307
scientists with decades of experience in earth observation. As illustration, the software used in the development and implementation of the EODESM system is open
source and freely available and is based primarily on python scripts and the
RSGISLib (Bunting et al. 2013; Clewley et al. 2014). Numerous options are becoming available for routinely evaluating the accuracy and reliability of these products,
giving confidence to many users. For these reasons, earth observation datasets can
now be better used to transform the way that our environment is managed and conserved. No longer are procedures and products remaining within the realms of the
scientific community; they now can transition into being used to support nature
conservation in a more practical sense. For this, we give credit to many individuals,
groups and organisations (e.g., space agencies, governments and businesses such as
Google) for facilitating free and public distribution.
Acknowledgements The authors would like to thank all participants of the H2020
ECOPOTENTIAL and FP7 BIOSOS funded projects. This project has received funding from the
European Union’s Horizon 2020 research and innovation programme under grant agreement No.
641762.
References
Bunting, P., Clewley, D., Lucas, R.M., Gillingham, S.: The remote Sensing and GIS Software
Library (RSGISLib). Comput. Geosci. 62, 206–226 (2013)
Clewley, D., Bunting, B., Shepherd, J., Gillingham, S., Flood, N., Dymond, J., Lucas, R., Armston,
J., Moghaddam, M.: A python-based open source system for geographic object-based image
analysis (GEOBIA) utilising raster attribute tables. Remote Sens. 6(7), 6111–6135 (2014)
Di Gregorio, A.: Land Cover Classification System (LCCS). Classification concepts and user manual for software version 2. FAO Environment and Natural Resources Series, 8, 190 pp (2005)
Feng, M., Sexton, J.O., Channan, S., Townshend, J.R.: A global, high-resolution (30 m) inland
water body dataset for 2000: first results of a topographic-spectral classification algorithm. Int.
J. Digit. Earth. 9(2), 113–133 (2016). doi:10.1080/17538947.2015.1026420
Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., Masek, J.L., Duke, N.: Status
and distribution of mangrove forests of the world using earth observation satellite data. Glob.
Ecol. Biogeogr. 20, 154–159 (2011)
Hall, D.K., Riggs, G.A.: MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid, version 6.
Boulder, Colorado, USA. NASA National Snow and Ice Data Center Distributed Active
Archive Center. doi:http://dx.doi.org/10.5067/MODIS/MOD10A2.006 (2016)
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D.,
Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice,
C.O., Townshend, J.R.G.: High-resolution global maps of 21st-century forest cover change.
Science. 342(6160), 850–853 (2013)
Kosmidou, V., Petrou, Z., Bunce, R.G., Mucher, C., Jongman, R.H., Bogers, M.M., Lucas, R.M.,
Tomaselli, V., Blonda, P., Pado-Schioppa, E., Manakos, I., Petrou, M.: Harmonization of the
land cover classification system (LCCS2) with the General Habitat Categories (GHC) classification system. Ecol. Indic. 36, 290–300 (2013)
Langanke, T., Büttner, G., Dufourmont, H., Iasillo, D., Probeck, M., Rosengren, M., Sousa, A.,
Strobl, P., Weichselbaum, J.: GIO land (GMES/Copernicus initial operations land) High
Resolution Layers (HRLs) – summary of product specifications. ESA GIO Land Team. 16p
(2013)
Integrated Land Cover and Change Classifications
scientists with decades of experience in earth observation. As illustration, the software used in the development and implementation of the EODESM system is open
source and freely available and is based primarily on python scripts and the
RSGISLib (Bunting et al. 2013; Clewley et al. 2014). Numerous options are becoming available for routinely evaluating the accuracy and reliability of these products,
giving confidence to many users. For these reasons, earth observation datasets can
now be better used to transform the way that our environment is managed and conserved. No longer are procedures and products remaining within the realms of the
scientific community; they now can transition into being used to support nature
conservation in a more practical sense. For this, we give credit to many individuals,
groups and organisations (e.g., space agencies, governments and businesses such as
Google) for facilitating free and public distribution.
Acknowledgements The authors would like to thank all participants of the H2020
ECOPOTENTIAL and FP7 BIOSOS funded projects. This project has received funding from the
European Union’s Horizon 2020 research and innovation programme under grant agreement No.
641762.
References
Bunting, P., Clewley, D., Lucas, R.M., Gillingham, S.: The remote Sensing and GIS Software
Library (RSGISLib). Comput. Geosci. 62, 206–226 (2013)
Clewley, D., Bunting, B., Shepherd, J., Gillingham, S., Flood, N., Dymond, J., Lucas, R., Armston,
J., Moghaddam, M.: A python-based open source system for geographic object-based image
analysis (GEOBIA) utilising raster attribute tables. Remote Sens. 6(7), 6111–6135 (2014)
Di Gregorio, A.: Land Cover Classification System (LCCS). Classification concepts and user manual for software version 2. FAO Environment and Natural Resources Series, 8, 190 pp (2005)
Feng, M., Sexton, J.O., Channan, S., Townshend, J.R.: A global, high-resolution (30 m) inland
water body dataset for 2000: first results of a topographic-spectral classification algorithm. Int.
J. Digit. Earth. 9(2), 113–133 (2016). doi:10.1080/17538947.2015.1026420
Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., Masek, J.L., Duke, N.: Status
and distribution of mangrove forests of the world using earth observation satellite data. Glob.
Ecol. Biogeogr. 20, 154–159 (2011)
Hall, D.K., Riggs, G.A.: MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid, version 6.
Boulder, Colorado, USA. NASA National Snow and Ice Data Center Distributed Active
Archive Center. doi:http://dx.doi.org/10.5067/MODIS/MOD10A2.006 (2016)
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D.,
Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice,
C.O., Townshend, J.R.G.: High-resolution global maps of 21st-century forest cover change.
Science. 342(6160), 850–853 (2013)
Kosmidou, V., Petrou, Z., Bunce, R.G., Mucher, C., Jongman, R.H., Bogers, M.M., Lucas, R.M.,
Tomaselli, V., Blonda, P., Pado-Schioppa, E., Manakos, I., Petrou, M.: Harmonization of the
land cover classification system (LCCS2) with the General Habitat Categories (GHC) classification system. Ecol. Indic. 36, 290–300 (2013)
Langanke, T., Büttner, G., Dufourmont, H., Iasillo, D., Probeck, M., Rosengren, M., Sousa, A.,
Strobl, P., Weichselbaum, J.: GIO land (GMES/Copernicus initial operations land) High
Resolution Layers (HRLs) – summary of product specifications. ESA GIO Land Team. 16p
(2013)
Integrated Land Cover and Change Classifications