Gergő Diószegi’s research while affiliated with University of Debrecen and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (2)


Testing treecbh in Central European forests: an R package for crown base height detection using high-resolution aerial laser-scanned data
  • Article

August 2024

·

26 Reads

·

1 Citation

Forestry

Gergő Diószegi

·

·

·

[...]

·

Accurate information regarding tree canopy characteristics is crucial for forest management, but it is often difficult to assess. This study presents an innovative framework designed for crown base height (CBH) detection using high-resolution laser-scanned data, with a specific focus on individual trees within forests. The framework comprises three key steps: (i) segmenting the input tree point cloud to identify the tree trunk and its branches using the treesio software; (ii) applying vertical cross-sectional K-means clustering to cluster the identified tree and to define the elevation threshold for removing low-lying understory vegetation; (iii) employing a novel 2D kernel method for detecting CBH after eliminating low-lying understory vegetation. The 2D kernel method, developed for broadleaf forests using leaf-off airborne laser scanning (ALS) data, underpins the treecbh tool. This tool features a visual CBH adjustment component that shows a 2D profile plot of the tree point cloud, and suggests a CBH value for user approval or adjustment. To evaluate accuracy, in situ measured CBH data from five forest plots in Germany and Hungary with varied species compositions were used. ALS data were collected during leaf-off conditions for the two Hungarian plots and during leaf-on conditions for the three German plots. Leaf-off terrestrial laser-scanned data from individual trees were also used in the accuracy assessment. A sensitivity analysis using random point decimation was conducted on the terrestrial laser-scanned data to assess treecbh’s sensitivity to point density. The initial results exhibited matching rates of 45% and 60% for leaf-off ALS plots, which significantly improved to 71% and 77%, respectively, when using the visual CBH adjustment feature of the tool. The leaf-on ALS results demonstrated matching rates between 24% and 33%, whereas the CBHs of individual terrestrial laser-scanned trees could be detected with 93% accuracy in visual mode. It was observed that treecbh operates effectively when the input ALS data have a minimum point density of 20 pts/m2{\text{m}}^2, with its optimal performance achieved at 110 pts/m2{\text{m}}^2. These findings indicated treecbh’s sensitivity to ALS data quality, scanning season (leaf-on and leaf-off), and point density. This sensitivity can be effectively mitigated in the case of leaf-off ALS data by utilizing the visual CBH adjustment feature of the tool.


Circular example of a CHM with a pixel size of 0.5∙0.5 m, displayed in decreasing order between 31.8 m (maximum height) and 6 m. The decrement interval is 2 m. In the top row on the left, the initial example CHM is depicted, with green representing value 1 and grey indicating no data. The locations of the two example patches are highlighted by filled red circles with transparency
Example patches (Fig. 1) provide an overview of the three-layer concept. Green denotes value 1, whereas grey represents no data
The three-layer concept (Fig. 2), which is at the core of the GTR algorithm, is visualized and mathematically explained. In the first three columns, green represents value 1, whereas grey indicates no data. The distinct colours in the fourth column and shades of grey in the sixth column represent the patch IDs. The red circle around the bottom-right raster highlights the ITD output (red unfilled dots) of the algorithm
Example CHM (0.5∙0.5 m pixel size) with treetops (a); example CHM with treetops after applying the distance-based treetop filter (b); (c) the two main functions, get_TREETOPS() and finalize_TREETOPS() from the R package TREETOPS, and the implementation of threshold settings using the min_H and max_H parameters
Locations of the four German forest plots (left) in the federal state of Baden-Württemberg and the Hungarian Nagyerdő forest site (right) in the federal state of Hajdú-Bihar

+7

A new method for individual treetop detection with low-resolution aerial laser scanned data
  • Article
  • Full-text available

June 2024

·

443 Reads

In the past decade, the use of three-dimensional forest information from airborne Light Detection and Ranging (LiDAR) has become widespread in forest inventories. Accurate Individual Treetop Detection (ITD) and crown boundary delineation using LiDAR data are critical for obtaining precise inventory metrics. To address this need, we introduced a novel growing tree region (GTR)-driven ITD method that utilizes canopy height models (CHM) derived from very low-resolution airborne LiDAR data. The GTR algorithm consists of three key stages: (i) preserving all height layers through incremental cutting and stacking of CHM; (ii) employing a three-layer concept to identify individual treetops; and (iii) refining the detected treetops using a distance-based filter. Our method was tested in five temperate forests across Central Europe and was compared against the widely-used local maxima (LM) search combined with an optimized variable window filtering (VWF) technique. Our results showed that the GTR method outperformed LM with VWF, particularly in forests with high canopy density. The achieved root mean square accuracies were 74% for the matching rate, 19% for commission errors, and 27% for omission errors. In comparison, the LM with the VWF method resulted in a matching rate of 71%, commission errors of 20%, and omission errors of 31%. To facilitate the application of our algorithm, we developed an R package called TREETOPS, which seamlessly integrates with the lidR package, ensuring compatibility with existing treetop-based segmentation methods. By introducing TREETOPS, we provide the most accurate open-source tool for detecting treetops using low-resolution LiDAR-derived CHM.

Download

Citations (1)


... In addition to the indicators related to tree height or DBH growth rates, as mentioned above, it may be necessary to consider variables such as canopy information that have been correlated with [30,31]. Recent advances in LiDAR survey technology have made it possible to acquire data on canopy information [30][31][32]. Therefore, it is necessary to elucidate the relationship between detailed tree information and , which was difficult to obtain prior to the advent of LiDAR. ...

Reference:

Empirical Relationships of the Characteristics of Standing Trees with the Dynamic Modulus of Elasticity of Japanese Cedar (Cryptomeria japonica) Logs: Case Study in the Kyoto Prefecture
Testing treecbh in Central European forests: an R package for crown base height detection using high-resolution aerial laser-scanned data
  • Citing Article
  • August 2024

Forestry