Schedule

Instructors: Dr. Sorin C. Popescu, Dr. Justinn J. Jones A current and accurate Canopy Height Model (CHM) is the basic building block of a forest assessment. It is not logistically feasible to measure every tree by hand in the field, so we rely on CHMs derived from various remote sensing technologies. In this workshop, we will explore the ICESat-2 spaceborne lidar platform and how it can be used in conjunction with ancillary datasets such as Landsat 8/9 imagery and topographic variables to generate a custom CHM. We will use a Google Colab notebook (coded in Python) to access Open-Source datasets from Google Earth Engine and use Machine Learning regression to generate and evaluate a custom model. We will also explore the SlideRule web client, a graphic user interface website, to access spaceborne lidar data for a specific area of interest with no coding required. The overall goal of this workshop is to introduce students to spaceborne lidar data for vegetation assessment. The specific objectives are to understand and utilize powerful tools such as 1) ICESat-2 data products, 2) Google Earth Engine platform, 3) Google Colab for Open-Source Data Science, and 4) Machine Learning algorithms for model generation and evaluation. The target audience for this workshop is anyone interested in Forest Ecology applications of Open-Source Data Science. More details here.