Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey
Abstract
:1. Introduction
1.1. Related Works
1.2. Paper Contributions
- Does BFASTMonitor produce accurate results in the detection of disturbances over coniferous and deciduous forests in the Marmara region of Turkey?
- Does BFASTMonitor produce accurate results in both large- and small-scale deforestation?
- Among water-sensitive and chlorophyll-sensitive VIs, which ones selected in this study indicated highly accurate results?
2. Study Area and Data
2.1. Study Area
2.1.1. Euxine–Colchic Deciduous Forests
2.1.2. Anatolian Coniferous and Deciduous Forests
2.2. Landsat Data
3. Methodology
3.1. Defining Forest and Deforestation Scale
3.2. Spectral Vegetation Indices
3.3. Forest Mask
3.4. BFASTMonitor Implementation
- Formula—regression model formula (harmonic and/or trend component);
- Order—order of the harmonic term;
- Start—starting date of the monitoring period;
- History—specification of the stable history period;
- h—bandwidth relative to the sample size in the MOSUM monitoring process, the numeric between 0–1.
3.5. Reference Data and Validation
4. Results
4.1. Breakpoints and Magnitude
4.2. Accuracy Assessment of Detected Changes
5. Discussions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Study Site | Coordinate (Lat/Long) | Climate |
---|---|---|
Site A (Selected part of Istanbul forest) | 41°11′44.38″ N | Warm and hot weather during the summer, and winter is rainy and mild. Annual precipitation in the year 2020 was 820 mm and the average temperature was 14.95 °C. |
28°45′57.42″ E | ||
Site B (Çanakkale province) | 40°9′4.50″ N | Transition climate type, summer is hot and dry, winter is cold and rainy. July is the warmest month while January is the coldest month with long-term averages of 6.4 °C and 25 °C. |
27°8′58.88″ E |
Vegetation Index | NDVI | NDMI | NBR | EVI | |
---|---|---|---|---|---|
Total Breakpoints | Deciduous forest | 124,822 | 141,229 | 173,126 | 199,275 |
Total Pixels | 275,913 | 275,913 | 275,913 | 275,913 | |
Percentage | 45.24% | 51.19% | 62.74% | 72.22% | |
Total Breakpoints | Coniferous forest | 375,605 | 388,647 | 420,210 | 249,464 |
Total Pixels | 471,281 | 471,281 | 471,281 | 471,281 | |
Percentage | 79.70% | 82.47% | 89.16% | 52.93% |
Vegetation Index | NDVI | NDMI | NBR | EVI | |
---|---|---|---|---|---|
Total Deforested Pixels | Deciduous forest | 45,195 | 35,515 | 49,510 | 61,679 |
Percentage | 16.38% | 12.87% | 17.94% | 22.35% | |
Total Deforested Pixels | Coniferous forest | 24,537 | 14,243 | 34,921 | 13,239 |
Percentage | 5.20% | 3.02% | 7.40% | 2.80% |
Deciduous Forest (A) | Coniferous Forest (B) | ||||||||
---|---|---|---|---|---|---|---|---|---|
NDVI | D | S | Total | UA | NDVI | D | S | Total | UA |
D | 101 | 14 | 115 | 87.82% | D | 126 | 10 | 136 | 92.64% |
S | 62 | 323 | 385 | 83.89% | S | 71 | 493 | 564 | 87.41% |
Total | 163 | 337 | 500 | Total | 197 | 503 | 700 | ||
PA | 61.96% | 95.84% | OA= | 84.80% | PA | 63.95% | 98.01% | OA= | 88.42% |
NDMI | D | S | Total | UA | NDMI | D | S | Total | UA |
D | 99 | 13 | 115 | 86.08% | D | 119 | 1 | 120 | 99.16% |
S | 64 | 324 | 385 | 84.15% | S | 78 | 502 | 580 | 86.55% |
Total | 163 | 337 | 500 | Total | 197 | 503 | 700 | ||
PA | 60.73% | 96.14% | OA= | 84.60% | PA | 60.40% | 99.80% | OA= | 88.71% |
NBR | D | S | Total | UA | NBR | D | S | Total | UA |
D | 103 | 19 | 122 | 84.42% | D | 131 | 23 | 154 | 85.06% |
S | 60 | 318 | 378 | 84.12% | S | 66 | 480 | 546 | 87.91% |
Total | 163 | 337 | 500 | Total | 197 | 503 | 700 | ||
PA | 63.19% | 94.36% | OA= | 84.20% | PA | 66.49% | 95.42% | OA= | 87.28% |
EVI | D | S | Total | UA | EVI | D | S | Total | UA |
D | 101 | 44 | 142 | 71.12% | D | 118 | 4 | 122 | 96.72% |
S | 62 | 293 | 355 | 82.53% | S | 79 | 499 | 578 | 86.33% |
Total | 163 | 337 | 500 | Total | 197 | 503 | 700 | ||
PA | 61.96% | 86.94% | OA= | 78.80% | PA | 59.89% | 99.20% | OA= | 88.14% |
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Mashhadi, N.; Alganci, U. Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey. ISPRS Int. J. Geo-Inf. 2022, 11, 573. https://doi.org/10.3390/ijgi11110573
Mashhadi N, Alganci U. Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey. ISPRS International Journal of Geo-Information. 2022; 11(11):573. https://doi.org/10.3390/ijgi11110573
Chicago/Turabian StyleMashhadi, Nooshin, and Ugur Alganci. 2022. "Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey" ISPRS International Journal of Geo-Information 11, no. 11: 573. https://doi.org/10.3390/ijgi11110573