Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems

International Journal of Applied Earth Observation and Geoinformation

Volumn 66, Issue , 2018, Pages 126-134

  Mura, Matteo ^a   Bottalico, Francesca ^a   Giannetti, Francesca ^a   Bertani, Remo ^b   Giannini, Raffaello ^c   Mancini, Marco ^a   Orlandini, Simone ^a   Travaglini, Davide ^a   Chirici, Gherardo ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

^b RDM Progetti Srl   (Italy)

^c Fondazione per il Clima e la Sostenibilità FCS   (Italy)

Author keywords

Forests; Growing stock volume; Multi spectral instrument; Prediction; Random forest; Sentinel 2

Indexed keywords

ALGORITHM; FOREST ECOSYSTEM; FOREST MANAGEMENT; LANDSAT; NEAREST NEIGHBOR ANALYSIS; PREDICTION; RAPIDEYE; REMOTE SENSING; SATELLITE IMAGERY; SENTINEL; SPATIAL RESOLUTION; STOCK ASSESSMENT; SUSTAINABILITY;

ITALY;

EID: 85047159058     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2017.11.013     Document Type: Article

References (48)

1. Ardö J., Volume quantification of coniferous forest compartments using spectral radiance recorded by Landsat Thematic Mapper. Int. J. Remote Sens. 13 (1992), 1779–1786, 10.1080/01431169208904227.
2. Atzberger, C., Richter, K., Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery. Remote Sens. Environ. 120 (2012), 208–218, 10.1016/j.rse.2011.10.035.
3. Barabesi, L., Franceschi, S., Sampling properties of spatial total estimators under tessellation stratified designs. Environmetrics 22 (2011), 271–278, 10.1002/env.1046.
4. Berger, M., Moreno, J., Johannessen, J.A., Levelt, P.F., Hanssen, R.F., ESA's sentinel missions in support of Earth system science. Remote Sens. Environ. 120 (2012), 84–90, 10.1016/j.rse.2011.07.023.
5. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32, 10.1023/A:1010933404324.
6. Chirici, G., Barbati, A., Corona, P., Marchetti, M., Travaglini, D., Maselli, F., Bertini, R., Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems. Remote Sens. Environ. 112 (2008), 2686–2700, 10.1016/j.rse.2008.01.002.
7. Chirici, G., Mura, M., McInerney, D., Py, N., Tomppo, E.O., Waser, L.T., Travaglini, D., McRoberts, R.E., A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data. Remote Sens. Environ. 176 (2016), 282–294, 10.1016/j.rse.2016.02.001.
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, 10.1080/2150704X.2017.1295479.
9. Clasen, A., Somers, B., Pipkins, K., Tits, L., Segl, K., Brell, M., Kleinschmit, B., Spengler, D., Lausch, A., Förster, M., Spectral unmixing of forest crown components at close range, airborne and simulated Sentinel-2 and EnMAP spectral imaging scale. Remote Sens. 7 (2015), 15361–15387, 10.3390/rs71115361.
10. 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. Int. J. Appl. Earth Obs. Geoinf. 23 (2013), 344–351, 10.1016/j.jag.2012.10.008.
11. Crookston, N.L., Finley, A.O., yaImpute: an R package for (NN imputation. J. Stat. Softw. 23 (2008), 1–16, 10.18637/jss.v023.i10.
12. Crookston, N.L., Moeur, M., Renner, D., Users Guide to the Most Similar Neighbor Imputation Program Version 2. RMRS-GTR-96. 2002 (Ogden,UT).
13. Dotzler, S., Hill, J., Buddenbaum, H., Stoffels, J., The potential of EnMAP and sentinel-2 data for detecting drought stress phenomena in deciduous forest communities. Remote Sens. 7 (2015), 14227–14258, 10.3390/rs71014227.
14. 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. Remote Sens. Environ. 120 (2012), 25–36, 10.1016/j.rse.2011.11.026.
15. ESA, Sentinel 2 MSI Technical Guides. Level 1-C Algorythm [WWW Document]. 2017.
16. Falkowski, M.J.F.J., Hudak, A.T.H.T., Crookston, N.L.C.L., Gessler, P.E.G.E., Uebler, E.H.U.H., Smith, A.M.S.S.M.S., Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data. Can. J. For. Res. 40 (2010), 184–199, 10.1139/X09-183.
17. Feilhauer, H., Dahlke, C., Doktor, D., Lausch, A., Schmidtlein, S., Schulz, G., Stenzel, S., Mapping the local variability of Natura 2000 habitats with remote sensing. Appl. Veg. Sci. 17 (2014), 765–779, 10.1111/avsc.12115.
18. Gasparini, P., Tabacchi, G., L'Inventario Nazionale Delle Foreste E Dei Serbatoi Forestali Di Carbonio INFC 2005. Secondo Inventario Forestale Nazionale Italiano. Metodi E Risultati. Edagricole-Il Sole 24 Ore. 2011.
19. Gemmell, F.M., Effects of forest cover, terrain, and scale on timber volume estimation with Thematic Mapper data in a rocky mountain site. Remote Sens. Environ. 51 (1995), 291–305, 10.1016/0034-4257(94)00056-S.
20. Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., Bonfil, D.J., LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sens. Environ. 115 (2011), 2141–2151, 10.1016/j.rse.2011.04.018.
21. Hojas-Gascón, L., Belward, A., Eva, H., Ceccherini, G., Hagolle, O., Garcia, J., Cerutti, P., Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ? ISPRS Archives, European Commission Joint Research Centre, Via E. Fermi 2749, Ispra (VA), Italy, 2015, 417–423, 10.5194/isprsarchives-XL-7-W3-417-2015.
22. Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E., Falkowski, M.J., Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sens. Environ. 112 (2008), 2232–2245, 10.1016/j.rse.2007.10.009.
23. Hyvärinen, A., Oja, E., Independent component analysis: algorithms and applications. Neural Netw. 13 (2000), 411–430, 10.1016/S0893-6080(00)00026-5.
24. 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, 10.3390/rs8030166.
25. Korhonen, L., Hadi, Packalen, P., Rautiainen, M., Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sens. Environ. 195 (2017), 259–274, 10.1016/j.rse.2017.03.021.
26. Laurin, G. Vaglio, Puletti, N., Hawthorne, W., Liesenberg, V., Corona, P., Papale, D., Chen, Q., Valentini, R., Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sens. Environ. 176 (2016), 163–176, 10.1016/j.rse.2016.01.017.
27. Mahalanobis, P.C., On the Generalised Distance in Statistics. 1936, Proceedings National Institute of Science, India, 49–55.
28. 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, 10.1080/2150704X.2016.1149251.
29. Maselli, F., Chirici, G., Bottai, L., Corona, P., Marchetti, M., Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images. Int. J. Remote Sens. 26 (2005), 3781–3796, 10.1080/01431160500166433.
30. Marchini, J.L., Heaton, C., Ripley, B.D., The fastICA package. 2002 Software and documentation available at http://www.stats.ox.ac.uk/marchini/software.html.
31. McRoberts, R.E., Tomppo, E.O., Remote sensing support for national forest inventories. Remote Sens. Environ. 110 (2007), 412–419, 10.1016/j.rse.2006.09.034.
32. McRoberts, R.E., Cohen, W.B., Næsset, E., Stehman, S.V., Tomppo, E.O., Using remotely sensed data to construct and assess forest attribute maps and related spatial products. Scand. J. For. Res. 25 (2010), 340–367, 10.1080/02827581.2010.497496.
33. Moeur, M., Stage, A.R., Most similar neighbor: an improved sampling inference procedure for natural resource planning. For. Sci. 41 (1995), 337–359.
34. Muukkonen, P., Heiskanen, J., Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data. Remote Sens. Environ. 99 (2005), 434–447, 10.1016/j.rse.2005.09.011.
35. Nink, S., Hill, J., Buddenbaum, H., Stoffels, J., Sachtleber, T., Langshausen, J., Assessing the suitability of future multi- and hyperspectral satellite systems for mapping the spatial distribution of norway spruce timber volume. Remote Sens. 7 (2015), 12009–12040, 10.3390/rs70912009.
36. Ohmann, J.L., Gregory, M.J., Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A. Can. J. For. Res. 32 (2002), 725–741, 10.1139/x02-011.
37. PLANET, Rapideye Imagery Product Specifications. 2016.
38. R Core Team, R: A Language and Environment for Statistical Computing. 2014.
39. Ramoelo, A., Cho, M., Mathieu, R., Skidmore, A.K., Potential of Sentinel-2 spectral configuration to assess rangeland quality. J. Appl. Remote Sens., 9, 2015, 94096, 10.1117/1.JRS.9 094096.
40. Richter, K., Atzberger, C., Vuolo, F., Weihs, P., D'Urso, G., Experimental assessment of the Sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize. Can. J. Remote Sens. 35 (2009), 230–247.
41. Richter, K., Hank, T.B., Vuolo, F., Mauser, W., D'Urso, G., Optimal exploitation of the sentinel-2 spectral capabilities for crop leaf area index mapping. Remote Sens. 4 (2012), 561–582, 10.3390/rs4030561.
42. Schlemmera, M., Gitelson, A., Schepersa, J., Fergusona, R., Peng, Y., Shanahana, J., Rundquist, D., Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. Int. J. Appl. Earth Obs. Geoinf. 25 (2013), 47–54, 10.1016/j.jag.2013.04.003.
43. Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A., Classification and change detection using landsat TM data: when and how to correct atmospheric effects?. Remote Sens. Environ. 75 (2001), 230–244, 10.1016/S0034-4257(00)00169-3.
44. Stage, A.R., Crookston, N.L., Partitioning error components for accuracy-Assessment of near-Neighbor methods of imputation. For. Sci. 53 (2007), 62–72.
45. Steensen, T., Müller, S., Jandewerth, M., Büscher, O., Mapping Biomass Availability to Decrease the Dependency on Fossil Fuels, In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences − ISPRS Archives. 2014, Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany, 165–171, 10.5194/isprsarchives-XL-7-165-2014.
46. Tabacchi, G., Di Cosmo, L., Gasparini, P., Morelli, S., Stima Del Volume E Della Fitomassa Delle Principali Specie Forestali Italiene Equazioni Di Previsione Tavole Del Volume E Tavole Della Fitomassa Arborea Epigea. 2011.
47. Tomppo, E., Olsson, H., Ståhl, G., Nilsson, M., Hagner, O., Katila, M., Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sens. Environ. 112 (2008), 1982–1999, 10.1016/j.rse.2007.03.032.
48. USGS, Landsat 8 (l8) Data Users Handbook. 2016.