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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 processing to condense and facilitate their use by land managers. Here, we propose to generate fire history metrics from the Landsat Burned Area Products for the southeastern US. We will develop code for a processing pipeline that utilizes USGS high-performance computing resources, evaluate Amazon cloud computing services,...
The Total Water Level and Coastal Change Forecast delivers 6-day forecasts of hourly water levels and the probability of waves impacting dunes along 5000 km of sandy coasts along the Atlantic and Gulf of Mexico and will soon expand to the Pacific. These forecasts provide needed information to local governments and federal partners and are used by the USGS to place sensors before a storm. The forecast data are presented in a publicly accessible web tool and stored in a database. Currently, model data are only accessible to project staff. A growing user community is requesting direct access to the data, to conduct scientific analyses and share forecasts on other platforms. To address this need, we will develop an...
Fighting wildfires and reducing their negative effects on natural resources costs billions of dollars annually in the U.S. We will develop the Wildfire Trends Tool (WTT), a data visualization and analysis tool that will calculate and display wildfire trends and patterns for the western U.S. based on user-defined regions of interest, time periods, and ecosystem types. The WTT will be publicly available via a web application that will retrieve fire data and generate graphically compelling maps and charts of fire activity. For an area of interest, users will be able ask questions such as: Is the area burned by wildfire each year increasing or decreasing over time? Are wildfires becoming larger? Are fire seasons becoming...
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 compiled North American Fire Scar Network — 2,592 sites, 35,602 trees, and > 300,000 fire records — to fire scientists, managers and the public through an online application that will provide tools to explore, visualize, and analyze fire history data. The app will provide raw and derived data products, graphics, statistical summaries,...
Scientists who study coastal ecosystems and hazards such as hurricanes, flooding, and cliff failure collect lots of photographs of coastal environments from airplanes and drones. A large area can be surveyed at high resolution and low cost. Additionally, satellites such as Landsat have provided imagery of the Nation’s coastlines every few days for decades. Scientist’s ability to understand coastal hazards would be greatly improved if this wealth of imagery could be ‘mined’ automatically by computers. We want to automate the process of identifying and labelling each region of the image from a set of categories (e.g. bare land, water, woody vegetation, herbaceous vegetation). We need to train a computer to recognize...
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 make decisions based on information pieced together from multiple sources because the available data and tools are scattered across various databases and websites; to aid coastal water managers, a website that consolidates data from multiple organizations and provides statistical analysis of hydrologic and water quality data...
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 software, and specific hardware. We will use CHS resources on their Pangeo JupyterHub to recast our data and workflows to a cloud agnostic open-source framework. Lessons learned will be shared at workshops, in reports, and on our website so others can increase the openness and accessibility of their data and workflows....
The USGS maintains an extensive monitoring network throughout the United States in order to protect the public and help manage natural resources. This network generates millions of data points each year, all of which must be evaluated and reviewed manually for quality assurance and control. Sensor malfunctions and issues can result in data losses and unexpected costs, and are typically only noticed after they occur during manual data checks. By connecting internal USGS databases to “always-on” artificial-intelligence applications, we can constantly scan data-streams for issues and predict problems before they occur. By connecting these algorithms to other cloud-hosted services, the system can automatically notify...
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 propose to combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary...
To make informed decisions, land managers require knowledge about the state of the ecosystems present. Vegetation structure is a key indicator of the state of forested systems; it influences habitat suitability, water quality and runoff, microclimate, and informs wildfire-related characteristics such as fuel loads, burn severity, and post-fire regeneration. Field data used to derive vegetation structure are limited in spatial and temporal extent. Alternatively, forest growth simulation models estimate vegetation structure, but do not capture all factors influencing vegetation growth. Assessment of vegetation structure can be improved by using observations to derive maps which can be used to calibrate modeled forest...
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 would support the planning and prioritization of fire-fighting crews, the placement and abundance of fire breaks, and the amount and type of seed needed for post-fire restoration. While the Southwest has seen exceptional increases in fire, these drier ecosystems are also notably difficult for fire predictions because of unique...
Mapping the occurrence of post-fire flooding and debris flow is crucial for 1) integrating observations into models used to define rainfall thresholds for early warning, 2) understanding patterns of inundation, and 3) improving models for predictive hazard assessment. Despite the critical role mapping plays in post-fire hazard assessment and early warning, there has not been a standardized approach for mapping floods and debris flows with consistent protocols. This project will develop and test a mapping schema using ArcGIS Collector, a mobile mapping platform. We will test the approach in five areas that burned in 2020 through a collaboration between the USGS and the Oregon Department of Geology and Mineral...
Drought is a major problem in the American Southwest that is expected to worsen under the effects of climate change. Currently, the Southwest Biological Science Center is monitoring the effects of drought with soil moisture probes in a range of ecosystems across an elevational gradient on the Colorado Plateau. These data are used in multiple studies to analyze the effects of drought on vegetation composition and demography. Accessing and analyzing that data still relies on traditional site visits, which can result in delayed recognition of erroneous data, a common pitfall in many field-science operations. We propose to improve upon this traditional data workflow by leveraging the use of Internet of Things (IoT)...