The use of NASA LANCE imagery and data for Near real-time applications

Davies, Erin L; Murphy, .; Conover, Justin L.; Regner,; Beaumont, Olivier; Masuoka,; Vollmer,; Durbin,; Michael, Cassia; Boller,; Schmaltz,; Horrocks,; Ilavajhala,; Ullah,; Teague,; Thompson, Daniel; Bingham,

doi:10.1109/igarss.2012.6352410

Cited by 6 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From our interactive web app tool (see Figure 20 below), we noticed that most work does not include hardware and software specifications (e.g., what CPU/GPU the authors used to run their models, what Python libraries they used to implement the DL models, etc.) and/or processing times [244]. Of the 200 total papers we reviewed, 101 ran strictly in cloud computing environments (i.e., they had no offline component).…”

Section: Brief Summary Of Reviewed Studiesmentioning

confidence: 99%

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

View full text Add to dashboard Cite

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

show abstract

Section: Brief Summary Of Reviewed Studiesmentioning

confidence: 99%

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua remote sensing satellites has been successfully imaging the Earth's surface since 2000 and 2002, respectively. With spatial resolutions of 250 m, 500 m and 1 km and 36 spectral bands, MODIS provides temporal composites and daily images with near realtime access to data (Davies et al 2015) that are critical to many applications. The portfolio of the MODIS-based land products has been expanding and improving through 5 Collections, to include surface reflectance (SR), vegetation indices (VIs), biophysical parameters (leaf area index and fraction of absorbed photosynthetically active radiation), net and gross primary productivity, bidirectional reflectance distribution function (BRDF), albedo, temperature and land cover.…”

Section: Introductionmentioning

confidence: 99%

Transitioning from MODIS to VIIRS: an analysis of inter-consistency of NDVI data sets for agricultural monitoring

Skakun

Justice

Vermote

et al. 2017

International Journal of Remote Sensing

View full text Add to dashboard Cite

The Visible/Infrared Imager/Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite was launched in 2011, in part to provide continuity with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard National Aeronautics and Space Administration's (NASA) Terra and Aqua remote sensing satellites. The VIIRS will eventually replace MODIS for both land science and applications and add to the coarse-resolution, long term data record. It is, therefore, important to provide the user community with an assessment of the consistency of equivalent products from the two sensors. For this study, we do this in the context of example agricultural monitoring applications. Surface reflectance that is routinely delivered within the M{O,Y}D09 and VNP09 series of products provide critical input for generating downstream products. Given the range of applications utilizing the normalized difference vegetation index (NDVI) generated from M{O,Y}D09 and VNP09 products and the inherent differences between MODIS and VIIRS sensors in calibration, spatial sampling, and spectral bands, the main objective of this study is to quantify uncertainties related the transitioning from using MODIS to VIIRS-based NDVI's. In particular, we compare NDVI's derived from two sets of Level 3 MYD09 and VNP09 products with various spatial-temporal characteristics, namely 8-day composites at 500 m spatial resolution and daily Climate Modelling Grid (CMG) images at 0.05° spatial resolution. Spectral adjustment of VIIRS I1 (red) and I2 (near infra-red - NIR) bands to match MODIS/Aqua b1 (red) and b2 (NIR) bands is performed to remove a bias between MODIS and VIIRS-based red, NIR, and NDVI estimates. Overall, red reflectance, NIR reflectance, NDVI uncertainties were 0.014, 0.029 and 0.056 respectively for the 500 m product and 0.013, 0.016 and 0.032 for the 0.05° product. The study shows that MODIS and VIIRS NDVI data can be used interchangeably for applications with an uncertainty of less than 0.02 to 0.05, depending on the scale of spatial aggregation, which is typically the uncertainty of the individual dataset.

show abstract

“…In the field of Emergency Response operations, improvements to satellite spatial resolution and latency have led to a wide adoption of geospatial informatics. Some data, such as those provided through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system, can commonly be made available within 3 h of overpass, making it suitable for NRT applications (Davies et al, 2015). Examples of such initiatives include NASA's NRT Flood Mapping Fayne et al, 2017;Policelli et al, 2017), UMD's Global Flood Monitoring System (GFMS, Wu et al, 2014), and the Dartmouth Flood Observatory (DFO, Brakenridge and Anderson, 2006).…”

Section: The Value Of Applied Earth Observations In Emergency Managementmentioning

confidence: 99%

The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response

Oddo

Bolten

2019

Front. Environ. Sci.

View full text Add to dashboard Cite

Information is a critical resource in disaster response scenarios. Data regarding the geographic extent, severity, and socioeconomic impacts of a disaster event can help guide emergency responders and relief operations, particularly when delivered within hours of data acquisition. Information from remote observations provides a valuable tool for assessing conditions "on the ground" more quickly and efficiently. Here, we evaluate the social value of a near real-time flood impact system using a disaster response case study, and quantify the Value of Information (VOI) of satellite-based observations for rapid response using a hypothetical flooding disaster in Bangkok, Thailand. MODIS imagery from NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system is used to produce operational estimates of inundation depths and economic damages. These rapid Earth observations are coupled with a decision-analytical model to inform decisions on emergency vehicle routing. Emergency response times from vehicles routed using flood damage data are compared with baseline routes without the benefit of advance information on road conditions. Our results illustrate how the application of near real-time Earth observations can improve the response time and reduce potential encounters with flood hazards when compared with baseline routing strategies. Results indicate a potential significant economic benefit (i.e., millions of dollars) from applying near real-time Earth observations for improved flood disaster response and management.

show abstract

The use of NASA LANCE imagery and data for Near real-time applications

Cited by 6 publications

References 3 publications

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Transitioning from MODIS to VIIRS: an analysis of inter-consistency of NDVI data sets for agricultural monitoring

The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response

Contact Info

Product

Resources

About