199
Acknowledgments Our gratitude to the Pommersche Farmereigesellschaft and their staff for
allowing us to work on the farm Erichsfelde. The work was financially supported by the SASSCAL
initiative, with funding by the German Federal Ministry of Education and Research; BMBF
Funding Nr: 01LG1201M.
References
Baldeck, C.A., Asner, G.P., Martin, R.E., et al.: Operational tree species mapping in a diverse tropical forest with airborne imaging spectroscopy. PLoS One. 10, e0118403 (2015). doi:10.1371/
journal.pone.0118403
Baret, F., Guyot, G., Major, D.: TSAVI: a vegetation index which minimizes soil brightness
effects on LAI and APAR estimation. In: 12th Canadian Symposium on Remote Sensing and
IGARSS’90, p. 4, Vancouver, Canada, 10–14 July 1989 (1989)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). doi:10.1023/A:1010933404324
Bunting, P., Lucas, R.: The delineation of tree crowns in Australian mixed species forests using
hyperspectral Compact Airborne Spectrographic Imager (CASI) data. Remote Sens. Environ.
101, 230–248 (2006). doi:10.1016/j.rse.2005.12.015
Cho, M.A., Mathieu, R., Asner, G.P., et al.: Mapping tree species composition in South African
savannas using an integrated airborne spectral and LiDAR system. Remote Sens. Environ. 125,
214–226 (2012). doi:10.1016/j.rse.2012.07.010
Cho, M.A., Malahlela, O., Ramoelo, A.: Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications:
Dukuduku forest patch as case study. Int. J. Appl. Earth Obs. Geoinf. 38, 349–357 (2015).
doi:10.1016/j.jag.2015.01.015
Colgan, M.S., Baldeck, C.A., Féret, J.-B., Asner, G.P.: Mapping savanna tree species at ecosystem
scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data. Remote Sens. 4, 3462–3480 (2012). doi:10.3390/rs4113462
Conrad, O., Bechtel, B., Bock, M., et al.: System for automated geoscientific analyses (SAGA) v.
2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015). doi:10.5194/gmd-8-1991-2015
Culvenor, D.S.: TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Comput. Geosci. 28, 33–44 (2002). doi:10.1016/
S0098-3004(00)00110-2
Dalponte, M., Coomes, D.A.: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods Ecol. Evol. 7, 1236–1245 (2016).
doi:10.1111/2041-210X.12575
Duro, D.C., Franklin, S.E., Dubé, M.G.: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes
using SPOT-5 HRG imagery. Remote Sens. Environ. 118, 259–272 (2012). doi:10.1016/j.
rse.2011.11.020
Dvořák, P.J., Müllerová J., Bartaloš, T., Brůna J.: Unmanned aerial vehicles for alien plant species detection and monitoring. ISPRS – international archives of the photogrammetry,
remote sensing and spatial information sciences XL-1/W4: 83–90 (2015). doi:10.5194/
isprsarchives-XL-1-W4-83-2015
Fassnacht, F.E., Latifi, H., Stereńczak, K., et al.: Review of studies on tree species classification from remotely sensed data. Remote Sens. Environ. 186, 64–87 (2016). doi:10.1016/j.
rse.2016.08.013
Fischer, T., Veste, M., Eisele, A., et al.: Small scale spatial heterogeneity of Normalized Difference
Vegetation Indices (NDVIs) and hot spots of photosynthesis in biological soil crusts. Flora –
Morphol. Distrib. Funct. Ecol. Plants. 207, 159–167 (2012). doi:10.1016/j.flora.2012.01.001
Giess, W.: A preliminary vegetation map of Namibia. Dinteria. 4, 1–112 (1998)
The Potential of UAV Derived Image Features for Discriminating Savannah Tree Species
Acknowledgments Our gratitude to the Pommersche Farmereigesellschaft and their staff for
allowing us to work on the farm Erichsfelde. The work was financially supported by the SASSCAL
initiative, with funding by the German Federal Ministry of Education and Research; BMBF
Funding Nr: 01LG1201M.
References
Baldeck, C.A., Asner, G.P., Martin, R.E., et al.: Operational tree species mapping in a diverse tropical forest with airborne imaging spectroscopy. PLoS One. 10, e0118403 (2015). doi:10.1371/
journal.pone.0118403
Baret, F., Guyot, G., Major, D.: TSAVI: a vegetation index which minimizes soil brightness
effects on LAI and APAR estimation. In: 12th Canadian Symposium on Remote Sensing and
IGARSS’90, p. 4, Vancouver, Canada, 10–14 July 1989 (1989)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). doi:10.1023/A:1010933404324
Bunting, P., Lucas, R.: The delineation of tree crowns in Australian mixed species forests using
hyperspectral Compact Airborne Spectrographic Imager (CASI) data. Remote Sens. Environ.
101, 230–248 (2006). doi:10.1016/j.rse.2005.12.015
Cho, M.A., Mathieu, R., Asner, G.P., et al.: Mapping tree species composition in South African
savannas using an integrated airborne spectral and LiDAR system. Remote Sens. Environ. 125,
214–226 (2012). doi:10.1016/j.rse.2012.07.010
Cho, M.A., Malahlela, O., Ramoelo, A.: Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications:
Dukuduku forest patch as case study. Int. J. Appl. Earth Obs. Geoinf. 38, 349–357 (2015).
doi:10.1016/j.jag.2015.01.015
Colgan, M.S., Baldeck, C.A., Féret, J.-B., Asner, G.P.: Mapping savanna tree species at ecosystem
scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data. Remote Sens. 4, 3462–3480 (2012). doi:10.3390/rs4113462
Conrad, O., Bechtel, B., Bock, M., et al.: System for automated geoscientific analyses (SAGA) v.
2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015). doi:10.5194/gmd-8-1991-2015
Culvenor, D.S.: TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Comput. Geosci. 28, 33–44 (2002). doi:10.1016/
S0098-3004(00)00110-2
Dalponte, M., Coomes, D.A.: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods Ecol. Evol. 7, 1236–1245 (2016).
doi:10.1111/2041-210X.12575
Duro, D.C., Franklin, S.E., Dubé, M.G.: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes
using SPOT-5 HRG imagery. Remote Sens. Environ. 118, 259–272 (2012). doi:10.1016/j.
rse.2011.11.020
Dvořák, P.J., Müllerová J., Bartaloš, T., Brůna J.: Unmanned aerial vehicles for alien plant species detection and monitoring. ISPRS – international archives of the photogrammetry,
remote sensing and spatial information sciences XL-1/W4: 83–90 (2015). doi:10.5194/
isprsarchives-XL-1-W4-83-2015
Fassnacht, F.E., Latifi, H., Stereńczak, K., et al.: Review of studies on tree species classification from remotely sensed data. Remote Sens. Environ. 186, 64–87 (2016). doi:10.1016/j.
rse.2016.08.013
Fischer, T., Veste, M., Eisele, A., et al.: Small scale spatial heterogeneity of Normalized Difference
Vegetation Indices (NDVIs) and hot spots of photosynthesis in biological soil crusts. Flora –
Morphol. Distrib. Funct. Ecol. Plants. 207, 159–167 (2012). doi:10.1016/j.flora.2012.01.001
Giess, W.: A preliminary vegetation map of Namibia. Dinteria. 4, 1–112 (1998)
The Potential of UAV Derived Image Features for Discriminating Savannah Tree Species