Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging
<p>Test site: summer barley experiment at Campus Klein-Altendorf agricultural research station in 2013. GCPs, ground control points used for crop surface model (CSM) generation.</p> "> Figure 2
<p>CSM over ground model (<b>top</b>) and derived plant height (<b>bottom</b>) of Plots 8, 7 and 1 (from left to right) of the eastern row of the test site for 14 June and 8 July 2013.</p> "> Figure 3
<p>Scatter plot for PH<sub>ref</sub> and PH<sub>CSM</sub> for all plots (n = 216). R<sup>2</sup> = coefficient of determination; p < 0.0001 for all R<sup>2</sup>.</p> "> Figure 4
<p>Cross-validation relationship of fresh/dry biomass and CSM<sub>PH</sub> for calibration datasets. Model 1 (M1): 70%; M2a: 40 kg N/m<sup>2</sup>; M2b: 80 kg N/m<sup>2</sup>; M3a: old cultivars; M3b: new cultivars and all values; p < 0.0001 for all R<sup>2</sup>.</p> "> Figure 5
<p>Cross-validation scatter plots for observed fresh and dry biomass <span class="html-italic">versus</span> predicted biomass derived from validation datasets M1–M3b (details in <a href="#remotesensing-06-10395-t004" class="html-table">Table 4</a>); p < 0.0001 for all R<sup>2</sup>.</p> "> Figure 6
<p>Scatter plot for dry biomass <span class="html-italic">versus</span> CSM<sub>PH</sub>: lodging and non-lodging plots; n = 216.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Site: Campus Klein-Altendorf, 2013
2.2. Biomass Sampling
Type | Date | Number of Images Collected | BBCH *1 | Point Density (pt./m²) | Ø Image Overlap *2 |
---|---|---|---|---|---|
UAV (ground model) | 30 April 2013 | 216 | |||
UAV | 14 May 2013 | 378 | 2878 | >9 | |
Biomass | 14 May 2013 | tillering (21–27) | |||
UAV | 28 May 2013 | 783 | 2675 | >9 | |
Biomass | 28 May 2013 | tillering-stem elongation (25–35) | |||
UAV | 14 June 2013 | 363 | 2958 | >9 | |
Biomass | 12 June 2013 | booting (41–47) | |||
UAV | 25 June 2013 | 300 | 3452 | >9 | |
Biomass | 25 June 2013 | inflorescence emergence, heading (51–59) | |||
UAV | 8 July 2013 | 342 | 2836 | >9 | |
Biomass | 9 July 2013 | development of fruit (71–75) | |||
UAV | 23 July 2013 | 265 | 2653 | >9 | |
Biomass | 22 July 2013 | development of fruit-ripening (77–89) |
2.3. Platform
2.4. Sensor
2.5. Generating CSMs
PHref (m) | PHCSM (m) | Fresh Biomass (kg/m²) | Dry Biomass (kg/m²) | |
---|---|---|---|---|
Min | 0.14 | −0.03 | 0.22 | 0.03 |
Max | 1.00 | 0.80 | 8.29 | 2.70 |
Mean | 0.55 | 0.43 | 3.24 | 0.81 |
SE | 0.25 | 0.25 | 1.96 | 0.68 |
n | 216 | 216 | 216 | 216 |
2.6. Statistical Analyses
R2 | PHref (m) | PHCSM (m) | Fresh Biomass (kg/m²) | Dry Biomass (kg/m²) |
---|---|---|---|---|
PHref (m) | 1 | |||
PHCSM (m) | 0.92 (lin.) | 1 | ||
fresh biomass (kg/m²) | 0.76 (exp.) | 0.81 (exp.) | 1 | |
dry biomass (kg/m²) | 0.79 (exp.) | 0.82 (exp.) | 0.67 (lin.) | 1 |
3. Results
3.1. Plant Height and Biomass Samples
Calibration/Validation Dataset | Regression Model | n | SE (kg/m2) | R2 | RMSE (kg/m2) | RE (%) |
---|---|---|---|---|---|---|
Fresh Biomass | ||||||
M1: 70%/30% | BIOM = 0.642 × exp(PH × 3.082) | 66 | 3.21 | 0.71 | 1.95 | 60.87 |
M2a: 40/80 kg N/m2 | BIOM = 0.534 × exp(PH × 3.411) | 108 | 3.46 | 0.61 | 2.35 | 67.72 |
M2b: 80/40 kg N/m2 | BIOM = 0.741 × exp(PH × 2.858) | 108 | 2.97 | 0.71 | 1.60 | 54.04 |
M3a: old/new cultivars | BIOM = 0.690 × exp(PH × 3.080) | 120 | 3.49 | 0.61 | 2.15 | 61.50 |
M3b: new/old cultivars | BIOM = 0.591 × exp(PH × 3.135) | 96 | 2.87 | 0.72 | 1.77 | 61.79 |
Dry Biomass | ||||||
M1: 70%/30% | BIOM = 0.073 × exp(PH × 4.309) | 66 | 0.77 | 0.60 | 0.59 | 76.50 |
M2a: 40/80 kg N/m2 | BIOM = 0.057 × exp(PH × 4.922) | 108 | 0.98 | 0.49 | 0.83 | 84.61 |
M2b: 80/40 kg N/m2 | BIOM = 0.083 × exp(PH × 3.960) | 108 | 0.61 | 0.61 | 0.42 | 68.41 |
M3a: old/new cultivars | BIOM = 0.081 × exp(PH × 4.242) | 120 | 0.67 | 0.39 | 0.54 | 79.88 |
M3b: new/old cultivars | BIOM = 0.063 × exp(PH × 4.469) | 96 | 0.83 | 0.68 | 0.64 | 76.28 |
3.2. Biomass Modelling
3.2.1. Model Development
3.2.2. Model Application
4. Discussion
5. Conclusions and Outlook
Acknowledgments
Author Contributions
Conflicts of Interest
References and Notes
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Bendig, J.; Bolten, A.; Bennertz, S.; Broscheit, J.; Eichfuss, S.; Bareth, G. Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging. Remote Sens. 2014, 6, 10395-10412. https://doi.org/10.3390/rs61110395
Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, Bareth G. Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging. Remote Sensing. 2014; 6(11):10395-10412. https://doi.org/10.3390/rs61110395
Chicago/Turabian StyleBendig, Juliane, Andreas Bolten, Simon Bennertz, Janis Broscheit, Silas Eichfuss, and Georg Bareth. 2014. "Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging" Remote Sensing 6, no. 11: 10395-10412. https://doi.org/10.3390/rs61110395