476
remain, including scaling mismatches, misuse of data and techniques, insufficiently
high spatiotemporal resolution of RS data, biases in in-situ data, and many more. It
is imperative that we acknowledge and work with these challenges to devise even
more accurate and suitable approaches to assessing biodiversity for the study of
ecosystem function, conservation, and other applications at large spatial scales.
Acknowledgments FS was supported by an Anne McLaren fellowship by the University of
Nottingham (UK). We thank Nathalie Pettorelli for useful discussions and suggestions. CW
acknowledges support from the British Geological Survey. MJS acknowledges the support of the
University Research Priority Program in Global Change and Biodiversity and the Department of
Geography at the University of Zurich. We also thank the NIMBioS Working Group on Remote
Sensing of Biodiversity.
References
Abelleira Martínez OJ et al (2016) Scaling up functional traits for ecosystem services with remote
sensing: concepts and methods. Ecol Evol 613:4359–4371. https://doi.org/10.1002/ece3.2201
De Araujo Barbosa CC, Atkinson PM, Dearing JA (2015) Remote sensing of ecosystem services:
a systematic review, ecological indicators. Elsevier Ltd 52:430–443. https://doi.org/10.1016/j.
ecolind.2015.01.007
Alves DS, Skole LD (1996) Characterizing land cover dynamics using multi-temporal imagery. Int
J Remote Sens 17:835–839
Ahmadalipour A, Moradkhani H, Yan H, Zarekarizi M (2017) Remote sensing of drought: vegetation, soil moisture, and data assimilation. In: Remote sensing of hydrological extremes,
pp 121–149
Andrew ME, Wulder MA, Nelson TA, Coops NC (2014) Spatial data, analysis approaches, and
information needs for spatial ecosystem service assessments: a review. GIScience Remote Sens
52:344–373
Aronson MFJ, Patel MV, ONeill KM, Ehrenfeld JG (2017) Urban riparian systems function as corridors for both native and invasive plant species. Biol Invasions 19:3645–3657
Asner GP et al (2005) Selective logging in the Brazilian Amazon. Science 310(5747):480–482
Asner GA, Olinger SV (2009) Remote sensing for terrestrial biogeochemical modelling. In:
Warner TA, Nellis MD, Foody GM (eds) The SAGE handbook of remote sensing. SAGE,
London, pp 411–422
Asner GP, Tupayachi R (2017) Accelerated losses of protected forests from gold mining in the
Peruvian Amazon. Environ Res Lett 12:094004
Baeten L et al (2013) A novel comparative research platform designed to determine the functional
significance of tree species diversity in European forests. Persp Pl Ecol Evol Syst 155:281–291.
https://doi.org/10.1016/j.ppees.2013.07.002
Barbet-Massin M et al (2012) Selecting pseudo-absences for species distribution models : how, where and how many? Methods Ecol Evol 3:327–338. https://doi.
org/10.1111/j.2041-210X.2011.00172.x
Beck J et al (2014) Spatial bias in the GBIF database and its effect on modeling species geographic
distributions. Eco Inform 19:10–15
Berger M, Aschbacher J (2012) Preface: The Sentinel missions—new opportunities for science.
Remote Sens Environ 120:1–2
Bierkens MFP et al (2015) Hyper-resolution global hydrological modelling: what is next?:
“Everywhere and locally relevant” M. F. P. Bierkens et al. Invited Commentary. Hydrol Process
292:310–320. https://doi.org/10.1002/hyp.10391
F. Schrodt et al.
remain, including scaling mismatches, misuse of data and techniques, insufficiently
high spatiotemporal resolution of RS data, biases in in-situ data, and many more. It
is imperative that we acknowledge and work with these challenges to devise even
more accurate and suitable approaches to assessing biodiversity for the study of
ecosystem function, conservation, and other applications at large spatial scales.
Acknowledgments FS was supported by an Anne McLaren fellowship by the University of
Nottingham (UK). We thank Nathalie Pettorelli for useful discussions and suggestions. CW
acknowledges support from the British Geological Survey. MJS acknowledges the support of the
University Research Priority Program in Global Change and Biodiversity and the Department of
Geography at the University of Zurich. We also thank the NIMBioS Working Group on Remote
Sensing of Biodiversity.
References
Abelleira Martínez OJ et al (2016) Scaling up functional traits for ecosystem services with remote
sensing: concepts and methods. Ecol Evol 613:4359–4371. https://doi.org/10.1002/ece3.2201
De Araujo Barbosa CC, Atkinson PM, Dearing JA (2015) Remote sensing of ecosystem services:
a systematic review, ecological indicators. Elsevier Ltd 52:430–443. https://doi.org/10.1016/j.
ecolind.2015.01.007
Alves DS, Skole LD (1996) Characterizing land cover dynamics using multi-temporal imagery. Int
J Remote Sens 17:835–839
Ahmadalipour A, Moradkhani H, Yan H, Zarekarizi M (2017) Remote sensing of drought: vegetation, soil moisture, and data assimilation. In: Remote sensing of hydrological extremes,
pp 121–149
Andrew ME, Wulder MA, Nelson TA, Coops NC (2014) Spatial data, analysis approaches, and
information needs for spatial ecosystem service assessments: a review. GIScience Remote Sens
52:344–373
Aronson MFJ, Patel MV, ONeill KM, Ehrenfeld JG (2017) Urban riparian systems function as corridors for both native and invasive plant species. Biol Invasions 19:3645–3657
Asner GP et al (2005) Selective logging in the Brazilian Amazon. Science 310(5747):480–482
Asner GA, Olinger SV (2009) Remote sensing for terrestrial biogeochemical modelling. In:
Warner TA, Nellis MD, Foody GM (eds) The SAGE handbook of remote sensing. SAGE,
London, pp 411–422
Asner GP, Tupayachi R (2017) Accelerated losses of protected forests from gold mining in the
Peruvian Amazon. Environ Res Lett 12:094004
Baeten L et al (2013) A novel comparative research platform designed to determine the functional
significance of tree species diversity in European forests. Persp Pl Ecol Evol Syst 155:281–291.
https://doi.org/10.1016/j.ppees.2013.07.002
Barbet-Massin M et al (2012) Selecting pseudo-absences for species distribution models : how, where and how many? Methods Ecol Evol 3:327–338. https://doi.
org/10.1111/j.2041-210X.2011.00172.x
Beck J et al (2014) Spatial bias in the GBIF database and its effect on modeling species geographic
distributions. Eco Inform 19:10–15
Berger M, Aschbacher J (2012) Preface: The Sentinel missions—new opportunities for science.
Remote Sens Environ 120:1–2
Bierkens MFP et al (2015) Hyper-resolution global hydrological modelling: what is next?:
“Everywhere and locally relevant” M. F. P. Bierkens et al. Invited Commentary. Hydrol Process
292:310–320. https://doi.org/10.1002/hyp.10391
F. Schrodt et al.