Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection
"> Figure 1
<p>Flowchart of the three-step methodology to assess the potential of a sensor for sub-decametric feature detection.</p> "> Figure 2
<p>Average surface reflectance spectral signatures of the nine considered classes derived from the Sentinel-2 image acquired on 1 January 2015 over Belgium (see <a href="#sec3-remotesensing-08-00488" class="html-sec">Section 3</a>).</p> "> Figure 3
<p>The overlap area (<span class="html-italic">S</span> ) between two normal probability density functions (in blue).</p> "> Figure 4
<p>Example of separability analysis. The error ribbons represent the 90% confidence interval around the distributions mean of a background/foreground mixture. The blue zone corresponds to the mixtures separable from the pure background distribution represented with the red dotted lines. With large background fractions, the overlap <span class="html-italic">S</span> (<b>black</b> line on the <b>bottom</b> graph) between the mixture and the pure background exceeds 10% and the distributions are considered non-separable (<b>red</b> zone) corresponding to a classification error larger than 5%. The black dashed lines indicate when the limit of <math display="inline"> <semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics> </math> of separability with the background class is reached.</p> "> Figure 5
<p>Shapes of interest: (<b>a</b>) continuous linear objects crossing the pixel in its center (LC); (<b>b</b>) continuous linear objects centered with relation to the pixel border (LB); (<b>c</b>) compact object centered in the pixel (CO).</p> "> Figure 6
<p>(<b>a</b>) Sentinel-2 10 m PSF; (<b>b</b>) example of simulated 8 m wide LC object; (<b>c</b>) bi-convolution of the PSF on the simulated LC object. Orange area shows the pixel footprint. Blue areas represent the footprints of the 8 adjacent pixels.</p> "> Figure 7
<p>Snapshots of an orthophoto (25 cm), Sentinel-2A (10 m), SPOT-5 (Take 5) (10 m) and Landsat-8 (30 m) images of a small pond (30 m × 30 m) surrounded by permanent grassland in the South of Belgium. The four images are presented using the same standard false color bands combination (R: NIR<math display="inline"> <semantics> <msub> <mrow/> <mrow> <mi>w</mi> <mi>i</mi> <mi>d</mi> <mi>e</mi> </mrow> </msub> </semantics> </math>, G: Red, B: Green).</p> "> Figure 8
<p>Discriminating power of spectral bands and indices to discriminate foreground classes according to the limit proportion of background. Each pair of classes is ordered as foreground - background and the limit proportion of the background allows an error of 5% of misclassification between front and back classes. All results are based on Sentinel-2 data. The left matrix includes all the bands and indices which are common to the 3 sensors, the central matrix includes bands and indices that are not present in SPOT-5 and the right matrix is based on bands and indices that are specific to Sentinel-2.</p> "> Figure 9
<p>Discriminating power difference between SPOT-5 and Sentinel-2 (S2) for each index and foreground/background pair. Blue tones indicate a better discrimination with SPOT-5 and red ones show the better performance of Sentinel-2. <span class="html-italic">Crosses</span> indicate that the discrimination threshold was not achieved by one of the sensors.</p> "> Figure 10
<p>Discriminating power difference between Landsat-8 (L8) and Sentinel-2 (S2) for each indices and foreground/background pair. Blue tones indicate a better discrimination with Landsat-8 and red ones show the better performance of Sentinel-2. <span class="html-italic">Crosses</span> indicate that the discrimination threshold was not achieved by one of the sensors.</p> "> Figure 11
<p>Probability of classification error for a pure foreground pixel with Sentinel-2’s indices and bands when classes are equiprobable two-by-two. The left matrix includes all the bands and indices which are common to the 3 sensors, the central matrix includes bands and indices that are not present in SPOT-5 and the right matrix is based on bands and indices that are specific to Sentinel-2.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Spectral Resolution for Spatial Object Detection
2.1.1. Class Pairs of Interest
- Water bodies: Besides their obvious importance to the hydrological cycle, a large number of species rely on the presence of small water bodies for their life cycle (fishes, batrachians, dragonflies...). The creation of water bodies in rural areas is furthermore encouraged by institutions through various policies such as the European Common Agricultural Policy (CAP). Small ponds are dynamic in time and could also be quickly filled or invaded by vegetation. As ponds are generally found in grassland areas, water bodies were paired with grassy background.
- Roads: Roads are most of the time obstacles for animal movement, but are sometimes sought for food foraging (hunting areas). The focus was on small consolidated roads (bitumen or concrete) across crop fields and grassland. Different pairs were therefore considered: Road/Maize, Road/Bare soil, Road/Sugar beet and Road/Grassland.
- Grass strips: Leaving grass strips provides corridors that improve landscape connectivity for birds and insects population in crop dominated landscapes as well as refuge zones for auxiliary crop species acting in biological pest control. They also play an important role to mitigate soil erosion. Those grass strips are most of the time located along crop fields, so that Grassland/Maize, Grassland/Sugar beet and Grassland/Bare soil pairs were considered.
- Small woody patches: Hedges and isolated trees contribute to woody habitat connectivity and provide food and shelter to a large range of species. Broadleaved trees were coupled with grass, maize and sugar beet because their contribution to ecological network is of major importance in agricultural landscapes.
2.1.2. Spectral Separability Analysis
2.2. Effective Spatial Resolution
2.3. Potential for Object Detection
3. Study Area and Data
4. Results
4.1. On the Spectral Resolution
4.1.1. Separability of Foreground/Background Pairs with Sentinel-2
4.1.2. Comparison with SPOT-5 and Landsat-8
4.2. On the Spatial Resolution
4.3. On the Potential for Object Detection
5. Discussion
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Sentinel-2 MSI | Landsat-8 OLI | SPOT-5 HRG | ||||
---|---|---|---|---|---|---|
Band [m] | Range [nm] | Band [m] | Range [nm] | Band [m] | Range [nm] | Name |
B1 * (60) | B1 (30) | Aerosol | ||||
B2 (10) | B2 (30) | Blue | ||||
B3 (10) | B3 (30) | B1 (10) | Green | |||
B4 (10) | B4 (30) | B2 (10) | Red | |||
B8 * (15) | PAN (5) | PAN | ||||
B5 (20) | Red-edge 1 | |||||
B6 (20) | Red-edge 2 | |||||
B7 (20) | Red-edge 3 | |||||
B8 (10) | B3 (10) | NIR | ||||
B8A (20) | B5 (30) | NIR | ||||
B9 * (60) | Cirrus | |||||
B10 * (60) | B9 (30) | Water Vapor | ||||
B11 (20) | B6 (30) | B4 (20) | SWIR 1 | |||
B12 (20) | B7 (30) | SWIR 2 | ||||
B10 * (100) | Thermal 1 | |||||
B11 * (100) | Thermal 2 |
Indices | Name | Formula | Reference |
---|---|---|---|
Vegetation discrimination | |||
Chlogreen | Chlorophyll Green index | [39] | |
GEMI | Global Environment Monitoring Vegetation Index | [40] | |
GI | Greenness Index | [41] | |
gNDVI | Green normalized difference vegetation index | [42] | |
MSAVI | Modified soil adjusted vegetation index | [43] | |
MSI | Moisture stress index | [44] | |
ND | Normalized Difference of Red-edge and SWIR2 | ||
NDVI | Normalized difference vegetation index | [45] | |
NDVIre | Red-edge normalized difference vegetation index | [42] | |
PVI | Perpendicular vegetation index | [46] | |
RededgePeakArea | Red-edge peak area | ||
RTVIcore | Red-edge Triangular Vegetation Index | [47] | |
SAVI | Soil Adjusted Vegetation Index | with | [48] |
SR | Simple ratio NIR narrow and Blue | [49] | |
SR | Simple ratio NIR narrow and Green | [41] | |
SR | Simple ratio NIR narrow and Red | [49] | |
TSAVI | Transformed Soil Adjusted Vegetation Index | [50] | |
WDVi | Weighted Difference Vegetation Index | [51] | |
Water detection | |||
NDWI | Normalized Difference Water Index 1 | [52] | |
NDWI | Normalized Difference Water Index 2 | [53] | |
NHI | Normalized Humidity Index | [31] | |
Canopy properties | |||
LAnthoC | Leaf Anthocyanid Content | [54] | |
LCaroC | Leaf Carotenoid Content | [54] | |
LChloC | Leaf Chlorophyll Content | [54] | |
Dry vegetation | |||
NDTI | Normalized Difference Tillage Index | [55] | |
RedSWIR | Bands difference | [56] | |
STI | Soil Tillage Index | [55] | |
Vegetation (with Red-edge) | |||
SR | Simple Blue and Red-edge 1 Ratio | [41] | |
SR | Simple Blue and Red-edge 2 Ratio | [57] | |
SR | Simple Blue and Red-edge Ratio | derived from [41,57] | |
SR | Simple NIR and Red-edge 1 Ratio | [39] | |
SR | Simple NIR and Red-edge 2 Ratio | derived from [39] | |
SR | Simple NIR and Red-edge 3 Ratio | derived from [39] | |
Artificial areas | |||
BAI | Built-up Area Index | [58] |
Satellite | Bare Soil | Broad-Leaved | Grassland | Maize | Needle-Leaved | Pasture | Roads | Sugar Beet | Water |
---|---|---|---|---|---|---|---|---|---|
Sentinel-2 | 1318 | 3561 | 406 | 558 | 1369 | 1131 | 49 | 1079 | 632 |
SPOT-5 | 1390 | 3565 | 412 | 210 | 1384 | 1124 | 50 | 1081 | 630 |
Landsat-8 | 328 | 364 | 33 | 74 | 113 | 126 | 14 | 126 | 53 |
Location | Centroid | Sensor | Acquisition Date | Atmospheric Correction | |
---|---|---|---|---|---|
Latitude | Longitude | ||||
Belgium | 502821.01 N | 55313.12E | Sentinel-2 | 1 October 2015 | Sen2Cor |
501628.32 N | 6150.84 E | Landsat-8 | 29 September 2015 | L8SR | |
50368.86 N | 4597.62 E | SPOT-5 | 23 August 2015 | MACCS | |
Sacramento, USA | 384755.29 N | 1214732.87 W | Sentinel-2 | 18 September 2015 | Sen2Cor |
38540.62 N | 12071.90 W | Landsat-8 | 6 September 2015 | L8SR | |
38419.21 N | 121478.93 W | SPOT-5 | 8 September 2015 | MACCS |
Satellite | Band | Channel | Resolution [m] | FWHM [Pixel] | SD [Pixel] | FWHM [m] | SD [m] | SNR |
---|---|---|---|---|---|---|---|---|
Landsat-8 | Band 3 | Red | 30 | 1.70 | 0.18 | 51.05 | 5.48 | 21 |
SPOT-5 | Band 2 | Red | 10 | 2.01 | 0.20 | 20.11 | 1.99 | 57 |
Sentinel-2 | Band 4 | Red | 10 | 2.21 | 0.17 | 22.06 | 1.7 | 88 |
Sentinel-2 | Band 5 | Red-edge | 20 | 1.67 | 0.17 | 33.48 | 3.44 | 50 |
Sentinel-2 | Band 11 | SWIR | 20 | 1.96 | 0.13 | 39.10 | 2.21 | 31 |
Landsat-8 (30 m) | Sentinel-2 (10 m) | SPOT-5 (10 m) | Sentinel-2 Red-Edge (20 m) | Sentinel-2 SWIR (20 m) | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Couple | Prop | LC | LB | CO | Prop | LC | LB | CO | Prop | LC | LB | CO | Prop | LC | LB | CO | Prop | LC | LB | CO |
Broadleaved - Grassland | 0.39 | 37.5 | 46.5 | 53.5 | 0.52 | 12.5 | 14 | 19.5 | 0.27 | 19 | 22.5 | 25 | 0.42 | 23 | 29 | 33.5 | 0.34 | 32 | 38 | 44 |
Broadleaved - Maize | 0.60 | 23 | 29 | 39 | 0.43 | 15 | 17 | 22 | 0.04 | 35 | 40.5 | 40 | / | / | / | / | 0.30 | 34.5 | 41 | 46.5 |
Broadleaved - Pasture | 0.59 | 23.5 | 29.5 | 40 | 0.67 | 8 | 9.5 | 15 | 0.59 | 9.5 | 11 | 16 | 0.64 | 13.5 | 17 | 24 | 0.71 | 12.5 | 15 | 24.5 |
Broadleaved - Sugar beet | 0.63 | 21 | 26.5 | 37.5 | / | / | / | / | 0.45 | 13 | 15.5 | 19.5 | 0.23 | 34 | 42.5 | 44 | 0.63 | 16.5 | 19.5 | 28.5 |
Grassland - Bare soil | 0.83 | 9.5 | 12 | 23.5 | 0.88 | 3 | 3 | 8.5 | 0.84 | 3.5 | 4 | 8.5 | 0.93 | 2.5 | 3.5 | 10 | 0.90 | 4.5 | 5.5 | 14 |
Grassland - Maize | 0.74 | 14.5 | 18.5 | 30 | 0.40 | 16 | 18.5 | 23 | / | / | / | / | 0.51 | 19 | 24 | 29.5 | 0.64 | 16 | 19 | 28 |
Grassland - Sugar beet | 0.79 | 12 | 15 | 26.5 | 0.43 | 15 | 17 | 22 | / | / | / | / | 0.50 | 19.5 | 24.5 | 30 | 0.78 | 9.5 | 11.5 | 21 |
Pasture - Bare soil | 0.81 | 10.5 | 13.5 | 25 | 0.89 | 3 | 3 | 8.5 | 0.87 | 3 | 3.5 | 8 | 0.93 | 2.5 | 3.5 | 10 | 0.90 | 4.5 | 5.5 | 14 |
Pasture - Sugar beet | 0.75 | 14 | 17.5 | 29.5 | 0.09 | 32 | 36 | 37.5 | / | / | / | / | 0.14 | 42 | 51.5 | 51 | 0.77 | 10 | 12 | 21.5 |
Roads - Bare soil | 0.52 | 28 | 35.5 | 44.5 | 0.56 | 11 | 13 | 18.5 | 0.64 | 8 | 9.5 | 14.5 | 0.47 | 20.5 | 26 | 31.5 | 0.52 | 21.5 | 25.5 | 34 |
Roads - Grassland | 0.89 | 6 | 8 | 19 | 0.89 | 3 | 3 | 8.5 | 0.64 | 8 | 9.5 | 14.5 | 0.89 | 4 | 5.5 | 12.5 | 0.76 | 10.5 | 12.5 | 22 |
Roads - Maize | 0.90 | 5.5 | 7 | 18 | 0.90 | 2.5 | 3 | 8 | 0.75 | 5.5 | 6.5 | 11.5 | 0.88 | 4.5 | 5.5 | 13 | 0.91 | 4 | 5 | 13 |
Roads - Sugar beet | 0.91 | 5 | 6.5 | 17 | 0.92 | 2 | 2.5 | 7 | 0.71 | 6.5 | 8 | 13 | 0.88 | 4.5 | 5.5 | 13 | 0.92 | 3.5 | 4 | 12.5 |
Water - Grassland | 0.74 | 14.5 | 18.5 | 30 | 0.81 | 5 | 5.5 | 11 | 0.62 | 8.5 | 10.5 | 15 | 0.82 | 6.5 | 8.5 | 16 | 0.79 | 9 | 11 | 20.5 |
Water - Pasture | 0.80 | 11 | 14 | 26 | 0.72 | 7 | 8 | 14 | 0.74 | 6 | 7 | 12 | 0.79 | 8 | 10 | 17.5 | 0.83 | 7.5 | 9 | 18.5 |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Radoux, J.; Chomé, G.; Jacques, D.C.; Waldner, F.; Bellemans, N.; Matton, N.; Lamarche, C.; D’Andrimont, R.; Defourny, P. Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection. Remote Sens. 2016, 8, 488. https://doi.org/10.3390/rs8060488
Radoux J, Chomé G, Jacques DC, Waldner F, Bellemans N, Matton N, Lamarche C, D’Andrimont R, Defourny P. Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection. Remote Sensing. 2016; 8(6):488. https://doi.org/10.3390/rs8060488
Chicago/Turabian StyleRadoux, Julien, Guillaume Chomé, Damien Christophe Jacques, François Waldner, Nicolas Bellemans, Nicolas Matton, Céline Lamarche, Raphaël D’Andrimont, and Pierre Defourny. 2016. "Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection" Remote Sensing 8, no. 6: 488. https://doi.org/10.3390/rs8060488