A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers
Remote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allow...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/3/353 |
| _version_ | 1797409010312282112 |
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| author | Maja Michałowska Jacek Rapiński |
| author_facet | Maja Michałowska Jacek Rapiński |
| author_sort | Maja Michałowska |
| collection | DOAJ |
| description | Remote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allowing for increased performance of object extraction and classification approaches. As many publications have shown, multiple LiDAR-derived metrics, with the assistance of classification algorithms, contribute to the high accuracy of tree species discrimination based on data obtained by laser scanning. The aim of this article is to review studies in the species classification literature which used data collected by Airborne Laser Scanning. We analyzed these studies to figure out the most efficient group of LiDAR-derived features in species discrimination. We also identified the most powerful classification algorithm, which maximizes the advantages of the derived metrics to increase species discrimination performance. We conclude that features extracted from full-waveform data lead to the highest overall accuracy. Radiometric features with height information are also promising, generating high species classification accuracies. Using random forest and support vector machine as classifiers gave the best species discrimination results in the reviewed publications. |
| first_indexed | 2024-03-09T04:08:06Z |
| format | Article |
| id | doaj.art-d44cd140ae8d44a9bab89e8eb2eed0fc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T04:08:06Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d44cd140ae8d44a9bab89e8eb2eed0fc2023-12-03T14:03:57ZengMDPI AGRemote Sensing2072-42922021-01-0113335310.3390/rs13030353A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied ClassifiersMaja Michałowska0Jacek Rapiński1Institute of Geodesy and Civil Engineering, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-724 Olsztyn, PolandInstitute of Geodesy and Civil Engineering, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-724 Olsztyn, PolandRemote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allowing for increased performance of object extraction and classification approaches. As many publications have shown, multiple LiDAR-derived metrics, with the assistance of classification algorithms, contribute to the high accuracy of tree species discrimination based on data obtained by laser scanning. The aim of this article is to review studies in the species classification literature which used data collected by Airborne Laser Scanning. We analyzed these studies to figure out the most efficient group of LiDAR-derived features in species discrimination. We also identified the most powerful classification algorithm, which maximizes the advantages of the derived metrics to increase species discrimination performance. We conclude that features extracted from full-waveform data lead to the highest overall accuracy. Radiometric features with height information are also promising, generating high species classification accuracies. Using random forest and support vector machine as classifiers gave the best species discrimination results in the reviewed publications.https://www.mdpi.com/2072-4292/13/3/353LiDARALSforestrytree speciesclassification |
| spellingShingle | Maja Michałowska Jacek Rapiński A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers Remote Sensing LiDAR ALS forestry tree species classification |
| title | A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers |
| title_full | A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers |
| title_fullStr | A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers |
| title_full_unstemmed | A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers |
| title_short | A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers |
| title_sort | review of tree species classification based on airborne lidar data and applied classifiers |
| topic | LiDAR ALS forestry tree species classification |
| url | https://www.mdpi.com/2072-4292/13/3/353 |
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