Parameterization of the Individual Tree Detection Method Using Large Dataset from Ground Sample Plots and Airborne Laser Scanning for Stands Inventory in Coniferous Forest
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Inventory Data
2.3. Airborne Laser Scanning Point Clouds
2.4. Homogenization of Field Dendrometric Measurements with Remotely Determined Trees
2.5. Individual Tree Detection on Sample Plots
2.5.1. Point Cloud Pre-Processing
2.5.2. Parameterization of Individual Tree Detection Methods
2.5.3. Post-Processing Tree Detection Outputs
2.5.4. Number of Trees Estimation in Sample Plots
2.5.5. Sample Plots Classification Based on Several Attributes
3. Results
3.1. Impact of Parameterization of ITD Methods on Estimation Errors
3.2. Choosing Optimal Parameters for ITD Methods
3.3. Analysis of ITD Results on Some Sample Plots (Case Study)
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Feature | Supraśl Forest District | Żednia Forest District |
---|---|---|
Airing date | 23 August 2015 | 30 August 2017 |
Scanning density [pts/m] | 7.30 | 6.70 |
Point cloud density [pts/m] | 27.13 | 19.64 |
Density of single point returns [pts/m] | 4.89 | 4.06 |
Flight Altitude (AGL) [m] | 550 (510–590) | 520 |
Flight Altitude (MSL) [m] | 690–717 | 687–713 |
Scanning coverage | 30% | 25% |
Number of strips | 26 | 19 |
Length of strips [km] | 390 | 186 |
Data coverage area [km] | 161.7 | 70 |
Scanning angle (FOV) | 60° | 60° |
Category: | A | B [m] | C [%] | D [m] | E [m] | F [%] |
---|---|---|---|---|---|---|
g1: | Żednia [112] | ≤12.62 [485] | 0–20 [328] | 0–100 [227] | 0–2 [182] | 0–10 [156] |
g2: | Supraśl [410] | >12.62 [37] | 20–30 [117] | 100–250 [180] | 2–5 [181] | 10–20 [196] |
g3: | - | - | 30–100 [77] | >250 [115] | >5 [159] | >20 [170] |
Cat. | Group | Minimizing RMSE | Minimizing MAPE | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LMF+GR Percentile | Ws—Window Size | Ws—Std. Dev. | Ws—Pixel Size | RMSE | RMSE | LMF+GR Percentile | Ws—Window Size | Ws—Std. Dev. | Ws—Pixel Size | MAPE | MAPE | ||
A | g1 | 3.50 | 1 | 1 | 0.35 | 8.18 | 7.53 | 4.50 | 1 | 1 | 0.35 | 17.81 | 17.60 |
A | g2 | 4.50 | 1 | 1 | 0.40 | 7.79 | 8.62 | 7.00 | 1 | 2 | 0.40 | 23.97 | 22.34 |
B | g1 | 3.50 | 1 | 1 | 0.40 | 7.36 | 8.09 | 7.00 | 1 | 1 | 0.40 | 23.53 | 22.01 |
B | g2 | 3.50 | 1 | 1 | 0.35 | 12.89 | 11.55 | 3.50 | 1 | 1 | 0.35 | 12.77 | 11.10 |
C | g1 | 3.50 | 1 | 1 | 0.35 | 8.57 | 9.49 | 6.50 | 1 | 1 | 0.40 | 21.58 | 20.88 |
C | g2 | 4.50 | 2 | 1 | 0.35 | 6.81 | 6.42 | 9.00 | 2 | 3 | 0.35 | 26.30 | 21.18 |
C | g3 | 3.50 | 1 | 1 | 0.35 | 5.86 | 5.72 | 9.00 | 1 | 1 | 0.40 | 22.34 | 19.45 |
D | g1 | 3.50 | 1 | 1 | 0.35 | 8.85 | 10.26 | 4.50 | 1 | 1 | 0.35 | 17.65 | 18.53 |
D | g2 | 3.00 | 1 | 2 | 0.35 | 6.17 | 5.65 | 5.50 | 2 | 1 | 0.35 | 21.21 | 17.63 |
D | g3 | 8.00 | 3 | 1 | 0.35 | 6.57 | 5.82 | 13.00 | 3 | 1 | 0.35 | 31.06 | 28.67 |
E | g1 | 3.00 | 1 | 2 | 0.35 | 8.27 | 10.54 | 4.50 | 1 | 1 | 0.35 | 17.81 | 20.91 |
E | g2 | 4.50 | 1 | 2 | 0.40 | 7.43 | 7.20 | 7.00 | 1 | 1 | 0.40 | 25.21 | 21.53 |
E | g3 | 4.50 | 2 | 1 | 0.35 | 6.84 | 6.69 | 10.50 | 1 | 1 | 0.40 | 24.27 | 24.40 |
F | g1 | 4.00 | 1 | 1 | 0.35 | 6.06 | 6.63 | 8.00 | 1 | 2 | 0.40 | 18.54 | 17.69 |
F | g2 | 3.00 | 1 | 1 | 0.40 | 8.04 | 9.59 | 4.50 | 1 | 1 | 0.40 | 22.29 | 20.73 |
F | g3 | 3.00 | 1 | 3 | 0.35 | 8.52 | 8.45 | 7.00 | 1 | 1 | 0.35 | 27.43 | 29.19 |
G | g1 | 3.50 | 1 | 1 | 0.35 | 7.88 | 8.45 | 6.50 | 1 | 1 | 0.40 | 22.99 | 21.46 |
Tree Height [m] | Segment Area [m] | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
SP | Field Tress | Trees Remotely Detected | Difference [%] | Max | Min | Mean | Std | Max | Min | Mean | Std | Method (Optimization) |
1 | 115 | 125 | 8.70 | 24.45 | 17.79 | 22.23 | 1.21 | 25.29 | 3.64 | 13.41 | 4.17 | LMF (RMSE) |
2 | 103 | 142 | 37.86 | 24.77 | 14.57 | 21.40 | 1.61 | 27.93 | 4.86 | 13.01 | 4.68 | |
3 | 55 | 57 | 3.64 | 35.52 | 14.56 | 28.05 | 6.30 | 52.61 | 2.83 | 26.82 | 12.36 | |
1 | 115 | 117 | 1.74 | 24.45 | 18.91 | 22.40 | 1.06 | 34.79 | 3.43 | 14.69 | 5.96 | WS (RMSE) |
2 | 103 | 120 | 16.50 | 24.77 | 18.27 | 21.75 | 1.32 | 43.73 | 3.18 | 14.78 | 8.68 | |
3 | 55 | 54 | −1.82 | 35.52 | 16.12 | 30.12 | 4.33 | 56.23 | 4.16 | 25.51 | 14.14 | |
1 | 115 | 115 | 0.00 | 24.45 | 17.79 | 22.34 | 1.17 | 28.94 | 3.24 | 14.67 | 4.39 | LMF (MAPE) |
2 | 103 | 125 | 21.36 | 24.77 | 14.57 | 21.44 | 1.74 | 31.97 | 3.64 | 14.69 | 5.60 | |
3 | 55 | 52 | −5.45 | 35.52 | 14.79 | 28.80 | 5.82 | 54.84 | 5.26 | 29.56 | 12.73 | |
1 | 115 | 116 | 0.87 | 24.45 | 18.91 | 22.41 | 1.06 | 33.92 | 3.04 | 15.05 | 6.12 | WS (MAPE) |
2 | 103 | 106 | 2.91 | 24.77 | 18.64 | 21.78 | 1.26 | 50.56 | 3.84 | 16.60 | 9.32 | |
3 | 55 | 53 | −3.64 | 35.52 | 16.12 | 30.05 | 4.54 | 59.84 | 5.28 | 28.20 | 15.36 |
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Kolendo, Ł.; Kozniewski, M.; Ksepko, M.; Chmur, S.; Neroj, B. Parameterization of the Individual Tree Detection Method Using Large Dataset from Ground Sample Plots and Airborne Laser Scanning for Stands Inventory in Coniferous Forest. Remote Sens. 2021, 13, 2753. https://doi.org/10.3390/rs13142753
Kolendo Ł, Kozniewski M, Ksepko M, Chmur S, Neroj B. Parameterization of the Individual Tree Detection Method Using Large Dataset from Ground Sample Plots and Airborne Laser Scanning for Stands Inventory in Coniferous Forest. Remote Sensing. 2021; 13(14):2753. https://doi.org/10.3390/rs13142753
Chicago/Turabian StyleKolendo, Łukasz, Marcin Kozniewski, Marek Ksepko, Szymon Chmur, and Bożydar Neroj. 2021. "Parameterization of the Individual Tree Detection Method Using Large Dataset from Ground Sample Plots and Airborne Laser Scanning for Stands Inventory in Coniferous Forest" Remote Sensing 13, no. 14: 2753. https://doi.org/10.3390/rs13142753