Martin Krucek

Martin Krucek
Silva Tarouca Research Institute · Department of Forest Ecology

PhD

About

25
Publications
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251
Citations

Publications

Publications (25)
Article
is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final...
Article
Full-text available
The height growth of the trees depends on sufficient mechanical support given by the stem and an effective hydraulic system. On unstable slopes, tree growth is affected by soil pressure from above and potential soil erosion from below the position of tree. The necessary stabilization is then provided by the production of mechanically stronger wood...
Article
Full-text available
NASAs Global Ecosystem Dynamics Investigation (GEDI) is collecting space-borne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDIs footprint-level (~25 m) AGBD (GEDI04_A) product, including a descript...
Article
Full-text available
We applied a supervised individual-tree segmentation algorithm to ultra-high-density drone lidar in a temperate mountain forest in the southern Czech Republic. We compared the number of trees correctly segmented, stem diameter at breast height (DBH), and tree height from drone-lidar segmentations to field-inventory measurements and segmentations fr...
Article
Tree radial growth is influenced by individual tree abilities, climate, competition, disturbance regimes, as well as by biogeomorphic processes including biomechanical interactions between trees and soil. Trees are actively involved in hillslope dynamics, both responding to and affecting many (bio)geomorphic processes. Using dendrochronology we stu...
Article
Full-text available
Current and planned space missions will produce aboveground biomass density data products at varying spatial resolution. Calibration and validation of these data products is critically dependent on the existence of field estimates of aboveground biomass and coincident remote sensing data from airborne or terrestrial lidar. There are few places that...
Article
There are many indications that for a true understanding of aboveground canopy competition, the concept of symmetric trees is oversimplified and unsatisfactory; in spite of that, this concept is still commonly used in forest ecology research. In this study we analyzed and quantified the effect of tree/crown asymmetry on crown-to-crown interactions...
Article
Recently there have been vital discussions about the validity of the European patch-mosaic conceptual model of forest dynamics – the traditional concept of a shifting patch-mosaic of developmental stages and phases, also known as the forest cycle concept. Here we try to answer the fundamental questions of this debate: How much do the forest dynamic...
Article
Full-text available
Terrestrial laser scanning is a powerful technology for capturing the three-dimensional structure of forests with a high level of detail and accuracy. Over the last decade, many algorithms have been developed to extract various tree parameters from terrestrial laser scanning data. Here we present 3D Forest, an open-source non-platform-specific soft...
Data
Analysis of sensitivity of DBH computation using logistic regression- goodness of fit. The goodness of fit was evaluated using analysis of deviance table where deviance column gives difference between models as variables (i.e. factors) are added to the model in turn. (XLSX)
Data
Dataset for all analysis presented in paper. Zipped files with data for analysis. File Automatic_segmentation-data.xlsx contains results of automatic segmentation. File DBH_height-data.xlsx contains manual data and corresponding computed data from 3D Forest. File Sensitivity-results-LSR.xlsx contains result of sensitivity analysis with all factors...
Data
Simple convex geometrical objects with computed concave hull. Point clouds (black dots) arranged in simple convex geometrical 3D objects of known metrics represented by concave triangulation by 0.1m horizontal sections provided by 3D Forest (blue surface). (PNG)
Data
Complex concave geometrical objects with computed concave hull. Point clouds (black dots) arranged in complex concave geometrical 3D objects of known metrics represented by concave triangulation by 0.1m horizontal sections provided by 3D Forest (blue surface). (PNG)
Data
The effect of factors S and N on the mapping accuracy of tree segmentation. Bold numbers denote statistically significant results. Asterisks denote the group of best segmentation settings (factor levels) according to Nemenyi post hoc test. (XLSX)
Data
The effect of factors S and N on the omission error of the tree segmentation. Bold numbers denote statistically significant results. Asterisks denote the group of best segmentation settings (factor levels) according to Nemenyi post hoc test. (XLSX)
Data
Verification of tree crown metrics provided by 3D Forest. (DOCX)
Data
Simple convex geometrical objects with computed convex hull. Point clouds (black dots) arranged in simple convex geometrical 3D objects of known metrics represented by 3D convex hulls produced by 3D Forest (blue surface). (PNG)
Data
Complex concave geometrical objects with computed convex hull. Point clouds (black dots) arranged in complex concave geometrical 3D objects of known metrics represented by 3D convex hull made in 3D Forest (blue surface). (PNG)
Data
Various parameters of 3D shapes as calculated by 3D Forest and compared to reference values. Basic parameters (height, length and width), planar projection, surface and volume of 3D geometrical shapes computed by 3D Forest and compared to reference values. (XLSX)
Data
The effect of factors S and N on the commission error of tree segmentation. Bold numbers denote statistically significant results. Asterisks denote the group of best segmentation settings (factor levels) according to Nemenyi post hoc test. (XLSX)

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