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Sankey TT et al (2018) UAV hyperspectral and LiDAR data and their fusion for arid and semi-arid
land vegetation monitoring. Remote Sens Ecol Conserv, Early View 4:20
Schmidtlein S, Fassnacht FE (2017) The spectral variability hypothesis does not hold across landscapes. Remote Sens Environ 192:114–125
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https://doi.org/10.1111/geb.12335
Schweiger AK, Cavender-Bares J, Townsend PA, Hobbie SE, Madritch MD, Wang R, Tilman D,
Gamon JA (2018) Plant spectral diversity integrates functional and phylogenetic components
of biodiversity and predicts ecosystem function. Nat Ecol Evol 2:976–982
Smith SA, Brown JW (2018) Constructing a broadly inclusive seed plant phylogeny. Am J Bot
1053:302–314. https://doi.org/10.1002/ajb2.1019.
Stefanski J et al (2014) Mapping land management regimes in western Ukraine using optical and
SAR data. Remote Sens 6:5279–5305
Stoms DM, Estes JE (1993) A remote sensing research agenda for mapping and monitoring biodiversity. Int J Remote Sensing 1:14
Sun B, Chen X, Zhou Q (2017) Analyzing the uncertainties of ground validation for remote sensing land cover mapping in the era of big geographic data. In: Zhou C (ed) Advances in geographic information science. Springer Nature, Singapore, pp 31–38
Svancara LK, Scott JM, Loveland TR, Pidgorna AB (2009) Assessing the landscape context
and conversion risk of protected areas using satellite data products. Remote Sens Environ
113:1357–1369
Swenson NG (2014) Phylogenetic imputation of plant functional trait databases. Ecography
37:105–110. https://doi.org/10.1111/j.1600-0587.2013.00528.x
Schwieder M, Leitão PJ, Bustamante MMC, Ferreira LG, Rabe A, Hostert P (2016) Mapping
Brazilian savanna vegetation gradients with Landsat time series. Int J Appl Earth Obs Geoinf
52:361–370
Sheppard JK, Lawler IR, Marsh H (2007) Seagrass as pasture for seacows: landscape-level dugong
habitat evaluation. Estuar Coast Shelf Sci 711–2:117–132
Siebert S, Henrich V, Frenken K, Burke J (2013) Global map of irrigation areas version 5. Rheinische
Friedrich-Wilhelms-University, Bonn, Germany/Food and Agriculture Organization of the
United Nations, Rome
Skidmore AK, Pettorelli N, Coops NC, Geller GN, Hansen M, Lucas R, Mücher CA, OConnor
B, Paganini M, Pereira HM, Schaepman ME, Turner W, Wang T, Wegmann M (2015)
Environmental science: agree on biodiversity metrics to track from space. Nature 523:403–405
Taubert F, Fischer R, Groeneveld J, Lehmann S, Müller MS, Rödig E, Wiegand T, Huth A (2018)
Global patterns of tropical forest fragmentation. Nature 554:519–522
Templ B et al (2018) Pan European phenological database PEP725: a single point of access for
European data. Int J Biometeorol 626:1109–1113. https://doi.org/10.1007/s00484-018-1512-8
Tong C et al (2004) A landscape-scale assessment of steppe degradation in the Xilin River
Basin, Inner Mongolia, China. J Arid Environ 591:133–149. https://doi.org/10.1016/j.
jaridenv.2004.01.004
Toth C, Jóźków G (2016) Remote sensing platforms and sensors: a survey. ISPRS J Photogramm
Remote Sens 115:22–36
Turak E et al (2017) Essential biodiversity variables for measuring change in global freshwater biodiversity, biological conservation. Elsevier Ltd 213:272–279. https://doi.org/10.1016/j.
biocon.2016.09.005
Turner W, Spectro S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing
for biodiversity science and conservation. Trends Ecol Evol 18:306–314
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…
Sandel B et al (2015) Estimating the missing species bias in plant trait measurements. J Veg Sci
265:828. https://doi.org/10.1111/jvs.12292.
Santos MJ, Smith AB, Thorne JH, Moritz C (2017) The relative roles of changing vegetation and
climate on elevation range dynamics of small mammals. Clim Chang Res 4:7
Sankey TT et al (2018) UAV hyperspectral and LiDAR data and their fusion for arid and semi-arid
land vegetation monitoring. Remote Sens Ecol Conserv, Early View 4:20
Schmidtlein S, Fassnacht FE (2017) The spectral variability hypothesis does not hold across landscapes. Remote Sens Environ 192:114–125
Schrodt F et al (2015) BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. Glob Ecol Biogeogr 2412:1510–1521.
https://doi.org/10.1111/geb.12335
Schweiger AK, Cavender-Bares J, Townsend PA, Hobbie SE, Madritch MD, Wang R, Tilman D,
Gamon JA (2018) Plant spectral diversity integrates functional and phylogenetic components
of biodiversity and predicts ecosystem function. Nat Ecol Evol 2:976–982
Smith SA, Brown JW (2018) Constructing a broadly inclusive seed plant phylogeny. Am J Bot
1053:302–314. https://doi.org/10.1002/ajb2.1019.
Stefanski J et al (2014) Mapping land management regimes in western Ukraine using optical and
SAR data. Remote Sens 6:5279–5305
Stoms DM, Estes JE (1993) A remote sensing research agenda for mapping and monitoring biodiversity. Int J Remote Sensing 1:14
Sun B, Chen X, Zhou Q (2017) Analyzing the uncertainties of ground validation for remote sensing land cover mapping in the era of big geographic data. In: Zhou C (ed) Advances in geographic information science. Springer Nature, Singapore, pp 31–38
Svancara LK, Scott JM, Loveland TR, Pidgorna AB (2009) Assessing the landscape context
and conversion risk of protected areas using satellite data products. Remote Sens Environ
113:1357–1369
Swenson NG (2014) Phylogenetic imputation of plant functional trait databases. Ecography
37:105–110. https://doi.org/10.1111/j.1600-0587.2013.00528.x
Schwieder M, Leitão PJ, Bustamante MMC, Ferreira LG, Rabe A, Hostert P (2016) Mapping
Brazilian savanna vegetation gradients with Landsat time series. Int J Appl Earth Obs Geoinf
52:361–370
Sheppard JK, Lawler IR, Marsh H (2007) Seagrass as pasture for seacows: landscape-level dugong
habitat evaluation. Estuar Coast Shelf Sci 711–2:117–132
Siebert S, Henrich V, Frenken K, Burke J (2013) Global map of irrigation areas version 5. Rheinische
Friedrich-Wilhelms-University, Bonn, Germany/Food and Agriculture Organization of the
United Nations, Rome
Skidmore AK, Pettorelli N, Coops NC, Geller GN, Hansen M, Lucas R, Mücher CA, OConnor
B, Paganini M, Pereira HM, Schaepman ME, Turner W, Wang T, Wegmann M (2015)
Environmental science: agree on biodiversity metrics to track from space. Nature 523:403–405
Taubert F, Fischer R, Groeneveld J, Lehmann S, Müller MS, Rödig E, Wiegand T, Huth A (2018)
Global patterns of tropical forest fragmentation. Nature 554:519–522
Templ B et al (2018) Pan European phenological database PEP725: a single point of access for
European data. Int J Biometeorol 626:1109–1113. https://doi.org/10.1007/s00484-018-1512-8
Tong C et al (2004) A landscape-scale assessment of steppe degradation in the Xilin River
Basin, Inner Mongolia, China. J Arid Environ 591:133–149. https://doi.org/10.1016/j.
jaridenv.2004.01.004
Toth C, Jóźków G (2016) Remote sensing platforms and sensors: a survey. ISPRS J Photogramm
Remote Sens 115:22–36
Turak E et al (2017) Essential biodiversity variables for measuring change in global freshwater biodiversity, biological conservation. Elsevier Ltd 213:272–279. https://doi.org/10.1016/j.
biocon.2016.09.005
Turner W, Spectro S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing
for biodiversity science and conservation. Trends Ecol Evol 18:306–314
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…