The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Forest Monitoring Using Landsat Time Series Data: A Review
Open accessAbstract
Unique among Earth observation programs, the Landsat program has provided continuous earth observation data for the past 41 years. Landsat data are systematically collected and archived following a global acquisition strategy. The provision of free, robust data products since 2008 has spurred a renaissance of interest in Landsat and resulted in an increasingly widespread use of Landsat time series (LTS) for multitemporal characterizations. The science and applications capacity has developed steadily since 1972, with the increase in sophistication offered over time incorporated into Landsat processing and analysis practices. With the successful launch of Landsat-8, the continuity of measures at scales of particular relevance to management and scientific activities is ensured in the short term. In particular, forest monitoring benefits from LTS, whereby a baseline of conditions can be interrogated for both abrupt and gradual changes and attributed to different drivers. Such benefits are enabled by data availability, analysis-ready image products, increased computing power and storage, as well as sophisticated image processing approaches. In this review, we present the status of remote sensing of forests and forest dynamics using LTS, including issues related to the sensors, data availability, data preprocessing, variables used in LTS, analysis approaches, and validation issues.
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Cites
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Self-citationLandsat-8: Science and product vision for terrestrial global change research
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A review of machine learning in processing remote sensing data for mineral exploration
Shirmard, H., Farahbakhsh, E., Müller, R. D., & Chandra, R. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112750 · Cited by 420
Pacheco-Angulo, C., Plata-Rocha, W., Serrano, J., Vilanova, E., Monjardin-Armenta, S., González, A., & Camargo, C. (2021) · Remote Sensing · doi:10.3390/rs13081435 · Cited by 12
Metadata
- Authors
- Banskota, A., Kayastha, N., Falkowski, M. J., Wulder, M. A., Froese, R. E., & White, J. C.
- Venue
- Canadian Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2014-09-03
- Volume
- 40
- Issue
- 5
- Pages
- 362-384
- ISSN
- 0703-8992 (ISSN-L)
- 1712-7971
- OpenAlex
- W1999635212
- Cited by
- 419
- References
- 113
- FWCI
- 15.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Banskota, A., Kayastha, N., Falkowski, M. J., Wulder, M. A., Froese, R. E., & White, J. C. (2014). Forest Monitoring Using Landsat Time Series Data: A Review. Canadian Journal of Remote Sensing. https://doi.org/10.1080/07038992.2014.987376
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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.
