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CANOPY INSTALL JUPYTER NOTEBOOK HOW TO
CANOPY INSTALL JUPYTER NOTEBOOK ARCHIVE
CANOPY INSTALL JUPYTER NOTEBOOK DOWNLOAD

CANOPY INSTALL JUPYTER NOTEBOOK DOWNLOAD

(1.33 GB) A NASA Earthdata Login account is required to download the data used in this tutorial. Use the links below to download the files directly from the LP DAAC Data Pool:

This tutorial uses the GEDI L2B observation from J(orbit 02932). If you prefer to not install Conda, the same setup and dependencies can be achieved by using another package manager such as pip. If you do not have Jupyter Notebook installed, you may need to run:Ĭonda install jupyter notebook Having trouble getting a compatible Python environment set up? Contact LP DAAC User Services.

Using your preferred command line interface (command prompt, terminal, cmder, etc.) type the following to successfully create a compatible python environment:Ĭonda create -n geditutorial -c conda-forge -yes python=3.7 h5py shapely geopandas pandas geoviews holoviews.

Conda was used to create the python environment. This Python Jupyter Notebook tutorial has been tested using Python version 3.7. Once you have Conda installed, Follow the instructions below to successfully setup a Python environment on Linux, MacOS, or Windows. It is recommended to use Conda, an environment manager to set up a compatible Python environment. These metrics are based on the directional gap probability profile derived from the L1B waveform and include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD) and Foliage Height Diversity (FHD).ġ.2 Set Up the Working Environment and Retrieve FilesĢ.1 Open a GEDI HDF5 File and Read File Metadataģ.1 Subset by Layer and Create a Geodataframeħ.1 Import, Subset, and Quality Filter all Beamsħ.3 Visualize All Beams: Canopy Height, Elevation, and PAIīefore Starting this Tutorial: Setup and Dependencies

The purpose of the L2B dataset is to extract biophysical metrics from each GEDI waveform.

GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level - GEDI02_B.001.

CANOPY INSTALL JUPYTER NOTEBOOK HOW TO

This tutorial will show how to use Python to open GEDI L2B files, visualize the full orbit of GEDI points (shots), subset to a region of interest, visualize GEDI canopy height and vertical profile metrics, and export subsets of GEDI science dataset (SDS) layers as GeoJSON files that can be loaded into GIS and/or Remote Sensing software programs. The goal of the project is to use GEDI L2B data to observe tree canopy height, cover, and profile over Redwood National Park in northern California. This tutorial was developed using an example use case for a project being completed by the National Park Service.

The L1B and L2 GEDI products are archived and distributed in the HDF-EOS5 file format.

CANOPY INSTALL JUPYTER NOTEBOOK ARCHIVE

The Land Processes Distributed Active Archive Center (LP DAAC) distributes the GEDI Level 1 and Level 2 products. GEDI is attached to the International Space Station and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. The Global Ecosystem Dynamics Investigation ( GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth's carbon cycle and biodiversity. Getting Started with GEDI L2B Data in Python This tutorial demonstrates how to work with the Canopy Cover and Vertical Profile Metrics ( GEDI02_B.001) data product. Users are advised to use the most recent version of GEDI. ⚠ Attention: The Global Ecosystem Dynamics Investigation (GEDI) Version 1 data products are no longer available for distribution from NASA’s Earthdata Search or LP DAAC’s Data Pool.