{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "<a href=\"https://githubtocolab.com/gee-community/geemap/blob/master/examples/notebooks/113_image_area.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>\n",
                "\n",
                "Uncomment the following line to install [geemap](https://geemap.org) if needed."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# !pip install geemap"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "import ee\n",
                "import geemap"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Add ESA Land Cover data."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "dataset = ee.ImageCollection(\"ESA/WorldCover/v100\").first()\n",
                "Map.addLayer(dataset, {'bands': ['Map']}, 'ESA Land Cover')\n",
                "Map.add_legend(builtin_legend='ESA_WorldCover')\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Calculate the area of each land cover type."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "df = geemap.image_area_by_group(\n",
                "    dataset, scale=1000, denominator=1e6, decimal_places=4, verbose=True\n",
                ")\n",
                "df"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Save the results to a CSV."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "df.to_csv('esa_area.csv')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Add NLCD land cover data."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map(center=[40, -100], zoom=4)\n",
                "Map.add_basemap('HYBRID')\n",
                "\n",
                "nlcd = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019')\n",
                "landcover = nlcd.select('landcover')\n",
                "\n",
                "Map.addLayer(landcover, {}, 'NLCD Land Cover 2019')\n",
                "Map.add_legend(\n",
                "    title=\"NLCD Land Cover Classification\", builtin_legend='NLCD', height='465px'\n",
                ")\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Calculate the area of each land cover type."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "df = geemap.image_area_by_group(\n",
                "    landcover, scale=1000, denominator=1e6, decimal_places=4, verbose=True\n",
                ")\n",
                "df"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Save the results to a CSV."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "df.to_csv('nlcd_area.csv')"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}