Geology Reports⌕ Search

USGS · 70155996

ASTER and USGS EROS disaster response: emergency imaging after Hurricane Katrina

Abstract

The value of remotely sensed imagery during times of crisis is well established, and the increasing spatial and spectral resolution in newer systems provides ever greater utility and ability to discriminate features of interest (International Charter, Space and Major Disasters, 2005). The existing suite of sensors provides an abundance of data, and enables warning alerts to be broadcast for many situations in advance. In addition, imagery acquired soon after an event occurs can be used to assist response and remediation teams in identifying the extent of the affected area and the degree of damage. The data characteristics of the Advanced Spaceborne Thermal Emission and Refl ection Radiometer (ASTER) are well-suited for monitoring natural hazards and providing local and regional views after disaster strikes. For this reason, and because of the system fl exibility in scheduling high-priority observations, ASTER is often tasked to support emergency situations. The Emergency Response coordinators at the United States Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) work closely with staff at the National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (LP DAAC) at EROS and the ASTER Science Team as they fulfi ll their mission to acquire and distribute data during critical situations. This article summarizes the role of the USGS/EROS Emergency Response coordinators, and provides further discussion of ASTER data and the images portrayed on the cover of this issue

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kenneth A. Duda, Michael Abrams. 2005. ASTER and USGS EROS disaster response: emergency imaging after Hurricane Katrina. https://pubs.usgs.gov/publication/70155996

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↗