typo fix

docs/appendix/community_guidelines.md

@@ -36,7 +36,7 @@ pDEMtools can be installed for development by cloning the github repository. We
 
 ```bash
 git clone [email protected]:trchudley/pdemtools.git
-cd pypromice/
+cd pdemtools/
 mamba env create --file environment.yml -n pdemtools_env
 mamba activate pdemtools_env
 pip install -e .

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).