Software

The Landsat STAC catalog provides a versatile and user-friendly interface for accessing and exploring the USGS Landsat Archive. Understanding the characteristics and differences between the various Landsat data products is helpful for choosing suitable data for your research and applications. This tutorial focuses on filtering the Landsat STAC catalog for products based on various criteria, includ

In this tutorial, we demonstrate the process of accessing Landsat data files through the STAC API and the AWS command-line tools, specifically the requester-pays functionality. Users can define their spatial and temporal parameters to query and download multiple Landsat data band files.

This tutorial will introduces the basics of selecting a subset of bands over a set area of interest (AOI) and time span, loading the desired cloud optimized geotiffs (COGs) into a Jupyter Notebook directly from the cloud, cropping the bands and creating a natural composite.

This tutorial demonstrates how to query using metadata specific to the various collections of Landsat data. We use the following Machine-to-Machine API filters: metadataValue, metadataBetween, metadataAnd, and metadataOr. Users can view queryable metadata after following one of these steps (1) Submitting a dataset-filters query and listing the results (2) Submitting a search on Earth Explorer and

This tutorial is designed to guide users through metadata filtering for Landsat-9 satellite data within the Landsat 8-9 Operational Land Imager and Thermal Infrared Sensor Collection 2 Level-2 dataset. The chosen location for use is Albemarle Sound, a large estuarine body of water in northeastern North Carolina, United States. The emphasis on metadata filtering is crucial, especially when dealing

This notebook shows how to download Landsat Collection 2 bands, bundles, and band groups using the M2M API. In this context, "bands" refer to individual band files in TIF format, "bundles" include all files for a product(s) as .tar files, and "band groups" include downloading all files for a given product(s) as individual files (unpacked from the .tar file within a bundle). The area of interest (A

This repository contains the code used to analyzed the water quality from a formalin study. We examined how formalin affected the total nitrogen and nitrite nitrogen in water that was passed through a biofilter.

Code to support the "Freshwater Habitat Potential for Chinook Salmon in the Yukon and Kuskokwim River Basins, Alaska" project dataset. These Python notebooks and scripts process synthetic stream networks and DEMs to create unique reach contributing areas (uRCAs) and uRCA valley bottoms (uRCA VBs). The Python notebooks are designed to be run within ESRI ArcPro software and utilize the 'arcpy' modul

Python package with functionality for the quality assurance, harmonization, and analysis of water-well records. It was developed in support of the U.S. Geological Survey (USGS) National Water-Well Database (NWWDB) as part of the National Hydrologic Geospatial Fabric (NHGF) project.

This python package works with Quarto MarkDown files to allow authors to supply dois for references while writing a document. Tools in this utilities package provide the ability to automatically query https://doi.org to return valid BiBTeX entries from dois. Further utilities allow users to supply a list of dois, look up the BiBTeX entries, and update them into a new or existing BiBTeX file. Final

This repository contains a journal template for the Journal of Fish and Wildlife Management. The template uses Quarto, a Markdown-based "open-source scientific and technical publishing system." The template uses an MS Word file as a template to format a manuscript for the Journal of Fish and Wildlife Management. Users will want to edit the qmd file and the bib file if they adapt this template for

Amid climate change and rapidly shifting land uses, effective methods for monitoring natural resources are critical to support scientifically-informed resource management decisions. The practice of using Autonomous Monitoring Units (AMUs) to monitor wildlife species has grown immensely in the past decade, with monitoring projects across species from birds, to bats, amphibians, insects, terrestrial