Shrub height estimation for habitat conservation in NW Iberian Peninsula (Spain) using UAV LiDAR point clouds
[EN] This study aimed to develop and validate a method of estimating 3D parameters from DJI Zenmuse L1 LiDAR on DJI Matrice 300 RTK UAV data to characterise and monitor the structure and conservation status of dense shrub formations. The shrub heights were estimated using Progressive Morphological F...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de León |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/23980 |
| Acceso en línea: | https://www.tandfonline.com/doi/full/10.1080/22797254.2024.2438626#abstract https://hdl.handle.net/10612/23980 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingeniería forestal LiDAR UAS Digital terrain model Canopy height model Shrubland height DJI Zenmuse L1 |
| Sumario: | [EN] This study aimed to develop and validate a method of estimating 3D parameters from DJI Zenmuse L1 LiDAR on DJI Matrice 300 RTK UAV data to characterise and monitor the structure and conservation status of dense shrub formations. The shrub heights were estimated using Progressive Morphological Filter (PMF) and the Ground Filter module of the FUSION/LDV software. A digital terrain model (DTM) was interpolated with RMSE 0.23 and 0.27 m, respectively, and a normalised canopy height model (nCHM) was calculated by subtracting it from the LiDAR data and the best DTM obtained. The reliability of the estimates was evaluated against georeferenced field data. In addition, the study examined the impact of vegetation characteristics and return reduction in the original point cloud on the accuracy of LiDAR-data derived . Significant differences were found in the correlations between observed and estimated data for the DTM (R2 = 0.9998) and nCHM heights (R2 = 0.51/0.54). The corresponding RMSE values were 0.23 and 0.34 m. Moreover, no significant differences in the reliability were found for different vegetation types, whereas reduction point cloud density (up to 25–50 returns/m2) did not significantly affect accuracy. In conclusion, lightweight UAV LiDAR can effectively detect sub-metric scale vegetation 3D structure, useful for fine-scale habitat conservation. |
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