Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index

Remote Sensing of Environment

Volumn 195, Issue , 2017, Pages 259-274

  Korhonen, Lauri ^a   Hadi, ^b   Packalen, Petteri ^a   Rautiainen, Miina ^b  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

^b AALTO UNIVERSITY   (Finland)

Author keywords

Biophysical; Canopy cover; Landsat; Leaf area index; Sentinel 2

Indexed keywords

FORESTRY; IMAGE RESOLUTION; OPTICAL RADAR;

BIOPHYSICAL; CANOPY COVER; LANDSAT; LEAF AREA INDEX; SENTINEL-2;

MEAN SQUARE ERROR;

AIRBORNE SENSOR; BIOPHYSICS; BOREAL FOREST; FOREST CANOPY; LANDSAT; LEAF AREA; LIDAR; PREDICTION; SENTINEL;

FINLAND; POHJOIS-SAVO; SUONENJOKI;

EID: 85018460110     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.03.021     Document Type: Article

References (76)

1. Axelsson, P., DEM generation from laser scanner data using adaptive TIN models. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 33 (2000), 111–118.
2. Broge, N.H., Leblanc, E., Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76 (2001), 156–172.
3. Brown, L., Chen, J.M., Leblanc, S.G., Cihlar, J., A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: An image and model analysis. Remote Sens. Environ. 71 (2000), 16–25.
4. Buermann, W., Dong, J., Zeng, X., Myneni, R.B., Dickinson, R.E., Evaluation of the utility of satellite-based vegetation leaf area index data for climate simulations. J. Clim. 14 (2001), 3536–3550.
5. Chen, J.M., Black, T.A., Defining leaf area index for non-flat leaves. Plant Cell Environ. 15 (1992), 421–429.
6. Chen, J.M., Black, T.A., Adams, R.S., Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand. Agric. For. Meteorol. 56 (1991), 129–143.
7. Chen, X., Vierling, L., Rowell, E., DeFelice, T., Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM + vegetation cover estimates in a ponderosa pine forest. Remote Sens. Environ. 91 (2004), 14–26.
8. Chrysafis, I., Mallinis, G., Siachalou, S., Patias, P., Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem. Remote Sens. Lett. 8 (2017), 508–517.
9. R Core Team, R: A Language and Environment for Statistical Computing. 2015, R Foundation for Statistical Computing, Vienna, Austria.
10. Dash, J., Curran, P., The MERIS Terrestrial Chlorophyll Index. 2004.
11. Daughtry, C., Walthall, C., Kim, M., De Colstoun, E.B., McMurtrey, J., Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74 (2000), 229–239.
12. Delegido, J., Verrelst, J., Alonso, L., Moreno, J., Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 11 (2011), 7063–7081.
13. Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens. Environ. 120 (2012), 25–36.
14. FAO, Global forest resources assessment 2005. Forestry Paper, 147, 2005, 350.
15. Faraway, J.J., Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. 2006, Chapman & Hall/CRC, Boca Raton, FL, USA, 301.
16. Fernández-Manso, A., Fernández-Manso, O., Quintano, C., SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. Int. J. Appl. Earth Obs. Geoinf. 50 (2016), 170–175.
17. Ferrari, S., Cribari-Neto, F., Beta regression for modelling rates and proportions. J. Appl. Stat. 31 (2004), 799–815.
18. Frampton, W.J., Dash, J., Watmough, G., Milton, E.J., Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J. Photogramm. Remote Sens. 82 (2013), 83–92.
19. Ganguly, S., Nemani, R.R., Baret, F., Bi, J., Weiss, M., Zhang, G., et al. Green leaf area and fraction of photosynthetically active radiation absorbed by vegetation. Hanes, J.M., (eds.) Biophysical Applications of Satellite Remote Sensing, 2014, Springer-Verlag, Berlin Heidelberg, 43–61.
20. Gaveau, D.L.A., Hill, R.A., Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data. Can. J. Remote. Sens. 29 (2003), 650–657.
21. Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58 (1996), 289–298.
22. Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80 (2002), 76–87.
23. Gitelson, A.A., Gritz, Y., Merzlyak, M.N., Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160 (2003), 271–282.
24. Global Climate Observing System, Implementation plan for the global observing system for climate in support of the UNFCCC (2010 update). GCOS Rep. 138 (2010), 1–180.
25. Goel, N.S., Qin, W., Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation. Remote Sens. Rev. 10 (1994), 309–347.
26. Gschwantner, T., Schadauer, K., Vidal, C., Lanz, A., Tomppo, E., Di Cosmo, L., et al. Common tree definitions for national forest inventories in Europe. Silva Fennica 43 (2009), 303–321.
27. Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81 (2002), 416–426.
28. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B., Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90 (2004), 337–352.
29. Hadi, Korhonen, L., Hovi, A., Rönnholm, P., Rautiainen, M., The accuracy of large-area forest canopy cover estimation using Landsat in boreal region. Int. J. Appl. Earth Obs. Geoinf. 53 (2016), 118–127.
30. Halperin, J., LeMay, V., Coops, N., Verchot, L., Marshall, P., Lochhead, K., Canopy cover estimation in miombo woodlands of Zambia: comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods. Remote Sens. Environ. 179 (2016), 170–182.
31. Hansen, M., DeFries, R., Townshend, J., Carroll, M., Dimiceli, C., Sohlberg, R., Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interact. 7 (2003), 1–15.
32. Hardisky, M., Klemas, V., Smart, R.M., The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm. Eng. Remote. Sens. 49 (1983), 77–83.
33. Heiskanen, J., Rautiainen, M., Korhonen, L., Mõttus, M., Stenberg, P., Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions. Int. J. Appl. Earth Obs. Geoinf. 13 (2011), 595–606.
34. Huete, A., Liu, H., Batchily, K.v., Van Leeuwen, W., A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 59 (1997), 440–451.
35. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83 (2002), 195–213.
36. Immitzer, M., Vuolo, F., Atzberger, C., First experience with sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sens., 8, 2016, 166.
37. Jordan, C.F., Derivation of leaf-area index from quality of light on the forest floor. Ecology 50 (1969), 663–666.
38. Key, C., Landscape assessment (LA). Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland, S., Gangi, L.J., (eds.) FIREMON: Fire Effects Monitoring and Inventory System, 2006, USDA Forest Service, Rocky Mountain Research Station.
39. Korhonen, L., Heikkinen, J., Automated analysis of in situ canopy images for the estimation of forest canopy cover. For. Sci. 55 (2009), 323–334.
40. Korhonen, L., Korhonen, K.T., Rautiainen, M., Stenberg, P., Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fennica, 40, 2006, 577.
41. Korhonen, L., Korpela, I., Heiskanen, J., Maltamo, M., Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index. Remote Sens. Environ. 115 (2011), 1065–1080.
42. Korhonen, L., Heiskanen, J., Korpela, I., Modelling lidar-derived boreal forest canopy cover with SPOT 4 HRVIR data. Int. J. Remote Sens. 34 (2013), 8172–8181.
43. Le Maire, G., François, C., Dufrêne, E., Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ. 89 (2004), 1–28.
44. Main, R., Cho, M.A., Mathieu, R., O'Kennedy, M.M., Ramoelo, A., Koch, S., An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS J. Photogramm. Remote Sens. 66 (2011), 751–761.
45. Majasalmi, T., Rautiainen, M., The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study. Remote Sens. Lett. 7 (2016), 427–436.
46. Majasalmi, T., Rautiainen, M., Stenberg, P., Rita, H., Optimizing the sampling scheme for LAI-2000 measurements in a boreal forest. Agric. For. Meteorol. 154-155 (2012), X38–X43.
47. Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., et al. A landsat surface reflectance dataset for North America, 1990-2000. IEEE Geosci. Remote Sens. Lett. 3 (2006), 68–72.
48. Mathieu, R., Naidoo, L., Cho, M.A., Leblon, B., Main, R., Wessels, K., et al. Toward structural assessment of semi-arid African savannahs and woodlands: The potential of multitemporal polarimetric RADARSAT-2 fine beam images. Remote Sens. Environ. 138 (2013), 215–231.
49. Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B., Rakitin, V.Y., Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106 (1999), 135–141.
50. Morsdorf, F., Kötz, B., Meier, E., Itten, K.I., Allgöwer, B., Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sens. Environ. 104 (2006), 50–61.
51. Næsset, E., Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 80 (2002), 88–99.
52. Nobis, M., Hunziker, U., Automatic thresholding for hemispherical canopy-photographs based on edge detection. Agric. For. Meteorol. 128 (2005), 243–250.
53. Peng, Y., Gitelson, A.A., Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agric. For. Meteorol. 151 (2011), 1267–1276.
54. Rautiainen, M., Stenberg, P., Nilson, T., Estimating canopy cover in scots pine stands. Silva Fennica 39 (2005), 137–142.
55. Ridler, T.W., Calvard, S., Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern. SMC-8 (1978), 630–632.
56. Roujean, J., Breon, F., Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 51 (1995), 375–384.
57. Schleppi, P., Conedera, M., Sedivy, I., Thimonier, A., Correcting non-linearity and slope effects in the estimation of the leaf area index of forests from hemispherical photographs. Agric. For. Meteorol. 144 (2007), 236–242.
58. Sexton, J.O., Song, X., Feng, M., Noojipady, P., Anand, A., Huang, C., et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digital Earth 6 (2013), 427–448.
59. Sims, D.A., Gamon, J.A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81 (2002), 337–354.
60. Solberg, S., Brunner, A., Hanssen, K.H., Lange, H., Næsset, E., Rautiainen, M., et al. Mapping LAI in a Norway spruce forest using airborne laser scanning. Remote Sens. Environ. 113 (2009), 2317–2327.
61. Sripada, R.P., Heiniger, R.W., White, J.G., Meijer, A.D., Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agron. J. 98 (2006), 968–977.
62. Stenberg, P., Correcting LAI-2000 estimates for the clumping of needles in shoots of conifers. Agric. For. Meteorol. 79 (1996), 1–8.
63. Stenberg, P., Nilson, T., Smolander, H., Voipio, P., Gap fraction based estimation of LAI in Scots pine stands subjected to experimental removal of branches and stems. Can. J. Remote. Sens. 29 (2003), 363–370.
64. Stenberg, P., Rautiainen, M., Manninen, T., Voipio, P., Smolander, H., Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands. Silva Fennica 38 (2004), 3–14.
65. Tian, Y., Gu, K., Chu, X., Yao, X., Cao, W., Zhu, Y., Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant Soil 376 (2013), 193–209.
66. Urbazaev, M., Thiel, C., Mathieu, R., Naidoo, L., Levick, S.R., Smit, I.P., et al. Assessment of the mapping of fractional woody cover in southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images. Remote Sens. Environ. 166 (2015), 138–153.
67. Verrelst, J., Rivera, J.P., Veroustraete, F., Muñoz-Marí, J., Clevers, J.G., Camps-Valls, G., et al. Experimental sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods – a comparison. ISPRS J. Photogramm. Remote Sens. 108 (2015), 260–272.
68. Vincini, M., Frazzi, E., D'Alessio, P., Angular dependence of maize and sugar beet VIs from directional CHRIS/Proba data. 4th ESA CHRIS PROBA Workshop, 2006, ESRIN, Frascati, Italy, 19–21.
69. Vuolo, F., Żółtak, M., Pipitone, C., Zappa, L., Wenng, H., Immitzer, M., Weiss, M., Baret, F., Atzberger, C., Data service platform for Sentinel-2 surface reflectance and value-added products: system use and examples. Remote Sens., 8, 2016, 938.
70. Wang, L., Qu, J.J., NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys. Res. Lett., 34, 2007, L20405.
71. Wang, W., Yao, X., Yao, X., Tian, Y., Liu, X., Ni, J., et al. Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crop Res. 129 (2012), 90–98.
72. Welles, J.M., Norman, J.M., Instrument for indirect measurement of canopy architecture. Agron. J. 83 (1991), 818–825.
73. van der Werff, H., van der Meer, F., Sentinel-2A MSI and Landsat 8 OLI provide data continuity for geological remote sensing. Remote Sens., 8, 2016, 883.
74. Wood, S.N., Modelling and smoothing parameter estimation with multiple quadratic penalties. J. R. Stat. Soc. Ser. B Stat Methodol. 62 (2000), 413–428.
75. Wu, C., Niu, Z., Tang, Q., Huang, W., Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric. For. Meteorol. 148 (2008), 1230–1241.
76. Wulder, M.A., White, J.C., Bater, C.W., Coops, N.C., Hopkinson, C., Chen, G., Lidar plots - a new large-area data collection option: context, concepts, and case study. Can. J. Remote. Sens. 38 (2012), 600–618.