881 research outputs found
Estimation of diameter distributions by means of airborne laser scanner data
No full textDiameter distributions are an important source of information for estimating the timber assortment in forest stands. In this paper, a one-step procedure for deriving the parameters of a Weibull function, itself used to describe diameter distributions, is presented. A generalized linear model (GLM) is employed that allows for an estimation of the shape and scale parameters as functions of different predictors. The GLM was fit using 495 sample plots from a conventional sample-plot inventory. Plotwise height metrics derived from airborne laser scanner data serve as covarkates (auxiliary variables). Each sample plot consists of four concentric circle plots, where the largest plot covers an area of 450 m(2) (12 m radius). Trees with a diameter at breast height (DBH) <30 cm are measured only on the smaller circle plots. Because of this design, left- and right-truncated Weibull distributions, conditional on the DBH, were used to fit the data. The frequently used two-step procedure - in which the Weibull distribution is firstly fitted via maximum likelihood, and is parameters are then estimated via linear regression - requires an adequate number of observations per sample plot in the first step. Hence, this method would have been unsuitable for the data source at hand, because a mean of just 12 trees per sample plot was recorded. The visual comparison of the predicted Weibull distributions with observed data shows a good fit to the data. The mean of the DBH distributions was estimated with a root mean square error (RMSE) of 2.44 cm and a bias of 0.41 cm
Response to the comment '"Phantom of the Opera?" or "Sex and the City"' by Thomas K. Bauer, Philipp Breidenbach, and Christoph M. Schmidt
No full textIn our paper "The Phantom of the Opera," published in this journal in 2011, we introduce proximity to a baroque opera house location as an instrument for concentrations of high-skilled individuals today. In the second stage, we assess productive spillovers from these high-skilled individuals attracted by cultural amenities. Thomas K. Bauer, Philipp Breidenbach, and Christoph M. Schmidt challenge our identification strategy in their comment. In our response, we provide further arguments and empirical evidence for the unambiguous validity of our strategy.</p
Our Dear-Bought Liberty: Book Discussion
No full textAuthor Michael Breidenbach of Ave Maria University will discuss how early American Catholics reshaped ideas of religious liberty.
Michael Breidenbach, Chair, History Department Ave Maria Universityhttps://scholarship.law.nd.edu/ndls_posters/1569/thumbnail.jp
Our Dear-Bought Liberty: Book Discussion
No full textAuthor Michael Breidenbach of Ave Maria University will discuss how early American Catholics reshaped ideas of religious liberty.
Michael Breidenbach, Chair, History Department Ave Maria Universityhttps://scholarship.law.nd.edu/ndls_posters/1569/thumbnail.jp
Instance segmentation of individual tree crowns with YOLOv5: A comparison of approaches using the ForInstance benchmark LiDAR dataset
No full textFine-grained information on the level of individual trees constitute key components for forest observation enabling forest management practices tackling the effects of climate change and the loss of biodiversity in forest ecosystems. Such information on individual tree crowns (ITC's) can be derived from the application of ITC segmentation approaches, which utilize remotely sensed data. However, many ITC segmentation approaches require prior knowledge about forest characteristics, which is difficult to obtain for parameterization. This can be avoided by the adoption of data-driven, automated workflows based on convolutional neural networks (CNN). To contribute to the advancements of efficient ITC segmentation approaches, we present a novel ITC segmentation approach based on the YOLOv5 CNN. We analyzed the performance of this approach on a comprehensive international unmanned aerial laser scanning (UAV-LS) dataset (ForInstance), which covers a wide range of forest types. The ForInstance dataset consists of 4192 individually annotated trees in high-density point clouds with point densities ranging from 498 to 9529 points m-2 collected across 80 sites. The original dataset was split into 70% for training and validation and 30% for model performance assessment (test data). For the best performing model, we observed a F1-score of 0.74 for ITC segmentation and a tree detection rate (DET %) of 64% in the test data. This model outperformed an ITC segmentation approach, which requires prior knowledge about forest characteristics, by 41% and 33% for F1-score and DET %, respectively. Furthermore, we tested the effects of reduced point densities (498, 50 and 10 points per m-2) on ITC segmentation performance. The YOLO model exhibited promising F1-scores of 0.69 and 0.62 even at point densities of 50 and 10 points m-2, respectively, which were between 27% and 34% better than the ITC approach that requires prior knowledge.Furthermore, the areas of ITC segments resulting from the application of the best performing YOLO model were close to the reference areas (RMSE = 3.19 m-2), suggesting that the YOLO-derived ITC segments can be used to derive information on ITC level
Phänotypische und genetische Reaktionen von Sequoia sempervirens Herkünften auf Frosttemperaturen
No full textPhänotypische und genetische Reaktionen von Sequoia sempervirens Herkünften auf Frosttemperaturen
No full textCelebrating the Nobel prize to Sam Ting in the Salle des Pas Perdus
No full text1st row: David Jackson, M, Breidenbach, Mary K, Gaillard, Marcello Conversi, Sam Ting 2nd row: Maurice Jacob, P.G. Hansen, R. Anthoine, Sau La
Breidenbach, August John (Death, 1904-07-08)
No full textAddress: 1037 Rittenhouse St.Age at death: 35 yrs.141/Pg. 88/1904/M W M/Ind./Dr. F. B. Baurichter/Ackerman & Busch/Madison Ind.Original record filed in drawer labeled 'BREHM-BRIN'
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