scholarly journals Developing laser scanning applications for mapping and monitoring single tree characteristics for the needs of urban forestry

2016 ◽  
Vol 2016 (230) ◽  
Author(s):  
Topi Tanhuanpää
2021 ◽  
Author(s):  
Puliti Stefano ◽  
Grant D. Pears ◽  
Michael S. Watt ◽  
Edward Mitchard ◽  
Iain McNicol ◽  
...  

<p>Survey-grade drone laser scanners suitable for unmanned aerial vehicles (UAV-LS) allow the efficient collection of finely detailed three-dimensional information of tree structures. This data type allows forests to be resolved into discrete individual trees and has shown promising results in providing accurate in-situ observations of key forestry variables. New and improved approaches for analyzing UAV-LS point clouds have to be developed to transform the vast amounts of data from UAV-LS into actionable insights and decision support. Many different studies have explored various methods for automating single tree detection, segmentation, parsing into different tree components, and measurement of biophysical variables (e.g., diameter at breast height). Despite the considerable efforts dedicated to developing automated ways to process UAV-LS data into useful data, current methods tend to be tailored to small datasets, and it remains challenging to evaluate the performance of different algorithms based on a consistent validation dataset. To fill this knowledge gap and to further advance our ability to measure forests from UAV-LS data, we present a new benchmarking dataset. This data is composed of manually labelled UAV-LS data acquired a number of continents and biomes which span tropical to boreal forests. The UAV-LS data was collected exclusively used survey-grade sensors such as the Riegl VUX and mini-VUX series which are characterized by a point density in the range 1 – 10 k points m<sup>2</sup>. Currently, such data represent the state-of-the-art in aerial laser scanning data. The benchmark data consists of a library of single-tree point clouds, aggregated to sample plots, with each point classified as either stem, branch, or leaves. With the objective of releasing such a benchmark dataset as a public asset, in the future, researchers will be able to leverage such pre-existing labelled trees for developing new methods to measure forests from UAV-LS data. The availability of benchmarking datasets represents an important driver for enabling the development of robust and accurate methods. Such a benchmarking dataset will also be important for a consistent comparison of existing or future algorithms which will guide future method development.</p>


Author(s):  
Ville Kankare ◽  
Minna Räty ◽  
Xiaowei Yu ◽  
Markus Holopainen ◽  
Mikko Vastaranta ◽  
...  

2013 ◽  
Vol 34 (16) ◽  
pp. 2144-2150 ◽  
Author(s):  
Ahlem Othmani ◽  
Lew F.C. Lew Yan Voon ◽  
Christophe Stolz ◽  
Alexandre Piboule

2016 ◽  
Vol 175 ◽  
pp. 231-241 ◽  
Author(s):  
Ángeles Casas ◽  
Mariano García ◽  
Rodney B. Siegel ◽  
Alexander Koltunov ◽  
Carlos Ramírez ◽  
...  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 102263-102277
Author(s):  
Chunhua Hu ◽  
Shaojie Pan ◽  
Huaiqing Zhang ◽  
Pingping Li

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 277 ◽  
Author(s):  
Barbara Del Perugia ◽  
Francesca Giannetti ◽  
Gherardo Chirici ◽  
Davide Travaglini

Nowadays, forest inventories are frequently carried out using a combination of field measurements and remote sensing data, often acquired with light detection and ranging (LiDAR) sensors. Several studies have investigated how three-dimensional laser scanning point clouds from different platforms can be used to acquire information traditionally collected with forest instruments, such as hypsometers and callipers to detect single-tree attributes like tree height and diameter at the breast height. The present study has tested the performances of the ZEB1 instrument, a type of hand-held mobile laser scanner, for single-tree attributes estimation in pure Castanea sativa Mill. stands cultivated for fruit production in Central Italy. In particular, the influence of walking scan path density on single-tree attributes estimation (number of trees, tree position, diameter at breast height, tree height, and crown base height) was investigated to test the efficiency of field measures. The point clouds were acquired by walking along straight lines drawn with different spacing: 10 and 15 m apart. A single-tree scan approach, which included walking with the instrument around each tree, was used as reference data. In order to evaluate the efficiency of the survey, the influence of the walking scan path was discussed in relation to the accuracy of single-tree attributes estimation, as well as the time and cost needed for data acquisition, pre-processing, and analysis. Our results show that the 10 m scan path provided the best results, with an omission error of 6%; the assessment of single-tree attributes was successful, with values of the coefficient of determination and the relative root mean square error similar to other studies. The 10 m scan path has also proved to decrease the costs by about €14 for data pre-processing, and a saving of time for data acquisition and data analysis of about 37 min compared to the reference data.


2017 ◽  
Vol 143 ◽  
pp. 165-176 ◽  
Author(s):  
Tiago de Conto ◽  
Kenneth Olofsson ◽  
Eric Bastos Görgens ◽  
Luiz Carlos Estraviz Rodriguez ◽  
Gustavo Almeida

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