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@M3nin0
Created April 23, 2021 21:26
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{
"cells": [
{
"cell_type": "markdown",
"id": "hydraulic-thanksgiving",
"metadata": {},
"source": [
"## Suavização de Séries Temporais de Imagens de Satélite\n",
"\n",
"Este *Computational Notebook* apresenta um exemplo de suavização de Séries Temporais de Imagens de Satélite (SITS) recuperadas através do serviço **W**eb **T**ime **S**eries **S**ervice (WTSS) (Queiroz *et al*, 2015). Este serviço oferece operações que permitem a aquisição de SITS de uma dada localização considerando diferentes coleções de dados. Atualmente, o serviço é mantido pelo projeto `Brazil Data Cube` e oferece coleções de dados do CBERS-4/WFI, Sentinel-2/MSI e Landsat-8/OLI.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "temporal-longer",
"metadata": {},
"outputs": [],
"source": [
"import numpy\n",
"from wtss import *\n",
"import matplotlib.pyplot as plt\n",
"from scipy.signal import savgol_filter"
]
},
{
"cell_type": "markdown",
"id": "conditional-seeking",
"metadata": {},
"source": [
"### Aquisição dos dados\n",
"\n",
"Para a aquisição dos dados, será feito a utilização do cliente [wtss.py](https://wtss.readthedocs.io/en/latest/), que permite a fácil comunicação com o serviço WTSS através da linguagem de programação Python.\n",
"\n",
"Neste processo, será feita a aquisição das séries temporais de **quatro** pontos de agricultura da região de Alto Graças, no estado do Mato Grosso, no período de **01/2017** à **12/2019**. Será feito o uso da coleção de dados **CBERS-4/WFI**, com a banda espectral `BAND 15`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "every-grocery",
"metadata": {},
"outputs": [],
"source": [
"service = WTSS('https://brazildatacube.dpi.inpe.br/', access_token='BDC_ACCESS_TOKEN')\n",
"cbers4_collection = service['CB4_64_16D_STK-1']\n",
"\n",
"time_series = []\n",
"for latitude in numpy.arange(-16.905,-16.955, -0.01): \n",
" extracted_time_series = cbers4_collection.ts(\n",
" attributes = ('BAND15'),\n",
" latitude = float(latitude), longitude = -53.989,\n",
" start_date = \"2017-01-01\", end_date = \"2019-12-31\")\n",
" time_series.append(extracted_time_series.values('BAND15'))"
]
},
{
"cell_type": "markdown",
"id": "thirty-dating",
"metadata": {},
"source": [
"### Suavização das séries temporais\n",
"\n",
"Abaixo é feita a suavização das séries temporais extraídas. Para isso são utilizados os seguintes parâmetros:\n",
"\n",
"- `Tamanho de janela`: 9\n",
"- `Grau do Polinômio`: 2\n",
"\n",
"O filtro será aplicado nos valores medianos das quatro séries temporais extraídas no passo anterior."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "beneficial-barrier",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"median_ts = numpy.median(time_series, axis=0)\n",
"median_ts_smoothed = savgol_filter(median_ts, window_length = 9, polyorder = 2)\n",
"\n",
"plt.plot(median_ts, color='blue')\n",
"plt.plot(median_ts_smoothed, color='red')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "looking-grammar",
"metadata": {},
"outputs": [],
"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": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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