From 15fae61e2e7eed2513fee0c8779704742bf22284 Mon Sep 17 00:00:00 2001
From: GRUPO3_HACKATHON <ydory007@gmail.com>
Date: Mon, 31 Jul 2023 04:08:50 +0000
Subject: [PATCH] Update file hackathon.ipynb
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-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "bf1e1f14-3a8f-4346-9268-0638f16257f2",
- "metadata": {},
- "source": [
- "# Agricultura Familiar Conectada: Plataforma Web para Optimizar Cultivos con NDVI y NDWI"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d9e05d42-0175-41fd-a589-680e8354f145",
- "metadata": {},
- "source": [
- "### Casos de estudio\n",
- "\n",
- "* Cuenca del rÃo Grande en el área Central de Panamá"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ae13de6f-8a8f-4bbe-995e-b1e1750b9999",
- "metadata": {},
- "source": [
- "### Objetivo General\n",
- "\n",
- "\"Desarrollar una plataforma web innovadora y accesible para agricultores familiares, basada en cálculos de los Ãndices NDVI y NDWI, que les permita mejorar la toma de decisiones y optimizar el manejo de cultivos mediante el monitoreo de la salud de las plantas y la disponibilidad de agua en sus terrenos agrÃcolas ee identificar posibles zonas inundables en el area.\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "622f4ddd-b1ce-447d-83c4-b0f5c8538271",
- "metadata": {},
- "source": [
- "### Objetivos EspecÃficos\n",
- "\n",
- "* Desarrollar la plataforma web: Crear una plataforma en lÃnea que sea intuitiva, fácil de usar y accesible desde diversos dispositivos (como computadoras, tabletas y teléfonos móviles) para garantizar que los agricultores familiares puedan acceder a ella y utilizarla de manera efectiva.\n",
- "* Integrar datos de teledetección: Automatizar la descarga de datos de los satilites y delimitados por las parcelas que el usuario seleccione.\n",
- "* Identificar la calidad de la cobertura vegetal\n",
- "* Identificar las potencialidades de concentración de agua superficial\n",
- "* Definir los principales usos de suelo\n",
- "* Delimitación de áreas de inundación por influencia topográfica\n",
- "* Establecer bases de datos de parcelas agrÃcolas\n",
- "* Implementar alertas y notificaciones cuando se detecten cambios significativos en los Ãndices NDVI y NDWI, señalando posibles situaciones de estrés hÃdrico o problemas en el crecimiento de los cultivos.\n",
- "* Facilitar la interacción y colaboración al implementar funciones de interacción social en la plataforma para que los agricultores familiares puedan compartir experiencias, hacer preguntas y recibir asesoramiento de otros agricultores y expertos agrÃcolas."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5c773f94-cbd3-4bc4-9a9c-73f23173ca1d",
- "metadata": {},
- "source": [
- "## MetodologÃa\n",
- "\n",
- "### Planteamiento del problema\n",
- "\n",
- "Las áreas geográficas de provincias centrales en Panamá presentan caracterÃsticas naturales morfológicas favorables para los procesos de inundaciones. Estas áreas geográficas presentan importantes actividades económicas de explotación del sector primario y sobresalientes ecosistemas.\n",
- "De igual manera nuestros agricultores se enfrentan a retos nunca antes vistos, fenómenos climáticos extremos como sequias, inundaciones, Ciclones tropicales, degradación de la tierra, entre otros. Estos fenómenos naturales alteran en gran medida los valores nutricionales y quÃmicos de los suelos en que los cultivos regularmente se desarrollan. \n",
- "\n",
- "Mediante el presente análisis, se proyecta identificar las áreas potenciales de concentración de agua superficial como datos relevantes para delimitar áreas de inundación por la naturaleza del territorio y como potencialidad para el aprovechamiento del recursos hÃdricos para actividades económicas importantes como la agricultura."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "4d5b2e96-7255-487f-81f9-ab9ecc136d17",
- "metadata": {},
- "source": [
- "## Procedimiento\n",
- "\n",
- "1. Delimitacion de Zonas agricolas mediante el uso de mapa de cobertura boscosa y uso de suelo gestionando de información de carácter satelital del sitio de Copernicus y procesadas en QGIS 3.22"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "595feab2-b299-4eed-89d6-629021683bdc",
- "metadata": {},
- "source": [
- "<img src=\"./img/landcover.png\" />"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9c2060e1-9132-425d-be8f-a43790e150b3",
- "metadata": {},
- "source": [
- "# for Raster files 'tiff' manipulation\n",
- "import rasterio\n",
- "# libraries for array manipulation\n",
- "import numpy as np\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "c177b48b",
- "metadata": {},
- "outputs": [],
- "source": [
- "def readTiffBand(tiff_file, band_number):\n",
- " \"\"\"\n",
- " Reading the specific band from the 'tiff' file.\n",
- " \"\"\"\n",
- " with rasterio.open(tiff_file) as dataset:\n",
- " band = dataset.read(band_number)\n",
- " return band"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a18e63b6-1b41-4f15-aeca-676956e3d3cb",
- "metadata": {},
- "source": [
- "2. Calculamos el Ãndice Normalizado de Diferencia de Vegetación, utilizando codigos en Python\n",
- "\n",
- "NDVI = (NIR-Red) / (NIR+Red)\n",
- "\n",
- "NIR= luz del infrarrojo cercano y \n",
- "Rojo= luz roja visible\n",
- "\n",
- "Entre los valores de referencia de Ãndice que se interpretarán calculando el NDVI, se encuentran los siguientes:\n",
- "\n",
- " | Min | Max | State |\n",
- "|------|------|-----------------------------|\n",
- "| 0.1 | 0.2 | Suelo desnudo |\n",
- "| 0.2 | 1 | Plantas |\n",
- "| 0.5 | 0.5 | Vegetación densa y saludable|\n",
- "| 0.2 | 0.5 | Vegetación dispersa |\n",
- "\n",
- "Valores negativos = Concentración de agua, estructuras artificiales y rocas entre otros.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "5868bef0",
- "metadata": {},
- "outputs": [],
- "source": [
- "def calculateNDVI(band_red, band_nir):\n",
- " \"\"\"\n",
- " Calculating NDVI index given the red and near infrared band.\n",
- " \"\"\"\n",
- " \n",
- " # In case you don't have cero values over the bands\n",
- " #ndvi = (band_nir - band_red) / (band_nir + band_red)\n",
- " \n",
- " # Cero values won't be valid\n",
- " element1 = (band_nir - band_red)\n",
- " element2 = (band_nir + band_red)\n",
- " \n",
- " ndvi = np.divide(element1, element2, out=np.zeros_like(element1), where=element2!=0)\n",
- " \n",
- " return ndvi"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "64302660-61a1-445b-a35c-3ebfafb71c8a",
- "metadata": {},
- "source": [
- "<img src=\"./img/NDVI.png\" />"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c87217b4-0fcd-46c5-a492-4d99c8be0be6",
- "metadata": {},
- "source": [
- "4. Cálculo de Ãndice de Agua de Diferencia Normalizada (NDWI), con la finalidad de identificar concentraciones de agua sobre la superficie de la tierra. Se aplicará la siguiente fórmula\n",
- "\n",
- "NDWI = (Green – NIR)/(Green + NIR)\n",
- "\n",
- "Green = luz de banda verde\n",
- "NIR = luz del infrarrojo cercano\n",
- "| Min | Max | State |\n",
- "|------|-----|--------------------------------------|\n",
- "| 0.2 | 1 | Superficie del agua |\n",
- "| 0,0 | 0,2 | Inundación, humedad |\n",
- "|- 0,3 | 0,0 | SequÃa moderada, superficies sin agua| \n",
- "| -1 | -0,3| SequÃa, superficies sin agua |\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "6a97d07d",
- "metadata": {},
- "outputs": [],
- "source": [
- "def calculateNDWI(band_green, band_nir):\n",
- " \"\"\"\n",
- " Calculating NDWI index given the green and near infrared band.\n",
- " \"\"\"\n",
- " \n",
- " # In case you don't have cero values over the bands\n",
- " #ndwi = (band_green - band_nir) / (band_green + band_nir)\n",
- " \n",
- " \n",
- " # Cero values won't be valid\n",
- " element1 = (band_green - band_nir)\n",
- " element2 = (band_green + band_nir)\n",
- " \n",
- " ndwi = np.divide(element1, element2, out=np.zeros_like(element1), where=element2!=0)\n",
- " \n",
- " return ndwi"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "71134f4a-04fe-4976-9f16-4b2bea08db92",
- "metadata": {},
- "source": [
- "<img src=\"./img/NDWI.png\" />"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "3ba41e31",
- "metadata": {},
- "outputs": [],
- "source": [
- "def write_index_to_tiff(index_array, output_tiff_file, tiff_metadata):\n",
- " \"\"\"\n",
- " Write the computed index array to the new 'tiff' file with the same metadata as the input 'tiff' file.\n",
- " \"\"\"\n",
- " \n",
- " with rasterio.open(output_tiff_file, 'w', **tiff_metadata) as dst:\n",
- " dst.write(index_array, 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "d8bdbf9f",
- "metadata": {},
- "outputs": [],
- "source": [
- "def update_index_metadata(band_file, name_index, index_array):\n",
- " \"\"\"\n",
- " Update metada from the computed index array\n",
- " \"\"\"\n",
- " \n",
- " # Reading metadata from 'tiff' file\n",
- " with rasterio.open(band_file) as dataset:\n",
- " tiff_metadata = dataset.profile\n",
- "\n",
- " # Updating metadata from computed index array\n",
- " tiff_metadata.update(count=1, dtype=str(index_array.dtype))\n",
- "\n",
- " # Saving computed index array to a 'tiff' file\n",
- " output_index_tiff = f'./outputs/output_{name_index}.tif'\n",
- " write_index_to_tiff(index_array, output_index_tiff, tiff_metadata)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "6a1e0f4f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Reading green band (band 3), red band (band 4) and near infrared band (band 8) from 'tiff' files\n",
- "band3_file = './sentinel_2/banda_3/T17PNK20230427T155529B03_10m.tif'\n",
- "band4_file = './sentinel_2/banda_4/T17PNK20230427T155529B04_10m.tif'\n",
- "band8_file = './sentinel_2/banda_8/T17PNK20230427T155529B08_10m.tif'\n",
- "\n",
- "band_green = readTiffBand(band3_file, 1).astype('float64')\n",
- "band_red = readTiffBand(band4_file, 1).astype('float64')\n",
- "band_nir = readTiffBand(band8_file, 1).astype('float64')\n",
- "\n",
- "\n",
- "# Calculating NDVI\n",
- "ndvi = calculateNDVI(band_red, band_nir)\n",
- "update_index_metadata(band8_file, \"ndvi\", ndvi)\n",
- "\n",
- "\n",
- "# Calculating NDWI\n",
- "ndwi = calculateNDWI(band_green, band_nir)\n",
- "update_index_metadata(band8_file, \"ndwi\", ndwi)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "75bcf2dd",
- "metadata": {},
- "outputs": [],
- "source": [
- "#Dataset for the calculated ndvi index\n",
- "df_ndvi = pd.DataFrame(ndvi)\n",
- "df_ndvi.to_csv(\"ndvi_index.csv\", index=False)\n",
- "#df_ndvi"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "3d50f7f7",
- "metadata": {},
- "outputs": [],
- "source": [
- "#Dataset for the calculated ndwi index\n",
- "df_ndwi = pd.DataFrame(ndwi)\n",
- "df_ndwi.to_csv(\"ndwi_index.csv\", index=False)\n",
- "#df_ndwi"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "889a6819",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "836a614b-9dcb-44c6-8114-dd96b52fd513",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.10.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--
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