Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas
2.2. Data
2.3. Methodology
2.3.1. Analysis of Spectral Characteristics and Separability
2.3.2. Analysis of Crown Textural Information
2.3.3. Analysis of Growth Environment
2.3.4. Development of Species Classification Algorithm
3. Results
3.1. Spectral Characteristics and Separability
3.2. Crown Texture
3.3. Comparison of Growth Environments
3.4. Tree Species Classification
3.4.1. Gwangneung Forest Area
3.4.2. Mt. Baekdu Area
3.4.3. North and South Goseong-gun
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Classification | Satellite Type | Gwangneung Forest Area | Mt. Baekdu Area | Goseong-Gun Area |
---|---|---|---|---|
Multispectral (13 bands, 10 m) | Sentinel-2 | 28 April 2018 23 May 2018 2 June 2018 7 July 2018 1 August 2018 25 September 2018 30 October 2018 8 April 2019 | 10 May 2019 ~10 June 2019 1 October 2019 ~30 November 2019 | 23 May 2019 25 October 2018 |
Very high-resolution satellite imagery (4 bands, 0.5 m) | GeoEye-1 WorldView-3 | 11 April 2016 26 June 2016 |
Korean Red Pine | Korean Pine | Japanese Larch | Needle Fir | Oak | |
---|---|---|---|---|---|
Elevation (m) | 185 ± 71 | 192 ± 76 | 196 ± 69 | 145 ± 58 | 211 ± 82 |
Slope (º) | 14.8 ± 5.9 | 15.1 ± 6.3 | 15.1 ± 5.9 | 12.0 ± 6.0 | 16.6 ± 6.8 |
Species | Korean Red Pine | Korean Pine | Japanese Larch | Needle Fir | Oak | Total | UA |
---|---|---|---|---|---|---|---|
Korean red pine | 1792 | 100 | 50 | 45 | 104 | 2091 | 0.86 |
Korean pine | 217 | 1406 | 274 | 80 | 117 | 2094 | 0.67 |
Japanese larch | 81 | 167 | 1702 | 32 | 113 | 2095 | 0.81 |
Needle fir | 28 | 36 | 37 | 1983 | 16 | 2100 | 0.94 |
Oak | 83 | 75 | 131 | 18 | 1785 | 2092 | 0.85 |
Total | 2201 | 1784 | 2194 | 2158 | 2135 | 10,472 | |
PA | 0.81 | 0.79 | 0.78 | 0.92 | 0.84 | ||
Overall accuracy | 0.83 | kappa statistic | 0.83 |
Species | Korean Red Pine | Korean Pine | Japanese Larch | Oak | Total | UA | |
---|---|---|---|---|---|---|---|
Korean red pine | 6266 | 112 | 74 | 42 | 6494 | 0.96 | |
Korean pine | 78 | 6745 | 75 | 52 | 6950 | 0.97 | |
Japanese larch | 312 | 191 | 5683 | 681 | 6867 | 0.83 | |
Oak | 107 | 94 | 521 | 6014 | 6736 | 0.89 | |
Total | 6763 | 7142 | 6353 | 6789 | 27,047 | ||
PA | 0.93 | 0.94 | 0.89 | 0.89 | |||
Overall accuracy | 0.91 | kappa statistic | 0.91 |
Species | Korean Red Pine | Korean Pine | Japanese Larch | Oak | Total | UA | |
---|---|---|---|---|---|---|---|
Korean red pine | 6266 | 112 | 74 | 42 | 6494 | 0.96 | |
Korean pine | 78 | 6745 | 75 | 52 | 6950 | 0.97 | |
Japanese larch | 312 | 191 | 5683 | 681 | 6867 | 0.83 | |
Oak | 107 | 94 | 521 | 6014 | 6736 | 0.89 | |
Total | 6763 | 7142 | 6353 | 6789 | 27,047 | ||
PA | 0.93 | 0.94 | 0.89 | 0.89 | |||
Overall accuracy | 0.91 | kappa statistic | 0.91 |
Species | Korean Red Pine | Korean Pine | Japanese Larch | Needle Fir | Oak | Total | UA |
---|---|---|---|---|---|---|---|
Korean red pine | 16,513 | 1441 | 390 | 1008 | 1683 | 21,035 | 0.79 |
Korean pine | 666 | 19,601 | 151 | 221 | 377 | 21,016 | 0.93 |
Japanese larch | 310 | 216 | 19,980 | 243 | 195 | 20,944 | 0.95 |
Needle fir | 1129 | 458 | 531 | 14,507 | 4337 | 20,962 | 0.69 |
Oak | 2705 | 1117 | 661 | 6134 | 10,313 | 20,930 | 0.49 |
Total | 21,323 | 22,833 | 21,713 | 22,113 | 16,905 | 104,887 | |
PA | 0.77 | 0.86 | 0.92 | 0.66 | 0.61 | ||
Overall accuracy | 0.77 | kappa statistic | 0.77 |
Species | Korean Red Pine | Korean Pine | Japanese Larch | Needle Fir | Oak | Total | UA |
---|---|---|---|---|---|---|---|
Korean red pine | 24,606 | 1615 | 533 | 1169 | 1629 | 29,552 | 0.83 |
Korean pine | 973 | 27,726 | 505 | 383 | 369 | 29,956 | 0.93 |
Japanese larch | 782 | 554 | 27,324 | 1069 | 204 | 29,933 | 0.91 |
Needle fir | 1341 | 697 | 1244 | 22,253 | 4230 | 29,765 | 0.75 |
Oak | 2710 | 1068 | 669 | 6204 | 10,279 | 20,930 | 0.49 |
Total | 30,412 | 31,660 | 30,275 | 31,078 | 16,711 | 140,136 | |
PA | 0.81 | 0.88 | 0.90 | 0.72 | 0.62 | ||
Overall accuracy | 0.80 | kappa statistic | 0.80 |
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Lim, J.; Kim, K.-M.; Kim, E.-H.; Jin, R. Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables. Remote Sens. 2020, 12, 2049. https://doi.org/10.3390/rs12122049
Lim J, Kim K-M, Kim E-H, Jin R. Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables. Remote Sensing. 2020; 12(12):2049. https://doi.org/10.3390/rs12122049
Chicago/Turabian StyleLim, Joongbin, Kyoung-Min Kim, Eun-Hee Kim, and Ri Jin. 2020. "Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables" Remote Sensing 12, no. 12: 2049. https://doi.org/10.3390/rs12122049