docs/source/science/GEDI/GEDI_L3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37756a5c",
   "metadata": {},
   "source": [
    "# GEDI_L3 Search and Download"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5124a49",
   "metadata": {},
   "source": [
    "Authors: Anish Bhusal (UAH), Sumant Jha (MSFC/USRA), Jamison French (DevSeed), Aimee Barciauskas (DevSeed), Alex Mandel (DevSeed)\n",
    "\n",
    "Date: February 8, 2023\n",
    "\n",
    "Description: In this tutorial, we will search for GEDI L3 data in NASA's Earthdata Search, and then download a GeoTIFF file from the ORNL DAAC S3. We will then visualize the GeoTIFF file, which consists of canopy heights."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd51e936",
   "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": "b62a45d6",
   "metadata": {},
   "source": [
    "## About the Data\n",
    "\n",
    "GEDI L3 Gridded Land Surface Metrics, Version 2\n",
    "\n",
    "This dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation, and counts of laser footprints per 1-km x 1-km grid cells globally within -52 and 52 degrees latitude. 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. \n",
    "\n",
    "GEDI L3 data products are gridded by spatially interpolating Level 2 footprint estimates of canopy cover, canopy height, Leaf Area Index (LAI), vertical foliage profile and their uncertainties. Level 2 data contains terrain elevation, canopy height, RH metrics and Leaf Area Index (LAI). The raw waveforms are collected by GEDI system and processed to provide canopy height and profile metrics. These metrics provide easy to use and interpret information about the vertical distribution of canopy material. \n",
    "\n",
    "Source: [GEDI L3 Gridded Land Surface Metrics, Version 2 Data Set Landing Page](https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1952)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5089d922",
   "metadata": {},
   "source": [
    "![Canopy](../../_static/canopy_footprint_level2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053cc63d",
   "metadata": {},
   "source": [
    "Source: https://gedi.umd.edu/data/products/\n",
    "\n",
    "Figure: Sample of GEDI lidar waveform (left). The light brown area under the curve represents the return energy from the canopy, while the dark brown area signifies the return from the underlying typography. The black line is cumulative return energey, starting from the bottom of the ground return (normalized to 0) to the top of canopy (normalized to 1). The diagram on the right shows the distribution of trees that produced the waveform."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00822213",
   "metadata": {},
   "source": [
    "## Additional Resources\n",
    "- [GEDI L3 Gridded Land Surface Metrics, Version 2 User Guide](https://daac.ornl.gov/GEDI/guides/GEDI_L3_LandSurface_Metrics_V2.html)\n",
    "- [GEDI Overview Page, LPDAAC](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/gedi-overview/) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d435622a",
   "metadata": {},
   "source": [
    "## Importing and Installing Packages\n",
    "\n",
    "This tutorial assumes you've all packages installed. If you haven't already, uncomment the following lines to install these packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19680200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install geopandas\n",
    "# !pip install contextily\n",
    "# !pip install backoff\n",
    "# !pip install folium\n",
    "# !pip install geojsoncontour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc332b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from maap.maap import MAAP\n",
    "import pandas as pd\n",
    "import folium\n",
    "from rasterio.plot import show \n",
    "import rasterio\n",
    "import boto3\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b292534",
   "metadata": {},
   "source": [
    "After importing necessary packages, the next step is to initialize MAAP constructor using `api.maap-project.org` as `maap_host` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e84b82ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "maap = MAAP(maap_host=\"api.maap-project.org\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "666b5e2d",
   "metadata": {},
   "source": [
    "Now, the next step is to seach granules from the CMR. To generate following query, you can use EarthData search feature from MAAP ADE. Refer to this [tutorial](https://docs.maap-project.org/en/latest/science/GEDI/SearchGEDI.html) for more info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "964d18aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "results=maap.searchGranule(cmr_host='cmr.earthdata.nasa.gov',concept_id=\"C2153683336-ORNL_CLOUD\", limit=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f46ce72",
   "metadata": {},
   "source": [
    "The above query gives 1000 results by default. The number of necessary results can be changed using `limit` argument.\n",
    "We can view the `GranuleUR` from results using: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fbf53e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GEDI_L3_LandSurface_Metrics_V2.GEDI03_elev_lowestmode_stddev_2019108_2020287_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_counts_2019108_2020287_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_counts_2019108_2021104_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_rh100_mean_2019108_2020287_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_rh100_stddev_2019108_2020287_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_elev_lowestmode_stddev_2019108_2021104_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_rh100_stddev_2019108_2021104_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_elev_lowestmode_mean_2019108_2020287_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_elev_lowestmode_mean_2019108_2021104_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_rh100_mean_2019108_2021104_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_elev_lowestmode_stddev_2019108_2021216_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_elev_lowestmode_mean_2019108_2021216_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_rh100_stddev_2019108_2021216_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_rh100_mean_2019108_2021216_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_counts_2019108_2021216_002_02.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_elev_lowestmode_mean_2019108_2022019_002_03.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_counts_2019108_2022019_002_03.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_rh100_mean_2019108_2022019_002_03.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_elev_lowestmode_stddev_2019108_2022019_002_03.tif',\n",
       " 'GEDI_L3_LandSurface_Metrics_V2.GEDI03_rh100_stddev_2019108_2022019_002_03.tif']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[result['Granule']['GranuleUR'] for result in results]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2034db42",
   "metadata": {},
   "source": [
    "Before downloading a particular tif, let's catch the collection and file name for first item in results: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5923097c",
   "metadata": {},
   "outputs": [],
   "source": [
    "granule_ur=results[0]['Granule']['GranuleUR'].split(\".\")\n",
    "collection_name=granule_ur[0]\n",
    "file_name=granule_ur[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d30dbc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collection name: GEDI_L3_LandSurface_Metrics_V2 | file_name: GEDI03_elev_lowestmode_stddev_2019108_2020287_002_02\n"
     ]
    }
   ],
   "source": [
    "print(f\"collection name: {collection_name} | file_name: {file_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03631ad",
   "metadata": {},
   "source": [
    "## Download file from ORNL DAAC S3\n",
    "\n",
    "To download the file from the source, temporary s3 credentials are required for `maap` package. You can explicitly request `s3_cred_endpoint` for the credentials. The code below wraps that request to get credentials and download the file to your workspace. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d684eafe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_s3_creds(url):\n",
    "    return maap.aws.earthdata_s3_credentials(url)\n",
    "\n",
    "def get_s3_client(s3_cred_endpoint):\n",
    "    creds=get_s3_creds(s3_cred_endpoint)\n",
    "    boto3_session = boto3.Session(\n",
    "            aws_access_key_id=creds['accessKeyId'],\n",
    "            aws_secret_access_key=creds['secretAccessKey'],\n",
    "            aws_session_token=creds['sessionToken']\n",
    "    )\n",
    "    return boto3_session.client(\"s3\")\n",
    "\n",
    "def download_s3_file(s3, bucket, collection_name, file_name):\n",
    "    os.makedirs(\"/projects/gedi_l3\", exist_ok=True) # create directories, as necessary\n",
    "    download_path=f\"/projects/gedi_l3/{file_name}.tif\"\n",
    "    s3.download_file(bucket, f\"gedi/{collection_name}/data/{file_name}.tif\", download_path)\n",
    "    return download_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f08470da",
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_cred_endpoint= 'https://data.ornldaac.earthdata.nasa.gov/s3credentials' \n",
    "s3=get_s3_client(s3_cred_endpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c5bcbb50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/projects/gedi_l3/GEDI03_elev_lowestmode_stddev_2019108_2020287_002_02.tif'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket=\"ornl-cumulus-prod-protected\"\n",
    "download_path=download_s3_file(s3, bucket, collection_name, file_name)\n",
    "download_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "751a02f7",
   "metadata": {},
   "source": [
    "Now, we have the file in our local workspace. It's time to visualize it using `rasterio` package"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee503aa3",
   "metadata": {},
   "source": [
    "## [Optional] Visualization using Rasterio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e700a3a",
   "metadata": {},
   "source": [
    "The downloaded file is too big to read and visualize directly so we might need to scale it down and view it as a small thumbnail. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4e54a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_thumbnail(path):\n",
    "    src=rasterio.open(path)\n",
    "    oview = src.overviews(1)[0]\n",
    "    thumbnail = src.read(1, out_shape=(1, int(src.height // oview), int(src.width // oview)))\n",
    "    show(thumbnail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "075f4cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_thumbnail(download_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c33ab1e",
   "metadata": {},
   "source": [
    "## [Optional] Overlay Raster Layer on top of Folium Map\n",
    "\n",
    "To properly visualize the canopy heights, we need to display the TIF image on the map. The TIF image file may be too memory and compute-intensive for the kernel causing the process to exit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "287ddc34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tif=rasterio.open(download_path)\n",
    "# arr=tif.read()\n",
    "# bounds=tif.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4e92dd2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import numpy as np\n",
    "\n",
    "# x1,y1,x2,y2=bounds\n",
    "# bbox=[(bounds.bottom, bounds.left), (bounds.top, bounds.right)]\n",
    "# m=folium.Map(location=[14.59, 120.98], zoom_start=10)\n",
    "# img = folium.raster_layers.ImageOverlay(image=np.moveaxis(arr, 0, -1), bounds=bbox, opacity=0.9, interactive=True, cross_origin=False, zindex=1)\n",
    "# m"
   ]
  }
 ],
 "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": "3.7.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "5c7b89af1651d0b8571dde13640ecdccf7d5a6204171d6ab33e7c296e100e08a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}