This document summarizes Asami Minei's undergraduate research project on classifying and calculating vegetation indices from drone imagery at the Regional Science Center in Minnesota. The research aims to monitor vegetation changes over time for a prairie restoration project and a global study on nutrient deposition effects. High-resolution drone images were taken every two weeks and processed to generate vegetation indices and unsupervised classifications of land cover types like healthy plants and bare ground. The analyses will help monitor total biomass and plant diversity/composition for the projects over multiple years.

1. Asami Minei
Minnesota State University Moorhead
Department of Anthropology and Earth Sciences
Advisor: David Kramar, Ph.D.
Classification and
Calculation of Vegetation
Indices from High
Resolution UAS Imagery

2. Regional Science
Center
(RSC)
▪ About 400 acres of tall grass
prairie
▪ Located 16 miles east of
Moorhead, MN
▪ 25 rare plants and animal
species

3. Research and Education at RSC
■ Prairie restoration project
– the Minnesota Environment and
Natural Resources Trust Fund (ENRTF)
■ Nutrient Network Project
– Collaboration with biology department
■ Conservation programs for over 7000
students every year
Interest in monitoring vegetation
changes in this area

4. Nutrient
Network
▪ Global, collaborative
project
▪ ~150 Sites
▪ Every continent
▪ Study effects of nutrient
deposition

5. Nutrient Network: Research Questions
■ How general is our current understanding of
productivity-diversity relationships?
■ To what extent are plant production and
diversity co-limited by nutrients in
grasslands?
■ Under what conditions does fertilization
control plant biomass, diversity, and
composition?

6. Project Purpose
1. Record Vegetation Change
2. Construct the Models
– Non-NIR Vegetation Indices
– Classification
3. Nutrient Network
– Total Biomass
– Plant Diversity/
Composition
Fly Drone
Georeferencing
Vegetation
Indices
Total Biomass
Classification
Plant Diversity

7. Process and the Focal Models
Fly Drone Georeferencing
Non-NIR
Vegetation Indices
Total Biomass
Classification Plant Diversity

8. Data
Collection
▪ DJI Phantom 4 Advanced
▪ Every two weeks from May
to Sept.
▪ At 70’ with 80% overlap
and 80% sidelap
Fly Drone Georeferencing
Vegetation
Indices
Classification

9. North Pond and Houston at RSC Fly Drone Georeferencing
Vegetation
Indices
Classification

10. Fly Drone Georeferencing
Vegetation
Indices
Classification

11. North Pond
▪ Post – georeferenced
▪ Cell size: Less than 5mm x
5mm
▪ Detailed enough to identify a
gopher mound or individual
plants
Fly Drone Georeferencing
Vegetation
Indices
Classification

12. NON-NIR VEGETATION
INDICES
Fly Drone Georeferencing
Vegetation
Indices
Classification

13. Vegetation
Indices
▪ Enhancing green vegetation
using mathematical equations
and transformations
▪ Indicate the AMOUNT of
vegetation
▪ Distinguish between soil and
vegetation
▪ This study only uses RGB
𝑁𝐷𝑉𝐼 =
𝑁𝐼𝑅 − 𝑅𝐸𝐷
𝑁𝐼𝑅 + 𝑅𝐸𝐷
𝐺𝑅𝑉𝐼 =
ρ𝐺𝑟𝑒𝑒𝑛 − 𝑝𝑅𝑒𝑑
ρ𝐺𝑟𝑒𝑒𝑛 + ρ𝑅𝑒𝑑
Fly Drone Georeferencing
Vegetation
Indices
Classification

14. Vegetation Indices
■ Put a imagery of the site here
Fly Drone Georeferencing
Vegetation
Indices
Classification
Vegetation Indices Equations
Modified Adjusted transformed
soil-adjusted VI
𝐴𝑇𝑆𝐴𝑉𝐼 = a ∗
𝐺REEN − 𝑎 ∗ 𝑅𝐸𝐷 − 𝑏
𝑎 ∗ 𝐺REEN + 𝑅𝐸𝐷 − 𝑎 ∗ 𝑏 + 𝑥(1 + 𝑎2)
x= 0.08, a = 1.22,b = 0.03
Modified Visible Vegetation
Index
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

15. Fly Drone Georeferencing
Vegetation
Indices
Classification

16. Fly Drone Georeferencing
Vegetation
Indices
Classification

17. Vegetation Indices: Outputs 2018
Fly Drone Georeferencing
Vegetation
Indices
Classification

18. Fly Drone Georeferencing
Vegetation
Indices
Classification

19. Fly Drone Georeferencing
Vegetation
Indices
Classification

20. Fly Drone Georeferencing
Vegetation
Indices
Classification

21. Comparison of VVI and GLI
VVI GLI

22. Fly Drone Georeferencing
Vegetation
Indices
Classification

23. UN/SUPERVISED
CLASSIFICATION
Fly Drone Georeferencing
Vegetation
Indices
Classification

24. Classification
▪ Process of sorting entities into
groups
▪ Groups
▪ Healthy Plant
▪ Unhealthy Plant
▪ Bare ground
Unhealthy Bare Ground Healthy
Fly Drone Georeferencing
Vegetation
Indices
Classification

25. Two Classifications
Classification Type Classification Tools
Unsupervised Classification
(Software Analysis)
Iterative Self-Organizing (ISO) cluster unsupervised
classification
Object-Oriented Supervised
Classifications (Training Samples)
Maximum likelihood classification
Fly Drone Georeferencing
Vegetation
Indices
Classification
True Color Mosaic/ Segment Mean Shift Before Reclassification

26. ISO North Pond, 2018
Fly Drone Georeferencing
Vegetation
Indices
Classification

27. ISO Houston, 2018
Fly Drone Georeferencing
Vegetation
Indices
Classification

28. ISO North Pond, 2019
Fly Drone Georeferencing
Vegetation
Indices
Classification

29. ■ f
Fly Drone Georeferencing
Vegetation
Indices
Classification

30. ISO Houston, 2019
■ f
Fly Drone Georeferencing
Vegetation
Indices
Classification

31. True Color
Segment Mean Shift
True Color
Mosaic/ Segment Mean Shift
Less Heterogeneity More Heterogeneity

32. Future Project
■ Classify down to
Individuals
– Goldenrods
– Gopher mounts
– Possibly Anything!

33. Conclusion/ Discussion
■ Vegetation Indices
– Non-Nir Vegetation Indicies
– Availability of Drone work
– Total Biomass Estimation
■ Classification
– Unsupervised > Supervised
– Plant Diversity/ Composition
■ Regional Science Center and NutNet
– Continue Collecting Data for Monitoring

34. Acknowledgements
■ Department of Anthropology and Earth Sciences
– Dr. David Kramar
– Dr. Karl Lenard
– Yoko Kosugi
■ Department of Biological Sciences
– Dr. Alison Wallace
– Andie Wood
– Patrice Delaney
■ Casey Coombs
■ Nafisa Mahabub

35. Thank You for
Listening
Any
Questions?
Email: mineias@mnstate.edu