Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar
Synthetic Aperture Radar has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths, such as L-band, are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/11/1899 |
| _version_ | 1797565561909018624 |
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| author | Johannes N. Hansen Edward T. A. Mitchard Stuart King |
| author_facet | Johannes N. Hansen Edward T. A. Mitchard Stuart King |
| author_sort | Johannes N. Hansen |
| collection | DOAJ |
| description | Synthetic Aperture Radar has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths, such as L-band, are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is much interest due to its high temporal resolution (five days) and “free, full, and open” data policy. We tested its ability to distinguish forest from non-forest in six study sites, located in Alaska, Colombia, Finland, Florida, Indonesia, and the UK. Using the time series for a full year significantly increases the classification accuracy compared to a single scene (a mean of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>85</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> compared to <inline-formula> <math display="inline"> <semantics> <mrow> <mn>77</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> across the study sites for the best classifier). Our results show that we can further improve the mean accuracy to <inline-formula> <math display="inline"> <semantics> <mrow> <mn>87</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> when only considering the annual mean and standard deviation of co-polarized (VV) and cross-polarized (VH) backscatter. In this case, separation accuracies of up to <inline-formula> <math display="inline"> <semantics> <mrow> <mn>93</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> (in Finland) are possible, though in the worst case (Alaska), the highest possible accuracy using these variables was <inline-formula> <math display="inline"> <semantics> <mrow> <mn>80</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula>. The best overall performance was observed when using a Support Vector Machine classifier, outperforming random forest, k-Nearest-Neighbors, and Quadratic Discriminant Analysis. We further show that the small information content we found in the phase data is an artifact of terrain slope orientation and has a negligible impact on classifier performance. We conclude that for the purposes of forest mapping the smaller file size and easier to process GRD products are sufficient, unless the SLC products are used to compute the temporal coherence which was not tested in this study. |
| first_indexed | 2024-03-10T19:13:54Z |
| format | Article |
| id | doaj.art-496f5aba987e45aeabf730fafe7be3d0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:13:54Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-496f5aba987e45aeabf730fafe7be3d02023-11-20T03:32:40ZengMDPI AGRemote Sensing2072-42922020-06-011211189910.3390/rs12111899Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture RadarJohannes N. Hansen0Edward T. A. Mitchard1Stuart King2School of Mathematics, University of Edinburgh, Edinburgh EH9 3FD, UKSchool of GeoSciences, University of Edinburgh, Edinburgh EH8 3FF, UKSchool of Mathematics, University of Edinburgh, Edinburgh EH9 3FD, UKSynthetic Aperture Radar has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths, such as L-band, are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is much interest due to its high temporal resolution (five days) and “free, full, and open” data policy. We tested its ability to distinguish forest from non-forest in six study sites, located in Alaska, Colombia, Finland, Florida, Indonesia, and the UK. Using the time series for a full year significantly increases the classification accuracy compared to a single scene (a mean of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>85</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> compared to <inline-formula> <math display="inline"> <semantics> <mrow> <mn>77</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> across the study sites for the best classifier). Our results show that we can further improve the mean accuracy to <inline-formula> <math display="inline"> <semantics> <mrow> <mn>87</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> when only considering the annual mean and standard deviation of co-polarized (VV) and cross-polarized (VH) backscatter. In this case, separation accuracies of up to <inline-formula> <math display="inline"> <semantics> <mrow> <mn>93</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> (in Finland) are possible, though in the worst case (Alaska), the highest possible accuracy using these variables was <inline-formula> <math display="inline"> <semantics> <mrow> <mn>80</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula>. The best overall performance was observed when using a Support Vector Machine classifier, outperforming random forest, k-Nearest-Neighbors, and Quadratic Discriminant Analysis. We further show that the small information content we found in the phase data is an artifact of terrain slope orientation and has a negligible impact on classifier performance. We conclude that for the purposes of forest mapping the smaller file size and easier to process GRD products are sufficient, unless the SLC products are used to compute the temporal coherence which was not tested in this study.https://www.mdpi.com/2072-4292/12/11/1899SARSentinel-1deforestationradarforestsLULUCF |
| spellingShingle | Johannes N. Hansen Edward T. A. Mitchard Stuart King Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar Remote Sensing SAR Sentinel-1 deforestation radar forests LULUCF |
| title | Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar |
| title_full | Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar |
| title_fullStr | Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar |
| title_full_unstemmed | Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar |
| title_short | Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar |
| title_sort | assessing forest non forest separability using sentinel 1 c band synthetic aperture radar |
| topic | SAR Sentinel-1 deforestation radar forests LULUCF |
| url | https://www.mdpi.com/2072-4292/12/11/1899 |
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