Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Sentinel-2 Data Collection and Processing
3.2. Classification Using U-Net Based Algorithm
3.3. Identifying Priority Afforestation Sites Using Topographic Data
4. Results and Discussion
4.1. Land Classification Results and Accuracy Verification
4.2. Comparison with Previous Studies for the Deforested Area
4.3. Identification of Priority Afforestation Target Site
4.4. Implication of Phenological Classification with Sentinel Satellite and Deep Learning Algorithm
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Used Area | Agriculture | Deciduous | Evergreen | Plateau | Reclaimed | Unstocked | Water | Total | UA | |
---|---|---|---|---|---|---|---|---|---|---|
Used Area | 2 | 1 | 1 | 1 | 0 | 0 | 0 | 8 | 13 | 15.4% |
Agriculture | 4 | 139 | 0 | 0 | 0 | 35 | 0 | 0 | 178 | 78.1% |
Deciduous | 0 | 3 | 484 | 26 | 0 | 3 | 1 | 0 | 517 | 93.6% |
Evergreen | 0 | 0 | 0 | 47 | 0 | 0 | 0 | 0 | 47 | 100% |
Plateau | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 10 | 100% |
Reclaimed | 6 | 12 | 16 | 14 | 0 | 125 | 3 | 0 | 176 | 71% |
Unstocked | 0 | 3 | 0 | 6 | 0 | 6 | 24 | 0 | 39 | 61.5% |
Water | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 10 | 90% |
Total | 13 | 158 | 501 | 94 | 10 | 169 | 28 | 17 | 990 | |
PA | 15.4% | 88.0% | 96.6% | 50% | 100% | 74.0% | 85.7% | 52.9% | 84.8% |
Macro-Class | Sub-Class | Classification Results (ha) | Korean Ministry of Environment (ha) |
---|---|---|---|
Used Area | Used Area | 160,536 | 201,404 |
Agricultural Land | Agricultural Land | 2,164,308 | - |
Reclaimed Forest | 2,137,396 | - | |
Total | 4,301,703 | 3,072,785 | |
Forest | Deciduous Forest | 6,451,421 | - |
Evergreen Forest | 573,256 | - | |
Unstocked Forest | 472,912 | - | |
Total | 7,024,677 | 8,503,797 | |
Grass | Plateau Vegetation | 118,515 | - |
Grass | - | 27,223 | |
Wet Land | Wet Land | - | 38,020 |
Barren | Barren | - | 150,632 |
Water | Water | 80,097 | 166,835 |
Total Area | 12,158,440 | 12,160,697 |
Province | Forested Area | Deforested Area | Deforestation Ratio |
---|---|---|---|
Pyongyang capital city | 50,710 | 88,667 | 64% |
Rason special city | 50,419 | 18,728 | 27% |
Nampho special city | 5513 | 27,641 | 83% |
Kaesong special city | 53,656 | 50,731 | 49% |
South Pyongan | 539,814 | 274,574 | 34% |
North Pyongan | 525,275 | 318,135 | 38% |
Chagang | 1,164,533 | 261,579 | 18% |
South Hwanghae | 184,160 | 283,653 | 61% |
North Hwanghae | 352,934 | 253,148 | 42% |
Kangwon | 749,944 | 214,287 | 22% |
South Hamgyong | 1,239,313 | 319,999 | 21% |
North Hamgyong | 1,051,034 | 278,154 | 21% |
Ryanggang | 1,057,372 | 222,750 | 17% |
Total Area | 7,024,677 | 2,610,308 | 27% |
Class | Classification Results (ha) | Statistics Korea (ha) | Jin et al. (ha) |
---|---|---|---|
Deciduous Forest (a) | 6,451,421 | - | - |
Evergreen Forest (b) | 573,256 | - | - |
Reclaimed Forest (c) | 2,137,395 | 1,217,000 | 2,700,000 |
Unstocked Forest (d) | 472,912 | 1,401,000 | 1,300,000 |
Healthy Forest (a) + (b) | 7,024,677 | 6,767,000 | 8,300,000 |
Deforested Land (c) + (d) | 2,610,308 | 2,618,000 | 4,000,000 |
Total Forest (a) + (b) + (c) + (d) | 9,634,985 | 9,386,000 | 11,695,905 |
Deforestation Ratio | 27.1% | 28% | 34.2% |
Priority Afforestation Site (8°<) | 1,709,618 | 2,618,000 | |
Priority Afforestation Site (15°<) | 1,124,194 | 1,475,000 | -- |
Afforestation Priority Grade | Description | Area (ha) |
---|---|---|
Grade 1 | Unstocked forests with altitudes < 600 m | 258,304 |
Grade 2 | Reclaimed forest with slope > 20° | 293,535 |
Grade 3 | Reclaimed forest with slope 15°–20° | 286,648 |
Grade 4 | Reclaimed forest with slope 8°–15° | 577,564 |
Grade 5 | Reclaimed forest with slope 0°–8° | 721,289 |
Natural restoration area | Deforested areas with altitudes > 600 m | 472,967 |
Total Area | 2,610,308 |
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Kim, J.; Lim, C.-H.; Jo, H.-W.; Lee, W.-K. Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea. Remote Sens. 2021, 13, 2946. https://doi.org/10.3390/rs13152946
Kim J, Lim C-H, Jo H-W, Lee W-K. Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea. Remote Sensing. 2021; 13(15):2946. https://doi.org/10.3390/rs13152946
Chicago/Turabian StyleKim, Joon, Chul-Hee Lim, Hyun-Woo Jo, and Woo-Kyun Lee. 2021. "Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea" Remote Sensing 13, no. 15: 2946. https://doi.org/10.3390/rs13152946
APA StyleKim, J., Lim, C. -H., Jo, H. -W., & Lee, W. -K. (2021). Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea. Remote Sensing, 13(15), 2946. https://doi.org/10.3390/rs13152946