Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry
Accurate collection of dendrometric information is essential for improving decision confidence and supporting potential advances in forest management planning (FMP). Total stem volume is an important forest inventory parameter that requires high accuracy. Terrestrial laser scanning (TLS) has emerged...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/12/6/717 |
| _version_ | 1797531854575763456 |
|---|---|
| author | Dimitrios Panagiotidis Azadeh Abdollahnejad |
| author_facet | Dimitrios Panagiotidis Azadeh Abdollahnejad |
| author_sort | Dimitrios Panagiotidis |
| collection | DOAJ |
| description | Accurate collection of dendrometric information is essential for improving decision confidence and supporting potential advances in forest management planning (FMP). Total stem volume is an important forest inventory parameter that requires high accuracy. Terrestrial laser scanning (TLS) has emerged as one of the most promising tools for automatically measuring total stem height and diameter at breast height (DBH) with very high detail. This study compares the accuracy of different methods for extracting the total stem height and DBH to estimate total stem volume from TLS data. Our results show that estimates of stem volume using the random sample consensus (RANSAC) and convex hull and H<sub>TSP</sub> methods are more accurate (bias = 0.004 for RANSAC and bias = 0.009 for convex hull and H<sub>TSP</sub>) than those using the circle fitting method (bias = 0.046). Furthermore, the RANSAC method had the best performance with the lowest bias and the highest percentage of accuracy (78.89%). The results of this study provide insight into the performance and accuracy of the tested methods for tree-level stem volume estimation, and allow for the further development of improved methods for point-cloud-based data collection with the goal of supporting potential advances in precision forestry. |
| first_indexed | 2024-03-10T10:50:45Z |
| format | Article |
| id | doaj.art-5ca8909b553040f0a539e1cae7dd75f7 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T10:50:45Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-5ca8909b553040f0a539e1cae7dd75f72023-11-21T22:16:51ZengMDPI AGForests1999-49072021-05-0112671710.3390/f12060717Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision ForestryDimitrios Panagiotidis0Azadeh Abdollahnejad1Faculty of Forestry and Wood Sciences, Czech University of Life Sciences (CZU Prague), Kamýcká 129, 165 21 Prague, Czech RepublicFaculty of Forestry and Wood Sciences, Czech University of Life Sciences (CZU Prague), Kamýcká 129, 165 21 Prague, Czech RepublicAccurate collection of dendrometric information is essential for improving decision confidence and supporting potential advances in forest management planning (FMP). Total stem volume is an important forest inventory parameter that requires high accuracy. Terrestrial laser scanning (TLS) has emerged as one of the most promising tools for automatically measuring total stem height and diameter at breast height (DBH) with very high detail. This study compares the accuracy of different methods for extracting the total stem height and DBH to estimate total stem volume from TLS data. Our results show that estimates of stem volume using the random sample consensus (RANSAC) and convex hull and H<sub>TSP</sub> methods are more accurate (bias = 0.004 for RANSAC and bias = 0.009 for convex hull and H<sub>TSP</sub>) than those using the circle fitting method (bias = 0.046). Furthermore, the RANSAC method had the best performance with the lowest bias and the highest percentage of accuracy (78.89%). The results of this study provide insight into the performance and accuracy of the tested methods for tree-level stem volume estimation, and allow for the further development of improved methods for point-cloud-based data collection with the goal of supporting potential advances in precision forestry.https://www.mdpi.com/1999-4907/12/6/717dendrometryterrestrial laser scannerArcGISRANSACcircle fittingconvex hull |
| spellingShingle | Dimitrios Panagiotidis Azadeh Abdollahnejad Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry Forests dendrometry terrestrial laser scanner ArcGIS RANSAC circle fitting convex hull |
| title | Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry |
| title_full | Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry |
| title_fullStr | Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry |
| title_full_unstemmed | Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry |
| title_short | Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry |
| title_sort | accuracy assessment of total stem volume using close range sensing advances in precision forestry |
| topic | dendrometry terrestrial laser scanner ArcGIS RANSAC circle fitting convex hull |
| url | https://www.mdpi.com/1999-4907/12/6/717 |
| work_keys_str_mv | AT dimitriospanagiotidis accuracyassessmentoftotalstemvolumeusingcloserangesensingadvancesinprecisionforestry AT azadehabdollahnejad accuracyassessmentoftotalstemvolumeusingcloserangesensingadvancesinprecisionforestry |