{ "cells": [ { "cell_type": "markdown", "source": "# Exercice 1 : Test de l'environnement \n\n**Durée estimée** : 1-2 minutes \n**Prérequis** : Section 3.3 de la formation \n**Objectif** : Vérifier que les librairies géospatiales sont installées et fonctionnelles\n\n---\n\n## Téléchargement des données\n\nCe notebook nécessite l'image Sentinel-2 de Saskatchewan-Athabasca.\n\n**Téléchargement automatique** : La cellule suivante télécharge automatiquement les données depuis Google Drive (recommandé).\n\n**Téléchargement manuel** : [Cliquez ici](https://drive.google.com/file/d/1ssjG8ZO4jP8U0bZ78jkDuotafv-Py3yH/view) et placez dans `atelier/data/saskatchewan_athabasca_clip.tif`\n\n---\n\nCliquez sur **Run All** en haut du notebook, ou exécutez chaque cellule individuellement.", "metadata": {} }, { "cell_type": "code", "source": "# Téléchargement automatique des données depuis Google Drive\nimport os\nimport requests\nfrom pathlib import Path\n\n# Configuration\nFILE_ID = \"1ssjG8ZO4jP8U0bZ78jkDuotafv-Py3yH\"\nDATA_DIR = Path(\"../data\")\nFILE_PATH = DATA_DIR / \"saskatchewan_athabasca_clip.tif\"\n\n# Créer dossier si nécessaire\nDATA_DIR.mkdir(exist_ok=True)\n\n# Télécharger si fichier absent\nif not FILE_PATH.exists():\n print(\"Téléchargement de l'image Saskatchewan-Athabasca depuis Google Drive...\")\n url = f\"https://drive.google.com/uc?export=download&id={FILE_ID}\"\n \n response = requests.get(url, allow_redirects=True)\n response.raise_for_status()\n \n with open(FILE_PATH, 'wb') as f:\n f.write(response.content)\n \n file_size_mb = FILE_PATH.stat().st_size / 1024 / 1024\n print(f\"Téléchargement terminé : {FILE_PATH}\")\n print(f\" Taille : {file_size_mb:.1f} MB\")\nelse:\n print(f\"Données déjà présentes : {FILE_PATH}\")", "metadata": {}, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Imports et versions\n", "import os\n", "import rasterio\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "print(f\"rasterio: {rasterio.__version__}\")\n", "print(f\"numpy: {np.__version__}\")\n", "print(\"\\nLes librairies géospatiales sont installées\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lecture et visualisation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "# ============================================\n# RASTERIO: Lecture des bandes spectrales\n# ============================================\nfrom pathlib import Path\nimage_path = Path(\"../data/saskatchewan_athabasca_clip.tif\")\n\nwith rasterio.open(image_path) as src:\n red = src.read(4) # Bande 4 - Rouge\n green = src.read(3) # Bande 3 - Vert\n blue = src.read(2) # Bande 2 - Bleu\n nir = src.read(8) # Bande 8 - NIR \n\n# ============================================\n# NUMPY: Normalisation des bandes [0-10000] -> [0-1]\n# ============================================\ndef normalize(band):\n # détecter les Nodata\n valid = band[~np.isnan(band)]\n # calculer percentiles\n p2, p98 = np.percentile(valid, (2, 98))\n # normaliser et clipper\n normalized = np.clip((band - p2) / (p98 - p2), 0, 1)\n return normalized\n\n# NumPy: normaliser les 3 bandes RGB\nred_norm = normalize(red)\ngreen_norm = normalize(green)\nblue_norm = normalize(blue)\n\n# ============================================\n# NUMPY: Correction brightness/gamma\n# ============================================\n# Paramètres de correction visuelle\nbrightness = 1.2 # > 1.0 = plus lumineux\ngamma = 0.6 # < 1.0 = plus clair, > 1.0 = plus sombre\n\n# appliquer la formule gamma \nred_corrected = np.clip(np.power(red_norm * brightness, gamma), 0, 1)\ngreen_corrected = np.clip(np.power(green_norm * brightness, gamma), 0, 1)\nblue_corrected = np.clip(np.power(blue_norm * brightness, gamma), 0, 1)\n\n# combiner les 3 bandes en RGB\nrgb = np.dstack([red_corrected, green_corrected, blue_corrected])\n\n# ============================================\n# MATPLOTLIB: Affichage de l'image\n# ============================================\nplt.figure(figsize=(10, 8))\nplt.imshow(rgb)\nplt.title('Image satellite Sentinel-2 - Composition RGB')\nplt.axis('off')\nplt.tight_layout()\nplt.show()\n\nprint(\"Validation réussie\")" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Distribution spectrale RGB + NIR" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Distribution spectrale affichée\n" ] } ], "source": [ "# Histogramme des 4 bandes (RGB + NIR)\n", "plt.figure(figsize=(10, 6))\n", "\n", "# Tracer les 4 distributions avec leurs couleurs respectives\n", "plt.hist(red[~np.isnan(red)].flatten(), bins=200, alpha=0.6, color='red', label='Rouge (B4)', edgecolor='darkred')\n", "plt.hist(green[~np.isnan(green)].flatten(), bins=200, alpha=0.6, color='green', label='Vert (B3)', edgecolor='darkgreen')\n", "plt.hist(blue[~np.isnan(blue)].flatten(), bins=200, alpha=0.6, color='blue', label='Bleu (B2)', edgecolor='darkblue')\n", "plt.hist(nir[~np.isnan(nir)].flatten(), bins=200, alpha=0.6, color='darkred', label='NIR (B8)', edgecolor='black')\n", "\n", "plt.xlim(500, 6000)\n", "plt.xlabel('Valeurs de réflectance')\n", "plt.ylabel('Fréquence (pixels)')\n", "plt.title('Distribution spectrale des bandes (RGB + NIR)')\n", "plt.legend()\n", "plt.grid(True, alpha=0.3)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Distribution spectrale affichée\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calcul et visualisation NDSI" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "# Calculer l'indice NDSI (Normalized Difference Snow Index)\nwith rasterio.open(image_path) as src:\n swir = src.read(11) # Bande 11 - SWIR (infrarouge moyen)\n\n# Normaliser les bandes (même fonction que pour RGB)\ngreen_norm = normalize(green)\nswir_norm = normalize(swir)\n\n# Calculer le NDSI avec formule standard (éviter division par zéro)\ndenom = green_norm + swir_norm\nndsi = np.where(denom > 0.0001, (green_norm - swir_norm) / denom, np.nan)\n\n# Afficher la carte NDSI\nplt.figure(figsize=(10, 8))\nplt.imshow(ndsi, cmap='Blues', vmin=-0.5, vmax=1)\nplt.colorbar(label='NDSI', shrink=0.6)\nplt.title('Indice de neige NDSI')\nplt.axis('off')\nplt.tight_layout()\nplt.show()\n\n# Statistiques\nprint(f\"NDSI calculé - Moyenne: {np.nanmean(ndsi):.3f}\")" } ], "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": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 4 }