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Leading change agents, people with the expertise and authority to effect change within their organization, industry, or discipline converged on a set of recommendations to address the growing costs of tactical fire management.
E. Natasha Stavros; Virginia IglesiasiD; Amy Decastro. The Wicked Wildfire Problem and Solution Space for Detecting and Tracking the Fires that Matter. 2021, 1 .
AMA StyleE. Natasha Stavros, Virginia IglesiasiD, Amy Decastro. The Wicked Wildfire Problem and Solution Space for Detecting and Tracking the Fires that Matter. . 2021; ():1.
Chicago/Turabian StyleE. Natasha Stavros; Virginia IglesiasiD; Amy Decastro. 2021. "The Wicked Wildfire Problem and Solution Space for Detecting and Tracking the Fires that Matter." , no. : 1.
Wicked problems result from complex systems and often have no single solution. WKID Innovation is a framework to tack wicked problems and is modeled after NASA’s science system engineering. NASA is a leader creating disruptive technologies that alter the way that people, companies, or industries operate. It has been pioneering innovation to advance human knowledge since 1958 engineering the first human landing on the moon, successfully landing rovers on Mars, and leaving our solar system, literally going where no man has gone before. NASA drives innovation to new frontiers in our galaxy and beyond, while also collecting accurate, reliable Earth observations that change the way we live our life in the day to day. WKID Innovation is a framework to scale NASA processes for innovation, specifically by using the knowledge hierarchy to bridge design thinking and complex systems science to system engineer and manage disruptive innovation. Keywords: innovation; NASA; information technology; process; wicked problem
E. Natasha Stavros. Wicked Problems need WKID Innovation: Innovation as a Process to Develop a Disruptive Technology Product. Qeios 2021, 1 .
AMA StyleE. Natasha Stavros. Wicked Problems need WKID Innovation: Innovation as a Process to Develop a Disruptive Technology Product. Qeios. 2021; ():1.
Chicago/Turabian StyleE. Natasha Stavros. 2021. "Wicked Problems need WKID Innovation: Innovation as a Process to Develop a Disruptive Technology Product." Qeios , no. : 1.
It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models.
Red Coleman; Natasha Stavros; Glynn Hulley; Nicholas Parazoo. Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California. Remote Sensing 2020, 12, 4102 .
AMA StyleRed Coleman, Natasha Stavros, Glynn Hulley, Nicholas Parazoo. Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California. Remote Sensing. 2020; 12 (24):4102.
Chicago/Turabian StyleRed Coleman; Natasha Stavros; Glynn Hulley; Nicholas Parazoo. 2020. "Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California." Remote Sensing 12, no. 24: 4102.
The Surface Biology and Geology global imaging spectrometer is primarily designed to observe the chemical fingerprint of the Earth’s surface. However imaging spectroscopy across the visible to shortwave infrared (VSWIR) can also provide important atmospheric observations of methane point sources, highly concentrated emissions from energy, waste management and livestock operations. Relating these point-source observations to greenhouse gas inventories and coarser, regional methane observations from sensors like the European Space Agency (ESA) TROPOMI will contribute to reducing uncertainties in local, regional and global carbon budgets. We present the Multi-scale Methane Analytic Framework (M2AF) that facilitates disentangling confounding processes by streamlining analysis of cross-scale, multi-sensor methane observations across three key, overlapping spatial scales: 1) global to regional scale, 2) regional to local scale, and 3) facility (point source scale). M2AF is an information system that bridges methane research and applied science by integrating tiered observations of methane from surface measurements, airborne sensors and satellite. Reducing uncertainty in methane fluxes with multi-scale analyses can improve carbon accounting and attribution which is valuable to both formulation and verification of mitigation actions. M2AF lays the foundation for extending existing methane analysis systems beyond their current experimental states, reducing latency and cost of methane data analysis and improving accessibility by researchers and decision makers. M2AF leverages the NASA Methane Source Finder (MSF), the NASA Science Data Analytics Platform (SDAP), Amazon Web Services (AWS) and two supercomputers for fast, on-demand analytics of cross-scale, integrated, quality-controlled methane flux estimates.
E. Natasha StavrosiD; Riley Duren; Andrew Thorpe; Daniel Cusworth; Brian Bue; Joseph Jacob; Winston Olson-DuvalliD; Robert Tapella; Kevin Gill; John Worden; Daniel Jacob; Vineet Yadav; Elizabeth Yam. Integrating Point-Source Methane Emissions from Imaging Spectroscopy Data into the Multi-scale Methane Analytic Framework (M2AF) Information System. 2020, 1 .
AMA StyleE. Natasha StavrosiD, Riley Duren, Andrew Thorpe, Daniel Cusworth, Brian Bue, Joseph Jacob, Winston Olson-DuvalliD, Robert Tapella, Kevin Gill, John Worden, Daniel Jacob, Vineet Yadav, Elizabeth Yam. Integrating Point-Source Methane Emissions from Imaging Spectroscopy Data into the Multi-scale Methane Analytic Framework (M2AF) Information System. . 2020; ():1.
Chicago/Turabian StyleE. Natasha StavrosiD; Riley Duren; Andrew Thorpe; Daniel Cusworth; Brian Bue; Joseph Jacob; Winston Olson-DuvalliD; Robert Tapella; Kevin Gill; John Worden; Daniel Jacob; Vineet Yadav; Elizabeth Yam. 2020. "Integrating Point-Source Methane Emissions from Imaging Spectroscopy Data into the Multi-scale Methane Analytic Framework (M2AF) Information System." , no. : 1.
The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC; formerly GeoSPEC) pioneers an on-demand science data processing system (SDPS) producing user-customized Level 1 calibrated radiance to Level 3+ data products in anticipation for the 2017-2027 Earth Decadal Survey prioritized spaceborne global imaging spectrometer to advance the study of Surface Biology and Geology (SBG). SBG data volumes (~20 TB/day) of high dimensionality (>224 bands) would be infeasible to download and the breadth of applications of the data across dozens of disciplines presents a need to evolve the traditional NASA SDPS. ImgSPEC streamlines processing data into key SBG observables that have demonstrated algorithms at local-to-regional scales and may vary locally. As such, a traditional, monolithic SDPS could not fully exploit the information in SBG measurements. To remove this barrier to use, ImgSPEC demonstrates an on-demand SDPS prototype that improves imaging spectroscopy data discovery, access, and utility enabling shared knowledge transfer from advanced imaging spectroscopy users to less experienced users such as decision makers and the general public. We test three use cases: 1) standard data processing workflows, 2) customized variants of standard workflows, and 3) algorithm development of new workflows. We create collaborative algorithm development environments that offer services typically restricted to NASA SDPSs such as data product provenance and bulk processing. We leverage existing NASA-funded information technologies such as the hybrid on-premise/ cloud science data system (HySDS), the Multi-mission Algorithm and Analysis Platform (MAAP), ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the SBG Science and Applications Communities.
E. Natasha StavrosiD; Philip Townsend; George Chang; Hook Hua; Thomas Huang; Justin MerziD; Winston Olson-DuvalliD; William Phyo; SuJen Shah; David Thompson. Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC). 2020, 1 .
AMA StyleE. Natasha StavrosiD, Philip Townsend, George Chang, Hook Hua, Thomas Huang, Justin MerziD, Winston Olson-DuvalliD, William Phyo, SuJen Shah, David Thompson. Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC). . 2020; ():1.
Chicago/Turabian StyleE. Natasha StavrosiD; Philip Townsend; George Chang; Hook Hua; Thomas Huang; Justin MerziD; Winston Olson-DuvalliD; William Phyo; SuJen Shah; David Thompson. 2020. "Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC)." , no. : 1.
The NASA open data policy and the increases in data volume from recent and future missions have resulted in a need to re-evaluate data archive information systems and services. For example, the Surface Water Ocean Topography mission (SWOT) will produce ∼15 TB of data every day that needs to be freely and openly accessible through the NASA Physical Oceanography Distribute Active Archive Center (PO.DAAC). Because of the computational, and distribution challenges associated with a system of data systems and making such volumes of Earth Observation data publicly available, the NASA Earth Science Data Information System (ESDIS) is moving their archive systems, like PO.DAAC, to the cloud. To facilitate this migration, system of system requirements need to accommodate expected use. As such, the objective of this study was to assesses expected user needs and develop a quantitative framework to inform system of system requirements that trace services enabling data discovery, access and utility to a cloud-based data archive system architecture. We used a two-tier data gathering method that included a short survey of potential users (identified at professional meetings and community listservs) and in-depth interviews. The survey provided a means for scaling findings from the in-depth interviews to a more representative sample, thus enabling assessment of expected impact to various user communities for different services that could be provided. This framework enables traceability for setting priorities for data services development to requirements for a cloud-based data archive system and services that enable data discovery, access and utility.
E. Natasha Stavros; Catalina M. Oaida; Jessica Hausman; Michelle M. Gierach. A Quantitative Framework to Inform Cloud Data System Architecture and Services Requirements Based on User Needs and Expected Demand. IEEE Access 2020, 8, 138088 -138101.
AMA StyleE. Natasha Stavros, Catalina M. Oaida, Jessica Hausman, Michelle M. Gierach. A Quantitative Framework to Inform Cloud Data System Architecture and Services Requirements Based on User Needs and Expected Demand. IEEE Access. 2020; 8 (99):138088-138101.
Chicago/Turabian StyleE. Natasha Stavros; Catalina M. Oaida; Jessica Hausman; Michelle M. Gierach. 2020. "A Quantitative Framework to Inform Cloud Data System Architecture and Services Requirements Based on User Needs and Expected Demand." IEEE Access 8, no. 99: 138088-138101.
High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities.
Red Coleman; Natasha Stavros; Vineet Yadav; Nicholas Parazoo. A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity. Remote Sensing 2020, 12, 2399 .
AMA StyleRed Coleman, Natasha Stavros, Vineet Yadav, Nicholas Parazoo. A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity. Remote Sensing. 2020; 12 (15):2399.
Chicago/Turabian StyleRed Coleman; Natasha Stavros; Vineet Yadav; Nicholas Parazoo. 2020. "A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity." Remote Sensing 12, no. 15: 2399.
Traditional methods for assessing fire danger often depend on meteorological forecasts, which have reduced reliability after ∼10 d. Recent studies have demonstrated long lead-time correlations between pre-fire-season hydrological variables such as soil moisture and later fire occurrence or area burned, yet the potential value of these relationships for operational forecasting has not been studied. Here, we use soil moisture data refined by remote sensing observations of terrestrial water storage from NASA's Gravity Recovery and Climate Experiment (GRACE) mission and vapor pressure deficit from NASA's Atmospheric Infrared Sounder (AIRS) mission to generate monthly predictions of fire danger at scales commensurate with regional management. We test the viability of predictors within nine US geographic area coordination centers (GACCs) using regression models specific to each GACC. Results show that the model framework improves interannual wildfire-burned-area prediction relative to climatology for all GACCs. This demonstrates the importance of hydrological information to extend operational forecast ability into the months preceding wildfire activity.
Alireza Farahmand; E. Natasha Stavros; John T. Reager; Ali Behrangi; James T. Randerson; Brad Quayle. Satellite hydrology observations as operational indicators of forecasted fire danger across the contiguous United States. Natural Hazards and Earth System Sciences 2020, 20, 1097 -1106.
AMA StyleAlireza Farahmand, E. Natasha Stavros, John T. Reager, Ali Behrangi, James T. Randerson, Brad Quayle. Satellite hydrology observations as operational indicators of forecasted fire danger across the contiguous United States. Natural Hazards and Earth System Sciences. 2020; 20 (4):1097-1106.
Chicago/Turabian StyleAlireza Farahmand; E. Natasha Stavros; John T. Reager; Ali Behrangi; James T. Randerson; Brad Quayle. 2020. "Satellite hydrology observations as operational indicators of forecasted fire danger across the contiguous United States." Natural Hazards and Earth System Sciences 20, no. 4: 1097-1106.
Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.
Alireza Farahmand; E. Natasha Stavros; John T. Reager; Ali Behrangi. Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States. Remote Sensing 2020, 12, 1252 .
AMA StyleAlireza Farahmand, E. Natasha Stavros, John T. Reager, Ali Behrangi. Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States. Remote Sensing. 2020; 12 (8):1252.
Chicago/Turabian StyleAlireza Farahmand; E. Natasha Stavros; John T. Reager; Ali Behrangi. 2020. "Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States." Remote Sensing 12, no. 8: 1252.
Methane emissions monitoring is rapidly expanding with increasing coverage of surface, airborne, and satellite instruments. However, no single methane instrument or observing strategy can both close emission budgets and pinpoint point sources on regional to global scales. Instead, we present a multi-tiered data analytics system that synthesizes information across various instruments into a single analytic framework. We highlight an example in Los Angeles, where we combine surface measurements from the Los Angeles megacities project, mountaintop measurements from the CLARS-FTS instrument, airborne AVIRIS-NG point source emission estimates, and TROPOMI total column retrievals into a single analytic framework. Surface, mountaintop, and satellite measurements are assimilated into a methane flux inverse model to constrain basin-wide emissions and pinpoint sub-basin methane hotspots. We show an example of a large urban landfill, whose anomalous emissions were detected by the inverse system, and validated using AVIRIS-NG methane plume maps. This general approach of quantifying both methane area and point source emissions is an avenue not just for closing regional to global scale budgets, but also for understanding which emission sources dominate the budget (i.e., so called methane super-emitters). We finally show how this multi-tiered analytic framework can be improved with future satellite missions, and present examples of unexpectedly large methane emissions that were detected by a new generation of satellite imaging spectrometers.
Daniel Cusworth; Riley Duren; Andrew Thorpe; Natasha Stavros; Brian Bue; Robert Tapella; Vineet Yadav; Charles Miller. A multi-tiered methane analytic framework for constraining budgets, point source attribution, and anomalous event detection. 2020, 1 .
AMA StyleDaniel Cusworth, Riley Duren, Andrew Thorpe, Natasha Stavros, Brian Bue, Robert Tapella, Vineet Yadav, Charles Miller. A multi-tiered methane analytic framework for constraining budgets, point source attribution, and anomalous event detection. . 2020; ():1.
Chicago/Turabian StyleDaniel Cusworth; Riley Duren; Andrew Thorpe; Natasha Stavros; Brian Bue; Robert Tapella; Vineet Yadav; Charles Miller. 2020. "A multi-tiered methane analytic framework for constraining budgets, point source attribution, and anomalous event detection." , no. : 1.
Traditional methods for assessing fire danger often depend on meteorological forecasts, which have reduced reliability after ~ 10 days. Recent studies have demonstrated long lead-time correlations between pre-fire-season hydrological variables such as soil moisture and later fire occurrence or area burned, yet no potential value of these relationships for operational forecasting have not been studied. Here, we use soil moisture data refined by remote sensing observations of terrestrial water storage from NASA’s GRACE mission and vapor pressure deficit from NASA’s AIRS mission to generate monthly predictions of fire danger at scales commensurate with regional management. We test the viability of predictors within nine US Geographic Area Coordination Centers (GACCs) using regression models specific to each GACC. Results show that the model framework improves interannual wildfire burned area prediction relative to climatology for all GACCs. This demonstrates the importance of hydrological information to extend operational forecast ability into the months preceding wildfire activity.
Alireza Farahmand; E. Natasha Stavros; John T. Reager; Ali Behrangi; James Randerson; Brad Quayle. Satellite Hydrology Observations as Operational Indicators of Forecasted Fire Danger across the Contiguous United States. 2019, 2019, 1 -16.
AMA StyleAlireza Farahmand, E. Natasha Stavros, John T. Reager, Ali Behrangi, James Randerson, Brad Quayle. Satellite Hydrology Observations as Operational Indicators of Forecasted Fire Danger across the Contiguous United States. . 2019; 2019 ():1-16.
Chicago/Turabian StyleAlireza Farahmand; E. Natasha Stavros; John T. Reager; Ali Behrangi; James Randerson; Brad Quayle. 2019. "Satellite Hydrology Observations as Operational Indicators of Forecasted Fire Danger across the Contiguous United States." 2019, no. : 1-16.
Remote sensing data are most useful if they are available with sufficient precision, accuracy, spatiotemporal and spectral sampling, as well as continuity across decades. The Landsat and Sentinel series, as well other satellites are currently covering significant parts of this observational trade space. It can be expected that growing demands and budget constraints will require new capabilities in orbit that can address as many observables as possible with a single instrument. Recent optical performance improvements of imaging spectrometers make them true alternatives to traditional multispectral imagers. However, they are much more adaptable to a wide range of Earth observation needs due to the combination of continuous high spectral sampling with spatial sampling consistent with previous sensors (e.g., Landsat). Unfortunately, there is a knowledge gap in demonstrating that imaging spectroscopy data can substitute for multi-spectral data while sustaining the long-term record. Thus, the objective of this analysis is to test the hypothesis that imaging spectroscopy data compare radiometrically with multi-spectral data to within 5%. Using a coincident Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flight with over-passing Operational Land Imager (OLI) data on Landsat 8, we document a procedure for simulating OLI multi-spectral bands from AVIRIS data, evaluate influencing factors on the observed radiance, and assess the difference in top-of-atmosphere radiance as compared to OLI. The procedure for simulating OLI data include spectral convolution, accounting for the minimal atmospheric effects between the two sensors, and spatial resampling. The remaining differences between the simulated and the real OLI data result mainly from differences in sensor calibration, surface bi-directional reflectance, and spatial sampling. The median relative radiometric difference for each band ranges from −8.3% to 0.6%. After bias-correction to minimize potential calibration discrepancies, we find no more than a 1.2% relative difference. This analysis therefore successfully demonstrates that imaging spectrometer data can contribute to Landsat-type or other multi-spectral data records. It also shows that cross-calibration from a spectrometer to a radiometer can be easily performed as a result of the imaging spectrometer high spectral sampling and its ability to recreate multi-spectral response functions.
Felix C. Seidel; E. Natasha Stavros; Morgan L. Cable; Robert Green; Anthony Freeman. Imaging spectrometer emulates Landsat: A case study with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Operational Land Imager (OLI) data. Remote Sensing of Environment 2018, 215, 157 -169.
AMA StyleFelix C. Seidel, E. Natasha Stavros, Morgan L. Cable, Robert Green, Anthony Freeman. Imaging spectrometer emulates Landsat: A case study with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Operational Land Imager (OLI) data. Remote Sensing of Environment. 2018; 215 ():157-169.
Chicago/Turabian StyleFelix C. Seidel; E. Natasha Stavros; Morgan L. Cable; Robert Green; Anthony Freeman. 2018. "Imaging spectrometer emulates Landsat: A case study with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Operational Land Imager (OLI) data." Remote Sensing of Environment 215, no. : 157-169.
Fire is a widespread Earth system process with important carbon and climate feedbacks. Multispectral remote sensing has enabled mapping of global spatiotemporal patterns of fire and fire effects, which has significantly improved our understanding of interactions between ecosystems, climate, humans and fire. With several upcoming spaceborne hyperspectral missions like the Environmental Mapping And Analysis Program (EnMAP), the Hyperspectral Infrared Imager (HyspIRI) and the Precursore Iperspettrale Della Missione Applicativa (PRISMA), we provide a review of the state-of-the-art and perspectives of hyperspectral remote sensing of fire. Hyperspectral remote sensing leverages information in many (often more than 100) narrow (smaller than 20 nm) spectrally contiguous bands, in contrast to multispectral remote sensing of few (up to 15) non-contiguous wider (greater than 20 nm) bands. To date, hyperspectral fire applications have primarily used airborne data in the visible to short-wave infrared region (VSWIR, 0.4 to 2.5 μm). This has resulted in detailed and accurate discrimination and quantification of fuel types and condition, fire temperatures and emissions, fire severity and vegetation recovery. Many of these applications use processing techniques that take advantage of the high spectral resolution and dimensionality such as advanced spectral mixture analysis. So far, hyperspectral VSWIR fire applications are based on a limited number of airborne acquisitions, yet techniques will approach maturity for larger scale application when spaceborne imagery becomes available. Recent innovations in airborne hyperspectral thermal (8 to 12 μm) remote sensing show potential to improve retrievals of temperature and emissions from active fires, yet these applications need more investigation over more fires to verify consistency over space and time, and overcome sensor saturation issues. Furthermore, hyperspectral information and structural data from, for example, light detection and ranging (LiDAR) sensors are highly complementary. Their combined use has demonstrated advantages for fuel mapping, yet its potential for post-fire severity and combustion retrievals remains largely unexplored.
Sander Veraverbeke; Philip Dennison; Ioannis Gitas; Glynn Hulley; Olga Kalashnikova; Thomas Katagis; Le Kuai; Ran Meng; Dar Roberts; Natasha Stavros. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment 2018, 216, 105 -121.
AMA StyleSander Veraverbeke, Philip Dennison, Ioannis Gitas, Glynn Hulley, Olga Kalashnikova, Thomas Katagis, Le Kuai, Ran Meng, Dar Roberts, Natasha Stavros. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment. 2018; 216 ():105-121.
Chicago/Turabian StyleSander Veraverbeke; Philip Dennison; Ioannis Gitas; Glynn Hulley; Olga Kalashnikova; Thomas Katagis; Le Kuai; Ran Meng; Dar Roberts; Natasha Stavros. 2018. "Hyperspectral remote sensing of fire: State-of-the-art and future perspectives." Remote Sensing of Environment 216, no. : 105-121.
In the version of this Comment previously published, in Box 1, the spacing of the GEDI footprints should have read 60 m along the track, not 25 m. Also the second affiliation for Susan Ustin was incorrect, she is only associated with the University of California, Davis. These errors have now been corrected.
E Natasha Stavros; David Schimel; Ryan Pavlick; Shawn Serbin; Abigail Swann; Laura Duncanson; Joshua B Fisher; Fabian Fassnacht; Susan Ustin; Ralph Dubayah; Anna Schweiger; Paul Wennberg. Author Correction: ISS observations offer insights into plant function. Nature Ecology & Evolution 2017, 1, 1584 -1584.
AMA StyleE Natasha Stavros, David Schimel, Ryan Pavlick, Shawn Serbin, Abigail Swann, Laura Duncanson, Joshua B Fisher, Fabian Fassnacht, Susan Ustin, Ralph Dubayah, Anna Schweiger, Paul Wennberg. Author Correction: ISS observations offer insights into plant function. Nature Ecology & Evolution. 2017; 1 (10):1584-1584.
Chicago/Turabian StyleE Natasha Stavros; David Schimel; Ryan Pavlick; Shawn Serbin; Abigail Swann; Laura Duncanson; Joshua B Fisher; Fabian Fassnacht; Susan Ustin; Ralph Dubayah; Anna Schweiger; Paul Wennberg. 2017. "Author Correction: ISS observations offer insights into plant function." Nature Ecology & Evolution 1, no. 10: 1584-1584.
E. Natasha Stavros; Donald McKenzie; Narasimhan K Larkin. The climate-wildfire-air quality system: interactions and feedbacks across spatial and temporal scales. WIREs Climate Change 2014, 5, 719 -733.
AMA StyleE. Natasha Stavros, Donald McKenzie, Narasimhan K Larkin. The climate-wildfire-air quality system: interactions and feedbacks across spatial and temporal scales. WIREs Climate Change. 2014; 5 (6):719-733.
Chicago/Turabian StyleE. Natasha Stavros; Donald McKenzie; Narasimhan K Larkin. 2014. "The climate-wildfire-air quality system: interactions and feedbacks across spatial and temporal scales." WIREs Climate Change 5, no. 6: 719-733.
Seasonal changes in the climatic potential for very large wildfires (VLWF ≥ 50,000 ac ~ 20,234 ha) across the western contiguous United States are projected over the 21st century using generalized linear models and downscaled climate projections for two representative concentration pathways (RCPs). Significant ( ≤ 0.05) increases in VLWF probability for climate of the mid-21st century (2031–2060) relative to contemporary climate are found, for both RCP 4.5 and 8.5. The largest differences are in the Eastern Great Basin, Northern Rockies, Pacific Northwest, Rocky Mountains, and Southwest. Changes in seasonality and frequency of VLWFs d7epend on changes in the future climate space. For example, flammability-limited areas such as the Pacific Northwest show that (with high model agreement) the frequency of weeks with VLWFs in a given year is 2–2.7 more likely. However, frequency of weeks with at least one VLWF in fuel-limited systems like the Western Great Basin is 1.3 times more likely (with low model agreement). Thus, areas where fire is directly associated with hot and dry climate, as opposed to experiencing lagged effects from previous years, experience more change in the likelihood of VLWF in future projections. The results provide a quantitative foundation for management to mitigate the effects of VLWFs.
E. Natasha Stavros; John Abatzoglou; Donald McKenzie; Narasimhan K. Larkin. Regional projections of the likelihood of very large wildland fires under a changing climate in the contiguous Western United States. Climatic Change 2014, 126, 455 -468.
AMA StyleE. Natasha Stavros, John Abatzoglou, Donald McKenzie, Narasimhan K. Larkin. Regional projections of the likelihood of very large wildland fires under a changing climate in the contiguous Western United States. Climatic Change. 2014; 126 (3):455-468.
Chicago/Turabian StyleE. Natasha Stavros; John Abatzoglou; Donald McKenzie; Narasimhan K. Larkin. 2014. "Regional projections of the likelihood of very large wildland fires under a changing climate in the contiguous Western United States." Climatic Change 126, no. 3: 455-468.
Very large wildfires can cause significant economic and environmental damage, including destruction of homes, adverse air quality, firefighting costs and even loss of life. We examine how climate is associated with very large wildland fires (VLWFs ≥50000 acres, or ~20234ha) in the western contiguous USA. We used composite records of climate and fire to investigate the spatial and temporal variability of VLWF–climatic relationships. Results showed quantifiable fire weather leading up and up to 3 weeks post VLWF discovery, thus providing predictors of the probability that VLWF occurrence in a given week. Models were created for eight National Interagency Fire Center Geographic Area Coordination Centers (GACCs). Accuracy was good (AUC>0.80) for all models, but significant fire weather predictors of VLWFs vary by GACC, suggesting that broad-scale ecological mechanisms associated with wildfires also vary across regions. These mechanisms are very similar to those found by previous analyses of annual area burned, but this analysis provides a means for anticipating VLWFs specifically and thereby the timing of substantial area burned within a given year, thus providing a quantifiable justification for proactive fire management practices to mitigate the risk and associated damage of VLWFs.
E. Natasha Stavros; John Abatzoglou; Narasimhan K. Larkin; Donald McKenzie; E. Ashley Steel. Climate and very large wildland fires in the contiguous western USA. International Journal of Wildland Fire 2014, 23, 899 -914.
AMA StyleE. Natasha Stavros, John Abatzoglou, Narasimhan K. Larkin, Donald McKenzie, E. Ashley Steel. Climate and very large wildland fires in the contiguous western USA. International Journal of Wildland Fire. 2014; 23 (7):899-914.
Chicago/Turabian StyleE. Natasha Stavros; John Abatzoglou; Narasimhan K. Larkin; Donald McKenzie; E. Ashley Steel. 2014. "Climate and very large wildland fires in the contiguous western USA." International Journal of Wildland Fire 23, no. 7: 899-914.