Pub Date : 2023-01-17DOI: 10.3389/frsen.2022.1079223
Lei Chen, Zhi-yong Xu, Zhao Zhao
In recent years, passive acoustic monitoring (PAM) has become increasingly popular. Many acoustic indices (AIs) have been proposed for rapid biodiversity assessment (RBA), however, most acoustic indices have been reported to be susceptible to abiotic sounds such as wind or rain noise when biotic sound is masked, which greatly limits the application of these acoustic indices. In this work, in order to take an insight into the influence mechanism of signal-to-noise ratio (SNR) on acoustic indices, four most commonly used acoustic indices, i.e., the bioacoustic index (BIO), the acoustic diversity index (ADI), the acoustic evenness index (AEI), and the acoustic complexity index (ACI), were investigated using controlled computational experiments with field recordings collected in a suburban park in Xuzhou, China, in which bird vocalizations were employed as typical biotic sounds. In the experiments, different signal-to-noise ratio conditions were obtained by varying biotic sound intensities while keeping the background noise fixed. Experimental results showed that three indices (acoustic diversity index, acoustic complexity index, and bioacoustic index) decreased while the trend of acoustic evenness index was in the opposite direction as signal-to-noise ratio declined, which was owing to several factors summarized as follows. Firstly, as for acoustic diversity index and acoustic evenness index, the peak value in the spectrogram will no longer correspond to the biotic sounds of interest when signal-to-noise ratio decreases to a certain extent, leading to erroneous results of the proportion of sound occurring in each frequency band. Secondly, in bioacoustic index calculation, the accumulation of the difference between the sound level within each frequency band and the minimum sound level will drop dramatically with reduced biotic sound intensities. Finally, the acoustic complexity index calculation result relies on the ratio between total differences among all adjacent frames and the total sum of all frames within each temporal step and frequency bin in the spectrogram. With signal-to-noise ratio decreasing, the biotic components contribution in both the total differences and the total sum presents a complex impact on the final acoustic complexity index value. This work is helpful to more comprehensively interpret the values of the above acoustic indices in a real-world environment and promote the applications of passive acoustic monitoring in rapid biodiversity assessment.
{"title":"Biotic sound SNR influence analysis on acoustic indices","authors":"Lei Chen, Zhi-yong Xu, Zhao Zhao","doi":"10.3389/frsen.2022.1079223","DOIUrl":"https://doi.org/10.3389/frsen.2022.1079223","url":null,"abstract":"In recent years, passive acoustic monitoring (PAM) has become increasingly popular. Many acoustic indices (AIs) have been proposed for rapid biodiversity assessment (RBA), however, most acoustic indices have been reported to be susceptible to abiotic sounds such as wind or rain noise when biotic sound is masked, which greatly limits the application of these acoustic indices. In this work, in order to take an insight into the influence mechanism of signal-to-noise ratio (SNR) on acoustic indices, four most commonly used acoustic indices, i.e., the bioacoustic index (BIO), the acoustic diversity index (ADI), the acoustic evenness index (AEI), and the acoustic complexity index (ACI), were investigated using controlled computational experiments with field recordings collected in a suburban park in Xuzhou, China, in which bird vocalizations were employed as typical biotic sounds. In the experiments, different signal-to-noise ratio conditions were obtained by varying biotic sound intensities while keeping the background noise fixed. Experimental results showed that three indices (acoustic diversity index, acoustic complexity index, and bioacoustic index) decreased while the trend of acoustic evenness index was in the opposite direction as signal-to-noise ratio declined, which was owing to several factors summarized as follows. Firstly, as for acoustic diversity index and acoustic evenness index, the peak value in the spectrogram will no longer correspond to the biotic sounds of interest when signal-to-noise ratio decreases to a certain extent, leading to erroneous results of the proportion of sound occurring in each frequency band. Secondly, in bioacoustic index calculation, the accumulation of the difference between the sound level within each frequency band and the minimum sound level will drop dramatically with reduced biotic sound intensities. Finally, the acoustic complexity index calculation result relies on the ratio between total differences among all adjacent frames and the total sum of all frames within each temporal step and frequency bin in the spectrogram. With signal-to-noise ratio decreasing, the biotic components contribution in both the total differences and the total sum presents a complex impact on the final acoustic complexity index value. This work is helpful to more comprehensively interpret the values of the above acoustic indices in a real-world environment and promote the applications of passive acoustic monitoring in rapid biodiversity assessment.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123399835","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 : 2023-01-16DOI: 10.3389/frsen.2022.1085808
Kelsey Huelsman, H. Epstein, Xi Yang, Lydia Mullori, L. Červená, Roderick Walker
Land managers are making concerted efforts to control the spread of invasive plants, a task that demands extensive ecosystem monitoring, for which unoccupied aerial vehicles (UAVs or drones) are becoming increasingly popular. The high spatial resolution of unoccupied aerial vehicles imagery may positively or negatively affect plant species differentiation, as reflectance spectra of pixels may be highly variable when finely resolved. We assessed this impact on detection of invasive plant species Ailanthus altissima (tree of heaven) and Elaeagnus umbellata (autumn olive) using fine-resolution images collected in northwestern Virginia in June 2020 by a unoccupied aerial vehicles with a Headwall Hyperspec visible and near-infrared hyperspectral imager. Though E. umbellata had greater intraspecific variability relative to interspecific variability over more wavelengths than A. altissima, the classification accuracy was greater for E. umbellata (95%) than for A. altissima (66%). This suggests that spectral differences between species of interest and others are not necessarily obscured by intraspecific variability. Therefore, the use of unoccupied aerial vehicles-based spectroscopy for species identification may overcome reflectance variability in fine resolution imagery.
{"title":"Spectral variability in fine-scale drone-based imaging spectroscopy does not impede detection of target invasive plant species","authors":"Kelsey Huelsman, H. Epstein, Xi Yang, Lydia Mullori, L. Červená, Roderick Walker","doi":"10.3389/frsen.2022.1085808","DOIUrl":"https://doi.org/10.3389/frsen.2022.1085808","url":null,"abstract":"Land managers are making concerted efforts to control the spread of invasive plants, a task that demands extensive ecosystem monitoring, for which unoccupied aerial vehicles (UAVs or drones) are becoming increasingly popular. The high spatial resolution of unoccupied aerial vehicles imagery may positively or negatively affect plant species differentiation, as reflectance spectra of pixels may be highly variable when finely resolved. We assessed this impact on detection of invasive plant species Ailanthus altissima (tree of heaven) and Elaeagnus umbellata (autumn olive) using fine-resolution images collected in northwestern Virginia in June 2020 by a unoccupied aerial vehicles with a Headwall Hyperspec visible and near-infrared hyperspectral imager. Though E. umbellata had greater intraspecific variability relative to interspecific variability over more wavelengths than A. altissima, the classification accuracy was greater for E. umbellata (95%) than for A. altissima (66%). This suggests that spectral differences between species of interest and others are not necessarily obscured by intraspecific variability. Therefore, the use of unoccupied aerial vehicles-based spectroscopy for species identification may overcome reflectance variability in fine resolution imagery.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121787646","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 : 2023-01-12DOI: 10.3389/frsen.2022.1038287
S. Skiles, Christopher P. Donahue, A. Hunsaker, J. Jacobs
Snow albedo, a measure of the amount of solar radiation that is reflected at the snow surface, plays a critical role in Earth’s climate and in regional hydrology because it is a primary driver of snowmelt timing. Satellite multi-spectral remote sensing provides a multi-decade record of land surface reflectance, from which snow albedo can be retrieved. However, this observational record is challenging to assess because discrete in situ observations are not well suited for validation of snow properties at the spatial resolution of satellites (tens to hundreds of meters). For example, snow grain size, a primary driver of snow albedo, can vary at the sub-meter scale driven by changes in aspect, elevation, and vegetation. Here, we present a new uncrewed aerial vehicle hyperspectral imaging (UAV-HSI) method for mapping snow surface properties at high resolution (20 cm). A Resonon near-infrared HSI was flown on a DJI Matrice 600 Pro over the meadow encompassing Swamp Angel Study Plot in Senator Beck Basin, Colorado. Using a radiative transfer forward modeling approach, effective snow grain size and albedo maps were produced from measured surface reflectance. Coincident ground observations were used for validation; relative to retrievals from a field spectrometer the mean grain size difference was 2 μm, with an RMSE of 12 μm, and the mean broadband albedo was within 1% of that measured near the center of the flight area. Even though the snow surface was visually homogenous, the maps showed spatial variability and coherent patterns in the freshly fallen snow. To demonstrate the potential for UAV-HSI to be used to improve validation of satellite retrievals, the high-resolution maps were used to assess grain size and albedo retrievals, and subpixel variability, across 17 Landsat 9 OLI pixels from a satellite overpass with similar conditions two days following the flight. Although Landsat 9 did not capture the same range of values and spatial variability as the UAV-HSI, on average the comparison showed good agreement, with a mean grain size difference of 9 μm and the same broadband albedo (86%).
{"title":"UAV hyperspectral imaging for multiscale assessment of Landsat 9 snow grain size and albedo","authors":"S. Skiles, Christopher P. Donahue, A. Hunsaker, J. Jacobs","doi":"10.3389/frsen.2022.1038287","DOIUrl":"https://doi.org/10.3389/frsen.2022.1038287","url":null,"abstract":"Snow albedo, a measure of the amount of solar radiation that is reflected at the snow surface, plays a critical role in Earth’s climate and in regional hydrology because it is a primary driver of snowmelt timing. Satellite multi-spectral remote sensing provides a multi-decade record of land surface reflectance, from which snow albedo can be retrieved. However, this observational record is challenging to assess because discrete in situ observations are not well suited for validation of snow properties at the spatial resolution of satellites (tens to hundreds of meters). For example, snow grain size, a primary driver of snow albedo, can vary at the sub-meter scale driven by changes in aspect, elevation, and vegetation. Here, we present a new uncrewed aerial vehicle hyperspectral imaging (UAV-HSI) method for mapping snow surface properties at high resolution (20 cm). A Resonon near-infrared HSI was flown on a DJI Matrice 600 Pro over the meadow encompassing Swamp Angel Study Plot in Senator Beck Basin, Colorado. Using a radiative transfer forward modeling approach, effective snow grain size and albedo maps were produced from measured surface reflectance. Coincident ground observations were used for validation; relative to retrievals from a field spectrometer the mean grain size difference was 2 μm, with an RMSE of 12 μm, and the mean broadband albedo was within 1% of that measured near the center of the flight area. Even though the snow surface was visually homogenous, the maps showed spatial variability and coherent patterns in the freshly fallen snow. To demonstrate the potential for UAV-HSI to be used to improve validation of satellite retrievals, the high-resolution maps were used to assess grain size and albedo retrievals, and subpixel variability, across 17 Landsat 9 OLI pixels from a satellite overpass with similar conditions two days following the flight. Although Landsat 9 did not capture the same range of values and spatial variability as the UAV-HSI, on average the comparison showed good agreement, with a mean grain size difference of 9 μm and the same broadband albedo (86%).","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125584676","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 : 2023-01-10DOI: 10.3389/frsen.2022.1100012
Hannah Ulman, Jonas Gütter, Julia Niebling
Obtaining high quality labels is a major challenge for the application of deep neural networks in the remote sensing domain. A common way of acquiring labels is the usage of crowd sourcing which can provide much needed training data sets but also often contains incorrect labels which can affect the training process of a deep neural network significantly. In this paper, we exploit uncertainty to identify a certain type of label noise for semantic segmentation of buildings in satellite imagery. That type of label noise is known as “omission noise,” i.e., missing labels for whole buildings which still appear in the satellite image. Following the literature, uncertainty during training can help in identifying the “sweet spot” between generalizing well and overfitting to label noise, which is further used to differentiate between noisy and clean labels. The differentiation between clean and noisy labels is based on pixel-wise uncertainty estimation and beta distribution fitting to the uncertainty estimates. For our study, we create a data set for building segmentation with different levels of omission noise to evaluate the impact of the noise level on the performance of the deep neural network during training. In doing so, we show that established uncertainty-based methods to identify noisy labels are in general not sufficient enough for our kind of remote sensing data. On the other hand, for some noise levels, we observe some promising differences between noisy and clean data which opens the possibility to refine the state-of-the-art methods further.
{"title":"Uncertainty is not sufficient for identifying noisy labels in training data for binary segmentation of building footprints","authors":"Hannah Ulman, Jonas Gütter, Julia Niebling","doi":"10.3389/frsen.2022.1100012","DOIUrl":"https://doi.org/10.3389/frsen.2022.1100012","url":null,"abstract":"Obtaining high quality labels is a major challenge for the application of deep neural networks in the remote sensing domain. A common way of acquiring labels is the usage of crowd sourcing which can provide much needed training data sets but also often contains incorrect labels which can affect the training process of a deep neural network significantly. In this paper, we exploit uncertainty to identify a certain type of label noise for semantic segmentation of buildings in satellite imagery. That type of label noise is known as “omission noise,” i.e., missing labels for whole buildings which still appear in the satellite image. Following the literature, uncertainty during training can help in identifying the “sweet spot” between generalizing well and overfitting to label noise, which is further used to differentiate between noisy and clean labels. The differentiation between clean and noisy labels is based on pixel-wise uncertainty estimation and beta distribution fitting to the uncertainty estimates. For our study, we create a data set for building segmentation with different levels of omission noise to evaluate the impact of the noise level on the performance of the deep neural network during training. In doing so, we show that established uncertainty-based methods to identify noisy labels are in general not sufficient enough for our kind of remote sensing data. On the other hand, for some noise levels, we observe some promising differences between noisy and clean data which opens the possibility to refine the state-of-the-art methods further.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114276115","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 : 2023-01-04DOI: 10.3389/frsen.2022.1010978
M. S. Dhillon, Thorsten Dahms, Carina Kuebert-Flock, Thomas Rummler, J. Arnault, Ingolf Stefan-Dewenter, T. Ullmann
The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R 2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.
{"title":"Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape","authors":"M. S. Dhillon, Thorsten Dahms, Carina Kuebert-Flock, Thomas Rummler, J. Arnault, Ingolf Stefan-Dewenter, T. Ullmann","doi":"10.3389/frsen.2022.1010978","DOIUrl":"https://doi.org/10.3389/frsen.2022.1010978","url":null,"abstract":"The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R 2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131962089","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 : 2022-12-22DOI: 10.3389/frsen.2022.1076471
L. Oreopoulos, N. Cho, Dongmin Lee
We update the parameterization capturing the variation of parameters that describe how cloud occurrence (layer cloud fraction) and layer cloud optical depth (COD) distributions overlap vertically. Our updated analysis is motivated by the availability of a new dataset constructed by combining two products describing the two-dimensional extinction properties of liquid and ice phase clouds (and their mixtures) according to active cloud observations by the CloudSat and CALIPSO satellites. As before, cloud occurrence overlap is modeled with the decorrelation length of an inverse exponential function describing the decay with separation distance of the relative likelihood that two cloudy layers are overlapped maximally versus randomly. Similarly, cloud optical depth distribution vertical overlap is described again with a decorrelation length that describes the assumed inverse exponential decay with separation distance of the rank correlation between cloud optical depth distribution members in two cloudy layers. We derive the climatological zonal variability of these two decorrelation lengths using 4 years of observations for scenes of ∼100 km scale length, a typical grid size of numerical models used for climate simulations. As previously, we find a strong latitudinal dependence reflecting systematic differences in dominant cloud types with latitude, but substantially different magnitudes of decorrelation length compared to the previous work. The previously used parameterization form is therefore updated with new parameters to describe the latitudinal dependence of decorrelation lengths and its seasonal shift. Similar zonal patterns of decorrelation length are found when the analysis is broken down by different cloud classes. When the revised parameterization is implemented in a cloud subcolumn generator, simulated column cloud properties compare to observations quite well, and so do their associated cloud radiative effects, but improvements over the earlier version of the parameterization are marginal.
{"title":"Revisiting cloud overlap with a merged dataset of liquid and ice cloud extinction from CloudSat and CALIPSO","authors":"L. Oreopoulos, N. Cho, Dongmin Lee","doi":"10.3389/frsen.2022.1076471","DOIUrl":"https://doi.org/10.3389/frsen.2022.1076471","url":null,"abstract":"We update the parameterization capturing the variation of parameters that describe how cloud occurrence (layer cloud fraction) and layer cloud optical depth (COD) distributions overlap vertically. Our updated analysis is motivated by the availability of a new dataset constructed by combining two products describing the two-dimensional extinction properties of liquid and ice phase clouds (and their mixtures) according to active cloud observations by the CloudSat and CALIPSO satellites. As before, cloud occurrence overlap is modeled with the decorrelation length of an inverse exponential function describing the decay with separation distance of the relative likelihood that two cloudy layers are overlapped maximally versus randomly. Similarly, cloud optical depth distribution vertical overlap is described again with a decorrelation length that describes the assumed inverse exponential decay with separation distance of the rank correlation between cloud optical depth distribution members in two cloudy layers. We derive the climatological zonal variability of these two decorrelation lengths using 4 years of observations for scenes of ∼100 km scale length, a typical grid size of numerical models used for climate simulations. As previously, we find a strong latitudinal dependence reflecting systematic differences in dominant cloud types with latitude, but substantially different magnitudes of decorrelation length compared to the previous work. The previously used parameterization form is therefore updated with new parameters to describe the latitudinal dependence of decorrelation lengths and its seasonal shift. Similar zonal patterns of decorrelation length are found when the analysis is broken down by different cloud classes. When the revised parameterization is implemented in a cloud subcolumn generator, simulated column cloud properties compare to observations quite well, and so do their associated cloud radiative effects, but improvements over the earlier version of the parameterization are marginal.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131721301","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 : 2022-12-14DOI: 10.3389/frsen.2022.1060144
Ritu Yadav, Andrea Nascetti , Yifang Ban
Floods are occurring across the globe, and due to climate change, flood events are expected to increase in the coming years. Current situations urge more focus on efficient monitoring of floods and detecting impacted areas. In this study, we propose two segmentation networks for flood detection on uni-temporal Sentinel-1 Synthetic Aperture Radar data. The first network is “Attentive U-Net”. It takes VV, VH, and the ratio VV/VH as input. The network uses spatial and channel-wise attention to enhance feature maps which help in learning better segmentation. “Attentive U-Net” yields 67% Intersection Over Union (IoU) on the Sen1Floods11 dataset, which is 3% better than the benchmark IoU. The second proposed network is a dual-stream “Fusion network”, where we fuse global low-resolution elevation data and permanent water masks with Sentinel-1 (VV, VH) data. Compared to the previous benchmark on the Sen1Floods11 dataset, our fusion network gave a 4.5% better IoU score. Quantitatively, the performance improvement of both proposed methods is considerable. The quantitative comparison with the benchmark method demonstrates the potential of our proposed flood detection networks. The results are further validated by qualitative analysis, in which we demonstrate that the addition of a low-resolution elevation and a permanent water mask enhances the flood detection results. Through ablation experiments and analysis we also demonstrate the effectiveness of various design choices in proposed networks. Our code is available on Github at https://github.com/RituYadav92/UNI_TEMP_FLOOD_DETECTION for reuse.
{"title":"Deep attentive fusion network for flood detection on uni-temporal Sentinel-1 data","authors":"Ritu Yadav, Andrea Nascetti , Yifang Ban ","doi":"10.3389/frsen.2022.1060144","DOIUrl":"https://doi.org/10.3389/frsen.2022.1060144","url":null,"abstract":"Floods are occurring across the globe, and due to climate change, flood events are expected to increase in the coming years. Current situations urge more focus on efficient monitoring of floods and detecting impacted areas. In this study, we propose two segmentation networks for flood detection on uni-temporal Sentinel-1 Synthetic Aperture Radar data. The first network is “Attentive U-Net”. It takes VV, VH, and the ratio VV/VH as input. The network uses spatial and channel-wise attention to enhance feature maps which help in learning better segmentation. “Attentive U-Net” yields 67% Intersection Over Union (IoU) on the Sen1Floods11 dataset, which is 3% better than the benchmark IoU. The second proposed network is a dual-stream “Fusion network”, where we fuse global low-resolution elevation data and permanent water masks with Sentinel-1 (VV, VH) data. Compared to the previous benchmark on the Sen1Floods11 dataset, our fusion network gave a 4.5% better IoU score. Quantitatively, the performance improvement of both proposed methods is considerable. The quantitative comparison with the benchmark method demonstrates the potential of our proposed flood detection networks. The results are further validated by qualitative analysis, in which we demonstrate that the addition of a low-resolution elevation and a permanent water mask enhances the flood detection results. Through ablation experiments and analysis we also demonstrate the effectiveness of various design choices in proposed networks. Our code is available on Github at https://github.com/RituYadav92/UNI_TEMP_FLOOD_DETECTION for reuse.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125499229","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 : 2022-12-12DOI: 10.3389/frsen.2022.970401
A. DeAngelis, S. V. Van Parijs, J. Barkowski, S. Baumann‐Pickering, Kourtney Burger, Genevieve E. Davis, J. Joseph, Annebelle C. M. Kok, A. Kügler, M. Lammers, T. Margolina, Nicola Pegg, Ally Rice, T. Rowell, J. Ryan, Allison Stokoe, Eden J. Zang, L. Hatch
The United States of America’s Office of National Marine Sanctuaries (ONMS) hosts 15 National Marine Sanctuaries (NMS) and two Monuments in its waters. Charismatic marine megafauna, such as fin whales (Balaenoptera physalus), humpback whales (Megaptera novaeangliae), and various delphinid species frequent these areas, but little is known about their occupancy. As part of a national effort to better understand the soundscapes of NMS, 22 near-continuous passive acoustic bottom mounted recorders and one bottom-mounted cable hydrophone were analyzed within seven NMS (Stellwagen Bank, Gray’s Reef, Florida Keys, Olympic Coast, Monterey Bay, Channel Islands, and Hawaiian Islands Humpback Whale sanctuaries). The daily acoustic presence of humpback and fin whales across 2 years (November 2018–October 2020) and hourly presence of delphinids over 1 year (June 2019–May 2020) were analyzed. Humpback whales showed variability in their acoustic presence across NMS, but in general were mostly present January through May and September through December, and more scarce or fully absent June through August. Consecutive days of humpback whale vocalizations were greatest at sites HI01 and HI05 in the Hawaiian Islands Humpback Whale NMS and fewest at the Channel Islands NMS. Fin whales exhibited a similar seasonal pattern across the West Coast NMS and Stellwagen Bank NMS. Monterey Bay NMS had the greatest number of median consecutive presence of fin whales with fewest at Stellwagen Bank NMS. Delphinid acoustic presence varied throughout and within NMS, with sites at the Channel Islands and Hawaiʻi NMS showing the highest occupancy. All NMS showed distinct monthly delphinid acoustic presence with differences in detected hours between day versus night. Sixteen sites had medians of delphinid presence between one and three consecutive days, while three sites had 5 days or more of consecutive presence, and one site had no consecutive delphinid presence, showing clear variation in how long they occupied different NMS. Marine mammals utilized all NMS and showed a wide range of occupancy, emphasizing the importance of understanding species use across different NMS as biological areas for migration, breeding and foraging.
美国国家海洋保护区办公室(ONMS)在其水域内设有15个国家海洋保护区(NMS)和两个纪念碑。迷人的海洋巨型动物，如长须鲸(Balaenoptera physalus)、座头鲸(Megaptera novaeangliae)和各种海豚物种经常出现在这些地区，但人们对它们的居住情况知之甚少。作为国家努力的一部分，为了更好地了解NMS的声景，在7个NMS (Stellwagen Bank, Gray 's Reef, Florida Keys, Olympic Coast, Monterey Bay, Channel Islands, and Hawaiian Islands座头鲸保护区)中分析了22个近连续被动声学底部安装记录仪和一个底部安装电缆水听器。分析了2年(2018年11月- 2020年10月)座头鲸和长须鲸的每日声音存在以及1年(2019年6月- 2020年5月)海豚的每小时声音存在。座头鲸在整个NMS中表现出不同的声音存在，但一般来说，1月至5月和9月至12月主要存在，6月至8月更少或完全缺席。夏威夷岛座头鲸保护区HI01点和HI05点座头鲸连续发声天数最多，海峡群岛保护区最少。长须鲸在西海岸NMS和斯特尔瓦根银行NMS中表现出类似的季节性模式。蒙特雷湾NMS中长须鲸连续存在的中位数数量最多，而Stellwagen Bank NMS中最少。海豚的声音存在于整个NMS和NMS内部，海峡群岛和夏威夷NMS的站点显示出最高的占用率。所有NMS都显示出明显的月海豚声存在，并且在白天和夜晚的探测小时之间存在差异。16个站点的海豚存在的中位数在连续1天到3天之间，3个站点连续5天或更长时间，1个站点没有连续的海豚存在，显示出它们在不同NMS中占用的时间有明显的差异。海洋哺乳动物利用了所有NMS，并表现出广泛的占用范围，强调了了解不同NMS作为迁移、繁殖和觅食生物区域的物种利用的重要性。
{"title":"Exploring marine mammal presence across seven US national marine sanctuaries","authors":"A. DeAngelis, S. V. Van Parijs, J. Barkowski, S. Baumann‐Pickering, Kourtney Burger, Genevieve E. Davis, J. Joseph, Annebelle C. M. Kok, A. Kügler, M. Lammers, T. Margolina, Nicola Pegg, Ally Rice, T. Rowell, J. Ryan, Allison Stokoe, Eden J. Zang, L. Hatch","doi":"10.3389/frsen.2022.970401","DOIUrl":"https://doi.org/10.3389/frsen.2022.970401","url":null,"abstract":"The United States of America’s Office of National Marine Sanctuaries (ONMS) hosts 15 National Marine Sanctuaries (NMS) and two Monuments in its waters. Charismatic marine megafauna, such as fin whales (Balaenoptera physalus), humpback whales (Megaptera novaeangliae), and various delphinid species frequent these areas, but little is known about their occupancy. As part of a national effort to better understand the soundscapes of NMS, 22 near-continuous passive acoustic bottom mounted recorders and one bottom-mounted cable hydrophone were analyzed within seven NMS (Stellwagen Bank, Gray’s Reef, Florida Keys, Olympic Coast, Monterey Bay, Channel Islands, and Hawaiian Islands Humpback Whale sanctuaries). The daily acoustic presence of humpback and fin whales across 2 years (November 2018–October 2020) and hourly presence of delphinids over 1 year (June 2019–May 2020) were analyzed. Humpback whales showed variability in their acoustic presence across NMS, but in general were mostly present January through May and September through December, and more scarce or fully absent June through August. Consecutive days of humpback whale vocalizations were greatest at sites HI01 and HI05 in the Hawaiian Islands Humpback Whale NMS and fewest at the Channel Islands NMS. Fin whales exhibited a similar seasonal pattern across the West Coast NMS and Stellwagen Bank NMS. Monterey Bay NMS had the greatest number of median consecutive presence of fin whales with fewest at Stellwagen Bank NMS. Delphinid acoustic presence varied throughout and within NMS, with sites at the Channel Islands and Hawaiʻi NMS showing the highest occupancy. All NMS showed distinct monthly delphinid acoustic presence with differences in detected hours between day versus night. Sixteen sites had medians of delphinid presence between one and three consecutive days, while three sites had 5 days or more of consecutive presence, and one site had no consecutive delphinid presence, showing clear variation in how long they occupied different NMS. Marine mammals utilized all NMS and showed a wide range of occupancy, emphasizing the importance of understanding species use across different NMS as biological areas for migration, breeding and foraging.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125772530","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 : 2022-12-12DOI: 10.3389/frsen.2022.1027065
Nathaniel R. Baurley, Chris Tomsett, J. Hart
Uncrewed Aerial Vehicles (UAVs), in combination with Structure from Motion (SfM) photogrammetry, have become an established tool for reconstructing glacial and ice-marginal topography, yet the method is highly dependent on several factors, all of which can be highly variable in glacial environments. However, recent technological advancements, related primarily to the miniaturisation of new payloads such as compact Laser Scanners (LS), has provided potential new opportunities for cryospheric investigation. Indeed, UAV-LS systems have shown promise in forestry, river, and snow depth research, but to date the method has yet to be deployed in glacial settings. As such, in this study we assessed the suitability of UAV-LS for glacial research by investigating short-term changes in ice surface elevation, calving front geometry and crevasse morphology over the near-terminus region of an actively calving glacier in southeast Iceland. We undertook repeat surveys over a 0.1 km2 region of the glacier at sub-daily, daily, and weekly temporal intervals, producing directly georeferenced point clouds at very high spatial resolutions (average of >300 points per m−2 at 40 m flying height). Our data has enabled us to: 1) Accurately map surface elevation changes (Median errors under 0.1 m), 2) Reconstruct the geometry and evolution of an active calving front, 3) Produce more accurate estimates of the volume of ice lost through calving, and 4) Better detect surface crevasse morphology, providing future scope to extract size, depth and improve the monitoring of their evolution through time. We also compared our results to data obtained in parallel using UAV-SfM, which further emphasised the relative advantages of our method and suitability in glaciology. Consequently, our study highlights the potential of UAV-LS in glacial research, particularly for investigating glacier mass balance, changing ice dynamics, and calving glacier behaviour, and thus we suggest it has a significant role in advancing our knowledge of, and ability to monitor, rapidly changing glacial environments in future.
{"title":"Assessing UAV-based laser scanning for monitoring glacial processes and interactions at high spatial and temporal resolutions","authors":"Nathaniel R. Baurley, Chris Tomsett, J. Hart","doi":"10.3389/frsen.2022.1027065","DOIUrl":"https://doi.org/10.3389/frsen.2022.1027065","url":null,"abstract":"Uncrewed Aerial Vehicles (UAVs), in combination with Structure from Motion (SfM) photogrammetry, have become an established tool for reconstructing glacial and ice-marginal topography, yet the method is highly dependent on several factors, all of which can be highly variable in glacial environments. However, recent technological advancements, related primarily to the miniaturisation of new payloads such as compact Laser Scanners (LS), has provided potential new opportunities for cryospheric investigation. Indeed, UAV-LS systems have shown promise in forestry, river, and snow depth research, but to date the method has yet to be deployed in glacial settings. As such, in this study we assessed the suitability of UAV-LS for glacial research by investigating short-term changes in ice surface elevation, calving front geometry and crevasse morphology over the near-terminus region of an actively calving glacier in southeast Iceland. We undertook repeat surveys over a 0.1 km2 region of the glacier at sub-daily, daily, and weekly temporal intervals, producing directly georeferenced point clouds at very high spatial resolutions (average of >300 points per m−2 at 40 m flying height). Our data has enabled us to: 1) Accurately map surface elevation changes (Median errors under 0.1 m), 2) Reconstruct the geometry and evolution of an active calving front, 3) Produce more accurate estimates of the volume of ice lost through calving, and 4) Better detect surface crevasse morphology, providing future scope to extract size, depth and improve the monitoring of their evolution through time. We also compared our results to data obtained in parallel using UAV-SfM, which further emphasised the relative advantages of our method and suitability in glaciology. Consequently, our study highlights the potential of UAV-LS in glacial research, particularly for investigating glacier mass balance, changing ice dynamics, and calving glacier behaviour, and thus we suggest it has a significant role in advancing our knowledge of, and ability to monitor, rapidly changing glacial environments in future.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132779426","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 : 2022-12-08DOI: 10.3389/frsen.2022.958207
R. Foskinis, A. Nenes, A. Papayannis, P. Georgakaki, K. Eleftheriadis, S. Vratolis, M. Gini, M. Komppula, V. Vakkari, M. Tombrou, E. Bossioli, P. Kokkalis
Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semi-automated approach that can address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL cloud droplet number (N d sat ) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the in situ aerosol measurements at ground level were used constrain as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (N d ), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated N d and N d sat are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece from March to May 2020, shows that droplet closure can be achieved to within 30%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km × 4.6 km), opens the possibility of continuous and reliable N d sat , giving rise to high value datasets for aerosol-cloud-climate interaction studies.
遥感已成为开发用于研究和限制气溶胶-云-气候相互作用的广泛而详细的数据集的关键资源。然而，气溶胶与云的搭配挑战、算法限制，以及从气溶胶相关的云微物理效应中揭示动态的困难，长期以来一直挑战着云滴数浓度的精确检索。通过结合一系列遥感技术和地面现场测量，我们开发了一种半自动方法，可以解决几个检索问题，以可靠地估计非降水行星边界层(PBL)云的云滴数。该方法基于利用欧洲气象卫星开发组织(EUMETSAT)的旋转增强可见光和红外成像仪(SEVIRI)获得的最优云分析(OCA)产品的地球静止气象卫星数据对PBL云滴数(N d sat)的卫星检索。通过结合地面遥感数据和地面现场观测得到的液滴数，优化了反演参数。更具体地说，遥感数据用于检索云尺度垂直速度，并将地面的原位气溶胶测量作为最先进的液滴激活参数化的输入，以预测各自的云凝结核(CCN)光谱、云最大过饱和度和液滴数浓度(N d)，考虑垂直速度分布和横向夹杂的影响。并置的N d和N d sat之间的闭合研究用于评估用于检索液滴数的现有液滴光谱宽度参数，并确定检索的最佳值。该方法用于研究2020年3月至5月希腊雅典大都会区(AMA)上空形成的非降水云的气溶胶-云相互作用，结果表明，液滴的封闭程度可以达到30%以内，与许多原位研究中获得的封闭水平相当。鉴于此，将这种方法应用于SEVIRI获得的高时间(15分钟)和高空间分辨率(3.6 km × 4.6 km)的卫星数据的易用性，开启了连续和可靠的N d卫星的可能性，为气溶胶-云-气候相互作用研究提供了高价值的数据集。
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