Integrating Phenological and Geographical Information with Artificial Intelligence Algorithm to Map Rubber Plantations in Xishuangbanna
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Landsat Images and Data Pre-Processing
2.3. Ground Reference Data
2.4. Seasonal Changes and Spectral Features of Rubber Plantations and Natural Forests
2.5. Classification Algorithm
2.6. Rubber Plantation Mapping in Xishuangbanna during 1990–2020
2.7. Validation and Comparison
3. Results
3.1. The Phenological Characteristics of Rubber Plantations and Natural Forests
3.2. Random Forest Algorithm-Based Classifier Performs Better Than the Other Two Classifiers
3.3. Either Phenology or Topography Could Improve Mapping Accuracy
3.4. Natural Forests and Shrublands Were the Major Sources of the Increased Rubber Plantations
4. Discussion
4.1. Combining Phenological and Topographical Information to Improve the Mapping Efficiency for Rubber Plantations
4.2. Random Forest Classifier Could Improve the Accuracy and Stability for Mapping Rubber Plantations
4.3. Reversal of Rubber Plantations Expansion in Xishuangbanna
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Year | Path/Row | |||
---|---|---|---|---|
129/045 | 130/044 | 130/045 | 131/045 | |
1989 | 1 | 1 | 1 | 3 |
1990 | 0 | 0 | 1 | 1 |
1991 | 3 | 3 | 2 | 0 |
1998 | 1 | 0 | 1 | 0 |
2000 | 1 | 2 | 3 | 0 |
2001 | 0 | 1 | 3 | 0 |
2009 | 1 | 1 | 1 | 1 |
2010 | 0 | 2 | 3 | 1 |
2011 | 1 | 0 | 1 | 1 |
2018 | 1 | 2 | 4 | 1 |
2019 | 2 | 1 | 5 | 1 |
2020 | 0 | 0 | 4 | 0 |
ML | PA | UA | |||||||
Land use types | L | PL | TL | PTL | L | PL | TL | PTL | |
Natural Forests | 99.29% | 98.43% | 99.77% | 99.47% | 98.66% | 98.91% | 96.37% | 97.33% | |
Rubber Plantations | 92.84% | 92.43% | 96.38% | 95.83% | 95.22% | 96.64% | 98.11% | 97.36% | |
Shrublands | 87.18% | 95.92% | 95.98% | 98.68% | 75.43% | 75.90% | 83.61% | 81.23% | |
Non-Forests | 95.60% | 95.48% | 92.13% | 91.66% | 98.93% | 99.20% | 99.17% | 99.36% | |
QDT | PA | UA | |||||||
Land use types | L | PL | TL | PTL | L | PL | TL | PTL | |
Natural Forests | 98.75% | 98.55% | 98.64% | 98.94% | 96.45% | 97.21% | 94.16% | 94.73% | |
Rubber Plantations | 79.02% | 89.51% | 92.72% | 94.52% | 90.19% | 93.82% | 94.98% | 95.63% | |
Shrublands | 70.29% | 76.78% | 84.25% | 88.05% | 54.28% | 70.43% | 77.41% | 81.87% | |
Non-Forests | 95.97% | 96.81% | 91.76% | 91.62% | 97.19% | 97.27% | 97.86% | 98.05% | |
RF | PA | UA | |||||||
Land use types | L | PL | TL | PTL | L | PL | TL | PTL | |
Natural Forests | 99.49% | 99.53% | 99.58% | 99.73% | 98.18% | 98.24% | 97.76% | 97.92% | |
Rubber Plantations | 83.76% | 95.46% | 95.60% | 97.53% | 93.50% | 97.64% | 98.28% | 98.80% | |
Shrublands | 81.95% | 88.51% | 94.66% | 95.86% | 62.30% | 84.35% | 86.81% | 90.04% | |
Non-Forests | 95.51% | 97.40% | 96.86% | 96.40% | 97.71% | 98.50% | 99.43% | 99.39% | |
KA | OA | ||||||||
Classifiers | L | PL | TL | PTL | L | PL | TL | PTL | |
ML | 93.73% | 94.23% | 94.87% | 94.69% | 95.41% | 95.76% | 96.24% | 96.10% | |
QDT | 86.99% | 91.47% | 91.54% | 92.68% | 90.22% | 93.70% | 93.72% | 94.59% | |
RF | 90.10% | 95.63% | 96.25% | 96.96% | 92.63% | 96.83% | 97.28% | 97.80% |
Area (1000 km2) | Change Rate (% yr) | |||||||
---|---|---|---|---|---|---|---|---|
Land Use Types | 1990 | 2000 | 2010 | 2020 | 1990–2000 | 2000–2010 | 2010–2020 | 1990–2020 |
Natural Forests | 12.91 | 11.87 | 10.50 | 10.69 | −0.81% | −1.15% | 0.18% | −0.57% |
Rubber Plantations | 1.45 | 2.65 | 4.32 | 4.46 | 8.23% | 6.31% | 0.31% | 6.89% |
Shrublands | 3.04 | 2.46 | 2.98 | 2.14 | −1.91% | 2.10% | −2.81% | −0.99% |
Non- Forests | 1.77 | 2.19 | 1.37 | 1.88 | 2.41% | −3.76% | 3.76% | 0.22% |
1990 | |||||
2000 | Land Use Types | Natural Forests | Rubber Plantations | Shrublands | Non-Forests |
Natural Forests | 53.99 | 0.98 | 5.31 | 1.62 | |
Rubber Plantations | 5.21 | 4.85 | 2.33 | 1.43 | |
Shrublands | 5.39 | 0.61 | 5.47 | 1.37 | |
Non-Forests | 2.74 | 1.14 | 2.76 | 4.79 | |
2000 | |||||
2010 | Land Use Types | Natural Forests | Rubber Plantations | Shrublands | Non-Forests |
Natural Forests | 48.12 | 2.00 | 3.31 | 1.34 | |
Rubber Plantations | 7.13 | 9.95 | 2.89 | 2.58 | |
Shrublands | 5.81 | 1.23 | 5.75 | 2.75 | |
Non-Forests | 0.85 | 0.63 | 0.89 | 4.77 | |
2010 | |||||
2020 | Land Use Types | Natural Forests | Rubber Plantations | Shrublands | Non-Forests |
Natural Forests | 45.30 | 4.30 | 5.20 | 0.96 | |
Rubber Plantations | 3.79 | 14.83 | 3.55 | 1.08 | |
Shrublands | 4.16 | 1.42 | 4.52 | 1.07 | |
Non-Forests | 1.53 | 2.01 | 2.26 | 4.03 |
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Yang, J.; Xu, J.; Zhai, D.-L. Integrating Phenological and Geographical Information with Artificial Intelligence Algorithm to Map Rubber Plantations in Xishuangbanna. Remote Sens. 2021, 13, 2793. https://doi.org/10.3390/rs13142793
Yang J, Xu J, Zhai D-L. Integrating Phenological and Geographical Information with Artificial Intelligence Algorithm to Map Rubber Plantations in Xishuangbanna. Remote Sensing. 2021; 13(14):2793. https://doi.org/10.3390/rs13142793
Chicago/Turabian StyleYang, Jianbo, Jianchu Xu, and De-Li Zhai. 2021. "Integrating Phenological and Geographical Information with Artificial Intelligence Algorithm to Map Rubber Plantations in Xishuangbanna" Remote Sensing 13, no. 14: 2793. https://doi.org/10.3390/rs13142793