Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales
Abstract
:1. Introduction
2. Methodology
2.1. Study Area and Long-Term Experimental Plots
2.2. Ground Measurements and UAV Flight Missions
2.2.1. Ground Measurements of AGB and CH
2.2.2. Acquisition and Pre-Processing of UAV Remote Sensing Data
2.3. Data Processing and Modeling
2.3.1. Generating Six Datasets
2.3.2. Statistical Analysis and Methods Used for AGB Estimation
3. Results
3.1. Generating Multi-Source UAV-Based Datasets at Multi-Spatial Resolutions
3.2. AGB Estimate Using 3D Point Clouds at Multi-Spatial Resolutions
3.2.1. AGB Estimate Based on the multiSPEC-4C Data
3.2.2. AGB Estimate Based on the Micasense RedEdge-M Data
3.2.3. AGB Estimate Based on the LiDAR Data
3.3. Comparing the AGB Estimate Accuracy of the 3D Data with Multi-Spatial Resolutions
4. Discussion
4.1. Effects of the 3D Information on AGB Estimation
4.2. Performances of Two Type Datasets for AGB Estimate
4.3. Effects of Spatial Resolution of Datasets on AGB Estimate
4.4. Performances of Four Methods on AGB Estimate
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Field | Plot Number | Plot Size | Treatments 1 |
---|---|---|---|
Field 1 | 25 | 5 m × 6 m | CK, NP, NK, PK, NPK, NPK-S |
Field 2 | 32 | 5 m × 10 m | 0%fc, 40%fc, 60%fc, 80%fc |
Field 3 | 32 | 5 m × 10 m | N0-60%fc, N70-60%fc, N140-60%fc, N210-60%fc, N280-60%fc; N0-80%fc, N70-80%fc, N140-80%fc, N210-80%fc, N280-80%fc |
Fresh AGB (t·ha−1) | Dry AGB (t·ha−1) | CH (cm) | |
---|---|---|---|
Min | 1.68 | 0.20 | 67.67 |
Mean | 10.23 | 1.25 | 113.92 |
Max | 34.00 | 3.74 | 166.50 |
sd | 7.18 | 0.83 | 22.23 |
cv (%) | 70.18 | 66.90 | 19.51 |
AGB | Methods | Dataset 1 (T10, Mean, Max, sd, cv) | Dataset 2 (T10, Mean) | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | MRE | R2 | RMSE | MRE | ||
Fresh | OLS | 0.51 | 5.02 | 0.53 | 0.49 | 5.06 | 0.51 |
RF | 0.41 | 5.49 | 0.53 | 0.44 | 5.36 | 0.47 | |
BP | 0.20 | 7.31 | 0.72 | 0.39 | 5.59 | 0.51 | |
SVM | 0.44 | 5.35 | 0.55 | 0.45 | 5.28 | 0.52 | |
Dry | OLS | 0.53 | 0.57 | 0.50 | 0.48 | 0.63 | 0.50 |
RF | 0.45 | 0.62 | 0.49 | 0.39 | 0.69 | 0.48 | |
BP | 0.50 | 0.59 | 0.50 | 0.47 | 0.63 | 0.51 | |
SVM | 0.47 | 0.61 | 0.50 | 0.45 | 0.64 | 0.49 |
AGB | Methods | Dataset 3 (T10, Mean, Max, sd, cv) | Dataset 4 (T10, Mean) | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | MRE | R2 | RMSE | MRE | ||
Fresh | OLS | 0.77 | 3.41 | 0.30 | 0.76 | 3.50 | 0.31 |
RF | 0.75 | 3.61 | 0.31 | 0.75 | 3.54 | 0.30 | |
BP | 0.64 | 4.48 | 0.43 | 0.67 | 4.15 | 0.33 | |
SVM | 0.74 | 3.61 | 0.31 | 0.77 | 3.48 | 0.29 | |
Dry | OLS | 0.73 | 0.45 | 0.29 | 0.75 | 0.44 | 0.30 |
RF | 0.69 | 0.48 | 0.31 | 0.72 | 0.46 | 0.29 | |
BP | 0.71 | 0.46 | 0.29 | 0.73 | 0.45 | 0.28 | |
SVM | 0.74 | 0.45 | 0.28 | 0.76 | 0.44 | 0.27 |
AGB | Methods | Dataset 5 (T10, Mean, Max, sd, cv) | Dataset 6 (T10, Mean) | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | MRE | R2 | RMSE | MRE | ||
Fresh | OLS | 0.85 | 2.72 | 0.25 | 0.86 | 2.70 | 0.25 |
RF | 0.83 | 3.18 | 0.29 | 0.90 | 2.29 | 0.22 | |
BP | 0.70 | 4.01 | 0.34 | 0.89 | 2.36 | 0.22 | |
SVM | 0.85 | 2.78 | 0.26 | 0.85 | 2.78 | 0.24 | |
Dry | OLS | 0.82 | 0.37 | 0.27 | 0.81 | 0.37 | 0.26 |
RF | 0.80 | 0.41 | 0.28 | 0.85 | 0.33 | 0.23 | |
BP | 0.77 | 0.42 | 0.27 | 0.80 | 0.39 | 0.26 | |
SVM | 0.81 | 0.38 | 0.26 | 0.81 | 0.38 | 0.25 |
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Zhu, W.; Sun, Z.; Peng, J.; Huang, Y.; Li, J.; Zhang, J.; Yang, B.; Liao, X. Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sens. 2019, 11, 2678. https://doi.org/10.3390/rs11222678
Zhu W, Sun Z, Peng J, Huang Y, Li J, Zhang J, Yang B, Liao X. Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sensing. 2019; 11(22):2678. https://doi.org/10.3390/rs11222678
Chicago/Turabian StyleZhu, Wanxue, Zhigang Sun, Jinbang Peng, Yaohuan Huang, Jing Li, Junqiang Zhang, Bin Yang, and Xiaohan Liao. 2019. "Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales" Remote Sensing 11, no. 22: 2678. https://doi.org/10.3390/rs11222678
APA StyleZhu, W., Sun, Z., Peng, J., Huang, Y., Li, J., Zhang, J., Yang, B., & Liao, X. (2019). Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sensing, 11(22), 2678. https://doi.org/10.3390/rs11222678