Pub Date : 2023-07-10DOI: 10.3389/frsen.2023.1161530
Daniel Jack Thomas, B. Robson, A. Racoviteanu
Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed. The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89.2%–93.7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.4%. We find our approach to be robust, transferable to other regions, and accurate over regional (>4,000 km2) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.
{"title":"An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers","authors":"Daniel Jack Thomas, B. Robson, A. Racoviteanu","doi":"10.3389/frsen.2023.1161530","DOIUrl":"https://doi.org/10.3389/frsen.2023.1161530","url":null,"abstract":"Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed. The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89.2%–93.7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.4%. We find our approach to be robust, transferable to other regions, and accurate over regional (>4,000 km2) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123384042","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 : 2023-06-20DOI: 10.3389/frsen.2023.1196554
J. Vogeler, P. Fekety, Lisa H. Elliott, Neal C. Swayze, S. Filippelli, Brent Barry, Joseph D. Holbrook, K. Vierling
Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R 2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.
{"title":"Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat","authors":"J. Vogeler, P. Fekety, Lisa H. Elliott, Neal C. Swayze, S. Filippelli, Brent Barry, Joseph D. Holbrook, K. Vierling","doi":"10.3389/frsen.2023.1196554","DOIUrl":"https://doi.org/10.3389/frsen.2023.1196554","url":null,"abstract":"Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R 2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124656180","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 : 2023-06-01DOI: 10.3389/frsen.2023.1188056
G. Wen, A. Marshak
The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) routinely captures reflected radiation from the whole sunlit side of the Earth in the near backward direction to monitor the changing planet. The instrument had routinely operated until 27 June 2019, when the spacecraft was placed in an extended safe hold due to degradation of an inertial navigation unit. DSCOVR returned to full operations on 2 March 2020. Since then, the range of scattering angles between the incident sunlight and sensor direction has been larger than before and the largest scattering angle reaches ∼178°, only 2° from perfect backscattering, proving a unique opportunity to study the top-of-atmosphere (TOA) reflectance under such extreme conditions. In the paper, we compare EPIC global spectral reflectances in 2021–2016. We found that there are four occasions when the scattering angle reaches about 178° and associated with them enhanced global daily average spectral reflectances in 2021. The scattering angle related reflectance enhancements are not found in 2016 data when the maximum scattering angle is about 174.5°. CERES data do not show such occasions in global daily reflected shortwave flux. As a result, those enhanced reflectance occasions are primarily due to the change in scattering angle. The enhancement due to changes in scattering angle depends strongly on wavelength, primarily because of wavelength dependence of cloud scattering phase function. Radiative transfer calculations show that the change in scattering angles has the largest impact on reflectance in the red and NIR channels at 680 nm and 780 nm and the smallest influence on reflectance in the UV channel at 388 nm, consistent with EPIC observations. The change of global average cloud amount also plays an important role in the reflectance enhancement. The influence of the cloud effect depends on whether the change is in phase or not with the change of scattering angle.
{"title":"Effect of scattering angle on DSCOVR/EPIC observations","authors":"G. Wen, A. Marshak","doi":"10.3389/frsen.2023.1188056","DOIUrl":"https://doi.org/10.3389/frsen.2023.1188056","url":null,"abstract":"The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) routinely captures reflected radiation from the whole sunlit side of the Earth in the near backward direction to monitor the changing planet. The instrument had routinely operated until 27 June 2019, when the spacecraft was placed in an extended safe hold due to degradation of an inertial navigation unit. DSCOVR returned to full operations on 2 March 2020. Since then, the range of scattering angles between the incident sunlight and sensor direction has been larger than before and the largest scattering angle reaches ∼178°, only 2° from perfect backscattering, proving a unique opportunity to study the top-of-atmosphere (TOA) reflectance under such extreme conditions. In the paper, we compare EPIC global spectral reflectances in 2021–2016. We found that there are four occasions when the scattering angle reaches about 178° and associated with them enhanced global daily average spectral reflectances in 2021. The scattering angle related reflectance enhancements are not found in 2016 data when the maximum scattering angle is about 174.5°. CERES data do not show such occasions in global daily reflected shortwave flux. As a result, those enhanced reflectance occasions are primarily due to the change in scattering angle. The enhancement due to changes in scattering angle depends strongly on wavelength, primarily because of wavelength dependence of cloud scattering phase function. Radiative transfer calculations show that the change in scattering angles has the largest impact on reflectance in the red and NIR channels at 680 nm and 780 nm and the smallest influence on reflectance in the UV channel at 388 nm, consistent with EPIC observations. The change of global average cloud amount also plays an important role in the reflectance enhancement. The influence of the cloud effect depends on whether the change is in phase or not with the change of scattering angle.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116967914","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 : 2023-06-01DOI: 10.3389/frsen.2023.1135501
M. Huot, F. Dalgleish, D. Beauchesne, M. Piché, P. Archambault
A better understanding of how spatial distribution patterns in important primary producers and ecosystem service providers such as macroalgae and coral are affected by climate-change and human activity-related events can guide us in anticipating future community and ecosystem response. In-person underwater field surveys are essential in capturing fine and/or subtle details but are rarely simple to orchestrate over large spatial scale (e.g., hundreds of km). In this work, we develop an automated spectral classifier for detection and classification of various macroalgae and coral species through a spectral response dataset acquired in a controlled setting and via an underwater multispectral laser serial imager. Transferable to underwater lidar detection and imaging methods, laser line scanning is known to perform in various types of water in which normal photography and/or video methods may be affected by water optical properties. Using off the shelf components, we show how reflectance and fluorescence responses can be useful in differentiating algal color groups and certain coral genera. Results indicate that while macroalgae show many different genera and species for which differentiation by their spectral response alone would be difficult, it can be reduced to a three color-type/class spectral response problem. Our results suggest that the three algal color groups may be differentiated by their fluorescence response at 580 nm and 685 nm using common 450 nm, 490 nm and 520 nm laser sources, and potentially a subset of these spectral bands would show similar accuracy. There are however classification errors between green and brown types, as they both depend on Chl-a fluorescence response. Comparatively, corals are also very diverse in genera and species, and reveal possible differentiable spectral responses between genera, form (i.e., soft vs. hard), partly related to their emission in the 685 nm range and other shorter wavelengths. Moreover, overlapping substrates and irregular edges are shown to contribute to classification error. As macroalgae are represented worldwide and share similar photopigment assemblages within respective color classes, inter color-class differentiability would apply irrespective of their provenance. The same principle applies to corals, where excitation-emission characteristics should be unchanged from experimental response when investigated in-situ.
{"title":"Machine learning for underwater laser detection and differentiation of macroalgae and coral","authors":"M. Huot, F. Dalgleish, D. Beauchesne, M. Piché, P. Archambault","doi":"10.3389/frsen.2023.1135501","DOIUrl":"https://doi.org/10.3389/frsen.2023.1135501","url":null,"abstract":"A better understanding of how spatial distribution patterns in important primary producers and ecosystem service providers such as macroalgae and coral are affected by climate-change and human activity-related events can guide us in anticipating future community and ecosystem response. In-person underwater field surveys are essential in capturing fine and/or subtle details but are rarely simple to orchestrate over large spatial scale (e.g., hundreds of km). In this work, we develop an automated spectral classifier for detection and classification of various macroalgae and coral species through a spectral response dataset acquired in a controlled setting and via an underwater multispectral laser serial imager. Transferable to underwater lidar detection and imaging methods, laser line scanning is known to perform in various types of water in which normal photography and/or video methods may be affected by water optical properties. Using off the shelf components, we show how reflectance and fluorescence responses can be useful in differentiating algal color groups and certain coral genera. Results indicate that while macroalgae show many different genera and species for which differentiation by their spectral response alone would be difficult, it can be reduced to a three color-type/class spectral response problem. Our results suggest that the three algal color groups may be differentiated by their fluorescence response at 580 nm and 685 nm using common 450 nm, 490 nm and 520 nm laser sources, and potentially a subset of these spectral bands would show similar accuracy. There are however classification errors between green and brown types, as they both depend on Chl-a fluorescence response. Comparatively, corals are also very diverse in genera and species, and reveal possible differentiable spectral responses between genera, form (i.e., soft vs. hard), partly related to their emission in the 685 nm range and other shorter wavelengths. Moreover, overlapping substrates and irregular edges are shown to contribute to classification error. As macroalgae are represented worldwide and share similar photopigment assemblages within respective color classes, inter color-class differentiability would apply irrespective of their provenance. The same principle applies to corals, where excitation-emission characteristics should be unchanged from experimental response when investigated in-situ.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130178322","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 : 2023-05-17DOI: 10.3389/frsen.2023.1060908
Jacopo Agagliate, Robert Foster, A. Ibrahim, A. Gilerson
Introduction: In preparation for the upcoming PACE mission, we explore the feasibility of a neural network-based approach for the conversion of measurements of the degree of linear polarization at the top of the atmosphere as carried out by the HARP2 instrument into estimations of the ratio of attenuation to absorption in the surface layer of the ocean. Polarization has been shown to contain information on the in-water inherent optical properties including the total attenuation coefficient, in contrast with approaches solely based on remote sensing reflectance that are limited to the backscattered fraction of the scattering. In turn, these properties may be further combined with inversion algorithms to retrieve projected values for the optical and physical properties of marine particulates. Methodology: Using bio-optical models to produce synthetic data in quantities sufficient for network training purposes, and with associated polarization values derived from vector radiative transfer modeling, we produce a two-step algorithm that retrieves surface-level polarization first and attenuation-to-absorption ratios second, with each step handled by a separate neural network. The networks use multispectral inputs in terms of the degree of linear polarization from the polarimeter and the remote sensing reflectance from the Ocean Color Instrument that are anticipated to be fully available within the PACE data environment. Result and Discussion: Produce results that compare favorably with expected values, suggesting that a neural network-mediated conversion of remotely sensed polarization into in-water IOPs is viable. A simulation of the PACE orbit and of the HARP2 field of view further shows these results to be robust even over the limited number of data points expected to be available for any given point on Earth’s surface over a single PACE transit.
{"title":"A neural network approach to the estimation of in-water attenuation to absorption ratios from PACE mission measurements","authors":"Jacopo Agagliate, Robert Foster, A. Ibrahim, A. Gilerson","doi":"10.3389/frsen.2023.1060908","DOIUrl":"https://doi.org/10.3389/frsen.2023.1060908","url":null,"abstract":"Introduction: In preparation for the upcoming PACE mission, we explore the feasibility of a neural network-based approach for the conversion of measurements of the degree of linear polarization at the top of the atmosphere as carried out by the HARP2 instrument into estimations of the ratio of attenuation to absorption in the surface layer of the ocean. Polarization has been shown to contain information on the in-water inherent optical properties including the total attenuation coefficient, in contrast with approaches solely based on remote sensing reflectance that are limited to the backscattered fraction of the scattering. In turn, these properties may be further combined with inversion algorithms to retrieve projected values for the optical and physical properties of marine particulates. Methodology: Using bio-optical models to produce synthetic data in quantities sufficient for network training purposes, and with associated polarization values derived from vector radiative transfer modeling, we produce a two-step algorithm that retrieves surface-level polarization first and attenuation-to-absorption ratios second, with each step handled by a separate neural network. The networks use multispectral inputs in terms of the degree of linear polarization from the polarimeter and the remote sensing reflectance from the Ocean Color Instrument that are anticipated to be fully available within the PACE data environment. Result and Discussion: Produce results that compare favorably with expected values, suggesting that a neural network-mediated conversion of remotely sensed polarization into in-water IOPs is viable. A simulation of the PACE orbit and of the HARP2 field of view further shows these results to be robust even over the limited number of data points expected to be available for any given point on Earth’s surface over a single PACE transit.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129435401","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 : 2023-05-16DOI: 10.3389/frsen.2023.1099430
P. Herrmann, V. Nascimento, M. W. D. Freitas, J. Ometto
Fire has been an intrinsic ecological component of the ecosystems, affecting the public, economic, and socio-cultural policies of human-nature interactions. Using fire over grassland vegetation is a traditional practice for livestock in the highland grasslands and has economic and environmental consequences that have not yet been understood. A better description of the spatio-temporal biomass burning patterns is needed to analyze the effects of creation and application in these areas. This study used remote sensing techniques based on Sentinel-2 data and machine learning algorithms to identify burning scars and compare them with a national fire collection database for the highland grasslands in the Atlantic Forest Biome in Brazil. The aim is to evaluate public management tools and legislation evolution during the 35 years of the time series analyzed. The results indicated that 12,285 ha of grasslands were converted to other uses, losing about 24% of their original formation, with 10% occurring after banned this practice in 2008. The burned areas classification using the Random Forest algorithm obtained an AUC = 0.9983. Divergences in the burned area’s extent and frequency were found between the municipality’s authorized license and those classified as burned. On average, only 43% of the burned area in the Parque Estadual do Tainhas and its buffer zone had an environmental permit in the last 5 years. This research’s results provide subsidies for revising and creating public policies and consequently help territorial management.
{"title":"Fire in highland grasslands in the Atlantic Forest Biome, a burned areas time series analysis and its correlation with the legislation","authors":"P. Herrmann, V. Nascimento, M. W. D. Freitas, J. Ometto","doi":"10.3389/frsen.2023.1099430","DOIUrl":"https://doi.org/10.3389/frsen.2023.1099430","url":null,"abstract":"Fire has been an intrinsic ecological component of the ecosystems, affecting the public, economic, and socio-cultural policies of human-nature interactions. Using fire over grassland vegetation is a traditional practice for livestock in the highland grasslands and has economic and environmental consequences that have not yet been understood. A better description of the spatio-temporal biomass burning patterns is needed to analyze the effects of creation and application in these areas. This study used remote sensing techniques based on Sentinel-2 data and machine learning algorithms to identify burning scars and compare them with a national fire collection database for the highland grasslands in the Atlantic Forest Biome in Brazil. The aim is to evaluate public management tools and legislation evolution during the 35 years of the time series analyzed. The results indicated that 12,285 ha of grasslands were converted to other uses, losing about 24% of their original formation, with 10% occurring after banned this practice in 2008. The burned areas classification using the Random Forest algorithm obtained an AUC = 0.9983. Divergences in the burned area’s extent and frequency were found between the municipality’s authorized license and those classified as burned. On average, only 43% of the burned area in the Parque Estadual do Tainhas and its buffer zone had an environmental permit in the last 5 years. This research’s results provide subsidies for revising and creating public policies and consequently help territorial management.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127086058","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 : 2023-05-15DOI: 10.3389/frsen.2023.1172819
K. Ali, D. Flanagan, M. Brandt, J. Ortiz, T. Smith
Coral reef health in the U.S. Virgin Islands (USVI) is in decline due to land-based sources of pollution associated with watershed development and global climate change. Water quality is a good indicator of stress in these nearshore environments as it plays a key role in determining the health and distribution of coral reef communities. Conventional water quality assessment methods based on in situ measurements are both time consuming and costly, and they lack the spatial coverage and temporal resolution that can be achieved using satellite remote sensing techniques. Water quality parameters (WQPs) such as Chlorophyll a (Chl-a), can be studied remotely using models that account for the inherent optical properties (IOPs) of the water. In this study, empirical based standard ocean color algorithm (OC4) and two semi-analytical algorithms, the Garver–Siegel–Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, were evaluated in retrieving Chl-a in the nearshore waters of the USVI. GSM and GIOP were also evaluated for modeling inherent optical properties such as absorption coefficient of phytoplankton (aph (443)). Analysis of the results from each model using a field database from six cruises during May/June and December between 2016 and 2018, showed that the OC4 performed poorly with R 2 of 0.14 and RMSE = 0.15. Effects of suspended particulates and benthic reflectance most likely contributed to the poor performance of the algorithm. GSM is a slightly better estimator for aph (443) and Chl-a (R 2 = 0.55, RMSE = 0.04; R 2 = 0.60, RMSE = 0.09) than GIOP (R 2 = 0.52, RMSE = 0.05; R 2 = 0.17, RMSE = 0.15). Performance of the semi-analytical models are limited in estimating particulate back scattering (bbp (443)) also due to the benthic albedo effects in the shallow waters. The calibrated GSM model was applied to Landsat 8 OLI satellite imagery spanning 2016–2018 to develop a time series of the spatial changes in Chl-a concentrations in the coastal waters of the USVI. The Landsat GSM Chl-a model produced promising results of R 2 = 0.45, RMSE = 0.07, in an environment where signal-to-noise ratio is significantly low.
{"title":"Semi-analytical inversion modelling of Chlorophyll a variability in the U.S. Virgin Islands","authors":"K. Ali, D. Flanagan, M. Brandt, J. Ortiz, T. Smith","doi":"10.3389/frsen.2023.1172819","DOIUrl":"https://doi.org/10.3389/frsen.2023.1172819","url":null,"abstract":"Coral reef health in the U.S. Virgin Islands (USVI) is in decline due to land-based sources of pollution associated with watershed development and global climate change. Water quality is a good indicator of stress in these nearshore environments as it plays a key role in determining the health and distribution of coral reef communities. Conventional water quality assessment methods based on in situ measurements are both time consuming and costly, and they lack the spatial coverage and temporal resolution that can be achieved using satellite remote sensing techniques. Water quality parameters (WQPs) such as Chlorophyll a (Chl-a), can be studied remotely using models that account for the inherent optical properties (IOPs) of the water. In this study, empirical based standard ocean color algorithm (OC4) and two semi-analytical algorithms, the Garver–Siegel–Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, were evaluated in retrieving Chl-a in the nearshore waters of the USVI. GSM and GIOP were also evaluated for modeling inherent optical properties such as absorption coefficient of phytoplankton (aph (443)). Analysis of the results from each model using a field database from six cruises during May/June and December between 2016 and 2018, showed that the OC4 performed poorly with R 2 of 0.14 and RMSE = 0.15. Effects of suspended particulates and benthic reflectance most likely contributed to the poor performance of the algorithm. GSM is a slightly better estimator for aph (443) and Chl-a (R 2 = 0.55, RMSE = 0.04; R 2 = 0.60, RMSE = 0.09) than GIOP (R 2 = 0.52, RMSE = 0.05; R 2 = 0.17, RMSE = 0.15). Performance of the semi-analytical models are limited in estimating particulate back scattering (bbp (443)) also due to the benthic albedo effects in the shallow waters. The calibrated GSM model was applied to Landsat 8 OLI satellite imagery spanning 2016–2018 to develop a time series of the spatial changes in Chl-a concentrations in the coastal waters of the USVI. The Landsat GSM Chl-a model produced promising results of R 2 = 0.45, RMSE = 0.07, in an environment where signal-to-noise ratio is significantly low.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129586946","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 : 2023-05-15DOI: 10.3389/frsen.2023.1156837
Colin A. Quinn, P. Burns, C. Hakkenberg, Leonardo Salas, B. Pasch, S. Goetz, M. Clark
Ecoacoustic monitoring has proliferated as autonomous recording units (ARU) have become more accessible. ARUs provide a non-invasive, passive method to assess ecosystem dynamics related to vocalizing animal behavior and human activity. With the ever-increasing volume of acoustic data, the field has grappled with summarizing ecologically meaningful patterns in recordings. Almost 70 acoustic indices have been developed that offer summarized measurements of bioacoustic activity and ecosystem conditions. However, their systematic relationships to ecologically meaningful patterns in varying sonic conditions are inconsistent and lead to non-trivial interpretations. We used an acoustic dataset of over 725,000 min of recordings across 1,195 sites in Sonoma County, California, to evaluate the relationship between 15 established acoustic indices and sonic conditions summarized using five soundscape components classified using a convolutional neural network: anthropophony (anthropogenic sounds), biophony (biotic sounds), geophony (wind and rain), quiet (lack of emergent sound), and interference (ARU feedback). We used generalized additive models to assess acoustic indices and biophony as ecoacoustic indicators of avian diversity. Models that included soundscape components explained acoustic indices with varying degrees of performance (avg. adj-R2 = 0.61 ± 0.16; n = 1,195). For example, we found the normalized difference soundscape index was the most sensitive index to biophony while being less influenced by ambient sound. However, all indices were affected by non-biotic sound sources to varying degrees. We found that biophony and acoustic indices combined were highly predictive in modeling bird species richness (deviance = 65.8%; RMSE = 3.9 species; n = 1,185 sites) for targeted, morning-only recording periods. Our analyses demonstrate the confounding effects of non-biotic soundscape components on acoustic indices, and we recommend that applications be based on anticipated sonic environments. For instance, in the presence of extensive rain and wind, we suggest using an index minimally affected by geophony. Furthermore, we provide evidence that a measure of biodiversity (bird species richness) is related to the aggregate biotic acoustic activity (biophony). This established relationship adds to recent work that identifies biophony as a reliable and generalizable ecoacoustic measure of biodiversity.
{"title":"Soundscape components inform acoustic index patterns and refine estimates of bird species richness","authors":"Colin A. Quinn, P. Burns, C. Hakkenberg, Leonardo Salas, B. Pasch, S. Goetz, M. Clark","doi":"10.3389/frsen.2023.1156837","DOIUrl":"https://doi.org/10.3389/frsen.2023.1156837","url":null,"abstract":"Ecoacoustic monitoring has proliferated as autonomous recording units (ARU) have become more accessible. ARUs provide a non-invasive, passive method to assess ecosystem dynamics related to vocalizing animal behavior and human activity. With the ever-increasing volume of acoustic data, the field has grappled with summarizing ecologically meaningful patterns in recordings. Almost 70 acoustic indices have been developed that offer summarized measurements of bioacoustic activity and ecosystem conditions. However, their systematic relationships to ecologically meaningful patterns in varying sonic conditions are inconsistent and lead to non-trivial interpretations. We used an acoustic dataset of over 725,000 min of recordings across 1,195 sites in Sonoma County, California, to evaluate the relationship between 15 established acoustic indices and sonic conditions summarized using five soundscape components classified using a convolutional neural network: anthropophony (anthropogenic sounds), biophony (biotic sounds), geophony (wind and rain), quiet (lack of emergent sound), and interference (ARU feedback). We used generalized additive models to assess acoustic indices and biophony as ecoacoustic indicators of avian diversity. Models that included soundscape components explained acoustic indices with varying degrees of performance (avg. adj-R2 = 0.61 ± 0.16; n = 1,195). For example, we found the normalized difference soundscape index was the most sensitive index to biophony while being less influenced by ambient sound. However, all indices were affected by non-biotic sound sources to varying degrees. We found that biophony and acoustic indices combined were highly predictive in modeling bird species richness (deviance = 65.8%; RMSE = 3.9 species; n = 1,185 sites) for targeted, morning-only recording periods. Our analyses demonstrate the confounding effects of non-biotic soundscape components on acoustic indices, and we recommend that applications be based on anticipated sonic environments. For instance, in the presence of extensive rain and wind, we suggest using an index minimally affected by geophony. Furthermore, we provide evidence that a measure of biodiversity (bird species richness) is related to the aggregate biotic acoustic activity (biophony). This established relationship adds to recent work that identifies biophony as a reliable and generalizable ecoacoustic measure of biodiversity.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126661571","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 : 2023-05-11DOI: 10.3389/frsen.2023.1174672
S. Stamnes, Michael Jones, James G. Allen, E. Chemyakin, A. Bell, J. Chowdhary, Xu Liu, S. Burton, B. van Diedenhoven, O. Hasekamp, J. Hair, Yongxiang Hu, C. Hostetler, R. Ferrare, K. Stamnes, B. Cairns
We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multispectral polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system. PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. The PACE-MAPP algorithm uses a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, this physically consistent treatment of the system across the ultraviolet, (UV: 300–400 nm), visible (VIS: 400–700 nm), near-infrared (NIR: 700–1100 nm), and shortwave infrared (SWIR: 1100–2400 nm) spectral bands ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of the atmosphere’s aerosol and ocean constituents, find the optimal solution, and evaluate the uncertainties of each parameter. This coupled approach, together with multiangle, multispectral polarimeter measurements, enables retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations in the forward model. We provide an overview of the PACE-MAPP framework and quantify its expected retrieval performance on simulated PACE-like data using a bimodal aerosol model for observations of fine-mode absorbing aerosols and coarse-mode sea salt particles. We also quantify its performance for observations over the ocean of dust-laden scenes using a trimodal aerosol model that incorporates non-spherical coarse-mode dust particles. Lastly, PACE-MAPP’s modular capabilities are described, and we discuss plans to implement a new ocean bio-optical model that uses a mixture of coated and uncoated particles, as well as a thin cirrus model for detecting and correcting for sub-visual ice clouds.
{"title":"The PACE-MAPP algorithm: Simultaneous aerosol and ocean polarimeter products using coupled atmosphere-ocean vector radiative transfer","authors":"S. Stamnes, Michael Jones, James G. Allen, E. Chemyakin, A. Bell, J. Chowdhary, Xu Liu, S. Burton, B. van Diedenhoven, O. Hasekamp, J. Hair, Yongxiang Hu, C. Hostetler, R. Ferrare, K. Stamnes, B. Cairns","doi":"10.3389/frsen.2023.1174672","DOIUrl":"https://doi.org/10.3389/frsen.2023.1174672","url":null,"abstract":"We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multispectral polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system. PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. The PACE-MAPP algorithm uses a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, this physically consistent treatment of the system across the ultraviolet, (UV: 300–400 nm), visible (VIS: 400–700 nm), near-infrared (NIR: 700–1100 nm), and shortwave infrared (SWIR: 1100–2400 nm) spectral bands ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of the atmosphere’s aerosol and ocean constituents, find the optimal solution, and evaluate the uncertainties of each parameter. This coupled approach, together with multiangle, multispectral polarimeter measurements, enables retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations in the forward model. We provide an overview of the PACE-MAPP framework and quantify its expected retrieval performance on simulated PACE-like data using a bimodal aerosol model for observations of fine-mode absorbing aerosols and coarse-mode sea salt particles. We also quantify its performance for observations over the ocean of dust-laden scenes using a trimodal aerosol model that incorporates non-spherical coarse-mode dust particles. Lastly, PACE-MAPP’s modular capabilities are described, and we discuss plans to implement a new ocean bio-optical model that uses a mixture of coated and uncoated particles, as well as a thin cirrus model for detecting and correcting for sub-visual ice clouds.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128860937","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 : 2023-05-10DOI: 10.3389/frsen.2023.1132208
P. Krause, Brieanne Forbes, Alexander Barajas-Ritchie, M. Clark, M. Disney, P. Wilkes, L. Bentley
A crucial part of carbon accounting is quantifying a tree’s aboveground biomass (AGB) using allometric equations, but species-specific equations are limited because data to inform these equations requires destructive harvesting of many trees which is difficult and time-consuming. Here, we used terrestrial laser scanning (TLS) to non-destructively estimate AGB for 282 trees from 5 species at 3 locations in Northern California using stem and branch volume estimates from quantitative structure models (QSMs) and wood density from the literature. We then compared TLS QSM estimates of AGB with published allometric equations and used TLS-based AGB, diameter at breast height (DBH), and height to derive new species-specific allometric AGB equations for our study species. To validate the use of TLS, we used traditional forestry approaches to collect DBH (n = 550) and height (n = 291) data on individual trees. TLS-based DBH and height were not significantly different from field inventory data (R 2 = 0.98 for DBH, R 2 = 0.95 for height). Across all species, AGB calculated from TLS QSM volumes were approximately 30% greater than AGB estimates using published Forest Service’s Forest Inventory and Analysis Program equations, and TLS QSM AGB estimates were 10% greater than AGB calculated with existing equations, although this variation was species-dependent. In particular, TLS AGB estimates for Quercus agrifolia and Sequoia sempervirens differed the most from AGB estimates calculated using published equations. New allometric equations created using TLS data with DBH and height performed better than equations that only included DBH and matched most closely with AGB estimates generated from QSMs. Our results support the use of TLS as a method to rapidly estimate height, DBH, and AGB of multiple trees at a plot-level when species are identified and wood density is known. In addition, the creation of new TLS-based non-destructive allometric equations for our 5 study species may have important applications and implications for carbon quantification over larger spatial scales, especially since our equations estimated greater AGB than previous approaches.
{"title":"Using terrestrial laser scanning to evaluate non-destructive aboveground biomass allometries in diverse Northern California forests","authors":"P. Krause, Brieanne Forbes, Alexander Barajas-Ritchie, M. Clark, M. Disney, P. Wilkes, L. Bentley","doi":"10.3389/frsen.2023.1132208","DOIUrl":"https://doi.org/10.3389/frsen.2023.1132208","url":null,"abstract":"A crucial part of carbon accounting is quantifying a tree’s aboveground biomass (AGB) using allometric equations, but species-specific equations are limited because data to inform these equations requires destructive harvesting of many trees which is difficult and time-consuming. Here, we used terrestrial laser scanning (TLS) to non-destructively estimate AGB for 282 trees from 5 species at 3 locations in Northern California using stem and branch volume estimates from quantitative structure models (QSMs) and wood density from the literature. We then compared TLS QSM estimates of AGB with published allometric equations and used TLS-based AGB, diameter at breast height (DBH), and height to derive new species-specific allometric AGB equations for our study species. To validate the use of TLS, we used traditional forestry approaches to collect DBH (n = 550) and height (n = 291) data on individual trees. TLS-based DBH and height were not significantly different from field inventory data (R 2 = 0.98 for DBH, R 2 = 0.95 for height). Across all species, AGB calculated from TLS QSM volumes were approximately 30% greater than AGB estimates using published Forest Service’s Forest Inventory and Analysis Program equations, and TLS QSM AGB estimates were 10% greater than AGB calculated with existing equations, although this variation was species-dependent. In particular, TLS AGB estimates for Quercus agrifolia and Sequoia sempervirens differed the most from AGB estimates calculated using published equations. New allometric equations created using TLS data with DBH and height performed better than equations that only included DBH and matched most closely with AGB estimates generated from QSMs. Our results support the use of TLS as a method to rapidly estimate height, DBH, and AGB of multiple trees at a plot-level when species are identified and wood density is known. In addition, the creation of new TLS-based non-destructive allometric equations for our 5 study species may have important applications and implications for carbon quantification over larger spatial scales, especially since our equations estimated greater AGB than previous approaches.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131854376","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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