From 14cda1c8fb952c9b136e28c978e5d56f717e7391 Mon Sep 17 00:00:00 2001 From: trchudley Date: Tue, 14 May 2024 22:57:07 +0100 Subject: [PATCH] typo fix --- docs/appendix/community_guidelines.md | 2 +- paper/paper.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/appendix/community_guidelines.md b/docs/appendix/community_guidelines.md index 58623b7..aa57dc1 100644 --- a/docs/appendix/community_guidelines.md +++ b/docs/appendix/community_guidelines.md @@ -36,7 +36,7 @@ pDEMtools can be installed for development by cloning the github repository. We ```bash git clone git@github.com:trchudley/pdemtools.git -cd pypromice/ +cd pdemtools/ mamba env create --file environment.yml -n pdemtools_env mamba activate pdemtools_env pip install -e . diff --git a/paper/paper.md b/paper/paper.md index 1d789af..b79d129 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -38,7 +38,7 @@ bibliography: paper.bib # Statement of need -[ArcicDEM](https://www.pgc.umn.edu/data/arcticdem/) and [REMA](https://www.pgc.umn.edu/data/rema/) are high-resolution, time-stamped 2-metre-resolution DEMs of the polar regions provided by the Polar Geospatial Center (PGC). They are extracted by applying stereo auto-correlation techniques [@noh_surface_2017] to pairs of submetre Maxar satellite imagery, including Worldview-1, Worldview-2, Worldview-3, and GeoEye-1, beginning in 2007 (ArcticDEM) or 2009 (REMA) and ongoing to the present day. Products are available as tens of thousands of time-stamped 'strips' [@porter_arcticdem_2022; @howat_remastrips_2022] constructed from individual scene-pairs, or as a single mosaic [@porter_arcticdem_2023; @howat_remamosaic_2022] compiled from the combined stack of strips. Strips allow users to perform change detection by comparing data from different seasons or years, whilst mosaics provide a consistent and comprehensive product over the entire polar regions. +[ArcticDEM](https://www.pgc.umn.edu/data/arcticdem/) and [REMA](https://www.pgc.umn.edu/data/rema/) are high-resolution, time-stamped 2-metre-resolution DEMs of the polar regions provided by the Polar Geospatial Center (PGC). They are extracted by applying stereo auto-correlation techniques [@noh_surface_2017] to pairs of submetre Maxar satellite imagery, including Worldview-1, Worldview-2, Worldview-3, and GeoEye-1, beginning in 2007 (ArcticDEM) or 2009 (REMA) and ongoing to the present day. Products are available as tens of thousands of time-stamped 'strips' [@porter_arcticdem_2022; @howat_remastrips_2022] constructed from individual scene-pairs, or as a single mosaic [@porter_arcticdem_2023; @howat_remamosaic_2022] compiled from the combined stack of strips. Strips allow users to perform change detection by comparing data from different seasons or years, whilst mosaics provide a consistent and comprehensive product over the entire polar regions. As Earth Science has moved into the 'big data' era, increasing amounts of Arctic- and Antarctic-focused resources are available as public, cloud-optimised datasets. New approaches are providing Python tools to act as combined API and processing tools, such as `icepyx` [@scheick_icepyx_2023] or `pypromice` [@how_pypromice_2023]. From 2022 (ArcticDEM v4.1 and REMA v2), the PGC DEM products are [hosted](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/pgc-data-stac.json) as Cloud Optimised GeoTIFFs (CoGs) in a SpatioTemporal Asset Catalog (STAC), a standardised structure for cataloguing spatiotemporal data. However, the PGC STAC is not currently a dynamic STAC, and as such lacks a RESTful API interface for searching and downloading datasets in response to user queries. This limits the ability of users to programmatically interact with ArcticDEM and REMA data in a quick and efficient manner. The `pdemtools` package has two aims: the first is to provide a Python-focussed alternative for searching and downloading ArcticDEM and REMA data, emulating dynamic STAC query tools such as `pystac` [@radiant_pystac_2024]; whilst the second is to provide commonly used processing functions specific to the needs of ArcticDEM and REMA users (a focus on ice sheet and cryosphere work), as well as the particular strengths of the ArcticDEM and REMA datasets (high-resolution and multitemporal).