Spectral recovery=# of yrs reqd for pixel to attain 80% of its pre-disturbance NBR value (Years to Recovery, Y2R). NBR=Normalized Burn Ratio, which exploits differences in spectral response of vegetation & exposed soil in NIR and SWIR wavelengths. Enabled by #Landsat time series.
We used an #opendata synthesis of #boreal pre- & post-fire field measurements shared by Baltzer @forestecogrp et al. (2021). Incredibly useful data for examining drivers of spectral #forestrecovery post-fire.
Plots w/ an absolute decrease in stem density post-fire had significantly longer median recovery rates (17 yrs) compared to plots that experienced no change (8 yrs). Likewise, the % of field plots that experienced a decrease in stem density increased with increasing Y2R.
Coniferous-dominated plots pre-fire had significantly longer median recovery rates (14 yrs) compared to broadleaf-dominated (8 yrs) plots. Compositional shifts among coniferous, broadleaf, and mixedwood classes varied by 5-year Y2R epoch & for not recovered plots.
Plots that had not yet spectrally recovered by the end of the #Landsat time series were associated with higher elevations, drier sites, greater pre-fire basal area, & greater change magnitudes. They were also more likely to have been labelled in the field as regeneration failure.
Our results emphasize that post-fire #forestrecovery in the #boreal is a process that is highly variable and that knowledge of pre-fire condition is useful for characterizing and interpreting measures of post-fire spectral recovery. #OpenAccess doi.org/10.1016/j.fore…
#Lidar is an operational technology for #forestinventory. Single-photon lidar (#SPL) may provide an acquisition advantage for large areas. How does #SPL perform in an area-based #forest inventory? Some things we have learned (i.e. a thread)!
2/ We used #SPL and 269 field plots to generate an area-based operational #forestinventory over a 15000 ha study area of temperate mixedwood forest representing a complex assemblage of tree species, forest structures, and management histories. bit.ly/37GTeZG
3/ We validated our #forestinventory estimates at the stand-level using independent field measured data. Why? Because the stand is the fundamental spatial unit for forest management and planning, and the unit at which decisions are made. #CFSEFIbit.ly/37GTeZG
An increasingly common question: “We generated an enhanced forest inventory with #lidar data and ground plots for one of our management units. Can we use the same models for our other management areas?” #CFSEFI#forestinventory
A great question and one that is top of mind for many of us who generate and use #forestinventory data. So what does the #science tell us about the transferability of area-based models? #CFSEFI
Fekety et al. 2015: "Mapping response variables at the landscape level demonstrates that the relationship between field data and LiDAR metrics holds true even though the data were collected in different years."