scholarly journals Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 294
Author(s):  
Blanca Sanz ◽  
Jukka Malinen ◽  
Jussi Heiskanen ◽  
Timo Tokola

The methodology presented here can assist in evaluating the need for pre-harvest clearing. In the long term, similar approaches may help with managing electronic standing sales and enhance the operational environment of roundwood e-marketplaces. In cut-to-length harvesting, pre-harvest clearing is needed when the understory vegetation hinders the visibility of the stems to be harvested. It can facilitate the work of the harvester operators and thereby enhance the productivity and quality of the harvesting operation. Information about where pre-harvest clearing is required is often not available, however, or else it has to be collected during time-consuming field visits. We report here on the development and evaluation of airborne laser scanning (ALS)-based models for estimating the need for pre-harvest clearing. The reference data consisted of 99 circular field sample plots that were photographed and in which stems with diameters at breast height from one to seven centimeters were measured. An online e-questionnaire survey responded to by 66 forest professionals classified the sample plots into five categories ranging from no need for pre-harvest clearing to compulsory pre-harvest clearing. A linear discriminant analysis was used to estimate the need for pre-harvest clearing with an accuracy of 63.6%, whereas a linear model-based method that predicted the understory stem density assessed the need with an accuracy of 64.6%. Use of this method could deliver information about the understory vegetation, offer guidelines for clearing the understory, and reduce the number of field visits before harvesting, thus reducing costs.

2020 ◽  
pp. 95
Author(s):  
P. Crespo-Peremarch ◽  
L. A. Ruiz

<p class="Bodytext">This PhD thesis addresses the development of full-waveform airborne laser scanning (ALS<sub>FW</sub>) processing and analysis methods to characterize the vertical forest structure, in particular the understory vegetation. In this sense, the influence of several factors such as pulse density, voxel parameters (voxel size and assignation value), scan angle at acquisition, radiometric correction and regression methods is analyzed on the extraction of ALS<sub>FW</sub> metric values and on the estimate of forest attributes. Additionally, a new software tool to process ALS<sub>FW</sub> data is presented, which includes new metrics related to understory vegetation. On the other hand, occlusion caused by vegetation in the ALS<sub>FW</sub>, discrete airborne laser scanning (ALS<sub>D</sub>) and terrestrial laser scanning (TLS) signal is characterized along the vertical structure. Finally, understory vegetation density is detected and determined by ALS<sub>FW</sub> data, as well as characterized by using the new proposed metrics.</p>


2019 ◽  
Vol 93 (1) ◽  
pp. 150-162 ◽  
Author(s):  
Stefano Puliti ◽  
Jonathan P Dash ◽  
Michael S Watt ◽  
Johannes Breidenbach ◽  
Grant D Pearse

Abstract This study addresses the use of multiple sources of auxiliary data from unmanned aerial vehicles (UAVs) and airborne laser scanning (ALS) data for inference on key biophysical parameters in small forest properties (5–300 ha). We compared the precision of the estimates using plot data alone under a design-based inference with model-based estimates that include plot data and the following four types of auxiliary data: (1) terrain-independent variables from UAV photogrammetric data (UAV-SfM); (2) variables obtained from UAV photogrammetric data normalized using external terrain data (UAV-SfMDTM); (3) UAV-LS and (4) ALS data. The inclusion of remotely sensed data increased the precision of DB estimates by factors of 1.5–2.2. The optimal data sources for top height, stem density, basal area and total stem volume were: UAV-LS, UAV-SfM, UAV-SfMDTM and UAV-SfMDTM. We conclude that the use of UAV data can increase the precision of stand-level estimates even under intensive field sampling conditions.


Author(s):  
J.-M. Monnet ◽  
C. Ginzler ◽  
J.-C. Clivaz

Airborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R&lt;sup&gt;2&lt;/sup&gt;&thinsp;=&thinsp;0.61 with a root mean square error of 47&thinsp;%) and basal area (respectively 0.51 and 45&thinsp;%) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy.


Author(s):  
J.-M. Monnet ◽  
C. Ginzler ◽  
J.-C. Clivaz

Airborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R<sup>2</sup>&thinsp;=&thinsp;0.61 with a root mean square error of 47&thinsp;%) and basal area (respectively 0.51 and 45&thinsp;%) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy.


Author(s):  
Денис Алексеевич Миягашев ◽  
Биликто Александрович Базаров ◽  
Ярослав Витальевич Дикий

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2018 ◽  
Vol 217 ◽  
pp. 400-413 ◽  
Author(s):  
Pablo Crespo-Peremarch ◽  
Piotr Tompalski ◽  
Nicholas C. Coops ◽  
Luis Ángel Ruiz

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