482
Neil K, Wu J (2006) Effects of urbanization on plant flowering phenology: a review. Urban Ecosyst
9:243–257
Niphadkar M, Nagendra H (2016) Remote sensing of invasive plants: incorporating functional
traits into the picture. Int J Remote Sens. Taylor & Francis 3713:3074–3085. https://doi.org/1
0.1080/01431161.2016.1193795
Olinger SV (2011) Sources of variability in canopy reflectance and the convergent properties of
plants. New Phytol 189:375–394
Paganini M et al (2016) The role of space agencies in remotely sensed essential biodiversity variables. Remote Sens Ecol Conserv 23:132–140. https://doi.org/10.1002/rse2.29
Pereira HM et al (2013) Essential biodiversity variables. Science 3396117:277–278. https://doi.
org/10.1126/science.1229931
Pereira HM, Belnap J, Böhm M, Brummitt N, Garcia-Moreno J, Gregory R, Martin L, Peng C,
Proença V, Schmeller D, van Swaay C (2015) Monitoring essential biodiversity variables at the
species level. In: Walters M, Scholes R (eds) The GEO handbook on biodiversity observation
networks. Springer, Cham
Pérez-Harguindeguy N et al (2013) New Handbook for standardized measurement of plant functional traits worldwide. Aust J Bot 6134:167–234. https://doi.org/10.1071/BT12225
Pettorelli N et al (2017) Satellite remote sensing of ecosystem functions: opportunities, challenges
and way forward. Remote Sens Ecol Conserv 4:1–23. https://doi.org/10.1002/rse2.59
Pontius RG, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation
disagreement for accuracy assessment. Int J Remote Sens 32:4407–4429
Pontius RG, Santacruz A (2014) Quantity, exchange, and shift components of difference in a
square contingency table. Int J Remote Sens 35:7543–7554
Potapov P, Hansen MC, Laestadius L, Turubanova S, Yaroshenko A, Thies C, Smith W, Zhuravleva
I, Komarova A, Minnemeyer S, Esipova E (2017) The last frontiers of wilderness: tracking loss
of intact forest landscapes from 2000 to 2013. Sci Adv 3:e1600821
Propastin P (2012) Modifying geographically weighted regression for estimating aboveground
biomass in tropical rainforests by multispectral remote sensing data. Int J Appl Earth Obs
Geoinf 18:82–90
Quattrochi DA, Wentz EA, Siu-Ngam Lam N, Emerson CW (2017) Integrating scale in remote
sensing and GIS. Routledge, New York
Reed BC, Brown JF, Vanderzee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci 55:703–714
Reddy CS et al (2017) Earth observation data for habitat monitoring in protected areas of India,
remote sensing applications: society and environment. Elsevier BV 8May:114–125. https://doi.
org/10.1016/j.rsase.2017.08.004
Reed B et al (2009) Integration of MODIS-derived metrics to assess interannual variability in
snowpack, lake ice, and NDVI in southwest Alaska, Remote Sensing of Environment. Elsevier
BV 1137:1443–1452. https://doi.org/10.1016/j.rse.2008.07.020
Richardson AD et al (2018) Intercomparison of phenological transition dates derived from the
PhenoCam Dataset V1.0 and MODIS satellite remote sensing. Sci Rep 81:1–12. https://doi.
org/10.1038/s41598-018-23804-6
Rocchini D, Luque S, Pettorelli N, Bastin L, Doktor D, Faedi N, Feilhauer H, Feret J-B, Foody GM,
Gavish Y, Godinho S, Kunin WE, Lausch A, Leitao PJ, Marcantonio M, Neteler M, Ricotta C,
Schmidtlein S, Vihervaara P, Wegmann M, Nagendra H (2017) Measuring beta-diversity by
remote sensing: a challenge for biodiversity monitoring. Methods Ecol Evol 9(8):1787–1798
Rockström J et al (2009) A safe operating space for humanity. Nature 461:472–478
Ronk A, Szava-kovats R, Pärtel M (2015) Applying the dark diversity concept to plants at the
European scale. Ecography 38:1015–1025. https://doi.org/10.1111/ecog.01236
Rose RA et al (2015) Ten ways remote sensing can contribute to conservation. Conserv Biol
292:350–359. https://doi.org/10.1111/cobi.12397.
Running SW, Nemani RR, Heinsch FA, Zhao MS, Reeves M, Hashimoto H (2004) A continuous
satellite-derived measure of global terrestrial primary production. Bioscience 546:547–560
F. Schrodt et al.
Neil K, Wu J (2006) Effects of urbanization on plant flowering phenology: a review. Urban Ecosyst
9:243–257
Niphadkar M, Nagendra H (2016) Remote sensing of invasive plants: incorporating functional
traits into the picture. Int J Remote Sens. Taylor & Francis 3713:3074–3085. https://doi.org/1
0.1080/01431161.2016.1193795
Olinger SV (2011) Sources of variability in canopy reflectance and the convergent properties of
plants. New Phytol 189:375–394
Paganini M et al (2016) The role of space agencies in remotely sensed essential biodiversity variables. Remote Sens Ecol Conserv 23:132–140. https://doi.org/10.1002/rse2.29
Pereira HM et al (2013) Essential biodiversity variables. Science 3396117:277–278. https://doi.
org/10.1126/science.1229931
Pereira HM, Belnap J, Böhm M, Brummitt N, Garcia-Moreno J, Gregory R, Martin L, Peng C,
Proença V, Schmeller D, van Swaay C (2015) Monitoring essential biodiversity variables at the
species level. In: Walters M, Scholes R (eds) The GEO handbook on biodiversity observation
networks. Springer, Cham
Pérez-Harguindeguy N et al (2013) New Handbook for standardized measurement of plant functional traits worldwide. Aust J Bot 6134:167–234. https://doi.org/10.1071/BT12225
Pettorelli N et al (2017) Satellite remote sensing of ecosystem functions: opportunities, challenges
and way forward. Remote Sens Ecol Conserv 4:1–23. https://doi.org/10.1002/rse2.59
Pontius RG, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation
disagreement for accuracy assessment. Int J Remote Sens 32:4407–4429
Pontius RG, Santacruz A (2014) Quantity, exchange, and shift components of difference in a
square contingency table. Int J Remote Sens 35:7543–7554
Potapov P, Hansen MC, Laestadius L, Turubanova S, Yaroshenko A, Thies C, Smith W, Zhuravleva
I, Komarova A, Minnemeyer S, Esipova E (2017) The last frontiers of wilderness: tracking loss
of intact forest landscapes from 2000 to 2013. Sci Adv 3:e1600821
Propastin P (2012) Modifying geographically weighted regression for estimating aboveground
biomass in tropical rainforests by multispectral remote sensing data. Int J Appl Earth Obs
Geoinf 18:82–90
Quattrochi DA, Wentz EA, Siu-Ngam Lam N, Emerson CW (2017) Integrating scale in remote
sensing and GIS. Routledge, New York
Reed BC, Brown JF, Vanderzee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci 55:703–714
Reddy CS et al (2017) Earth observation data for habitat monitoring in protected areas of India,
remote sensing applications: society and environment. Elsevier BV 8May:114–125. https://doi.
org/10.1016/j.rsase.2017.08.004
Reed B et al (2009) Integration of MODIS-derived metrics to assess interannual variability in
snowpack, lake ice, and NDVI in southwest Alaska, Remote Sensing of Environment. Elsevier
BV 1137:1443–1452. https://doi.org/10.1016/j.rse.2008.07.020
Richardson AD et al (2018) Intercomparison of phenological transition dates derived from the
PhenoCam Dataset V1.0 and MODIS satellite remote sensing. Sci Rep 81:1–12. https://doi.
org/10.1038/s41598-018-23804-6
Rocchini D, Luque S, Pettorelli N, Bastin L, Doktor D, Faedi N, Feilhauer H, Feret J-B, Foody GM,
Gavish Y, Godinho S, Kunin WE, Lausch A, Leitao PJ, Marcantonio M, Neteler M, Ricotta C,
Schmidtlein S, Vihervaara P, Wegmann M, Nagendra H (2017) Measuring beta-diversity by
remote sensing: a challenge for biodiversity monitoring. Methods Ecol Evol 9(8):1787–1798
Rockström J et al (2009) A safe operating space for humanity. Nature 461:472–478
Ronk A, Szava-kovats R, Pärtel M (2015) Applying the dark diversity concept to plants at the
European scale. Ecography 38:1015–1025. https://doi.org/10.1111/ecog.01236
Rose RA et al (2015) Ten ways remote sensing can contribute to conservation. Conserv Biol
292:350–359. https://doi.org/10.1111/cobi.12397.
Running SW, Nemani RR, Heinsch FA, Zhao MS, Reeves M, Hashimoto H (2004) A continuous
satellite-derived measure of global terrestrial primary production. Bioscience 546:547–560
F. Schrodt et al.