The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Fire models and methods to map fuel types: The role of remote sensing
Open accessAbstract
Understanding fire is essential to improving forest management strategies. More specifically, an accurate knowledge of the spatial distribution of fuels is critical when analyzing, modelling and predicting fire behaviour. First, we review the main concepts and terminology associated with forest fuels and a number of fuel type classifications. Second, we summarize the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling. We pay special attention to more contemporary techniques, which involve the use of remote sensing systems. In general, remote sensing systems are low-priced, can be regularly updated and are less time-consuming than traditional methods, but they are still facing important limitations. Recent work has shown that the integration of different sources of information and methods in a complementary way helps to overcome most of these limitations. Further research is encouraged to develop novel and enhanced remote sensing techniques.
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Cites
A mathematical model for predicting fire spread in wildland fuels
Rothermel, R. C. (1972) · doi:10.2737/int-rp-115 · Cited by 2,247
Lidar Remote Sensing for Ecosystem Studies
LEFSKY, M. A., COHEN, W. B., PARKER, G. G., & HARDING, D. J. (2002) · BioScience · doi:10.1641/0006-3568(2002)052[0019:lrsfes]2.0.co;2 · Cited by 1,824
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Finney, M. A. (1998) · doi:10.2737/rmrs-rp-4 · Cited by 1,617
Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models
Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., & Green, R. O. (1998) · Remote Sensing of Environment · doi:10.1016/s0034-4257(98)00037-6 · Cited by 1,363
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Aids to determining fuel models for estimating fire behavior
Anderson, H. E. (1982) · doi:10.2737/int-gtr-122 · Cited by 984
Development and Structure of the Canadian Forest FireWeather Index System
Wagner, C. E. V., Forest, P., Station, E., Ontario, C. R., Fran�ais, R. U. E., & Davis, H. J. (1987) · Cited by 830
Adams, J. (1995) · Remote Sensing of Environment · doi:10.1016/0034-4257(94)00098-8 · Cited by 811
McArthur's fire‐danger meters expressed as equations
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Lidar Remote Sensing for Forestry
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Cited by
Review of optical-based remote sensing for plant trait mapping
Homolová, L., Malenovský, Z., Clevers, J. G. P. W., García-Santos, G., & Schaepman, M. E. (2013) · Ecological Complexity · doi:10.1016/j.ecocom.2013.06.003 · Cited by 437
Ottmar, R. D., Sandberg, D. V., Riccardi, C. L., & Prichard, S. J. (2007) · Canadian Journal of Forest Research · doi:10.1139/x07-077 · Cited by 294
A Review of the Applications of Remote Sensing in Fire Ecology
Szpakowski, D., & Jensen, J. (2019) · Remote Sensing · doi:10.3390/rs11222638 · Cited by 292
Fusion of LiDAR and imagery for estimating forest canopy fuels
Erdody, T. L., & Moskal, L. M. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.11.002 · Cited by 270
Metadata
- Authors
- Arroyo, L. A., Pascual, C., & Manzanera, J. A.
- Venue
- Forest Ecology and Management
- Publisher
- Elsevier BV
- Published
- 2008-09
- Volume
- 256
- Issue
- 6
- Pages
- 1239-1252
- ISSN
- 0378-1127 (ISSN-L)
- OpenAlex
- W2075704050
- Cited by
- 272
- References
- 84
- FWCI
- 10.28
- Type
- journal article
- Language
- en
- Open access
- green
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- Arroyo, L. A., Pascual, C., & Manzanera, J. A. (2008). Fire models and methods to map fuel types: The role of remote sensing. Forest Ecology and Management. https://doi.org/10.1016/j.foreco.2008.06.048
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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