{ "cells": [ { "cell_type": "markdown", "id": "078dd796", "metadata": {}, "source": [ "# GEDI_L2B Search and Visualize" ] }, { "cell_type": "markdown", "id": "78d4037b", "metadata": {}, "source": [ "Authors: Samuel Ayers (UAH), Sumant Jha (MSFC/USRA), Anish Bhusal (UAH), Alex Mandel (Development Seed), Aimee Barciauskas (Development Seed)\n", "\n", "Date: December 19, 2022\n", "\n", "Description: In this tutorial, we will use the integrated Earthdata search function in MAAP Algorithm Development Environment (ADE) to search for GEDI L2B data for an area of interest. We will then download some of this data and read its attributes in preparation for some analysis. We will perform a spatial subset on the data to reduce data volumes, and then make some basic plots of our data." ] }, { "cell_type": "markdown", "id": "4e049ed3", "metadata": {}, "source": [ "## Run This Notebook\n", "To access and run this tutorial within MAAP's Algorithm Development Environment (ADE), please refer to the [\"Getting started with the MAAP\"](https://docs.maap-project.org/en/latest/getting_started/getting_started.html) section of our documentation.\n", "\n", "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors." ] }, { "cell_type": "markdown", "id": "f92161a0", "metadata": {}, "source": [ "## About the Data\n", "\n", "GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V002\n", "\n", "This dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 2 (L2) data, which has the purpose of extracting biophysical metrics and consists of Canopy Cover and Vertical Profile Metrics. These metrics are derived from the L1B waveform, and include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). GEDI is attached to the International Space Station (ISS) 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; specifically, GEDI L2B data has a spatial resolution of 25m. (Source: [GEDI L2B CMR Search](https://cmr.earthdata.nasa.gov/search/concepts/C1908350066-LPDAAC_ECS.html))" ] }, { "cell_type": "markdown", "id": "4352c920", "metadata": {}, "source": [ "## Additional Resources\n", "- [GEDI_L2B Version 2 Dataset Landing Page](https://lpdaac.usgs.gov/products/gedi02_bv002/)\n", "- [GEDI Level 2 Version 2 User Guide](https://lpdaac.usgs.gov/documents/998/GEDI02_UserGuide_V21.pdf)\n", "- [The GEDI Website](https://gedi.umd.edu/)" ] }, { "cell_type": "markdown", "id": "cd7bc7b1", "metadata": {}, "source": [ "## Importing and Installing Packages\n", "\n", "We will begin by installing any packages we need and importing the packages that we will use." ] }, { "cell_type": "markdown", "id": "2f1ebae0", "metadata": {}, "source": [ "**Prerequisites** \n", "\n", "* geopandas\n", "* folium" ] }, { "cell_type": "code", "execution_count": null, "id": "e008ff2c", "metadata": {}, "outputs": [], "source": [ "# Uncomment the following lines to install these packages if you haven't already.\n", "# !pip install geopandas\n", "# !pip install folium" ] }, { "cell_type": "code", "execution_count": 1, "id": "d7388fd7", "metadata": {}, "outputs": [], "source": [ "from maap.maap import MAAP\n", "maap = MAAP(maap_host='api.maap-project.org')\n", "import geopandas as gpd\n", "import folium\n", "import h5py\n", "import pandas\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import shapely\n", "import os" ] }, { "cell_type": "markdown", "id": "115748f1", "metadata": {}, "source": [ "## Search Data Using an AOI" ] }, { "cell_type": "markdown", "id": "564bbad0", "metadata": {}, "source": [ "We will search and download GEDI L2B data using the bounding box of a vector AOI. Firstly, an AOI over the Shenandoah National Park will be created and then we will plot its location on a map." ] }, { "cell_type": "code", "execution_count": 2, "id": "92e3f1c8", "metadata": {}, "outputs": [], "source": [ "# Using bounding coordinates to create a polygon around Shenandoah National Park\n", "lon_coords = [-78.32129105072025, -78.04618813890727, -78.72985973163064, -79.0158578082679, -78.32129105072025]\n", "lat_coords = [38.88703610703791, 38.74909216350823, 37.88789051477522, 38.03177640342157, 38.88703610703791]\n", "\n", "polygon_geom = shapely.geometry.polygon.Polygon(zip(lon_coords, lat_coords))\n", "crs = 'epsg:4326'\n", "AOI = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[polygon_geom])" ] }, { "cell_type": "markdown", "id": "ac237d81", "metadata": {}, "source": [ "We can get the bounding box of the AOI so we can use it as a spatial limit on our data search. GeoPandas has a function for returning the spatial coordinates of a bounding box:" ] }, { "cell_type": "code", "execution_count": 3, "id": "851d3490", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "minx = -79.0158578082679\n", "miny = 37.88789051477522\n", "maxx = -78.04618813890727\n", "maxy = 38.88703610703791\n" ] } ], "source": [ "# Get the bounding box of the shp\n", "bbox = AOI.bounds\n", "# print the bounding box coords\n", "print('minx = ', bbox['minx'][0])\n", "print('miny = ', bbox['miny'][0])\n", "print('maxx = ', bbox['maxx'][0])\n", "print('maxy = ', bbox['maxy'][0])" ] }, { "cell_type": "markdown", "id": "a62b03b2", "metadata": {}, "source": [ "Let's look at our AOI on an interactive map using folium." ] }, { "cell_type": "code", "execution_count": 4, "id": "e5935b6e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = folium.Map(location=[38.5, -78], zoom_start=9, tiles='CartoDB positron')\n", "geo_j = folium.GeoJson(data=AOI, style_function=lambda x: {'fillColor': 'orange'})\n", "geo_j.add_to(m)\n", "m" ] }, { "cell_type": "markdown", "id": "1128aa69", "metadata": {}, "source": [ "## Search using the EarthData Search Integration" ] }, { "cell_type": "markdown", "id": "6fc03b2c", "metadata": {}, "source": [ "Search Data: To search GEDI data we can use the EarthData search integration in the MAAP ADE. Open the Earthdata search toolbar by clicking on the following:" ] }, { "cell_type": "markdown", "id": "e9ea32c0", "metadata": {}, "source": [ "![EarthData](../../_static/EarthdataSearch.png)" ] }, { "cell_type": "markdown", "id": "06089e6b", "metadata": {}, "source": [ "This will open up the EarthData search interface in a new tab. We can use the search bar to search GEDI L2B data. By entering \"L2B\" in the search bar, we can see the relevant GEDI data is filtered. Click on the dataset to get more details.\n", "\n", "![EarthDataInterface](../../_static/EDinterface.png)" ] }, { "cell_type": "markdown", "id": "d18b4e17", "metadata": {}, "source": [ "While we have been searching for data, the MAAP ADE has been keeping a track of our search parameters. This means that we can easily insert our search for GEDI data straight into our notebook." ] }, { "cell_type": "markdown", "id": "b07ad963", "metadata": {}, "source": [ "![PasteResults](../../_static/PasteResults.png)" ] }, { "cell_type": "markdown", "id": "35ad01ff", "metadata": {}, "source": [ "## Inspect and Filter through the Data" ] }, { "cell_type": "markdown", "id": "b82c11d7", "metadata": {}, "source": [ "This gives us our search parameters in our notebook using the GEDI dataset ID. The default limit for the number of returned results is 1000. Running this will produce 1000 results, but we can look at the first one by indexing the list of returned results. This is what the data entry looks like. You can see a large amount of metadata for the file along with the URL for where this specific file is stored." ] }, { "cell_type": "code", "execution_count": 6, "id": "b1bf0a03", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'concept-id': 'G2164246777-LPCLOUD',\n", " 'collection-concept-id': 'C2142776747-LPCLOUD',\n", " 'revision-id': '4',\n", " 'format': 'application/echo10+xml',\n", " 'Granule': {'GranuleUR': 'GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002',\n", " 'InsertTime': '2021-02-22T19:16:30.675Z',\n", " 'LastUpdate': '2021-09-16T13:36:07.965Z',\n", " 'Collection': {'ShortName': 'GEDI02_B', 'VersionId': '002'},\n", " 'DataGranule': {'SizeMBDataGranule': '16.5413',\n", " 'ProducerGranuleId': 'GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002',\n", " 'DayNightFlag': 'UNSPECIFIED',\n", " 'ProductionDateTime': '2021-02-21T14:45:08Z',\n", " 'AdditionalFile': {'Name': 'GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.png',\n", " 'SizeInBytes': '246767',\n", " 'Checksum': {'Value': 'b5b51ea64c588467ebdcfe41c877525fd231cb390db005cef0f1a56b4dc3ae95',\n", " 'Algorithm': 'SHA256'}}},\n", " 'PGEVersionClass': {'PGEVersion': '003'},\n", " 'Temporal': {'RangeDateTime': {'BeginningDateTime': '2019-04-18T00:20:12.000000Z',\n", " 'EndingDateTime': '2019-04-18T01:52:53.000000Z'}},\n", " 'Spatial': {'HorizontalSpatialDomain': {'Geometry': {'GPolygon': {'Boundary': {'Point': [{'PointLongitude': '80.2890335089',\n", " 'PointLatitude': '-4.6168623465'},\n", " {'PointLongitude': '82.4542052313', 'PointLatitude': '-1.5568021223'},\n", " {'PointLongitude': '83.6402279084', 'PointLatitude': '0.1277767863'},\n", " {'PointLongitude': '83.6814118126', 'PointLatitude': '0.0987901632'},\n", " {'PointLongitude': '82.4953794073', 'PointLatitude': '-1.5858244175'},\n", " {'PointLongitude': '80.3302671214',\n", " 'PointLatitude': '-4.6459691487'}]}}}}},\n", " 'OrbitCalculatedSpatialDomains': {'OrbitCalculatedSpatialDomain': {'StartOrbitNumber': '1959',\n", " 'StopOrbitNumber': '1959'}},\n", " 'MeasuredParameters': {'MeasuredParameter': {'ParameterName': 'Level2B'}},\n", " 'Platforms': {'Platform': {'ShortName': 'ISS',\n", " 'Instruments': {'Instrument': {'ShortName': 'GEDI',\n", " 'Sensors': {'Sensor': {'ShortName': 'LIDAR'}}}}}},\n", " 'Campaigns': {'Campaign': {'ShortName': 'GEDI'}},\n", " 'AdditionalAttributes': {'AdditionalAttribute': [{'Name': 'identifier_product_doi',\n", " 'Values': {'Value': '10.5067/GEDI/GEDI02_B.002'}},\n", " {'Name': 'identifier_product_doi_authority',\n", " 'Values': {'Value': 'https://doi.org'}},\n", " {'Name': 'Reference_Ground_Track', 'Values': {'Value': '3909'}},\n", " {'Name': 'SEGMENT_NUMBER', 'Values': {'Value': '01'}}]},\n", " 'InputGranules': {'InputGranule': ['GEDI01_B_2019108002012_O01959_01_T03909_02_005_01_V002.h5',\n", " 'GEDI02_A_2019108002012_O01959_01_T03909_02_003_01_V002_10algs.h5']},\n", " 'OnlineAccessURLs': {'OnlineAccessURL': [{'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/GEDI02_B.002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.h5',\n", " 'URLDescription': 'Download GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.h5'},\n", " {'URL': 's3://lp-prod-protected/GEDI02_B.002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.h5',\n", " 'URLDescription': 'This link provides direct download access via S3 to the granule'}]},\n", " 'OnlineResources': {'OnlineResource': [{'URL': 'https://doi.org/10.5067/GEDI/GEDI02_B.001 ',\n", " 'Description': 'The Landing Page for this file may be accessed directly from this link',\n", " 'Type': 'DOI',\n", " 'MimeType': 'text/html'},\n", " {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/GEDI02_B.002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.cmr.xml',\n", " 'Description': 'Download GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.cmr.xml',\n", " 'Type': 'EXTENDED METADATA'},\n", " {'URL': 's3://lp-prod-protected/GEDI02_B.002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.cmr.xml',\n", " 'Description': 'This link provides direct download access via S3 to the granule',\n", " 'Type': 'EXTENDED METADATA'},\n", " {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',\n", " 'Description': 'api endpoint to retrieve temporary credentials valid for same-region direct s3 access',\n", " 'Type': 'VIEW RELATED INFORMATION'}]},\n", " 'DataFormat': 'HDF5',\n", " 'AssociatedBrowseImageUrls': {'ProviderBrowseUrl': [{'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/GEDI02_B.002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.png',\n", " 'Description': 'Download GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.png'},\n", " {'URL': 's3://lp-prod-public/GEDI02_B.002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002/GEDI02_B_2019108002012_O01959_01_T03909_02_003_01_V002.png',\n", " 'Description': 'This link provides direct download access via S3 to the granule'}]}}}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = maap.searchGranule(cmr_host='cmr.earthdata.nasa.gov', concept_id=\"C2142776747-LPCLOUD\", limit=1000)[0]\n", "data" ] }, { "cell_type": "markdown", "id": "4da794c7", "metadata": {}, "source": [ "So far, this search function requests all of the GEDI data but we can add a spatial subset filter using our AOI from above to limit the results. Adding a spatial filter returns 259 GEDI L2B files that intersect with our AOI." ] }, { "cell_type": "code", "execution_count": 8, "id": "faf2e2cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "324\n" ] } ], "source": [ "data_aoi = maap.searchGranule(cmr_host='cmr.earthdata.nasa.gov', concept_id=\"C2142776747-LPCLOUD\", bounding_box=\"-79.0158578082679,37.88789051477522,-78.04618813890727,38.887036107037915\", limit=1000)\n", "print(len(data_aoi))" ] }, { "cell_type": "markdown", "id": "7ec8eca0", "metadata": {}, "source": [ "This is more data than we need, so let's look at the contents of a single GEDI file. Firstly we need to bring it from the server side (S3) to our local side. This can be done using the MAAP function getData. We'll create a new data directory, and then we will pull the 7th file in our search results." ] }, { "cell_type": "code", "execution_count": 9, "id": "c7c36429", "metadata": {}, "outputs": [], "source": [ "# set data directory\n", "dataDir = './data'\n", "\n", "# check if directory exists -> if directory doesn't exist, directory is created\n", "if not os.path.exists(dataDir):\n", " os.mkdir(dataDir)" ] }, { "cell_type": "code", "execution_count": 10, "id": "64299360", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "./data/GEDI02_B_2019145051352_O02537_03_T04809_02_003_01_V002.h5\n" ] } ], "source": [ "# pulling the 7th file into the new directory\n", "gedi_data = data_aoi[6].getData(dataDir)\n", "print(gedi_data)" ] }, { "cell_type": "markdown", "id": "136e1736", "metadata": {}, "source": [ "GEDI data has 8 beams. So, we will check that all beams are in our file and print a list of the available beams." ] }, { "cell_type": "code", "execution_count": 11, "id": "d8c4345b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['BEAM0000', 'BEAM0001', 'BEAM0010', 'BEAM0011', 'BEAM0101', 'BEAM0110', 'BEAM1000', 'BEAM1011']\n" ] } ], "source": [ "gedi_h5_file = h5py.File(gedi_data, 'r')\n", "gedi_keys = list(gedi_h5_file.keys())\n", "gedi_beams = ['BEAM0000', 'BEAM0001', 'BEAM0010', 'BEAM0011', 'BEAM0101', 'BEAM0110', 'BEAM1000', 'BEAM1011']\n", "gedi_beams_lst = []\n", "for gedi_beam_name in gedi_keys:\n", " if gedi_beam_name in gedi_beams:\n", " gedi_beams_lst.append(gedi_beam_name)\n", "print(gedi_beams_lst)" ] }, { "cell_type": "markdown", "id": "2f4210e5", "metadata": {}, "source": [ "For each beam, we need to get all of the data and metrics associated with it. For this, we have a function that will gather all of the metrics we want and put them into a geopandas dataframe:" ] }, { "cell_type": "code", "execution_count": 12, "id": "132efb8e", "metadata": {}, "outputs": [], "source": [ "def get_gedi_df(gedi_h5_file, gedi_beam_name):\n", " gedi_beam = gedi_h5_file[gedi_beam_name]\n", " \n", " # Get location info.\n", " gedi_beam_geoloc = gedi_beam['geolocation']\n", " # Get land cover data.\n", " gedi_beam_landcover = gedi_beam['land_cover_data']\n", "\n", " gedi_beam_df = pandas.DataFrame(\n", " {'elevation_bin0' : gedi_beam_geoloc['elevation_bin0'],\n", " 'elevation_lastbin' : gedi_beam_geoloc['elevation_lastbin'],\n", " 'height_bin0' : gedi_beam_geoloc['height_bin0'],\n", " 'height_lastbin' : gedi_beam_geoloc['height_lastbin'],\n", " 'shot_number' : gedi_beam_geoloc['shot_number'],\n", " 'solar_azimuth' : gedi_beam_geoloc['solar_azimuth'],\n", " 'solar_elevation' : gedi_beam_geoloc['solar_elevation'],\n", " 'latitude_bin0' : gedi_beam_geoloc['latitude_bin0'],\n", " 'latitude_lastbin' : gedi_beam_geoloc['latitude_lastbin'],\n", " 'longitude_bin0' : gedi_beam_geoloc['longitude_bin0'],\n", " 'longitude_lastbin' : gedi_beam_geoloc['longitude_lastbin'],\n", " 'degrade_flag' : gedi_beam_geoloc['degrade_flag'],\n", " 'digital_elevation_model': gedi_beam_geoloc['digital_elevation_model'],\n", " 'landsat_treecover' : gedi_beam_landcover['landsat_treecover'],\n", " 'modis_nonvegetated' : gedi_beam_landcover['modis_nonvegetated'],\n", " 'modis_nonvegetated_sd' : gedi_beam_landcover['modis_nonvegetated_sd'],\n", " 'modis_treecover' : gedi_beam_landcover['modis_treecover'],\n", " 'modis_treecover_sd' : gedi_beam_landcover['modis_treecover_sd'],\n", " 'beam' : gedi_beam['beam'],\n", " 'cover' : gedi_beam['cover'],\n", " 'master_frac' : gedi_beam['master_frac'],\n", " 'master_int' : gedi_beam['master_int'],\n", " 'num_detectedmodes' : gedi_beam['num_detectedmodes'],\n", " 'omega' : gedi_beam['omega'],\n", " 'pai' : gedi_beam['pai'],\n", " 'pgap_theta' : gedi_beam['pgap_theta'],\n", " 'pgap_theta_error' : gedi_beam['pgap_theta_error'],\n", " 'rg' : gedi_beam['rg'],\n", " 'rh100' : gedi_beam['rh100'],\n", " 'rhog' : gedi_beam['rhog'],\n", " 'rhog_error' : gedi_beam['rhog_error'],\n", " 'rhov' : gedi_beam['rhov'],\n", " 'rhov_error' : gedi_beam['rhov_error'],\n", " 'rossg' : gedi_beam['rossg'],\n", " 'rv' : gedi_beam['rv'],\n", " 'sensitivity' : gedi_beam['sensitivity'],\n", " 'stale_return_flag' : gedi_beam['stale_return_flag'],\n", " 'surface_flag' : gedi_beam['surface_flag'],\n", " 'l2a_quality_flag' : gedi_beam['l2a_quality_flag'],\n", " 'l2b_quality_flag' : gedi_beam['l2b_quality_flag']})\n", "\n", " gedi_beam_gdf = gpd.GeoDataFrame(gedi_beam_df, crs='EPSG:4326',\n", " geometry=gpd.points_from_xy(gedi_beam_df.longitude_lastbin,\n", " gedi_beam_df.latitude_lastbin))\n", " return gedi_beam_gdf" ] }, { "cell_type": "markdown", "id": "939d027c", "metadata": {}, "source": [ "To access the data with this function, we can call the function for each beam id that we have:\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "0c9dbe10", "metadata": {}, "outputs": [], "source": [ "BEAM0000 = get_gedi_df(gedi_h5_file, 'BEAM0000')\n", "BEAM0001 = get_gedi_df(gedi_h5_file, 'BEAM0001')\n", "BEAM0010 = get_gedi_df(gedi_h5_file, 'BEAM0010')\n", "BEAM0011 = get_gedi_df(gedi_h5_file, 'BEAM0011')\n", "BEAM0101 = get_gedi_df(gedi_h5_file, 'BEAM0101')\n", "BEAM0110 = get_gedi_df(gedi_h5_file, 'BEAM0110')\n", "BEAM1000 = get_gedi_df(gedi_h5_file, 'BEAM1000')\n", "BEAM1011 = get_gedi_df(gedi_h5_file, 'BEAM1011')" ] }, { "cell_type": "markdown", "id": "eaa89406", "metadata": {}, "source": [ "Now we can look at the data in one of the dataframes (beams). We can see that there are 108583 GEDI shots (108583 entries in each column)." ] }, { "cell_type": "code", "execution_count": 14, "id": "1ce9b78c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " elevation_bin0 elevation_lastbin height_bin0 height_lastbin \\\n", "0 533.320205 532.870899 -9999.000000 -9999.000000 \n", "1 533.610735 533.161429 -9999.000000 -9999.000000 \n", "2 835.586133 790.954687 42.284134 -2.373195 \n", "3 822.361914 780.875651 38.280968 -3.229356 \n", "4 788.459662 760.602475 25.957445 -1.915897 \n", "\n", " shot_number solar_azimuth solar_elevation latitude_bin0 \\\n", "0 25370000300165164 -34.511044 -10.500773 51.821288 \n", "1 25370000300165165 -34.510292 -10.501078 51.821282 \n", "2 25370000300165166 -34.509689 -10.501341 51.821258 \n", "3 25370000300165167 -34.508926 -10.501648 51.821253 \n", "4 25370000300165168 -34.508163 -10.501955 51.821249 \n", "\n", " latitude_lastbin longitude_bin0 ... rhov rhov_error rossg \\\n", "0 51.821288 -127.096372 ... 0.6 -9999.0 0.5 \n", "1 51.821282 -127.095550 ... 0.6 -9999.0 0.5 \n", "2 51.821260 -127.094876 ... 0.6 -9999.0 0.5 \n", "3 51.821255 -127.094048 ... 0.6 -9999.0 0.5 \n", "4 51.821250 -127.093210 ... 0.6 -9999.0 0.5 \n", "\n", " rv sensitivity stale_return_flag surface_flag \\\n", "0 -9999.000000 -14.502214 1 1 \n", "1 -9999.000000 -8.689309 1 1 \n", "2 3615.732178 0.965246 0 1 \n", "3 4290.229980 0.969620 0 1 \n", "4 2557.771240 0.943713 0 1 \n", "\n", " l2a_quality_flag l2b_quality_flag geometry \n", "0 0 0 POINT (-127.09637 51.82129) \n", "1 0 0 POINT (-127.09555 51.82128) \n", "2 1 1 POINT (-127.09485 51.82126) \n", "3 1 1 POINT (-127.09403 51.82126) \n", "4 1 1 POINT (-127.0932 51.82125) \n", "\n", "[5 rows x 41 columns]\n", "number of rows = 108583\n" ] } ], "source": [ "print(BEAM0000.head())\n", "print('number of rows = ', len(BEAM0000))" ] }, { "cell_type": "markdown", "id": "3a5e3b14", "metadata": {}, "source": [ "## Create the Subset and Display the Data" ] }, { "cell_type": "markdown", "id": "a52e54be", "metadata": {}, "source": [ "Before displaying this data we can do a spatial subset to remove the data outside of our AOI by doing a spatial subset. We use the Geopandas clip function to clip out the GEDI data based on the extent of our AOI. This reduces the size of the dataframe from 108583 rows to 508." ] }, { "cell_type": "code", "execution_count": 15, "id": "1bfb1bef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "508" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BEAM0000_sub = gpd.clip(BEAM0000, AOI)\n", "len(BEAM0000_sub)" ] }, { "cell_type": "markdown", "id": "06552a1b", "metadata": {}, "source": [ "We can display this subset of data over our AOI extent." ] }, { "cell_type": "code", "execution_count": 16, "id": "5450b5d4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = folium.Map(location=[38.5, -78], zoom_start=9, tiles='CartoDB positron')\n", "\n", "geo_j = folium.GeoJson(data=AOI, style_function=lambda x: {'fillColor': 'orange'})\n", "geo_j.add_to(m)\n", "\n", "geo_g = folium.GeoJson(data=BEAM0000_sub,marker = folium.CircleMarker(radius = 1, # Radius in metres\n", " weight = 0, #outline weight\n", " fill_color = '#FF0000', \n", " fill_opacity = 1))\n", "geo_g.add_to(m)\n", "m" ] }, { "cell_type": "markdown", "id": "977cf3e0", "metadata": {}, "source": [ "Now we have this subset of data, we can look at some of the GEDI metrics over our area of interest. The 'rh100' metric should give us the top of the canopy heights. What does this look like over Shenandoah National Park? We will look at the ground height and the 'rh100' above ground. The DEM metric is in meters (m) and the 'rh100' metric is in centimeters (cm) above the ground height. Therefore we must add the 'rh100' value to the ground height to get the total height of the tree for display purposes. The 'rh100' metric is also converted to meters (m) to normalize the units" ] }, { "cell_type": "code", "execution_count": 17, "id": "1f046135", "metadata": {}, "outputs": [], "source": [ "latitude = BEAM0000_sub['latitude_lastbin']\n", "rh100 = BEAM0000_sub['rh100']\n", "ground = BEAM0000_sub['digital_elevation_model']\n", "\n", "TreeHeight = ground + rh100/100" ] }, { "cell_type": "markdown", "id": "fd5d1766", "metadata": {}, "source": [ "Finally, we can make a plot of the ground surface and the canopy heights above the ground surface. We can see the forest canopies in green above the topographically complex ground in brown." ] }, { "cell_type": "code", "execution_count": 18, "id": "5c948500", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20, 5))\n", "plt.scatter(latitude, TreeHeight, c='green')\n", "plt.plot(latitude, ground, c='brown')" ] }, { "cell_type": "markdown", "id": "17a67ae2", "metadata": {}, "source": [ "Now you have this basic structure you can investigate some of the other metrics and GEDI beams to understand more about the data." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }