Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Aboveground Biomass and Carbon Estimation Using Remote Sensing
3.2. Tree Species Identification Using Remote Sensing
3.3. Tree Species Diversity Mapping Using Remote Sensing
3.4. Forest Cover Mapping and Change Detection with Remote Sensing
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Reference | Country | Sensor Name | Algorithm/Method | Area | Accuracy |
---|---|---|---|---|---|
[39] | Bolivia | Quickbird | LR | 4 ha | R2 = 0.70 |
[50] | Hong Kong | AVNIR-2 | SWR, LR | 100 km2 | R2 = 0.88 RMSE = 32 t/ha |
[47] | Costa Rica | LiDAR, HYDICE | OLS, GLS, | Not specified | R2 = 0.90 RMSE = 38.3 Mg/ha |
[40] | Yucatan Peninsula | LiDAR | OLS | 9 ha | R2 = 0.89 |
[43] | Nepal | LiDAR, GeoEye-1 | LR | 5821 ha | R2 = 0.81 |
[42] | Taiwan | LiDAR | MLR | Not specified | R2 = 0.91 RMSE = 15–210 tons/ha |
[51] | Nepal | Landsat 8 | RF, MLR | Not specified | R2 = 0.95 RMSE = 13.3 t/ha |
[48] | Ecuador | LiDAR | LR | ~85 km2 | R2 = 0.91 |
[52] | India | Sentinel 1 SAR, Sentinel 2 | RF, SGB | 400 km2 | R2 = 0.71 RMSE = 105.027 t/ha |
[45] | China | Sentinel 1 SAR, Sentinel 2 | RF, ANN, GWR, SVR | Not specified | r = 1 RMSE = 0.08 Mg/ha |
[53] | China | Landsat 8, Landsat TM | RF | 6.06 million ha | R2 = 0.73 RMSE = 6.66 Mg/ha |
[46] | China | Sentinel 1 SAR, Sentinel 2 | SWR, GWR, ANN, SVR, RF | 17,481 ha | R2 = 0.97 RMSE = 61.11 Mg/ha |
[54] | China | Landsat 8 | LR, RF, XGBoost | 13.00 × 104 km2 | R2 = 0.37 |
[49] | India | MODIS | LR | 6292.68 km2 | R2 = 0.94 |
[55] | Ecuador | Landsat 8 | PLSR | R2 = 0.31 | |
[44] | Nepal | GeoEye-1, RapidEye, LiDAR | MLR | 1888 ha | R2 = 0.88 RMSE = 44 kg/tree |
[20] | India | Sentinel 2 | RF, ANN, SVM | 84.46 km2 | R2 = 0.86 RMSE = 0.26% |
Reference | Country | Sensor/ Data Set | Algorithm | Area | Average Accuracy |
---|---|---|---|---|---|
[58] | Costa Rica | HYDICE | LDA, ML, SAM | Not specified | ≥92% |
[59] | Costa Rica | HYDICE | SMA | Not specified | Not specified |
[60] | Costa Rica | HYDICE | RF | Not specified | ≥85% |
[67] | USA | CAO-Alpha System, LiDAR | SVM | 70 ha | ≥90% |
[68] | USA | CAO-Alpha System | LDA, RDA, QDA, Linear-SVM, Radial-SVM, ANN, KNN | 70 ha | ≥73% |
[56] | Brazil | ASD Spectroradiometer | LDA | Not specified | 96% |
[61] | Panama | DAP | LR, Visual Analysis | 150 ha | 76% |
[69] | Hawaii, USA | EO-1 Hyperion | MESMA, WASMA | 1500 ha | R2 = 0.74 KC = 0.65 |
[62] | South Africa | WorldView-2 | SVM | 6000 ha | ≥89% |
[63] | South Africa | WorldView-2 | SVM, ANN | Not specified | ≥77% |
[65] | Taiwan | QuickBird | ML | Not specified | SCKC = 0.99 |
[30] | Brazil | AISA EAGLE, AISA HAWK, WorldView-3 | LDA, Radial-SVM, L-SVM, RF | Not specified | ≥84% |
[66] | China | LiDAR | RF | Not specified | 86.2% |
[19] | Ghana | AISA EAGLE, Sentinel 2 | SVM, ML | 815 km2 | 92.34% |
[64] | South Africa | ASD Spectroradiometer, WorldView-2, RapidEye, | PLS-RF | Not specified | >92% |
[21] | Brazil | WorldView-2 | SVM | 237.6 ha | 96% |
[57] | Brazil | WorldView-3 | MESMA | ≥70% |
Reference | Country | Sensor name | Algorithm | Area | Accuracy |
[77] | Malaysia | Landsat TM | GRNN, MLPNN | 300 km2 | r = 0.69 |
[78] | Hawaii, USA | AVIRIS | LR, MCS | Not specified | R2 = 0.85 |
[74] | Kyrgyzstan | ASTER | DCA | 90 km2 | R2 = 0.61 |
[76] | Panama | Landsat ETM+, AIRSAR | LR | Not specified | R2 = 0.51 |
[73] | India | IKONOS, Landsat ETM+ | Not specified | Not specified | r = 0.33 |
[70] | Sierra Leone | AISA EAGLE | RF | Not specified | R2 = 0.84.9 RMSE = 0.30 |
[71] | Kenya | Landsat-5 TM, Landsat-7 ETM+ | LR | 850 km2 | R2 = 0.36 |
[72] | Kenya | AISA EAGLE | K Means clustering, LR | Not specified | R2 = 0.50 RMSE = 3 |
[75] | China | PHI-3, LiDAR | RF | Not specified | R2 = 0.83, RMSE = 0.25 |
Reference | Country | Sensor Name | Algorithm | Area | Accuracy |
---|---|---|---|---|---|
[84] | India | IRS-1 C WiFS | K-means clustering | Not Specified | 85% |
[83] | Bangladesh | SIR-C, ALOS PALSAR | ML | Not specified | 83% |
[82] | Bhutan | Landsat ETM+ | ML | Not specified | 87.5% |
[79] | Nigeria | Landsat 7 ETM+ | ML | Not specified | 97% |
[81] | Belize | Landsat 8 | CART | Not specified | 97% |
[80] | South Africa | Landsat 8 | RF, SVM | 2218 | 95% |
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Gyamfi-Ampadu, E.; Gebreslasie, M. Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review. Forests 2021, 12, 739. https://doi.org/10.3390/f12060739
Gyamfi-Ampadu E, Gebreslasie M. Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review. Forests. 2021; 12(6):739. https://doi.org/10.3390/f12060739
Chicago/Turabian StyleGyamfi-Ampadu, Enoch, and Michael Gebreslasie. 2021. "Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review" Forests 12, no. 6: 739. https://doi.org/10.3390/f12060739
APA StyleGyamfi-Ampadu, E., & Gebreslasie, M. (2021). Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review. Forests, 12(6), 739. https://doi.org/10.3390/f12060739