Source Code

We propose to bring in expertise from multiple USGS environmental DNA (eDNA) labs to create a database to track samples from initial collection through analysis and reporting and to long-term storage. We will publish this tracking template so it can be implemented within eDNA labs across the USGS.

To help users connect and comprehend USGS data, we propose to develop an interactive viewer for multi-channel geophysical data using existing Python PyViz tools.

We synthesize local high-resolution urban landcover imagery with microclimate data and regional meteorology to determine landcover drivers of extreme urban heat. Resulting outputs are mappable items spatially describing urban temperatures at fine scales, and a web application to analyze changes in urban heat under different climate scenarios.

Effectively documenting and distributing information about restoration projects is essential for measuring progress towards national conservation goals. We will improve the National Fish Habitat Partnership Project Tracking database by creating a data pipeline to compile project information and link data with other decision support tools.

This proposal would fund the testing of quantitative methods for extracting total water level from imagery, with add-on applications including satellite shoreline detection, digital stream gauges, and flood detection. This project supports national scale USGS coastal hazards products.

This project develops an approach to common questions USGS scientists are faced with when working on multidisciplinary teams to address complex challenges — what models are available? When is it appropriate to couple/integrate models? And how can we apply technology to support an appropriate approach?

The sustainability of coastal water resources is being affected by climate change, sea level rise, and modifications to land use and hydrologic systems. To prepare for and respond to these drivers of hydrologic change, coastal water managers need real-time data, an understanding of temporal trends, and information about how current and historical data compare. Coastal water managers often must mak

Fire has increased dramatically across the western U.S. and these increases are expected to continue. With this reality, it is critical that we improve our ability to forecast the timing, extent, and intensity of fire to provide resource managers and policy makers the information needed for effective decisions. For example, an advanced, spatially-explicit prediction of the upcoming fire season wou

Wildfires are increasing across the western U.S., causing damage to ecosystems and communities. Addressing the fire problem requires understanding the trends and drivers of fire, yet most fire data is limited only to recent decades. Tree-ring fire scars provide fire records spanning 300-500 years, yet these data are largely inaccessible to potential users. Our project will deliver the newly compil

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. We pr

Detailed information about past fire history is critical for understanding fire impacts and risk, as well as prioritizing conservation and fire management actions. Yet, fire history information is neither consistently nor routinely tracked by many agencies and states, especially on private lands in the Southeast. Remote sensing data products offer opportunities to do so but require additional pr

We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise, proprietary