# Training Data We used two different training data sources to model forest structure. We predicted canopy height and % canopy cover using the [Global Ecosystem Dynamics Investigation](https://gedi.umd.edu/) (GEDI) space-based [LIDAR](https://oceanservice.noaa.gov/facts/lidar.html#:~:text=Lidar%20%E2%80%94%20Light%20Detection%20and%20Ranging,Lighthouse%2C%20Dry%20Tortugas%2C%20Florida.) data. Specifically, we used the [Level 2A Geolocated Elevation and Height Metrics](https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GEDI02_A_002_MONTHLY) product available in the Google Earth Engine data catalog. This dataset contains monthly summaries of canopy height and % canopy cover for paths flown by the mission globally from 2019 to present. We selected the 95th percentile of both metrics in the month of July (corresponding to the month of our Landsat covariates) as our response variables. From across our modeling boundary, we sampled 5000 observations of each variable evenly across the years 2019-2022. In addition, we also derived a training dataset for tree diameter following a 'transfer learning' approach, with the goal to 'learn' to predict tree diameter from the [LEMMA GNN](https://lemma.forestry.oregonstate.edu/data/structure-maps) QMD\_DOM model, which predicts the quadratic mean diameter of all dominant and codominant trees. We extracted 5000 observations of the QMD\_DOM variable distributed across our modeling region, sampled evening across the years 1990, 2000, 2005, 2010, and 2015. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://terradapt.gitbook.io/terradapt-cascadia-documentation/methods-and-validation/forest-structure/training-data.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.