Pre-Emptive Detection of Mature Pine Drought Stress Using Multispectral Aerial Imagery
Abstract
:1. Introduction
2. Methods
2.1. Site Description
2.2. Study Overview
2.3. Crown Morphological Traits
2.4. Remote Sensing and Imagery Processing
2.5. Analysis Approach
3. Results
3.1. Crown Morphology
3.2. Multivariate Analysis of Morphological Traits
3.3. Crown Spectral Traits
3.4. Multivariate Analysis of Spectral Traits
3.5. Correlation of Morphological and Spectral Traits
3.6. Classification Tree Model Outcome
4. Discussion
4.1. Effects of Environmental Stresses on Crown Morphology
4.2. Spectral Delineation of Environmental Stresses
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Crown Morphological, and Insect and Disease Traits on Branchlets
Appendix B. List of All Whole Tree, Crown Morphological, and Spectral Traits
TRAIT | M BN | M+N | X BN | X+N | DESCRIPTION |
---|---|---|---|---|---|
WHL | 5.2 | 5.4 | 5.1 | 5.4 | number of retained needle ages on branchlets |
CHL1 | 0 | 1 | 0 | 0.4 | % chlorosis or chlorotic mottle on 1-year needles |
CHL2 | 2 | 3 | 3 | 3 | % chlorosis or chlorotic mottle on 2-year needles |
CHL4 | 12 | 14 | 16 | 15 | % chlorosis or chlorotic mottle on 4-year needles |
CHL6 | 48 | 52 | 44 | 49 | % chlorosis or chlorotic mottle on 6-year needles |
%MxNL1 | 81 | 80 | 77 | 79 | Relative current year needle length |
%FOLLN | 59 | 57 | 61 | 59 | proportion of branchlet length with retained needles |
BRDIA2 | 10.7 | 10.8 | 11.4 | 11.7 | prior year branchlet diameter at its base |
ES | 1.2 | 0.9 | 1.2 | 0.9 | early senescence of needles, whole tree |
Strobili ♂ | 0.02 | 0.02 | 0.02 | 0.00 | frequency of male strobili |
LF DF | 1.72 | 0.01 | 1.34 | 0.00 | additive frequency of both needle defoliators |
Elytroderma | 0.19 | 0.25 | 0.60 | 0.43 | needle cast fungi, presence/absence, whole tree |
DMR | 0.07 | 0.00 | 0.50 | 0.31 | dwarf mistletoe rank |
R TOP | 125.7 | 121.0 | 136.6 | 136.3 | intensity of R wavelength, tree-top |
NDVI TOP | 0.57 | 0.57 | 0.53 | 0.54 | index of productivity, tree-top |
NIR TOP | 475.0 | 459.2 | 444.4 | 452.9 | intensity of NIR wavelength, tree-top |
NIR SC TOP | 0.79 | 0.79 | 0.76 | 0.77 | inverse of NDVI, indexed water content, tree-top |
THERM TOP | 24.6 | 24.2 | 26.3 | 25.8 | needle temperature, tree-top |
R MID | 129.1 | 124.6 | 138.2 | 135.1 | intensity of R wavelength, upper mid crown |
NDVI MID | 0.58 | 0.58 | 0.54 | 0.55 | index of productivity, upper mid crown |
NIR MID | 491.3 | 482 | 459.2 | 479.8 | intensity of NIR wavelength, upper mid crown |
NIR SC MID | 0.79 | 0.79 | 0.77 | 0.78 | indexed water content, upper mid crown |
THERM MID | 24.7 | 24.3 | 27.7 | 27.3 | needle temperature, upper mid crown |
R Δ | −3.46 | −3.58 | −1.56 | −6.64 | [tree-top] − [upper mid crown] R |
NDVI Δ | 0.00 | −0.01 | −0.01 | 0.00 | [tree-top] − [upper mid crown] NDVI |
NIR Δ | −16.31 | −22.90 | −14.94 | −26.88 | [tree-top] − [upper mid crown] NIR |
NIR SC Δ | 0.00 | 0.00 | 0.00 | 0.00 | [tree-top] − [upper mid crown] NIR SC |
THERM Δ | −0.1 | −0.1 | −1.6 | −1.8 | [tree-top] − [upper mid crown] THERM |
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a. | Mesic/Xeric | BN/+N | H2O * N |
---|---|---|---|
CHL4 | 0.018 | 0.241 | 0.096 |
BRDIA2 | 0.011 | 0.905 | 0.751 |
LF DF | <0.0001 | <0.0001 | <0.0001 |
DMR | 0.043 | 0.725 | 0.679 |
b. | |||
R TOP | 0.005 | 0.276 | 0.976 |
NDVI TOP | 0.012 | 0.246 | 0.558 |
NIR SC TOP | 0.005 | 0.274 | 0.590 |
THERM Δ | <0.0001 | 0.348 | 0.327 |
Test | Value | Approx. F | # DF | DenDF | Prob > F |
---|---|---|---|---|---|
Wilks’ λ | 0.4341 | 1.93 | 16 | 98.40 | 0.0150 |
Pillai’s Trace | 0.6450 | 1.68 | 16 | 140.00 | 0.0269 |
Hotelling-Lawley | 1.1275 | 2.19 | 16 | 58.25 | 0.0103 |
Roy’s Max Root | 0.9550 | 8.36 | 4 | 35.00 | <0.0001 |
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Grulke, N.; Maxfield, J.; Riggan, P.; Schrader-Patton, C. Pre-Emptive Detection of Mature Pine Drought Stress Using Multispectral Aerial Imagery. Remote Sens. 2020, 12, 2338. https://doi.org/10.3390/rs12142338
Grulke N, Maxfield J, Riggan P, Schrader-Patton C. Pre-Emptive Detection of Mature Pine Drought Stress Using Multispectral Aerial Imagery. Remote Sensing. 2020; 12(14):2338. https://doi.org/10.3390/rs12142338
Chicago/Turabian StyleGrulke, Nancy, Jason Maxfield, Phillip Riggan, and Charlie Schrader-Patton. 2020. "Pre-Emptive Detection of Mature Pine Drought Stress Using Multispectral Aerial Imagery" Remote Sensing 12, no. 14: 2338. https://doi.org/10.3390/rs12142338
APA StyleGrulke, N., Maxfield, J., Riggan, P., & Schrader-Patton, C. (2020). Pre-Emptive Detection of Mature Pine Drought Stress Using Multispectral Aerial Imagery. Remote Sensing, 12(14), 2338. https://doi.org/10.3390/rs12142338