From 9b85caadde0468d0ef7575656b8b1463e0398dc6 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 24 Jun 2024 03:07:09 +0000 Subject: [PATCH] Deployed 27fc18e with MkDocs version: 1.6.0 --- index.html | 2 +- .../__pycache__/shortcodes.cpython-312.pyc | Bin 11304 -> 11304 bytes .../__pycache__/translations.cpython-312.pyc | Bin 5710 -> 5710 bytes search/search_index.json | 2 +- sitemap.xml.gz | Bin 127 -> 127 bytes 5 files changed, 2 insertions(+), 2 deletions(-) diff --git a/index.html b/index.html index 8ab84d1b..9b09610b 100644 --- a/index.html +++ b/index.html @@ -1 +1 @@ - TheroPoDa Documentation
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Vinícius Mesquita / DALEE - theropod, jurassic landscape, digital art, hight quality

Time Series Extraction for Polygonal Data

Name

  • T(h)eroPoDa + - Time Series Extraction for Polygonal Data and Trend Analysis ⬛

Description

  • Toolkit created to extract Time Series information from Sentinel 2 🛰 data stored in Earth Engine, gap filling and trend analysis image

Author

Co-author

Version

  • 1.1.0

How to use

  • At this version of TheroPoDa (1.1.0), you could extract a series of NDVI data from Sentinel 2 for a Feature Collection of polygons simplily by adjusting some variables at the end of code:
variable usage example
asset Choosed Earth Engine Vector Asset users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m
id_field Vector column used as ID (use unique identifiers!) ID_POINTS
output_name Output filename LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork.csv

Roadmap

  • Implement arguments to choose other zonal reducers (i.e. percentile, variance, etc.)
  • Implement arguments to choose other satellite data series (i.e. Landsat series, MODIS products)
  • Implement a visualization of the processed data (or samples of it)
\ No newline at end of file + TheroPoDa Documentation
Skip to content

Vinícius Mesquita / DALEE - theropod, jurassic landscape, digital art, hight quality

Time Series Extraction for Polygonal Data

Name

  • T(h)eroPoDa + - Time Series Extraction for Polygonal Data and Trend Analysis ⬛

Description

  • Toolkit created to extract Time Series information from Sentinel 2 🛰 data stored in Earth Engine, gap filling and trend analysis image

Author

Co-author

Version

  • 1.1.0

Requirements

  • Python 3.10
  • GDAL
  • Rasterio
  • Pandas
  • Geopandas
  • Scikit-learn
  • Joblib
  • Psutil
  • scikit-map

How to use

  • At this version of TheroPoDa (1.1.0), you could extract a series of NDVI data from Sentinel 2 for a Feature Collection of polygons simplily by adjusting some variables at the end of code:
variable usage example
asset Choosed Earth Engine Vector Asset users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m
id_field Vector column used as ID (use unique identifiers!) ID_POINTS
output_name Output filename LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork.csv

Roadmap

  • Implement arguments to choose other zonal reducers (i.e. percentile, variance, etc.)
  • Implement arguments to choose other satellite data series (i.e. Landsat series, MODIS products)
  • Implement a visualization of the processed data (or samples of it)
\ No newline at end of file diff --git a/material/overrides/hooks/__pycache__/shortcodes.cpython-312.pyc b/material/overrides/hooks/__pycache__/shortcodes.cpython-312.pyc index 0f06784364afd2bf3dccd6a7989f85028737ab4b..ceddefd5b0bbe3da84b019944431e0200dd62536 100644 GIT binary patch delta 20 acmZ1xu_A)|G%qg~0}z~iRI!m;RtEq?J_aKI delta 20 acmZ1xu_A)|G%qg~0}woTRkD#=RtEq?`vyJ$ diff --git a/material/overrides/hooks/__pycache__/translations.cpython-312.pyc b/material/overrides/hooks/__pycache__/translations.cpython-312.pyc index eddd9c4009903f4e5c1d7eb7e08c216a8591d8aa..9f8919a59fced8a6b1c409e94d66145619b8b796 100644 GIT binary patch delta 20 acmX@7b54i*G%qg~0}z~iRI!oUSquO}=msYM delta 20 acmX@7b54i*G%qg~0}woTRkD%WSquO~r3OL( diff --git a/search/search_index.json b/search/search_index.json index de7f5d79..71090702 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"lang":["en"],"separator":"[\\s\\u200b\\-_,:!=\\[\\]()\"`/]+|\\.(?!\\d)|&[lg]t;|(?!\\b)(?=[A-Z][a-z])","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":""},{"location":"#time-series-extraction-for-polygonal-data","title":"Time Series Extraction for Polygonal Data","text":""},{"location":"#name","title":"Name","text":""},{"location":"#description","title":"Description","text":""},{"location":"#author","title":"Author","text":""},{"location":"#co-author","title":"Co-author","text":""},{"location":"#version","title":"Version","text":""},{"location":"#how-to-use","title":"How to use","text":" variable usage example asset Choosed Earth Engine Vector Asset users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m id_field Vector column used as ID (use unique identifiers!) ID_POINTS output_name Output filename LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork.csv"},{"location":"#roadmap","title":"Roadmap","text":""},{"location":"theropoda/","title":"Theropoda Module","text":"

This module includes functionalities related to theropoda.py code.

"},{"location":"theropoda/#overview","title":"Overview","text":"

The theropoda.py module provides functions to extract time series information from Sentinel 2 data stored in Earth Engine.

"},{"location":"theropoda/#attributes","title":"Attributes","text":""},{"location":"theropoda/#example-usage","title":"Example Usage","text":"
asset   = 'users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m'\nid_field = 'ID_POINTS'\noutput_name = 'LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork.csv'\n
"},{"location":"theropoda/#functions","title":"Functions","text":""},{"location":"theropoda/#1gettimeseries","title":"1.getTimeSeries","text":"

This function is responsible to get the time series of Sentinel 2 data throught Earth Engine. It needs a geometry object in the ee.Feature() formart and the choosed vector propertie ID as the id_field.

"},{"location":"theropoda/#parameters","title":"Parameters","text":""},{"location":"theropoda/#returns","title":"Returns","text":""},{"location":"theropoda/#2build_time_series","title":"2.build_time_series","text":"

Builds and writes NDVI time series data for a target vector asset, processing one polygon at a time.

"},{"location":"theropoda/#parameters_1","title":"Parameters","text":""},{"location":"theropoda/#returns_1","title":"Returns","text":""},{"location":"theropoda/#3build_time_series_check","title":"3.build_time_series_check","text":"

Checks the consistency of the NDVI time series library and handles errors during processing.

"},{"location":"theropoda/#parameters_2","title":"Parameters","text":""},{"location":"theropoda/#returns_2","title":"Returns","text":""},{"location":"theropoda/#4build_id_list","title":"4.build_id_list","text":"

Builds and writes a text file containing each Polygon ID used to extract the time series.

"},{"location":"theropoda/#parameters_3","title":"Parameters","text":""},{"location":"theropoda/#5run","title":"5.run","text":"

Manages the overall workflow by catching argument information and initiating the process of extracting NDVI time series data for specified polygonal areas.

"},{"location":"theropoda/#parameters_4","title":"Parameters","text":""},{"location":"trend_analysis/","title":"Trend Analysis Module","text":"

This module includes functionalities for trend analysis.

"},{"location":"trend_analysis/#overview","title":"Overview","text":"

The trend_analysis module provides functions to analyze trends in time series data.

"},{"location":"trend_analysis/#functions","title":"Functions","text":""},{"location":"trend_analysis/#calculate_moving_averagedata-window_size","title":"calculate_moving_average(data, window_size)","text":"

Calculates the moving average of a time series.

"},{"location":"trend_analysis/#parameters","title":"Parameters","text":""},{"location":"trend_analysis/#returns","title":"Returns","text":""},{"location":"trend_analysis/#example-usage","title":"Example Usage","text":"
from trend_analysis import calculate_moving_average\n\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nmoving_average = calculate_moving_average(data, window_size=3)\nprint(moving_average)  # Output: [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]\n
"},{"location":"trend_analysis/#detect_trenddata","title":"detect_trend(data)","text":"

Detects the trend in a time series.

"},{"location":"trend_analysis/#parameters_1","title":"Parameters","text":""},{"location":"trend_analysis/#returns_1","title":"Returns","text":""},{"location":"trend_analysis/#example-usage_1","title":"Example Usage","text":"
from trend_analysis import detect_trend\n\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\ntrend = detect_trend(data)\nprint(trend)  # Output: upward\n
"},{"location":"trend_analysis/#forecastdata-periods","title":"forecast(data, periods)","text":"

Forecasts future values of a time series.

"},{"location":"trend_analysis/#parameters_2","title":"Parameters","text":""},{"location":"trend_analysis/#returns_2","title":"Returns","text":""},{"location":"trend_analysis/#example-usage_2","title":"Example Usage","text":"
from trend_analysis import forecast\n\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nfuture_values = forecast(data, periods=3)\nprint(future_values)  # Output: [11, 12, 13]\n
"},{"location":"blog/","title":"Blog","text":""}]} \ No newline at end of file +{"config":{"lang":["en"],"separator":"[\\s\\u200b\\-_,:!=\\[\\]()\"`/]+|\\.(?!\\d)|&[lg]t;|(?!\\b)(?=[A-Z][a-z])","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":""},{"location":"#time-series-extraction-for-polygonal-data","title":"Time Series Extraction for Polygonal Data","text":""},{"location":"#name","title":"Name","text":""},{"location":"#description","title":"Description","text":""},{"location":"#author","title":"Author","text":""},{"location":"#co-author","title":"Co-author","text":""},{"location":"#version","title":"Version","text":""},{"location":"#requirements","title":"Requirements","text":""},{"location":"#how-to-use","title":"How to use","text":" variable usage example asset Choosed Earth Engine Vector Asset users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m id_field Vector column used as ID (use unique identifiers!) ID_POINTS output_name Output filename LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork.csv"},{"location":"#roadmap","title":"Roadmap","text":""},{"location":"theropoda/","title":"Theropoda Module","text":"

This module includes functionalities related to theropoda.py code.

"},{"location":"theropoda/#overview","title":"Overview","text":"

The theropoda.py module provides functions to extract time series information from Sentinel 2 data stored in Earth Engine.

"},{"location":"theropoda/#attributes","title":"Attributes","text":""},{"location":"theropoda/#example-usage","title":"Example Usage","text":"
asset   = 'users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m'\nid_field = 'ID_POINTS'\noutput_name = 'LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork.csv'\n
"},{"location":"theropoda/#functions","title":"Functions","text":""},{"location":"theropoda/#1gettimeseries","title":"1.getTimeSeries","text":"

This function is responsible to get the time series of Sentinel 2 data throught Earth Engine. It needs a geometry object in the ee.Feature() formart and the choosed vector propertie ID as the id_field.

"},{"location":"theropoda/#parameters","title":"Parameters","text":""},{"location":"theropoda/#returns","title":"Returns","text":""},{"location":"theropoda/#2build_time_series","title":"2.build_time_series","text":"

Builds and writes NDVI time series data for a target vector asset, processing one polygon at a time.

"},{"location":"theropoda/#parameters_1","title":"Parameters","text":""},{"location":"theropoda/#returns_1","title":"Returns","text":""},{"location":"theropoda/#3build_time_series_check","title":"3.build_time_series_check","text":"

Checks the consistency of the NDVI time series library and handles errors during processing.

"},{"location":"theropoda/#parameters_2","title":"Parameters","text":""},{"location":"theropoda/#returns_2","title":"Returns","text":""},{"location":"theropoda/#4build_id_list","title":"4.build_id_list","text":"

Builds and writes a text file containing each Polygon ID used to extract the time series.

"},{"location":"theropoda/#parameters_3","title":"Parameters","text":""},{"location":"theropoda/#5run","title":"5.run","text":"

Manages the overall workflow by catching argument information and initiating the process of extracting NDVI time series data for specified polygonal areas.

"},{"location":"theropoda/#parameters_4","title":"Parameters","text":""},{"location":"trend_analysis/","title":"Trend Analysis Module","text":"

This module includes functionalities for trend analysis.

"},{"location":"trend_analysis/#overview","title":"Overview","text":"

The trend_analysis module provides functions to analyze trends in time series data.

"},{"location":"trend_analysis/#functions","title":"Functions","text":""},{"location":"trend_analysis/#calculate_moving_averagedata-window_size","title":"calculate_moving_average(data, window_size)","text":"

Calculates the moving average of a time series.

"},{"location":"trend_analysis/#parameters","title":"Parameters","text":""},{"location":"trend_analysis/#returns","title":"Returns","text":""},{"location":"trend_analysis/#example-usage","title":"Example Usage","text":"
from trend_analysis import calculate_moving_average\n\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nmoving_average = calculate_moving_average(data, window_size=3)\nprint(moving_average)  # Output: [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]\n
"},{"location":"trend_analysis/#detect_trenddata","title":"detect_trend(data)","text":"

Detects the trend in a time series.

"},{"location":"trend_analysis/#parameters_1","title":"Parameters","text":""},{"location":"trend_analysis/#returns_1","title":"Returns","text":""},{"location":"trend_analysis/#example-usage_1","title":"Example Usage","text":"
from trend_analysis import detect_trend\n\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\ntrend = detect_trend(data)\nprint(trend)  # Output: upward\n
"},{"location":"trend_analysis/#forecastdata-periods","title":"forecast(data, periods)","text":"

Forecasts future values of a time series.

"},{"location":"trend_analysis/#parameters_2","title":"Parameters","text":""},{"location":"trend_analysis/#returns_2","title":"Returns","text":""},{"location":"trend_analysis/#example-usage_2","title":"Example Usage","text":"
from trend_analysis import forecast\n\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nfuture_values = forecast(data, periods=3)\nprint(future_values)  # Output: [11, 12, 13]\n
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