335
RS: optical (multispectral, hyperspectral), thermal, radar, LiDAR data, laboratory, tower, camera traps, WSNs, drones, and close-range, air- and spaceborne RS platforms. Additionally, it should link monitoring databases,
networks, citizen science information, abiotic (soil, water, air) information,
and social and economic information.
(iii) Data science, linked open data, and semantic web as a bridge for understanding and monitoring vegetation diversity. For further information see also
Lausch et al. (2015c, 2018a, b) (Fig. 13.7).
Acknowledgments We particularly thank the researchers for the Hyperspectral Equipment of the
Helmholtz Centre for Environmental Research—UFZ and TERENO funded by the Helmholtz
Association and the Federal Ministry of Education and Research, Germany. At the same time, we
truly appreciate the support that we received from the project “GEOEssential: Essential Variables
workflows for resource efficiency and environmental management” (ERA-NET Cofund Grant,
Grant Agreement No. 689443). Finally, we thank the NIMBioS working group on Remote Sensing
of Biodiversity.
References
Andersen HE, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LIDAR data. Remote Sens Environ 94:441–449. https://doi.org/10.1016/j.
rse.2004.10.013
Andersen HE, McGaughey RJ, Reutebuch SE, Andersen H-E, McGaughey RJ, Reutebuch
SE, Andersen HE, McGaughey RJ, Reutebuch SE (2008) Assessing the influence of flight
parameters, interferometric processing, slope and canopy density on the accuracy of X-band
IFSAR-derived forest canopy height models. Int J Remote Sens 29:1495–1510. https://doi.
org/10.1080/01431160701736430
Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial
ecology. Front Ecol Environ 11:138–146. https://doi.org/10.1890/120150
Asner GP, Martin RE (2009) Airborne spectranomics: mapping canopy chemical and taxonomic
diversity in tropical forests. Front Ecol Environ 7:269–276. https://doi.org/10.1890/070152
Asner GP, Anderson CB, Martin RE, Tupayachi R, Knapp DE, Sinca F (2015) Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy. Nat
Geosci 8:567–573. https://doi.org/10.1038/ngeo2443
Baldocchi D, Falge E, Lianhong G, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C,
Davis K, Evans R, Gu LH, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C, Davis
K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee XH, Malhi Y, Meyers T, Munger
W, Oechel W, Paw UKT, Pilegaard K, Schmid HP, Valentini R, Verma S, Vesala T, Wilson K,
Wofsy S, Paw UKT, Pilegaard K, Schmid HP, Valentini R, Verma S, Vesala T, Wilson K, Wofsy
S (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem- scale
carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82:2415–2434.
https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2
Balzter H (2001) Forest mapping and monitoring with interferometric synthetic aperture radar
(InSAR). Prog Phys Geogr 25:159–177. https://doi.org/10.1177/030913330102500201
Baltzer H (2017) Earth observation for land and emergency monitoring. University of Leicester
Leicester
13 A Range of Earth Observation Techniques for Assessing Plant Diversity
RS: optical (multispectral, hyperspectral), thermal, radar, LiDAR data, laboratory, tower, camera traps, WSNs, drones, and close-range, air- and spaceborne RS platforms. Additionally, it should link monitoring databases,
networks, citizen science information, abiotic (soil, water, air) information,
and social and economic information.
(iii) Data science, linked open data, and semantic web as a bridge for understanding and monitoring vegetation diversity. For further information see also
Lausch et al. (2015c, 2018a, b) (Fig. 13.7).
Acknowledgments We particularly thank the researchers for the Hyperspectral Equipment of the
Helmholtz Centre for Environmental Research—UFZ and TERENO funded by the Helmholtz
Association and the Federal Ministry of Education and Research, Germany. At the same time, we
truly appreciate the support that we received from the project “GEOEssential: Essential Variables
workflows for resource efficiency and environmental management” (ERA-NET Cofund Grant,
Grant Agreement No. 689443). Finally, we thank the NIMBioS working group on Remote Sensing
of Biodiversity.
References
Andersen HE, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LIDAR data. Remote Sens Environ 94:441–449. https://doi.org/10.1016/j.
rse.2004.10.013
Andersen HE, McGaughey RJ, Reutebuch SE, Andersen H-E, McGaughey RJ, Reutebuch
SE, Andersen HE, McGaughey RJ, Reutebuch SE (2008) Assessing the influence of flight
parameters, interferometric processing, slope and canopy density on the accuracy of X-band
IFSAR-derived forest canopy height models. Int J Remote Sens 29:1495–1510. https://doi.
org/10.1080/01431160701736430
Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial
ecology. Front Ecol Environ 11:138–146. https://doi.org/10.1890/120150
Asner GP, Martin RE (2009) Airborne spectranomics: mapping canopy chemical and taxonomic
diversity in tropical forests. Front Ecol Environ 7:269–276. https://doi.org/10.1890/070152
Asner GP, Anderson CB, Martin RE, Tupayachi R, Knapp DE, Sinca F (2015) Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy. Nat
Geosci 8:567–573. https://doi.org/10.1038/ngeo2443
Baldocchi D, Falge E, Lianhong G, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C,
Davis K, Evans R, Gu LH, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C, Davis
K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee XH, Malhi Y, Meyers T, Munger
W, Oechel W, Paw UKT, Pilegaard K, Schmid HP, Valentini R, Verma S, Vesala T, Wilson K,
Wofsy S, Paw UKT, Pilegaard K, Schmid HP, Valentini R, Verma S, Vesala T, Wilson K, Wofsy
S (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem- scale
carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82:2415–2434.
https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2
Balzter H (2001) Forest mapping and monitoring with interferometric synthetic aperture radar
(InSAR). Prog Phys Geogr 25:159–177. https://doi.org/10.1177/030913330102500201
Baltzer H (2017) Earth observation for land and emergency monitoring. University of Leicester
Leicester
13 A Range of Earth Observation Techniques for Assessing Plant Diversity