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ID_POINTS output_name Output filename LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork <pre><code>python theropoda.py --asset users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m --id_field ID_POINTS --output_name LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork\n</code></pre> <p>If you don't know how to upload your vector data in Earth Engine, you can follow the tutotial clicking this link.</p>"},{"location":"#roadmap","title":"Roadmap","text":"<ul> <li>Implement arguments to choose other zonal reducers (i.e. percentile, variance, etc.)</li> <li>Implement arguments to choose other satellite data series (i.e. Landsat series, MODIS products)</li> <li>Implement a visualization of the processed data (or samples of it)</li> </ul>"},{"location":"theropoda/","title":"Theropoda Module","text":"<p>This module includes functionalities related to <code>theropoda.py</code> code.</p>"},{"location":"theropoda/#overview","title":"Overview","text":"<p>The <code>theropoda.py</code> module provides functions to extract time series information from Sentinel 2 data stored in Earth Engine.</p>"},{"location":"theropoda/#attributes","title":"Attributes","text":"<ul> <li><code>asset</code> (str): Choosed Earth Engine vector asset.</li> <li><code>id_field</code> (str): Vector column used as ID (use unique identifiers!).</li> <li><code>output_name</code> (str): Output filename.</li> </ul>"},{"location":"theropoda/#example-usage","title":"Example Usage","text":"<pre><code>asset   = 'users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m'\nid_field = 'ID_POINTS'\noutput_name = 'LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork.csv'\n</code></pre>"},{"location":"theropoda/#functions","title":"Functions","text":""},{"location":"theropoda/#1gettimeseries","title":"1.<code>getTimeSeries</code>","text":"<p>This function is responsible to get the time series of Sentinel 2 data throught Earth Engine. 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Time series data and corresponding dates.</p>"},{"location":"trend_analysis/#2gapfill","title":"2.<code>gapfill</code>","text":"<p>Fills gaps in the time series data.</p>"},{"location":"trend_analysis/#parameters_1","title":"Parameters","text":"<ul> <li><code>ts</code>: Time series data.</li> <li><code>dates</code>: List of dates corresponding to the time series data.</li> <li><code>season_size</code>: Size of the seasonal period.</li> </ul> <p>Returns: - Filled time series data and updated dates.</p>"},{"location":"trend_analysis/#3sm_trend","title":"3.<code>sm_trend</code>","text":"<p>Applies seasonal decomposition and trend smoothing to the time series data.</p>"},{"location":"trend_analysis/#parameters_2","title":"Parameters","text":"<ul> <li><code>ts</code>: Time series data.</li> <li><code>season_size</code>: Size of the seasonal period.</li> <li><code>seasonal_smooth</code>: Size of the seasonal smoothing.</li> </ul> <p>Returns: - Trend analysis results and column names.</p>"},{"location":"trend_analysis/#4run","title":"4.<code>run</code>","text":"<p>Executes the trend analysis workflow for a given polygon ID.</p>"},{"location":"trend_analysis/#parameters_3","title":"Parameters","text":"<ul> <li><code>input_file</code>: Input database file.</li> <li><code>id_pol</code>: ID of the polygon.</li> <li><code>dt_5days</code>: List of 5-day intervals.</li> <li><code>season_size</code>: Size of the seasonal period.</li> <li><code>output_file</code>: Output file path.</li> </ul>"},{"location":"blog/","title":"Blog","text":""}]}
+{"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":"<ul> <li>T(h)eroPoDa + - Time Series Extraction for Polygonal Data and Trend Analysis \u2b1b</li> </ul>"},{"location":"#description","title":"Description","text":"<ul> <li>Toolkit created to extract Time Series information from Sentinel 2 \ud83d\udef0 data stored in Earth Engine, gap filling and trend analysis </li> </ul>"},{"location":"#author","title":"Author","text":"<ul> <li>Vin\u00edcius Vieira Mesquita - [email protected] (Main Theropoda)</li> </ul>"},{"location":"#co-author","title":"Co-author","text":"<ul> <li>Leandro Leal Parente - [email protected] (Gap Filling and Trend Analysis implementation)</li> </ul>"},{"location":"#version","title":"Version","text":"<ul> <li>1.1.0</li> </ul>"},{"location":"#requirements-installation-order-from-top-to-bottom","title":"Requirements (installation order from top to bottom)","text":"<ul> <li>Python 3.10</li> <li>GDAL</li> <li>Rasterio </li> <li>Pandas</li> <li>Geopandas</li> <li>Scikit-learn</li> <li>Joblib</li> <li>Psutil</li> <li>Earthengine-api</li> <li>scikit-map</li> </ul>"},{"location":"#how-to-use","title":"How to use","text":"<ul> <li>In this version of TheroPoDa (1.1.0), you could extract a series of median NDVI from Sentinel 2 for a Feature Collection of polygons simplily by adjusting some variables at the end of code:</li> </ul> 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 <pre><code>python theropoda.py --asset users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m --id_field ID_POINTS --output_name LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork\n</code></pre> <p>If you don't know how to upload your vector data in Earth Engine, you can follow the tutotial clicking this link.</p>"},{"location":"#roadmap","title":"Roadmap","text":"<ul> <li>Implement arguments to choose other zonal reducers (i.e. percentile, variance, etc.)</li> <li>Implement arguments to choose other satellite data series (i.e. Landsat series, MODIS products)</li> <li>Implement a visualization of the processed data (or samples of it)</li> </ul>"},{"location":"theropoda/","title":"Theropoda Module","text":"<p>This module includes functionalities related to <code>theropoda.py</code> code.</p>"},{"location":"theropoda/#overview","title":"Overview","text":"<p>The <code>theropoda.py</code> module provides functions to extract time series information from Sentinel 2 data stored in Earth Engine.</p>"},{"location":"theropoda/#attributes","title":"Attributes","text":"<ul> <li><code>asset</code> (str): Choosed Earth Engine vector asset.</li> <li><code>id_field</code> (str): Vector column used as ID (use unique identifiers!).</li> <li><code>output_name</code> (str): Output filename.</li> </ul>"},{"location":"theropoda/#example-usage","title":"Example Usage","text":"<pre><code>asset   = 'users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m'\nid_field = 'ID_POINTS'\noutput_name = 'LAPIG_Pasture_S2_NDVI_Monitoring_FieldWork.csv'\n</code></pre>"},{"location":"theropoda/#functions","title":"Functions","text":""},{"location":"theropoda/#1gettimeseries","title":"1.<code>getTimeSeries</code>","text":"<p>This function is responsible to get the time series of Sentinel 2 data throught Earth Engine. It needs a <code>geometry</code> object in the <code>ee.Feature()</code> formart and the choosed vector propertie ID as the <code>id_field</code>.</p>"},{"location":"theropoda/#parameters","title":"Parameters","text":"<ul> <li><code>geometry</code>: An ee.Feature() object representing the area of interest.</li> <li><code>bestEffort</code>: A boolean indicating whether to use a larger pixel (10m to 30m) if the polygon area is too big (default is False).</li> </ul>"},{"location":"theropoda/#returns","title":"Returns","text":"<ul> <li>NDVI time series data along with other information for the specified geometry.</li> </ul>"},{"location":"theropoda/#2build_time_series","title":"2.<code>build_time_series</code>","text":"<p>Builds and writes NDVI time series data for a target vector asset, processing one polygon at a time.</p>"},{"location":"theropoda/#parameters_1","title":"Parameters","text":"<ul> <li><code>index</code>: Index of the object being processed.</li> <li><code>obj</code>: Object ID for which the time series is being generated.</li> <li><code>id_field</code>: Field name representing the ID in the vector asset.</li> <li><code>outfile</code>: Output file path to write the time series data.</li> <li><code>asset</code>: Earth Engine vector asset.</li> <li><code>bestEffort</code>: A boolean indicating whether to use a larger scale if needed (default is False).</li> </ul>"},{"location":"theropoda/#returns_1","title":"Returns","text":"<ul> <li>True if processing is successful, None if the polygon area is too small, False if an error occurs during processing and restart the process using the bestEffort approach.</li> </ul>"},{"location":"theropoda/#3build_time_series_check","title":"3.<code>build_time_series_check</code>","text":"<p>Checks the consistency of the NDVI time series library and handles errors during processing.</p>"},{"location":"theropoda/#parameters_2","title":"Parameters","text":"<ul> <li><code>index</code>: Index of the object being processed.</li> <li><code>obj</code>: Object ID for which the time series is being checked.</li> <li><code>id_field</code>: Field name representing the ID in the vector asset.</li> <li><code>outfile</code>: Output file path where time series data is stored.</li> <li><code>asset</code>: Earth Engine vector asset.</li> <li><code>checker</code>: A boolean indicating whether to check if the polygon has been processed before (default is False).</li> </ul>"},{"location":"theropoda/#returns_2","title":"Returns","text":"<ul> <li>A dictionary containing information about errors and processing time.</li> </ul>"},{"location":"theropoda/#4build_id_list","title":"4.<code>build_id_list</code>","text":"<p>Builds and writes a text file containing each Polygon ID used to extract the time series.</p>"},{"location":"theropoda/#parameters_3","title":"Parameters","text":"<ul> <li><code>asset</code>: Earth Engine vector asset.</li> <li><code>id_field</code>: Field name representing the ID in the vector asset.</li> <li><code>colab_folder</code>: Path of the folder where the text file will be saved.</li> </ul>"},{"location":"theropoda/#5run","title":"5.<code>run</code>","text":"<p>Manages the overall workflow by catching argument information and initiating the process of extracting NDVI time series data for specified polygonal areas.</p>"},{"location":"theropoda/#parameters_4","title":"Parameters","text":"<ul> <li><code>asset</code>: Earth Engine vector asset.</li> <li><code>id_field</code>: Field name representing the ID in the vector asset.</li> <li><code>output_name</code>: Name of the output file.</li> <li><code>colab_folder</code>: Path of the folder where the output file will be saved.</li> </ul>"},{"location":"trend_analysis/","title":"Trend Analysis Module","text":"<p>This module includes functionalities related to <code>trend_analysis.py</code> code.</p>"},{"location":"trend_analysis/#overview","title":"Overview","text":"<p>The <code>trend_analysis</code> module provides functions to gap filling and analyze trends in time series data.</p>"},{"location":"trend_analysis/#functions","title":"Functions","text":""},{"location":"trend_analysis/#1extract_ts","title":"1.<code>extract_ts</code>","text":"<p>Extracts time series data from the DataFrame for 5-day intervals.</p>"},{"location":"trend_analysis/#parameters","title":"Parameters","text":"<ul> <li><code>df</code>: DataFrame containing the data.</li> <li><code>dt_5days</code>: List of 5-day intervals.</li> </ul> <p>Returns: - 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