477
Bischof R, Loe LE, Meisingset EL, Zimmentmann B, van Moorter B, Mysterud A (2012) A migratory northern ungulate in the pursuit of Spring: jumping or surfing the green wave? Am Nat
180:407–424
Botzat A, Fischer LK, Kowarik I (2016) Unexploited opportunities in understanding liveable
and biodiverse cities. A review on urban biodiversity perception and valuation. Glob Environ
Chang 39:220–233
Bouttier F, Courtier P (2002) Data assimilation concepts and methods. Meteorol Train Course Lect
Ser 1–58
Bouvet A, Mermoz S, Le Toan T, Villard L, Mathieu R, Naidoo L, Asner GP (2017) An aboveground biomass map of African savannahs and woodlands at 25 m resolution derived from
ALOS PALSAR. Remote Sens Environ 206:156–173
Boyd DS et al (2012) Evaluation of Envisat MERIS terrestrial chlorophyll index-based models
for the estimation of terrestrial gross primary productivity. IEEE Geosci Remote Sens Lett
93:457–461. https://doi.org/10.1109/LGRS.2011.2170810
Boyd DS, Foody GM (2011) An overview of recent remote sensing and GIS based research in ecological informatics, Ecological Informatics. Elsevier BV 61:25–36. https://doi.org/10.1016/j.
ecoinf.2010.07.007
Bradley BA, Fleishman E (2008) Can remote sensing of land cover improve species distribution
modelling? J Biogeogr 35:1158–1159
Brown TB et al (2016) Using phenocams to monitor our changing earth: toward a global phenocam
network. Front Ecol Environ 142:84–93. https://doi.org/10.1002/fee.1222
Brus DJ et al (2012) Statistical mapping of tree species over Europe. Eur J For Res 1311:145–157.
https://doi.org/10.1007/s10342-011-0513-5
Buitenwerf R, Rose L, Higgins SI (2015) Three decades of multi-dimensional change in global
leaf phenology. Nat Clim Chang 54:364–368. https://doi.org/10.1038/nclimate2533
Bustamante MMC et al (2016) Toward an integrated monitoring framework to assess the effects
of tropical forest degradation and recovery on carbon stocks and biodiversity. Glob Chang Biol
221:92–109. https://doi.org/10.1111/gcb.13087
Butler EE et al (2017) Mapping local and global variability in plant trait distributions. Proc Natl
Acad Sci 114(51):201708984. https://doi.org/10.1073/pnas.1708984114
Cabello J et al (2012) The ecosystem functioning dimension in conservation: insights from remote
sensing. Biodivers Conserv 2113:3287–3305. https://doi.org/10.1007/s10531-012-0370-7
Carreiras JMB, Jones J, Lucas RM, Shimabukuro YE (2017) Mapping major land cover types and
retrieving the age of secondary forests in the Brazilian Amazon by combining single-date optical and radar remote sensing data. Remote Sens Environ 194:16–32
Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh,
pp 161–168
Cerreta M, Poli G (2017) Landscape services assessment: a hybrid Multi-Criteria Spatial Decision
Support System (MC-SDSS). Sustainability 9:1310–1328
Choa MA, Mathieu R, Asner GP, Naidoo L, Aardt J, Ramoelo A, Debba P, Wessels K, Main R,
Smit IPJ, Erasmus B (2012) Mapping tree species composition in south African savannas using
an integrated airborne spectral and LiDAR system. Remote Sens Environ 125:214–226
Chave J et al (2014) Improved allometric models to estimate the aboveground biomass of tropical
trees. Glob Chang Biol 20:3177–3190
Claverie M, Ju J, Masek JG, Dungan JL, Vermote EF, Roger J-C, Skakun SV, Justice C (2018)
The harmonized landsat and sentinel-2 surface reflectance dataset. Remote Sens Environ
219:145–161
Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in
response to global change. Trends Ecol Evol 22:357–365
Clinton N, Yu L, Gong P (2015) Geographic stacking: decision fusion to increase global land cover
map accuracy. Glob L Cover Mapp Monit 103:57–65
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…
Bischof R, Loe LE, Meisingset EL, Zimmentmann B, van Moorter B, Mysterud A (2012) A migratory northern ungulate in the pursuit of Spring: jumping or surfing the green wave? Am Nat
180:407–424
Botzat A, Fischer LK, Kowarik I (2016) Unexploited opportunities in understanding liveable
and biodiverse cities. A review on urban biodiversity perception and valuation. Glob Environ
Chang 39:220–233
Bouttier F, Courtier P (2002) Data assimilation concepts and methods. Meteorol Train Course Lect
Ser 1–58
Bouvet A, Mermoz S, Le Toan T, Villard L, Mathieu R, Naidoo L, Asner GP (2017) An aboveground biomass map of African savannahs and woodlands at 25 m resolution derived from
ALOS PALSAR. Remote Sens Environ 206:156–173
Boyd DS et al (2012) Evaluation of Envisat MERIS terrestrial chlorophyll index-based models
for the estimation of terrestrial gross primary productivity. IEEE Geosci Remote Sens Lett
93:457–461. https://doi.org/10.1109/LGRS.2011.2170810
Boyd DS, Foody GM (2011) An overview of recent remote sensing and GIS based research in ecological informatics, Ecological Informatics. Elsevier BV 61:25–36. https://doi.org/10.1016/j.
ecoinf.2010.07.007
Bradley BA, Fleishman E (2008) Can remote sensing of land cover improve species distribution
modelling? J Biogeogr 35:1158–1159
Brown TB et al (2016) Using phenocams to monitor our changing earth: toward a global phenocam
network. Front Ecol Environ 142:84–93. https://doi.org/10.1002/fee.1222
Brus DJ et al (2012) Statistical mapping of tree species over Europe. Eur J For Res 1311:145–157.
https://doi.org/10.1007/s10342-011-0513-5
Buitenwerf R, Rose L, Higgins SI (2015) Three decades of multi-dimensional change in global
leaf phenology. Nat Clim Chang 54:364–368. https://doi.org/10.1038/nclimate2533
Bustamante MMC et al (2016) Toward an integrated monitoring framework to assess the effects
of tropical forest degradation and recovery on carbon stocks and biodiversity. Glob Chang Biol
221:92–109. https://doi.org/10.1111/gcb.13087
Butler EE et al (2017) Mapping local and global variability in plant trait distributions. Proc Natl
Acad Sci 114(51):201708984. https://doi.org/10.1073/pnas.1708984114
Cabello J et al (2012) The ecosystem functioning dimension in conservation: insights from remote
sensing. Biodivers Conserv 2113:3287–3305. https://doi.org/10.1007/s10531-012-0370-7
Carreiras JMB, Jones J, Lucas RM, Shimabukuro YE (2017) Mapping major land cover types and
retrieving the age of secondary forests in the Brazilian Amazon by combining single-date optical and radar remote sensing data. Remote Sens Environ 194:16–32
Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh,
pp 161–168
Cerreta M, Poli G (2017) Landscape services assessment: a hybrid Multi-Criteria Spatial Decision
Support System (MC-SDSS). Sustainability 9:1310–1328
Choa MA, Mathieu R, Asner GP, Naidoo L, Aardt J, Ramoelo A, Debba P, Wessels K, Main R,
Smit IPJ, Erasmus B (2012) Mapping tree species composition in south African savannas using
an integrated airborne spectral and LiDAR system. Remote Sens Environ 125:214–226
Chave J et al (2014) Improved allometric models to estimate the aboveground biomass of tropical
trees. Glob Chang Biol 20:3177–3190
Claverie M, Ju J, Masek JG, Dungan JL, Vermote EF, Roger J-C, Skakun SV, Justice C (2018)
The harmonized landsat and sentinel-2 surface reflectance dataset. Remote Sens Environ
219:145–161
Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in
response to global change. Trends Ecol Evol 22:357–365
Clinton N, Yu L, Gong P (2015) Geographic stacking: decision fusion to increase global land cover
map accuracy. Glob L Cover Mapp Monit 103:57–65
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…