# Lidar Remote Sensing Uncertainty - Compare Ground to Lidar Measurements of Tree Height in Python

## 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.

or using the earthpy package:

et.data.get_data("spatial-vector-lidar")

TimeTopicSpeaker
1:00 - 1:30Questions / Review
1:30 - 2:30Coding: Use lidar to characterize vegetation / uncertainty
2:30 - 2:40BREAK
2:40 - 3:50Coding: Use lidar to characterize vegetation / uncertainty

## 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