Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

Remote Sensing of Environment

Volumn 191, Issue , 2017, Pages 95-109

  Meng, Ran ^a   Wu, Jin ^a   Schwager, Kathy L ^a   Zhao, Feng ^b   Dennison, Philip E ^c   Cook, Bruce D ^d   Brewster, Kristen ^a,e   Green, Timothy M ^a   Serbin, Shawn P ^a  

^a BROOKHAVEN NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

^c UNIVERSITY OF UTAH   (United States)

^d NASA GODDARD SPACE FLIGHT CENTER   (United States)

^e STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Error matrix; Frequency distributions; High spatial resolution; Random Forests; Scale effect; Spectral library

Indexed keywords

DECISION TREES; DEFORESTATION; ECOLOGY; ECOSYSTEMS; FIRE HAZARDS; FIRES; FORESTRY; IMAGE RESOLUTION; PHOTOMAPPING; REMOTE SENSING; SATELLITE IMAGERY;

ERROR MATRICES; FREQUENCY DISTRIBUTIONS; HIGH SPATIAL RESOLUTION; RANDOM FORESTS; SCALE EFFECTS; SPECTRAL LIBRARIES;

MAPPING;

AERIAL PHOTOGRAPHY; ERROR ANALYSIS; FOREST ECOSYSTEM; LANDSAT; REMOTE SENSING; SATELLITE IMAGERY; SPATIAL RESOLUTION; SPECTRAL ANALYSIS;

CHANGDAO ISLANDS; CHINA; NEW JERSEY; PINE BARRENS; SHANDONG; UNITED STATES;

EID: 85009863176     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.01.016     Document Type: Article

References (89)

1. Amiro, B.D., Orchansky, A.L., Barr, A.G., Black, T.A., Chambers, S.D., Chapin, F.S. III, Gouldenf, M.L., Litvakg, M., Liu, H.P., McCaughey, J.H., McMillan, A., Randerson, J.T., The effect of post-fire stand age on the boreal forest energy balance. Agric. For. Meteorol. 140 (2006), 41–50.
2. Arnett, J., Coops, N.C., Daniels, L.D., Falls, R.W., Detecting forest damage after a low-severity fire using remote sensing at multiple scales. Int. J. Appl. Earth Obs. Geoinf. 35 (2015), 239–246.
3. Bowman, D.M.J.S., Balch, J.K., Artaxo, P., Bond, W.J., Carlson, J.M., Cochrane, M.A., D'Antonio, C.M., DeFries, R.S., Doyle, J.C., Harrison, S.P., Johnston, F.H., Keeley, J.E., Krawchuk, M.A., Kull, C.A., Marston, J.B., Moritz, M.A., Prentice, I.C., Roos, C.I., Scott, A.C., Swetnam, T.W., Van Der Werf, G.R., Pyne, S.J., Fire in the earth system. Science 324 (2009), 481–484.
4. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
5. Brewer, C.K., Winne, J.C., Redmond, R.L., Opitz, D.W., Mangrich, M.V., Classifying and mapping wildfire severity: a comparison of methods. Photogramm. Eng. Remote. Sens. 71 (2005), 1311–1320.
6. Canty, M.J., Nielsen, A.A., Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sens. Environ. 112 (2008), 1025–1036.
7. Chen, G., Metz, M.R., Rizzo, D.M., Dillon, W.W., Meentemeyer, R.K., Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery. ISPRS J. Photogramm. Remote Sens. 102 (2015), 38–47.
8. Chen, G., Metz, M.R., Rizzo, D.M., Meentemeyer, R.K., Mapping burn severity in a disease-impacted forest landscape using Landsat and MASTER imagery. Int. J. Appl. Earth Obs. Geoinf. 40 (2015), 91–99.
9. Cho, M.A., Malahlela, O., Ramoelo, A., Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study. Int. J. Appl. Earth Obs. Geoinf. 38 (2015), 349–357.
10. Chuvieco, E., Martin, M.P., Palacios, A., Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int. J. Remote Sens. 23 (2002), 5103–5110.
11. Chuvieco, E., Riaño, D., Danson, F., Martin, P., Use of a radiative transfer model to simulate the postfire spectral response to burn severity. J. Geophys. Res. Biogeosci., 111, 2006.
12. Cocke, A.E., Fule, P.Z., Crouse, J.E., Comparison of burn severity assessments using differenced normalized burn ratio and ground data. Int. J. Wildland Fire 14 (2005), 189–198.
13. Congalton, R.G., Remote sensing and geographic information system data integration: Error sources and research issues. Photogramm. Eng. Remote Sens. 57 (1991), 677–687.
14. Congalton, R.G., A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37 (1991), 35–46.
15. Cook, B.D., Nelson, R.F., Middleton, E.M., Morton, D.C., McCorkel, J.T., Masek, J.G., Ranson, K.J., Ly, V., Montesano, P.M., NASA Goddard's lidar, hyperspectral and thermal (G-LiHT) airborne imager. Remote Sens. 5 (2013), 4045–4066.
16. Coops, N.C., Johnson, M., Wulder, M.A., White, J.C., Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 103 (2006), 67–80.
17. Craney, T.A., Surles, J.G., Model-dependent variance inflation factor cutoff values. Qual. Eng. 14 (2002), 391–403.
18. Davranche, A., Lefebvre, G., Poulin, B., Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sens. Environ. 114 (2010), 552–562.
19. De Santis, A., Chuvieco, E., GeoCBI: a modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sens. Environ. 113 (2009), 554–562.
20. De Santis, A., Chuvieco, E., Vaughan, P.J., Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models. Remote Sens. Environ. 113 (2009), 126–136.
21. Dennison, P.E., Roberts, D.A., Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sens. Environ. 87 (2003), 123–135.
22. Dennison, P.E., Halligan, K.Q., Roberts, D.A., A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper. Remote Sens. Environ. 93 (2004), 359–367.
23. Dragozi, E., Gitas, I.Z., Bajocco, S., Stavrakoudis, D.G., Exploring the relationship between burn severity field data and very high resolution GeoEye images: the case of the 2011 Evros Wildfire in Greece. Remote Sens., 8(7), 2016, 566.
24. Drake, N.A., Mackin, S., Settle, J.J., Mapping vegetation, soils, and geology in semiarid shrublands using spectral matching and mixture modeling of SWIR AVIRIS imagery. Remote Sens. Environ. 68 (1999), 12–25.
25. Dudley, K.L., Dennison, P.E., Roth, K.L., Roberts, D.A., Coates, A.R., A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients. Remote Sens. Environ. 167 (2015), 121–134.
26. Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z., Quayle, B., Howard, S., A project for monitoring trends in burn severity. Fire Ecol. 3 (2007), 3–21.
27. Epting, J., Verbyla, D., Sorbel, B., Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM +. Remote Sens. Environ. 96 (2005), 328–339.
28. Fernandez-Manso, A., Quintano, C., Roberts, D.A., Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems. Remote Sens. Environ. 184 (2016), 112–123.
29. Flannigan, M.D., Stocks, B.J., Wotton, B.M., Climate change and forest fires. Sci. Total Environ. 262 (2000), 221–229.
30. Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H., Hurtt, G.C., Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J. Geophys. Res. G: Biogeosci., 114, 2009.
31. Goulden, M.L., Winston, G.C., McMillan, A.M.S., Litvak, M.E., Read, E.L., Rocha, A.V., Elliot, J.R., An eddy covariance mesonet to measure the effect of forest age on land-atmosphere exchange. Glob. Chang. Biol. 12 (2006), 2146–2162.
32. Griffith, D.A., Peres-Neto, P.R., Spatial modeling in ecology: The flexibility of eigenfunction spatial analyses. Ecology 87:10 (2006), 2603–2613.
33. Hammill, K.A., Bradstock, R.A., Remote sensing of fire severity in the Blue Mountains: influence of vegetation type and inferring fire intensity. Int. J. Wildland Fire 15 (2006), 213–226.
34. Hart, S.J., Veblen, T.T., Detection of spruce beetle-induced tree mortality using high-and medium-resolution remotely sensed imagery. Remote Sens. Environ. 168 (2015), 134–145.
35. Holden, Z.A., Morgan, P., Smith, A.M., Vierling, L., Beyond Landsat: a comparison of four satellite sensors for detecting burn severity in ponderosa pine forests of the Gila Wilderness, NM, USA. Int. J. Wildland Fire 19 (2010), 449–458.
36. Huete, A.R., A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25 (1988), 295–309.
37. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83 (2002), 195–213.
38. Jin, S.M., Sader, S.A., Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens. Environ. 94 (2005), 364–372.
39. Jordan, M.J., Patterson, W.A., Windisch, A.G., Conceptual ecological models for the Long Island pitch pine barrens: Implications for managing rare plant communities. For. Ecol. Manag. 185 (2003), 151–168.
40. Keeley, J.E., Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int. J. Wildland Fire 18 (2009), 116–126.
41. Key, C.H., Benson, N.C., Landscape assessment:sampling and analysis methods. Rocky Mountain Research Station General Technical Report, RMRS-GTR-164-CD, 2006.
42. Klonus, S., Ehlers, M., Performance of evaluation methods in image fusion. Information Fusion, 2009. FUSION'09 12th International Conference on (pp. 1409–1416): IEEE, 2009.
43. Kurczewski, F.E., Boyle, H.F., Historical changes in the pine barrens of central Suffolk County, New York. Northeast. Nat. 7 (2000), 95–112.
44. Lawrence, R.L., Wood, S.D., Sheley, R.L., Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sens. Environ. 100 (2006), 356–362.
45. 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., Remote sensing techniques to assess active fire characteristics and post-fire effects. Int. J. Wildland Fire 15 (2006), 319–345.
46. Lentile, L.B., Smith, A.M., Hudak, A.T., Morgan, P., Bobbitt, M.J., Lewis, S.A., Robichaud, P.R., Remote sensing for prediction of 1-year post-fire ecosystem condition. Int. J. Wildland Fire 18:5 (2009), 594–608.
47. Lu, B., He, Y., Tong, A., Evaluation of spectral indices for estimating burn severity in semiarid grasslands. Int. J. Wildland Fire 25 (2015), 147–157.
48. Marvin, D.C., Asner, G.P., Schnitzer, S.A., Liana canopy cover mapped throughout a tropical forest with high-fidelity imaging spectroscopy. Remote Sens. Environ. 176 (2016), 98–106.
49. Meng, R., Dennison, P.E., Spectroscopic analysis of green, desiccated and dead tamarisk canopies. Photogramm. Eng. Remote Sens. 81 (2015), 199–207.
50. Meng, R., Dennison, P., Jamison, L., van Riper, C., Nager, P., Hultine, K., Bean, D., Dudley, T., Detection of tamarisk defoliation by the northern tamarisk beetle based on multitemporal Landsat 5 thematic mapper imagery. GIScience Remote Sens. 49 (2012), 510–537.
51. Meng, R., Dennison, P.E., Huang, C., Moritz, M.A., D'Antonio, C., Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California. Remote Sens. Environ. 171 (2015), 311–325.
52. Miller, J.D., Thode, A.E., Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens. Environ. 109 (2007), 66–80.
53. Miller, J.D., Knapp, E.E., Key, C.H., Skinner, C.N., Isbell, C.J., Creasy, R.M., Sherlock, J.W., 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 (2009), 645–656.
54. Mitri, G.H., Gitas, I.Z., Fire type mapping using object-based classification of Ikonos imagery. Int. J. Wildland Fire 15 (2006), 457–462.
55. Mitri, G.H., Gitas, I.Z., Mapping the severity of fire using object-based classification of IKONOS imagery. Int. J. Wildland Fire 17 (2008), 431–442.
56. Mitri, G.H., Gitas, I.Z., Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery. Int. J. Appl. Earth Obs. Geoinf. 20 (2013), 60–66.
57. Morgan, P., Keane, R.E., Dillon, G.K., Jain, T.B., Hudak, A.T., Karau, E.C., Sikkink, P.G., Holden, Z.A., Strand, E.K., Challenges of assessing fire and burn severity using field measures, remote sensing and modelling. Int. J. Wildland Fire 23 (2014), 1045–1060.
58. Norton, J., Glenn, N., Germino, M., Weber, K., Seefeldt, S., Relative suitability of indices derived from Landsat ETM + and SPOT 5 for detecting fire severity in sagebrush steppe. Int. J. Appl. Earth Obs. Geoinf. 11 (2009), 360–367.
59. Pal, M., Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26 (2005), 217–222.
60. Pereira, J.M.C., A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping. IEEE Trans. Geosci. Remote Sens. 37 (1999), 217–226.
61. Pérez-Cabello, F., Cerdà, A., De la Riva, J., Echeverría, M., García-Martín, A., Ibarra, P., Lasanta, T., Montorio, R., Palacios, V., Micro-scale post-fire surface cover changes monitored using high spatial resolution photography in a semiarid environment: a useful tool in the study of post-fire soil erosion processes. J. Arid Environ. 76 (2012), 88–96.
62. Qi, J., Chehbouni, A., Huete, A., Kerr, Y., Sorooshian, S., A modified soil adjusted vegetation index. Remote Sens. Environ. 48 (1994), 119–126.
63. Quintano, C., Fernández-Manso, A., Roberts, D.A., Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sens. Environ. 136 (2013), 76–88.
64. Reiners, W., Relationships between vegetational strata in the pine barrens of central Long Island, New York. Bull. Torrey. Bot. Club, 1967, 87–99.
65. Riaño, D., Chuvieco, E., Ustin, S., Zomer, R., Dennison, P., Roberts, D., Salas, J., Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sens. Environ. 79 (2002), 60–71.
66. Roberts, D.A., Gardner, M., Church, R., Ustin, S., Scheer, G., Green, R.O., Mapping Chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sens. Environ. 65 (1998), 267–279.
67. Roberts, D.A., Dennison, P.E., Gardner, M.E., Hetzel, Y., Ustin, S.L., Lee, C.T., Evaluation of the potential of Hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer. IEEE Trans. Geosci. Remote Sens. 41 (2003), 1297–1310.
68. Roberts, D.A., Halligan, K., Dennison, P.E., VIPER Tools User Manual (Version1. 5). 2007.
69. Roberts, D.A., Dennison, P.E., Roth, K.L., Dudley, K., Hulley, G., Relationships between dominant plant species, fractional cover and land surface temperature in a Mediterranean ecosystem. Remote Sens. Environ. 167 (2015), 152–167.
70. Schepers, L., Haest, B., Veraverbeke, S., Spanhove, T., Vanden Borre, J., Goossens, R., Burned area detection and burn severity assessment of a heathland fire in Belgium using airborne imaging spectroscopy (APEX). Remote Sens. 6 (2014), 1803–1826.
71. Schroeder, T.A., Cohen, W.B., Song, C., Canty, M.J., Yang, Z., Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens. Environ. 103 (2006), 16–26.
72. Serbin, S.P., Ahl, D.E., Gower, S.T., Spatial and temporal validation of the MODIS LAI and FPAR products across a boreal forest wildfire chronosequence. Remote Sens. Environ. 133 (2013), 71–84.
73. Settle, J., Campbell, N., On the errors of two estimators of sub-pixel fractional cover when mixing is linear. IEEE Trans. Geosci. Remote Sens. 36 (1998), 163–170.
74. Smith, A.M.S., Lentile, L.B., Hudak, A.T., Morgan, P., Evaluation of linear spectral unmixing and DNBR for predicting postfire recovery in a North American ponderosa pine forest. Int. J. Remote Sens. 28 (2007), 5159–5166.
75. Smith, A.M., Sparks, A.M., Kolden, C.A., Abatzoglou, J.T., Talhelm, A.F., Johnson, D.M., Boschetti, L., Lutz, J.A., Apostol, K.G., Yedinak, K.M., Towards a new paradigm in fire severity research using dose–response experiments. Int. J. Wildland Fire, 2016.
76. Sparks, A.M., Kolden, C.A., Talhelm, A.F., Smith, A., Apostol, K.G., Johnson, D.M., Boschetti, L., Spectral indices accurately quantify changes in seedling physiology following fire: towards mechanistic assessments of post-fire carbon cycling. Remote Sens., 8, 2016, 572.
77. Speybroeck, N., Classification and regression trees. Int. J. Public Health 57 (2012), 243–246.
78. Sugihara, N.G., Wagtendonk, J.W.V., Fites-Kaufman, J., Fire as ecological process. Sugihara, N.G., Wagtendonk, J.W.V., Shaffer, K.E., Fites-Kaufman, J., Andrea, E.T., (eds.) Fire in California's Ecosystems, 2006, University of California Press, Berkeley Los Angeles London.
79. Team, R.C., R: A Language and Environment for Statistical Computing. 2013.
80. Tucker, C.J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8 (1979), 127–150.
81. Van Wagtendonk, J.W., Root, R.R., Key, C.H., Comparison of AVIRIS and Landsat ETM + detection capabilities for burn severity. Remote Sens. Environ. 92 (2004), 397–408.
82. Veraverbeke, S., Hook, S., Hulley, G., An alternative spectral index for rapid fire severity assessments. Remote Sens. Environ. 123 (2012), 72–80.
83. Werf, V.D., Randerson, G.R., Giglio, J.T., Collatz, L., Mu, G.J., Kasibhatla, M., Morton, D.C., DeFries, R.S., van Jin, Y., van Leeuwen, T.T., Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10 (2010), 11707–11735.
84. White, J.D., Ryan, K.C., Key, C.C., Running, S.W., Remote sensing of forest fire severity and vegetation recovery. Int. J. Wildland Fire 6 (1996), 125–136.
85. Whitman, E., Rapaport, E., Sherren, K., Modeling fire susceptibility to delineate Wildland-urban Interface for municipal-scale fire risk management. Environ. Manag. 52 (2013), 1427–1439.
86. Whittaker, R.H., Woodwell, G.M., Structure, production and diversity of the oak-pine Forest at Brookhaven, New York. J. Ecol. 57 (1969), 155–174.
87. Wilson, A.M., Latimer, A.M., Silander, J.A., Climatic Controls on Ecosystem Resilience: Postfire Regeneration in the Cape Floristic Region of South Africa. Proceedings of the National Academy of Sciences of the United States of America, 112, 2015, 9058–9063.
88. Wulder, M.A., White, J.C., Coops, N.C., Butson, C.R., Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sens. Environ. 112 (2008), 2729–2740.
89. Yu, X., Hyyppä, J., Vastaranta, M., Holopainen, M., Viitala, R., Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS J. Photogramm. Remote Sens. 66 (2011), 28–37.