Pub Date : 2023-02-16DOI: 10.3389/frsen.2023.973944
Madjid Hadjal, Ross Paterson, D. McKee
Estimation of chlorophyll (CHL) using ocean colour remote sensing (OCRS) signals in coastal waters is difficult due to the presence of two other constituents altering the light signal: coloured dissolved organic material (CDOM) and mineral suspended sediments (MSS). Artificial neural networks (NNs) have the capacity to deal with signal complexity and are a potential solution to the problem. Here NNs are developed to operate on two datasets replicating MODIS Aqua bands simulated using Hydrolight 5.2. Artificial noise is added to the simulated signal to improve realism. Both datasets use the same ranges of in water constituent concentrations, and differ by the type of logarithmic concentration distributions. The first uses a Gaussian distribution to simulate samples from natural water conditions. The second uses a flat distribution and is intended to allow exploration of the impact of undersampling extremes at both high and low concentrations in the Gaussian distribution. The impact of the concentration distribution structure is assessed and no benefits were found by switching to a flat distribution. The normal distribution performs better because it reduces the number of low concentration samples that are relatively difficult to resolve against varying concentrations of other constituents. In this simulated environment NNs have the capacity to estimate CHL with outstanding performance compared to real in situ algorithms, except for low values when other constituents dominate the light signal in coastal waters. CDOM and MSS can also be predicted with very high accuracies using NNs. It is found that simultaneous retrieval of all three constituents using multitask learning (MTL) does not provide any advantage over single parameter retrievals. Finally it is found that increasing the number of wavebands generally improves NN performance, though there appear to be diminishing returns beyond ∼8 bands. It is also shown that a smaller number of carefully selected bands performs better than a uniformly distributed band set of the same size. These results provide useful insight into future performance for NNs using hyperspectral satellite sensors and highlight specific wavebands benefits.
{"title":"Neural networks to retrieve in water constituents applied to radiative transfer models simulating coastal water conditions","authors":"Madjid Hadjal, Ross Paterson, D. McKee","doi":"10.3389/frsen.2023.973944","DOIUrl":"https://doi.org/10.3389/frsen.2023.973944","url":null,"abstract":"Estimation of chlorophyll (CHL) using ocean colour remote sensing (OCRS) signals in coastal waters is difficult due to the presence of two other constituents altering the light signal: coloured dissolved organic material (CDOM) and mineral suspended sediments (MSS). Artificial neural networks (NNs) have the capacity to deal with signal complexity and are a potential solution to the problem. Here NNs are developed to operate on two datasets replicating MODIS Aqua bands simulated using Hydrolight 5.2. Artificial noise is added to the simulated signal to improve realism. Both datasets use the same ranges of in water constituent concentrations, and differ by the type of logarithmic concentration distributions. The first uses a Gaussian distribution to simulate samples from natural water conditions. The second uses a flat distribution and is intended to allow exploration of the impact of undersampling extremes at both high and low concentrations in the Gaussian distribution. The impact of the concentration distribution structure is assessed and no benefits were found by switching to a flat distribution. The normal distribution performs better because it reduces the number of low concentration samples that are relatively difficult to resolve against varying concentrations of other constituents. In this simulated environment NNs have the capacity to estimate CHL with outstanding performance compared to real in situ algorithms, except for low values when other constituents dominate the light signal in coastal waters. CDOM and MSS can also be predicted with very high accuracies using NNs. It is found that simultaneous retrieval of all three constituents using multitask learning (MTL) does not provide any advantage over single parameter retrievals. Finally it is found that increasing the number of wavebands generally improves NN performance, though there appear to be diminishing returns beyond ∼8 bands. It is also shown that a smaller number of carefully selected bands performs better than a uniformly distributed band set of the same size. These results provide useful insight into future performance for NNs using hyperspectral satellite sensors and highlight specific wavebands benefits.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129905700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-13DOI: 10.3389/frsen.2023.1105627
B. Lin, Matthew Walker Mclinden, G. Heymsfield, Yongxiang Hu, N. Privé, Lihua Li, S. Harrah, K. Horgan, Xia Cai, J. Carswell
This study simulates V-band sea surface reflectance and normalized radar cross-section (NRCS) for sea surface air pressure barometry using a differential absorption radar operating at three spectrally even spaced close frequency bands (65.5, 67.75 and 70.0 GHz) with ± 15° cross-track scanning angle. The reflectance ratios of two neighboring frequency pairs and the ratio of the two ratios or three-channel approach are the focus of this study. Impacts of major sea surface geophysical variables such as sea surface temperature, wind, salinity, whitecap, and incidence angle on these reflection properties are analyzed. The reflection simulation is essentially based on geometric optics of rough sea surface. Simulation shows that NRCS values are sufficiently strong within the scanning angle and sea surface salinity would only introduce minimal variations in the surface reflection. The impact of sea surface reflection variations with sea surface temperature, wind, and whitecaps on sea surface barometry are mitigated when the ratios of frequency-paired radar signals are used. Furthermore, the ratios of a three-channel approach are very close to unity and calibration or compensation for the reflectance ratios may not be needed for sea level pressure retrievals. These results improve our understanding of sea surface reflection variations and would help the system design and development.
{"title":"Simulations of sea surface reflection for V-band O2 differential absorption radar barometry","authors":"B. Lin, Matthew Walker Mclinden, G. Heymsfield, Yongxiang Hu, N. Privé, Lihua Li, S. Harrah, K. Horgan, Xia Cai, J. Carswell","doi":"10.3389/frsen.2023.1105627","DOIUrl":"https://doi.org/10.3389/frsen.2023.1105627","url":null,"abstract":"This study simulates V-band sea surface reflectance and normalized radar cross-section (NRCS) for sea surface air pressure barometry using a differential absorption radar operating at three spectrally even spaced close frequency bands (65.5, 67.75 and 70.0 GHz) with ± 15° cross-track scanning angle. The reflectance ratios of two neighboring frequency pairs and the ratio of the two ratios or three-channel approach are the focus of this study. Impacts of major sea surface geophysical variables such as sea surface temperature, wind, salinity, whitecap, and incidence angle on these reflection properties are analyzed. The reflection simulation is essentially based on geometric optics of rough sea surface. Simulation shows that NRCS values are sufficiently strong within the scanning angle and sea surface salinity would only introduce minimal variations in the surface reflection. The impact of sea surface reflection variations with sea surface temperature, wind, and whitecaps on sea surface barometry are mitigated when the ratios of frequency-paired radar signals are used. Furthermore, the ratios of a three-channel approach are very close to unity and calibration or compensation for the reflectance ratios may not be needed for sea level pressure retrievals. These results improve our understanding of sea surface reflection variations and would help the system design and development.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121320604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-13DOI: 10.3389/frsen.2023.1041466
Yogita Karale, M. Yuan
Fine particulate matter, also known as PM2.5, has many adverse impacts on human health. However, there are few ground monitoring stations measuring PM2.5. Satellite data help fill the gaps in ground measurements, but most studies focus on estimating daily PM2.5 levels. Studies examining the effects of environmental exposome need accurate PM2.5 estimates at fine temporal intervals. This work developed a Convolutional Neural Network (CNN) to estimate the PM2.5 concentration at an hourly average using high-resolution Aerosol Optical Depth (AOD) from the MODIS MAIAC algorithm and meteorological data. Satellite-acquired AOD data are instantaneous measurements, whereas stations on the ground provide an hourly average of PM2.5 concentration. The current work aimed to refine PM2.5 estimates at temporal intervals from 24-h to 1-h averages. Our premise posited the enabling effects of spatial convolution on temporal refinements in PM2.5 estimates. We trained a CNN to estimate PM2.5 corresponding to the hour of AOD acquisition in the Dallas-Fort Worth and surrounding area using 10 years of data from 2006–2015. The CNN accepts images as input. For each PM2.5 station, we strategically subset temporal MODIS images centering at the PM2.5 station. Hence, the resulting image-patch size represented the size of the area around the PM2.5 station. It thus was analogous to spatial lag in spatial statistics. We systematically increased the image-patch size from 3 × 3, 5 × 5, … , to 19 × 19 km2 and observed how increasing the spatial lag impacted PM2.5 estimation. Model performance improved with a larger spatial lag; the model with a 19 × 19 km2 image-patch as input performed best, with a correlation coefficient of 0.87 and a RMSE of 2.57 g/m3 to estimate PM2.5 at in situ stations corresponding to the hour of satellite acquisition time. To overcome the problem of a reduced number of image-patches available for training due to missing AOD, the study employed a data augmentation technique to increase the number of samples available to train the model. In addition to avoiding overfitting, data augmentation also improved model performance.
{"title":"Spatially lagged predictors from a wider area improve PM2.5 estimation at a finer temporal interval—A case study of Dallas-Fort Worth, United States","authors":"Yogita Karale, M. Yuan","doi":"10.3389/frsen.2023.1041466","DOIUrl":"https://doi.org/10.3389/frsen.2023.1041466","url":null,"abstract":"Fine particulate matter, also known as PM2.5, has many adverse impacts on human health. However, there are few ground monitoring stations measuring PM2.5. Satellite data help fill the gaps in ground measurements, but most studies focus on estimating daily PM2.5 levels. Studies examining the effects of environmental exposome need accurate PM2.5 estimates at fine temporal intervals. This work developed a Convolutional Neural Network (CNN) to estimate the PM2.5 concentration at an hourly average using high-resolution Aerosol Optical Depth (AOD) from the MODIS MAIAC algorithm and meteorological data. Satellite-acquired AOD data are instantaneous measurements, whereas stations on the ground provide an hourly average of PM2.5 concentration. The current work aimed to refine PM2.5 estimates at temporal intervals from 24-h to 1-h averages. Our premise posited the enabling effects of spatial convolution on temporal refinements in PM2.5 estimates. We trained a CNN to estimate PM2.5 corresponding to the hour of AOD acquisition in the Dallas-Fort Worth and surrounding area using 10 years of data from 2006–2015. The CNN accepts images as input. For each PM2.5 station, we strategically subset temporal MODIS images centering at the PM2.5 station. Hence, the resulting image-patch size represented the size of the area around the PM2.5 station. It thus was analogous to spatial lag in spatial statistics. We systematically increased the image-patch size from 3 × 3, 5 × 5, … , to 19 × 19 km2 and observed how increasing the spatial lag impacted PM2.5 estimation. Model performance improved with a larger spatial lag; the model with a 19 × 19 km2 image-patch as input performed best, with a correlation coefficient of 0.87 and a RMSE of 2.57 g/m3 to estimate PM2.5 at in situ stations corresponding to the hour of satellite acquisition time. To overcome the problem of a reduced number of image-patches available for training due to missing AOD, the study employed a data augmentation technique to increase the number of samples available to train the model. In addition to avoiding overfitting, data augmentation also improved model performance.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129636588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-09DOI: 10.3389/frsen.2023.1106533
P. Cauchy, K. Heywood, N. Merchant, D. Risch, B. Queste, P. Testor
Ocean gliders are quiet, buoyancy-driven, long-endurance, profiling autonomous platforms. Gliders therefore possess unique advantages as platforms for Passive Acoustic Monitoring (PAM) of the marine environment. In this paper, we review available glider platforms and passive acoustic monitoring systems, and explore current and potential uses of passive acoustic monitoring-equipped gliders for the study of physical oceanography, biology, ecology and for regulatory purposes. We evaluate limiting factors for passive acoustic monitoring glider surveys, such as platform-generated and flow noise, weight, size and energy constraints, profiling ability and slow movement. Based on data from 34 passive acoustic monitoring glider missions, it was found that <13% of the time spent at sea was unsuitable for passive acoustic monitoring measurements, either because of surface communications or glider manoeuvre, leaving the remainder available for subsequent analysis. To facilitate the broader use of passive acoustic monitoring gliders, we document best practices and include workarounds for the typical challenges of a passive acoustic monitoring glider mission. Three research priorities are also identified to improve future passive acoustic monitoring glider observations: 1) Technological developments to improve sensor integration and preserve glider endurance; 2) improved sampling methods and statistical analysis techniques to perform population density estimation from passive acoustic monitoring glider observations; and 3) calibration of the passive acoustic monitoring glider to record absolute noise levels, for anthropogenic noise monitoring. It is hoped this methodological review will assist glider users to broaden the observational capability of their instruments, and help researchers in related fields to deploy passive acoustic monitoring gliders in their studies.
{"title":"Gliders for passive acoustic monitoring of the oceanic environment","authors":"P. Cauchy, K. Heywood, N. Merchant, D. Risch, B. Queste, P. Testor","doi":"10.3389/frsen.2023.1106533","DOIUrl":"https://doi.org/10.3389/frsen.2023.1106533","url":null,"abstract":"Ocean gliders are quiet, buoyancy-driven, long-endurance, profiling autonomous platforms. Gliders therefore possess unique advantages as platforms for Passive Acoustic Monitoring (PAM) of the marine environment. In this paper, we review available glider platforms and passive acoustic monitoring systems, and explore current and potential uses of passive acoustic monitoring-equipped gliders for the study of physical oceanography, biology, ecology and for regulatory purposes. We evaluate limiting factors for passive acoustic monitoring glider surveys, such as platform-generated and flow noise, weight, size and energy constraints, profiling ability and slow movement. Based on data from 34 passive acoustic monitoring glider missions, it was found that <13% of the time spent at sea was unsuitable for passive acoustic monitoring measurements, either because of surface communications or glider manoeuvre, leaving the remainder available for subsequent analysis. To facilitate the broader use of passive acoustic monitoring gliders, we document best practices and include workarounds for the typical challenges of a passive acoustic monitoring glider mission. Three research priorities are also identified to improve future passive acoustic monitoring glider observations: 1) Technological developments to improve sensor integration and preserve glider endurance; 2) improved sampling methods and statistical analysis techniques to perform population density estimation from passive acoustic monitoring glider observations; and 3) calibration of the passive acoustic monitoring glider to record absolute noise levels, for anthropogenic noise monitoring. It is hoped this methodological review will assist glider users to broaden the observational capability of their instruments, and help researchers in related fields to deploy passive acoustic monitoring gliders in their studies.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124133937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-08DOI: 10.3389/frsen.2023.1072930
Jing Tan, R. Frouin, H. Murakami
A generic methodology is presented to cross-calibrate satellite ocean-color sensors in polar orbit via an intermediary geostationary sensor of reference. In this study, AHI onboard Hiwamari-8 is used as the intermediary sensor to cross-calibrate SGLI onboard GCOM-C and MODIS onboard Aqua and Terra (MODIS-A and MODIS-T) after system vicarious calibration (SVC). Numerous coincidences were obtained near the Equator using 3 days of imagery, i.e., 11 May 2018, 22 January 2019, and 25 January 2020. Spectral matching to AHI spectral bands was first performed for a wide range of angular geometry, aerosol conditions, and Case 1 waters using a single band or multiple bands of SGLI, MODIS-A and MODIS-T, yielding root mean square differences of 0.1–0.7% in the blue and green and 0.7%–3.7% in the red depending on the band combination. Limited by the inherent AHI instrument noise and the system vicarious calibration of individual polar-orbiting sensors, cross-calibration was only performed for equivalent AHI bands centered on at 471, 510, and 639 nm. Results show that MODIS-A and MODIS-T are accurately cross-calibrated, with cross-calibration ratios differing by 0.1%–0.8% in magnitude. These differences are within or slightly outside the estimated uncertainties of ±0.6% to ±1.0%. In contrast, SGLI shows larger cross-calibration differences, i.e., 1.4%, 3.4%, and 1.1% with MODIS-A and 1.5%, 4.6%, and 1.5% with MODIS-T, respectively. These differences are above uncertainties of ±0.8–1.0% at 471 and 510 nm and within uncertainties of ±2.3% and ±1.9% at 639 nm. Such differences may introduce significant discrepancies between ocean-color products generated from SGLI and MODIS data, although some compensation may occur because different atmospheric correction schemes are used to process SGLI and MODIS imagery, and SVC is based on the selected scheme. Geostationary sensors with ocean color capability have potential to improve the spectral matching and reduce uncertainties, as long as they provide imagery at sufficient cadence over equatorial regions. The methodology is applicable to polar-orbiting optical sensors in general and can be implemented operationally to ensure consistency of products generated by individual sensors in establishing long-term data records for climate studies.
{"title":"Feasibility of cross-calibrating ocean-color sensors in polar orbit using an intermediary geostationary sensor of reference","authors":"Jing Tan, R. Frouin, H. Murakami","doi":"10.3389/frsen.2023.1072930","DOIUrl":"https://doi.org/10.3389/frsen.2023.1072930","url":null,"abstract":"A generic methodology is presented to cross-calibrate satellite ocean-color sensors in polar orbit via an intermediary geostationary sensor of reference. In this study, AHI onboard Hiwamari-8 is used as the intermediary sensor to cross-calibrate SGLI onboard GCOM-C and MODIS onboard Aqua and Terra (MODIS-A and MODIS-T) after system vicarious calibration (SVC). Numerous coincidences were obtained near the Equator using 3 days of imagery, i.e., 11 May 2018, 22 January 2019, and 25 January 2020. Spectral matching to AHI spectral bands was first performed for a wide range of angular geometry, aerosol conditions, and Case 1 waters using a single band or multiple bands of SGLI, MODIS-A and MODIS-T, yielding root mean square differences of 0.1–0.7% in the blue and green and 0.7%–3.7% in the red depending on the band combination. Limited by the inherent AHI instrument noise and the system vicarious calibration of individual polar-orbiting sensors, cross-calibration was only performed for equivalent AHI bands centered on at 471, 510, and 639 nm. Results show that MODIS-A and MODIS-T are accurately cross-calibrated, with cross-calibration ratios differing by 0.1%–0.8% in magnitude. These differences are within or slightly outside the estimated uncertainties of ±0.6% to ±1.0%. In contrast, SGLI shows larger cross-calibration differences, i.e., 1.4%, 3.4%, and 1.1% with MODIS-A and 1.5%, 4.6%, and 1.5% with MODIS-T, respectively. These differences are above uncertainties of ±0.8–1.0% at 471 and 510 nm and within uncertainties of ±2.3% and ±1.9% at 639 nm. Such differences may introduce significant discrepancies between ocean-color products generated from SGLI and MODIS data, although some compensation may occur because different atmospheric correction schemes are used to process SGLI and MODIS imagery, and SVC is based on the selected scheme. Geostationary sensors with ocean color capability have potential to improve the spectral matching and reduce uncertainties, as long as they provide imagery at sufficient cadence over equatorial regions. The methodology is applicable to polar-orbiting optical sensors in general and can be implemented operationally to ensure consistency of products generated by individual sensors in establishing long-term data records for climate studies.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131392306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-07DOI: 10.3389/frsen.2023.1104681
Ray H. Watkins, Michael J. Sayers , Robert A. Shuchman , Karl R. Bosse
The Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was launched in 2006 with the primary goal of measuring the properties of clouds and aerosols in Earth’s atmosphere using LiDAR. Since then, numerous studies have shown the viability of using CALIPSO to observe day/night differences in subsurface optical properties of oceans and large seas from space. To date no studies have been done on using CALIPSO to monitor the subsurface optical properties of large, freshwater-lakes. This is likely due to the limited spatial resolution of CALIPSO, which makes the mapping of subsurface properties of regions smaller than large seas impractical. Still, CALIPSO does pass over some of the world’s largest, freshwater-lakes, yielding important information about the water. Here we use the entire CALIPSO data record (approximately 15 years) to measure the particulate backscatter coefficient (b bp , m −1) across Lake Michigan. We then compare the LiDAR derived values of b bp to optical imagery values obtained from MODIS and to in situ measurements. Critically, we find that the LiDAR derived b bp aligns better in non-summer months with in situ values when compared to the optically imagery. However, due to both high cloud coverage and high wind speeds on Lake Michigan, this comes with the caveat that the CALIPSO product is limited in its usability. We close by speculating on the roll that spaceborne LiDAR, including CALIPSO and other satitlites, have on the future of monitoring the Great Lakes and other large bodies of fresh water.
{"title":"Assessment of using spaceborne LiDAR to monitor the particulate backscatter coefficient on large, freshwater lakes: A test using CALIPSO on Lake Michigan","authors":"Ray H. Watkins, Michael J. Sayers , Robert A. Shuchman , Karl R. Bosse ","doi":"10.3389/frsen.2023.1104681","DOIUrl":"https://doi.org/10.3389/frsen.2023.1104681","url":null,"abstract":"The Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was launched in 2006 with the primary goal of measuring the properties of clouds and aerosols in Earth’s atmosphere using LiDAR. Since then, numerous studies have shown the viability of using CALIPSO to observe day/night differences in subsurface optical properties of oceans and large seas from space. To date no studies have been done on using CALIPSO to monitor the subsurface optical properties of large, freshwater-lakes. This is likely due to the limited spatial resolution of CALIPSO, which makes the mapping of subsurface properties of regions smaller than large seas impractical. Still, CALIPSO does pass over some of the world’s largest, freshwater-lakes, yielding important information about the water. Here we use the entire CALIPSO data record (approximately 15 years) to measure the particulate backscatter coefficient (b bp , m −1) across Lake Michigan. We then compare the LiDAR derived values of b bp to optical imagery values obtained from MODIS and to in situ measurements. Critically, we find that the LiDAR derived b bp aligns better in non-summer months with in situ values when compared to the optically imagery. However, due to both high cloud coverage and high wind speeds on Lake Michigan, this comes with the caveat that the CALIPSO product is limited in its usability. We close by speculating on the roll that spaceborne LiDAR, including CALIPSO and other satitlites, have on the future of monitoring the Great Lakes and other large bodies of fresh water.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116674100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-03DOI: 10.3389/frsen.2023.994518
E. Pearson, William K. Oestreich, J. Ryan, Samara M. Haver, Jason Gedamke, R. Dziak, C. Wall
The NOAA-NPS Ocean Noise Reference Station Network (NRS) is a passive acoustic monitoring effort to record the low-frequency (<2 kHz) sound field throughout the U.S. Exclusive Economic Zone. Data collection began in 2014 and spans 12 acoustic recording locations. To date, NRS datasets have been analyzed to understand spatial variation of large-scale sound levels, however, assessment of specific sound sources is an area where these datasets can provide additional insights. To understand seasonal patterns of blue whale, Balaenoptera musculus, and fin whale, B. physalus, sound production in the eastern North Pacific Ocean, this study explored data recorded between 2014 and 2020 from four NRS recording sites. A call index (CI) was used to quantify the intensity of blue whale B calls and fin whale 20 Hz pulses. Diel and seasonal patterns were then determined in the context of their migratory patterns. Most sites shared similar patterns in blue whale CI: persistent acoustic presence for 4–5 months starting by August and ending by February with a CI maximum in October or November. Fin whale patterns included persistent acoustic presence for 5–7 months starting by October and ending before April with a CI maximum between October and December. The diel patterning of blue whale song varied across the sites with the Gulf of Alaska, Olympic Coast, Cordell Bank, and Channel Islands (2014–2015) exhibiting a tendency towards nighttime song detection. However, this diel pattern was not observed at Channel Islands (2018–2020). Fin whale song detection was distributed evenly across day and night at most recording sites and months, however, a tendency toward nighttime song detection was observed in Cordell Bank during fall, and Gulf of Alaska and Olympic Coast during spring. Understanding call and migration patterns for blue and fin whales is essential for conservation efforts. By using passive acoustic monitoring and efficient detection methods, such as CI, it is possible to process large amounts of bioacoustic data and better understand the migratory behaviors of endangered marine species.
{"title":"Widespread passive acoustic monitoring reveals spatio-temporal patterns of blue and fin whale song vocalizations in the Northeast Pacific Ocean","authors":"E. Pearson, William K. Oestreich, J. Ryan, Samara M. Haver, Jason Gedamke, R. Dziak, C. Wall","doi":"10.3389/frsen.2023.994518","DOIUrl":"https://doi.org/10.3389/frsen.2023.994518","url":null,"abstract":"The NOAA-NPS Ocean Noise Reference Station Network (NRS) is a passive acoustic monitoring effort to record the low-frequency (<2 kHz) sound field throughout the U.S. Exclusive Economic Zone. Data collection began in 2014 and spans 12 acoustic recording locations. To date, NRS datasets have been analyzed to understand spatial variation of large-scale sound levels, however, assessment of specific sound sources is an area where these datasets can provide additional insights. To understand seasonal patterns of blue whale, Balaenoptera musculus, and fin whale, B. physalus, sound production in the eastern North Pacific Ocean, this study explored data recorded between 2014 and 2020 from four NRS recording sites. A call index (CI) was used to quantify the intensity of blue whale B calls and fin whale 20 Hz pulses. Diel and seasonal patterns were then determined in the context of their migratory patterns. Most sites shared similar patterns in blue whale CI: persistent acoustic presence for 4–5 months starting by August and ending by February with a CI maximum in October or November. Fin whale patterns included persistent acoustic presence for 5–7 months starting by October and ending before April with a CI maximum between October and December. The diel patterning of blue whale song varied across the sites with the Gulf of Alaska, Olympic Coast, Cordell Bank, and Channel Islands (2014–2015) exhibiting a tendency towards nighttime song detection. However, this diel pattern was not observed at Channel Islands (2018–2020). Fin whale song detection was distributed evenly across day and night at most recording sites and months, however, a tendency toward nighttime song detection was observed in Cordell Bank during fall, and Gulf of Alaska and Olympic Coast during spring. Understanding call and migration patterns for blue and fin whales is essential for conservation efforts. By using passive acoustic monitoring and efficient detection methods, such as CI, it is possible to process large amounts of bioacoustic data and better understand the migratory behaviors of endangered marine species.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131005684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-19DOI: 10.3389/frsen.2023.944615
E. Gokul, Dionysios E. Raitsos, R. Brewin, I. Hoteit
Harmful algal blooms (HABs) have adverse effects on marine ecosystems. An effective approach for detecting, monitoring, and eventually predicting the occurrences of such events is required. By combining a singular value decomposition (SVD) approach and satellite remote sensing observations, we propose a remote sensing algorithm to detect and delineate species-specific HABs. We implemented and tested the proposed SVD algorithm to detect HABs associated with the mixed assemblages of different phytoplankton functional type (PFT) groupings in the Red Sea. The results were validated with concurrent in-situ data from surface samples, demonstrating that the SVD-model performs remarkably well at detecting and distinguishing HAB species in the Red Sea basin. The proposed SVD-model offers a cost-effective tool for implementing an automated remote-sensing monitoring system for detecting HAB species in the basin. Such a monitoring system could be used for predicting HAB outbreaks based on near real-time measurements, essential to support aquaculture industries, desalination plants, tourism, and public health.
{"title":"A singular value decomposition approach for detecting and delineating harmful algal blooms in the Red Sea","authors":"E. Gokul, Dionysios E. Raitsos, R. Brewin, I. Hoteit","doi":"10.3389/frsen.2023.944615","DOIUrl":"https://doi.org/10.3389/frsen.2023.944615","url":null,"abstract":"Harmful algal blooms (HABs) have adverse effects on marine ecosystems. An effective approach for detecting, monitoring, and eventually predicting the occurrences of such events is required. By combining a singular value decomposition (SVD) approach and satellite remote sensing observations, we propose a remote sensing algorithm to detect and delineate species-specific HABs. We implemented and tested the proposed SVD algorithm to detect HABs associated with the mixed assemblages of different phytoplankton functional type (PFT) groupings in the Red Sea. The results were validated with concurrent in-situ data from surface samples, demonstrating that the SVD-model performs remarkably well at detecting and distinguishing HAB species in the Red Sea basin. The proposed SVD-model offers a cost-effective tool for implementing an automated remote-sensing monitoring system for detecting HAB species in the basin. Such a monitoring system could be used for predicting HAB outbreaks based on near real-time measurements, essential to support aquaculture industries, desalination plants, tourism, and public health.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"483 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123060346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-18DOI: 10.3389/frsen.2023.1063188
J. Southworth, S. Ryan, H. Herrero, Reza Khatami, Erin L. Bunting, Mehedy Hassan, Carly S. Muir, P. Waylen
Introduction: The dynamics of terrestrial vegetation are shifting globally due to environmental changes, with potential repercussions for the proper functioning of the Earth system. However, the response of global vegetation, and the variability of the responses to their changing environment, is highly variable. In addition, the study of such changes and the methods used to monitor them, have in of themselves, been found to significantly impact the findings. Methods: This research builds on a recently developed vegetation persistence metric, which is simple to use, is user‐controlled to assess levels of statistical significance, and is readily reproducible, all designed to avoid these potential pitfalls. This study uses this vegetation persistence metric to present a global exploration of vegetation responses to climatic, latitudinal, and land‐use changes at a biomes level across three decades (1982–2010) of seasonal vegetation activity via the Normalized Difference Vegetation Index (NDVI). Results: Results demonstrated that positive vegetation persistence was found to be greater in June, July, August (JJA), and September, October, November (SON), with an increasing vegetation persistence found in the Northern Hemisphere (NH) over the Southern Hemisphere (SH). While vegetation showed positive persistence overall, this was not constant across all studied biomes. Overall forested biomes along with mangroves showed positive responses towards enhanced vegetation persistence in both the northern hemisphere and southern hemisphere. Contrastingly, desert, xeric shrubs, and savannas exhibited no significant persistence patterns, but the grassland biomes showed more negative persistence patterns and much higher variability over seasons, compared to the other biomes. The main drivers of changes appear to relate to climate, with tropical biomes linking to the availability of seasonal moisture, whereas the northern hemisphere forested biomes are driven more by temperature. Grasslands respond to moisture also, with high precipitation seasonality driving the persistence patterns. Land-use change also affected biomes and their responses, with many biomes having been significantly impacted by humans such that the vegetation response matched land use and not biome type. Discussion: The use here of a novel statistical time series analysis of NDVI at a pixel level, and looking historically back in time, highlights the utility and power of such techniques within global change studies. Overall, the findings match greening trends of other research but within a finer scale both temporally and spatially which is a critical new development in understanding global vegetation shifts.
{"title":"Latitudes and land use: Global biome shifts in vegetation persistence across three decades","authors":"J. Southworth, S. Ryan, H. Herrero, Reza Khatami, Erin L. Bunting, Mehedy Hassan, Carly S. Muir, P. Waylen","doi":"10.3389/frsen.2023.1063188","DOIUrl":"https://doi.org/10.3389/frsen.2023.1063188","url":null,"abstract":"Introduction: The dynamics of terrestrial vegetation are shifting globally due to environmental changes, with potential repercussions for the proper functioning of the Earth system. However, the response of global vegetation, and the variability of the responses to their changing environment, is highly variable. In addition, the study of such changes and the methods used to monitor them, have in of themselves, been found to significantly impact the findings. Methods: This research builds on a recently developed vegetation persistence metric, which is simple to use, is user‐controlled to assess levels of statistical significance, and is readily reproducible, all designed to avoid these potential pitfalls. This study uses this vegetation persistence metric to present a global exploration of vegetation responses to climatic, latitudinal, and land‐use changes at a biomes level across three decades (1982–2010) of seasonal vegetation activity via the Normalized Difference Vegetation Index (NDVI). Results: Results demonstrated that positive vegetation persistence was found to be greater in June, July, August (JJA), and September, October, November (SON), with an increasing vegetation persistence found in the Northern Hemisphere (NH) over the Southern Hemisphere (SH). While vegetation showed positive persistence overall, this was not constant across all studied biomes. Overall forested biomes along with mangroves showed positive responses towards enhanced vegetation persistence in both the northern hemisphere and southern hemisphere. Contrastingly, desert, xeric shrubs, and savannas exhibited no significant persistence patterns, but the grassland biomes showed more negative persistence patterns and much higher variability over seasons, compared to the other biomes. The main drivers of changes appear to relate to climate, with tropical biomes linking to the availability of seasonal moisture, whereas the northern hemisphere forested biomes are driven more by temperature. Grasslands respond to moisture also, with high precipitation seasonality driving the persistence patterns. Land-use change also affected biomes and their responses, with many biomes having been significantly impacted by humans such that the vegetation response matched land use and not biome type. Discussion: The use here of a novel statistical time series analysis of NDVI at a pixel level, and looking historically back in time, highlights the utility and power of such techniques within global change studies. Overall, the findings match greening trends of other research but within a finer scale both temporally and spatially which is a critical new development in understanding global vegetation shifts.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134600667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-18DOI: 10.3389/frsen.2023.1086085
Chloe M. Mattilio, Daniel R. Tekiela, U. Norton
Leafy spurge (Euphorbia esula L.) has been introduced to the Yampa River in Northwestern Colorado for over 40 years and flood and runoff events transport leafy spurge propagules onto adjacent landscapes. The spread of leafy spurge beyond the river channels has yet to be mapped and recorded, and this research was conducted to map leafy spurge occurrence in the Yampa River Valley. Significant stakeholder mapping efforts took place in the summer of 2019–2021, leading to excellent spatial data on leafy spurge presence and absence along the main channel. In summer 2019, multispectral SPOT seven satellite imagery, stakeholder ground mapping efforts, and bright yellow-green leafy spurge bracts were used to interpret imagery, identify dense, unobscured patches of leafy spurge, and digitize them. Spectral signatures from training samples for leafy spurge and other land cover classes (generalized as “not leafy spurge”) were then used to train a Random Forest machine learning classification. In the summer of 2021, generated classification maps were compared to multispectral satellite imagery and stakeholder ground mapped leafy spurge presence. Mismatches were identified, and 271 validation locations were identified, navigated to, and evaluated for leafy spurge presence. Leafy spurge training samples were classified with 96% accuracy. Correctly classified leafy spurge locations had higher leafy spurge coverage and lower overstory canopy than missed leafy spurge locations. Leafy spurge growing beneath shrub canopy or growing as individual plants along the riverbanks were more likely to be missed. A frequency analysis for other plant species found at validation locations determined that smooth brome (Bromus inermis Leyss.), dandelion (Taraxacum officinale L.), and willow (Salix sp.) were most frequently misclassified as leafy spurge. In conclusion, multispectral satellite imagery was useful at remote detection of leafy spurge in open areas with dense leafy spurge coverage, but more work must be done for identification of sparse and diffuse leafy spurge infestations.
{"title":"Remote mapping of leafy spurge (Euphorbia esula, L.) in Northwestern Colorado","authors":"Chloe M. Mattilio, Daniel R. Tekiela, U. Norton","doi":"10.3389/frsen.2023.1086085","DOIUrl":"https://doi.org/10.3389/frsen.2023.1086085","url":null,"abstract":"Leafy spurge (Euphorbia esula L.) has been introduced to the Yampa River in Northwestern Colorado for over 40 years and flood and runoff events transport leafy spurge propagules onto adjacent landscapes. The spread of leafy spurge beyond the river channels has yet to be mapped and recorded, and this research was conducted to map leafy spurge occurrence in the Yampa River Valley. Significant stakeholder mapping efforts took place in the summer of 2019–2021, leading to excellent spatial data on leafy spurge presence and absence along the main channel. In summer 2019, multispectral SPOT seven satellite imagery, stakeholder ground mapping efforts, and bright yellow-green leafy spurge bracts were used to interpret imagery, identify dense, unobscured patches of leafy spurge, and digitize them. Spectral signatures from training samples for leafy spurge and other land cover classes (generalized as “not leafy spurge”) were then used to train a Random Forest machine learning classification. In the summer of 2021, generated classification maps were compared to multispectral satellite imagery and stakeholder ground mapped leafy spurge presence. Mismatches were identified, and 271 validation locations were identified, navigated to, and evaluated for leafy spurge presence. Leafy spurge training samples were classified with 96% accuracy. Correctly classified leafy spurge locations had higher leafy spurge coverage and lower overstory canopy than missed leafy spurge locations. Leafy spurge growing beneath shrub canopy or growing as individual plants along the riverbanks were more likely to be missed. A frequency analysis for other plant species found at validation locations determined that smooth brome (Bromus inermis Leyss.), dandelion (Taraxacum officinale L.), and willow (Salix sp.) were most frequently misclassified as leafy spurge. In conclusion, multispectral satellite imagery was useful at remote detection of leafy spurge in open areas with dense leafy spurge coverage, but more work must be done for identification of sparse and diffuse leafy spurge infestations.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128655996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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