Pub Date : 2023-09-13DOI: 10.3389/frsen.2023.1254242
Rennan de Freitas Bezerra Marujo, Felipe Menino Carlos, Raphael Willian da Costa, Jeferson de Souza Arcanjo, José Guilherme Fronza, Anderson Reis Soares, Gilberto Ribeiro de Queiroz, Karine Reis Ferreira
Clouds and cloud shadows significantly impact optical remote sensing. Combining images from different sources can help to obtain more frequent time series of the Earth’s surface. Nevertheless, sensor differences must be accounted for and treated before combining images from multiple sensors. Even after geometric correction, inter-calibration, and bandpass, disparities in image measurements can persist. One potential factor contributing to this phenomenon is directional effects. Bidirectional reflectance distribution function (BRDF) corrections have emerged as an optional processing method to soften differences in surface reflectance (SR) measurements, where the c-factor is one of the available options for this task. The c-factor efficiency is well-proven for medium spatial resolution products. However, its use should be restricted to images from sensors with a narrow view since it causes subtle changes in the processed images. There are currently a limited number of open tools for users to independently process their images. Here, we implemented the required tools to generate a Nadir BRDF-Adjusted Surface Reflectance (NBAR) product through the c-factor approach, and we evaluated them for a study area using Landsat-8 and Sentinel-2 images. Several comparisons were conducted to verify the SR and NBAR differences. Initially, a single-sensor approach was adopted and later a multi-source approach. Notably, NBAR products exhibit fewer disparities compared to SR products (prior to BRDF corrections). The results reinforce that the c-factor can be used to improve time series compatibility and, most importantly, provide the tools to allow users to generate the NBAR products themselves.
{"title":"A reproducible and replicable approach for harmonizing Landsat-8 and Sentinel-2 images","authors":"Rennan de Freitas Bezerra Marujo, Felipe Menino Carlos, Raphael Willian da Costa, Jeferson de Souza Arcanjo, José Guilherme Fronza, Anderson Reis Soares, Gilberto Ribeiro de Queiroz, Karine Reis Ferreira","doi":"10.3389/frsen.2023.1254242","DOIUrl":"https://doi.org/10.3389/frsen.2023.1254242","url":null,"abstract":"Clouds and cloud shadows significantly impact optical remote sensing. Combining images from different sources can help to obtain more frequent time series of the Earth’s surface. Nevertheless, sensor differences must be accounted for and treated before combining images from multiple sensors. Even after geometric correction, inter-calibration, and bandpass, disparities in image measurements can persist. One potential factor contributing to this phenomenon is directional effects. Bidirectional reflectance distribution function (BRDF) corrections have emerged as an optional processing method to soften differences in surface reflectance (SR) measurements, where the c-factor is one of the available options for this task. The c-factor efficiency is well-proven for medium spatial resolution products. However, its use should be restricted to images from sensors with a narrow view since it causes subtle changes in the processed images. There are currently a limited number of open tools for users to independently process their images. Here, we implemented the required tools to generate a Nadir BRDF-Adjusted Surface Reflectance (NBAR) product through the c-factor approach, and we evaluated them for a study area using Landsat-8 and Sentinel-2 images. Several comparisons were conducted to verify the SR and NBAR differences. Initially, a single-sensor approach was adopted and later a multi-source approach. Notably, NBAR products exhibit fewer disparities compared to SR products (prior to BRDF corrections). The results reinforce that the c-factor can be used to improve time series compatibility and, most importantly, provide the tools to allow users to generate the NBAR products themselves.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135734163","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 : 2023-09-07DOI: 10.3389/frsen.2023.1157609
Kate C. Fickas, Ryan E. O'Shea, N. Pahlevan, Brandon Smith, Sarah L. Bartlett, Jennifer L. Wolny
Cyanobacteria harmful algal blooms (cyanoHABs) present a critical public health challenge for aquatic resource and public health managers. Satellite remote sensing is well-positioned to aid in the identification and mapping of cyanoHABs and their dynamics, giving freshwater resource managers a tool for both rapid and long-term protection of public health. Monitoring cyanoHABs in lakes and reservoirs with remote sensing requires robust processing techniques for generating accurate and consistent products across local and global scales at high revisit rates. We leveraged the high spatial and temporal resolution chlorophyll-a (Chla) and phycocyanin (PC) maps from two multispectral satellite sensors, the Sentinel-2 (S2) MultiSpectral Instrument (MSI) and the Sentinel-3 (S3) Ocean Land Colour Instrument (OLCI) respectively, to study bloom dynamics in Utah Lake, United States, for 2018. We used established Mixture Density Networks (MDNs) to map Chla from MSI and train new MDNs for PC retrieval from OLCI, using the same architecture and training dataset previously proven for PC retrieval from hyperspectral imagery. Our assessment suggests lower median uncertainties and biases (i.e., 42% and -4%, respectively) than that of existing top-performing PC algorithms. Additionally, we compared bloom trends in MDN-based PC and Chla products to those from a satellite-derived cyanobacteria cell density estimator, the cyanobacteria index (CI-cyano), to evaluate their utility in the context of public health risk management. Our comprehensive analyses indicate increased spatiotemporal coherence of bloom magnitude, frequency, occurrence, and extent of MDN-based maps compared to CI-cyano and potential for use in cyanoHAB monitoring for public health and aquatic resource managers.
{"title":"Leveraging multimission satellite data for spatiotemporally coherent cyanoHAB monitoring","authors":"Kate C. Fickas, Ryan E. O'Shea, N. Pahlevan, Brandon Smith, Sarah L. Bartlett, Jennifer L. Wolny","doi":"10.3389/frsen.2023.1157609","DOIUrl":"https://doi.org/10.3389/frsen.2023.1157609","url":null,"abstract":"Cyanobacteria harmful algal blooms (cyanoHABs) present a critical public health challenge for aquatic resource and public health managers. Satellite remote sensing is well-positioned to aid in the identification and mapping of cyanoHABs and their dynamics, giving freshwater resource managers a tool for both rapid and long-term protection of public health. Monitoring cyanoHABs in lakes and reservoirs with remote sensing requires robust processing techniques for generating accurate and consistent products across local and global scales at high revisit rates. We leveraged the high spatial and temporal resolution chlorophyll-a (Chla) and phycocyanin (PC) maps from two multispectral satellite sensors, the Sentinel-2 (S2) MultiSpectral Instrument (MSI) and the Sentinel-3 (S3) Ocean Land Colour Instrument (OLCI) respectively, to study bloom dynamics in Utah Lake, United States, for 2018. We used established Mixture Density Networks (MDNs) to map Chla from MSI and train new MDNs for PC retrieval from OLCI, using the same architecture and training dataset previously proven for PC retrieval from hyperspectral imagery. Our assessment suggests lower median uncertainties and biases (i.e., 42% and -4%, respectively) than that of existing top-performing PC algorithms. Additionally, we compared bloom trends in MDN-based PC and Chla products to those from a satellite-derived cyanobacteria cell density estimator, the cyanobacteria index (CI-cyano), to evaluate their utility in the context of public health risk management. Our comprehensive analyses indicate increased spatiotemporal coherence of bloom magnitude, frequency, occurrence, and extent of MDN-based maps compared to CI-cyano and potential for use in cyanoHAB monitoring for public health and aquatic resource managers.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116225564","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 : 2023-09-04DOI: 10.3389/frsen.2023.1202234
Yongxiang Hu, Xiaomei Lu, Xubin Zeng, Charles Gatebe, Q. Fu, Ping Yang, Carl Weimer, S. Stamnes, R. Baize, Ali Omar, Garfield Creary, Anum Ashraf, K. Stamnes, Yuping Huang
Lidar multiple scattering measurements provide the probability distribution of the distance laser light travels inside snow. Based on an analytic two-stream radiative transfer solution, the present study demonstrates why/how these lidar measurements can be used to derive snow depth and snow density. In particular, for a laser wavelength with little snow absorption, an analytical radiative transfer solution is leveraged to prove that the physical snow depth is half of the average distance photons travel inside snow and that the relationship linking lidar measurements and the extinction coefficient of the snow is valid. Theoretical formulas that link lidar measurements to the extinction coefficient and the effective grain size of snow are provided. Snow density can also be derived from the multi-wavelength lidar measurements of the snow extinction coefficient and snow effective grain size. Alternatively, lidars can provide the most direct snow density measurements and the effective discrimination between snow and trees by adding vibrational Raman scattering channels.
{"title":"Linking lidar multiple scattering profiles to snow depth and snow density: an analytical radiative transfer analysis and the implications for remote sensing of snow","authors":"Yongxiang Hu, Xiaomei Lu, Xubin Zeng, Charles Gatebe, Q. Fu, Ping Yang, Carl Weimer, S. Stamnes, R. Baize, Ali Omar, Garfield Creary, Anum Ashraf, K. Stamnes, Yuping Huang","doi":"10.3389/frsen.2023.1202234","DOIUrl":"https://doi.org/10.3389/frsen.2023.1202234","url":null,"abstract":"Lidar multiple scattering measurements provide the probability distribution of the distance laser light travels inside snow. Based on an analytic two-stream radiative transfer solution, the present study demonstrates why/how these lidar measurements can be used to derive snow depth and snow density. In particular, for a laser wavelength with little snow absorption, an analytical radiative transfer solution is leveraged to prove that the physical snow depth is half of the average distance photons travel inside snow and that the relationship linking lidar measurements and the extinction coefficient of the snow is valid. Theoretical formulas that link lidar measurements to the extinction coefficient and the effective grain size of snow are provided. Snow density can also be derived from the multi-wavelength lidar measurements of the snow extinction coefficient and snow effective grain size. Alternatively, lidars can provide the most direct snow density measurements and the effective discrimination between snow and trees by adding vibrational Raman scattering channels.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114726351","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 : 2023-09-01DOI: 10.3389/frsen.2023.1123254
Andressa Garcia Fontana, Victor Fernandez Nascimento, J. P. Ometto, Francisco Hélter Fernandes do Amaral
This research investigates Land Use and Land Cover (LULC) changes in the Porto Alegre Metropolitan Region (RMPA). A 30-year historical analysis using Landsat satellite imagery was made and used to develop LULC scenarios for the next 20 years using a Multilayer Perceptrons (MLP) model through an Artificial Neural Network (ANN). These maps analyze the urban area’s expansion over the years and project their potential development in the future. This research considered several critical factors influencing urban growth, including shaded relief, slope, distances from main roadways, railway stations, urban centers, and the state capital, Porto Alegre. These spatial variables were incorporated into the model’s learning processes to generate future urbanization scenarios. The LULC historical maps precision showed excellent performance with a Kappa index greater than 88% for the studied years. The results indicate that the urbanization class witnessed an increase of 236.78 km2 between 1990 and 2020. Additionally, it was observed that the primary concentration of urbanized areas since 1990 has predominantly occurred around Porto Alegre and Canoas. Lastly, the future forecasts for LULC changes in 2030 and 2040 indicate that the urban area of the RMPA is projected to reach 1,137.48 km2 and 1,283.62 km2, respectively. In conclusion, based on the observed urban perimeter in 2020, future projections indicate that urban areas are expected to increase by more than 443.29 km2 by 2040. The combination of remote sensing data and Geographic Information System (GIS) enables the monitoring and modeling the metropolitan area expansion. The findings provide valuable insights for policymakers to develop more informed and conscientious urban plans, as well as enhance management techniques for urban development.
{"title":"Analysis of past and future urban growth on a regional scale using remote sensing and machine learning","authors":"Andressa Garcia Fontana, Victor Fernandez Nascimento, J. P. Ometto, Francisco Hélter Fernandes do Amaral","doi":"10.3389/frsen.2023.1123254","DOIUrl":"https://doi.org/10.3389/frsen.2023.1123254","url":null,"abstract":"This research investigates Land Use and Land Cover (LULC) changes in the Porto Alegre Metropolitan Region (RMPA). A 30-year historical analysis using Landsat satellite imagery was made and used to develop LULC scenarios for the next 20 years using a Multilayer Perceptrons (MLP) model through an Artificial Neural Network (ANN). These maps analyze the urban area’s expansion over the years and project their potential development in the future. This research considered several critical factors influencing urban growth, including shaded relief, slope, distances from main roadways, railway stations, urban centers, and the state capital, Porto Alegre. These spatial variables were incorporated into the model’s learning processes to generate future urbanization scenarios. The LULC historical maps precision showed excellent performance with a Kappa index greater than 88% for the studied years. The results indicate that the urbanization class witnessed an increase of 236.78 km2 between 1990 and 2020. Additionally, it was observed that the primary concentration of urbanized areas since 1990 has predominantly occurred around Porto Alegre and Canoas. Lastly, the future forecasts for LULC changes in 2030 and 2040 indicate that the urban area of the RMPA is projected to reach 1,137.48 km2 and 1,283.62 km2, respectively. In conclusion, based on the observed urban perimeter in 2020, future projections indicate that urban areas are expected to increase by more than 443.29 km2 by 2040. The combination of remote sensing data and Geographic Information System (GIS) enables the monitoring and modeling the metropolitan area expansion. The findings provide valuable insights for policymakers to develop more informed and conscientious urban plans, as well as enhance management techniques for urban development.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115856030","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 : 2023-08-30DOI: 10.3389/frsen.2023.1221757
K. S. R. Aka, S. Akpavi, N. H. Dibi, Amos T. Kabo-bah, A. Gyilbag, E. Boamah
Land use and land cover (LULC) changes are one of the main factors contributing to ecosystem degradation and global climate change. This study used the Gontougo Region as a study area, which is fast changing in land occupation and most vulnerable to climate change. The machine learning (ML) method through Google Earth Engine (GEE) is a widely used technique for the spatiotemporal evaluation of LULC changes and their effects on land surface temperature (LST). Using Landsat 8 OLI and TIRS images from 2015 to 2022, we analyzed vegetation cover using the Normalized Difference Vegetation Index (NDVI) and computed LST. Their correlation was significant, and the Pearson correlation (r) was negative for each correlation over the year. The correspondence of the NDVI and LST reclassifications has also shown that non-vegetation land corresponds to very high temperatures (34.33°C–45.22°C in 2015 and 34.26°C–45.81°C in 2022) and that high vegetation land corresponds to low temperatures (17.33°C–28.77°C in 2015 and 16.53 29.11°C in 2022). Moreover, using a random forest algorithm (RFA) and Sentinel-2 images for 2015 and 2022, we obtained six LULC classes: bareland and settlement, forest, waterbody, savannah, annual crops, and perennial crops. The overall accuracy (OA) of each LULC map was 93.77% and 96.01%, respectively. Similarly, the kappa was 0.87 in 2015 and 0.92 in 2022. The LULC classes forest and annual crops lost 48.13% and 65.14%, respectively, of their areas for the benefit of perennial crops from 2015 to 2022. The correlation between LULC and LST showed that the forest class registered the low mean temperature (28.69°C in 2015 and 28.46°C in 2022), and the bareland/settlement registered the highest mean temperature (35.18°C in 2015 and 35.41°C in 2022). The results show that high-resolution images can be used for monitoring biophysical parameters in vegetation and surface temperature and showed benefits for evaluating food security.
{"title":"Toward understanding land use land cover changes and their effects on land surface temperature in yam production area, Côte d'Ivoire, Gontougo Region, using remote sensing and machine learning tools (Google Earth Engine)","authors":"K. S. R. Aka, S. Akpavi, N. H. Dibi, Amos T. Kabo-bah, A. Gyilbag, E. Boamah","doi":"10.3389/frsen.2023.1221757","DOIUrl":"https://doi.org/10.3389/frsen.2023.1221757","url":null,"abstract":"Land use and land cover (LULC) changes are one of the main factors contributing to ecosystem degradation and global climate change. This study used the Gontougo Region as a study area, which is fast changing in land occupation and most vulnerable to climate change. The machine learning (ML) method through Google Earth Engine (GEE) is a widely used technique for the spatiotemporal evaluation of LULC changes and their effects on land surface temperature (LST). Using Landsat 8 OLI and TIRS images from 2015 to 2022, we analyzed vegetation cover using the Normalized Difference Vegetation Index (NDVI) and computed LST. Their correlation was significant, and the Pearson correlation (r) was negative for each correlation over the year. The correspondence of the NDVI and LST reclassifications has also shown that non-vegetation land corresponds to very high temperatures (34.33°C–45.22°C in 2015 and 34.26°C–45.81°C in 2022) and that high vegetation land corresponds to low temperatures (17.33°C–28.77°C in 2015 and 16.53 29.11°C in 2022). Moreover, using a random forest algorithm (RFA) and Sentinel-2 images for 2015 and 2022, we obtained six LULC classes: bareland and settlement, forest, waterbody, savannah, annual crops, and perennial crops. The overall accuracy (OA) of each LULC map was 93.77% and 96.01%, respectively. Similarly, the kappa was 0.87 in 2015 and 0.92 in 2022. The LULC classes forest and annual crops lost 48.13% and 65.14%, respectively, of their areas for the benefit of perennial crops from 2015 to 2022. The correlation between LULC and LST showed that the forest class registered the low mean temperature (28.69°C in 2015 and 28.46°C in 2022), and the bareland/settlement registered the highest mean temperature (35.18°C in 2015 and 35.41°C in 2022). The results show that high-resolution images can be used for monitoring biophysical parameters in vegetation and surface temperature and showed benefits for evaluating food security.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115181623","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 : 2023-08-14DOI: 10.3389/frsen.2023.1095275
M. Verfaillie, E. Cho, Lauren Dwyre, Imran Khan, Cameron Wagner, J. Jacobs, A. Hunsaker
Unoccupied aerial systems (UAS) are an established technique for collecting data on cold region phenomenon at high spatial and temporal resolutions. While many studies have focused on remote sensing applications for monitoring long term changes in cold regions, the role of UAS for detection, monitoring, and response to rapid changes and direct exposures resulting from abrupt hazards in cold regions is in its early days. This review discusses recent applications of UAS remote sensing platforms and sensors, with a focus on observation techniques rather than post-processing approaches, for abrupt, cold region hazards including permafrost collapse and event-based thaw, flooding, snow avalanches, winter storms, erosion, and ice jams. The pilot efforts highlighted in this review demonstrate the potential capacity for UAS remote sensing to complement existing data acquisition techniques for cold region hazards. In many cases, UASs were used alongside other remote sensing techniques (e.g., satellite, airborne, terrestrial) and in situ sampling to supplement existing data or to collect additional types of data not included in existing datasets (e.g., thermal, meteorological). While the majority of UAS applications involved creation of digital elevation models or digital surface models using Structure-from-Motion (SfM) photogrammetry, this review describes other applications of UAS observations that help to assess risks, identify impacts, and enhance decision making. As the frequency and intensity of abrupt cold region hazards changes, it will become increasingly important to document and understand these changes to support scientific advances and hazard management. The decreasing cost and increasing accessibility of UAS technologies will create more opportunities to leverage these techniques to address current research gaps. Overcoming challenges related to implementation of new technologies, modifying operational restrictions, bridging gaps between data types and resolutions, and creating data tailored to risk communication and damage assessments will increase the potential for UAS applications to improve the understanding of risks and to reduce those risks associated with abrupt cold region hazards. In the future, cold region applications can benefit from the advances made by these early adopters who have identified exciting new avenues for advancing hazard research via innovative use of both emerging and existing sensors.
{"title":"UAS remote sensing applications to abrupt cold region hazards","authors":"M. Verfaillie, E. Cho, Lauren Dwyre, Imran Khan, Cameron Wagner, J. Jacobs, A. Hunsaker","doi":"10.3389/frsen.2023.1095275","DOIUrl":"https://doi.org/10.3389/frsen.2023.1095275","url":null,"abstract":"Unoccupied aerial systems (UAS) are an established technique for collecting data on cold region phenomenon at high spatial and temporal resolutions. While many studies have focused on remote sensing applications for monitoring long term changes in cold regions, the role of UAS for detection, monitoring, and response to rapid changes and direct exposures resulting from abrupt hazards in cold regions is in its early days. This review discusses recent applications of UAS remote sensing platforms and sensors, with a focus on observation techniques rather than post-processing approaches, for abrupt, cold region hazards including permafrost collapse and event-based thaw, flooding, snow avalanches, winter storms, erosion, and ice jams. The pilot efforts highlighted in this review demonstrate the potential capacity for UAS remote sensing to complement existing data acquisition techniques for cold region hazards. In many cases, UASs were used alongside other remote sensing techniques (e.g., satellite, airborne, terrestrial) and in situ sampling to supplement existing data or to collect additional types of data not included in existing datasets (e.g., thermal, meteorological). While the majority of UAS applications involved creation of digital elevation models or digital surface models using Structure-from-Motion (SfM) photogrammetry, this review describes other applications of UAS observations that help to assess risks, identify impacts, and enhance decision making. As the frequency and intensity of abrupt cold region hazards changes, it will become increasingly important to document and understand these changes to support scientific advances and hazard management. The decreasing cost and increasing accessibility of UAS technologies will create more opportunities to leverage these techniques to address current research gaps. Overcoming challenges related to implementation of new technologies, modifying operational restrictions, bridging gaps between data types and resolutions, and creating data tailored to risk communication and damage assessments will increase the potential for UAS applications to improve the understanding of risks and to reduce those risks associated with abrupt cold region hazards. In the future, cold region applications can benefit from the advances made by these early adopters who have identified exciting new avenues for advancing hazard research via innovative use of both emerging and existing sensors.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122724651","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 : 2023-07-27DOI: 10.3389/frsen.2023.1194580
Sayoob Vadakke-Chanat, C. Jamet
Introduction: Space-borne lidar measurements from sensors such as CALIOP were recently used to retrieve the particulate back-scattering coefficient, bbp, in the upper ocean layers at a global scale and those observations have a strong potential for the future of ocean color with depth-resolved observations thereby complementing the conventional ocean color remote sensed observations as well as overcoming for some of its limitations. It is critical to evaluate and validate the space-borne lidar measurements for ocean applications as CALIOP was not originally designed for ocean applications. Few validation exercises of CALIOP were published and each exercise designed its own validation protocol. We propose here an objective validation protocol that could be applied to any current and future space-borne lidars for ocean applications.Methods: We, first, evaluated published validation protocols for CALIOP bbp product. Two published validation schemes were evaluated in our study, by using in-situ measurements from the BGC-Argo floats. These studies were either limited to day- or nighttime, or by the years used or by the geographical extent. We extended the match-up exercise to day-and nighttime observations and for the period 2010–2017 globally. We studied the impact of the time and distance differences between the in-situ measurements and the CALIOP footprint through a sensitivities study. Twenty combinations of distance (from 9-km to 50-km) and time (from 9 h to 16 days) differences were tested.Results & Discussion: A statistical score was used to objectively selecting the best optimal timedistance windows, leading to the best compromise in term of number of matchups and low errors in the CALIOP product. We propose to use either a 24 h/9 km or 24 h/15 km window for the evaluation of space-borne lidar oceanic products.
{"title":"Validation protocol for the evaluation of space-borne lidar particulate back-scattering coefficient bbp","authors":"Sayoob Vadakke-Chanat, C. Jamet","doi":"10.3389/frsen.2023.1194580","DOIUrl":"https://doi.org/10.3389/frsen.2023.1194580","url":null,"abstract":"Introduction: Space-borne lidar measurements from sensors such as CALIOP were recently used to retrieve the particulate back-scattering coefficient, bbp, in the upper ocean layers at a global scale and those observations have a strong potential for the future of ocean color with depth-resolved observations thereby complementing the conventional ocean color remote sensed observations as well as overcoming for some of its limitations. It is critical to evaluate and validate the space-borne lidar measurements for ocean applications as CALIOP was not originally designed for ocean applications. Few validation exercises of CALIOP were published and each exercise designed its own validation protocol. We propose here an objective validation protocol that could be applied to any current and future space-borne lidars for ocean applications.Methods: We, first, evaluated published validation protocols for CALIOP bbp product. Two published validation schemes were evaluated in our study, by using in-situ measurements from the BGC-Argo floats. These studies were either limited to day- or nighttime, or by the years used or by the geographical extent. We extended the match-up exercise to day-and nighttime observations and for the period 2010–2017 globally. We studied the impact of the time and distance differences between the in-situ measurements and the CALIOP footprint through a sensitivities study. Twenty combinations of distance (from 9-km to 50-km) and time (from 9 h to 16 days) differences were tested.Results & Discussion: A statistical score was used to objectively selecting the best optimal timedistance windows, leading to the best compromise in term of number of matchups and low errors in the CALIOP product. We propose to use either a 24 h/9 km or 24 h/15 km window for the evaluation of space-borne lidar oceanic products.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133464330","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 : 2023-07-26DOI: 10.3389/frsen.2023.1229745
Hiroki Murata, R. Shibasaki, Naoto Imura, K. Nishinari
Container terminals are cargo gateways in the global maritime supply chain network. Major container terminals generally operate throughout the year, but do not operate at night, when container vessels are not calling at ports, or when there is no need to handle containers. Terminal congestion can delay containers’ shipping schedules, which impacts the supply chain network. To optimize global logistics, it is therefore important to understand fully the daily operational status of container terminals. A vessels’ automatic identification system data are not sufficient to determine whether containers are being handled in container terminals at night. Remote sensing, especially nighttime-light (NTL) imagery, might solve this problem. Recently, high-resolution images for the CE-SAT-IIB satellite with a pixel resolution of 5.1 m became available to observe NTL. This study assessed the operational status of container terminals based on satellite image taken at night. Eight terminals in the Port of Tokyo, Japan, were selected for the study. A Sentinel-2A image recorded during the day on 7 April 2021, and a CE-SAT-IIB image recorded during the night on 6 April 2021, were obtained. The digital numbers (DNs) of each red-, green-, and blue-(RGB) band image were analyzed, revealing that the red, green, and blue bands, in that order, had higher DNs in the Sentinel-2A daytime image and the CE-SAT-IIB NTL image at all terminals. One of the eight terminals had a low DN in the CE-SAT-IIB RGB image because its lights were off at the time the image was taken. The operational status of the terminals could be verified from the CE-SAT-IIB image by setting the DN threshold to the green or red bands. We also found that the CE-SAT-IIB image could distinguish white-light-emitting diode (LED) lamps from high-pressure sodium lamps based on color differences in the DNs of the RGB bands. If high-resolution NTL sensors were placed onboard microsatellites, a high-frequency observation constellation network could be constructed using a combination of NTL data and daytime images. This study showed the benefits and usefulness of NTL images of ports; the results will contribute to the overall optimization of the global maritime supply chain network.
{"title":"Identifying the operational status of container terminals from high-resolution nighttime-light satellite image for global supply chain network optimization","authors":"Hiroki Murata, R. Shibasaki, Naoto Imura, K. Nishinari","doi":"10.3389/frsen.2023.1229745","DOIUrl":"https://doi.org/10.3389/frsen.2023.1229745","url":null,"abstract":"Container terminals are cargo gateways in the global maritime supply chain network. Major container terminals generally operate throughout the year, but do not operate at night, when container vessels are not calling at ports, or when there is no need to handle containers. Terminal congestion can delay containers’ shipping schedules, which impacts the supply chain network. To optimize global logistics, it is therefore important to understand fully the daily operational status of container terminals. A vessels’ automatic identification system data are not sufficient to determine whether containers are being handled in container terminals at night. Remote sensing, especially nighttime-light (NTL) imagery, might solve this problem. Recently, high-resolution images for the CE-SAT-IIB satellite with a pixel resolution of 5.1 m became available to observe NTL. This study assessed the operational status of container terminals based on satellite image taken at night. Eight terminals in the Port of Tokyo, Japan, were selected for the study. A Sentinel-2A image recorded during the day on 7 April 2021, and a CE-SAT-IIB image recorded during the night on 6 April 2021, were obtained. The digital numbers (DNs) of each red-, green-, and blue-(RGB) band image were analyzed, revealing that the red, green, and blue bands, in that order, had higher DNs in the Sentinel-2A daytime image and the CE-SAT-IIB NTL image at all terminals. One of the eight terminals had a low DN in the CE-SAT-IIB RGB image because its lights were off at the time the image was taken. The operational status of the terminals could be verified from the CE-SAT-IIB image by setting the DN threshold to the green or red bands. We also found that the CE-SAT-IIB image could distinguish white-light-emitting diode (LED) lamps from high-pressure sodium lamps based on color differences in the DNs of the RGB bands. If high-resolution NTL sensors were placed onboard microsatellites, a high-frequency observation constellation network could be constructed using a combination of NTL data and daytime images. This study showed the benefits and usefulness of NTL images of ports; the results will contribute to the overall optimization of the global maritime supply chain network.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128835163","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 : 2023-07-25DOI: 10.3389/frsen.2023.1217568
Y. Meijer, E. Andersson, H. Boesch, O. Dubovik, S. Houweling, J. Landgraf, R. Lang, H. Lindqvist
{"title":"Editorial: Anthropogenic emission monitoring with the Copernicus CO2 monitoring mission","authors":"Y. Meijer, E. Andersson, H. Boesch, O. Dubovik, S. Houweling, J. Landgraf, R. Lang, H. Lindqvist","doi":"10.3389/frsen.2023.1217568","DOIUrl":"https://doi.org/10.3389/frsen.2023.1217568","url":null,"abstract":"","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121732493","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 : 2023-07-13DOI: 10.3389/frsen.2023.1197523
Di Yang, Chiung-Shiuan Fu, H. Herrero, J. Southworth, Michael Binford
The Southeastern United States has high landscape heterogeneity, with heavily managed forestlands, developed agriculture, and multiple metropolitan areas. The spatial pattern of land use is dynamic. Expansion of urban areas convert forested and agricultural land, scrub forests are converted to citrus groves, and some croplands transition to pine plantations. Previous studies have recognized that forest management is the predominant factor in structural and functional changes forests, but little is known about how forest management practices interact with surrounding land uses at the regional scale. The first step in studying the spatial relationships of forest management with surrounding landscapes is to be able to map management practices and describe their proximity to various land uses. There are two major difficulties in generating land use and land management maps at the regional scale by any method: the necessity of large training data sets and expensive computation. The combination of crowdsourced, citizen-science mapping and cloud-based computing may help overcome those difficulties. In this study, OpenStreetMap is incorporated into mapping land use and shows great potential for justifying and monitoring land use at a regional scale. Google Earth Engine enables large-scale spatial analysis and imagery processing by providing a variety of Earth observation datasets and computational resources. By incorporating the OpenStreetMap dataset into Earth observation images to map forest land management practices and determine the distribution of other nearby land uses, we develop a robust regional land-use mapping approach and describe the patterns of how different land uses may affect forest management and vice versa. We find that cropland is more likely to be near ecological forest management patches; few close spatial relationships exist between land uses and preservation forest management, which fulfills the preservation management strategy of sustaining the forests, and production forests have the strongest spatial relationships with croplands. This approach leads to increased understanding of land-use patterns and management practices at local to regional scales.
{"title":"Linking forest management to surrounding lands: a citizen-based approach towards the regional understanding of land-use transitions","authors":"Di Yang, Chiung-Shiuan Fu, H. Herrero, J. Southworth, Michael Binford","doi":"10.3389/frsen.2023.1197523","DOIUrl":"https://doi.org/10.3389/frsen.2023.1197523","url":null,"abstract":"The Southeastern United States has high landscape heterogeneity, with heavily managed forestlands, developed agriculture, and multiple metropolitan areas. The spatial pattern of land use is dynamic. Expansion of urban areas convert forested and agricultural land, scrub forests are converted to citrus groves, and some croplands transition to pine plantations. Previous studies have recognized that forest management is the predominant factor in structural and functional changes forests, but little is known about how forest management practices interact with surrounding land uses at the regional scale. The first step in studying the spatial relationships of forest management with surrounding landscapes is to be able to map management practices and describe their proximity to various land uses. There are two major difficulties in generating land use and land management maps at the regional scale by any method: the necessity of large training data sets and expensive computation. The combination of crowdsourced, citizen-science mapping and cloud-based computing may help overcome those difficulties. In this study, OpenStreetMap is incorporated into mapping land use and shows great potential for justifying and monitoring land use at a regional scale. Google Earth Engine enables large-scale spatial analysis and imagery processing by providing a variety of Earth observation datasets and computational resources. By incorporating the OpenStreetMap dataset into Earth observation images to map forest land management practices and determine the distribution of other nearby land uses, we develop a robust regional land-use mapping approach and describe the patterns of how different land uses may affect forest management and vice versa. We find that cropland is more likely to be near ecological forest management patches; few close spatial relationships exist between land uses and preservation forest management, which fulfills the preservation management strategy of sustaining the forests, and production forests have the strongest spatial relationships with croplands. This approach leads to increased understanding of land-use patterns and management practices at local to regional scales.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116373982","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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