Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements
The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavitie...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/5/867 |
| _version_ | 1798026005613379584 |
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| author | Julian Frey Thomas Asbeck Jürgen Bauhus |
| author_facet | Julian Frey Thomas Asbeck Jürgen Bauhus |
| author_sort | Julian Frey |
| collection | DOAJ |
| description | The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown deadwood, are an established tool to quantify the suitability of habitat trees for biodiversity conservation. The aim to predict TreMs based on remote sensing (RS) parameters is supposed to assist a more objective and efficient selection of retention elements. The RS parameters were collected by the use of terrestrial laser scanning as well as unmanned aerial vehicles structure from motion point cloud generation to provide a 3D distribution of plant tissue. Data was recorded on 135 1-ha plots in Germany. Statistical models were used to test the influence of 28 RS predictors, which described TreM richness (R<sup>2</sup>: 0.31) and abundance (R<sup>2</sup>: 0.31) in moderate precision and described a deviance of 44% for the abundance and 38% for richness of TreMs. Our results indicate that multiple RS techniques can achieve moderate predictions of TreM occurrence. This method allows a more efficient and objective selection of retention elements such as habitat trees that are keystone features for biodiversity conservation, even if it cannot be considered a full replacement of TreM inventories due to the moderate statistical relationship at this stage. |
| first_indexed | 2024-04-11T18:28:15Z |
| format | Article |
| id | doaj.art-fccd2ba3c0fc4778ba474f11beef4359 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:28:15Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fccd2ba3c0fc4778ba474f11beef43592022-12-22T04:09:34ZengMDPI AGRemote Sensing2072-42922020-03-0112586710.3390/rs12050867rs12050867Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention ElementsJulian Frey0Thomas Asbeck1Jürgen Bauhus2Chair of Remote Sensing and Landscape Information Systems, University of Freiburg, D-79106 Freiburg, GermanyChair of Silviculture, University of Freiburg, D-79106 Freiburg, GermanyChair of Silviculture, University of Freiburg, D-79106 Freiburg, GermanyThe retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown deadwood, are an established tool to quantify the suitability of habitat trees for biodiversity conservation. The aim to predict TreMs based on remote sensing (RS) parameters is supposed to assist a more objective and efficient selection of retention elements. The RS parameters were collected by the use of terrestrial laser scanning as well as unmanned aerial vehicles structure from motion point cloud generation to provide a 3D distribution of plant tissue. Data was recorded on 135 1-ha plots in Germany. Statistical models were used to test the influence of 28 RS predictors, which described TreM richness (R<sup>2</sup>: 0.31) and abundance (R<sup>2</sup>: 0.31) in moderate precision and described a deviance of 44% for the abundance and 38% for richness of TreMs. Our results indicate that multiple RS techniques can achieve moderate predictions of TreM occurrence. This method allows a more efficient and objective selection of retention elements such as habitat trees that are keystone features for biodiversity conservation, even if it cannot be considered a full replacement of TreM inventories due to the moderate statistical relationship at this stage.https://www.mdpi.com/2072-4292/12/5/867forest biodiversitytree related microhabitatsterrestrial laser scanninguavstructure from motionforest structure |
| spellingShingle | Julian Frey Thomas Asbeck Jürgen Bauhus Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements Remote Sensing forest biodiversity tree related microhabitats terrestrial laser scanning uav structure from motion forest structure |
| title | Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements |
| title_full | Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements |
| title_fullStr | Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements |
| title_full_unstemmed | Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements |
| title_short | Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements |
| title_sort | predicting tree related microhabitats by multisensor close range remote sensing structural parameters for the selection of retention elements |
| topic | forest biodiversity tree related microhabitats terrestrial laser scanning uav structure from motion forest structure |
| url | https://www.mdpi.com/2072-4292/12/5/867 |
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