add challenge section

tutorials/Python/AOP/Lidar/uncertainty-validation/lidar_uncertainty/lidar_uncertainty.md

@@ -419,3 +419,23 @@ plt.xlabel('Height Difference(m)'); plt.ylabel('Frequency');
 In the open ground scenario we are able to see the error characteristics we expect, with a mean difference of only 0.011 m and a variation of 0.068 m. 
 
 This shows that the uncertainty we expect in the NEON LiDAR system (~0.15 m) is only valid in bare, open, hard-surfaces. We cannot expect the LiDAR to be as accurate when vegetation is present. Quantifying the top of the canopy is particularly difficult and can lead to uncertainty in excess of 1 m for any given pixel.  
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+<div id="ds-challenge" markdown="1">
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+**Challenge: Repeat this uncertainty analysis on another NEON site**
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+1. There are a number of other instances where AOP has flown repeat flights in short proximity (within a few days, to a few months apart). Try repeating this analysis for one of these sites, listed below:
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+- 2017 SERC
+- 2019 CHEQ
+- 2020 CPER
+- 2024 KONZ
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+2. Repeat this analysis for a site that was flown twice in the same year, but with different lidar sensors (payloads).
+- 2023 SOAP (Visit 6: Riegl Q780, Visit 7: Optech Galaxy Prime)
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+Tip: You may wish to read this <a href="https://www.neonscience.org/about/faq" target="_blank">FAQ: Have AOP sensors changed over the years? How do different sensors affect the data?</a> This discusses the differences between lidar sensors that NEON AOP operates, and some of the implications for the data products derived from the lidar sensor.
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+</div>