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Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

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Radoux, Julien [UCL] Chomé, Guillaume [UCL] Jacques, Damien Christophe [UCL] Waldner, François [UCL] Bellemans, Nicolas [UCL] Matton, Nicolas [UCL] Lamarche, Céline [UCL] d'Andrimont, Raphaël [UCL] Defourny, Pierre [UCL]

Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès libre
Publication date 2016
Language Anglais
Journal information "Remote Sensing" - Vol. 8, no.6, p. 488 (2016)
Peer reviewed yes
Publisher MDPI
issn 2072-4292
e-issn 2072-4292
Publication status Publié
Affiliation UCL - SST/ELI/ELIE - Environmental Sciences
Links
  1. Antrop Marc, Reflecting upon 25 years of landscape ecology, 10.1007/s10980-007-9170-y
  2. Hilty (2006)
  3. Metzger Jean Paul, Landscape ecology: perspectives based on the 2007 IALE world congress, 10.1007/s10980-008-9217-8
  4. LaRue Michelle A., Nielsen Clayton K., Modelling potential dispersal corridors for cougars in midwestern North America using least-cost path methods, 10.1016/j.ecolmodel.2007.10.036
  5. Nagendra Harini, Pareeth Sajid, Ghate Rucha, People within parks—forest villages, land-cover change and landscape fragmentation in the Tadoba Andhari Tiger Reserve, India, 10.1016/j.apgeog.2005.11.002
  6. Forman, 619 (1986)
  7. Thornton M. W., Atkinson P. M., Holland D. A., Sub‐pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super‐resolution pixel‐swapping, 10.1080/01431160500207088
  8. Suter, 157 (2007)
  9. Frazier A.E., Wang L., Characterizing spatial patterns of invasive species using sub-pixel classifications, 10.1016/j.rse.2011.04.002
  10. Lindenmayer (2006)
  11. Shao Guofan, Wu Jianguo, On the accuracy of landscape pattern analysis using remote sensing data, 10.1007/s10980-008-9215-x
  12. Malenovský Zbyněk, Rott Helmut, Cihlar Josef, Schaepman Michael E., García-Santos Glenda, Fernandes Richard, Berger Michael, Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land, 10.1016/j.rse.2011.09.026
  13. Drusch M., Del Bello U., Carlier S., Colin O., Fernandez V., Gascon F., Hoersch B., Isola C., Laberinti P., Martimort P., Meygret A., Spoto F., Sy O., Marchese F., Bargellini P., Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services, 10.1016/j.rse.2011.11.026
  14. Du Yun, Zhang Yihang, Ling Feng, Wang Qunming, Li Wenbo, Li Xiaodong, Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band, 10.3390/rs8040354
  15. Pesaresi Martino, Corbane Christina, Julea Andreea, Florczyk Aneta, Syrris Vasileios, Soille Pierre, Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas, 10.3390/rs8040299
  16. Immitzer Markus, Vuolo Francesco, Atzberger Clement, First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe, 10.3390/rs8030166
  17. Eitel Jan U.H., Vierling Lee A., Litvak Marcy E., Long Dan S., Schulthess Urs, Ager Alan A., Krofcheck Dan J., Stoscheck Leo, Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland, 10.1016/j.rse.2011.09.002
  18. Schuster Christian, Förster Michael, Kleinschmit Birgit, Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data, 10.1080/01431161.2012.666812
  19. Zarco-Tejada Pablo J., Miller John R., Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery, 10.1029/1999jd900161
  20. Verrelst Jochem, Muñoz Jordi, Alonso Luis, Delegido Jesús, Rivera Juan Pablo, Camps-Valls Gustavo, Moreno José, Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3, 10.1016/j.rse.2011.11.002
  21. Delegido Jesús, Verrelst Jochem, Alonso Luis, Moreno José, Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content, 10.3390/s110707063
  22. Clevers J.G.P.W., Gitelson A.A., Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3, 10.1016/j.jag.2012.10.008
  23. Sibanda Mbulisi, Mutanga Onisimo, Rouget Mathieu, Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments, 10.1016/j.isprsjprs.2015.10.005
  24. Foschi, Photogramm. Eng. Remote Sens., 63, 493 (1997)
  25. Oki, Photogramm. Eng. Remote Sens., 68, 77 (2002)
  26. Lechner Alex Mark, Stein Alfred, Jones Simon D., Ferwerda Jelle Garke, Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping, 10.1016/j.rse.2009.06.002
  27. Boucher, 143 (2007)
  28. Congalton Russell G, Birch Kevin, Jones Rick, Schriever James, Evaluating remotely sensed techniques for mapping riparian vegetation, 10.1016/s0168-1699(02)00108-4
  29. Lausch A, Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability, 10.1016/s1470-160x(02)00053-5
  30. Jensen, Photogramm. Eng. Remote Sens., 65, 611 (1999)
  31. Lacaux J.P., Tourre Y.M., Vignolles C., Ndione J.A., Lafaye M., Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal, 10.1016/j.rse.2006.07.012
  32. Townsend Philip A., Lookingbill Todd R., Kingdon Clayton C., Gardner Robert H., Spatial pattern analysis for monitoring protected areas, 10.1016/j.rse.2008.05.023
  33. Knight Edward, Kvaran Geir, Landsat-8 Operational Land Imager Design, Characterization and Performance, 10.3390/rs61110286
  34. Storey James, Choate Michael, Lee Kenton, Landsat 8 Operational Land Imager On-Orbit Geometric Calibration and Performance, 10.3390/rs61111127
  35. Morfitt Ron, Barsi Julia, Levy Raviv, Markham Brian, Micijevic Esad, Ong Lawrence, Scaramuzza Pat, Vanderwerff Kelly, Landsat-8 Operational Land Imager (OLI) Radiometric Performance On-Orbit, 10.3390/rs70202208
  36. de Lussy F., Greslou D., Dechoz C., Amberg V., Delvit J. M., Lebegue L., Blanchet G., Fourest S., PLEIADES HR IN FLIGHT GEOMETRICAL CALIBRATION : LOCATION AND MAPPING OF THE FOCAL PLANE, 10.5194/isprsarchives-xxxix-b1-519-2012
  37. Sentinel-2 ESA’s Optical High-Resolution Mission for GMES Operational Serviceshttps://sentinels.copernicus.eu/documents/247904/349490/S2_SP-1322_2.pdf
  38. Price J.C., Spectral band selection for visible-near infrared remote sensing: spectral-spatial resolution tradeoffs, 10.1109/36.628794
  39. Datt B., Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves, 10.1080/014311699211778
  40. Pinty B., Verstraete M. M., GEMI: a non-linear index to monitor global vegetation from satellites, 10.1007/bf00031911
  41. le Maire G., François C., Dufrêne E., Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements, 10.1016/j.rse.2003.09.004
  42. Gitelson Anatoly A., Kaufman Yoram J., Merzlyak Mark N., Use of a green channel in remote sensing of global vegetation from EOS-MODIS, 10.1016/s0034-4257(96)00072-7
  43. Qi J., Chehbouni A., Huete A.R., Kerr Y.H., Sorooshian S., A modified soil adjusted vegetation index, 10.1016/0034-4257(94)90134-1
  44. Tucker Compton J., Red and photographic infrared linear combinations for monitoring vegetation, 10.1016/0034-4257(79)90013-0
  45. Richardson, Photogramm. Eng. Remote Sens., 43, 1541 (1977)
  46. Chen, Guang Pu Xue Yu Guang Pu Fen Xi/Spectrosc. Spectr. Anal., 30, 512 (2010)
  47. Huete A.R, A soil-adjusted vegetation index (SAVI), 10.1016/0034-4257(88)90106-x
  48. Blackburn George Alan, Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales, 10.1016/s0034-4257(98)00059-5
  49. Clevers J.G.P.W., The derivation of a simplified reflectance model for the estimation of leaf area index, 10.1016/0034-4257(88)90041-7
  50. Gao Bo-cai, NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space, 10.1016/s0034-4257(96)00067-3
  51. McFEETERS S. K., The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, 10.1080/01431169608948714
  52. Van Deventer, Photogramm. Eng. Remote Sens., 63, 87 (1997)
  53. Jacques Damien Christophe, Kergoat Laurent, Hiernaux Pierre, Mougin Eric, Defourny Pierre, Monitoring dry vegetation masses in semi-arid areas with MODIS SWIR bands, 10.1016/j.rse.2014.07.027
  54. Lichtenthaler H.K., Lang M., Sowinska M., Heisel F., Miehé J.A., Detection of Vegetation Stress Via a New High Resolution Fluorescence Imaging System, 10.1016/s0176-1617(96)80081-2
  55. Shahi Kaveh, Shafri Helmi Z.M., Taherzadeh Ebrahim, Mansor Shattri, Muniandy Ratnasamy, A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery, 10.1016/j.ejrs.2014.12.003
  56. (2016)
  57. Radoux Julien, Defourny Pierre, Automated Image-to-Map Discrepancy Detection using Iterative Trimming, 10.14358/pers.76.2.173
  58. Worton Bruce J., Using Monte Carlo Simulation to Evaluate Kernel-Based Home Range Estimators, 10.2307/3801959
  59. Jones M. C., Marron J. S., Sheather S. J., A Brief Survey of Bandwidth Selection for Density Estimation, 10.1080/01621459.1996.10476701
  60. Schowengerdt, 300 (2007)
  61. Joseph George, How well do we understand Earth observation electro-optical sensor parameters?, 10.1016/s0924-2716(00)00004-6
  62. Pagnutti Mary, Blonski Slawomir, Cramer Michael, Helder Dennis, Holekamp Kara, Honkavaara Eija, Ryan Robert, Targets, methods, and sites for assessing the in-flight spatial resolution of electro-optical data products, 10.5589/m10-078
  63. Schowengerdt, Photogramm. Eng. Remote Sens., 51, 1395 (1985)
  64. Ruiz C. Pinilla, López F. J. Ariza, Restoring SPOT images using PSF-derived deconvolution filters, 10.1080/01431160110075857
  65. Campagnolo Manuel L., Montano Enrique L., Estimation of Effective Resolution for Daily MODIS Gridded Surface Reflectance Products, 10.1109/tgrs.2013.2291496
  66. Wenny Brian, Helder Dennis, Hong Jungseok, Leigh Larry, Thome Kurtis, Reuter Dennis, Pre- and Post-Launch Spatial Quality of the Landsat 8 Thermal Infrared Sensor, 10.3390/rs70201962
  67. Irons James R., Dwyer John L., Barsi Julia A., The next Landsat satellite: The Landsat Data Continuity Mission, 10.1016/j.rse.2011.08.026
  68. Fletcher (2012)
  69. Fedorov Alexander A., Barbosa Sandrina P., Berberan-Santos Mário N., Radiation propagation time broadening of the instrument response function in time-resolved fluorescence spectroscopy, 10.1016/j.cplett.2006.01.049
  70. (2010)
  71. Sentinel-2 MSI—Level-2A Prototype Processor Installation and User Manualhttp://s2tbx.telespazio-vega.de/sen2cor/sen2cor-sum-2.0.pdf
  72. Vermote Eric, Justice Chris, Claverie Martin, Franch Belen, Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product, 10.1016/j.rse.2016.04.008
  73. Hagolle Olivier, Huc Mireille, Pascual David, Dedieu Gerard, A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENS and Sentinel-2 Images, 10.3390/rs70302668
  74. Wardlow Brian D., Egbert Stephen L., Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains, 10.1016/j.rse.2007.07.019
  75. Foody G.M., Fuzzy modelling of vegetation from remotely sensed imagery, 10.1016/0304-3800(95)00012-7
  76. Wickham, Photogramm. Eng. Remote Sens., 63, 397 (1997)
  77. Arnot C., Fisher P.F., Wadsworth R., Wellens J., Landscape metrics with ecotones: pattern under uncertainty, 10.1023/b:land.0000021723.24247.ee
  78. Gallego F. J., Remote sensing and land cover area estimation, 10.1080/01431160310001619607
  79. RADOUX J, DEFOURNY P, A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery, 10.1016/j.rse.2007.02.031
  80. (2012)
Bibliographic reference Radoux, Julien ; Chomé, Guillaume ; Jacques, Damien Christophe ; Waldner, François ; Bellemans, Nicolas ; et. al. Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection. In: Remote Sensing, Vol. 8, no.6, p. 488 (2016)
Permanent URL http://hdl.handle.net/2078.1/174665