Welcome to Week 5!
Welcome to week 5 of Earth Analytics! This week, you will explore the concept of uncertainty surrounding lidar raster data (and remote sensing data in general). You will use the same data that you downloaded last week for class. You will also use pipes again this week to work with tabular data.
For your homework you’ll also need to download the data below.
or using the
|1:00 - 1:30||Questions / Review|
|1:30 - 2:30||Coding: Use lidar to characterize vegetation / uncertainty|
|2:30 - 2:40||BREAK|
|2:40 - 3:50||Coding: Use lidar to characterize vegetation / uncertainty|
- Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested Riparian Buffers During Leaf-Off and Leaf-On Conditions
- The characterization and measurement of land cover change through remote sensing: problems in operational applications?
- Learn more about the various uncertainty terms.
Example Homework Plots
The plots below are examples of what your plot could look like. Feel free to customize or modify plot settings as you see fit!
Calculated Regression Fit
The above plots show the regression fit as calculated by the
seaborn python package. Use
stats.linregression() to calculate the slope and intercept of the regresion fit for each of the plots above.
Print the outputs below.
SJER - Mean Height Comparison slope: print-slope-value-here intercept: print-intercept-value-here SJER - Max Height Comparison slope: print-slope-value-here intercept: print-intercept-value-here