# Investigating Greenland's ice marginal lakes under a changing climate (GrIML)
[](https://badge.fury.io/py/griml) [](https://zenodo.org/badge/latestdoi/444752900) [](https://griml.readthedocs.io/en/latest/?badge=latest) [](https://actions-badge.atrox.dev/PennyHow/GrIML/goto?ref=main)
The **GrIML** processing package for classifying water bodies from satellite imagery using a multi-sensor, multi-method remote sensing approach. This workflow is part of the [ESA GrIML project](https://eo4society.esa.int/projects/griml/), and this repository also holds all project-related materials.
## Installation
The GrIML post-processing Python package can be installed using pip:
```
$ pip install griml
```
Or cloned from the Github repository:
```
$ git clone git@github.com:PennyHow/GrIML.git
$ cd GrIML
$ pip install .
```
## Workflow outline
<img src="https://github.com/PennyHow/GrIML/blob/main/other/reporting/figures/griml_workflow_with_gee.png?raw=true" alt="The GrIML workflow." width="1500" align="aligncenter" />
**GrIML** proposes to examine ice marginal lake changes across Greenland using a multi-sensor and multi-method remote sensing approach to better address their influence on sea level contribution forecasting.
Ice marginal lakes are detected using a remote sensing approach, based on offline workflows developed within the [ESA Glaciers CCI](https://catalogue.ceda.ac.uk/uuid/7ea7540135f441369716ef867d217519") (Option 6, An Inventory of Ice-Marginal Lakes in Greenland) ([How et al., 2021](https://www.nature.com/articles/s41598-021-83509-1)). Initial classifications are performed on Google Earth Engine with the scripts available [here](https://github.com/PennyHow/GrIML/tree/main/gee_scripts). Lake extents are defined through a multi-sensor approach using:
- Multi-spectral indices classification from Sentinel-2 optical imagery
- Backscatter classification from Sentinel-1 SAR (synthetic aperture radar) imagery
- Sink detection from ArcticDEM digital elevation models
Post-processing of these classifications is performed using the GrIML post-processing Python package, including raster-to-vector conversion, filtering, merging, metadata population, and statistical analysis.
## Project links
- ESA [project outline](https://eo4society.esa.int/projects/griml/) and [fellow information](https://eo4society.esa.int/lpf/penelope-how/)
- Information about the [ESA Living Planet Fellowship](https://eo4society.esa.int/communities/scientists/living-planet-fellowship/)
- [GrIML project description](https://pennyhow.github.io/blog/investigating-griml/)
- 2017 ice marginal lake inventory [Scientific Reports paper](https://www.nature.com/articles/s41598-021-83509-1) and [dataset](https://catalogue.ceda.ac.uk/uuid/7ea7540135f441369716ef867d217519)
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"description": "# Investigating Greenland's ice marginal lakes under a changing climate (GrIML)\n\n[](https://badge.fury.io/py/griml) [](https://zenodo.org/badge/latestdoi/444752900) [](https://griml.readthedocs.io/en/latest/?badge=latest) [](https://actions-badge.atrox.dev/PennyHow/GrIML/goto?ref=main)\n\nThe **GrIML** processing package for classifying water bodies from satellite imagery using a multi-sensor, multi-method remote sensing approach. This workflow is part of the [ESA GrIML project](https://eo4society.esa.int/projects/griml/), and this repository also holds all project-related materials.\n\n## Installation\n\nThe GrIML post-processing Python package can be installed using pip: \n\n```\n$ pip install griml\n```\nOr cloned from the Github repository: \n\n```\n$ git clone git@github.com:PennyHow/GrIML.git\n$ cd GrIML\n$ pip install .\n```\n\n## Workflow outline\n\n<img src=\"https://github.com/PennyHow/GrIML/blob/main/other/reporting/figures/griml_workflow_with_gee.png?raw=true\" alt=\"The GrIML workflow.\" width=\"1500\" align=\"aligncenter\" />\n\n**GrIML** proposes to examine ice marginal lake changes across Greenland using a multi-sensor and multi-method remote sensing approach to better address their influence on sea level contribution forecasting.\n\nIce marginal lakes are detected using a remote sensing approach, based on offline workflows developed within the [ESA Glaciers CCI](https://catalogue.ceda.ac.uk/uuid/7ea7540135f441369716ef867d217519\") (Option 6, An Inventory of Ice-Marginal Lakes in Greenland) ([How et al., 2021](https://www.nature.com/articles/s41598-021-83509-1)). Initial classifications are performed on Google Earth Engine with the scripts available [here](https://github.com/PennyHow/GrIML/tree/main/gee_scripts). Lake extents are defined through a multi-sensor approach using:\n\n- Multi-spectral indices classification from Sentinel-2 optical imagery\n- Backscatter classification from Sentinel-1 SAR (synthetic aperture radar) imagery\n- Sink detection from ArcticDEM digital elevation models \n\nPost-processing of these classifications is performed using the GrIML post-processing Python package, including raster-to-vector conversion, filtering, merging, metadata population, and statistical analysis.\n\n## Project links\n\n- ESA [project outline](https://eo4society.esa.int/projects/griml/) and [fellow information](https://eo4society.esa.int/lpf/penelope-how/)\n\n- Information about the [ESA Living Planet Fellowship](https://eo4society.esa.int/communities/scientists/living-planet-fellowship/)\n\n- [GrIML project description](https://pennyhow.github.io/blog/investigating-griml/)\n\n- 2017 ice marginal lake inventory [Scientific Reports paper](https://www.nature.com/articles/s41598-021-83509-1) and [dataset](https://catalogue.ceda.ac.uk/uuid/7ea7540135f441369716ef867d217519)\n",
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