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.

For your homework you’ll also need to download the data below.

Download Spatial Lidar Teaching Data Subset 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 

1. Readings

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!

Plots of lidar min and max vs insitu min and max with a 1:1 line a regression fit for the NEON SJER field site.
Plots of lidar min and max vs insitu min and max with a 1:1 line a regression fit for the NEON SJER field site.
Plots of lidar min and max vs insitu min and max with a 1:1 line a regression fit for the NEON SOAP field site.
Plots of lidar min and max vs insitu min and max with a 1:1 line a regression fit for the NEON SOAP field site.

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

Updated: