Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Materials
2.2.1. Mask of Initial Agricultural Lands
2.2.2. Field Data
2.2.3. Landsat Data
2.2.4. Auxiliary Data
2.3. Methods
2.3.1. Multi-Temporal Mosaics Generation
2.3.2. Mosaics Normalization
2.3.3. Spectral Indices
2.3.4. FCI Threshold Identification
2.3.5. NDVISI Threshold Identification
2.3.6. Threshold Verification
2.3.7. Water and Urban Objects Masks Generation
2.3.8. Forest Cover Products Generation
2.3.9. Assessment of Forest Cover Area Dynamics
3. Results
3.1. Landsat Mosaics Generation and Normalization
3.2. FCI Threshold Identification and Its Validation
3.3. NDVISI Threshold Identification and Its Validation
3.4. Forest Cover Dynamic Map and Its Analysis
4. Discussion
4.1. Landsat Mosaics
4.2. Forest Cover Index
4.3. Bare Soil Index (NDVISI)
4.4. Accuracy of Forest Cover Assessment
4.5. Natural Afforestation Drivers
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Region | Population | Area, sq. km | Agricultural Lands, % | Forest Cover, % | Coniferous Forests, %1 | Deciduous Forests, % 1 |
---|---|---|---|---|---|---|
Rudnya district (RU) | 21,490 | 2111 | 55.5 | 24.5 | 22.5 | 77.5 |
Liozno district (BY) | 15,777 | 1418 | 43.7 | 44.0 | 27.2 | 72.8 |
NP “Smolenskoe Poozerie” (RU) | n/a | 1462 | 13.1 | 74.0 | 26.0 | 74.0 |
Band Name | Sensor (Satellite) and Its Temporal Coverage 1 | |||||
---|---|---|---|---|---|---|
TM (Landsat 4, 5), 1984–2011 | ETM+ (Landsat 7), 1999–2003 | OLI (Landsat 8), 2013–2020 | ||||
Band Number | Wavelength (µm) | Band Number | Wavelength (µm) | Band Number | Wavelength (µm) | |
Blue | 1 | 0.45–0.52 | 1 | 0.44–0.51 | 2 | 0.45–0.51 |
Green | 2 | 0.52–0.60 | 2 | 0.52–0.60 | 3 | 0.53–0.59 |
Red | 3 | 0.63–0.69 | 3 | 0.63–0.69 | 4 | 0.64–0.67 |
NIR | 4 | 0.76–0.90 | 4 | 0.77–0.90 | 5 | 0.85–0.88 |
SWIR1 | 5 | 1.55–1.75 | 5 | 1.55–1.75 | 6 | 1.57–1.65 |
SWIR2 | 7 | 2.08–2.35 | 7 | 2.07–2.35 | 7 | 2.11–2.29 |
Metric | Statistic Type | Landsat Bands | |||||
---|---|---|---|---|---|---|---|
Blue | Green | Red | NIR | SWIR1 | SWIR2 | ||
R2 | Max | 0.970 | 0.988 | 0.989 | 0.987 | 0.996 | 0.994 |
Median | 0.858 | 0.943 | 0.950 | 0.958 | 0.981 | 0.973 | |
Min | 0.536 | 0.861 | 0.891 | 0.912 | 0.964 | 0.955 | |
RMSE 1 | Max | 0.003 | 0.003 | 0.003 | 0.015 | 0.006 | 0.003 |
Median | 0.001 | 0.002 | 0.001 | 0.010 | 0.004 | 0.002 | |
Min | 0.001 | 0.001 | 0.001 | 0.006 | 0.002 | 0.001 |
Class Type | Sample Points | |||
---|---|---|---|---|
Forest Cover | Grassland | Total Points | ||
Classification by FCI threshold | Forest Cover | 71 | 2 | 73 |
Grassland | 5 | 102 | 107 | |
Total points | 76 | 104 | 173 | |
Po | Observed probability | 0.961 | ||
Pe | Expected probability | 0.515 | ||
k | Cohen’s kappa | 0.920 |
Class Type | Control Points | |||
---|---|---|---|---|
Forest Cover | Grassland | Total Points | ||
Classification by FCI threshold | Forest Cover | 40 | 0 | 40 |
Grassland | 2 | 41 | 43 | |
Total points | 42 | 41 | 81 | |
Po | Observed probability | 0.976 | ||
Pe | Expected probability | 0.500 | ||
k | Cohen’s kappa | 0.952 |
Class Type | Sample Points | |||
---|---|---|---|---|
Bare Soil | Grassland | Total points | ||
Classification by NDVISI threshold | Bare Soil | 83 | 0 | 83 |
Grassland | 0 | 104 | 104 | |
Total points | 83 | 104 | 187 | |
Po | Observed probability | 1.0 | ||
Pe | Expected probability | 0.506 | ||
k | Cohen’s kappa | 1.0 |
Class Type | Control Points | |||
---|---|---|---|---|
Bare Soil | Grassland | Total points | ||
Classification by NDVISI threshold | Bare Soil | 54 | 0 | 54 |
Grassland | 0 | 41 | 41 | |
Total points | 54 | 41 | 95 | |
Po | Observed probability | 1.0 | ||
Pe | Expected probability | 0.509 | ||
k | Cohen’s kappa | 1.0 |
District | 1985 | 2000 | 2010 1 | |||
---|---|---|---|---|---|---|
FCI | GLAD | FCI | GLAD | FCI | GLAD | |
Rudnya (RU) | 31.1 | 34.0 | 36.3 | 38.6 | 44.7 | 43.8 |
Liozno (BY) | 48.3 | 46.3 | 46.3 | 47.3 | 48.2 | 47.9 |
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Ershov, D.V.; Gavrilyuk, E.A.; Koroleva, N.V.; Belova, E.I.; Tikhonova, E.V.; Shopina, O.V.; Titovets, A.V.; Tikhonov, G.N. Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus. Remote Sens. 2022, 14, 322. https://doi.org/10.3390/rs14020322
Ershov DV, Gavrilyuk EA, Koroleva NV, Belova EI, Tikhonova EV, Shopina OV, Titovets AV, Tikhonov GN. Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus. Remote Sensing. 2022; 14(2):322. https://doi.org/10.3390/rs14020322
Chicago/Turabian StyleErshov, Dmitry V., Egor A. Gavrilyuk, Natalia V. Koroleva, Elena I. Belova, Elena V. Tikhonova, Olga V. Shopina, Anastasia V. Titovets, and Gleb N. Tikhonov. 2022. "Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus" Remote Sensing 14, no. 2: 322. https://doi.org/10.3390/rs14020322
APA StyleErshov, D. V., Gavrilyuk, E. A., Koroleva, N. V., Belova, E. I., Tikhonova, E. V., Shopina, O. V., Titovets, A. V., & Tikhonov, G. N. (2022). Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus. Remote Sensing, 14(2), 322. https://doi.org/10.3390/rs14020322