Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning
Abstract
:1. Introduction
- compare the performance of the different feature sets derived from IS and ALS data using SVM and RF classifiers;
- find species or groups of species with ecological or economical function that can be detected relatively accurately; and
- evaluate the impact of up-sampling and different approaches to group the species on the classification accuracy.
2. Material and Methods
2.1. Study Site
2.2. Field Data
2.3. Remote Sensing Data
2.4. Remote Sensing Data Preprocessing
2.5. Segmentation and Preparing Training Data
2.6. Minimum Noise Fraction Transformation
2.7. Narrowband Vegetation Indices
2.8. Point cloud Features
2.9. Feature Selection
2.10. Classification Methods
2.11. Measures of Performance
2.12. Jeffries–Matusita Distance
2.13. Statistical Significance Tests
2.14. Classification Trials
3. Results
3.1. Comparison of Feature Sets and Classifiers
3.2. Feature Selection
3.3. Jeffries–Matusita Distance
3.4. Data Balancing
3.5. Grouping by Frequency
3.6. Single Species Classfication
3.7. Grouping Species Based on Jeffries–Matusita Distance
3.8. Comparison of Different Aproaches
4. Discussion
4.1. Impacts of Classifier, Feature Selection and Data Fusion
4.2. The Impact of Up-Sampling and Grouping of Species on the Classification Results
4.3. Evaluation of the Quality of Airborne Data, Field Measurements and Segmentation
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Species | Abbreviation | Type | Crowns | Pixels |
---|---|---|---|---|
Grevillea robusta | Grerob | exotic | 109 | 5485 |
Acacia mearnsii | Acamea | exotic | 53 | 2437 |
Eucalyptus spp. | Eucspp | exotic | 42 | 2577 |
Persea americana | Perame | exotic | 42 | 1989 |
Cupressus lusitanica | Cuplus | exotic | 31 | 1641 |
Euphorbia kibwezensis | Eupkib | native | 31 | 1181 |
Eriobotrya japonica | Erijap | exotic | 14 | 516 |
Ficus thonningii | Fictho | native | 14 | 948 |
Maesa lanceolata | Maelan | native | 14 | 612 |
Mangifera indica | Manind | exotic | 14 | 668 |
Zimmermania ovata | Zimova | native | 13 | 674 |
Zimmermania commiphora | Zimcom | native | 11 | 426 |
Psidium guajava | Psigua | exotic | 10 | 459 |
Erythrina abyssinica | Eryaby | native | 9 | 415 |
Acacia seyal | Acasey | native | 8 | 417 |
Phoenix reclinata | Phorec | native | 8 | 442 |
Albizia gummifera | Albgum | native | 7 | 387 |
Prunus africana | Pruafr | native | 7 | 410 |
Bridelia micrantha | Brimic | native | 6 | 335 |
Dombeya kirkii | Domkir | native | 6 | 138 |
Ficus sur | Ficsur | native | 6 | 311 |
Combretum collinum | Comcol | native | 5 | 234 |
Cussonia spicata | Cusspi | native | 5 | 215 |
Macademia spp. | Macspp | exotic | 5 | 122 |
Millettia oblata | Milobl | native | 5 | 401 |
Acacia tortilis | Acator | native | 4 | 90 |
Dombeya rotundifolia | Domrot | native | 4 | 169 |
Ficus sycomorus | Ficsyc | native | 4 | 228 |
Newtonia buchananii | Newbuc | native | 4 | 385 |
Syzygium spp. | Syzspp | native | 4 | 174 |
Xymalos monospora | Xymmon | native | 4 | 123 |
Support Vector Machine | Random Forest | ||||
---|---|---|---|---|---|
Feature Set | Accuracy | Kappa | Accuracy | Kappa | |
Refl. | 37.9 | 30.9 | 31.7 | 21.6 | |
NVI | 45.5 | 37.5 | 35.9 | 25.8 | |
MNF | 53.3 | 46.8 | 51.3 | 44.8 | |
ALS | 31.7 | 21.6 | 30.7 | 21.8 | |
Refl.+ALS | 42.9 | 35.7 | 42.9 | 35.6 | |
NVI+ALS | 43.5 | 37.2 | 44.3 | 37.5 | |
MNF + ALS | 57.1 | 52.1 | 54.1 | 48.2 |
Feature Set | No Var. | Feature Names |
---|---|---|
Refl. | 19 | R406, R401, R553, R549, R414, R562, R419, R572, R769, R717, R576, R530, R526, R521, R581, R458, R688, R632, R674 |
NVI | 8 | ACI, ARI, CIred edge, PRI, PSSR, mCARI, CRI1, EVI |
MNF | 10 | MNF9, MNF1, MNF5, MNF7, MNF6, MNF4, MNF8, MNF10, MNF14, MNF2 |
ALS | 5 | HD, MADmedian, P95, AADmedian, min |
Refl. + ALS | 15 | HD, max, MADmedian, MADmean, AADmedian, R406, R562, R558, min, R414, R423, R726, R731, R540, R774 |
NVI + ALS | 17 | ACI, HD, ARI, P95, max, MADmedian, CARI, AADmedian, AADmean, CRI2, CIred edge, HC, min, PRI, PSSR, SR, VIgreen |
MNF + ALS | 13 | MNF9, HD, MNF5, MNF1, MNF4, MNF8, MNF7, MNF6, MNF11, MNF12, P95, MNF10, MNF14 |
R = reflectance, MNF = minimum noise fraction |
Group | Species in the Group | Samples |
---|---|---|
1 | Macspp, Maelan, Erijap, Domrot, Xymmon, ComCol, Zimova | 59 |
2 | Zimcom, Eryaby, Acasey, Acator, Domkir | 38 |
3 | Perame, Ficsur, Phorec, Manind, Brimic, Syzspp | 80 |
4 | Psigua, Cusspi, Cuplus, Pruafr, Ficsyc, Newbuc, Fictho, Milobl, Albgum | 87 |
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Piiroinen, R.; Heiskanen, J.; Maeda, E.; Viinikka, A.; Pellikka, P. Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning. Remote Sens. 2017, 9, 875. https://doi.org/10.3390/rs9090875
Piiroinen R, Heiskanen J, Maeda E, Viinikka A, Pellikka P. Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning. Remote Sensing. 2017; 9(9):875. https://doi.org/10.3390/rs9090875
Chicago/Turabian StylePiiroinen, Rami, Janne Heiskanen, Eduardo Maeda, Arto Viinikka, and Petri Pellikka. 2017. "Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning" Remote Sensing 9, no. 9: 875. https://doi.org/10.3390/rs9090875