Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning
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 n...
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doaj-846c3a38447b4883bd89419530d5c4cd2020-11-25T01:48:39ZengMDPI AGForests1999-49072020-03-0111329410.3390/f11030294f11030294Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser ScanningBlanca Sanz0Jukka Malinen1Jussi Heiskanen2Timo Tokola3School of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, FinlandSchool of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, FinlandSavonia University of Applied Sciences, Opiskelijankatu 3, FI-78210 Varkaus, FinlandSchool of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, FinlandThe 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.https://www.mdpi.com/1999-4907/11/3/294pre-harvest clearing operationsharvesting productivityunderstory treesundergrowth vegetationlight detection and ranging (lidar)operator’s visibilityremote sensinggeographic information system (gis)linear discriminant analysis (lda)linear model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Blanca Sanz Jukka Malinen Jussi Heiskanen Timo Tokola |
spellingShingle |
Blanca Sanz Jukka Malinen Jussi Heiskanen Timo Tokola Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning Forests pre-harvest clearing operations harvesting productivity understory trees undergrowth vegetation light detection and ranging (lidar) operator’s visibility remote sensing geographic information system (gis) linear discriminant analysis (lda) linear model |
author_facet |
Blanca Sanz Jukka Malinen Jussi Heiskanen Timo Tokola |
author_sort |
Blanca Sanz |
title |
Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning |
title_short |
Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning |
title_full |
Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning |
title_fullStr |
Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning |
title_full_unstemmed |
Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning |
title_sort |
need for pre-harvest clearing of understory vegetation determined by airborne laser scanning |
publisher |
MDPI AG |
series |
Forests |
issn |
1999-4907 |
publishDate |
2020-03-01 |
description |
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. |
topic |
pre-harvest clearing operations harvesting productivity understory trees undergrowth vegetation light detection and ranging (lidar) operator’s visibility remote sensing geographic information system (gis) linear discriminant analysis (lda) linear model |
url |
https://www.mdpi.com/1999-4907/11/3/294 |
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