Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series
Abstract
:1. Introduction
- Develop an optimal classification strategy for mapping tree species in natural forests and tree plantations at three class hierarchy levels using dense Formosat-2 SITS.
- Quantify the effect of removing noise (i.e., clouds and cloud shadows) in the time series on classification accuracy.
- Identify the best supervised learning classifier among parametric and nonparametric methods.
- Evaluate the sensitivity of the classification accuracy to the dimensionality of the data and to the feature space, by comparing the classification results based on different feature sets: spectral bands, NDVI index or spectral bands and NDVI.
2. Study Area and Data
2.1. Study Site
2.2. Image Data and Forest Map
2.3. Field Data
3. Methods
3.1. Smoothing of Temporal Profiles
3.2. Training and Validating the Models
4. Results
4.1. Influence of the Classifier
4.2. Influence of the Spectral Features
4.3. Influence of the Smoothing
4.4. Confusions between Species
4.5. Classification Stability
5. Discussion
6. Conclusions
- The classification performance is slighlty influenced by the classifier. RBF-SVM classifier demonstrated the best accuracy at the three levels of the class hierarchy. GMM performed the worst.
- There is any clear benefit of removing cloudy and shady pixels using the Whittaker smoother in our context, even if 32% of the reference pixels were contaminated at least once. By contrast, adding all the dates in the SITS instead of only the cloud-free and shadow-free images enables the classification accuracy to be increased.
- There is no benefit of adding NDVI to spectral bands to discriminate tree species. By contrast, using NDVI alone led to a significant decrease in classification performance, even if the dimensionality of the data is reduced.
- Classification uncertainty exists for complex mixed forests, regarding the spatial disagreements that appear between the maps produced by all the classifiers. By contrast, a high consistency is observed within monospecific broadleaf plantations.
- Among the broadleaf tree species, Oak and Black locust are the most difficult to discriminate. For conifers, the lowest accuracy is observed for Douglas fir.
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A. Confusion Matrix in Pixels from the Smoothed Wbands Dataset and the SVM Classifier
Reference Class | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Predicted Class | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
1 | 29.00 | 0.64 | 0.04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 37.04 | 0.04 | 0.12 | 0 | 0.12 | 0.16 | 0.32 | 0.68 | 0.72 | 0.48 | 0 | 0 |
3 | 0 | 0.20 | 49.92 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 20.04 | 0 | 0 | 0 | 0.32 | 0 | 0 | 0 | 0.40 | 0 |
5 | 0 | 0.56 | 0 | 0.12 | 49.88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
6 | 0 | 0.24 | 0 | 0 | 0 | 27.72 | 0.04 | 0 | 0 | 0 | 0.16 | 0 | 0 |
7 | 0 | 0.04 | 0 | 0 | 0 | 0.16 | 71.80 | 0 | 0 | 0 | 0 | 0 | 0 |
8 | 0 | 0.24 | 0 | 0.08 | 0 | 0 | 0 | 20.60 | 0.92 | 0.40 | 0 | 0 | 0 |
9 | 0 | 0.04 | 0 | 0.56 | 0.12 | 0 | 0 | 0.64 | 27.52 | 0.12 | 0 | 0 | 0 |
10 | 0 | 0 | 0 | 0.52 | 0 | 0 | 0 | 0.12 | 0.56 | 17.72 | 0 | 0.44 | 0 |
11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20.36 | 0 | 0 |
12 | 0 | 0 | 0 | 1.56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25.16 | 0 |
13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.32 | 0 | 0 | 0 | 16.00 |
Appendix B. Temporal Signatures in Each Spectral Band of the SITS for Each Broadleaf and Conifer Tree Species
Appendix C. Boxplots of the NDVI Index of the SITS for Each Broadleaf and Conifer Tree Species
Appendix D. Smoothing of Temporal Profiles Using Whittaker
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Level 1 | Level 2 | Level 3 | Sample Size |
---|---|---|---|
Broadleaf | Deciduous | Silver birch (Betula pendula) | 85 |
Broadleaf | Deciduous | Oak (Quercus robur/pubescens/petraea) | 113 |
Broadleaf | Deciduous | Red oak (Quercus rubra) | 145 |
Broadleaf | Deciduous | European ash (Fraxinus excelsior) | 80 |
Broadleaf | Deciduous | Aspen (Populus tremula) | 209 |
Broadleaf | Deciduous | Black locust (Robinia pseudoacacia) | 59 |
Broadleaf | Deciduous | Willow (Salix spp.) | 51 |
Broadleaf | Evergreen | Eucalyptus (Eucalyptus spp.) | 148 |
Conifer | Pine | Corsican pine (Pinus nigra subsp. laricio) | 62 |
Conifer | Pine | Maritime pine (Pinus pinaster) | 87 |
Conifer | Pine | Black pine (Pinus nigra) | 55 |
Conifer | Other conifer | Douglas fir (Pseudotsuga menziesii) | 66 |
Conifer | Other conifer | Silver fir (Abies alba) | 75 |
Dataset | Composition |
---|---|
W | Smoothed time series based on Whittaker including spectral bands only (17 dates) |
W | Smoothed time series based on Whittaker including NDVI only (17 dates) |
W | Smoothed time series based on Whittaker including spectral bands and NDVI (17 dates) |
C | Non-smoothed (cloudy) time series including spectral bands only (17 dates) |
C | Non-smoothed (cloudy) time series including NDVI only (17 dates) |
C | Non-smoothed (cloudy) time series including spectral bands and NDVI (17 dates) |
R | Non-smoothed time series with no cloud coverage or cloud shadows on forests including spectral bands only (14 dates) |
R | Non-smoothed time series with no cloud coverage or cloud shadows on forests including NDVI only (14 dates) |
R | Non-smoothed time series with no cloud coverage or cloud shadows on forests including spectral bands and NDVI (14 dates) |
GMM | SVM | RF | k-NN | |
---|---|---|---|---|
Level 1/all datasets | 0.91 ± 0.02 | 0.96 ± 0.01 | 0.93 ± 0.02 | 0.95 ± 0.01 |
Level 2/all datasets | 0.93 ± 0.01 | 0.95 ± 0.01 | 0.93 ± 0.01 | 0.94 ± 0.01 |
Level 3/all datasets | 0.92 ± 0.01 | 0.93 ± 0.01 | 0.90 ± 0.01 | 0.91 ± 0.01 |
Reference Class | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Predicted Class | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
1 | 100 | 1.64 | 0.08 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 94.97 | 0.08 | 0.52 | 0 | 0.43 | 0.22 | 1.45 | 2.27 | 3.80 | 2.29 | 0 | 0 |
3 | 0 | 0.51 | 99.84 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 87.13 | 0 | 0 | 0 | 1.45 | 0 | 0 | 0 | 1.54 | 0 |
5 | 0 | 1.44 | 0 | 0.52 | 99.76 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
6 | 0 | 0.62 | 0 | 0 | 0 | 99.00 | 0.06 | 0 | 0 | 0 | 0.76 | 0 | 0 |
7 | 0 | 0.10 | 0 | 0 | 0 | 0.57 | 99.72 | 0 | 0 | 0 | 0 | 0 | 0 |
8 | 0 | 0.62 | 0 | 0.35 | 0 | 0 | 0 | 93.64 | 3.07 | 2.11 | 0 | 0 | 0 |
9 | 0 | 0.10 | 0 | 2.43 | 0.24 | 0 | 0 | 2.91 | 91.73 | 0.63 | 0 | 0 | 0 |
10 | 0 | 0 | 0 | 2.26 | 0 | 0 | 0 | 0.55 | 1.87 | 93.46 | 0 | 1.69 | 0 |
11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 96.95 | 0 | 0 |
12 | 0 | 0 | 0 | 6.78 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 96.77 | 0 |
13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.07 | 0 | 0 | 0 | 100 |
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Sheeren, D.; Fauvel, M.; Josipović, V.; Lopes, M.; Planque, C.; Willm, J.; Dejoux, J.-F. Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series. Remote Sens. 2016, 8, 734. https://doi.org/10.3390/rs8090734
Sheeren D, Fauvel M, Josipović V, Lopes M, Planque C, Willm J, Dejoux J-F. Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series. Remote Sensing. 2016; 8(9):734. https://doi.org/10.3390/rs8090734
Chicago/Turabian StyleSheeren, David, Mathieu Fauvel, Veliborka Josipović, Maïlys Lopes, Carole Planque, Jérôme Willm, and Jean-François Dejoux. 2016. "Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series" Remote Sensing 8, no. 9: 734. https://doi.org/10.3390/rs8090734