201
Qian, Y., Zhou, W., Yan, J., et al.: Comparing machine learning classifiers for object-based land
cover classification using very high resolution imagery. Remote Sens. 7, 153–168 (2014).
doi:10.3390/rs70100153
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for
Statistical Computing, Vienna (2016)
Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S., Streibig, J.C.: Potential uses of
small unmanned aircraft systems (UAS) in weed research. Weed Res. 53, 242–248 (2013).
doi:10.1111/wre.12026
Rasmussen, J., Ntakos, G., Nielsen, J., et al.: Are vegetation indices derived from consumer-grade
cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Eur. J. Agron.
74, 75–92 (2016). doi:10.1016/j.eja.2015.11.026
Richards, J.A.: Remote Sensing Digital Image Analysis: An Introduction, 5th edn. Springer, Berlin
(2013)
Schirrmann, M., Giebel, A., Gleiniger, F., et al.: Monitoring agronomic parameters of winter wheat
crops with low-cost UAV imagery. Remote Sens. 8, 706 (2016). doi:10.3390/rs8090706
SenseFly. User Manual: S110 RGB/NIR /RE camera. SenseFly Ltd., Lausanne, Switzerland (2014)
SenseFly. eBee Sensefly: Extended User MANUAL eBee and eBee Ag. Revision 17, June 2015.
SenseFly Ltd., Lausanne, Switzerland (2015)
Silleos, N.G., Alexandridis, T.K., Gitas, I.Z., Perakis, K.: Vegetation indices: advances made in
biomass estimation and vegetation monitoring in the Last 30 years. Geocarto Int. 21, 21–28
(2006). doi:10.1080/10106040608542399
Singh, M., Evans, D., Tan, B.S., Nin, C.S.: Mapping and characterizing selected canopy tree species at the Angkor World Heritage Site in Cambodia using aerial data. PLoS One. 10, e0121558
(2015). doi:10.1371/journal.pone.0121558
Thiam A.K.: Geographic Information Systems and Remote Sensing Methods for Assessing and
Monitoring Land Degradation in the Sahel Region: The Case of Southern Mauritania (1998)
Tucker, C.J.: Red and photographic infrared linear combinations for monitoring vegetation.
Remote Sens. Environ. 8, 127–150 (1979)
Vergara-Díaz, O., Zaman-Allah, M.A., Masuka, B., et al.: A novel remote sensing approach for
prediction of maize yield under different conditions of nitrogen fertilization. Front. Plant Sci.
(2016). doi:10.3389/fpls.2016.00666
Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A.: Color Indices for Weed
Identification Under Various Soil, Residue, and Lighting Conditions. Trans. ASAE. 38, 259–
269 (1995). doi:10.13031/2013.27838
Zhang, F., Zaman, Q.U., Percival, D.C., Schumann, A.W.: Detecting bare spots in wild blueberry
fields using digital color photography. Appl. Eng. Agric. 26, 723–728 (2010)
The Potential of UAV Derived Image Features for Discriminating Savannah Tree Species
Qian, Y., Zhou, W., Yan, J., et al.: Comparing machine learning classifiers for object-based land
cover classification using very high resolution imagery. Remote Sens. 7, 153–168 (2014).
doi:10.3390/rs70100153
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for
Statistical Computing, Vienna (2016)
Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S., Streibig, J.C.: Potential uses of
small unmanned aircraft systems (UAS) in weed research. Weed Res. 53, 242–248 (2013).
doi:10.1111/wre.12026
Rasmussen, J., Ntakos, G., Nielsen, J., et al.: Are vegetation indices derived from consumer-grade
cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Eur. J. Agron.
74, 75–92 (2016). doi:10.1016/j.eja.2015.11.026
Richards, J.A.: Remote Sensing Digital Image Analysis: An Introduction, 5th edn. Springer, Berlin
(2013)
Schirrmann, M., Giebel, A., Gleiniger, F., et al.: Monitoring agronomic parameters of winter wheat
crops with low-cost UAV imagery. Remote Sens. 8, 706 (2016). doi:10.3390/rs8090706
SenseFly. User Manual: S110 RGB/NIR /RE camera. SenseFly Ltd., Lausanne, Switzerland (2014)
SenseFly. eBee Sensefly: Extended User MANUAL eBee and eBee Ag. Revision 17, June 2015.
SenseFly Ltd., Lausanne, Switzerland (2015)
Silleos, N.G., Alexandridis, T.K., Gitas, I.Z., Perakis, K.: Vegetation indices: advances made in
biomass estimation and vegetation monitoring in the Last 30 years. Geocarto Int. 21, 21–28
(2006). doi:10.1080/10106040608542399
Singh, M., Evans, D., Tan, B.S., Nin, C.S.: Mapping and characterizing selected canopy tree species at the Angkor World Heritage Site in Cambodia using aerial data. PLoS One. 10, e0121558
(2015). doi:10.1371/journal.pone.0121558
Thiam A.K.: Geographic Information Systems and Remote Sensing Methods for Assessing and
Monitoring Land Degradation in the Sahel Region: The Case of Southern Mauritania (1998)
Tucker, C.J.: Red and photographic infrared linear combinations for monitoring vegetation.
Remote Sens. Environ. 8, 127–150 (1979)
Vergara-Díaz, O., Zaman-Allah, M.A., Masuka, B., et al.: A novel remote sensing approach for
prediction of maize yield under different conditions of nitrogen fertilization. Front. Plant Sci.
(2016). doi:10.3389/fpls.2016.00666
Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A.: Color Indices for Weed
Identification Under Various Soil, Residue, and Lighting Conditions. Trans. ASAE. 38, 259–
269 (1995). doi:10.13031/2013.27838
Zhang, F., Zaman, Q.U., Percival, D.C., Schumann, A.W.: Detecting bare spots in wild blueberry
fields using digital color photography. Appl. Eng. Agric. 26, 723–728 (2010)
The Potential of UAV Derived Image Features for Discriminating Savannah Tree Species