Skip to main content

A workflow for classifying lakes from satellite imagery and compiling lake inventories

Project description

Investigating Greenland's ice marginal lakes under a changing climate (GrIML)

PyPI version DOI Documentation Status Build Status

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, 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

The GrIML workflow.

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 (Option 6, An Inventory of Ice-Marginal Lakes in Greenland) (How et al., 2021). Initial classifications are performed on Google Earth Engine with the scripts available here. 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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

griml-0.1.0.tar.gz (45.2 MB view hashes)

Uploaded Source

Built Distribution

griml-0.1.0-py3-none-any.whl (45.3 MB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page