The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae)
Abstract
:1. Introduction
2. Experimental Section
2.1. Sample Collection
Epidermal Type (ET) | Species | Subfamily |
---|---|---|
Bladder cells | Drosanthemum curtophyllum | Ruschioideae |
L. Bolus NM | ||
Drosanthemum archeri | Ruschioideae | |
L. Bolus NM | ||
Mesembryanthemum crystallinum | Mesembryanthemoideae | |
Linnaeus | ||
Delosperma vogtsii | Ruschioideae | |
L. Bolus NM | ||
Trichodiadema rupicola | Ruschioideae | |
L. Bolus NM | ||
Hair cover | Gibbaeum shandii | Ruschioideae |
N. E. Brown GC | ||
Cheiridopsis purpurea | Ruschioideae | |
L. Bolus NM | ||
Braunsia apiculata | Ruschioideae | |
(Kensit)L. Bolus | ||
Juttadineteria albata | Ruschioideae | |
(L. Bolus) L. Bolus | ||
Odontophorus marlothii | Ruschioideae | |
N. E. Brown GC | ||
Rough | Neohenricia spiculata | Ruschioideae |
S. A. Hammer CSJ | ||
Hereroa puttkameriana | Ruschioideae | |
(Dinter & A. Berger) Dinter & Schwantes | ||
Nananthus aloides | Ruschioideae | |
(Haworth) Schwantes GF | ||
Rhombophyllum dolabriforme | Ruschioideae | |
(Linnaeus) Schwantes GF | ||
Stomatium bolusiae | Ruschioideae | |
Schwantes MDK | ||
Smooth | Glottiphyllum longum | Ruschioideae |
(Haworth) N. E. Brown | ||
Malotigena frantiskae-niederlovae | Ruschioideae | |
Niederle | ||
Aptenia haeckeliana | Mesembryanthemoideae | |
(A. Berger) Bittrich ex Gerbaulet | ||
Schlechteranthus hallii | Ruschioideae | |
L. Bolus | ||
Bergeranthus scapiger | Ruschioideae | |
(Haworth) Schwantes | ||
Wax cover | Malephora purpurea-crocea | Ruschioideae |
(Haworth) Schwantes GF | ||
Oscularia steenbergensis | Ruschioideae | |
(L. Bolus) H. E. K. Hartmann Bradleya | ||
Scopelogena verruculata | Ruschioideae | |
(Linnaeus) L. Bolus | ||
Amphibolia succulent | Ruschioideae | |
(L.Bolus) H. E. K. Hartmann Bradleya | ||
Enarganthe octonaria | Ruschioideae | |
(L. Bolus) N. E. Brown GC |
2.2. Hyperspectral Data Collection
2.3. Data Preprocessing
2.3.1. Jump Correction
2.3.2. Standard Normal Variate Transformation
2.4. Classification
2.4.1. Partial Least Squares Discriminant Analysis
2.4.2. Validation
2.4.3. Accuracy Assessment
3. Results
3.1. Partial Least Squares Discriminant Analysis: Classification
SNV/no SNV | Segment | Validation | OA | 95% CI | p-Value | Kappa | |
---|---|---|---|---|---|---|---|
Min | Max | ||||||
SNV | Full | Training | 0.81 | 0.67 | 0.91 | <0.05 | 0.77 |
SNV | Full | Cross | 0.55 | 0.48 | 0.61 | <0.05 | 0.44 |
SNV | VIS | Training | 0.54 | 0.39 | 0.69 | <0.05 | 0.43 |
SNV | VIS | Cross | 0.52 | 0.46 | 0.58 | <0.05 | 0.40 |
SNV | NIR | Training | 0.52 | 0.37 | 0.67 | <0.05 | 0.40 |
SNV | NIR | Cross | 0.50 | 0.44 | 0.57 | <0.05 | 0.38 |
SNV | SWIR I | Training | 0.21 | 0.10 | 0.35 | 0.01 | |
SNV | SWIR I | Cross | 0.37 | 0.31 | 0.43 | <0.05 | 0.21 |
SNV | SWIR II | Training | 0.67 | 0.52 | 0.80 | <0.05 | 0.59 |
SNV | SWIR II | Cross | 0.77 | 0.71 | 0.82 | <0.05 | 0.71 |
no SNV | Full | Training | 0.60 | 0.45 | 0.74 | <0.05 | 0.51 |
no SNV | Full | Cross | 0.92 | 0.88 | 0.95 | <0.05 | 0.90 |
no SNV | VIS | Training | 0.46 | 0.31 | 0.61 | <0.05 | 0.32 |
no SNV | VIS | Cross | 0.54 | 0.48 | 0.60 | <0.05 | 0.43 |
no SNV | NIR | Training | 0.29 | 0.17 | 0.44 | 0.11 | |
no SNV | NIR | Cross | 0.34 | 0.28 | 0.40 | <0.05 | 0.17 |
no SNV | SWIR I | Training | 0.27 | 0.15 | 0.42 | 0.09 | |
no SNV | SWIR I | Cross | 0.35 | 0.29 | 0.41 | 0.19 | |
no SNV | SWIR II | Training | 0.56 | 0.41 | 0.71 | <0.05 | 0.45 |
no SNV | SWIR II | Cross | 0.55 | 0.48 | 0.61 | <0.05 | 0.44 |
Ground Truth (GT) | |||||||||
---|---|---|---|---|---|---|---|---|---|
Dataset | Bladder | Hairs | Rough | Smooth | Wax | # of Classified Spectra | UA | ||
CV/no SNV | Classification | Bladder | 47 | 0 | 1 | 2 | 0 | 50 | 94.0% |
Hairs | 0 | 47 | 0 | 0 | 1 | 48 | 97.9% | ||
Rough | 0 | 2 | 43 | 1 | 0 | 46 | 93.5% | ||
Smooth | 3 | 1 | 6 | 44 | 1 | 55 | 80.0% | ||
Wax | 0 | 0 | 0 | 2 | 47 | 49 | 95.9% | ||
# of GT Spectra | 50 | 50 | 50 | 49 | 49 | 248 | |||
PA | 94.0% | 94.0% | 86.0% | 89.8% | 95.9% | ||||
Ground Truth (GT) | |||||||||
Bladder | Hairs | Rough | Smooth | Wax | # of Classified Spectra | UA | |||
Training/SNV | Classification | Bladder | 10 | 0 | 1 | 1 | 0 | 12 | 83.3% |
Hairs | 0 | 7 | 1 | 0 | 0 | 8 | 87.5% | ||
Rough | 0 | 1 | 7 | 1 | 0 | 9 | 77.8% | ||
Smooth | 0 | 0 | 1 | 6 | 0 | 7 | 85.7% | ||
Wax | 0 | 2 | 0 | 1 | 9 | 12 | 75.0% | ||
# of GT Spectra | 10 | 10 | 10 | 9 | 9 | 48 | |||
PA | 100.0% | 70.0% | 70.0% | 66.7% | 100.0% |
3.2. Variable Importance for Projection
4. Discussion
5. Conclusions
Supplementary Files
Supplementary File 1Acknowledgments
Author Contributions
Conflicts of Interest
References
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Heim, R.H.-J.; Jürgens, N.; Große-Stoltenberg, A.; Oldeland, J. The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae). Remote Sens. 2015, 7, 16901-16914. https://doi.org/10.3390/rs71215862
Heim RH-J, Jürgens N, Große-Stoltenberg A, Oldeland J. The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae). Remote Sensing. 2015; 7(12):16901-16914. https://doi.org/10.3390/rs71215862
Chicago/Turabian StyleHeim, René Hans-Jürgen, Norbert Jürgens, André Große-Stoltenberg, and Jens Oldeland. 2015. "The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae)" Remote Sensing 7, no. 12: 16901-16914. https://doi.org/10.3390/rs71215862
APA StyleHeim, R. H. -J., Jürgens, N., Große-Stoltenberg, A., & Oldeland, J. (2015). The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae). Remote Sensing, 7(12), 16901-16914. https://doi.org/10.3390/rs71215862