Atmospheric effects on the performance and threshold extrapolation of multi-temporal Landsat derived dNBR for burn severity assessment

International Journal of Applied Earth Observation and Geoinformation

Volumn 33, Issue 1, 2014, Pages 10-20

  Fang, Lei ^a,b   Yang, Jian ^a  

^a INSTITUTE OF APPLIED ECOLOGY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Atmospheric correction; Burn severity; Chinese boreal forest; dNBR; Landsat; Optimality

Indexed keywords

ACCURACY ASSESSMENT; BOREAL FOREST; CLIMATE CHANGE; CLIMATE EFFECT; CORRECTION; CORRELATION; DATA SET; LANDSAT; MAPPING; PERFORMANCE ASSESSMENT; SATELLITE IMAGERY; WILDFIRE;

CHINA;

EID: 84904755359     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.04.017     Document Type: Article

References (64)

1. Allen, J.L., Sorbel, B., 2008. Assessing the differenced Normalized Burn Ratio's ability to map burn severity in the boreal forest and tundra ecosystems of Alaska's national parks. IJWF 17, 463-475.
2. Bobbe, T., Finco, M.V., Quayle, B., Lannom, K, Sohlberg, R., Parsons, A., 2001. Field measurements forthe training and validation of burn severity maps from spaceborne, remotely sensed imagery. USDI Joint Fire Science Program Final Project Report JFSPRFP.
3. Boby, L.A., Schuur, E.A.G., Mack, M.C., Verbyla, D., Johnstone, J.F., 2010. Quantifying fire severity, carbon, and nitrogen emissions in Alaska's boreal forest. Ecol. Appl. 20, 1633-1647.
4. Cai, W., Yang, J., Liu, Z., Hu, Y., Weisberg, P.J., 2013. Post-fire tree recruitment of a boreal larch forest in Northeast China. For. Ecol. Manage. 307, 20-29.
5. Canty, M.J., Nielsen, A.A., 2008. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sens. Environ. 112, 1025-1036.
6. Canty, M.J., Nielsen, A.A., Schmidt, M., 2004. Automatic radiometric normalization of multitemporal satellite imagery. Remote Sens. Environ. 91, 441-451.
7. Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893-903.
8. Chang, Y., He, H.S., Hu, Y., Bu, R., Li, X., 2008. Historic and current fire regimes in the Great Xing'an Mountains, northeastern China: implications for long-term forest management. For. Ecol. Manage. 254, 445-453.
9. Chavez, P.S., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 24, 459-479.
10. Chavez, P.S., 1996. Image-based atmospheric corrections-revisited and improved. Photogramm. Eng. Remote Sens. 62, 1025-1035.
11. Cocke, A.E., Fule, P.Z., Crouse, J.E., 2005. Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data. IJWF 14, 189-198.
12. Cohen, W.B., Goward, S.N., 2004. Landsat's role in ecological applications of remote sensing. Bioscience 54, 535-545.
13. Cohen, W.B., Yang, Z., Kennedy, R., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2, TimeSync - Tools for calibration and validation. Remote Sens. Environ. 114, 2911-2924.
14. Conard, S.G., Solomon, A.M., 2008. Effects of wildland fire on regional and global carbon stocks in a changing environment. Dev. Environ. Sci. 8, 109-138.
15. Coppin, P., Jonckheere, I., Nackaerts, It, Muys, B., Lambin, E., 2004. Review article: digital change detection methods in ecosystem monitoring: a review. Int. J. Remote Sens. 25, 1565-1596.
16. de Groot, W.J., Cantin, A.S., Flannigan, M.D., Soja, A.J., Gowman, L.M., Newbery, A., 2013. A comparison of Canadian and Russian boreal forest fire regimes. For. Ecol. Manage. 294, 23-34.
17. De Santis, A., Asner, G.P., Vaughan, P.J., Knapp, D.E., 2010. Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery. Remote Sens. Environ. 114, 1535-1545.
18. Eidenshink, J., Schwind, B., Brewer, It, Zhu, Z., Quayle, B., Howard, S., 2007. A project for monitoring trends in burn severity. Fire Ecol. Spec. Issue 3, 4.
19. Epting, J., Verbyla, D., Sorbel, B., 2005. Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sens. Environ. 96, 328-339.
20. French, N.H.F., Kasischke, E.S., Hall, R.J., Murphy, K.A., Verbyla, D.L., Hoy, E.E., Allen, J.L., 2008. Using Landsat data to assess fire and burn severity in the North American boreal forest region: an overview and summary of results. IJWF 17, 443-462.
21. Hall, R., Freeburn.J., De Groot, W., Pritchard, J., Lynham, T., Landry, R., 2008. Remote sensing of burn severity: experience from western Canada boreal fires. IJWF 17, 476-489.
22. Healey, S.P., Cohen, W.B., Spies, T.A., Moeur, M., Pflugmacher, D., Whitley, M.G., Lefsky, M., 2008. The relative impact of harvest and fire upon landscape-level dynamics of older forests: lessons from the Northwest Forest Plan. Ecosystems 11, 1106-1119.
23. Hoy, E.E., French, N.H.F., Turetsky, M.R., Trigg, S.N., Kasischke, E.S., 2008. Evaluating the potential of Landsat TM/ETM+ imagery for assessing fire severity in Alaskan black spruce forests. IJWF 17, 500-514.
24. Huang, C, Goward, S.N., Masek, J.G., Thomas, N., Zhu, Z., Vogelmann, J.E., 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens. Environ. 114, 183-198.
25. Hudak, A., Morgan, P., Bobbin, M., Smith, A., Lewis, S., Lentile, L, Robichaud, P., Clark, J., McKinley, R., 2007. The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecol. 3(1), 64-90 (Fire Ecol. Spec. Issue, Vol. 3, 66).
26. Johnstone, J.F., Chapin, F.S., 2006. Effects of soil burn severity on post-fire tree recruitment in boreal forest. Ecosystems 9, 14-31.
27. Kasischke, E.S., Turetsky, M.R., 2006. Recent changes in the fire regime across the North American boreal region - spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. 33, L09703.
28. Kasischke, E.S., Turetsky, M.R., Ottmar, R.D., French, N.H.F., Hoy, E.E., Kane, E.S., 2008. Evaluation of the composite burn index for assessing fire severity in Alaskan black spruce forests. IJWF 17, 515-526.
29. Keeley, J.E., 2009. Fire intensity, fire severity and burn severity: a brief review and suggested usage. IJWF 18, 116-126.
30. Key, C.H., Benson, N.C., 2006. Landscape Assessment (LA). FIREMON: fire effects monitoring and inventory system. General Technical Report RMRS-GTR-164-CD.
31. Kobayashi, M., Nemilostiv, Y.P., Zyryanova, O.A., Kajimoto, T., Matsuura, Y., Yoshida, T., Satoh, F., Sasa, It, Koike, T., 2007. Regeneration after forest fires in mixed conifer broad-leaved forests of the Amur Region in Far Eastern Russia: the relationship between species specific traits against fire and recent fire regimes. Eurasian J. For. Res. 10, 51-58.
32. Lentile, L.B., Holden, Z.A., Smith, A.M.S., Falkowski, M.J., Hudak, A.T., Morgan, P., Lewis, S.A., Gessler, P.E., Benson, N.C., 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects. IJWF 15, 319-345.
33. Lentile, L.B., Smith, A.M.S., Hudak, A.T., Morgan, P., Bobbin, M.J., Lewis, S.A., Robichaud, P.R., 2009. Remote sensing for prediction of 1-year post-fire ecosystem condition. IJWF 18, 594-608.
34. Liu, Z.H., Yang, J., Chang, Y., Weisberg, P.J., He, H.S., 2012. Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Global Change Biol. 18, 2041-2056.
35. Masek, J.G., Huang, C, Wolfe, R., Cohen, W., Hall, F., Kutler.J., Nelson, P., 2008. North American forest disturbance mapped from a decadal Landsat record. Remote Sens. Environ. 112, 2914-2926.
36. Miller, J.D., Thode, A.E., 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens. Environ. 109, 66-80.
37. Miller, J.D., Yool, S.R., 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.
38. Miller, J.D., Knapp, E.E., Key, C.H., Skinner, C.N., Isbell, C.J., Creasy, R.M., Sherlock, J.W., 2009. Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens. Environ. 113, 645-656.
39. Riano, D., Chuvieco, E., Salas, J., Aguado, I., 2003. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEETrans. Geosci. Remote Sens. 41, 1056-1061.
40. Roy, D.P., Boschetti, L., Trigg, S.N., 2006. Remote sensing of fire severity: assessing the performance of the normalized burn ratio. IEEE Geosci. Remote Sens. Lett. 3, 112-116.
41. Schroeder, T.A., Cohen, W.B., Song, C, Canty, M.J., Yang, Z., 2006. Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens. Environ. 103, 16-26.
42. Schroeder, T.A., Cohen, W.B., Yang, Z., 2007. Patterns of forest regrowth following clearcutting in western Oregon as determined from a Landsat time-series. For. Ecol. Manage. 243, 259-273.
43. Smith, A.M.S., Wooster, M.J., Drake, N.A., Dipotso, F.M., Falkowski, M.J., Hudak, A.T., 2005. Testing the potential of multispectral remote sensing for retrospectively estimating fire severity in African Savannahs. Remote Sens. Environ. 97, 92- 115.
44. Song, C.H., Woodcock, C.E., 2003. Monitoring forest succession with multitemporal Landsat images: factors of uncertainty. IEEE Trans. Geosci. Remote Sens. 41, 1535-1545.
45. Song, C.H., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A., 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens. Environ. 75, 230-244.
46. Soverel, N.O., Perrakis, D.D.B., Coops, N.C., 2010. Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sens. Environ. 114, 1896-1909.
47. Soverel, N.O., Coops, N.C., Perrakis, D.D.B., Daniels, L.D., Gergel, S.E., 2011. The transferability of a dNBR-derived model to predict burn severity across 10 wildland fires in western Canada. IJWF 20, 518-531.
48. Stocks, B., Wotton, B., Flannigan, M., Fosberg, M., Cahoon, D., Goldammer, J., 2003. Boreal forest fire regimes and climate change. Remote Sens. Climate Model., 233-246.
49. Sunderman, S.O., Weisberg, P.J., 2012. Predictive modelling of burn probability and burn severity in a desert spring ecosystem. IJWF 21, 1014-1024.
50. Turetsky, M.R., Kane, E.S., Harden, J.W., Ottmar, R.D., Manies, K.L., Hoy, E., Kasischke, E.S., 2010. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nat. Geosci. 4, 27-31.
51. Turner, M.G., Romme, W.H., Gardner, R.H., 1999. Prefire heterogeneity, fire severity, and early postfire plant reestablishment in subalpine forests of Yellowstone National Park, Wyoming. IJWF 9, 21-36.
52. Van Leeuwen, W.J.D., Casady, G.M., Neary, D.G., Bautista, S., Alloza, J.A., Carmel, Y., Wittenberg, L., Malkinson, D., Orr, B.J., 2010. Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel. IJWF 19, 75-93.
53. van Wagtendonk, J.W., Root, R.R., Key, C.H., 2004. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sens. Environ. 92, 397-408.
54. Veraverbeke, S., Verstraeten, W., Lhermitte, S., Goossens, R., 2010. Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity. Int. J. Appl. Earth Observ. Geoinf. 12, 60-70.
55. Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., Goossens, R., 2011. Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper. Int. J. Remote Sens. 32, 3521-3537.
56. Verbyla, D.L., Kasischke, E.S., Hoy, E.E., 2008. Seasonal and topographic effects on estimating fire severity from Landsat TM/ETM+ data. IJWF 17, 527-534.
57. Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M., Morcrette.JJ., 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans. Geosci. Remote Sens. 35, 675-686.
58. Verstraete, M.M., Pinty, B., 1996. Designing optimal spectral indexes for remote sensing applications. IEEETrans. Geosci. Remote Sens. 34, 1254-1265.
59. Wang, C, Gower, S.T., Wang, Y., Zhao, H., Yan, P., Lamberty, B.P., 2001. The influence of fire on carbon distribution and net primary production of boreal Larixgmelinii forests in north-eastern China. Global Change Biol. 7, 719-730.
60. Westerling, A.L., Turner, M.G., Smithwick, E.A., Romme, W.H., Ryan, M.G., 2011. Continued warming could transform Greater Yellowstone fire regimes by mid-21st century. Proc. Natl. Acad. Sci. 108, 13165-13170.
61. Xu, H.C., Li, Z.D., Qju, Y., 1997. Fire disturbance history in virgin forest in northern region of Daxing'anling Mountains. Acta Ecol. Sin. 17, 337-343.
62. Zhang, Y.P., Hu, H.Q., 2008. Climatic change and its impact on forest fire in Daxing'anling Mountains. J. Northeast For. Univ. 36, 29-31.
63. Zhu, Z., Key, C, Ohlen, D., Benson, N., 2006. Evaluate sensitivities of burn severity mapping algorithms for different ecosystems and fire histories in the United States. Final report JFSP 01-1-4-12, October., pp. 35.
64. Zhu, Z., Woodcock, C.E., Olofsson, P., 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ. 122, 75-91.