This page has only limited features, please log in for full access.
As droughts become more frequent and more severe under anthropogenic climate change, water stress due to diminished subsurface supplies may threaten the health and function of semi-arid riparian woodlands, which are assumed to be largely groundwater dependent. To better support the management of riparian woodlands under changing climatic conditions, it is essential to understand the sensitivity of riparian woodlands to depth to groundwater (DTG) across space and time. In this study, we examined six stands of riparian woodland along 28 km of the Santa Clara River in southern California. Combining remote sensing data of fractional land cover, based on spectral mixture analysis, with historical groundwater data, we assessed changes in riparian woodland health in response to DTG during the unprecedented 2012–2019 California drought. We observed a coherent 'brown wave' of tree mortality, characterized by decreases in healthy vegetation cover and increases in dead/woody vegetation cover, which progressed downstream through the Santa Clara River corridor between 2012 and 2016. We also found consistent, significant relationships between DTG and healthy vegetation cover, and separately between DTG and dead/woody vegetation cover, indicating that woodland health deteriorated in a predictable fashion as the water table declined at different sites and different times. Based on these findings, we conclude that the brown wave of vegetation dieback was likely caused by local changes in DTG associated with the propagation of precipitation deficits into a depleted shallow alluvial aquifer. These factors suggest that semi-arid riparian woodlands are strongly dependent on shallow groundwater availability, which is in turn sensitive to climate forcing.
Christopher L. Kibler; E. Claire Schmidt; Dar A. Roberts; John C. Stella; Li Kui; Adam M. Lambert; Michael Bliss Singer. A brown wave of riparian woodland mortality following groundwater declines during the 2012-2019 California drought. Environmental Research Letters 2021, 16, 084030 .
AMA StyleChristopher L. Kibler, E. Claire Schmidt, Dar A. Roberts, John C. Stella, Li Kui, Adam M. Lambert, Michael Bliss Singer. A brown wave of riparian woodland mortality following groundwater declines during the 2012-2019 California drought. Environmental Research Letters. 2021; 16 (8):084030.
Chicago/Turabian StyleChristopher L. Kibler; E. Claire Schmidt; Dar A. Roberts; John C. Stella; Li Kui; Adam M. Lambert; Michael Bliss Singer. 2021. "A brown wave of riparian woodland mortality following groundwater declines during the 2012-2019 California drought." Environmental Research Letters 16, no. 8: 084030.
Riparian ecosystems fundamentally depend on groundwater, especially in dryland regions, yet their water requirements and sources are rarely considered in water resource management decisions. Until recently, technological limitations and data gaps have hindered assessment of groundwater influences on riparian ecosystem health at the spatial and temporal scales relevant to policy and management. Here, we analyze Sentinel-2–derived normalized difference vegetation index (NDVI; n = 5,335,472 observations), field-based groundwater elevation (n = 32,051 observations), and streamflow alteration data for riparian woodland communities (n = 22,153 polygons) over a 5-y period (2015 to 2020) across California. We find that riparian woodlands exhibit a stress response to deeper groundwater, as evidenced by concurrent declines in greenness represented by NDVI. Furthermore, we find greater seasonal coupling of canopy greenness to groundwater for vegetation along streams with natural flow regimes in comparison with anthropogenically altered streams, particularly in the most water-limited regions. These patterns suggest that many riparian woodlands in California are subsidized by water management practices. Riparian woodland communities rely on naturally variable groundwater and streamflow components to sustain key ecological processes, such as recruitment and succession. Altered flow regimes, which stabilize streamflow throughout the year and artificially enhance water supplies to riparian vegetation in the dry season, disrupt the seasonal cycles of abiotic drivers to which these Mediterranean forests are adapted. Consequently, our analysis suggests that many riparian ecosystems have become reliant on anthropogenically altered flow regimes, making them more vulnerable and less resilient to rapid hydrologic change, potentially leading to future riparian forest loss across increasingly stressed dryland regions.
Melissa M. Rohde; John C. Stella; Dar A. Roberts; Michael Bliss Singer. Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow. Proceedings of the National Academy of Sciences 2021, 118, 1 .
AMA StyleMelissa M. Rohde, John C. Stella, Dar A. Roberts, Michael Bliss Singer. Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow. Proceedings of the National Academy of Sciences. 2021; 118 (25):1.
Chicago/Turabian StyleMelissa M. Rohde; John C. Stella; Dar A. Roberts; Michael Bliss Singer. 2021. "Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow." Proceedings of the National Academy of Sciences 118, no. 25: 1.
Romy Sabathier; Michael Bliss Singer; John C Stella; Dar A Roberts; Kelly K Caylor. Vegetation responses to climatic and geologic controls on water availability in southeastern Arizona. Environmental Research Letters 2021, 16, 064029 .
AMA StyleRomy Sabathier, Michael Bliss Singer, John C Stella, Dar A Roberts, Kelly K Caylor. Vegetation responses to climatic and geologic controls on water availability in southeastern Arizona. Environmental Research Letters. 2021; 16 (6):064029.
Chicago/Turabian StyleRomy Sabathier; Michael Bliss Singer; John C Stella; Dar A Roberts; Kelly K Caylor. 2021. "Vegetation responses to climatic and geologic controls on water availability in southeastern Arizona." Environmental Research Letters 16, no. 6: 064029.
The extensive record of Landsat imagery is commonly used to map urban land-cover and land-use change. Random forest (RF) classification was applied for mapping more detailed urban land-use and change categories than is typically attempted with Landsat data. Two dates of Landsat imagery (1990 and 2015) were utilized with surface reflectance, Vegetation-Impervious-Soil (V-I-S) fractions, grey-level cooccurrence matrix (GLCM) of V-I-S, and temporal variation of V-I-S inputs. GLCM V-I-S and temporal variation of Vegetation as input features of RF classifiers slightly improved accuracies of land use maps. A change map derived from an overlay analysis between the 2015 map and a Landsat-derived urban expansion map was more accurate than one from post-classification comparison of 1990 and 2015 maps. For the Taiwan study area, Transportation Corridor land use tended to lead conversion to Residential and Employment types in relatively undeveloped districts, and extensive urban land-use change occurred in peri-urban areas.
Hsiao-Chien Shih; Douglas A. Stow; Kou-Chen Chang; Dar A. Roberts; Konstadinos G. Goulias. From land cover to land use: Applying random forest classifier to Landsat imagery for urban land-use change mapping. Geocarto International 2021, 1 -24.
AMA StyleHsiao-Chien Shih, Douglas A. Stow, Kou-Chen Chang, Dar A. Roberts, Konstadinos G. Goulias. From land cover to land use: Applying random forest classifier to Landsat imagery for urban land-use change mapping. Geocarto International. 2021; ():1-24.
Chicago/Turabian StyleHsiao-Chien Shih; Douglas A. Stow; Kou-Chen Chang; Dar A. Roberts; Konstadinos G. Goulias. 2021. "From land cover to land use: Applying random forest classifier to Landsat imagery for urban land-use change mapping." Geocarto International , no. : 1-24.
Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.
Anushree Badola; Santosh Panda; Dar Roberts; Christine Waigl; Uma Bhatt; Christopher Smith; Randi Jandt. Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska. Remote Sensing 2021, 13, 1693 .
AMA StyleAnushree Badola, Santosh Panda, Dar Roberts, Christine Waigl, Uma Bhatt, Christopher Smith, Randi Jandt. Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska. Remote Sensing. 2021; 13 (9):1693.
Chicago/Turabian StyleAnushree Badola; Santosh Panda; Dar Roberts; Christine Waigl; Uma Bhatt; Christopher Smith; Randi Jandt. 2021. "Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska." Remote Sensing 13, no. 9: 1693.
COVID-19’s impact on society and our daily habits has been unprecedented. With a decrease in vehicular traffic and industrial production, a decrease in local emissions was expected to occur. In order to capture any trends in ambient trace gas concentrations, approximately one thousand whole air samples were collected in intervals across the United States from April to July 2020 as part of the NASA Student Airborne Research Program (SARP). These samples were then analyzed by the UCI Rowland-Blake Lab using multi-column gas chromatography for over one hundred unique trace gases, including methane, non-methane hydrocarbons, and halocarbons, as described in Colman et al. (2001) and Barletta et al. (2002). Initial samples collected in April coincided with the peak of stay-at-home/social distancing orders in most states while samples collected later in the spring and early summer reflect the easing of these measures and initial state reopenings. Overall trends in emissions over time in select metropolitan areas will be discussed and compared to trends observed across the entire United States.
Alex Jarnot; Donald Blake; Melissa Yang; James Flynn; Sergio Alvarez; Travis Griggs; Maya Zimmerman; Jordan Zachmann; Mackenzie Warner; Gabriela Vidad; Graham Trolley; Jacob Schenthal; Morgan Schachterle; Everett Rzeszowski; Dominick Ryan; Amanda Rodell; McKenna Price-Patak; Elena Press; Scarlet Passer; Nathan Pappalardo; Joseph Palmo; David Moore; An Li; Jessica Kasamoto; Tatiana Jimenez; Amelia Hurst; Kendra Herweck; Paola Granados; Katey Dong; Walker Demel; Ariana Deegan; Mackenzie Conkling; John Carlson; Joel Been; Patrick Sullivan; Alexander MacDonald; Nicole Taylor; Jesse Bausell; Simone Meinardi; Raphael Kudela; Barbara Barletta; Dar Roberts; Gloria Weitz; Andreas Beyersdorf; Brent Love; Roya Bahreini; Barbara Chisholm; Barry Lefer; Jack Kaye; Ryan Stauffer; Joseph Bennett; Hal Maring; Emily Schaller. A Comparison of Trace Gas Trends in Urban Areas Collected via Whole Air Sampling during the COVID-19 Pandemic. 2021, 1 .
AMA StyleAlex Jarnot, Donald Blake, Melissa Yang, James Flynn, Sergio Alvarez, Travis Griggs, Maya Zimmerman, Jordan Zachmann, Mackenzie Warner, Gabriela Vidad, Graham Trolley, Jacob Schenthal, Morgan Schachterle, Everett Rzeszowski, Dominick Ryan, Amanda Rodell, McKenna Price-Patak, Elena Press, Scarlet Passer, Nathan Pappalardo, Joseph Palmo, David Moore, An Li, Jessica Kasamoto, Tatiana Jimenez, Amelia Hurst, Kendra Herweck, Paola Granados, Katey Dong, Walker Demel, Ariana Deegan, Mackenzie Conkling, John Carlson, Joel Been, Patrick Sullivan, Alexander MacDonald, Nicole Taylor, Jesse Bausell, Simone Meinardi, Raphael Kudela, Barbara Barletta, Dar Roberts, Gloria Weitz, Andreas Beyersdorf, Brent Love, Roya Bahreini, Barbara Chisholm, Barry Lefer, Jack Kaye, Ryan Stauffer, Joseph Bennett, Hal Maring, Emily Schaller. A Comparison of Trace Gas Trends in Urban Areas Collected via Whole Air Sampling during the COVID-19 Pandemic. . 2021; ():1.
Chicago/Turabian StyleAlex Jarnot; Donald Blake; Melissa Yang; James Flynn; Sergio Alvarez; Travis Griggs; Maya Zimmerman; Jordan Zachmann; Mackenzie Warner; Gabriela Vidad; Graham Trolley; Jacob Schenthal; Morgan Schachterle; Everett Rzeszowski; Dominick Ryan; Amanda Rodell; McKenna Price-Patak; Elena Press; Scarlet Passer; Nathan Pappalardo; Joseph Palmo; David Moore; An Li; Jessica Kasamoto; Tatiana Jimenez; Amelia Hurst; Kendra Herweck; Paola Granados; Katey Dong; Walker Demel; Ariana Deegan; Mackenzie Conkling; John Carlson; Joel Been; Patrick Sullivan; Alexander MacDonald; Nicole Taylor; Jesse Bausell; Simone Meinardi; Raphael Kudela; Barbara Barletta; Dar Roberts; Gloria Weitz; Andreas Beyersdorf; Brent Love; Roya Bahreini; Barbara Chisholm; Barry Lefer; Jack Kaye; Ryan Stauffer; Joseph Bennett; Hal Maring; Emily Schaller. 2021. "A Comparison of Trace Gas Trends in Urban Areas Collected via Whole Air Sampling during the COVID-19 Pandemic." , no. : 1.
The 2020 COVID-19 pandemic provided a unique opportunity to sample atmospheric gases during a period of very low industrial/human activity. Over 1000 Whole Air Samples were collected in over 30 cities and towns across the United States from April through July 2020 as part of the NASA Student Airborne Research Program (SARP). Sample locations leveraged the geographic distribution across the United States of the undergraduate and graduate students, faculty, and NASA personnel associated with the internship program (44 people total). Each person collected approximately 24 air samples in their city/town with the goal of characterizing local emissions with time during the pandemic. Samples were collected in 2-Liter stainless steel evacuated canisters at approximately 2 meters above ground level. The canisters were shipped to the Rowland/Blake Laboratory at the University of California Irvine and analyzed for methane, carbon dioxide, carbon monoxide, non-methane hydrocarbons, and halocarbons using the gas chromatographic system described in Colman et al. (2001) and Barletta et al. (2002). Initial samples collected in April coincided with the peak of stay-at-home/social distancing orders across most of the United States while samples collected later in the spring and early summer reflect the easing of these measures in most locations. Overall trends in emissions with time across the United States during the pandemic (in several large metro areas as well as rural locations) will be discussed.
Melissa Yang; Donald Blake; Alex Jarnot; Simone Meinardi; Gloria Weitz; Brent Love; Barbara Barletta; Barbara Chisholm; James Flynn; Sergio Alvarez; Travis Griggs; Maya Zimmerman; Jordan Zachmann; Mackenzie Warner; Gabriela Vidad; Graham Trolley; Jacob Schenthal; Morgan Schachterle; Everett Rzeszowski; Dominick Ryan; Amanda Rodell; McKenna Price-Patak; Elena Press; Scarlet Passer; Nathan Pappalardo; Joseph Palmo; David Moore; An Li; Jessica Kasamoto; Tatiana Jimenez; Amelia Hurst; Kendra Herweck; Paola Granados; Katey Dong; Walker Demel; Ariana Deegan; Mackenzie Conkling; John Carlson; Joel Been; Nicole Taylor; Patrick Sullivan; Alexander MacDonald; Jesse Bausell; Dar Roberts; Raphael Kudela; Andreas Beyersdorf; Roya Bahreini; Barry Lefer; Jack Kaye; Hal Maring; Ryan Stauffer; Joseph Bennett; Emily Schaller. NASA Student Airborne Research Program (SARP) Whole Air Sampling across the United States during the COVID-19 Pandemic. 2021, 1 .
AMA StyleMelissa Yang, Donald Blake, Alex Jarnot, Simone Meinardi, Gloria Weitz, Brent Love, Barbara Barletta, Barbara Chisholm, James Flynn, Sergio Alvarez, Travis Griggs, Maya Zimmerman, Jordan Zachmann, Mackenzie Warner, Gabriela Vidad, Graham Trolley, Jacob Schenthal, Morgan Schachterle, Everett Rzeszowski, Dominick Ryan, Amanda Rodell, McKenna Price-Patak, Elena Press, Scarlet Passer, Nathan Pappalardo, Joseph Palmo, David Moore, An Li, Jessica Kasamoto, Tatiana Jimenez, Amelia Hurst, Kendra Herweck, Paola Granados, Katey Dong, Walker Demel, Ariana Deegan, Mackenzie Conkling, John Carlson, Joel Been, Nicole Taylor, Patrick Sullivan, Alexander MacDonald, Jesse Bausell, Dar Roberts, Raphael Kudela, Andreas Beyersdorf, Roya Bahreini, Barry Lefer, Jack Kaye, Hal Maring, Ryan Stauffer, Joseph Bennett, Emily Schaller. NASA Student Airborne Research Program (SARP) Whole Air Sampling across the United States during the COVID-19 Pandemic. . 2021; ():1.
Chicago/Turabian StyleMelissa Yang; Donald Blake; Alex Jarnot; Simone Meinardi; Gloria Weitz; Brent Love; Barbara Barletta; Barbara Chisholm; James Flynn; Sergio Alvarez; Travis Griggs; Maya Zimmerman; Jordan Zachmann; Mackenzie Warner; Gabriela Vidad; Graham Trolley; Jacob Schenthal; Morgan Schachterle; Everett Rzeszowski; Dominick Ryan; Amanda Rodell; McKenna Price-Patak; Elena Press; Scarlet Passer; Nathan Pappalardo; Joseph Palmo; David Moore; An Li; Jessica Kasamoto; Tatiana Jimenez; Amelia Hurst; Kendra Herweck; Paola Granados; Katey Dong; Walker Demel; Ariana Deegan; Mackenzie Conkling; John Carlson; Joel Been; Nicole Taylor; Patrick Sullivan; Alexander MacDonald; Jesse Bausell; Dar Roberts; Raphael Kudela; Andreas Beyersdorf; Roya Bahreini; Barry Lefer; Jack Kaye; Hal Maring; Ryan Stauffer; Joseph Bennett; Emily Schaller. 2021. "NASA Student Airborne Research Program (SARP) Whole Air Sampling across the United States during the COVID-19 Pandemic." , no. : 1.
In this short communication, we describe the shortcomings and pitfalls of a commonly used method to detect ground materials that relies on setting thresholds for normalized difference indices. We analyze this method critically and present some experimental results on the USGS and ECOSTRESS spectral libraries and on real Sentinel-2 and Landsat-8 images. We demonstrate the risk of commission errors and provide some suggestions to reduce it.
Fen Chen; Tim Van de Voorde; Dar Roberts; Haojie Zhao; Jingbo Chen. Detection of Ground Materials Using Normalized Difference Indices with a Threshold: Risk and Ways to Improve. Remote Sensing 2021, 13, 450 .
AMA StyleFen Chen, Tim Van de Voorde, Dar Roberts, Haojie Zhao, Jingbo Chen. Detection of Ground Materials Using Normalized Difference Indices with a Threshold: Risk and Ways to Improve. Remote Sensing. 2021; 13 (3):450.
Chicago/Turabian StyleFen Chen; Tim Van de Voorde; Dar Roberts; Haojie Zhao; Jingbo Chen. 2021. "Detection of Ground Materials Using Normalized Difference Indices with a Threshold: Risk and Ways to Improve." Remote Sensing 13, no. 3: 450.
Community forests have been established worldwide to sustainably manage forest ecosystem services while maintaining the livelihoods of local residents. The Chitwan National Park in Nepal is a world-renowned biodiversity hotspot, where community forests were consolidated in the park’s buffer zone after 1993. These western Chitwan community forests stand as the frontiers of human–environment interactions, nurturing endangered large mammal species while providing significant natural resources for local residents. Nevertheless, no systematic forest cover assessment has been conducted for these forests since their establishment. In this study, we examined the green vegetation dynamics of these community forests for the years 1988–2018 using Landsat surface reflectance products. Combining an automatic water extraction index, spectral mixture analysis and the normalized difference fraction index (NDFI), we developed water masks and quantified the water-adjusted green vegetation fractions and NDFI values in the forests. Results showed that all forests have been continuously greening up since their establishment, and the average green vegetation cover of all forests increased from approximately 30% in 1988 to above 70% in 2018. With possible contributions from the invasion of exotic understory plant species, we credit community forestry programs for some of the green-up signals. Monitoring of forest vegetation dynamics is critical for evaluating the effectiveness of community forestry as well as developing sustainable forest management policies. Our research will provide positive feedbacks to local community forest committees and users.
Jie Dai; Dar Roberts; Douglas Stow; Li An; Qunshan Zhao. Green Vegetation Cover Has Steadily Increased Since Establishment of Community Forests in Western Chitwan, Nepal. Remote Sensing 2020, 12, 4071 .
AMA StyleJie Dai, Dar Roberts, Douglas Stow, Li An, Qunshan Zhao. Green Vegetation Cover Has Steadily Increased Since Establishment of Community Forests in Western Chitwan, Nepal. Remote Sensing. 2020; 12 (24):4071.
Chicago/Turabian StyleJie Dai; Dar Roberts; Douglas Stow; Li An; Qunshan Zhao. 2020. "Green Vegetation Cover Has Steadily Increased Since Establishment of Community Forests in Western Chitwan, Nepal." Remote Sensing 12, no. 24: 4071.
Regrowth after fire is critical to the persistence of chaparral shrub communities in southern California, which has been subject to frequent fire events in recent decades. Fires that recur at short intervals of 10 years or less have been considered an inhibitor of recovery and the major cause of ‘community type-conversion’ in chaparral, primarily based on studies of small extents and limited time periods. However, recent sub-regional investigations based on remote sensing suggest that short-interval fire (SIF) does not have ubiquitous impact on postfire chaparral recovery. A region-wide analysis including a greater spatial extent and time period is needed to better understand SIF impact on chaparral. This study evaluates patterns of postfire recovery across southern California, based on temporal trajectories of Normalized Difference Vegetation Index (NDVI) derived from June-solstice Landsat image series covering the period 1984–2018. High spatial resolution aerial images were used to calibrate Landsat NDVI trajectory-based estimates of change in fractional shrub cover (dFSC) for 294 stands. The objectives of this study were (1) to assess effects of time between fires and number of burns on recovery, using stand-aggregate samples (n = 294) and paired single- and multiple-burn sample plots (n = 528), and (2) to explain recovery variations among predominant single-burn locations based on shrub community type, climate, soils, and terrain. Stand-aggregate samples showed a significant but weak effect of SIF on recovery (p < 0.001; R2 = 0.003). Results from paired sample plots showed no significant effect of SIF on dFSC among twice-burned sites, although recovery was diminished due to SIF at sites that burned three times within 25 years. Multiple linear regression showed that annual precipitation and temperature, chaparral community type, and edaphic variables explain 28% of regional variation in recovery of once-burned sites. Many stands that exhibited poor recovery had burned only once and consist of xeric, desert-fringe chamise in soils of low clay content.
Emanuel A. Storey; Douglas A. Stow; John F. O'Leary; Frank W. Davis; Dar A. Roberts. Does short-interval fire inhibit postfire recovery of chaparral across southern California? Science of The Total Environment 2020, 751, 142271 -142271.
AMA StyleEmanuel A. Storey, Douglas A. Stow, John F. O'Leary, Frank W. Davis, Dar A. Roberts. Does short-interval fire inhibit postfire recovery of chaparral across southern California? Science of The Total Environment. 2020; 751 ():142271-142271.
Chicago/Turabian StyleEmanuel A. Storey; Douglas A. Stow; John F. O'Leary; Frank W. Davis; Dar A. Roberts. 2020. "Does short-interval fire inhibit postfire recovery of chaparral across southern California?" Science of The Total Environment 751, no. : 142271-142271.
The authors wish to make the following corrections to this paper
Simona Niculescu; Jean-Baptiste Boissonnat; Cédric Lardeux; Dar Roberts; Jenica Hanganu; Antoine Billey; Adrian Constantinescu; Mihai Doroftei. Correction: Niculescu, S., et al. Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta. Remote Sensing 2020, 12(14), 2188. Remote Sensing 2020, 12, 2529 .
AMA StyleSimona Niculescu, Jean-Baptiste Boissonnat, Cédric Lardeux, Dar Roberts, Jenica Hanganu, Antoine Billey, Adrian Constantinescu, Mihai Doroftei. Correction: Niculescu, S., et al. Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta. Remote Sensing 2020, 12(14), 2188. Remote Sensing. 2020; 12 (16):2529.
Chicago/Turabian StyleSimona Niculescu; Jean-Baptiste Boissonnat; Cédric Lardeux; Dar Roberts; Jenica Hanganu; Antoine Billey; Adrian Constantinescu; Mihai Doroftei. 2020. "Correction: Niculescu, S., et al. Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta. Remote Sensing 2020, 12(14), 2188." Remote Sensing 12, no. 16: 2529.
Communities like Santa Barbara, California appear to have it all – beaches, mountains, sunshine, moderate temperatures, small urban population, and close proximity to the large metropolis of Los Angeles. What is not to love? Climate change, drought, flammable vegetation, and naturally prevailing weather conditions make a significant portion of the population vulnerable in many ways. Earthquakes and tsunamis might come to mind, but perhaps more of a threat is fire and/or flooding at, on or near the wildland-urban interface. The recent Thomas fire in December of 2017 and subsequent flooding, debris flow and mudslides in Montecito that followed in January of 2018 highlight what coastal vulnerability means under the new normal of extreme wildfire and flooding danger for this region. This paper discusses the unique hazards along with local weather conditions that contribute to vulnerability. We then detail spatial analytics to assess, model and predict risks. Insights are offered for the Santa Barbara region associated with extreme weather vulnerabilities.
Alan T. Murray; Leila Carvalho; Richard L. Church; Charles Jones; Dar Roberts; Jing Xu; Katelyn Zigner; Deanna Nash. Coastal Vulnerability under Extreme Weather. Applied Spatial Analysis and Policy 2020, 14, 497 -523.
AMA StyleAlan T. Murray, Leila Carvalho, Richard L. Church, Charles Jones, Dar Roberts, Jing Xu, Katelyn Zigner, Deanna Nash. Coastal Vulnerability under Extreme Weather. Applied Spatial Analysis and Policy. 2020; 14 (3):497-523.
Chicago/Turabian StyleAlan T. Murray; Leila Carvalho; Richard L. Church; Charles Jones; Dar Roberts; Jing Xu; Katelyn Zigner; Deanna Nash. 2020. "Coastal Vulnerability under Extreme Weather." Applied Spatial Analysis and Policy 14, no. 3: 497-523.
Extreme, downslope mountain winds often generate dangerous wildfire conditions. We used the wildfire spread model Fire Area Simulator (FARSITE) to simulate two wildfires influenced by strong wind events in Santa Barbara, CA. High spatial-resolution imagery for fuel maps and hourly wind downscaled to 100 m were used as model inputs, and sensitivity tests were performed to evaluate the effects of ignition timing and location on fire spread. Additionally, burn area rasters from FARSITE simulations were compared to minimum travel time rasters from FlamMap simulations, a wildfire model similar to FARSITE that holds environmental variables constant. Utilization of two case studies during strong winds revealed that FARSITE was able to successfully reconstruct the spread rate and size of wildfires when spotting was minimal. However, in situations when spotting was an important factor in rapid downslope wildfire spread, both FARSITE and FlamMap were unable to simulate realistic fire perimeters. We show that this is due to inherent limitations in the models themselves, related to the slope-orientation relative to the simulated fire spread, and the dependence of ember launch and land locations. This finding has widespread implications, given the role of spotting in fire progression during extreme wind events.
Katelyn Zigner; Leila M. V. Carvalho; Seth Peterson; Francis Fujioka; Gert-Jan Duine; Charles Jones; Dar Roberts; Max Moritz. Evaluating the Ability of FARSITE to Simulate Wildfires Influenced by Extreme, Downslope Winds in Santa Barbara, California. Fire 2020, 3, 29 .
AMA StyleKatelyn Zigner, Leila M. V. Carvalho, Seth Peterson, Francis Fujioka, Gert-Jan Duine, Charles Jones, Dar Roberts, Max Moritz. Evaluating the Ability of FARSITE to Simulate Wildfires Influenced by Extreme, Downslope Winds in Santa Barbara, California. Fire. 2020; 3 (3):29.
Chicago/Turabian StyleKatelyn Zigner; Leila M. V. Carvalho; Seth Peterson; Francis Fujioka; Gert-Jan Duine; Charles Jones; Dar Roberts; Max Moritz. 2020. "Evaluating the Ability of FARSITE to Simulate Wildfires Influenced by Extreme, Downslope Winds in Santa Barbara, California." Fire 3, no. 3: 29.
In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.
Simona Niculescu; Jean-Baptiste Boissonnat; Cédric Lardeux; Dar Roberts; Jenica Hanganu; Antoine Billey; Adrian Constantinescu; Mihai Doroftei. Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta. Remote Sensing 2020, 12, 2188 .
AMA StyleSimona Niculescu, Jean-Baptiste Boissonnat, Cédric Lardeux, Dar Roberts, Jenica Hanganu, Antoine Billey, Adrian Constantinescu, Mihai Doroftei. Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta. Remote Sensing. 2020; 12 (14):2188.
Chicago/Turabian StyleSimona Niculescu; Jean-Baptiste Boissonnat; Cédric Lardeux; Dar Roberts; Jenica Hanganu; Antoine Billey; Adrian Constantinescu; Mihai Doroftei. 2020. "Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta." Remote Sensing 12, no. 14: 2188.
Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (
Fernanda F. Ribeiro; Dar A. Roberts; Laura L. Hess; Frank W. Davis; Kelly K. Caylor; Gabriel Antunes Daldegan. Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas. Remote Sensing 2020, 12, 1721 .
AMA StyleFernanda F. Ribeiro, Dar A. Roberts, Laura L. Hess, Frank W. Davis, Kelly K. Caylor, Gabriel Antunes Daldegan. Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas. Remote Sensing. 2020; 12 (11):1721.
Chicago/Turabian StyleFernanda F. Ribeiro; Dar A. Roberts; Laura L. Hess; Frank W. Davis; Kelly K. Caylor; Gabriel Antunes Daldegan. 2020. "Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas." Remote Sensing 12, no. 11: 1721.
Forest managers rely on accurate burn severity estimates to evaluate post-fire damage and to establish revegetation policies. Burn severity estimates based on reflective data acquired from sensors onboard satellites are increasingly complementing field-based ones. However, fire not only induces changes in reflected and emitted radiation measured by the sensor, but also on energy balance. Evapotranspiration (ET), land surface temperature (LST) and land surface albedo (LSA) are greatly affected by wildfires. In this study, we examine the usefulness of these elements of energy balance as indicators of burn severity and compare the accuracy of burn severity estimates based on them to the accuracy of widely used approaches based on spectral indexes. We studied a mega-fire (more than 450 km2 burned) in Central Portugal, which occurred from 17 to 24 June 2017. The official burn severity map acted as a ground reference. Variations induced by fire during the first year following the fire event were evaluated through changes in ET, LST and LSA derived from Landsat data and related to burn severity. Fisher’s least significant difference test (ANOVA) revealed that ET and LST images could discriminate three burn severity levels with statistical significance (uni-temporal and multi-temporal approaches). Burn severity was estimated from ET, LST and LSA using thresholding. Accuracy of ET and LST based on burn severity estimates was adequate (κ = 0.63 and 0.57, respectively), similar to the accuracy of the estimate based on dNBR (κ = 0.66). We conclude that Landsat-derived surface energy balance variables, in particular ET and LST, in addition to acting as useful indicators of burn severity for mega-fires in Mediterranean ecosystems, may provide critical information about how energy balance changes due to fire.
Alfonso Fernández-Manso; Carmen Quintano; Dar A. Roberts. Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques? Remote Sensing 2020, 12, 890 .
AMA StyleAlfonso Fernández-Manso, Carmen Quintano, Dar A. Roberts. Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques? Remote Sensing. 2020; 12 (5):890.
Chicago/Turabian StyleAlfonso Fernández-Manso; Carmen Quintano; Dar A. Roberts. 2020. "Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques?" Remote Sensing 12, no. 5: 890.
Regrowth after fire is critical to long-term persistence of chaparral shrub communities in southern California. This region is subject to frequent fire, habitat fragmentation, and protracted droughts linked to climatic change. Short-interval fire (SIF) is considered an inhibitor of recovery and cause of “type conversion” in chaparral, based on field studies of small extents and limited time periods. Sub-regional scale investigations based on remotely sensed data, however, suggest that SIF may explain little variance in postfire chaparral recovery. Drought may contribute to poor recovery or worsen the impact of repeated, short-interval fires. Previous studies have not shown whether drought reduces chaparral recovery significantly across the region, while variations in response among community types and climate zones are not well resolved. This research evaluates a regional pattern of chaparral recovery, based on series of Normalized Difference Vegetation Index (NDVI) from annual, June-solstice Landsat images (1984–2018). High resolution aerial images were used in validation and calibration. The main objectives were (1) to assess effects of fire-return interval and number of burns on chaparral recovery using 0.25 km2 sample plots (n = 528) which were paired and stratified for experimental control, and (2) to explain recovery variations across the region based on geospatial climate, vegetation, soil, terrain, and temporal drought metric data (seasonal precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index (PDSI)) from 982 locations. Results suggest that SIF is most impactful in sites that burned three times within 25 years. More substantial effects were observed due to drought. In particular, ecotonal chaparral bounding the Colorado Desert is most subject to drought impact. We also highlight utility in landscape-scale predictors of drought impact on recovering chaparral, including Very Atmospherically Resistant Index (VARI).
Emanuel StoreyiD; Douglas Stow; Dar Roberts; Frank DavisiD; John O'leary. Evaluating Response of Southern California Chaparral Landscapes to Short-interval Fire and Drought (1984-2018). 2019, 1 .
AMA StyleEmanuel StoreyiD, Douglas Stow, Dar Roberts, Frank DavisiD, John O'leary. Evaluating Response of Southern California Chaparral Landscapes to Short-interval Fire and Drought (1984-2018). . 2019; ():1.
Chicago/Turabian StyleEmanuel StoreyiD; Douglas Stow; Dar Roberts; Frank DavisiD; John O'leary. 2019. "Evaluating Response of Southern California Chaparral Landscapes to Short-interval Fire and Drought (1984-2018)." , no. : 1.
This study evaluates a new generation of satellite imaging spectrometers to measure point source methane emissions from anthropogenic sources. We used the Airborne Visible and Infrared Imaging Spectrometer Next Generation(AVIRIS-NG) images with known methane plumes to create two simulated satellite products. One simulation had a 30 m spatial resolution with ~200 Signal-to-Noise Ratio (SNR) in the Shortwave Infrared (SWIR) and the other had a 60 m spatial resolution with ~400 SNR in the SWIR; both products had a 7.5 nm spectral spacing. We applied a linear matched filter with a sparsity prior and an albedo correction to detect and quantify the methane emission in the original AVIRIS-NG images and in both satellite simulations. We also calculated an emission flux for all images. We found that all methane plumes were detectable in all satellite simulations. The flux calculations for the simulated satellite images correlated well with the calculated flux for the original AVIRIS-NG images. We also found that coarsening spatial resolution had the largest impact on the sensitivity of the results. These results suggest that methane detection and quantification of point sources will be possible with the next generation of satellite imaging spectrometers.
Alana K. Ayasse; Philip E. Dennison; Markus Foote; Andrew K. Thorpe; Sarang Joshi; Robert O. Green; Riley M. Duren; David R. Thompson; Dar A. Roberts. Methane Mapping with Future Satellite Imaging Spectrometers. Remote Sensing 2019, 11, 3054 .
AMA StyleAlana K. Ayasse, Philip E. Dennison, Markus Foote, Andrew K. Thorpe, Sarang Joshi, Robert O. Green, Riley M. Duren, David R. Thompson, Dar A. Roberts. Methane Mapping with Future Satellite Imaging Spectrometers. Remote Sensing. 2019; 11 (24):3054.
Chicago/Turabian StyleAlana K. Ayasse; Philip E. Dennison; Markus Foote; Andrew K. Thorpe; Sarang Joshi; Robert O. Green; Riley M. Duren; David R. Thompson; Dar A. Roberts. 2019. "Methane Mapping with Future Satellite Imaging Spectrometers." Remote Sensing 11, no. 24: 3054.
Recovery trajectories derived from remote sensing data are widely used to monitor ecosystem recovery after disturbance events, but these trajectories are often retrieved without a precise understanding of the land cover within a scene. As a result, the sources of variability in post-disturbance recovery trajectories are poorly understood. In this study, we monitored the recovery of chaparral and conifer species following the 2007 Zaca Fire, which burned 97,270 ha in Santa Barbara County, California. We combined field survey data with two time series remote sensing products: the relative delta normalized burn ratio (RdNBR) and green vegetation (GV) fractions derived from spectral mixture analysis. Recovery trajectories were retrieved for stands dominated by six different chaparral species. We also retrieved recovery trajectories for stands of mixed conifer forest. We found that the two remote sensing products were equally effective at mapping vegetation cover across the burn scar. The GV fractions (r(78) = 0.552, p < 0.001) and normalized burn ratio (r(78) = 0.555, p < 0.001) had nearly identical correlations with ground reference data of green vegetation cover. Recovery of the chaparral species was substantially affected by the 2011–2017 California drought. GV fractions for the chaparral species generally declined between 2011 and 2016. Physiological responses to fire and drought were important sources of variability between the species. The conifer stands did not exhibit a drought signal that was directly correlated with annual precipitation, but the drought likely delayed the return to pre-fire conditions. As of 2018, 545 of the 756 conifer stands had not recovered to their pre-fire GV fractions. Spatial and temporal variation in species composition were important sources of spectral variability in the chaparral and conifer stands. The chaparral stands in particular had highly heterogeneous species composition. Dominant species accounted for between 30% and 53% of the land cover in the surveyed chaparral patches, so non-dominant land cover types strongly influenced remote sensing signals. Our study reveals that prolonged drought can delay or alter the post-fire recovery of Mediterranean ecosystems. It is also the first study to critically examine how fine-scale variability in land cover affects time series remote sensing analyses.
Christopher L. Kibler; Anne-Marie L. Parkinson; Seth H. Peterson; Dar A. Roberts; Carla M. D’Antonio; Susan K. Meerdink; Stuart H. Sweeney. Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series. Remote Sensing 2019, 11, 2963 .
AMA StyleChristopher L. Kibler, Anne-Marie L. Parkinson, Seth H. Peterson, Dar A. Roberts, Carla M. D’Antonio, Susan K. Meerdink, Stuart H. Sweeney. Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series. Remote Sensing. 2019; 11 (24):2963.
Chicago/Turabian StyleChristopher L. Kibler; Anne-Marie L. Parkinson; Seth H. Peterson; Dar A. Roberts; Carla M. D’Antonio; Susan K. Meerdink; Stuart H. Sweeney. 2019. "Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series." Remote Sensing 11, no. 24: 2963.
Gabriel Antunes Daldegan; Dar A. Roberts; Fernanda De Figueiredo Ribeiro. Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone. Remote Sensing of Environment 2019, 232, 1 .
AMA StyleGabriel Antunes Daldegan, Dar A. Roberts, Fernanda De Figueiredo Ribeiro. Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone. Remote Sensing of Environment. 2019; 232 ():1.
Chicago/Turabian StyleGabriel Antunes Daldegan; Dar A. Roberts; Fernanda De Figueiredo Ribeiro. 2019. "Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone." Remote Sensing of Environment 232, no. : 1.