The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux
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Citations
Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests
Bonan, G. B. (2008) · Science · doi:10.1126/science.1155121 · Cited by 6,238
Introductory digital image processing: A remote sensing perspective
Jensen, J. R., & Lulla, K. (1987) · Geocarto International · doi:10.1080/10106048709354084 · Cited by 5,006
BALDOCCHI, D. D. (2003) · Global Change Biology · doi:10.1046/j.1365-2486.2003.00629.x · Cited by 2,594
Introductory digital image processing: a remote sensing perspective
Tailor, A. (1986) · Image and Vision Computing · doi:10.1016/0262-8856(86)90052-1 · Cited by 1,958
Monitoring and estimating tropical forest carbon stocks: making REDD a reality
Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007) · Environmental Research Letters · doi:10.1088/1748-9326/2/4/045023 · Cited by 1,653
DEM Generation from Laser Scanner Data Using Adaptive TIN Models
Axelsson, P. (2000) · ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences · Cited by 1,233
The potential and challenge of remote sensing‐based biomass estimation
Lu, D. (2006) · International Journal of Remote Sensing · doi:10.1080/01431160500486732 · Cited by 1,213
Opening the archive: How free data has enabled the science and monitoring promise of Landsat
Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R., & Woodcock, C. E. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.01.010 · Cited by 1,206
A New Method for Segmenting Individual Trees from the Lidar Point Cloud
Li, W., Guo, Q., Jakubowski, M. K., & Kelly, M. (2012) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.78.1.75 · Cited by 753
Cited by
Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick, B., Zhang, X., et al. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.04.034 · Cited by 514
Self-citationMaimaitijiang, M., Sagan, V., Sidike, P., Maimaitiyiming, M., Hartling, S., Peterson, K. T., Maw, M. J. W., Shakoor, N., Mockler, T., & Fritschi, F. B. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.03.003 · Cited by 253
Standardizing Ecosystem Morphological Traits from 3D Information Sources
Valbuena, R., O’Connor, B., Zellweger, F., Simonson, W., Vihervaara, P., Maltamo, M., Silva, C. A., Almeida, D. R. A., Danks, F., Morsdorf, F., et al. (2020) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2020.03.006 · Cited by 126
Roy, D. P., Kashongwe, H. B., & Armston, J. (2021) · Science of Remote Sensing · doi:10.1016/j.srs.2021.100024 · Cited by 112
Metadata
- Authors
- Zhao, K., Suarez, J. C., Garcia, M., Hu, T., Wang, C., & Londo, A.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 204
- Pages
- 883-897
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2757142005
- Cited by
- 287
- References
- 71
- FWCI
- 12.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. https://app.cochat.ai/papers/10.1016/j.rse.2017.09.007
- The paper
- Zhao, K., Suarez, J. C., Garcia, M., Hu, T., Wang, C., & Londo, A. (2018). Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.09.007
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
