Use of digital aerial photogrammetry via images collected by drone in the quantitative inventory of na urban forest
Name: LAÍS GONCALVES PIRES DE SOUZA
Publication date: 30/10/2023
Examining board:
Name |
Role |
|---|---|
| ADRIANO RIBEIRO DE MENDONCA | Advisor |
Summary: In urban areas, trees play a crucial role in changing the landscape and local microclimate, in addition to promoting carbon sequestration and providing leisure and recreation spaces for the population. However, the establishment and maintenance of trees in cities pose a challenge for local administration, requiring environmental knowledge of the region, species, and deployment location. Currently, forest inventory enhanced with remote sensing data emerges as a facilitator of urban planning, expediting the tree inventory process and, consequently, decision-making. This study aimed to assess the accuracy of digital aerial photogrammetry (FAD) using images collected by a remotely piloted aircraft (RPA) in detecting trees and estimation of biometric variables in an urban forest inventory. The inventory was conducted on Governador Lindemberg Avenue, located in the municipality of Jerônimo Monteiro, Espírito Santo. High spatial resolution images were obtained by a multirotor RPA during the field inventory period. Subsequently, tree individuals were automatically identified, and their canopies were segmented using FAD-3D data. Finally, total height (H), diameter at 1,3m above ground (D), and canopy diameter (dc) values were estimated from regression models fitted with 3D point cloud height metrics. A total of 144 individuals were inventoried. For FAD validation, errors found were 0,32% for Digital Terrain Model (MDT) and 16,23% for total height. The windowed Variable detection algorithm (wV) using the point cloud as data source automatically identified 78% of individuals. For the comparison of canopy diameters, errors were 17,94%, 21,2% and 29,5% for manual measurements, FAD images, and field measurements with four rays, eight rays, and automatically obtained diameters through canopy identification and segmentation in FAD images, respectively. Regression models errors for H, D and dc were 8,97%, 36,76% and 15,68% respectively. The survey demonstrated the automatic identification of trees and extraction of traditional metrics for generating models to obtain variables of interest. The MDT obtained obtained provided satisfactory results for tree height estimation through FAD-RPA. Manual measurements with FAD images were considered satisfactory for canopy diameter, proving to be the best method for this variable. Additionally, regression models with tradicional metrics obtained were satisfactory for H and dc estimation, showing accurate RMSE and R² values. However, the trunk diameter model showed different results. In conclusion, conducting an aerial photogrammetric survey of urban areas using a remotely piloted aircraft is feasible and can provide valuable data for urban tree planning.
