A disturbance weighting analysis model (DWAM) for mapping wildfire burn severity in the presence of forest disease

REMOTE SENSING OF ENVIRONMENT

卷:221

Article

He, Yinan¹  Chen, Gang¹  De Santis, Angela²  Roberts, Dar A.³  Zhou, Yuyu⁴  Meentemeyer, Ross K.^5,6 

[1] Univ N Carolina, Dept Geog & Earth Sci, LRSEC, Charlotte, NC 28223 USA

[2] HOPE Humanitarian Operat Fdn, Brussels, Belgium

[3] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA

[4] Iowa State Univ, Dept Geol & Atmospher Sci, Ames, IA USA

[5] North Carolina State Univ, Ctr Geospatial Analyt, Raleigh, NC 27695 USA

[6] North Carolina State Univ, Dept Forestry & Environm Resources, Raleigh, NC 27695 USA

关键词: Burn severity;    Disturbance Weighting Analysis Model (DWAM);    Forestry;    MESMA;    Landsat;    AVIRIS;    Sudden oak death;    Landscape epidemiology;   

DOI  :  10.1016/j.rse.2018.11.015

来源: Elsevier

【 摘 要 】

Forest ecosystems are subject to recurring fires as one of their most significant disturbances. Accurate mapping of burn severity is crucial for post-fire land management and vegetation regeneration monitoring. Remote-sensing-based monitoring of burn severity faces new challenges when forests experience both fire and non-fire disturbances, which may change the biophysical and biochemical properties of trees in similar ways. In this study, we develop a Disturbance Weighting Analysis Model (DWAM) for accurately mapping burn severity in a forest landscape that is jointly affected by wildfire and an emerging infectious disease - sudden oak death. Our approach treats burn severity in each basic mapping unit (e.g., 30 m grid from a post-fire Landsat image) as a linear combination of burn severity of trees affected (diseased) and not affected by the disease (healthy), weighted by their areal fractions in the unit. DWAM is calibrated using two types of inputs: i) look-up tables (LUTs) linking burn severity and post-fire spectra for diseased and healthy trees, derived from field observations, hyperspectral sensors [e.g., Airborne Visible InfraRed Imaging Spectrometer (AVIRIS)], and radiative transfer models; and ii) pre-fire fractional maps of diseased and healthy trees, derived by decomposing a pre-fire Landsat image using Multiple Endmember Spectral Mixture Analysis (MESMA). Considering the presence of tree disease in DWAM improved the overall map accuracy by 42%. The superior performance is consistent across all three stages of disease progression. Our approach demonstrates the potential for improved mapping of forest burn severity by reducing the confounding effects of other biotic disturbances.

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