| Name | lt-gee-py JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | Python interface to the Google Earth Engine implementation of the LandTrendr spectral-temporal segmentation algorithm. |
| upload_time | 2024-05-02 17:04:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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| keywords |
google earth engine
landtrendr
gee
spectral-temporal segmentation
time series analysis
land cover change detection
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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# lt-gee-py
Python interface to the Google Earth Engine implementation of the LandTrendr spectral-temporal segmentation algorithm.
## Introduction
**LandTrendr** is set of spectral-temporal segmentation algorithms that are useful for change detection in a time series of moderate resolution satellite imagery (primarily Landsat) and for generating trajectory-based spectral time series data largely absent of inter-annual signal noise. LT was originally implemented in IDL (Interactive Data Language), but with the help of engineers at Google, it has been ported to the GEE platform.
The **LandTrendr** class from **lt-gee-py** is a light wrapper around the Google Earth Engine API that includes convenience methods to generate images and collections in the format required for LandTrendr on GEE. Please be aware this package is in alpha and may change.
## Getting Started
### Download and Install packages and dependencies
- Install the Python API for Google Earth Engine. This is the only dependency thus far.
```
conda install -c conda-forge earthengine-api
```
- Install lt-gee-py package using the pip command.
```
pip install lt-gee-py
```
### Authenticate on Google Earth Engine
- [Earth Engine Authentication and Initialization](https://developers.google.com/earth-engine/guides/auth)
```
earthengine authenticate # There are several alternatives for this. See link above.
```
## Basic Usage
```python
import ee
from ltgee import LandTrendr, LandsatComposite, LtCollection
# Initialize access to Google's EE servers
ee.Initialize("my_project_name")
# Initialize variables for LandTrendr algorithm
composite_params = {
"start_date": date(1985, 6,1),
"end_date": date(2017, 9,1),
"area_of_interest": ee.Geometry({
'type': 'Polygon',
'coordinates': [
[
[-122.37202331327023,44.62585686599272],
[-122.26765319608273,44.62585686599272],
[-122.26765319608273,44.696185837887384],
[-122.37202331327023,44.696185837887384],
[-122.37202331327023,44.62585686599272],
]
]
}),
"mask_labels": ['cloud', 'shadow', 'snow', 'water'],
"debug": True
}
lt_collection_params = {
"sr_collection": LandsatComposite(**composite_params),
# "sr_collection": composite_params, # - you may also just pass in your own collection or the params directly. Note: in the former, some methods in the class may not work.
"index": 'NBR',
"ftv_list": ['TCB', 'TCG', 'TCW', 'NBR'],
}
lt_params = {
"lt_collection": LtCollection(**lt_collection_params),
# "lt_collection": lt_collection_params, # - you may also just pass in your own collection or the params directly. Note: in the former, some methods in the class may not work.
"run_params": {
"maxSegments": 6,
"spikeThreshold": 0.9,
"vertexCountOvershoot": 3,
"preventOneYearRecovery": True,
"recoveryThreshold": 0.25,
"pvalThreshold": .05,
"bestModelProportion": 0.75,
"minObservationsNeeded": 6,
}
}
# Instantiating LandTrendr object. Note: The object will immediately request to run the algorithm on Google's servers.
lt = LandTrendr(**lt_params)
# Access resulting image using the data attribute.
lt_data = lt.data
```
## Features / Methods
- **run**:
Initiates the LandTrendr algorithm on Google's servers using the specified run_params and generates an image. This is a wrapper around build_sr_collection and build_lt_collection functions. The array image result is saved to LandTrendr.data as an ee.Image.
- **build_sr_collection**:
Builds an annual cloud and cloud shadow masked medoid composite of Landsat surface reflectance TM-equivalent bands 1,2,3,4,5,7. This collection can be useful outside of use by LandTrendr, but is also the base for creating the input collection for LandTrendr.
- **build_lt_collection**:
Builds a collection as input to LandTrendr. It will prepare a collection where the first band is the spectral index to base temporal segmentation on, and the subsequent bands will be fitted to segmentation structure of the segmentation index.
- **get_change_map**:
Generates a set of map layers describing either vegetation loss or gain events with attributes including: year of change detection, spectral delta, duration of change event, pre-change event spectral value, and the rate of spectral change. Each attribute is a band of an ee.Image.
- **get_fitted_data**:
Generates an annual band stack for a given index provided as ftvList indices to either buildLTcollection or runLT. It flattens the FTV array format to a band per year for a given FTV index.
- **get_segment_data**:
Generates an array of information about spectral-temporal segments from the breakpoint vertices identified by LandTrendr. Returns either all spectral-temporal segments, or just vegetation loss segments, or just vegetation growth segments.
- **get_segment_count**:
Given a segment data array produced by the getSegmentData function, this function returns the number of segments identified by LandTrendr as an ee.Image.
- **collection_to_band_stack**:
Transforms an image collection into an image stack where each band of each image in the collection is concatenated as a band into a single image. Useful for mapping a function over a collection, like transforming surface reflectance to NDVI, and then transforming the resulting collection into a band sequential time series image stack.
- **transform_sr_collection**:
Transforms the images within an annual surface reflectance collection built by buildSRcollection to a list of provided indices or bands.
- **get_fitted_rgb_col**:
Creates a collection of RGB visualization images from three FTV bands resulting from a call to LandTrendr segmentation. This is useful for creating thumbnails, filmstrips, and GIFs.
## [Manuscript](http://www.mdpi.com/2072-4292/10/5/691)
## Citation
>Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing. 10, 691.
Except as otherwise noted, the content of this repository and accompanying description site (https://emapr.github.io/LT-GEE/) are licensed under the [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/), and code samples are licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
Raw data
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"keywords": "Google Earth Engine, LandTrendr, GEE, spectral-temporal segmentation, time series analysis, land cover change detection",
"author": null,
"author_email": "Myscon Truong <myscontruong@gmail.com>, Robert Kennedy <robert.kennedy@oregonstate.edu>, Peter Clary <clarype@oregonstate.edu>",
"download_url": "https://files.pythonhosted.org/packages/2b/6a/56daa886587c181cc9f8b05a8229aca9d1d247399e280d3698c5cc9046d0/lt_gee_py-0.0.2.tar.gz",
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"description": "# lt-gee-py\nPython interface to the Google Earth Engine implementation of the LandTrendr spectral-temporal segmentation algorithm.\n\n## Introduction\n\n**LandTrendr** is set of spectral-temporal segmentation algorithms that are useful for change detection in a time series of moderate resolution satellite imagery (primarily Landsat) and for generating trajectory-based spectral time series data largely absent of inter-annual signal noise. LT was originally implemented in IDL (Interactive Data Language), but with the help of engineers at Google, it has been ported to the GEE platform.\n\nThe **LandTrendr** class from **lt-gee-py** is a light wrapper around the Google Earth Engine API that includes convenience methods to generate images and collections in the format required for LandTrendr on GEE. Please be aware this package is in alpha and may change.\n\n## Getting Started\n\n### Download and Install packages and dependencies\n\n- Install the Python API for Google Earth Engine. This is the only dependency thus far.\n\n```\nconda install -c conda-forge earthengine-api\n```\n\n- Install lt-gee-py package using the pip command.\n\n```\npip install lt-gee-py\n```\n\n### Authenticate on Google Earth Engine\n\n- [Earth Engine Authentication and Initialization](https://developers.google.com/earth-engine/guides/auth)\n\n```\nearthengine authenticate # There are several alternatives for this. See link above.\n```\n\n## Basic Usage\n\n```python\nimport ee\nfrom ltgee import LandTrendr, LandsatComposite, LtCollection\n\n# Initialize access to Google's EE servers\nee.Initialize(\"my_project_name\")\n\n# Initialize variables for LandTrendr algorithm\ncomposite_params = {\n \"start_date\": date(1985, 6,1),\n \"end_date\": date(2017, 9,1),\n \"area_of_interest\": ee.Geometry({\n 'type': 'Polygon',\n 'coordinates': [\n [\n [-122.37202331327023,44.62585686599272],\n [-122.26765319608273,44.62585686599272],\n [-122.26765319608273,44.696185837887384],\n [-122.37202331327023,44.696185837887384],\n [-122.37202331327023,44.62585686599272],\n ]\n ]\n }),\n \"mask_labels\": ['cloud', 'shadow', 'snow', 'water'],\n \"debug\": True\n}\nlt_collection_params = {\n \"sr_collection\": LandsatComposite(**composite_params),\n # \"sr_collection\": composite_params, # - you may also just pass in your own collection or the params directly. Note: in the former, some methods in the class may not work.\n \"index\": 'NBR',\n \"ftv_list\": ['TCB', 'TCG', 'TCW', 'NBR'],\n}\nlt_params = {\n \"lt_collection\": LtCollection(**lt_collection_params),\n # \"lt_collection\": lt_collection_params, # - you may also just pass in your own collection or the params directly. Note: in the former, some methods in the class may not work.\n \"run_params\": {\n \"maxSegments\": 6,\n \"spikeThreshold\": 0.9,\n \"vertexCountOvershoot\": 3,\n \"preventOneYearRecovery\": True,\n \"recoveryThreshold\": 0.25,\n \"pvalThreshold\": .05,\n \"bestModelProportion\": 0.75,\n \"minObservationsNeeded\": 6,\n }\n}\n\n# Instantiating LandTrendr object. Note: The object will immediately request to run the algorithm on Google's servers.\nlt = LandTrendr(**lt_params)\n\n# Access resulting image using the data attribute.\nlt_data = lt.data\n```\n\n## Features / Methods\n\n- **run**:\n Initiates the LandTrendr algorithm on Google's servers using the specified run_params and generates an image. This is a wrapper around build_sr_collection and build_lt_collection functions. The array image result is saved to LandTrendr.data as an ee.Image.\n\n- **build_sr_collection**:\n Builds an annual cloud and cloud shadow masked medoid composite of Landsat surface reflectance TM-equivalent bands 1,2,3,4,5,7. This collection can be useful outside of use by LandTrendr, but is also the base for creating the input collection for LandTrendr.\n\n- **build_lt_collection**:\n Builds a collection as input to LandTrendr. It will prepare a collection where the first band is the spectral index to base temporal segmentation on, and the subsequent bands will be fitted to segmentation structure of the segmentation index.\n\n- **get_change_map**:\n Generates a set of map layers describing either vegetation loss or gain events with attributes including: year of change detection, spectral delta, duration of change event, pre-change event spectral value, and the rate of spectral change. Each attribute is a band of an ee.Image.\n\n- **get_fitted_data**:\n Generates an annual band stack for a given index provided as ftvList indices to either buildLTcollection or runLT. It flattens the FTV array format to a band per year for a given FTV index.\n\n- **get_segment_data**:\n Generates an array of information about spectral-temporal segments from the breakpoint vertices identified by LandTrendr. Returns either all spectral-temporal segments, or just vegetation loss segments, or just vegetation growth segments.\n\n- **get_segment_count**:\n Given a segment data array produced by the getSegmentData function, this function returns the number of segments identified by LandTrendr as an ee.Image.\n\n- **collection_to_band_stack**:\n Transforms an image collection into an image stack where each band of each image in the collection is concatenated as a band into a single image. Useful for mapping a function over a collection, like transforming surface reflectance to NDVI, and then transforming the resulting collection into a band sequential time series image stack.\n\n- **transform_sr_collection**:\n Transforms the images within an annual surface reflectance collection built by buildSRcollection to a list of provided indices or bands.\n\n- **get_fitted_rgb_col**:\n Creates a collection of RGB visualization images from three FTV bands resulting from a call to LandTrendr segmentation. This is useful for creating thumbnails, filmstrips, and GIFs.\n\n## [Manuscript](http://www.mdpi.com/2072-4292/10/5/691) \n\n## Citation\n\n>Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing. 10, 691.\n\nExcept as otherwise noted, the content of this repository and accompanying description site (https://emapr.github.io/LT-GEE/) are licensed under the [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/), and code samples are licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. 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