Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNet
The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicl...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/11/1863 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850161027359965184 |
|---|---|
| author | Diego Pacheco-Prado Esteban Bravo-López Emanuel Martínez Luis Á. Ruiz |
| author_facet | Diego Pacheco-Prado Esteban Bravo-López Emanuel Martínez Luis Á. Ruiz |
| author_sort | Diego Pacheco-Prado |
| collection | DOAJ |
| description | The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicles (UAVs) for automating tree species classification. A dataset of 809 trees (crowns) for eight species was analyzed using a random forest classifier and deep learning with PointNet and PointNet++. In the first case, eleven variables such as the normalized red–blue difference index (NRBDI), intensity, brightness (BI), Green Leaf Index (GLI), points density (normalized), and height (maximum and percentiles 10, 50, and 90), produced the highest reliability values, with an overall accuracy of 0.70 and a Kappa index of 0.65. In the second case, the PointNet model had an overall accuracy of 0.62, and 0.64 with PointNet++; using the features Z, red, green, blue, NRBDI, intensity, and BI. Likewise, there was a high accuracy in the identification of the species <i data-eusoft-scrollable-element="1">Populus alba</i> L., and <i data-eusoft-scrollable-element="1">Melaleuca armillaris</i> (Sol. ex Gaertn.) Sm. This work contributes to a cost-effective workflow for urban tree monitoring using UAV data, comparing classical machine learning with deep learning approaches and analyzing the trade-offs. |
| format | Article |
| id | doaj-art-5db1e52da55749d8a32ca5e6686fb563 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-5db1e52da55749d8a32ca5e6686fb5632025-08-20T02:22:59ZengMDPI AGRemote Sensing2072-42922025-05-011711186310.3390/rs17111863Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNetDiego Pacheco-Prado0Esteban Bravo-López1Emanuel Martínez2Luis Á. Ruiz3Instituto de Estudios de Régimen Seccional del Ecuador (IERSE), Universidad del Azuay, Cuenca 010204, EcuadorInstituto de Estudios de Régimen Seccional del Ecuador (IERSE), Universidad del Azuay, Cuenca 010204, EcuadorInstituto de Estudios de Régimen Seccional del Ecuador (IERSE), Universidad del Azuay, Cuenca 010204, EcuadorGeo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, SpainThe management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicles (UAVs) for automating tree species classification. A dataset of 809 trees (crowns) for eight species was analyzed using a random forest classifier and deep learning with PointNet and PointNet++. In the first case, eleven variables such as the normalized red–blue difference index (NRBDI), intensity, brightness (BI), Green Leaf Index (GLI), points density (normalized), and height (maximum and percentiles 10, 50, and 90), produced the highest reliability values, with an overall accuracy of 0.70 and a Kappa index of 0.65. In the second case, the PointNet model had an overall accuracy of 0.62, and 0.64 with PointNet++; using the features Z, red, green, blue, NRBDI, intensity, and BI. Likewise, there was a high accuracy in the identification of the species <i data-eusoft-scrollable-element="1">Populus alba</i> L., and <i data-eusoft-scrollable-element="1">Melaleuca armillaris</i> (Sol. ex Gaertn.) Sm. This work contributes to a cost-effective workflow for urban tree monitoring using UAV data, comparing classical machine learning with deep learning approaches and analyzing the trade-offs.https://www.mdpi.com/2072-4292/17/11/1863point cloudPointNetrandom forestRGBtree identificationUAV |
| spellingShingle | Diego Pacheco-Prado Esteban Bravo-López Emanuel Martínez Luis Á. Ruiz Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNet Remote Sensing point cloud PointNet random forest RGB tree identification UAV |
| title | Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNet |
| title_full | Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNet |
| title_fullStr | Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNet |
| title_full_unstemmed | Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNet |
| title_short | Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNet |
| title_sort | urban tree species identification based on crown rgb point clouds using random forest and pointnet |
| topic | point cloud PointNet random forest RGB tree identification UAV |
| url | https://www.mdpi.com/2072-4292/17/11/1863 |
| work_keys_str_mv | AT diegopachecoprado urbantreespeciesidentificationbasedoncrownrgbpointcloudsusingrandomforestandpointnet AT estebanbravolopez urbantreespeciesidentificationbasedoncrownrgbpointcloudsusingrandomforestandpointnet AT emanuelmartinez urbantreespeciesidentificationbasedoncrownrgbpointcloudsusingrandomforestandpointnet AT luisaruiz urbantreespeciesidentificationbasedoncrownrgbpointcloudsusingrandomforestandpointnet |