Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery
"> Figure 1
<p>Picture of red leaf blotch symptoms: first ochre spots on a leaf (<b>a</b>); moderately affected leaf with some necrotic spots (<b>b</b>); necrotic leaf (<b>c</b>); and severely affected tree (<b>d</b>). Pictures were taken on 18 July 2014 in Córdoba.</p> "> Figure 2
<p>Reflectance spectrum of healthy leaves and, green, ochre, and necrotic areas of affected leaves measured with PolyPen RP400 operating in the spectral mode of 256 bands in the 400–790 nm region with 8 nm FWHM. Measurements were taken on fully-expanded sunny leaves from 1–1.5 meters high in the canopy on 18 July.</p> "> Figure 3
<p>Hyperspectral mosaic (<b>a</b>) obtained with the hyperspectral sensor on board the manned platform at 30-cm resolution. Pure almond crowns were identified using automatic object-based crown detection (<b>b</b>). Sample crown reflectance obtained by the hyperspectral imagery from red leaf blotch asymptomatic, initially symptomatic, and severely symptomatic almond trees is shown in (<b>c</b>). Red square (<b>a</b>) is shown in detail in (<b>b</b>).</p> "> Figure 4
<p>Thermal scene (<b>a</b>) obtained with the thermal camera on board the manned platform at 40-cm resolution, enabling pure almond crown identification (<b>b</b>). Pure almond crowns were identified using automatic object-based crown detection. Red square (<b>a</b>) is shown in detail in (<b>b</b>).</p> "> Figure 5
<p>Mean values of chlorophyll meter readings (SPAD units) (<b>a</b>); chlorophyll fluorescence (Fs) (<b>b</b>); photochemical reflectance index (PRI<sub>570</sub>) (<b>c</b>); and stomatal conductance (gs, mmols·m<sup>−2</sup>·s<sup>−1</sup>) (<b>d</b>); and measured on 24 asymptomatic, 29 ochre, and nine necrotic areas in affected leaves, and 20 locations within healthy leaves, located in the same stems as the affected leaves. Measurements were taken on fully-expanded, sunny leaves, between 11:30 and 12:30 GMT on 18 July. Analysis of variance of each index was conducted and asterisks indicate significant differences from the asymptomatic plants according to Tukey or Kruskall–Walli’s test at <span class="html-italic">p</span> < 0.05. Error bars indicate standard errors.</p> "> Figure 6
<p>Mean values of VOG1 (<b>a</b>); SIPI (<b>b</b>); Blue/Red ratio (BR1) (<b>c</b>) and healthy-index (HI) (<b>d</b>) of 11 green, 11 ochre, and 10 necrotic areas in affected leaves, and of six locations within healthy leaves nearby. Measurements were taken on sunny fully-expanded leaves with a Polypen RP400 device, between 11:30 and 12:30 GMT on 18 July. Analysis of variance of each index was conducted and asterisks indicate significant differences from the asymptomatic plants according to Tukey or Kruskall–Walli’s test at <span class="html-italic">p</span> < 0.05. Error bars indicate standard errors.</p> "> Figure 7
<p>Mean values of VOG (<b>a</b>); SIPI (<b>b</b>); chlorophyll fluorescence FLD2 (<b>c</b>); PRI<sub>570</sub> (<b>d</b>); red index (R) (<b>e</b>); Tc-Ta (<b>f</b>); NDVI (<b>g</b>); and healthy-index (HI) (<b>h</b>) for every red leaf blotch severity level calculated from thermal and hyperspectral imagery acquired on 11 July 2014. Analysis of variance of each index was conducted and asterisks indicate significant differences from the asymptomatic plants according to Dunnett‘s two-tailed test or Mann–Whitney U test at <span class="html-italic">p</span> < 0.05. Error bars indicate standard errors. Disease severity levels are shown as: A = asymptomatic, I = initial, M = moderate and S = severe.</p> "> Figure 8
<p>Classification of 52 almond trees assessed in the flight of 11 July 2014 into four red leaf blotch severity classes based on the first and second canonical variates from the canonical discriminant analysis.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site Description
2.2. Leaf-Level Measurements
2.3. Disease Assessment at Tree Crown Level
2.4. Airborne Imagery
2.5. Data Analyses
3. Results
3.1. Leaf-Level Observations
3.2. Airborne Imagery Results
3.2.1. Red Leaf Blotch Detection by Univariate Analyses
3.2.2. Red Leaf Blotch Quantification by Multivariate Analyses
4. Discussion
5. Conclusions
Acknowledgements
Author Contributions
Conflicts of Interest
Abbreviations
AGL | Above Ground Level |
ANOVA | standard Analysis of Variance |
FLD | Fraunhofer Line Depth |
FOV | Field Of View |
fps | frames per second |
Fs | Steady-state chlorophyll fluorescence |
FWHM | Full Width at Half Maximum |
GMT | Greenwich Mean Time |
gs | Stomatal conductance |
IFOV | Instantaneous Field of View |
IMU | Inertial Measuring Unit |
LDA | Linear Discriminant Analysis |
SVM | Support Vector Machine |
Ta | Air Temperature |
Tc-Ta | Normalized canopy temperature |
Tc | Canopy Temperature |
VNIR | Visible and Near-Infrared |
κ | Kappa coefficient |
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Vegetation Indices | Equation | Reference |
---|---|---|
Structural Indices | ||
Normalized Difference Vegetation Index | [29] | |
Renormalized Difference Vegetation Index | [30] | |
Simple Ratio | [31] | |
Modified Simple Ratio | [32] | |
Optimized Soil-Adjusted Vegetation Index | [33] | |
Modified Soil-Adjusted Vegetation Index | [34] | |
Modified Triangular Vegetation Indices | [35] | |
[35] | ||
Modified Chlorophyll Absorption Ratio Indices | [35] | |
[35] | ||
Enhanced Vegetation Index | EVI = 2.5(R800 − R670)/(R800 + 6R670 − 7.5R400 + 1) | [36] |
Lichtenthaler Index | [37] | |
Pigment indices Chlorophyll a+b indices | ||
Vogelmann Indices | [38] | |
[38] | ||
[38] | ||
Gitelson and Merzlyak indices | [39] | |
[39] | ||
Chlorophyll Index Red Edge | [40] | |
Simple Ratio Pigment Index | [41] | |
Normalized Phaeophytinization Index | [42] | |
Normalized Pigments Index | [43] | |
Carter Indices | [44] | |
Pigment Specific Simple Ratio Chlorophyll a | [45] | |
Pigment Specific Simple Ratio Chlorophyll b | [45] | |
Pigment Specific Normalized Difference | [45] | |
Reflectance Band Ratio Index | [46] | |
Carotenoid Indices | ||
Structure-Intensive Pigment Index | [41] | |
Plant Senescencing Reflectance Index | [47] | |
Pigment Specific Simple Ratio Carotenoids | [45] | |
Lichtenhaler Index | [37] | |
Carotenoid Reflectance Indices | [48] | |
[48] | ||
Ratio Analysis of Reflectance Spectra | [49] | |
Xanthophyll indices | ||
Photochemical Reflectance Index (570) | [50] | |
Photochemical Reflectance Index (515) | [51] | |
R/G/B Indices | ||
Redness Index | [52] | |
Greenness Index | [18] | |
Blue Index | [18] | |
Blue/Green Indices | [53] | |
[53] | ||
Blue/Red Indices | [54] | |
[54] | ||
Red/Green Indices | [53] | |
Lichtenthaler Index | [37] | |
Chlorophyll Fluorescence | ||
FLD2 | [54,55,56] | |
Plant Disease Index | ||
Healthy-Index | [57] |
Vegetation Indices | ANOVA | Kruskal–Wallis | ||
---|---|---|---|---|
P | HSD Tukey | P | Hom. Groups | |
Chlorophyll a+b indices | ||||
VOG1 | 0.000 | A-A-B-B | ||
VOG2 | 0.000 | B-B-A-AB | ||
VOG3 | 0.000 | B-B-A-AB A-A-AB-B | ||
GM1 | 0.002 | AB-A-B-B | ||
GM2 | 0.000 | |||
CI | 0.000 | A-A-B-B | ||
SRPI | 0.000 | A-A-B-C | B-B-B-A | |
NPQI | 0.000 | |||
NPCI | 0.000 | B-B-AB-A | ||
CTRI1 | 0.000 | A-A-B-B | ||
PSSRa | 0.000 | A-A-B-B | ||
PSSRb | 0.000 | A-A-B-C | ||
PSNDc | 0.000 | A-A-A-B | ||
Carotenoid indices | ||||
SIPI | 0.000 | AB-A-BC-C | ||
PSRI | 0.000 | C-CB-AB-A | ||
PSSRc | 0.000 | AB-A-AB-B | ||
LIC3 | 0.000 | AB-B-B-A | ||
Xanthophyll indices | ||||
PRI570 | 0.000 | A-A-B-B | ||
PRI515 | 0.000 | AB-B-B-A | ||
R/G/B indices | ||||
R | 0.000 | AB-AB-A-B | ||
G | 0.000 | A-A-A-B | ||
B | 0.040 | AB-AB-A-B | ||
BG1 | 0.000 | B-B-B-A | ||
BG2 | 0.000 | B-B-B-A | ||
BR1 | 0.000 | A-A-B-B | ||
BR2 | 0.000 | A-A-B-B | ||
RGI | 0.000 | B-B-AB-A | ||
LIC2 | 0.000 | A-A-B-B | ||
Plant disease index | ||||
HI | 0.000 | A-A-B-B |
Source | 11 July 2014 | 30 July 2014 | ||||||
---|---|---|---|---|---|---|---|---|
P | Disease Severity Levels | P | Disease Severity Levels | |||||
Init | Mod | Sev | Init | Mod | Sev | |||
Thermal | ||||||||
Tc-Ta | 0.001 | X | X | 0.000 | X | X | ||
Structural indices | ||||||||
NDVI | 0.000 | X | X | 0.000 | X | X | ||
RDVI | 0.001 | X | X | 0.000 | X | X | ||
SR | 0.000 | X | X | 0.000 | X | X | X | |
MSR | 0.000 | X | X | 0.000 | X | X | X | |
OSAVI | 0.000 | X | X | 0.000 | X | X | ||
MSAVI | 0.001 | X | X | 0.000 | X | X | ||
MTVI1 | 0.002 | X | X | 0.000 | X | X | X | |
MTVI2 | 0.001 | X | X | 0.000 | X | X | ||
MCARI1 | 0.002 | X | X | 0.000 | X | X | X | |
MCARI2 | 0.001 | X | X | 0.000 | X | X | ||
EVI | 0.001 | X | X | 0.000 | X | X | ||
LIC1 | 0.000 | X | X | 0.000 | X | X | ||
Pigment indices | ||||||||
Chlor a+b indices | ||||||||
VOG | 0.000 | X | X | X | 0.000 | X | X | X |
VOG2 | 0.000 | X | X | X | 0.000 | X | X | X |
VOG3 | 0.000 | X | X | X | 0.000 | X | X | X |
GM1 | 0.000 | X | X | X | 0.000 | X | X | X |
GM2 | 0.000 | X | X | X | 0.000 | X | X | X |
CI | 0.000 | X | X | X | 0.000 | X | X | X |
SRPI | 0.002 | X | X | 0.000 | X | X | X | |
NPQI | 0.815 | 0.015 | X | |||||
NPCI | 0.002 | X | X | 0.000 | X | X | X | |
CTR1 | 0.003 | X | X | 0.004 | X | X | X | |
DattNIRCabCx+c | 0.000 | X | X | X | 0.000 | X | X | X |
PSSRa | 0.000 | X | X | X | 0.000 | X | X | X |
PSSRb | 0.000 | X | X | X | 0.000 | X | X | X |
PSNDc | 0.000 | X | X | X | 0.000 | X | X | X |
Carotenoid indices | ||||||||
SIPI | 0.000 | X | X | X | 0.000 | X | X | X |
PSRI | 0.001 | X | X | 0.000 | X | X | ||
PSSRc | 0.000 | X | X | X | 0.000 | X | X | X |
LIC3 | 0.002 | X | X | X | 0.000 | X | X | |
CRI550 | 0.000 | X | X | 0.000 | X | X | ||
CRI550_515 | 0.000 | X | X | 0.000 | X | X | ||
RARS | 0.000 | X | X | X | 0.000 | X | ||
Xanthophyll indices | ||||||||
PRI570 | 0.002 | X | X | 0.002 | X | X | ||
PRI515 | 0.001 | X | X | X | 0.000 | X | X | |
R/G/B indices | ||||||||
B | 0.323 | 0.082 | ||||||
G | 0.002 | X | X | 0.000 | X | X | ||
R | 0.000 | X | X | 0.000 | X | X | ||
BGI1 | 0.599 | 0.083 | ||||||
BGI2 | 0.070 | 0.001 | X | |||||
BRI1 | 0.014 | X | X | 0.000 | X | X | X | |
BRI2 | 0.002 | X | X | 0.000 | X | X | ||
RGI | 0.001 | X | X | 0.000 | X | X | ||
LIC2 | 0.001 | X | X | 0.000 | X | X | ||
Fluorescence | ||||||||
FLD2 | 0.000 | X | X | X | 0.000 | X | X | X |
Plant disease index | ||||||||
HI | 0.000 | X | X | 0.000 | X | X |
Source | Wilks’ Lambda | Partial R2 | F Value | Pr > F | Pr < Lambda |
---|---|---|---|---|---|
VOG | 0.484 | 0.5163 | 35.58 | <0.0001 | <0.0001 |
PSRI | 0.341 | 0.2941 | 13.75 | <0.0001 | <0.0001 |
PSNDc | 0.159 | 0.1323 | 4.63 | 0.0047 | <0.0001 |
FLD2 | 0.300 | 0.1205 | 4.48 | 0.0055 | <0.0001 |
GM1 | 0.211 | 0.1191 | 4.24 | 0.0074 | <0.0001 |
CRI550 | 0.257 | 0.0771 | 2.67 | 0.0517 | <0.0001 |
LIC2 | 0.147 | 0.0747 | 2.42 | 0.0710 | <0.0001 |
CRI550_515 | 0.278 | 0.0742 | 2.59 | 0.0571 | <0.0001 |
BRI2 | 0.184 | 0.0708 | 2.34 | 0.0789 | <0.0001 |
MTVI2 | 0.239 | 0.0670 | 2.27 | 0.0851 | <0.0001 |
NPQI | 0.198 | 0.0631 | 2.09 | 0.1070 | <0.0001 |
Prediction | Training | Validation | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Ground Truth | Class precision | Ground Truth | Class precision | |||||||
Asymp | Init | Mod | Sev | Asymp | Init | Mod | Sev | |||
Asymp | 13 | 0 | 0 | 0 | 100% | 9 | 1 | 0 | 1 | 81.8% |
Init | 1 | 12 | 0 | 0 | 92.3% | 2 | 2 | 1 | 2 | 28.6% |
Mod | 1 | 0 | 14 | 1 | 87.5% | 3 | 2 | 12 | 0 | 70.6% |
Sev | 1 | 4 | 0 | 5 | 50.0% | 0 | 5 | 4 | 8 | 47.1% |
Class recall | 81.3% | 85.7% | 100% | 83.3% | 84.6% | 64.3% | 20.0% | 70.6% | 72.7% | 59.6% |
SCCs | CCCs | |||
---|---|---|---|---|
Source | Variate 1 | Variate 2 | Variate 1 | Variate 2 |
VOG | 3.612 | −4.381 | 0.770 | −0.175 |
PSRI | 2.889 | 4.727 | −0.560 | 0.007 |
PSNDc | 1.580 | 7.278 | 0.731 | −0.145 |
FLD2 | −0.284 | −0.771 | 0.543 | −0.173 |
GM1 | −2.472 | −3.264 | 0.756 | −0.234 |
CRI550 | −4.985 | −3.955 | 0.703 | −0.150 |
LIC2 | 1.050 | −0.723 | 0.669 | 0.015 |
CRI550_515 | 5.577 | 2.701 | 0.714 | −0.111 |
BRI2 | −0.025 | 7.092 | 0.642 | 0.049 |
MTVI2 | 0.568 | 0.546 | 0.637 | 0.097 |
NPQI | −0.228 | −0.516 | −0.047 | −0.055 |
Cumulative proportion | 0.656 | 0.247 | ··· | ··· |
(a) | ||||||||||||||||||||
Prediction | Training | Validation | ||||||||||||||||||
Ground Truth | Class Precision | Ground Truth | Class Precision | |||||||||||||||||
Asymp | Init | Mod | Sev | Asymp | Init | Mod | Sev | |||||||||||||
Asymp | 14 | 0 | 0 | 0 | 100% | 9 | 1 | 0 | 0 | 90.0% | ||||||||||
Init | 1 | 13 | 0 | 0 | 92.9% | 2 | 2 | 0 | 2 | 33.3% | ||||||||||
Mod | 1 | 3 | 13 | 0 | 76.5% | 3 | 5 | 16 | 3 | 59.3% | ||||||||||
Sev | 0 | 0 | 1 | 6 | 85.7% | 0 | 2 | 1 | 6 | 66.7% | ||||||||||
Class recall | 87.5% | 81.3% | 92.9% | 100% | 88.5% | 64.3% | 15.4% | 94.1% | 54.5% | 63.5% | ||||||||||
(b) | ||||||||||||||||||||
Prediction | Training | Validation | ||||||||||||||||||
Ground Truth | Class Precision | Ground Truth | Class Precision | |||||||||||||||||
Asymp | Init | Mod | Sev | Asymp | Init | Mod | Sev | |||||||||||||
Asymp | 16 | 0 | 1 | 0 | 94.1% | 8 | 1 | 0 | 0 | 88.9% | ||||||||||
Init | 0 | 15 | 0 | 0 | 100% | 4 | 8 | 4 | 3 | 42.1% | ||||||||||
Mod | 0 | 1 | 13 | 0 | 92.9% | 2 | 1 | 13 | 8 | 54.2% | ||||||||||
Sev | 0 | 0 | 0 | 6 | 100% | 0 | 0 | 0 | 0 | 0% | ||||||||||
Class recall | 100% | 93.8% | 92.9% | 100% | 96.2% | 57.1% | 80.0% | 76.5% | 0% | 55.8% |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
López-López, M.; Calderón, R.; González-Dugo, V.; Zarco-Tejada, P.J.; Fereres, E. Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery. Remote Sens. 2016, 8, 276. https://doi.org/10.3390/rs8040276
López-López M, Calderón R, González-Dugo V, Zarco-Tejada PJ, Fereres E. Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery. Remote Sensing. 2016; 8(4):276. https://doi.org/10.3390/rs8040276
Chicago/Turabian StyleLópez-López, Manuel, Rocío Calderón, Victoria González-Dugo, Pablo J. Zarco-Tejada, and Elías Fereres. 2016. "Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery" Remote Sensing 8, no. 4: 276. https://doi.org/10.3390/rs8040276