NDGeospatialSummit2019 - Estimation of Total Above Ground Biomass from High-Resolution UAS Imagery
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
• Collect and Classify High-Resolution
Imagery from Two Sites using an
• Compare multiple Non-NIR
Vegetation Indices to Determine
which Produces the Best Results
• Determine Which Index Best
Estimates Above Ground Biomass
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.**
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
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
• Comprised of more than 40 grassland sites worldwide.
• Grassroots effort.
• We have two sites that we are currently monitoring.
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)
• 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
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
• 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 −
+ 10 1 −
+ 10 1 −
• Modified Global Environmental Monitoring Index
• 𝐺𝐸𝑀𝐼 = 𝑒𝑡𝑎 1 − 0.25 ∗ 𝑒𝑡𝑎 −
• where 𝑒𝑡𝑎 =
2∗ 𝑝𝐺𝑟𝑒𝑒𝑛2−𝑝𝑅𝑒𝑑2 +1.5 ∗𝑝𝐺𝑟𝑒𝑒𝑛+0.5 ∗𝑝𝑅𝑒𝑑
• Green Leaf Index
• GLI =
2 ∗ρ𝐺𝑟𝑒𝑒𝑛−𝑝𝑅𝑒𝑑 −ρ𝐵𝑙𝑢𝑒
• 𝑉𝐴𝑅𝐼 =
• Green-Red Vegetation Index
• 𝐺𝑅𝑉𝐼 =
• 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.
• 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.
• Initial Models were Developed in R using both command line and
• 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-
• 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
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.
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
• 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!
• 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
• 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
• We recently acquired a Sentera NDVI sensor mounted on a DJI Mavic. We
are anxious to see how this performs in comparison.