Application of UAV photogrammetry for the assessment of forest structure and species network in the tropical forests of Southern Nigeria

Figures & data

Figure 1. Map of the study area.

Figure 2. Drone sampling plots derived from Unsupervised Classification of Worldview 1 imagery.

Table 1. Flight characteristics, and image quality parameters derived from Structure for Motion (SfM).

Sampled plots	Average ground sampling distance	Area covered	Geolocation errors
			Mean (m)			Sigma (m)			RMS (m)
			X	Y	Z	X	Y	Z	X	Y	Z
Omo FR	10.39 cm	2.960 Km^2	−0.143	0.074	0.022	1.539	2.384	3.436	1.546	2.386	3.436

Table 2. Pearson Correlation matrix showing the strength of association between the biophysical variables and location.

Variables	Tree diameter	CPA	Lat	Long
Tree diameter	1	0.590	−0.337	−0.150
CPA	0.590	1	−0.107	−0.077
Lat	−0.337	−0.107	1	0.178
Long	−0.150	−0.077	0.178	1

Figure 3. Flight planning of the study area in e-Motion software for the Sensefly Ebee Unmanned Aerial Vehicle (UAV).

Figure 4. Orthomosaics in False Color Composite and different stages of segmentation to obtain the final tree canopies.

Figure 5. The distribution and tree crown characteristics in the sampled plot.

Figure 6. Carbon stock estimates of individual trees in kilograms in the sampled plot.

Figure 7. Relationships between the variables within the Principal Component Sampled Space.

Figure 8. Observed variables in 2-dimensional sampled space of Principal Component Analysis.

Figure 9. Biplot relationship and directions of biophysical variables to location and topography.

Figure 10. Distribution between tree crown and stem diameter.

Figure 11. Groupings and variations of tree families within the sampled plot.