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NASA NTRS2012

Alabama Disasters: Leveraging NASA EOS to Explore the Environmental and Economic Impact of the April 27 Tornado Outbreak

The disastrous tornado outbreak in Alabama on April 27, 2011 greatly impacted the economy of the state. On record, the tornado outbreak was the second deadliest tornado outbreak in U.S. When considering the agricultural and value-added activities such as food and timber processing, farm inputs, manufacturing, transportation, and retail sales, the dollar value of Alabama agribusiness annually exceeds $40 billion (NASS, 2011). This research aims to examine how the timber and agriculture damage affected the state economy of Alabama and will be used to aid in long-term economic recovery. ASTER imagery was used along with ground-truthed NASS (National Agriculture Statistics Service) crop location records to verify the economic impact tornadoes had on the agricultural economy of the state. This swath damage can be calculated by correlating tornado path with NASS statistics on crop yield, precisely showing the fields affected and dollars lost to this disaster. Not only can this be executed manually using ENVI and ArcGIS, but also through the use of Python, a programming language that has the ability to automate the process, creating a product for initial damage assessment.

Herdy, Claire↗

NASA NTRS2012

Alabama Disasters: Leveraging NASA EOS to Explore the Environmental and Economic Impact of the April 27 Tornado Outbreak

Herdy, Claire↗

NASA NTRS2013

Geospatial Modeling of Asthma Population in Relation to Air Pollution

Current observations indicate that asthma is growing every year in the United States, specific reasons for this are not well understood. This study stems from an ongoing research effort to investigate the spatio-temporal behavior of asthma and its relatedness to air pollution. The association between environmental variables such as air quality and asthma related health issues over Mississippi State are investigated using Geographic Information Systems (GIS) tools and applications. Health data concerning asthma obtained from Mississippi State Department of Health (MSDH) for 9-year period of 2003-2011, and data of air pollutant concentrations (PM2.5) collected from USEPA web resources, and are analyzed geospatially to establish the impacts of air quality on human health specifically related to asthma. Disease mapping using geospatial techniques provides valuable insights into the spatial nature, variability, and association of asthma to air pollution. Asthma patient hospitalization data of Mississippi has been analyzed and mapped using quantitative Choropleth techniques in ArcGIS. Patients have been geocoded to their respective zip codes. Potential air pollutant sources of Interstate highways, Industries, and other land use data have been integrated in common geospatial platform to understand their adverse contribution on human health. Existing hospitals and emergency clinics are being injected into analysis to further understand their proximity and easy access to patient locations. At the current level of analysis and understanding, spatial distribution of Asthma is observed in the populations of Zip code regions in gulf coast, along the interstates of south, and in counties of Northeast Mississippi. It is also found that asthma is prevalent in most of the urban population. This GIS based project would be useful to make health risk assessment and provide information support to the administrators and decision makers for establishing satellite clinics in future.

Kethireddy, Swatantra R.↗

NASA NTRS2014

Myanmar Ecological Forecasting: Utilizing NASA Earth Observations to Monitor, Map, and Analyze Mangrove Forests in Myanmar for Enhanced Conservation

Mangroves supply many essential environmental amenities, such as preventing soil erosion, filtering water pollution, and protecting shorelines from harmful waves, floods, storms and winds. The Mangroves in Myanmar not only provide citizens with a food source, but they also offer firewood, charcoal, and construction materials. The depletion of mangroves is threatening more than the biodiversity however; Myanmar's fiscal livelihood is also in harm's way. Mangroves are valued at $100,000 to $277,000 per square kilometer and if managed in a sustainable fashion, can infuse constant income to the emerging Myanmarese economy. This study analyzed three coastline regions, the Ayeyarwady Delta, Rakhine and Tanintharyi, and mapped the spatial extent of mangrove forest during the dry season in 2000 and 2013. The classifications were derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operation Land Imager (OLI) imagery, as well as the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model information. This data was atmospherically corrected, mosaicked, masked and classified in ENVI, followed by ArcGIS to perform raster calculations and create final products. Forest degradation collected from 2000 to 2013 was later used to forecast the density and health of Mangroves in the year 2030. These results were subsequently presented to project partners Dr. Peter Leimgruber and Ellen Aiken at the Smithsonian Conservation Biology Institute in Front Royal, VA. After the presentation of the project to the partners, these organizations formally passed on to the Myanmar Ministry of Environment, Conservation and Forestry for policy makers and forest managers to utilize in order to protect the Myanmar mangrove ecosystem while sustaining a healthy economy.

Weber, Samuel J.↗

NASA NTRS2013

KINEROS2-AGWA: Model Use, Calibration, and Validation

KINEROS (KINematic runoff and EROSion) originated in the 1960s as a distributed event-based model that conceptualizes a watershed as a cascade of overland flow model elements that flow into trapezoidal channel model elements. KINEROS was one of the first widely available watershed models that interactively coupled a finite difference approximation of the kinematic overland flow equations to a physically based infiltration model. Development and improvement of KINEROS continued from the 1960s on a variety of projects for a range of purposes, which has resulted in a suite of KINEROS-based modeling tools. This article focuses on KINEROS2 (K2), a spatially distributed, event-based watershed rainfall-runoff and erosion model, and the companion ArcGIS-based Automated Geospatial Watershed Assessment (AGWA) tool. AGWA automates the time-consuming tasks of watershed delineation into distributed model elements and initial parameterization of these elements using commonly available, national GIS data layers. A variety of approaches have been used to calibrate and validate K2 successfully across a relatively broad range of applications (e.g., urbanization, pre- and post-fire, hillslope erosion, erosion from roads, runoff and recharge, and manure transport). The case studies presented in this article (1) compare lumped to stepwise calibration and validation of runoff and sediment at plot, hillslope, and small watershed scales; and (2) demonstrate an uncalibrated application to address relative change in watershed response to wildfire.

Erosion↗

NASA NTRS2016

Using Remotely Sensed Data for Climate Change Mitigation and Adaptation: A Collaborative Effort Between the Climate Change Adaptation Science Investigators Workgroup (CASI), NASA Johnson Space Center, and Jacobs Technology

With ever changing landscapes and environmental conditions due to human induced climate change, adaptability is imperative for the long-term success of facilities and Federal agency missions. To mitigate the effects of climate change, indicators such as above-ground biomass change must be identified to establish a comprehensive monitoring effort. Researching the varying effects of climate change on ecosystems can provide a scientific framework that will help produce informative, strategic and tactical policies for environmental adaptation. As a proactive approach to climate change mitigation, NASA tasked the Climate Change Adaptation Science Investigators Workgroup (CASI) to provide climate change expertise and data to Center facility managers and planners in order to ensure sustainability based on predictive models and current research. Generation of historical datasets that will be used in an agency-wide effort to establish strategies for climate change mitigation and adaptation at NASA facilities is part of the CASI strategy. Using time series of historical remotely sensed data is well-established means of measuring change over time. CASI investigators have acquired multispectral and hyperspectral optical and LiDAR remotely sensed datasets from NASA Earth Observation Satellites (including the International Space Station), airborne sensors, and astronaut photography using hand held digital cameras to create a historical dataset for the Johnson Space Center, as well as the Houston and Galveston area. The raster imagery within each dataset has been georectified, and the multispectral and hyperspectral imagery has been atmospherically corrected. Using ArcGIS for Server, the CASI-Regional Remote Sensing data has been published as an image service, and can be visualized through a basic web mapping application. Future work will include a customized web mapping application created using a JavaScript Application Programming Interface (API), and inclusion of the CASI data for the NASA Johnson Space Center into a NASA-Wide GIS Institutional Portal.

Jagge, Amy↗

NASA NTRS2017

Challenges in Obtaining and Visualizing Satellite Level 2 Data in GIS

Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted. Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. One way to help users better understand the satellite data is to provide data along with Images, including accurate pixel coverage area delineation, and science team recommended quality screening for individual geophysical parameters. However, there are challenges of visualizing remote sensed non-gridded products: (1) different geodetics of space-borne instruments (2) data often arranged in a long-track and a cross-track axes (3) spatially and temporally continuous data chunked into granule files: data for a portion (or all) of a satellite orbit (4) no general rule of resampling or interpolations to a grid (5) geophysical retrieval only based on pixel center location without shape information. In this presentation, we will unravel a new Goddard Earth Sciences Data and Information Services Center (GES DISC) Level 2 (L2) visualization on-demand service. The service's front end provides various visualization and data accessing capabilities, such as overlay and swipe of multiply variables and subset and download of data in different formats. The backend of the service consists of Open Geospatial Consortium (OGC) standard-compliant Web Mapping Service (WMS) and Web Coverage Service. The infrastructure allows inclusion of outside data sources served in OGC compliant protocols and allows other interoperable clients, such as ArcGIS clients, to connect to our L2 WCS/WMS.

GI↗

NASA NTRS2019

Earth Science and Remote Sensing Data Portal Earth Science and Remote Sensing Geospatial Data Portal

The Earth Science and Remote Sensing (ESRS) Data Portal, managed by the ESRS Unit at NASA's Johnson Space Center, is a web GIS environment built on Esri's ArcGIS Enterprise platform. Its current intent is to visualize, analyze, and distribute geospatial data to our internal organization and the communities of Houston and Galveston. Applications on the ESRS Data Portal include a Regional Remote Sensing web app, story maps developed for astronaut training, and a web scene for visualizing LiDAR.

Jagge, Amy M.↗

NASA NTRS2019

OPeNDAP Clients, Aggregation and S3

In this talk, we will discuss our work for testing OPeNDAP client access of data stored in the Amazon S3 cloud storage using a set of common analysis tools including Panoply, Jupyter Notebooks with Python xarray, NCO command line tool package, ArcGIS, and GDAL. We will also discuss our ongoing work on improving performance in Hyrax aggregation functionality.

Amazon S3↗

NASA NTRS2018

NASA’s Mid-Atlantic Communities and Areas at Intensive Risk Demonstration: Translating Compounding Hazards to Societal Risk

Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASA’s Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASA’s Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.

Rogers, Laura↗

NASA NTRS2019

Flood Mapping of Recent Major Hurricane Events with Synthetic Aperture Radar, Commercial Imaging, and Aerial Observations

Floodwater mapping is an important remote sensing process that is used for disaster response, recovery, and damage assessment practices. Developing a system to read in Synthetic Aperture Radar (SAR) data and perform land cover classification will allow for the production of near real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of the situation. SAR is a unique remote sensing tool. Data in this project was obtained by NASA Jet Propulsion Laboratory’s Uninhabited Aerial Vehicle SAR (UAVSAR), an L-band radar mounted to a Gulfstream III jet. Data collected by UAVSAR is similar to what will be available from the NASA-Indian Space Research Organization (NISAR) mission starting in early 2022. Using Python and ArcGIS applications, a model was developed using training samples taken from NOAA post-event aerial photography and UAVSAR data gathered in the aftermath of Hurricane Florence in September 2018.

Melancon, Alexander M.↗

NASA NTRS2019

Explore Air Quality from NASA Satellite Data

Space-borne earth observation has been important to monitor the earth condition and played acritical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISChas been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Giovanni to ArcGIS, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.

Wei, J.↗

NASA NTRS2019

Completeness Index for Earthquake-Induced Landslide Inventories

Understanding the global relation between Earthquake-Induced Landslides (EQILs) and the factors contributing to their initiation is still an open topic within the geomorphological community. Accessing EQIL inventories and analyzing them concerning their potential causes is the key to explore such relation. However, each of the existing EQIL inventories has its level of completeness and associated uncertainty which makes any unified relationship which is challenging to obtain. So far, the completeness of EQIL inventories has never been clearly defined. As a result, it has never been accounted for in global EQIL predictive models. In this technical note, we propose a simple definition for the completeness of EQIL inventories. We analyze 30 digital EQIL for 21 earthquakes and develop a semi-quantitative method to estimate the completeness level, which we refer to as Completeness Index (CI). The CI results from a combination of topographic factors, ground shaking parameters, and a measure derived from landslide size statistics. The proposed CI consists of Low, Moderate, and High completeness classes which can be used to evaluate any landslide inventory. We made the whole procedure to compute the CI accessible in an ArcGIS toolbox together with a test dataset.

Hakan Tanyas↗

NASA NTRS2020

Eastern Washington Disasters: Integrating NASA Earth Observations to Analyze Spatiotemporal Distributions of Lightning-Caused Wildfires in Eastern Washington

According to the Washington Department of Natural Resources, roughly 36% of large fires in the state since 2010 were caused by lightning. General trends also show a greater increase in the number of lightning-ignited fires over the last three decades. The NASA DEVELOP Eastern Washington Disasters team partnered with The Nature Conservancy’s Washington Chapter to analyze the relationship between lightning strikes and wildfire events in Eastern Washington, with an emphasis on Kittitas and Yakima Counties. Using the International Space Station Lightning Imaging Sensor, the Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager vegetation moisture index, and Washington Department of Natural Resources historical fire data, the team generated a lightning-caused fire vulnerability index for 2001-2019. Climatology maps of lightning, wildfire, and vegetation moisture of the study area, along with an Esri ArcGIS StoryMap, further communicated project findings. The project results demonstrated that spatiotemporal patterns of lightning-ignited wildfires in Eastern Washington can be useful to inform land management practices and better predict areas that may be more vulnerable to these events.

Disasters↗

NASA NTRS2020

Eastern Washington Disasters: Integrating NASA Earth Observations to Analyze Spatiotemporal Distributions of Lightning-Caused Wildfires in Eastern Washington

According to the Washington Department of Natural Resources, roughly 36% of large fires in the statesince 2010 were caused by lightning. General trends also show a greater increase in the number of lightning-ignited fires over the last three decades. The NASA DEVELOP Eastern Washington Disasters team partnered with The Nature Conservancy’s Washington Chapter to analyze the relationship between lightning strikes and wildfire events in Eastern Washington, with an emphasis on Kittitas and Yakima Counties. Using the International Space Station Lightning Imaging Sensor, the Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager vegetation moisture index, and Washington Department of Natural Resources historical fire data, the team generated a lightning-caused fire vulnerability index for 2001-2019. Climatology maps of lightning, wildfire, and vegetation moisture of the study area, along with an Esri ArcGIS StoryMap, further communicated project findings. The project results demonstrated that spatiotemporal patterns of lightning-ignited wildfires in Eastern Washington can be useful to inform land management practices and better predict areas that may be more vulnerable to these events.

Disasters↗

NASA NTRSDate not supplied

Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce the Urban Heat Island Effect in Cambridge, Massachusetts

Elevated temperatures resulting from the urban heat island (UHI) effect can have widespread impacts on human health, infrastructure, and ecosystems. These impacts can be exacerbated by changes in climate and extreme variation in regional temperature. By 2030, experts expect Cambridge, Massachusetts will experience warmer than average temperatures, more heat waves, and triple the number of abnormally warm days above 90°F per year. The NASA DEVELOP Program partnered with the City of Cambridge’s Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing efforts aimed at reducing the impacts of urban heat in the city of Cambridge. The team used scenes obtained from High Resolution Orthoimagery and the National Agriculture Imagery Program in conjunction with building footprint data, to calculate rooftop albedo between 2008 and 2018. Using these results, maps displaying building-specific variation in albedo across Cambridge were created. A nighttime land surface temperature (LST) record for June, July, and August between 2003 and 2019 was constructed using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) nighttime LST product. Lastly, temperature anomalies were calculated for Cambridge using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. The results of this project were then incorporated into an interactive ArcGIS Dashboard. This work will allow end users to explore spatial and temporal trends in albedo, nighttime LST, and temperature anomalies to assess whether the City of Cambridge is successfully reducing the effects of UHIs.

NASA DEVELOP↗

NASA NTRS2020

Mark Twain National Forest Ecological Forecasting - Utilizing NASA Earth Observations to Classify Ground Cover Types in the Mark Twain National Forest

The Mark Twain National Forest (MTNF) encompasses 1.5 million acres of public land in the Ozarks region of southeastern Missouri. The industrial boom between the 1880s and 1920s had devastating effects on the shortleaf pine (Pinus echinata),Missouri’s only native pine species. The combination of fire suppression on this fire-dependent species and timber harvest of mature pine stands inhibited the development of pine seedlings and promoted the establishment of hardwood stands. Partners at the US Forest Service’s MTNF are currently involved in restoration efforts in two ranger districts, which includes removing invasive eastern red cedar (Juniperus virginiana) and prescribed burning. To expand spatial coverage for the MTNF beyond in situ observation sites, the NASA DEVELOP team analyzed land cover change from 1986 through 2019 and forecasted changes based on a ‘business-as-usual' scenario out to 2040. The team incorporated remotely sensed data from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) into the random trees supervised classification tool in ArcGIS Pro. This tool spectrally separated pixels into five distinct land cover classes and produced classifications for 1986 and 2019, with kappa statistics of 0.87 and 0.81, respectively. Overall, there was a net decrease in conifer and meadow land cover between 1986 and 2019 along with a net increase in water, developed, and deciduous land cover. The team used TerrSet’s Land Change Modeler to forecast land cover through 2040. Results showed an increase in coniferous land cover and a decrease in deciduous cover, indicating a high probability that current restoration efforts will produce the intended effect.

Kaitlyn Bretz↗

NASA NTRS2020

Southern Bhutan Ecological Forecasting Tech Paper: Modeling Asian Elephant (Elephas maximus) Habitat Suitability along the Southern Bhutan Border with NASA Earth Observations

Asian elephants (Elephas maximus) are a flagship species essential for the functioning of forest ecosystems, and they also have cultural significance in Bhutan. Elephants receive the highest legal protection as listed under Schedule I of the Bhutan Forests and Nature Conservation Act, 1995. Yet, they face threats of extinction due to poaching for ivory as well as the loss and fragmentation of their habitat. Due to the recent clearing of forests and the growing populations in these areas, there has been an increase in incidents of human-elephant conflict. These conflicts have been detrimental to farmer’s annual harvests and livelihoods and have led to retaliatory killing and injury of elephants in southern Bhutan. The DEVELOP Southern Bhutan Ecological Forecasting team partnered with the Bhutan Foundation and Bhutan Tiger Center to help address this problem. The team integrated NASA Earth observations, including Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Shuttle Radar Topography Mission (SRTM) to acquire information on land cover change and elephant habitat suitability along the southern border of Bhutan. The team utilized Esri ArcGIS Pro and Software for Assisted Habitat Modeling (SAHM) for data analysis, modeling, and visualization. The team used elephant occurrence data and environmental variables to model current habitat suitability for migrating elephant populations. This analysis provided partners with maps to inform decisions about the placement and conservation of elephant corridors and helped build their capacity to use satellite data for future studies and project planning.

Palchen Wangchuk↗