Presented during the 2019 North Dakota Geospatial Summit in Bismarck, ND, October 22-23.

1. Estimation of Total Above-Ground Biomass Using
High Resolution UAS Imagery
David Kramar1, Valquiria Quirino1, Asami Minei1, Alison Wallace, and Breanna Huynh2
Minnesota State University Moorhead
1Department of Anthropology and Earth Sciences
2Department of Biological Sciences

2. Objectives
• Collect and Classify High-Resolution
Imagery from Two Sites using an
Off-The-Shelf UAS
• Compare multiple Non-NIR
Vegetation Indices to Determine
which Produces the Best Results
• Determine Which Index Best
Estimates Above Ground Biomass
Source: https://www.mnstate.edu/sciencecenter/

3. The Regional Science Center
• The Minnesota State University
Moorhead Regional Science Center
(RSC) is located 16 miles east of
Moorhead, adjacent to Buffalo River
State Park (BRSP)1.*
• There are 25 rare plants and animal
species within the complex BRSP,
RSC, and Bluestem Prairie2.**
Source: https://www.mnstate.edu/sciencecenter/

4. The Regional Science Center
• Currently several projects are underway in the Regional Science Center
o One is a prairie restoration project funded under the Minnesota Environment
and Natural Resources Trust Fund (ENRTF).
o A second project supporting the Nutrient Network (NutNet)* – which is a
coordinated research network that is quantifying the impacts of human activities
on ecological systems (see http://www.nutnet.org/home for additional
information about NutNet) - is also underway.
o There is a rich archeology history here.
• It provides programs for over 7000 Pre-K-12 students every year.
• It is an excellent “Living Laboratory”.
Thus, there is an INTEREST IN MONITORING VEGETATION CHANGES in
this area.

5. Nutrient Network Overview

6. Nutrient Network Overview
• At Nutrient Network ecologists and scientists are collecting data in a
standardized experimental design, across a broad range of site types.
• Designed to quantify community and ecosystem responses across a range of
vegetative ecosystems.
• Comprised of more than 40 grassland sites worldwide.
• Grassroots effort.
• We have two sites that we are currently monitoring.

7. Plot Design of the Northern Study Area

8. Software, UAS Platform, and Tools
• Software: (GIS/Remote Sensing)
o ArcGIS, QGIS, ORFEO TB
• Software: (Statistics)
o R (RATTLE Package)
o R (random forest, party, tree, etc)
o Statistica
• Software: (UAS/Processing)
o Pix4D Desktop
o Pix4D Capture
o Drone Harmony
• UAS Platform:
• Phantom 4 Advanced
• Example of Tools (GIS)
• Spatial Analyst Extension
• Segment Mean Shift
• Nibble
• Region
• Etc

9. Vegetation Indices (Minei 2019)
• We focused on two sites.
• Imagery was collected every 2
weeks from mid-May until the
end of September.
• We wanted to determine
which index did the best job
identifying healthy versus
unhealthy vegetation, and
which index would best
predict total above-ground
biomass.
• All vegetation indexes were
generated and interpreted by
student Asami Minei. The
sheer volume of data she has
analyzed is overwhelming.
• Modified VVI
• 𝑉𝑉𝐼 = 𝐿𝑜𝑔 10 1 −
𝑅−𝑅𝑜
𝑅+𝑅𝑜
+ 10 1 −
𝐺−𝐺𝑜
𝐺+𝐺𝑜
+ 10 1 −
𝐵−𝐵𝑜
𝐵+𝐵𝑜
+ 10
• Modified Global Environmental Monitoring Index
• 𝐺𝐸𝑀𝐼 = 𝑒𝑡𝑎 1 − 0.25 ∗ 𝑒𝑡𝑎 −
𝑝𝑅𝑒𝑑−0.125
1−𝑝𝑅𝑒𝑑
• where 𝑒𝑡𝑎 =
2∗ 𝑝𝐺𝑟𝑒𝑒𝑛2−𝑝𝑅𝑒𝑑2 +1.5 ∗𝑝𝐺𝑟𝑒𝑒𝑛+0.5 ∗𝑝𝑅𝑒𝑑
𝑝𝐺𝑟𝑒𝑒𝑛+𝑝𝑅𝑒𝑑+0.5
• Green Leaf Index
• GLI =
2 ∗ρ𝐺𝑟𝑒𝑒𝑛−𝑝𝑅𝑒𝑑 −ρ𝐵𝑙𝑢𝑒
2 ∗ρ𝐺𝑟𝑒𝑒𝑛+𝑝𝑅𝑒𝑑+ρ𝐵𝑙𝑢𝑒
• VariGreen
• 𝑉𝐴𝑅𝐼 =
𝑝𝐺𝑟𝑒𝑒𝑛−𝑝𝑅𝑒𝑑
𝑝𝐺𝑟𝑒𝑒𝑛+𝑝𝑅𝑒𝑑−𝑝𝐵𝑙𝑢𝑒
• Green-Red Vegetation Index
• 𝐺𝑅𝑉𝐼 =
ρ𝐺𝑟𝑒𝑒𝑛 −𝑝𝑅𝑒𝑑
ρ𝐺𝑟𝑒𝑒𝑛+ρ𝑅𝑒𝑑
• We also calculated a soil-adjusted index (modified version of SAVI) which
appeared to perform well. Due to time limitations, we do not present that here.

10. North Pond
2018
2019

11. Minei, A. 2019

12. Vegetation Indices
VVI – Greater Detail GLI – More Washed Out
Minei, A. 2019

13. Minei, A. 2019

14. HoustonMinei, A. 2019

15. Minei, A. 2019

16. Minei, A. 2019

17. Biomass Estimates
• Biomass was collected in 10 cm wide x 100 cm long strips from two
locations in each plot.
• This resulted in 60 sub-plots. During the first collection, the sub-plots
were combined (DOH!), which reduced the total sample size to 30.
Moving forward these will be kept separate.
• We extracted the min, max, mean, and standard deviation of the
different vegetation indices as they related to the biomass sub-plots.
• Models were developed using polynomial regression, random
forests/CART, and neural networks.
• Today I will present results from Decision Trees (CART), Random Forests,
and Neural Networks.

18. Biomass Estimates

19. Biomass Estimates
1. Collected
2. Sorted
3. Identified
4. Weighed

20. Biomass Estimates
• Initial Models were Developed in R using both command line and
Rattle.
• Methods were implemented into an R-Package that will be distributed
via the R CRAN in the future (later talk at conference).
• Models were implemented spatially in both ArcGIS and R (due to R-
package development).
• Results were cross-validated. This year we have training AND validation
biomass plots that allow us to assess the models in areas outside of the
Nutrient Network proper.
• While we present the estimates as “High, Medium, Low”, actual
estimates were in grams, and then converted to grams per meter
squared.

21. Biomass Estimates

22. Biomass-Decision Tree

23. Biomass-Decision Tree (VVI)
Generally predicted total biomass well based
off the modified VVI.

24. Biomass-Decision Tree (VVI)
Generally predicted grass biomass well based
off the modified VVI.

25. Biomass-Decision Tree (VVI)
Prediction of forbes biomass was not as robust
as grass and total biomass.

26. Biomass-Decision Tree (GLI)
GLI performed better than GEMI, but not as
well as the VVI.

27. Biomass-Decision Tree (GLI)
GLI performed better than GEMI, but not as
well as the VVI. Total Biomass

28. Biomass-Decision Tree (GLI)
Similar to forbes estimates for the VVI,
estimates from the GLI were also lower

29. Biomass-Decision Tree (VariGreen)
VariGreen also performed better than GEMI,
But not as well as the VVI and not as well as
the GLI. As such – No Map For You!
Come Back 1 Month!

30. Biomass-Decision Tree (GEMI)
GEMI did not perform well in terms of
biomass estimates. As a result, the map is a
HOT MESS and not presented!

31. Biomass-Random Forests

32. Biomass-Random Forests(VVI)
Random Forests provided a better solution to
Biomass estimates than a traditional
regression tree.

33. Biomass-Random Forests(VVI)
Random Forests provided a better solution to
Biomass estimates of total grass than a
traditional regression tree.

34. Biomass-Random Forests(VVI)
Random Forests provided a better solution to
Biomass estimates of total forbes than a
traditional regression tree.

35. Biomass-Random Forests(GLI)
GLI did not Perform as well as the VVI, however, the
results are still acceptable. GEMI and Varigreen did
not perform well, and are not presented here. Total
Biomass.

36. Biomass-Random Forests(GLI)
Forbes Biomass.

37. Biomass - Neural Networks

38. Biomass - Neural Networks (VVI)
Neural Networks provided a good solution to
Biomass estimates using the VVI. Whereas
the relationship was not as strong as the
Random Forests, the fit was adequate.

39. Biomass - Neural Networks (GLI)
Neural Networks also provided a good
solution to Biomass estimates using the GLI.
Whereas the relationship was not as strong as
the Random Forests, the fit was adequate,
and performs on par with the VVI.
Varigreen and GEMI performed poorly with
the resulting pseudo R2 values ~ R = 0.23

40. Conclusions
• The Modified VVI worked well, as did the GLI. Overall, the VVI was a
better predictor of total above ground biomass, however GLI also
performed at an acceptable rate.
• I am really on the fence with object oriented versus traditional ISO
classification. I have had great luck with object-oriented classification
using imagery such as NAIP, or even resolution down to about 6”. In
some respects object oriented worked better, in other cases it did not.
I am not aware of this being done at sub ¼” pixel resolution.
• Random Forests, CART, and Neural Networks work well in predicting
total above ground biomass based on this study.
• The project was a lot of fun!

41. Moving Forward
• Biomass collection from each sub-plot will not be collapsed into a single
sample. Samples for 2019 were processed a couple weeks ago, models are
in progress – promising!
• There is a continued interest in determining if we can estimate biomass for
certain plant types (equisitem, forbes, big bluestem, etc). To that end,
biomass has been separated based on type. Currently grass estimates are
robust.
• While we have been able to get reasonable results in classifying down to
species (not presented here), additional work is warranted.
• Focus efforts more heavily on issues pertaining to non-native grass
encroachment.
• We recently acquired a Sentera NDVI sensor mounted on a DJI Mavic. We
are anxious to see how this performs in comparison.
Questions?