Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification

By Yinghai Ke, Lindi J. Quackenbush, Jungho Im. 2010

314 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Insect Science

View Paper: https://www.semanticscholar.org/paper/5388ed0edea2f1d8fe3a2e18cf80bb2de06f66aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Lianjun Zhang
2. N. E. Zimmermann
3. Zhen Zhen
4. Robin Engler
5. Christian Ginzler
6. Erle C. Ellis
7. Jonathan P. Dandois
8. Achilleas Psomas

In Environmental Engineering

1. Lianjun Zhang
2. Zhen Zhen
3. Erle C. Ellis
4. Jonathan P. Dandois
5. David Gudex‐Cross
6. Christian Ginzler
7. Alison B. Adams
8. Lars T. Waser

In Insect Science

1. Christian Ginzler
2. Zhen Zhen
3. Lianjun Zhang
4. Lars T. Waser
5. Erle C. Ellis
6. Lindi J. Quackenbush
7. Jonathan P. Dandois

Citations in the corpus

• Pre-harvest Sugarcane Yield Estimation Using UAV-Based RGB Images and Ground Observation (2018, methodology): “This study used mean grey-level co-occurrence matrix (GLCM) as it can calculate the distribution of similar segments in rows and columns (Ke et al. 2010).”
• High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision (2013, background): “…structural and spectral traits for applications including the measurement of aboveground biomass and carbon, identifying individual species, and modeling the spatial heterogeneity of vegetation biochemistry (Anderson et al., 2008; Ke et al., 2010; Turner et al., 2003; Vitousek et al., 2009).”
• Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013, background): “A similar idea is to combine LiDAR and multi-spectral data, the former to delineate individual trees and the later to distinguish among the different species (Holmgren et al., 2008; Chubey et al., 2009; Ke et al., 2010), or data from sensors providing additional spectral or 3-”
• Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery (2017, background): “Several remote sensing studies have successfully mapped specieslevel distributions, though largely at highly localized spatial scales (Carleer and Wolff, 2004; Immitzer et al., 2012; Ke et al., 2010; Martin et al., 1998; Plourde et al., 2007).”
• Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification (2013, methodology): “It is often convenient and efficient for researchers to collect training and validation data using reference polygons rather than by performing point-by-point interpretation (e.g. Pugh 2005; Chen et al. 2009; Ke, Quackenbush, and Im 2010).”