Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping
Abstract
:1. Introduction
2. Study Area and Data Employed
- 2016 global urban footprint (GUF), generated at 12 m resolution with texture and intensity of TanDEM-X imagery, with an accuracy of 85–88% [38].
- 2011–2015 TanDEM-X forest non-forest map (TFNF), generated with 50 m resolution from TanDEM-X bistatic coherence data, with an estimated accuracy of 85–93% [13].
- 2017 Advanced land observing satellite phased array type L-band synthetic aperture radar forest/non-forest map (ALOS PALSAR FNF, shortened to AFNF) generated at 25 m resolution using backscatter data, with an accuracy of 85–95% [11].
3. Methods
3.1. SAR Data Processing
3.2. Land Cover Reference Dataset
3.3. Training Data Preparation
3.4. Classification Scheme
3.5. Validation
4. Results
4.1. Data Distribution
4.2. Classification with Feature Sets
4.3. Classification Stability
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
- Sentinel-1 and -2 data: https://scihub.copernicus.eu/dhus/ (accessed on 20 November 2021)
- High-resolution TanDEM-X DEM request: https://tandemx-science.dlr.de/cgi-bin/wcm.pl?page=TDM-Proposal-Submission-Procedure (accessed on 20 November 2021)
- NASADEM data: https://lpdaac.usgs.gov/products/nasadem_hgtv001 (accessed on 20 November 2021)
- Corine Land Cover data: https://land.copernicus.eu/pan-european/corine-land-cover/ (accessed on 20 November 2021)
- CCI Land cover data: https://www.esa-landcover-cci.org/ (accessed on 20 November 2021)
- TanDEM-X forest/non-forest map: https://download.geoservice.dlr.de/FNF50/ (accessed on 20 November 2021)
- Global urban footprint data can be requested following the instructions outlined in https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-11725/20508_read-47944/ (accessed on 20 November 2021)
- ALOS forest/non-forest map: https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm (accessed on 20 November 2021)
- GEDI L2B data: https://lpdaac.usgs.gov/products/gedi02_bv001/ (accessed on 20 November 2021)
Acknowledgments
Conflicts of Interest
Appendix A
Backscatter Statistics (B00) | Backscatter Statistics, Long-Term Coherence (B0C) | Backscatter and Short-Term Coherence Statistics, Long-Term Coherence (BCC) | |||||||||||||||||
Ur, urban | LV, low vegetation | Fo, forest | Wa, water | Comm. error | Ur, urban | LV, low vegetation | Fo, forest | Wa, water | Comm. error | Ur, urban | LV, low vegetation | Fo, forest | Wa, water | Comm. error | |||||
2017 | Urban | 156,219 | 55,550 | 715,668 | 658 | 83 | 162,320 | 37,453 | 55,637 | 488 | 37 | 167,829 | 29,648 | 3342 | 130 | 16 | |||
Low vegetation | 3657 | 16,924,371 | 176,319 | 5994 | 1 | 1439 | 17,010,367 | 78,215 | 5904 | 1 | 605 | 17,455,162 | 71,215 | 3200 | 0 | ||||
Forest | 8643 | 592,541 | 6,284,942 | 1474 | 9 | 4874 | 524,324 | 7,042,834 | 1764 | 7 | 240 | 96,711 | 7,101,180 | 3769 | 1 | ||||
Water | 200 | 11,807 | 774 | 80,258 | 14 | 86 | 12,125 | 1017 | 80,228 | 14 | 45 | 2748 | 1966 | 81,285 | 6 | ||||
Omission error | 7 | 4 | 12 | 9 | 4 | 3 | 2 | 9 | 1 | 1 | 1 | 8 | |||||||
2018 | Urban | 145,629 | 46,935 | 168,236 | 1173 | 60 | 161,350 | 33,984 | 35,227 | 723 | 30 | 167,339 | 23224 | 2067 | 84 | 13 | |||
Low vegetation | 3767 | 16,918,692 | 145,035 | 5343 | 1 | 1667 | 16,981,859 | 93,019 | 5574 | 1 | 1072 | 17,458,751 | 146,454 | 3239 | 1 | ||||
Forest | 19,092 | 609,823 | 6,863,700 | 1502 | 8 | 5594 | 560,937 | 7,048,591 | 1712 | 7 | 245 | 99,990 | 7,027,733 | 4110 | 1 | ||||
Water | 230 | 8826 | 732 | 80,366 | 11 | 107 | 7496 | 866 | 80,375 | 10 | 62 | 2311 | 1449 | 80,951 | 5 | ||||
Omission error | 14 | 4 | 4 | 9 | 4 | 3 | 2 | 9 | 1 | 1 | 2 | 8 | |||||||
2019 | Urban | 149,579 | 49,470 | 258,754 | 1242 | 67 | 158,263 | 27,764 | 34,328 | 654 | 28 | 167,557 | 32,022 | 2929 | 106 | 17 | |||
Low vegetation | 3492 | 16,927,829 | 143,335 | 5639 | 1 | 1972 | 17,020,180 | 103,283 | 5730 | 1 | 887 | 17,464,510 | 140,887 | 3525 | 1 | ||||
Forest | 15,427 | 592,422 | 6,774,737 | 1371 | 8 | 8365 | 524,202 | 7,039,024 | 1863 | 7 | 220 | 85,246 | 7,031,885 | 3936 | 1 | ||||
Water | 217 | 14,549 | 874 | 80,132 | 16 | 115 | 12,124 | 1065 | 80,137 | 14 | 51 | 2492 | 1999 | 80,817 | 5 | ||||
Omission error | 11 | 4 | 6 | 9 | 6 | 3 | 2 | 9 | 1 | 1 | 2 | 9 |
Backscatter Statistics (B00) | Backscatter Statistics, Long-Term Coherence (B0C) | Backscatter and Short-Term Coherence Statistics, Long-Term Coherence (BCC) | |||||||||||
Forest | Other | Commission error | Forest | Other | Commission error | Forest | Other | Commission error | |||||
2017 | Forest | 132,335 | 20,733 | 14 | 153,689 | 16,252 | 10 | 144,063 | 3162 | 2 | |||
Other | 28,749 | 412,269 | 7 | 7308 | 416,645 | 2 | 16,934 | 429,735 | 4 | ||||
Omission error | 18 | 5 | 5 | 4 | 11 | 1 | |||||||
2018 | Forest | 148,331 | 25,887 | 15 | 155,003 | 21,617 | 12 | 143,374 | 3911 | 3 | |||
Other | 12,753 | 407,116 | 3 | 5994 | 411,281 | 1 | 17,623 | 428,987 | 4 | ||||
Omission error | 8 | 6 | 4 | 5 | 11 | 1 | |||||||
2019 | Forest | 144,210 | 25,552 | 15 | 154,712 | 19,966 | 11 | 140,655 | 2581 | 2 | |||
Other | 16,874 | 407,451 | 4 | 6372 | 413,037 | 2 | 20,429 | 430,422 | 5 | ||||
Omission error | 10 | 6 | 4 | 5 | 13 | 1 |
Backscatter Statistics (B00) | Backscatter Statistics, Long-Term Coherence (B0C) | Backscatter and Short-Term Coherence Statistics, Long-Term Coherence (BCC) | |||||||||||||||||
Ur, Urban | LV, Low vegetation | Fo, forest | Wa, Water | Comm. error | Ur, Urban | LV, Low vegetation | Fo, forest | Wa, Water | Comm. error | Ur, Urban | LV, Low vegetation | Fo, forest | Wa, Water | Comm. error | |||||
2018 | Urban | 238 | 8677 | 32,889 | 0 | 99 | 634 | 18,288 | 207,037 | 15 | >99 | 775 | 5743 | 618 | 14 | 89 | |||
Low vegetation | 248 | 2,072,595 | 437,681 | 278 | 17 | 96 | 2,069,778 | 357,844 | 313 | 15 | 57 | 2,086,317 | 247,654 | 221 | 11 | ||||
Forest | 345 | 84,345 | 22,139,966 | 0 | <1 | 103 | 75,279 | 22,041,792 | 0 | <1 | 2 | 71,001 | 22,357,746 | 0 | <1 | ||||
Water | 4 | 770 | 5734 | 33,914 | 16 | 2 | 1124 | 8265 | 33,863 | 22 | 1 | 1356 | 8176 | 33,924 | 22 | ||||
Omission error | 71 | 4 | 2 | 1 | 24 | 4 | 3 | 1 | 7 | 4 | 1 | 1 | |||||||
2019 | Urban | 316 | 15310 | 29671 | 0 | 99 | 606 | 22,379 | 125,915 | 9 | >99 | 760 | 5903 | 372 | 12 | 89 | |||
Low vegetation | 219 | 2,079,824 | 187,958 | 275 | 8 | 125 | 2,074,418 | 142,987 | 311 | 6 | 72 | 2,097,155 | 106,030 | 234 | 5 | ||||
Forest | 293 | 70,427 | 22,393,056 | 0 | <1 | 97 | 68,317 | 22,336,185 | 0 | <1 | 0 | 61,805 | 22,499,246 | 0 | <1 | ||||
Water | 5 | 826 | 5585 | 33,917 | 16 | 5 | 1273 | 6785 | 33,871 | 19 | 1 | 1472 | 5491 | 33,913 | 17 | ||||
Omission error | 62 | 4 | 1 | 1 | 27 | 4 | 1 | 1 | 9 | 3 | <1 | 1 |
Backscatter Statistics (B00) | Backscatter Statistics, Long-Term Coherence (B0C) | Backscatter and Short-Term Coherence Statistics, Long-Term Coherence (BCC) | |||||||||||
Forest | Other | Commission error | Forest | Other | Commission error | Forest | Other | Commission error | |||||
2018 | Forest | 377,770 | 2298 | 1 | 376,550 | 2096 | 1 | 383,515 | 1957 | 1 | |||
Other | 20,381 | 43,775 | 32 | 21,548 | 43,946 | 33 | 14,572 | 44,083 | 25 | ||||
Omission error | 5 | 5 | 5 | 5 | 4 | 4 | |||||||
2019 | Forest | 379,793 | 1839 | <1 | 379,675 | 1842 | <1 | 384,333 | 1727 | <1 | |||
Other | 18,359 | 44,236 | 29 | 18,201 | 44,219 | 29 | 13,532 | 44,332 | 23 | ||||
Omission error | 5 | 4 | 5 | 4 | 3 | 4 |
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Class | Subclass | CLC 2018 | CCI LC 2015 | AFNF 2017 | TFNF 2018 |
---|---|---|---|---|---|
Urban | Artificial | 1xx: Artificial surfaces | 190: Urban areas | - | 0: Urban |
Low vegetation | Crops | 211: Non-irrigated Arable land | 10: Cropland 11: Herbaceous | !=1: Other (not forest) | 2: Not forest |
Pasture | 231: Pastures | 11: Herbaceous 130: Grassland | |||
Grassland | 321: Grassland | ||||
Permanent crops | 222: orchards 242: agriculture mix | 12: Tree or shrub | |||
Transitional woodland-shrub | 324: transitional Woodland-shrub | 40–153: natural vegetation | - | ||
Forest | Broadleaf | 311: broadleaf | 50–62: broadleaf | 1: Forest | 1: Forest |
Needleleaf | 312: needleleaf | 70–82: needleleaf | |||
Mixed | 313: mixed | 90: mixed | |||
Water | Water | - | 210: Water | !=1: Other | !=1: Other |
Class | Subclass, CCI LC 2015 | AFNF 2017 | TFNF 2018 | Sentinel-1 2018, 2019 | Sentinel-2 2020 |
---|---|---|---|---|---|
Urban | 190: Urban areas | !=1: Other | 0: Urban | - | NDVI < 0.6 NDWI < 0 |
Low vegetation | 30: Mosaic of cropland with natural vegetation | 2: Not forest | VV < −8 | NDMI < 0.05 NDVI < 0.6 NDWI < 0 | |
40: Mosaic of natural vegetation with cropland | |||||
100: Mosaic tree/shrub and herbaceous | |||||
120: Shrubland | |||||
Forest | 50–62: broadleaf | 1: Forest | 1: Forest | - | NDVI > 0.6 NDWI < 0 |
Water | 210: Water | !=1: Other | !=1: Other | VH < −20 dB VV < −15 dB | NDWI > 0 |
Temperate | Tropical | |||||||||||||||
Overall accuracy | Kappa statistic | Overall accuracy | Kappa statistic | |||||||||||||
B00 | B0C | BCC | B00 | B0C | BCC | B00 | B0C | BCC | B00 | B0C | BCC | |||||
LC | 2017 | 94 | 97 | 99 | 0.86 | 0.93 | 0.98 | |||||||||
2018 | 96 | 97 | 99 | 0.91 | 0.93 | 0.97 | 98 | 97 | 99 | 0.87 | 0.85 | 0.92 | ||||
2019 | 96 | 97 | 99 | 0.90 | 0.93 | 0.97 | 99 | 99 | 99 | 0.93 | 0.91 | 0.96 | ||||
GEDI | 2017 | 92 | 96 | 97 | 0.79 | 0.90 | 0.91 | |||||||||
2018 | 93 | 95 | 96 | 0.84 | 0.89 | 0.91 | 95 | 95 | 96 | 0.77 | 0.76 | 0.82 | ||||
2019 | 93 | 96 | 96 | 0.82 | 0.89 | 0.90 | 95 | 95 | 97 | 0.79 | 0.79 | 0.83 |
2017 | 2018 | 2019 | |||||||||||||
Ur | LV | Fo | Wa | Ur | LV | Fo | Wa | Ur | LV | Fo | Wa | ||||
Commission error | B00 | 83 | 1 | 9 | 14 | 60 | 1 | 8 | 11 | 67 | 1 | 8 | 16 | ||
B0C | 37 | 1 | 7 | 14 | 30 | 1 | 7 | 10 | 28 | 1 | 7 | 14 | |||
BCC | 16 | <1 | 1 | 6 | 13 | 1 | 1 | 5 | 17 | 1 | 1 | 5 | |||
Omission error | B00 | 7 | 4 | 12 | 9 | 14 | 4 | 4 | 9 | 11 | 4 | 6 | 9 | ||
B0C | 4 | 3 | 2 | 9 | 4 | 3 | 2 | 9 | 6 | 3 | 2 | 9 | |||
BCC | 1 | 1 | 1 | 8 | 1 | 1 | 2 | 8 | 1 | 1 | 2 | 9 |
2017 | 2018 | 2019 | ||||||||||
B00 | B0C | BCC | B00 | B0C | BCC | B00 | B0C | BCC | ||||
Commission error | Forest | 14 | 10 | 2 | 15 | 12 | 3 | 15 | 11 | 2 | ||
Other | 7 | 2 | 4 | 3 | 1 | 4 | 4 | 2 | 5 | |||
Omission error | Forest | 18 | 5 | 11 | 8 | 4 | 11 | 10 | 4 | 13 | ||
Other | 5 | 4 | 1 | 6 | 5 | 1 | 6 | 5 | 1 |
2018 | 2019 | |||||||||
Ur | LV | Fo | Wa | Ur | LV | Fo | Wa | |||
Commission error | B00 | 99 | 17 | <1 | 16 | 99 | 8 | <1 | 16 | |
B0C | >99 | 15 | <1 | 22 | >99 | 6 | <1 | 19 | ||
BCC | 89 | 11 | <1 | 22 | 89 | 5 | <1 | 17 | ||
Omission error | B00 | 71 | 4 | 2 | 1 | 62 | 4 | 1 | 1 | |
B0C | 24 | 4 | 3 | 1 | 27 | 4 | 1 | 1 | ||
BCC | 7 | 4 | 1 | 1 | 9 | 3 | <1 | 1 |
2018 | 2019 | |||||||
B00 | B0C | BCC | B00 | B0C | BCC | |||
Commission error | Forest | 1 | 1 | 1 | <1 | <1 | <1 | |
Other | 32 | 33 | 25 | 29 | 29 | 23 | ||
Omission error | Forest | 5 | 5 | 4 | 5 | 5 | 3 | |
Other | 5 | 5 | 4 | 4 | 4 | 4 |
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Borlaf-Mena, I.; Badea, O.; Tanase, M.A. Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping. Remote Sens. 2021, 13, 4814. https://doi.org/10.3390/rs13234814
Borlaf-Mena I, Badea O, Tanase MA. Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping. Remote Sensing. 2021; 13(23):4814. https://doi.org/10.3390/rs13234814
Chicago/Turabian StyleBorlaf-Mena, Ignacio, Ovidiu Badea, and Mihai Andrei Tanase. 2021. "Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping" Remote Sensing 13, no. 23: 4814. https://doi.org/10.3390/rs13234814