From c841c11487019e9eff87c97a3a92b2e8be6e215d Mon Sep 17 00:00:00 2001 From: valentina Date: Sun, 7 Jul 2024 11:49:26 -0700 Subject: [PATCH] update images --- docs/batch-computing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/batch-computing.md b/docs/batch-computing.md index 1144bcd..e0d0338 100644 --- a/docs/batch-computing.md +++ b/docs/batch-computing.md @@ -18,7 +18,7 @@ We use three workflows to batch process image pairs for glacier surface velocity ### 1. `image_correlation_pair` This workflow calls a Python script (image_correlation.py) that runs autoRIFT on a pair of spatially overlapping [Sentinel-2 L2A](https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l2a/) images. It requires the [product names](https://sentiwiki.copernicus.eu/web/s2-products) of the two images. The images are downloaded from aws using the [Element 84 Earth Search API](https://element84.com/earth-search/). Only the near infrared band (NIR, B08) is used which has a spatial resolution of 10 m. autoRIFT is used to perform image correlation. Search distances are scaled with temporal baseline assuming a maximum surface velocity of 1000 m/yr, so images acquired farther apart in time take longer to process. Surface velocity maps are saved as geotifs and uploaded as [Github Artifacts](https://docs.github.com/en/actions/using-workflows/storing-workflow-data-as-artifacts). -![plot](./images/input_images.png) +![plot](https://github.com/uwescience/SciPy2024-GitHubActionsTutorial/blob/main/glacier_image_correlation/images/input_images.png) ### 2. `batch_image_correlation` This workflow can be used to create surface velocity maps from many pairs of Sentinel-2 images. Required inputs include maximum cloud cover percent, start month (recommend >=5 to minimize snow cover), end month (recommend <=10 to minimize snow cover), and number of pairs per image, e.g.: @@ -31,7 +31,7 @@ Only the first suitable image is selected for each month. Once image pairs are i ### 3. `summary_statistics` This workflow downloads all of the velocity maps created during a `batch_image_correlation` run and uses them to calculate and plot median velocity, standard deviation of velocity, and valid pixel count across all velocity maps. The summary statistics plot is uploaded as a Github Artifact. -![plot](./images/velocity_summary_statistics.png) +![plot](https://github.com/uwescience/SciPy2024-GitHubActionsTutorial/blob/main/glacier_image_correlation/images/velocity_summary_statistics.png) ## Acknowledgements