added the drought monitoring project code (#1)

Co-authored-by: Akram Zaytar <[email protected]>

.ipynb_checkpoints/6_Project_Drought_Monitoring-checkpoint.ipynb

@@ -0,0 +1,340 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1c38785d",
+   "metadata": {},
+   "source": [
+    "# Drought Monitoring\n",
+    "\n",
+    "- This project shows how satellite-based data can be used for drought monitoring.\n",
+    "- Rainfall is the most important indicator in the determination of drought.\n",
+    "- A deviation in rainfall from its Long Period Average (LPA) of 30 years is considered as fairly credible indicator of drought.\n",
+    "- For any given region and time period, we will calculate the percentage deviation of rainfall from long-term average and determine which regions are likely experiencing drought."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8558b153",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Automatic pdb calling has been turned ON\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%pdb on\n",
+    "%config InlineBackend.figure_format ='retina'\n",
+    "from IPython.core.debugger import set_trace\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_theme()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "18787795",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d60f82c130b407f9bd897b49f286111",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map(center=[40, -100], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(T…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import ee\n",
+    "import geemap\n",
+    "ee.Initialize()\n",
+    "m = geemap.Map()\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac57198",
+   "metadata": {},
+   "source": [
+    "# Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e542e8e",
+   "metadata": {},
+   "source": [
+    "The data set we are going to use is called **CHIRPS** (Climate Hazards group Infrared Precipitation with Station data). \n",
+    "\n",
+    "- 30+ year gridded rainfall data: available from 1981 to present.\n",
+    "- 0.05 deg (~6km) spatial resolution.\n",
+    "- Combines satellite rainfall measurements with ground station data.\n",
+    "- Provides consistent, long time-series data.\n",
+    "- Primary applications\n",
+    "    - Drought monitoring\n",
+    "    - Global environmental change over lands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "412166e6",
+   "metadata": {},
+   "source": [
+    "## CHIRPS Pentad\n",
+    "\n",
+    "- The primary computing time step for CHIRP is the pentad.\n",
+    "- Pentad represents the grouping of 5 days.\n",
+    "- There are 6 pentads in a calendar month.\n",
+    "- Daily images are derived by disaggregating pentadal product.\n",
+    "- CHIRPS data has a 3-week latency.\n",
+    "- The dataset has a single band (precipitation). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "608830e7",
+   "metadata": {},
+   "source": [
+    "## Caclulate Global Diviation of Monthly Total Rainfall\n",
+    "\n",
+    "Steps:\n",
+    "1. [x] Sum-Aggregate total rainfall on a monthly scale for all images in the collection.\n",
+    "2. [x] Average-aggregate the image collection to get the mean monthly precipitation. Visualize January's map.\n",
+    "3. [x] Select the precip image of a year-month of interest. Visualize it.\n",
+    "4. [x] Calculate the global diviation by `(img - mean) * 100 / mean`.\n",
+    "5. [x] Visualize the gloabal diviation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09c84005",
+   "metadata": {},
+   "source": [
+    "Let's sum aggregate the total rainfall on a monthly basis (output: 12-image-per year image collection):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "042b4820",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_year = 1981\n",
+    "end_year = 2020\n",
+    "\n",
+    "years = ee.List.sequence(start_year, end_year)\n",
+    "months = ee.List.sequence(1, 12)\n",
+    "\n",
+    "daily = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY')\n",
+    "m.addLayer(daily.first(), {\"palette\": [\"white\", \"blue\"]}, \"CHRIPS Example\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "23a5e730",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make monthly-summed mosaic\n",
+    "# Loop over years-months to get summed monthly images\n",
+    "def map_month(year_collection, y, m):\n",
+    "    summed_img = year_collection.filter(ee.Filter.calendarRange(m, m, \"month\")).reduce(ee.Reducer.sum())\n",
+    "    return summed_img.set(\"system:time_start\", ee.Date.fromYMD(y, m, 1)).set(\"year\", y).set(\"month\", m).rename(\"ppt\")\n",
+    "\n",
+    "def map_year(y):\n",
+    "    year_collection = daily.filter(ee.Filter.calendarRange(y, y, \"year\"))\n",
+    "    return months.map(lambda m: map_month(year_collection, y, m))\n",
+    "\n",
+    "monthly = ee.ImageCollection.fromImages(years.map(map_year).flatten())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "49318413",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.addLayer(monthly.first(), {\"palette\": [\"white\", \"blue\"]}, \"CHRIPS Example\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d996efe",
+   "metadata": {},
+   "source": [
+    "Let's average aggregate the monthly image collection to get a monthly climatology: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4f9da836",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def agg_clim(collection, m):\n",
+    "    \"\"\"Mean-aggregate the images from a specific `month`.\"\"\"\n",
+    "    imgs = collection.filter(ee.Filter.eq(\"month\", m))\n",
+    "    return imgs.mean().set(\"month\", m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7ddc1c41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "monthly_clim = ee.ImageCollection.fromImages(months.map(lambda m: agg_clim(monthly, m)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e8d1d3d",
+   "metadata": {},
+   "source": [
+    "Visualize January's climatology:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "96059962",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.addLayer(ee.Image(monthly_clim.toList(monthly_clim.size()).get(6)), \n",
+    "           {\"palette\": [\"white\", \"blue\"]}, \"CHRIPS Example\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7341adfe",
+   "metadata": {},
+   "source": [
+    "Let's select the precip image of a year-month of interest and visualize it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6a0cb8ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_of_interest = monthly.filter(ee.Filter.And(ee.Filter.eq(\"year\", 2020), ee.Filter.eq(\"month\", 6))).first()\n",
+    "m.addLayer(img_of_interest, \n",
+    "           {\"min\": 0.0, \"max\": 112.0 * 6, \"palette\": ['001137', '0aab1e', 'e7eb05', 'ff4a2d', 'e90000']}, \n",
+    "           \"Image of Interest\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c84e6467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clim_of_interest = monthly_clim.filter(ee.Filter.eq(\"month\", 6)).first()\n",
+    "m.addLayer(clim_of_interest, \n",
+    "           {\"min\": 0.0, \"max\": 112.0 * 6, \"palette\": ['001137', '0aab1e', 'e7eb05', 'ff4a2d', 'e90000']}, \n",
+    "           \"Clim of Interest\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17295d6d",
+   "metadata": {},
+   "source": [
+    "Let's calculate the diviation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "397036d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# diviation = (img_of_interest.subtract(clim_of_interest)).divide(clim_of_interest).multiply(100)\n",
+    "diviation = img_of_interest.subtract(clim_of_interest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "91f8b58d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stack = img_of_interest.rename(\"ppt\").addBands(clim_of_interest.rename(\"clim\"))\n",
+    "diviation = stack.expression(\n",
+    "      '(PPT - CLIM) / CLIM', {\n",
+    "      'PPT': stack.select('ppt'),\n",
+    "      'CLIM': stack.select('clim')\n",
+    "})\n",
+    "m.addLayer(diviation, {\"palette\": ['001137', '0aab1e', 'e7eb05', 'ff4a2d', 'e90000']}, \"Diviation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd74a70e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geemap.ee_search()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ad5b1ad",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}