Use of a radiative transfer model to simulate the postfire spectral response to burn severity

Journal of Geophysical Research: Biogeosciences

Volumn 111, Issue 4, 2006, Pages

  Chuvieco, Emilio ^a   Riano D ^a,b   Danson, F M ^c   Martin, Pilar ^a,d  

^a UNIVERSITY OF ALCALÁ   (Spain)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF SALFORD   (United Kingdom)

^d CSIC Instituto de Economia Geografia y Demografia IEGD   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CANOPY; FIRE; FOLIAGE; LEAF AREA INDEX; MODELING; RADIATIVE TRANSFER; REGRESSION ANALYSIS; REMOTE SENSING; SPECTRAL REFLECTANCE;

EID: 34848921516     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2005JG000143     Document Type: Article

References (42)

1. Aldakheel, Y. Y., and F. M. Danson (1997), Spectral reflectance of dehydrating leaves: Measurements and modelling, Int. J. Remote Sens., 18, 3683-3690.
2. Andreae, M. O., and P. Merlet (2001), Emission of trace gases and aerosols from biomass burning, Global Biogeochem. Cycles, 15(4), 955-966.
3. Caetano, M. S., L. A. K. Mertes, and J. M. C. Pereira (1994), Using spectral mixture analysis for fire severity mapping, paper presented at 2nd International Conference Forest Fire Research, ADAI, Coimbra, Portugal.
4. Ceccato, P., S. Flasse, S. Tarantola, S. Jacquemoud, and J. M. Grégoire (2001), Detecting vegetation leaf water content using reflectance in the optical domain, Remote Sens. Environ., 77, 22-33.
5. Chuvieco, E., M. P., Martín, and A. Palacios (2002), Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination, Int. J. Remote Sens., 23, 5103-5110.
6. Chuvieco, E., D. Cocero, I. Aguado, A. Palacios-Orueta, and E. Prado (2004), Improving burning efficiency estimates through satellite assessment of fuel moisture content, J. Geophys. Res., 109, D14S07, doi:10.1029/2003JD00467.
7. Cocke, A. E., P. Z. Fule, and J. E. Crouse (2005), Comparison of burn severity assessments using differenced normalized burn ratio and ground data, Int. J. Wildland Fire, 14, 189-198.
8. Danson, F. M., and P. Bowyer (2004), Estimating live fuel moisture content from remotely sensed reflectance, Remote Sens. Environ., 92, 309-321.
9. Díaz-Delgado, R., X. Pons, and F. Lloret (2001), Fire severity effects on vegetation recovery after fire: The Bigues i Riells wildfire case study, in 3rd International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: New Methods and Sensors, edited by E. Chuvieco and M. P. Martín, pp. 152-155, Eur. Assoc. of Remote Sens. Lab., Paris.
10. Epting, J., D. L. Verbyla, and B. Sorbel (2005), Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+, Remote Sens. Environ., 96, 328-339.
11. Fearnside, P. M., P. M. Lima de Alencastro Graça, and F. J. Alves Rodriguez (2001), Burning of Amazonian rainforests: Burning efficiency and charcoal formation in forest cleared for cattle pasture near Manaus, Brazil, For. Ecol. Manage., 146, 115-128.
12. Fraser, R. H., Z. Li, and J. Cihlar (2000), Hotspot and NDVI differencing synergy (HANDS): A new technique for burned area mapping over boreal forest, Remote Sens. Environ., 74, 362-376.
13. Goel, N. S. (1988), Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data, Remote Sens. Rev., 4, 1-212.
14. Hosgood, B., S. Jacquemoud, G. Andreoli, J. Verdebout, G. Pedrini, and G. Schmuck (1994), Leaf Optical Properties Experiment 93 (LOPEX93), Rep. EUR 16095 EN, 21 pp., Inst. for Remote Sens. Appl., Joint Res. Cent., Eur. Comm., Ispra, Italy.
15. Huemmrich, K. F. (2001), The GeoSail model: A simple addition to the SAIL model to describe discontinuous canopy reflectance, Remote Sens. Environ., 75, 423-431.
16. Jacquemoud, S. (1990), PROSPECT: A model to leaf optical properties spectra, Remote Sens. Environ., 34, 74-91.
17. Jain, T. B. (2004), Confused meanings for common fire terminology can lead to fuels mismanagement: A new framework is needed to clarify and communicate the concepts, Wildfire, July-Aug., 22-26.
18. Jakubauskas, M. E., K. P. Lulla, and P. W. Mausel (1990), Assessment of vegetation change in a fire-altered forest landscape, Photogramm. Eng. Remote Sens., 56(3), 371-377.
19. Kasischke, E., and N. H. French (1995), Locating and estimating the areal extent of wildfires in Alaskan boreal forest using multiple-season AVHRR NDVI composite data, Remote Sens. Environ., 51, 263-275.
20. Key, C. C. (2005), Remote sensing sensitivity of fire severity and fire recovery, in Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment, edited by J, Riva, F. Pérez-Cabello, and E. Chuvieco, pp. 29-39, Univ. de Zaragoza, Zaragoza, Spain.
21. Key, C. H., and N. Benson (2005), Landscape assessment: Ground measure of severity, the composite burn index, and remote sensing of severity, the normalized burn ratio, in FIREMON: Fire Effects Monitoring and Inventory System, edited by D. C. Lutes et al., Gen. Tech. Rep. RMRS-GTR-164, pp. LA1-LA51, Rocky Mtn. Res. Stn., For. Serv., U.S. Dep. of Agric., Ogden, Utah.
22. Kuusk, A. (2001), A two-layer canopy reflectance model, J. Quant. Spectrosc. Radiat. Transfer, 71, 1-9.
23. López García, M. J., and V. Caselles (1991), Mapping burns and natural reforestation using Thematic Mapper data, Geocarto Int., 1, 31-37.
24. Martín, M. P. (1998), Cartografia e inventario de incendios forestales en la Península Ibérica a partìr de imágenes NOAA-AVHRR, doctoral thesis, Univ. de Alcalá, Alcalá de Henares, Spain.
25. Martín, M. P., I. Gómez, and E. Chuvieco (2005), Performance of a burned-area index (BAIM) for mapping Mediterranean burned scars from MODIS data, in Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment, edited by J. Riva, F. Pérez-Cabello, and E. Chuvieco, pp. 193-198, Univ. de Zaragoza, Zaragoza, Spain.
26. Miller, H. J., and S. R. Yool (2002), Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data, Remote Sens. Environ., 82, 481-496.
27. Minick, G. R., and W. A. Shain (1981), Comparison of satellite imagery and conventional aerial photography in evaluating a large forest fire, paper presented at 7th International Symposium Machine Processing of Remotely Sensed Data, Purdue Univ., West Lafayette, Indiana.
28. Moreno, J. M., and W. C. Oechel (1989), A simple method for estimating fire intensity after a burn in California chaparral, Acta Oecol. Oecologia Plant., 10(1), 57-68.
29. Parra, A., and E. Chuvieco (2005), Assessing burn severity using Hyperion data, in Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment, edited by J. Riva, F. Pérez-Cabello, and E. Chuvieco, pp. 239-244, Univ. de Zaragoza, Zaragoza, Spain.
30. Pereira, J. M. C. (1999), A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping, IEEE Trans. Geosci. Remote Sens., 37(1), 217-226.
31. Pereira, J. M. C., A. C. L. Sa, A. M. O. Sousa, J. M. N. Silva, T. N. Santos, and J. M. B. Carreiras (1999), Spectral characterisation and discrimination of burnt areas, in Remote Sensing of Large Wildfires in the European Mediterranean Basin, edited by E. Chuvieco, pp. 123-138, Springer, New York.
32. Pereira, J. M. C., B. Mota, J. L. Privette, K. K. Caylor, J. M. N. Silva, A. C. L. Sa, and W. Ni-Meister (2004), A simulation analysis of the detectability of understory burns in Miombo woodlands, Remote Sens. Environ., 93, 296-310.
33. Pérez, B., and J. M. Moreno (1998), Methods for quantifying fire severity in shrubland-fires, Plant Ecol., 139, 91-101.
34. Riaño, D., E. Chuvieco, S. L. Ustin, R. Zomer, P. Dennison, D. Roberts, and J. Salas (2002), Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains, Remote Sens. Environ., 79, 60-71.
35. Riaño, D., P. Vaughan, E. Chuvieco, P. Zarco-Tejada, and S. L. Ustin (2005), Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: Analysis at leaf and canopy level, IEEE Trans. Geosci. Remote Sens., 43(4), 819-826.
36. Rogan, J., and J. Franklin (2001), Mapping wildfire burn severity in southern California forests and shrublands using enhanced Thematic Mapper imagery, Geocarto Int., 16(4), 89-99.
37. Scurlock, J. M. O., G. P. Asner, and S. T. Gower (2001), Worldwide historical estimates of leaf area index, 1932-2000, Rep. ORNL/TM-2001/ 268, 34 pp., Oak Ridge Natl. Lab., Oak Ridge, Tenn.
38. van Wagtendonk, J. W., R. R. Root, and C. H. Key (2004), Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity, Remote Sens. Environ., 92, 397-408.
39. Verhoef, W. (1984), Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model, Remote Sens. Environ., 16, 125-141.
40. Wheatherspoon, C. P., and C. N. Skiner (1995), An assessment of factors associate with damage to tree crowns from the 1987 wildfires in northern California, For. Sci., 41(3), 430-451.
41. White, J. D., K. C. Ryan, C. C. Key, and S. W. Running (1996), Remote sensing of forest fire severity and vegetation recovery, Int. J. Wildland Fire, 6(3), 125-136.
42. Zarco-Tejada, P. J., J. R. Miller, T. L. Noland, G. H. Mohammed, and P. H. Sampson (2001), Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data, IEEE Trans. Geosci. Remote Sens., 39(7), 1491-1507.