Inventory of Small Forest Areas Using an Unmanned Aerial System
Abstract
:1. Introduction
1.1. Background
1.2. UAS Application in Forest Inventories
1.3. Objectives
- Assess the accuracy of Lore’s mean height (hL), hdom, stem number (N), basal area (G), and stem volume (V) determined with an ABA combining UAS-SfM and field data in a small forest property (200 ha) in Norway.
- Evaluate the importance of using spectral information in modeling the abovementioned properties.
2. Study Area and Materials
2.1. Study Area
2.2. Field Measurements
Property | Range | Mean |
---|---|---|
hL (m) | 6.7–17.1 | 11.4 |
hdom (m) | 13.1–28.4 | 19.8 |
N (n∙ha−1) | 350.0–3625 | 1372 |
G (m2∙ha−1) | 19.7–43.8 | 29.2 |
V (m3∙ha−1) | 136.6–580.9 | 256.1 |
2.3. Remotely Sensed Data
3. Methods
3.1. UAS Imagery Collection—Planning and Implementation
Date | Flight#. | No. of Images | Flight Time (min) | Coverage (ha) | Size (MB) | Wind-Speed (m·sec−1) | Weather |
---|---|---|---|---|---|---|---|
27.11.14 | 1 | 247 | 26 | 17.2 | 678 | 6–7 | Full cloud cover + snow |
29.11.14 | 2 | 223 | 24 | 19.7 | 734 | 4–5 | Full cloud cover |
3 | 252 | 27 | 17.5 | 832 | |||
4 | 197 | 23 | 14.6 | 654 | |||
5 | 186 | 23 | 15.7 | 613 | |||
30.11.14 | 6 | 194 | 23 | 14.4 | 636 | 4–5 | Full cloud cover |
7 | 226 | 25 | 14.2 | 742 | |||
8 | 227 | 25 | 17.1 | 775 | |||
9 | 237 | 26 | 17.2 | 825 | |||
02.12.14 | 10 | 220 | 24 | 18.2 | 686 | 3–5 | Full cloud cover + snow |
11 | 223 | 24 | 17.3 | 677 | |||
12 | 231 | 25 | 16.4 | 774 | |||
13 | 184 | 21 | 12.5 | 613 | |||
03.12.14 | 14 | 218 | 24 | 17.6 | 526 | 2–5 | Fog |
15 | 185 | 21 | 13.1 | 711 | Sun | ||
TOTAL | 15 | 3250 | 361 | 242.7 | 10476 |
3.2. Photogrammetric Processing
Task | Parameter |
---|---|
Align photos | Accuracy: high b |
Pair selection: reference b | |
Key point limit: 40000 b | |
Tie point limit: 1000 b | |
Build mesh | Surface type: height field b |
Source data: sparse point cloud b | |
Facecount: low (13544) a | |
Interpolation: enabled b | |
Guided marker positioning | |
Optimize camera alignment | Marker accuracy (m): 0.005 b Projection accuracy (pix): 0.1 b Tie point accuracy (pix): 4 b Fit all except for k4 b Number of GCPs: 13 a |
Build dense cloud | Quality: Medium a |
Depth filtering: mild a |
3.3. Variable Extraction and Statistical Methods
4. Results
4.1. Regression Modeling
Dependent Variable | Predictive Model a | Adj. R2 b | RMSE b | Relative RMSE b | b | Relative b |
---|---|---|---|---|---|---|
ln(hL) | p30 + hsd | 0.68 | 1.55 | 13.66 | 0.01 | 0.13 |
ln(hL) | p20 + hsd + Gm | 0.71 | 1.51 | 13.28 | 0.00 | 0.03 |
hdom | p50 + hsd + d7 | 0.96 | 0.72 | 3.64 | 0.01 | 0.05 |
hdom | p50 + hsd + d7 + Gm | 0.97 | 0.69 | 3.48 | 0.01 | 0.04 |
ln(N) | p30 + d0 + d9 | 0.57 | 529.03 | 38.57 | -8.28 | -0.60 |
ln(N) | p30 + d0 + d9 + Gsd | 0.60 | 538.31 | 39.24 | -4.90 | -0.36 |
G | p100 + d0 + d9 | 0.60 | 4.49 | 15.38 | 0.03 | 0.09 |
ln(V) | p80 + d0 | 0.85 | 38.30 | 14.95 | 0.54 | 0.21 |
4.2. Plot-Level Validation
5. Discussion
5.1. UAS-SfM Forest Inventory Accuracy
5.2. Importance of Spectral Variables
5.3. General Considerations
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Puliti, S.; Ørka, H.O.; Gobakken, T.; Næsset, E. Inventory of Small Forest Areas Using an Unmanned Aerial System. Remote Sens. 2015, 7, 9632-9654. https://doi.org/10.3390/rs70809632
Puliti S, Ørka HO, Gobakken T, Næsset E. Inventory of Small Forest Areas Using an Unmanned Aerial System. Remote Sensing. 2015; 7(8):9632-9654. https://doi.org/10.3390/rs70809632
Chicago/Turabian StylePuliti, Stefano, Hans Ole Ørka, Terje Gobakken, and Erik Næsset. 2015. "Inventory of Small Forest Areas Using an Unmanned Aerial System" Remote Sensing 7, no. 8: 9632-9654. https://doi.org/10.3390/rs70809632