Abiotic and biotic factors affecting the denning behaviors in Asiatic black bears Ursus thibetanus

2015 ◽  
Vol 97 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Toshiaki Yamamoto ◽  
Hiroo Tamatani ◽  
Junpei Tanaka ◽  
Gen Oshima ◽  
Serina Mura ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Abiotic Factors ◽  
Black Bears ◽  
Biotic Factors ◽  
Factors Affecting ◽  
Average Temperature ◽  
Males And Females ◽  
Abiotic And Biotic Factors ◽  
Mast Production

Abstract For bears, numerous associations between biotic and abiotic factors have been reported to correlate with the timing of den entry and emergence; however, an analysis showing which factors influence the timing of den entry and emergence has not been performed enough. In this study, a generalized linear mixed model was generated using 66 entry dates for 26 females and 40 entry dates for 26 males, and 56 emergence dates for 26 females and 25 emergence dates for 18 males between 1999 and 2012. Regarding factors for den entry, the average temperature in November and mast production of Mongolian oaks were significant for both males and females. For the date of den emergence, the average temperature in March affected strongly. For males, good mast production of Mongolian oaks in the previous year was found to be associated with early den emergence. For females, the presence of newborns had a significant influence on their den entry and emergence. This study indicated that denning behavior appears to be regulated by several abiotic and biotic factors and regulation factors are sex specific.

Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

2017 ◽  
Vol 168 (3) ◽  
pp. 127-133
Author(s):  
Matthew Parkan
Keyword(s):  
Cross Sections ◽  
Forest Edge ◽  
Airborne Lidar ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Small Areas ◽  
Interactive 3D ◽  
The Individual ◽  
Airborne Lidar Data ◽  
Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

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.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

scholarly journals Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation

2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Dajun Li ◽  
Yao Yevenyo Ziggah ◽  
Bo Liu
Keyword(s):  
Transfer Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Principal Component ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Initial Segmentation ◽  
Tree Extraction

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

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