Issues in species classification of trees in old growth conifer stands
By D. Leckie, S. Tinis, Trisalyn A. Nelson, Charles Burnett, François A. Gougeon, Ed Cloney, D. Paradine. 2005
104 citations recorded by the source corpus.
Subfields: Ecological Modeling, Ecology, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/319fa3871946ecad58673069c7efdb0f8a2745fc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecological Modeling
- N. E. Zimmermann
- Robin Engler
- Christian Ginzler
- Achilleas Psomas
- Lars T. Waser
- Lorenzo Bruzzone
- Clement Atzberger
- Emmanuel Baltsavias
In Ecology
- Clement Atzberger
- Markus Immitzer
- Tatjana Koukal
- N. E. Zimmermann
- Robin Engler
- Christian Ginzler
- Peter Wolter
- Philip A. Townsend
In Environmental Engineering
- Markus Immitzer
- Tatjana Koukal
- Clement Atzberger
- Philip A. Townsend
- Peter Wolter
- Christian Ginzler
- Lorenzo Bruzzone
- Emmanuel Baltsavias
Citations in the corpus
- Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas (2008, background): “…problems, hyperspectral images have been used in a wide number of forest applications, ranging from general cases focusing on the discrimination between forest and other land covers, to a more detailed analysis dealing with the distinction of different tree species (e.g., [6], [12]–[14]).”
- Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data (2011, background): “Spectral separability between species and the variability of trees within species have also been analysed and described in Leckie et al. (2005).”
- Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013, background): “Airborne hyperspectral imagery has also been found to produce high accuracies for identifying individual tree species (e.g. Aspinal, 2002; Leckie et al., 2003, 2005; Boschetti et al., 2007; Jones et al., 2010).”
- Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data (2012, methodology): “Furthermore, we suppose that considering only the sunlit regions of the tree crowns in the classification process, as demonstrated also for other sensors [38,39,61,86], significantly contributed to high classification accuracies.”
- Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota (2011, background): “…satellite sensor data (e.g., Landsat or SPOT) poses unique challenges in regions where species diversity and structural complexity arehigh (Wolter et al., 1995, 2008; Franco-Lopezet al., 2001;Reeseet al., 2002; Leckie et al., 2005; McRoberts, 2008; Stabach et al., 2009; Bauer et al., 2009).”