Geology ReportsSearch

USGS · 70124910

Unmanned aircraft systems (UAS) activities at the Department of the Interior

Abstract

The U.S. Department of the Interior (DOI) is responsible for protecting and managing the natural resources and heritage on almost 20% of the land in the United States. The DOI’s mission requires access to remotely sensed data over vast lands, including areas that are remote and potentially dangerous to access. Unmanned Aircraft Systems (UAS) technology has the potential to enable the DOI to be a better steward of the land by: (1) Improving natural hazard forecasting and the analysis of the impacts. (2) Improving the understanding of climate change to better plan for likely impacts. (3) Developing precipitation and evaporation forecasting to better manage water resources. (4) Monitoring Arctic ice change and its impacts on ecosystems, coasts, and transportation. (5) Increasing safety and effectiveness of wildland fire management. (6) Enhancing search and rescue capabilities. (7) Broadening the abilities to monitor environmental or landscape conditions and changes. (8) Better understanding and protecting the Nation’s ecosystems. The initial operational testing and evaluations performed by the DOI have proven that UAS technology can be used to support many of the Department’s activities. UAS technology provides scientists a way to look longer, closer and more frequently at some of Earth’s most remote areas—places that were previously too dangerous or expensive to monitor in detail. The flexibility of operations and relative low cost to purchase and operate Small Unmanned Aerial System (sUAS) enhances the ability to track long-term landscape and environmental change. The initial testing indicates the operational costs are approximately 10% of traditional manned aircraft. In addition, users can quickly assess landscape-altering events such as wildland fires, floods and volcanoes. UAS technology will allow the DOI to do more with less and in the process enhance the Department’s ability to provide unbiased scientific information to help stakeholders make informed decisions. It will also provide a digital baseline record that can be archived and used when monitoring future events or conditions. One possible future scenario has scientists carrying sUAS into the field allowing quick deployment and operation to observe the environment or for emergency response. This scenario could also include a persistent monitoring capability provided by a UAS that can stay airborne over a small geographic area for days or weeks, or possibly longer. While the DOI focus is on sUAS, the Department recognizes that larger UAS systems will also play a role in meeting its mission. The Department anticipates meeting long-duration or specialized acquisition commitments, such as state or national aerial photography, by collaboration with other agencies or through commercial contracts. Even though the DOI continues to evaluate UAS and sensor technology to meet the Department’s mission, some of its bureaus are already moving towards an operational capability. The authors fully anticipate that by 2020 UAS will emerge as one of the primary platforms for DOI remote sensing applications.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bruce K. Quirk, Michael E. Hutt. 2014. Unmanned aircraft systems (UAS) activities at the Department of the Interior. https://pubs.usgs.gov/publication/70124910

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Evaluating Three-Dimensional Elevation Program lidar consistency and accuracy at scale using cloud-native, open-source methods

The U.S. Geological Survey three-dimensional elevation program (3DEP) has significantly expanded national lidar coverage, necessitating scalable, reproducible methods for assessing data quality across diverse terrains and acquisition conditions. This study introduces a cloud-native, open-source workflow designed to evaluate the geometric accuracy and consistency of 3DEP lidar data sets at a national scale. Leveraging tools such as the Point Data Abstraction Library, Open3D, and Amazon Web Services infrastructure, the workflow integrates global navigation satellite system‐surveyed ground control points and terrestrial laser scanning data to validate airborne lidar collections. Two case studies demonstrate the application of this process. In Puerto Rico, the process identified vertical biases and inconsistencies in vegetated areas, while in Iowa and Arizona, the process confirmed high vertical accuracy with minimal bias. The results underscore the effectiveness of combining cloud computing with open-source tools to perform large-scale lidar data quality assessments. This process offers a reproducible, efficient solution for nationwide validation of 3DEP data sets, supporting enhanced decision-making in geospatial applications.

Photogrammetric Engineering and Remote Sensing

Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data

Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.

California