478
Comber A, Brunsdon C, Charlton M, Harris P (2017) Geographically weighted correspondence
matrices for local error reporting and change analyses: mapping the spatial distribution of
errors and change. Remote Sens Lett 8:234–243
Comber A, Fisher P, Brunsdon C, Khmag A (2012) Spatial analysis of remote sensing image classification accuracy. Remote Sens Environ 127:237–246
Comber A, Mooney P, Purves R, Rocchini D, Walz A (2016) Crowdsourcing: it matters who
the crowd are. The impacts of between group variations in recording land cover. PLoS One
11:e0158329
Coops NC, Wulder MA, Duro DC, Han T, Berry S (2008) The development of a Canadian dynamic
habitat index using multi-temporal satellite estimates of canopy light absorbance. Ecol Indic
8:754–766
Coops NC, Waring RH, Wulder MA, Pidgeon AM, Radeloff VC (2009) Bird diversity: a predictable function of satellite-derived estimates of seasonal variation in canopy light absorbance
across the United States. J Biogeogr 365:905–918
Cord AF et al (2017) Priorities to advance monitoring of ecosystem services using earth observation, trends in ecology & evolution. Elsevier Ltd 326:416–428. https://doi.org/10.1016/j.
tree.2017.03.003
Dakos V, Kefi S, Rietkerk M, van Nes EH, Scheffer M (2011) Slowing down in spatially patterned
ecosystems at the brink of collapse. Am Nat 177:E153–E166
De Frenne P et al (2018) No title. Methods Ecol Evol 00:1–9
De Groot RS, Wilson MA, Boumans RMJ (2002) A typology for the classification, description and
valuation of ecosystem functions, goods and services. Ecol Econ 41:393
Dengler J, sPlot Core Team (2014) sPlot: the first global vegetation-plot database and opportunities
to contribute. IAVS Bulletin 2:34–37
Dodge S, Bohrer G, Weinzierl R, Davidson SC, Kays R, Douglas D, Cruz S, Han J, Brandes
D, Wikelski M (2013) The environmental-data automated track annotation Env-DATA
system: linking animal tracks with environmental data. Mov Ecol 1:3. https://doi.
org/10.1186/2051-3933-1-3
Dorigo E, Wagner W, Albergel C, Albrecht F, Balsamo G, Brocca L, Chung D, Ertl M, Forkel
M, Gruber A, Haas E, Hamer P, Hirschi M, Ikonen J, de Jeu R, Kidd R, Lahoz W, Liu Y,
Miralles D, Mistelbauer T, Nicolai-Shaw N, Parinussa R, Pratola C, Reimer C, van der Schlie
R, Senebiratne S, Smolander T, Lecomte P (2017) ESA CCI Soil Moisture for improved Earth
system understanding: state-of-the art and future directions. Remote Sens Environ 15:185–215
Dornelas M et al (2018) BioTIME: a database of biodiversity time series for the anthropocene.
Glob Ecol Biogeogr 27:760. https://doi.org/10.1111/geb.12729
Duarte L et al (2018) QPhenoMetrics: an open source software application to assess vegetation
phenology metrics, computers and electronics in agriculture. Elsevier 148May 2016:82–94.
https://doi.org/10.1016/j.compag.2018.03.007
Dusseux P et al (2014) Combined use of multi-temporal optical and radar satellite images for
grassland monitoring. Remote Sens 6:6163–6182
Eddy IMS et al (2017) Integrating remote sensing and local ecological knowledge to monitor rangeland dynamics. Ecol Indic 82May:106–116. https://doi.org/10.1016/j.ecolind.2017.06.033
Eklundh L, Jonsson P (2015) TIMESAT: a software package for time-series processing and assessment of vegetation dynamics. In: Remote sensing time series. Springer, New York, pp 141–
158. https://doi.org/10.1007/978-3-319-15967-6_7
Ellis EC et al (2013) Used planet: a global history. Proc Natl Acad Sci 11020:7978–7985. https://
doi.org/10.1073/pnas.1217241110
Englund O, Berndes G, Cederberg C (2017) How to analyse ecosystem services in landscapes – a
systematic review, ecological indicators. Elsevier Ltd 73:492–504. https://doi.org/10.1016/j.
ecolind.2016.10.009
Evensen G (2002) Data assimilation: the ensemble Kalman filter. Springer-Verlage, Berlin
Fassnacht FE, Latifi H, Stereńczak K, Modzelewska A, Lefsky M, Waser LT, Straub C, Ghosh A
(2016) Review of studies on tree species classification from remotely sensed data. Remote Sens
Environ 186:64–87
F. Schrodt et al.
Comber A, Brunsdon C, Charlton M, Harris P (2017) Geographically weighted correspondence
matrices for local error reporting and change analyses: mapping the spatial distribution of
errors and change. Remote Sens Lett 8:234–243
Comber A, Fisher P, Brunsdon C, Khmag A (2012) Spatial analysis of remote sensing image classification accuracy. Remote Sens Environ 127:237–246
Comber A, Mooney P, Purves R, Rocchini D, Walz A (2016) Crowdsourcing: it matters who
the crowd are. The impacts of between group variations in recording land cover. PLoS One
11:e0158329
Coops NC, Wulder MA, Duro DC, Han T, Berry S (2008) The development of a Canadian dynamic
habitat index using multi-temporal satellite estimates of canopy light absorbance. Ecol Indic
8:754–766
Coops NC, Waring RH, Wulder MA, Pidgeon AM, Radeloff VC (2009) Bird diversity: a predictable function of satellite-derived estimates of seasonal variation in canopy light absorbance
across the United States. J Biogeogr 365:905–918
Cord AF et al (2017) Priorities to advance monitoring of ecosystem services using earth observation, trends in ecology & evolution. Elsevier Ltd 326:416–428. https://doi.org/10.1016/j.
tree.2017.03.003
Dakos V, Kefi S, Rietkerk M, van Nes EH, Scheffer M (2011) Slowing down in spatially patterned
ecosystems at the brink of collapse. Am Nat 177:E153–E166
De Frenne P et al (2018) No title. Methods Ecol Evol 00:1–9
De Groot RS, Wilson MA, Boumans RMJ (2002) A typology for the classification, description and
valuation of ecosystem functions, goods and services. Ecol Econ 41:393
Dengler J, sPlot Core Team (2014) sPlot: the first global vegetation-plot database and opportunities
to contribute. IAVS Bulletin 2:34–37
Dodge S, Bohrer G, Weinzierl R, Davidson SC, Kays R, Douglas D, Cruz S, Han J, Brandes
D, Wikelski M (2013) The environmental-data automated track annotation Env-DATA
system: linking animal tracks with environmental data. Mov Ecol 1:3. https://doi.
org/10.1186/2051-3933-1-3
Dorigo E, Wagner W, Albergel C, Albrecht F, Balsamo G, Brocca L, Chung D, Ertl M, Forkel
M, Gruber A, Haas E, Hamer P, Hirschi M, Ikonen J, de Jeu R, Kidd R, Lahoz W, Liu Y,
Miralles D, Mistelbauer T, Nicolai-Shaw N, Parinussa R, Pratola C, Reimer C, van der Schlie
R, Senebiratne S, Smolander T, Lecomte P (2017) ESA CCI Soil Moisture for improved Earth
system understanding: state-of-the art and future directions. Remote Sens Environ 15:185–215
Dornelas M et al (2018) BioTIME: a database of biodiversity time series for the anthropocene.
Glob Ecol Biogeogr 27:760. https://doi.org/10.1111/geb.12729
Duarte L et al (2018) QPhenoMetrics: an open source software application to assess vegetation
phenology metrics, computers and electronics in agriculture. Elsevier 148May 2016:82–94.
https://doi.org/10.1016/j.compag.2018.03.007
Dusseux P et al (2014) Combined use of multi-temporal optical and radar satellite images for
grassland monitoring. Remote Sens 6:6163–6182
Eddy IMS et al (2017) Integrating remote sensing and local ecological knowledge to monitor rangeland dynamics. Ecol Indic 82May:106–116. https://doi.org/10.1016/j.ecolind.2017.06.033
Eklundh L, Jonsson P (2015) TIMESAT: a software package for time-series processing and assessment of vegetation dynamics. In: Remote sensing time series. Springer, New York, pp 141–
158. https://doi.org/10.1007/978-3-319-15967-6_7
Ellis EC et al (2013) Used planet: a global history. Proc Natl Acad Sci 11020:7978–7985. https://
doi.org/10.1073/pnas.1217241110
Englund O, Berndes G, Cederberg C (2017) How to analyse ecosystem services in landscapes – a
systematic review, ecological indicators. Elsevier Ltd 73:492–504. https://doi.org/10.1016/j.
ecolind.2016.10.009
Evensen G (2002) Data assimilation: the ensemble Kalman filter. Springer-Verlage, Berlin
Fassnacht FE, Latifi H, Stereńczak K, Modzelewska A, Lefsky M, Waser LT, Straub C, Ghosh A
(2016) Review of studies on tree species classification from remotely sensed data. Remote Sens
Environ 186:64–87
F. Schrodt et al.