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We collected field and remotely sensed data spanning 10 years after three 2003 Montana wildfires to monitor ecological change across multiple temporal and spatial scales. Multiple endmember spectral mixture analysis was used to create post-fire maps of: char, soil, green (GV) and non-photosynthetic (NPV) vegetation from high-resolution 2003 hyperspectral (HS) and 2007 QuickBird (QB) imagery, and from Landsat 5 and 8 imagery collected on anniversary dates in 2002, 2003 (post fire), 2004, 2007 and 2013. Initial estimates of char and NPV from the HS images were significantly correlated with their ground-measured counterparts (ρ = 0.60 (P = 0.03) and 0.68 (P = 0.01) respectively), whereas HS GV and Landsat GV were correlated with canopy GV (ρ = 0.75 and 0.70 (P = 0.003) respectively). HS imagery had stronger direct correlations with all classes of fine-scale ground data than Landsat and also had stronger predictive correlations with 10-year canopy data (r=0.65 (P=0.02) to 0.84 (P=0.0003)). There was less than 5% understorey GV cover on the sites initially, but by 2013, it had increased to nearly 60% regardless of initial condition. The data suggest it took twice as long for understorey GV and NPV to replace char and soil as primary ground cover components on the high-burn-severity sites compared with other sites. [ABSTRACT FROM AUTHOR]

Many scientists and managers have an interest in describing the environment following a fire to understand the effects on soil productivity, vegetation growth, and wildlife habitat, but little research has focused on the scientific rationale for classifying the post-fire environment. We developed an empirically-grounded soil post-fire index (PFI) based on available science and ecological thresholds. Using over 50 literature sources, we identified a minimum of five broad categories of post-fire outcomes: (a) unburned, (b) abundant surface organic matter (>85% surface organic matter), (c) moderate amount of surface organic matter (≥40 through 85%), (d) small amounts of surface organic matter (<40%), and (e) absence of surface organic matter (no organic matter left). We then subdivided each broad category on the basis of post-fire mineral soil colors providing a more fine-tuned post-fire soil index. We related each PFI category to characteristics such as soil temperature and duration of heating during fire, and physical, chemical, and biological responses. Classifying or describing post-fire soil conditions consistently will improve interpretations of fire effects research and facilitate communication of potential responses or outcomes (e.g., erosion potential) from fires of varying severities. [ABSTRACT FROM AUTHOR]

Wildfire is a major forest disturbance in interior Alaska that can both directly and indirectly alter ecological processes. We used a combination of pre- and post-fire forest floor depths and post-fire ground cover assessments measured in the field, and high-resolution airborne hyperspectral imagery, to map forest floor conditions after the 2004 Taylor Complex in Alaska's boreal forest. We applied a linear spectral unmixing model with five endmembers representing green moss, non-photosynthetic moss, charred moss, ash and soil to reflectance data to produce fractional cover maps. Our study sites spanned low to moderately high burn severity, and both black and white spruce forest types; high cover of green or non-photosynthetic moss in the remotely sensed imagery indicated low consumption, whereas high cover of charred moss, ash or soil indicated higher consumption. Strong relationships (R²=0.5 to 0.6) between green moss estimated from the imagery and both post-fire depth and percentage consumption suggest that potential burn severity may be predicted by a map of green (live) moss. Given that the depth of the post-fire forest floor is ecologically significant, the method of mapping the condition of the organic forest floor with hyperspectral imagery presented here may be a useful tool to assess the effect of future fires in the boreal region. [ABSTRACT FROM AUTHOR]

Appropriate use of satellite data in predicting >1 year post-fire effects requires remote measurement of surface properties that can be mechanistically related to ground measures of post-fire condition. The present study of burned ponderosa pine (Pinus ponderosa) forests in the Black Hills of South Dakota evaluates whether immediate fractional cover estimates of char, green vegetation and brown (non-photosynthetic) vegetation within a pixel are improved predictors of 1-year post-fire field measures, when compared with single-date and differenced Normalized Burn Ratio (NBR and dNB R) indices. The modeled estimate of immediate char fraction either equaled or outperformed all other immediate metrics in predicting 1-year post-fire effects. Brown cover fraction was a poor predictor of all effects (r2 <0.30), and each remote measure produced only poor predictions of crown scorch (r2 <0.20). Application of dNBR (1 year post) provided a considerable increase in regression performance for predicting tree survival. Immediate post-fire NBR or dNBR produced only marginal differences in predictions of all the 1-year post-fire effects, perhaps limiting the need for prefire imagery. Although further research is clearly warranted to evaluate fire effects data available 2-20 years after fire, char and green vegetation fractions may be viable alternatives to dNBR and similar indices to predict longer-term post-fire ecological effects. [ABSTRACT FROM AUTHOR]

While nadir-viewing passive multispectral sensors have limited utility for characterizing the full dimensionality of forest canopies, multi-angle remote sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) may improve detection of canopy architecture, canopy cover, and leaf area index (LAI) of forest canopy versus understory vegetation. Our objective was to determine whether data from the MISR sensor could improve estimates of LAI across a post-fire ponderosa pine forest located in the Black Hills of South Dakota. We measured LAI during the 2002 and 2003 growing seasons and created continuous LAI maps using Landsat TM and ETM+ data (mean R 2 =0. 81). We fit linear regression models of total and canopy LAI, using a series of MISR at-nadir and off-nadir or anisotropic vegetation indices as predictor variables, for each of five sampling periods. We found the best LAI model fits using either a new hotspot-adjusted normalized difference vegetation index, NDVIHS, NDVI calculated from the −60° view angle, or NDVI at-nadir and either the Hotspot–DarkSpot Index (HDS) or the normalized difference anisotrophic index (NDAX) (R 2 =0. 56–0. 91). However, differences in fits of these best models and those including NDVI at-nadir ranged from only 1 to 8%. Reflectance anisotropy patterns related strongly to understory vegetation phenology. We found that the relationships among NDVI or the enhanced vegetation index (EVI) and canopy or total LAI showed little variation across view angles when the understory vegetation was senesced and significant anisotropy when understory green LAI was greatest. These findings demonstrate the value of multi-temporal measurements during periods of understory phenological change, even if the overstory LAI is relatively stable. We also evaluated the performance of the MISR LAI product and found moderate fits between the MISR LAI and our field-and Landsat-derived canopy LAI (R 2 =0. 21–0. 44). [Copyright &y& Elsevier]