1. Author- Jeffrey Smyczek; Faculty Mentor- Dr. Eric Compas
Mapping the prairie: correlations between biomass and vegetation indices using small unmanned aerial systems
Author- Jeffrey Smyczek; Faculty Mentor- Dr. Eric Compas
University of Wisconsin – Whitewater, Department of Geography and Geology
Introduction
Small unmanned aerial systems (sUAS) have opened new possibilities for studying environmental systems. With sUAS, we have derived vegetation indices from high-quality multispectral imagery obtained at low altitude, enabling vegetation health analysis at a finer scale in comparison to satellite imagery. The main purpose of the project was developing new techniques to map vegetation health, particularly in a heterogeneous landscape, throughout a growing season on a small scale using sUAS. The intent of this research was to measure biomass in a more efficient manner for practical purposes such as vegetation reestablishment after prescribed burns and habitat utilization by specific species.
Research question: Can multispectral drone imagery be used to accurately correlate between biomass and vegetation indices, such as normalized difference vegetation index (NDVI), in a variable natural setting?
Hypothesis: We theorised that biomass and NDVI would have a strong, positive correlation suitable for generalizing across a study area.
We used a new Tetracam RGB+3 multispectral camera (Figure 1) to attain aerial imagery. The multispectral camera’s four lenses include RGB, normalized difference vegetation index (NDVI) red, red edge, and NDVI near infrared (NIR). NDVI is useful in analysing whether the target has live green vegetation. The field procedures were conducted at Fair Meadows, a privately owned state natural area in Milton Wisconsin, containing highly diverse natural plant species.
Results and discussion
Figure 3: RGB (top), NDVI (middle), and NDVI change (bottom) portray our final results
Figure 1: 3DR Solo Quadcopter with the Tetracam RGB+3 attached beneath
Results and discussion
Acknowledgements and references
Does NDVI give a feasible option for modeling biomass?
From analysis, we cannot accept our hypothesis as an answer to measuring biomass from NDVI
Methodological errors may be the difference in success
Radiometric calibration- applying 2nd order polynomial
Too much biomass = saturated sensors
Biomass sampling is also left to human error
Sample collections may not always be consistent
High resolution imagery of about 2.5 cm
Biophysical processes could also skew data by introducing senescence with only labshere targets as a constant
Complex vegetation causes deep shadows
Unable to collect May samples due to technical camera difficulties
During this research, we have been able to fulfill our new techniques for data collection.
Successfully collected aerial imagery from June to October.
Collected 36 biomass samples for our designated study area throughout the growing season
Derived NDVI for each flight, June through October
Calculated based on near-infrared (NIR) and red bands in orthomosaics
Used image values within sample locations
Correlation between biomass and NDVI
Progression of NDVI was not easily recognized
Low r2 value (.14) does not correlate well
NDVI inconsistency between months inhibits direct correlation
Methods
The overarching goal during the study was to capture one set of data per month from May through October. To obtain imagery throughout the growing season, we needed to over come a plethora of unplanned methodological challenges.
Vignette correction to restore an images uneven brightness
Field-of-view (FOV) correction to optimize band alignment
Converting images to useable 10-bit TIFFs
Formatting images for use within Pix4D
Radiometric calibration for analysing across dates
Troubleshooting camera issues
Once in the field, we began our mapping within our proposed methodology:
Place 8 random biomass samples within the study area.
Capture calibration target images.
Plan and conduct flight that takes roughly 10 minutes for our study area.
Collect biomass samples (225 sq. cm) from each site and store in air tight bags.
Post process imagery in PixelWrench2, Pix4D, and ArcMap
Dry and weigh both live and dead biomass samples
I would like to acknowledge the University of Wisconsin-Whitewater Undergraduate Research Program, Dr. Rocio Duchesne, and Dr. Eric Compas for their help in respective parts of my research.
Laliberte, Andrea S., Mark A. Goforth, Caitriana M. Steele, and Albert Rango “Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments.” Remote Sensing 3 (11): 2529–51.
Vega, Francisco Agüera, Fernando Carvajal Ramírez, Mónica Pérez Saiz, and Francisco Orgaz Rosúa "Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop." Biosystems Engineering 132,
Figure 2: Intermediate step within Pix4D generation of the orthomosaic
Figure 4: Alive dried biomass is plotted with respect to average NDVI for sample locations across all dates