Team: 16
School: Eldorado High
Area of Science: Agriculture
Interim: Team ID: Team #16
Schools: Eldorado High School
Area of Science: Agriculture
Project Title: It’s Raining Seeds. Hallelujah!
Mentors: Patty Meyers (pmeyer2843@gmail.com)
Sponsor Teacher: Karen Glennon (kglennon25@gmail.com)
Team Members: Savannah Phelps (sav.phelps04@gmail.com)
Brendan Kuncel (bboyk2222@gmail.com)
Problem Definition:
Our project is addressing the issue of how best to replant after a forest fire. Several initiatives are already in place for replanting using drones to drop the seeds from the air much faster than a human could do themselves, but the drones those organizations use are mainly human operated. We would like to automate the process of navigating the drones, as well as optimizing where the seeds are dropped. Our project is not about the hardware of seed delivery, but in the flight path based on available GIS data. We will be specifically working with Ponderosa Pine seeds, the most common tree breed in the sprawling New Mexico forests prone to forest fires.
Problem Solution:
First, we will create a random terrain generator to work out the mechanics of created terrain. We are using the diamond-square method to create altitudes and randomly generating patches of soil, rock, or existing trees. Later on, our random terrains will be joined by terrains generated from state GIS data, providing real applications for our project. Since the GIS terrains we scan will probably not be burned areas, the random generator can help simulate such a situation.
Once the terrain is created and the model understands the type of raster cell in the terrain, it will consider where seeds are able to be planted. Our current plan is to have the drones divide the terrain into squares for each drone (the number of drones is dictated by the user), and find the fastest path between each possible seed delivery spot. We plan on having “walls†to prevent seeds from being dropped onto rocks or non fertile areas in a similar way to how robotic vacuums have virtual barriers to prevent them from going to certain areas.
Lastly, our drones will know when to drop the seeds so that they land right where they belong while they are in motion. This is assuming no or negligible wind, but if there is time we may incorporate weather conditions.
Expected Results:
We hope to have a customizable model of a New Mexico landscape incorporating altitude, terrain, and soil type that will allow us to find the optimal path a variable number of drones can take through the air to drop seeds and reforest most quickly and efficiently.
At the conclusion of the project, our model can be used for future reforestation, and, when compared to the success of hand-operated drones, our model should have more trees grow for fewer seeds planted. Our model will be variable in terms of number of drones and terrain area covered (we will only have a few terrain plots completely integrated into our code, but that is open for growth). It will display how many seeds were used, the time taken to deliver them, and possibly how successful the delivery was.
Progress to Date:
To date, we have gotten access to the UNM Resource Geographic Information System and contacted a member of their team with questions about data processing. We have made significant progress on our model code-wise.
For our program, we have gotten a terrain generation algorithm working using the diamond-square algorithm. This algorithm works by using the average heights of corners of a box, then the midpoints of sides procedurally in order to make realistic looking terrain. We use a matrix for this in order to visualize the information in an easily understandable way. If this was two dimensional, the y-coordinate would just be stored in the patch itself, but since there are several patches at the same x/z coordinate, we need to have a way outside of the paths themselves to store the data. We plan on using this for initial planning of the drone flight patterns to work purely computationally until we can use GIS data to model real life scenes and therefore have the need to be able to input information in a more realistic way.
Works Cited
Browse Rgis Data, rgis.unm.edu/rgis6/.
“ArcGIS Online Pricing for Teams & Individuals: Subscription Cost.†Esri, www.esri.com/en-us/arcgis/products/arcgis-online/pricing.
Casa, Raffaele, et al. “Geophysical and Hyperspectral Data Fusion Techniques for In-Field Estimation of Soil Properties.†Vadose Zone Journal, vol. 12, no. 4, 2013, doi:10.2136/vzj2012.0201.
Castaldi, F., et al. “Estimation of Soil Properties at the Field Scale from Satellite Data: a Comparison between Spatial and Nonâ€Spatial Techniques.†Wiley Online Library, John Wiley & Sons, Ltd (10.1111), 24 Nov. 2014, onlinelibrary.wiley.com/doi/abs/10.1111/ejss.12202.
“DEM Analysis – The Many Uses and Derivatives of a Digital Elevation Model.†NM RGIS, rgis.unm.edu/dem_analysis/.
“Digital Soil Mapping.†Developments in Soil Science Digital Soil Mapping - An Introductory Perspective, 2006, p. iii., doi:10.1016/s0166-2481(06)31060-4.
“Digital Soil Mapping.†2010, doi:10.1007/978-90-481-8863-5.
“FIRMS.†NASA, NASA, firms.modaps.eosdis.nasa.gov/map/#z:14;c:-101.3,32.5;d:2019-11-18..2019-11-25.
“Help.†What Is Raster Data?-Help | ArcGIS for Desktop, desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/what-is-raster-data.htm.
Krol, B.g.c.m. “Towards a Data Quality Management Framework for Digital Soil Mapping with Limited Data.†Digital Soil Mapping with Limited Data, pp. 137–149., doi:10.1007/978-1-4020-8592-5_11.
Miller, Bradley A., et al. “Impact of Multi-Scale Predictor Selection for Modeling Soil Properties.†Geoderma, vol. 239-240, 2015, pp. 97–106., doi:10.1016/j.geoderma.2014.09.018.
Silva, Sérgio Henrique Godinho, et al. “A Technique for Low Cost Soil Mapping and Validation Using Expert Knowledge on a Watershed in Minas Gerais, Brazil.†Soil Science Society of America Journal, vol. 78, no. 4, 2014, p. 1310., doi:10.2136/sssaj2013.09.0382.
Team Members:
Brendan Kuncel
Savannah Phelps
Sponsoring Teacher: Karen Glennon