ls_stats_est.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pLBvTp_PCPgx"
   },
   "source": [
    "# Maastikuökoloogia\n",
    "\n",
    "__Evelyn Uuemaa, PhD__\n",
    "__Alexander Kmoch, PhD__\n",
    "\n",
    "- Praktikumi lisa\n",
    "\n",
    "Tudengid laevad üles oma ZIP-faili, mis sisaldab maastike rasterfaile (geotifid). Rasterfailidele arvutatakse maastikuindeksid. Tulemuse saab tabelina alla laadida.\n",
    "\n",
    "Kasutatud materjalid:\n",
    "- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0225734\n",
    "- https://github.com/martibosch/pylandstats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dU09-eocCPg5"
   },
   "source": [
    "## Kuidas seda skripti kasutada\n",
    "\n",
    "Mine hiirega kõige esimesele kastile ja vajuta \"Run\" nuppu, et üksus läbi jooksutada ja minna järgmiste plokkide juurde. Kui midagi valesti läheb, saad sa lihtsasti uuesti algusest alustada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "izMKLW4VCPg6"
   },
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import ipywidgets as widgets\n",
    "import zipfile\n",
    "import glob\n",
    "from tqdm import tqdm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ülejärgmise ploki juures tuleb sul arvutist valida ZIP-fail, mis sisaldab maastike rasterfaile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "GB-ljpDACPg9"
   },
   "outputs": [],
   "source": [
    "upload = widgets.FileUpload( accept='.zip', multiple=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "1121b6bb4c684ea3bc98d7954bb282f9",
      "ca4e78308c6e429ea8d63198fa38c4fb",
      "eaad029d3e6b4c589b38cdbb72401785"
     ]
    },
    "id": "BjDuXlkECPg9",
    "outputId": "43a4ef7d-88ee-4013-af00-5ba18689f22f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78ae7ed3b3d341e2919efbb918a66030",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUpload(value={}, accept='.zip', description='Upload')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "upload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EGNME6bpCPg_"
   },
   "source": [
    "Kui oled faili oma arvutist valinud, peaks üleslaadimise lahter näitama \"Upload (1)\", mis tähendab, et fail on nüüd valitud.\n",
    "\n",
    "Jooksuta läbi järgmine plokk. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "id": "UIpA7Dx6CPhA",
    "outputId": "2157e166-ff29-4adb-b47a-69cc6c156529"
   },
   "outputs": [],
   "source": [
    "t = upload.data[0]\n",
    "\n",
    "with open('tmp.zip', 'wb') as fh:\n",
    "    fh.write(t)\n",
    "\n",
    "\n",
    "with zipfile.ZipFile('tmp.zip', 'r') as zip_ref:\n",
    "    zip_ref.extractall(\".\")\n",
    "\n",
    "all_tifs = glob.glob(\"*.tif\")\n",
    "\n",
    "# uncomment to see the names of all *.tif files inside the uploaded zip \n",
    "# print(all_tifs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G3G7rVvLCPhB"
   },
   "source": [
    "Nüüd on andmed üles laetud ja saame arvutada igale maastikule maastikuindeksid: servatihedus (edge density), eraldiste tihedus (patch density), kuju indeks (shape index) ja keskmine eraldise suurus (mean patch size).\n",
    "Tulemused saad allalaadida csv-failina, mille nimi on landscape_metrics.csv. Palun muuda faili nimi pärast allalaadimist nimi sobivaks vastavalt sellele, millise linnu elupaikadele indekseid parasjagu arvutad. Näiteks, \"maastikuindeksid_rukkiraak.csv\" kui arvutad rukkiräägule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:01<00:00, 38.61it/s]\n"
     ]
    },
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       "      <th>46601_clip.tif</th>\n",
       "      <td>41.769357</td>\n",
       "      <td>2.554769</td>\n",
       "      <td>1.423210</td>\n",
       "      <td>1.640148</td>\n",
       "      <td>39.142488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67910_clip.tif</th>\n",
       "      <td>51.102488</td>\n",
       "      <td>4.439798</td>\n",
       "      <td>1.741889</td>\n",
       "      <td>1.451895</td>\n",
       "      <td>22.523549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126669_clip.tif</th>\n",
       "      <td>45.445060</td>\n",
       "      <td>3.832153</td>\n",
       "      <td>1.575866</td>\n",
       "      <td>1.508586</td>\n",
       "      <td>26.094992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63580_clip.tif</th>\n",
       "      <td>43.288772</td>\n",
       "      <td>3.500567</td>\n",
       "      <td>1.491801</td>\n",
       "      <td>1.485714</td>\n",
       "      <td>28.566800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        ED        PD       SDI       MSI        MPS\n",
       "107946_clip.tif  43.470298  2.219899  1.402347  1.767347  45.047099\n",
       "79200_clip.tif   28.737318  1.916076  0.835417  1.544709  52.189984\n",
       "107973_clip.tif  38.761963  3.832153  1.463337  1.362103  26.094992\n",
       "63408_clip.tif   38.824617  2.854156  1.386288  1.561111  35.036633\n",
       "46540_clip.tif   41.101047  3.193461  1.088451  1.566708  31.313990\n",
       "108009_clip.tif  40.957838  3.818800  1.566654  1.374988  26.186233\n",
       "80662_clip.tif   40.766892  3.193461  1.841722  1.533052  31.313990\n",
       "123623_clip.tif  31.524262  2.854156  1.222211  1.309402  35.036633\n",
       "77340_clip.tif   40.766892  3.832153  1.691389  1.376389  26.094992\n",
       "73708_clip.tif   38.556861  2.834472  1.234223  1.458322  35.279942\n",
       "63574_clip.tif   39.215953  2.834472  1.077711  1.479806  35.279942\n",
       "64383_clip.tif   28.403163  2.235423  1.338072  1.490476  44.734272\n",
       "64431_clip.tif   21.873024  1.607975  0.943917  1.517701  62.190023\n",
       "125270_clip.tif  28.737318  2.554769  1.046957  1.371528  39.142488\n",
       "107940_clip.tif  37.425344  2.554769  1.405389  1.540756  39.142488\n",
       "125250_clip.tif  44.263207  3.845599  1.680983  1.440192  26.003751\n",
       "64432_clip.tif   24.393305  1.916076  1.081551  1.434091  52.189984\n",
       "102311_clip.tif  25.574613  1.607975  1.290216  1.542273  62.190023\n",
       "41062_clip.tif   31.410556  2.554769  1.234121  1.414641  39.142488\n",
       "126674_clip.tif  43.927880  3.845599  1.482718  1.493437  26.003751\n",
       "107939_clip.tif  26.746074  1.870300  1.312414  1.511623  53.467361\n",
       "79202_clip.tif   31.524262  1.902770  0.908090  1.619907  52.554949\n",
       "63576_clip.tif   38.959895  2.227634  1.497740  1.713469  44.890685\n",
       "107943_clip.tif  27.258309  2.824731  0.826117  1.361905  35.401597\n",
       "126673_clip.tif  31.856096  2.884199  1.275340  1.465482  34.671668\n",
       "73713_clip.tif   42.437667  3.193461  1.293912  1.483855  31.313990\n",
       "55745_clip.tif   31.856096  2.854156  1.493009  1.335548  35.036633\n",
       "64081_clip.tif   47.454599  4.409178  1.718627  1.396079  22.679963\n",
       "46602_clip.tif   31.634103  2.227634  1.381232  1.496297  44.890685\n",
       "64385_clip.tif   31.634103  2.227634  1.468762  1.563534  44.890685\n",
       "107945_clip.tif  34.874042  2.243266  1.356577  1.555764  44.577858\n",
       "107952_clip.tif  18.980462  1.909400  0.951391  1.403813  52.372466\n",
       "46597_clip.tif   37.015887  2.251165  1.618734  1.641126  44.421445\n",
       "107941_clip.tif  39.233298  3.204666  1.241471  1.363491  31.204501\n",
       "107942_clip.tif  31.405833  2.904582  1.426863  1.377072  34.428358\n",
       "108029_clip.tif  35.552708  2.805450  1.140444  1.478583  35.644907\n",
       "63219_clip.tif   12.941539  1.268514  0.503788  1.385417  78.832423\n",
       "64381_clip.tif   29.949217  1.929570  1.293428  1.575873  51.825019\n",
       "55746_clip.tif   36.215352  3.845599  1.233802  1.347631  26.003751\n",
       "62927_clip.tif   30.968121  1.591167  0.939963  1.667863  62.846960\n",
       "55747_clip.tif   33.415486  2.554769  1.396931  1.468304  39.142488\n",
       "64380_clip.tif   28.637188  1.909400  1.234219  1.534005  52.372466\n",
       "52517_clip.tif   33.068612  2.528249  1.434421  1.516113  39.553073\n",
       "63082_clip.tif   24.641301  1.909400  1.171473  1.442402  52.372466\n",
       "78355_clip.tif   34.510771  2.854156  1.468327  1.542489  35.036633\n",
       "78354_clip.tif   38.761963  4.470845  1.481434  1.268718  22.367136\n",
       "46601_clip.tif   41.769357  2.554769  1.423210  1.640148  39.142488\n",
       "67910_clip.tif   51.102488  4.439798  1.741889  1.451895  22.523549\n",
       "126669_clip.tif  45.445060  3.832153  1.575866  1.508586  26.094992\n",
       "63580_clip.tif   43.288772  3.500567  1.491801  1.485714  28.566800"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pylandstats as pls\n",
    "\n",
    "all_metrics = []\n",
    "\n",
    "for landscape in tqdm(all_tifs):\n",
    "    ls = pls.Landscape(landscape)\n",
    "    metrics_df = ls.compute_landscape_metrics_df(metrics=[\"edge_density\", \"patch_density\"])\n",
    "    metrics_df = metrics_df.rename(columns={\"edge_density\": \"ED\", \"patch_density\": \"PD\"})\n",
    "    metrics_df[\"ED\"] = ls.compute_landscape_metrics_df(metrics=[\"edge_density\"])\n",
    "    metrics_df[\"PD\"] = ls.compute_landscape_metrics_df(metrics=[\"patch_density\"])\n",
    "    metrics_df[\"MSI\"] = ls.shape_index()['shape_index'].mean()\n",
    "    metrics_df[\"MPS\"] = ls.area(class_val=None, hectares=True)['area'].mean()\n",
    "    metrics_df.index = [landscape]\n",
    "    all_metrics.append(metrics_df)\n",
    "    \n",
    "all_metrics_df = pd.concat(all_metrics, axis=0)\n",
    "\n",
    "all_metrics_df.to_csv(\"landscape_metrics.csv\", sep=\",\", encoding=\"utf-8\")\n",
    "\n",
    "display(all_metrics_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oU1U5pMxCPhC"
   },
   "source": [
    "[lae tabel alla siit](landscape_metrics.csv)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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