Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation
"> Figure 1
<p>Field high-throughput phenotyping: (<b>a</b>) Unmanned aerial vehicle mounted with a RedEdge MicaSense multispectral camera, a global positioning system and downwelling light sensor; (<b>b</b>) Aerial mission on a field experiment at Plant Breeding Centre, Horsham, Victoria, Australia.</p> "> Figure 2
<p>The processing workflow used for: Modelling—dry weight and fresh weight; Computing intermediate traits—crop height model, crop coverage and crop volume; Using—vegetation index, digital surface model (DSM) and digital terrain model (DTM).</p> "> Figure 3
<p>Correlation between computed crop height model and observed ground truth height measurements at two time points.</p> "> Figure 4
<p>A visual evaluation of the performance of the employed workflow for computing intermediate metrics—crop coverage (CC) and crop volume (CV) at different growth stages (30, 50, 90, 130 and 160 DAS) on three genotypes. The classified CC and CC × VIs images are compared against RGB composite images generated using multispectral bands blue (475 nm), green (560 nm) and red (668 nm).</p> "> Figure 5
<p>Estimation of dry biomass or dry weight (DW): Variability in the correlation of determination (R<sup>2</sup>) for modelling performed using crop volume (CV) and standard vegetation indices (<b>a</b>) across different time points and (<b>b</b>) at all the time points combined against observed DW; (<b>c</b>) Modelling results for DW derived from CV against observed DW data.</p> "> Figure 6
<p>Estimation of fresh biomass or fresh weight (FW): Variability in the correlation of determination (R<sup>2</sup>) of crop volume multiplied using standard vegetation indices (CV × VIs) (<b>a</b>) across different time points and (<b>b</b>) at all the time points combined against observed FW; (<b>c</b>) Modelling results for FW derived from CV × EVI against observed FW readings.</p> "> Figure 7
<p>Growth profile of wheat genotypes at different time points. Using modelled (<b>a</b>) Dry weight and (<b>b</b>) Fresh Weight.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Field Experiment
2.2. Aerial Data Acquisition System and Field Data Collection
2.3. Reflectance Orthomosaic, Digital Surface Model and Digital Terrain Model
2.4. Image Processing and Data Analysis
2.4.1. Vegetation Indices (VIs)
2.4.2. Crop Height Model (CHM)
2.4.3. Crop Coverage (CC)
2.4.4. Crop Volume (CV) and Dry Weight (DW) Modelling
2.4.5. Crop Volume Multiplication with Vegetation Indices (CV×VIs) and Fresh Weight (FW) Modelling
2.4.6. Plot Level Data Analytics
3. Results
3.1. Crop Height Model
3.2. Crop Coverage
3.3. Dry Weight Modelling
3.4. Fresh Weight Modelling
3.5. Genotypic Ranking Using Ground Measurements and UAV-Based Measurements
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Indices | Equation | Reference |
---|---|---|
Normalized difference vegetation index | [10] | |
Enhanced vegetation index | [71] | |
Green normalized difference vegetation index | [72] | |
Normalized difference red-edge index | [73] | |
Renormalized difference vegetation index | [74] | |
Optimized soil adjusted vegetation index | [12] | |
Modified simple ratio | [75] | |
Modified chlorophyll absorption ratio index 1 | [76] | |
Modified chlorophyll absorption ratio index 2 | [76] | |
Modified triangular vegetation index 1 | [76] | |
Modified triangular vegetation index 2 | [76] | |
Pigment specific simple ratio for chlorophyll a | [68] |
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Banerjee, B.P.; Spangenberg, G.; Kant, S. Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation. Remote Sens. 2020, 12, 3164. https://doi.org/10.3390/rs12193164
Banerjee BP, Spangenberg G, Kant S. Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation. Remote Sensing. 2020; 12(19):3164. https://doi.org/10.3390/rs12193164
Chicago/Turabian StyleBanerjee, Bikram Pratap, German Spangenberg, and Surya Kant. 2020. "Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation" Remote Sensing 12, no. 19: 3164. https://doi.org/10.3390/rs12193164