Greening Up Before Growing Up:

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## Greening Up Before Growing Up:
### Challenges in Modeling Forest Biomass Recovery Post-Harvest Using Satellite Imagery

#### Lucas Johnson
#### Mike Mahoney
#### Colin Beier
#### 2021-10-20

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Hi everyone - I'm Lucas Johnson - a PhD student at SUNY ESF in Syracuse, 
NY.

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# Outline

### 1. Satellite imagery and forest biomass

### 2. Greening up before growing up (the problem)

### 3. Recovery rate comparisons

### 4. Potential solution and next steps

<br />

Access these slides at https://lucas-johnson.github.io/saf-2021/slides.html

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First I'm going to share a bit of background on modeling forest biomass
using optical satellite imagery, including some of the methods 
and data I have used.

Second, I'm going to describe one common limitation of this approach, the
greening-up before growing-up problem, and why it is important to address.

Third I'm going to discuss how we compared post-harvest recovery rates between
Landsat-derived predictions and forest inventory data.

Finally, I'm going to share how we are thinking about addressing this issue 
in our modeling approach given the insights gained through this analysis.

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# Forest biomass and Landsat

### Landsat

- Measures spectral reflectance (i.e. color)
???

So why use landsat to model forest biomass? Data sources like LiDAR are better
at representing forest structure, while optical imagery like Landsat
is only able to measure the spectral reflectance of the surface, or in other
words the color.

- Unparalleled historical record and temporal consistency

- Globally available

???

Landsat offers the longest history of publicly available remote sensing data,
from 1984 to present day, and has repeat observations for the same location
roughly twice a month.
These data are available everywhere, and Landsat missions are well supported
meaning that any methods developed now can likely applied for future monitoring.

### Approach
 
- Forest Inventory and Analysis (FIA) plots

- Landsat spectral indices

- Landtrendr disturbance metrics

-  <svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> [Hudak et al. (2020)](https://www.fs.fed.us/rm/pubs_journals/2020/rmrs_2020_hudak_a002.pdf), 
<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> [Kennedy et al. (2018)](https://www.mdpi.com/2072-4292/10/5/691)

???
The approach co-locates Landsat-derived spectral indices and disturbance related 
information with field inventory plots on the ground.
This combined dataset is then used to train machine learning models for
aboveground biomass prediction.

---

# Greening up before growing up

???

So, our models are dependent on measures of spectral reflectance,
or 'greenness', to predict forest biomass. 
The problem with this is that greenness doesn't account for structure. 
Vegetation close to the ground floor can and does appear quite green.

- Harvest -> shrub or non-woody cover = FAST

- Harvest -> significant biomass accretion = SLOW

- Both are green!

???

Following a harvest, a stand is likely to "green-up" relatively quickly. 
Within a few years. 
This of course depends on the type of harvest, but without some adequately 
established advanced regeneration, 
this quick greening up is likely to be the result of early-successional species,
or small diameter saplings.

However, we know that it may take a harvested stand many years to "grow up", 
or return to pre-harvest conditions. 
Because of the type of remote sensing data that informs our model predictions, 
the models are likely to assume that a stand has snapped back to 
pre-harvest levels of biomass much faster than is reasonable. 
This is the "green-up before grow-up" problem.

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.center[
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]

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???

Here is a time series of RGB composites derived from 3 of the spectral indices 
used as predictors our model. 
The scene is of a working forest in the Adirondack region 
and spans 1990 to 2019.
You can see how quickly the spectral indices revert to greenness after the 
appearance of brown harvest patches in the early 90s and near 2010.
It follows that our modeled recovery rates would be fast given these kinds
of inputs.

.pull-right[

.center[
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???

Here are a set of predicted biomass maps for the same area and time-span.
The map on the left shows pixel predictions, with LCMAP landcover 
classifications overlaid.
Near-zero predictions are white, while larger predictions, upwards of 200
metric tons per hectare are in dark green. 
The bar chart on the right shows the total biomass in metric tons contained
in this scene for each year.
As I play this video, and the predictions progress through time we can see a few
harvests.
One in the early 1990s and a larger one near 2010
both of which recover to pre-harvest levels of biomass in 5-10 years. 
I'll play it again...

This problem is particularly relevant to carbon accounting as sequestration 
rates are more important than carbon stocks.
Faster rates of carbon sequestration would mean faster atmospheric removals, 
and so rates of recovery will have an impact on how working forests are assessed
as components in any greenhouse gas budgeting framework. 
While we would all like for forests to accumulate carbon at lightspeed, we
would rather have our predictions be accurate.

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# Comparison

### Data
- SUNY ESF continuous forest inventory (CFI) plots (1970-2017)

- Forest Inventory and Analysis (FIA) plots (2000-2019)

- Landsat-modeled AGB predictions at CFI plots (1990-2019)

???

We compared our modeled recovery rates to those measured through networks of 
repeated forest inventory plots. 
We used both FIA plots across the entire state, 
as well as CFI plots from SUNY ESF forest properties.
The density of forest plots in the CFI network, and long history of measurement
dating back to the 70s proved to be more useful in this analysis.

### Plot Selection Criteria

- Harvest occurrence = AGB loss > 30% of pre-harvest AGB

- No additional harvests/disturbances recorded (< 5% AGB loss)

???

We identified harvested plots as those which lost at least 30% of their 
plot-level biomass between inventories.
Additionally, we ensured that plots were not disturbed a second time, by 
excluding plots that lost more than 5% of their biomass between 
post-harvest inventories.

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### Recovery trajectories

.center[

]

???

So here we can see the average post-harvest growth trajectories 
for our three data sources.
The y-axis marks cumulative plot-level biomass gain post-harvest in 
metric-tons per hectare, and the x-axis represents time since harvest.
There are different post-harvest time-horizons available for 
each dataset which is a function of data availability and our plot-selection
criteria.

As expected, the landsat-derived growth-rates are much faster in the first 
5-10 years post harvest. 
However, the rates actually slow, down, and seem to "correct themselves" 
in comparison to the CFI trajectory at larger time-horizons of 20-25 years.

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### Recovery trajectory 95% CIs

.center[

]

???
Here we show the same data, excluding FIA, and with the addition of 95% 
confidence intervals around our recovery rates. 
For our Landsat-derived predictions, we simply don't have enough observations
beyond 10-15 years post-harvest to produce a narrow range of confidence.

While the whole post-harvest time-series might not be off, 
the outsized early rates of carbon sequestration are significant, 
and could have a large impact on how working forests are assessed in greenhouse
gas budgeting.

---

# Potential solution
???
So, we have identified this problem, and have a decent way to assess the
extent of it, but we are still thinking about how to address it in our
modeling approach.

### - Harvests identified by model predictions

???
One solution is to produce initial model predictions as described and
shown here, allowing these predictions to identify when harvests have 
occurred in the time-series.

### - Post-hoc correction of recovery trajectories

???
Then, following a harvest we might apply a different recovery trajectory, 
one developed with CFI, or other forest inventory data, to more accurately
represent forest growth.

### - Collect more regional CFI data

???
Ideally we would be able to apply location or environmentally relevant 
trajectories as we expect the growth rates to vary widely depending on site 
conditions.
This will require further collection of forest inventory data, as FIA plots
are too sparsely distributed and our SUNY ESF CFI data is geographically 
limited.
We hope to engage in this process with partners who might be willing to share
detailed historical inventory records.

---

## Thank you!

This work was supported by the [Climate and Applied Forest Research Institute](http://cafri-ny.org/)
and [SUNY ESF](https://www.esf.edu/),
with funding from the New York State Department of Environmental Conservation, 
Office of Climate change.

#### Links:

<br />

Access these slides at https://lucas-johnson.github.io/saf-2021/slides.html

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???
Thanks to everyone for listening! 
Here are a few links to where you find me or these slides later. 
I welcome any questions and am open to any perspectives anybody might want to 
share.