Dr. Joanne C. White Profile picture
Apr 28, 2021 12 tweets 13 min read Read on X
#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)!

#OpenAccess: bit.ly/37GTeZG
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. #CFSEFI bit.ly/37GTeZG
4/ We predicted a suite of #forestinventory attributes and assessed model performance overall and by forest type. bit.ly/37GTeZG
5/ Accuracy and bias varied by forest type. Stand-level validation was important for assessing model performance in different stand conditions. Overall, plot-level results (OOB) overestimated model error and underestimated bias relative to the stand-level validation results.
6/ #Forestinventory attributes such as volume and basal area were most challenging in managed pine stands and red pine plantations, which have very different stand structures. bit.ly/37GTeZG
7/ Results were on par with area-based outcomes achieved using linear-mode #lidar in this same study area, as well as with results reported in other studies using SPL data (in boreal forests): bit.ly/3u0dhLE and bit.ly/2PyHpPy.
8/ For large FMAs, the ability to acquire #lidar data from a single instrument with a consistent specification can be critical for operational implementation. From a model performance perspective, SPL data provided accurate #forestinventory attribute estimates with minimal bias.
9/ Collaborators: Dr. Margaret Penner and Murray Woods. Partners: @CIF_IFC, @cwfc_ccfb & @PRF_CFS. Data provided by @ONresources. Funding provided by the Ontario Forestry Future's Trust.
#OpenAccess: bit.ly/37GTeZG
10/ To learn more about our project "Exploring the innovation potential of single photon lidar for Ontario's eFRI" (including recorded webinar): bit.ly/32ReN72 and also bit.ly/3e134cE
11/ It is worth noting that although #SPL offers an advantage for #lidar data acquisition over large areas because of the high altitude used for acquisition (~3800 m agl and as documented bit.ly/3u0dhLE and bit.ly/2PyHpPy), low clouds/smoke can be problematic.
12/ Finally, to access the SPL and field plot data used in our research, as well as the predicted EFI attribute layers, visit the PRF remote sensing supersite [bit.ly/3vqaADD] and use search terms PRF and SPL. #PRFSS #CFSEFI
#OpenAccess: bit.ly/37GTeZG

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More from @Joanne_C_White

Apr 12, 2023
📢NEW PUBLICATION in Forest Ecology and Management: Pre-fire measures of boreal forest structure and composition inform interpretation of post-fire spectral recovery rates #OpenAccess #forestrecovery #boreal #fire
doi.org/10.1016/j.fore…

A short 🧵 follows... Image
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. ImageImage
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.

Paper: doi.org/10.1073/pnas.2…
Data: doi.org/10.3334/ORNLDA…
Read 8 tweets
Jan 6, 2020
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."

Read: dx.doi.org/10.1139/cjfr-2…
Read 4 tweets

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