Three total column dry-air mole fractions of CO_2(XCO_2) products from satellite retrievals, namely SCIAMACHY, NIES-GOSAT, and ACOS-GOSAT, in the Northern Hemisphere were validated by ground data from the Total Carbon...Three total column dry-air mole fractions of CO_2(XCO_2) products from satellite retrievals, namely SCIAMACHY, NIES-GOSAT, and ACOS-GOSAT, in the Northern Hemisphere were validated by ground data from the Total Carbon Column Observing Network(TCCON). The results showed that the satellite data have the same seasonal fluctuations as in the TCCON data, with maximum in April or May and minimum in August or September. The three products all underestimate the XCO2. The ACOS-GOSAT and the NIES-GOSAT products are roughly equivalent, and their mean standard deviations are 2.26 × 10^(-6)and 2.27 × 10^(-6)respectively. The accuracy of the SCIMACHY product is slightly lower, with a mean standard deviation of 2.91 × 10^(-6).展开更多
The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America.Despite much progress in targeting habitat management in sta...The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America.Despite much progress in targeting habitat management in staging and wintering areas,methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required.This is particularly true for species that breed in remote,inaccessible areas such as the American black duck,an intensively managed game bird in Eastern North America.Although evidence suggests that black ducks prefer productive,nutrient-rich waterbodies,such as beaver ponds,information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge.Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap.In this study,we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario.The use of multi-source data,including a Digital Elevation Model,a Sentinel-2 Multi-Spectral Image,and RadarSat 2 Polarimetric data,enabled us to identify individual beaver ponds on the landscape.Our model correctly identified an average of 83.0%of the known beaver dams and 72.5%of the known beaver ponds based on validation with an independent dataset.This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents.Furthermore,the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife.展开更多
A series of experiments are designed to propose a new method to study the characteristics of convex mode-2internal solitary waves(ISWs)in optical remote sensing images using a laboratory-based optical remote sensing s...A series of experiments are designed to propose a new method to study the characteristics of convex mode-2internal solitary waves(ISWs)in optical remote sensing images using a laboratory-based optical remote sensing simulation platform.The corresponding wave parameters of large-amplitude convex mode-2 ISWs under smooth surfaces are investigated along with the optical remote sensing characteristic parameters.The mode-2 ISWs in the experimentally obtained optical remote sensing image are produced by their overall modulation effect on the water surface,and the extreme points of the gray value of the profile curve of bright-dark stripes appear at the same location as the real optical remote sensing image.The present data extend to a larger range than previous studies,and for the characteristics of large amplitude convex mode-2 ISWs,the experimental results show a second-order dependence of wavelength on amplitude.There is a close relationship between optical remote sensing characteristic parameters and wave parameters of mode-2 ISWs,in which there is a positive linear relationship between the bright-dark spacing and wavelength and a nonlinear relationship with the amplitude,especially when the amplitude is very large,there is a significant increase in bright-dark spacing.展开更多
The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied...The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.展开更多
Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineatio...Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineation and mapping have been done using the conventional techniques, such as the utilization of ground truth facts together with orthophotos. These techniques have been proven to be very precise, but they are expensive, cumbersome, and challenging to employ in remote regions. To resolve this shortfall, this research investigates the potential of data from the commercial, PlanetScope CubeSat and the freely available, Sentinel 2 data from Copernicus to discriminate commercial forest tree species in the Usutu Forest, Eswatini. Two approaches for image classification, Random Forest (RF) and the Support Vector Machine (SVM) were investigated at different levels of the forest database classification which is the genus (family of tree species) and species levels. The result of the study indicates that, the Sentinel 2 images had the highest species classification accuracy compared to the PlanetScope image. Both classification methods achieved a 94% maximum OA and 0.90 kappa value at the genus level with the Sentinel 2 imagery. At the species level, the Sentinel 2 imagery again showed highly acceptable results with the SVM method, with an OA of 82%. The PlanetScope images performed badly with less than 64% OA for both RF and SVM at the genus level and poorer at the species level with a low OA figure, 47% and 53% for the SVM and RF respectively. Our results suggest that the freely available Sentinel 2 data together with the SVM method has a high potential for identifying differences between commercial tree species than the PlanetScope. The study uncovered that both classification methods are highly capable of classifying species under the gum genus group (esmi, egxu, and egxn) using both imageries. However, it was difficult to separate species types under the pine genus group, particularly discriminating the hybrid species such as pech and pell since pech is a hybrid species for pell.展开更多
Obtaining accurate ship positions and headings in remote sensing images plays a crucial role in various applications.However,current deep learning-based methods primarily focus on ship position detection,while the det...Obtaining accurate ship positions and headings in remote sensing images plays a crucial role in various applications.However,current deep learning-based methods primarily focus on ship position detection,while the detection of ship wakes relies on traditional non-deep learning approaches,which often underperform in complex marine environments.We proposed a novel,simple,and efficient method called Point-Vector Net.The proposed method leverages convolutional neural networks(CNN)for feature extraction and subsequently integrates multi-scale features to generate high-resolution feature maps.In the final stage,ship positions and headings are represented using a combination of points and vectors.Comparative experiments with results from automatic identification system(AIS)reports demonstrate that our method achieved impressive performance in two-class ship target detection,with an average precision of 96.4%,recall rate of 94.3%,and an F 1 score of 95.2%.Notably,the average heading error was 3.3°.The proposed model achieved a practical inference speed(FPS>30),and the average processing time for inferring a large-scale Sentinel-2 remote sensing image was 11.4 s.展开更多
Loss of multiyear ice(MYI)is of great importance for Arctic climate and marine systems and can be monitored using active and passive microwave satellite data.In this paper,we describe an upgraded classification algori...Loss of multiyear ice(MYI)is of great importance for Arctic climate and marine systems and can be monitored using active and passive microwave satellite data.In this paper,we describe an upgraded classification algorithm using the data from the scatterometer and radiometer sensors onboard the Chinese Haiyang-2B(HY-2B)satellite to identify MYI and first-year ice(FYI).The proposed method was established based on K-means and fuzzy clustering(K-means+FC)and was used to focus on the transition zone where the ice condition is complex due to the highly commixing of MYI and FYI,leading to the high challenge for accurate classification of sea ice.The K-means algorithm was applied to preliminarily classify MYI using the combination of scatterometer and radiometer data,followed by applying fuzzy clustering to reclassify MYI in the transition zone.The HY-2B K-means+FC results were compared with the ice type products[including the Ocean and Sea Ice Satellite Application Facility(OSI SAF)sea ice type product and the Equal-Area Scalable Earth-Grid sea ice age dataset],and showed agreement in the time series of MYI extent.Intercomparisons in the transition zone indicated that the HY-2B K-means+FC results can identify more old ice than the OSI SAF product,but with an underestimation in identifying second-year ice.Comparisons between K-means and Kmeans+FC results were performed using regional ice charts and Sentinel-1 synthetic aperture radar(SAR)data.By adding fuzzy clustering,the MYI is more consistent with the ice charts,with the overall accuracy(OA)increasing by 0.9%–6.5%.Comparing against SAR images,it is suggested that more scattered MYI floes can be identified by fuzzy clustering,and the OA is increased by about 3%in middle freezing season and 7%–20%in early and late freezing season.展开更多
The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus...The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus making them invaluable for monit-oring forest health.To compare the performance of these two satellites’red-edge bands in monitoring forest health,this study selected forests in Liuyang City,Hunan Province and Tonggu County,Jiangxi Province and Hanzhong City,Shaanxi Province in China as study areas and used three commonly used red-edge indices and the Random Forest(RF)algorithm for the comparison.The three selected red-edge indices were the Normalized Difference Red-Edge Index 1(NDRE1),the Missouri emergency resource information system Ter-restrial Chlorophyll Index(MTCI),and the Inverted Red-Edge Chlorophyll Index(IRECI).Through training of sample regions,this study determined the spectral differences among three forest health levels and established classification criteria for these levels.The res-ults showed that GF-6 imagery provided higher accuracy in distinguishing forest health levels than Sentinel-2A,with an average accur-acy of 90.22%versus 76.55%.This difference is attributed to variations in the wavelengths used to construct the red-edge indices between GF-6 and Sentinel-2A.In the RF algorithm,this study employed three distinct band combinations for classification:all bands including red-edge bands,excluding red-edge bands,and only red-edge bands.The results indicated that GF-6 outperformed Sentinel-2A when using the first and second band combinations,yet slightly underperforming with the third.This outcome was closely associ-ated with the importance of each band’s contribution to classification accuracy reveled by the Gini importance score,their sensitivity in detecting forest health conditions,and the total number of bands employed in the classification process.Overall,the NDRE1 derived from GF-6 achieved the highest average accuracy(90.22%).This study provides a scientific basis for selecting appropriate remote sens-ing data and techniques for forest health monitoring,which is of significant importance for the future ecological protection of forests.展开更多
基金funded by the 863 Project (2011AA12A104)National Natural Science Foundation of China (41375025)
文摘Three total column dry-air mole fractions of CO_2(XCO_2) products from satellite retrievals, namely SCIAMACHY, NIES-GOSAT, and ACOS-GOSAT, in the Northern Hemisphere were validated by ground data from the Total Carbon Column Observing Network(TCCON). The results showed that the satellite data have the same seasonal fluctuations as in the TCCON data, with maximum in April or May and minimum in August or September. The three products all underestimate the XCO2. The ACOS-GOSAT and the NIES-GOSAT products are roughly equivalent, and their mean standard deviations are 2.26 × 10^(-6)and 2.27 × 10^(-6)respectively. The accuracy of the SCIMACHY product is slightly lower, with a mean standard deviation of 2.91 × 10^(-6).
基金supported by the Natural Sciences and Engineering Research Council of Canada[RGPIN-2021-03624].
文摘The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America.Despite much progress in targeting habitat management in staging and wintering areas,methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required.This is particularly true for species that breed in remote,inaccessible areas such as the American black duck,an intensively managed game bird in Eastern North America.Although evidence suggests that black ducks prefer productive,nutrient-rich waterbodies,such as beaver ponds,information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge.Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap.In this study,we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario.The use of multi-source data,including a Digital Elevation Model,a Sentinel-2 Multi-Spectral Image,and RadarSat 2 Polarimetric data,enabled us to identify individual beaver ponds on the landscape.Our model correctly identified an average of 83.0%of the known beaver dams and 72.5%of the known beaver ponds based on validation with an independent dataset.This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents.Furthermore,the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife.
基金The National Natural Science Foundation of China under contract No.61871353。
文摘A series of experiments are designed to propose a new method to study the characteristics of convex mode-2internal solitary waves(ISWs)in optical remote sensing images using a laboratory-based optical remote sensing simulation platform.The corresponding wave parameters of large-amplitude convex mode-2 ISWs under smooth surfaces are investigated along with the optical remote sensing characteristic parameters.The mode-2 ISWs in the experimentally obtained optical remote sensing image are produced by their overall modulation effect on the water surface,and the extreme points of the gray value of the profile curve of bright-dark stripes appear at the same location as the real optical remote sensing image.The present data extend to a larger range than previous studies,and for the characteristics of large amplitude convex mode-2 ISWs,the experimental results show a second-order dependence of wavelength on amplitude.There is a close relationship between optical remote sensing characteristic parameters and wave parameters of mode-2 ISWs,in which there is a positive linear relationship between the bright-dark spacing and wavelength and a nonlinear relationship with the amplitude,especially when the amplitude is very large,there is a significant increase in bright-dark spacing.
文摘The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.
文摘Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineation and mapping have been done using the conventional techniques, such as the utilization of ground truth facts together with orthophotos. These techniques have been proven to be very precise, but they are expensive, cumbersome, and challenging to employ in remote regions. To resolve this shortfall, this research investigates the potential of data from the commercial, PlanetScope CubeSat and the freely available, Sentinel 2 data from Copernicus to discriminate commercial forest tree species in the Usutu Forest, Eswatini. Two approaches for image classification, Random Forest (RF) and the Support Vector Machine (SVM) were investigated at different levels of the forest database classification which is the genus (family of tree species) and species levels. The result of the study indicates that, the Sentinel 2 images had the highest species classification accuracy compared to the PlanetScope image. Both classification methods achieved a 94% maximum OA and 0.90 kappa value at the genus level with the Sentinel 2 imagery. At the species level, the Sentinel 2 imagery again showed highly acceptable results with the SVM method, with an OA of 82%. The PlanetScope images performed badly with less than 64% OA for both RF and SVM at the genus level and poorer at the species level with a low OA figure, 47% and 53% for the SVM and RF respectively. Our results suggest that the freely available Sentinel 2 data together with the SVM method has a high potential for identifying differences between commercial tree species than the PlanetScope. The study uncovered that both classification methods are highly capable of classifying species under the gum genus group (esmi, egxu, and egxn) using both imageries. However, it was difficult to separate species types under the pine genus group, particularly discriminating the hybrid species such as pech and pell since pech is a hybrid species for pell.
基金Supported by the National Key R&D Program of China(No.2022YFB3902400)the China High Resolution Earth Observation System Program(No.41-Y30F07-9001-20/22)。
文摘Obtaining accurate ship positions and headings in remote sensing images plays a crucial role in various applications.However,current deep learning-based methods primarily focus on ship position detection,while the detection of ship wakes relies on traditional non-deep learning approaches,which often underperform in complex marine environments.We proposed a novel,simple,and efficient method called Point-Vector Net.The proposed method leverages convolutional neural networks(CNN)for feature extraction and subsequently integrates multi-scale features to generate high-resolution feature maps.In the final stage,ship positions and headings are represented using a combination of points and vectors.Comparative experiments with results from automatic identification system(AIS)reports demonstrate that our method achieved impressive performance in two-class ship target detection,with an average precision of 96.4%,recall rate of 94.3%,and an F 1 score of 95.2%.Notably,the average heading error was 3.3°.The proposed model achieved a practical inference speed(FPS>30),and the average processing time for inferring a large-scale Sentinel-2 remote sensing image was 11.4 s.
基金the National Key Research and Development Program of China under contract No.2021YFC2803301the Fundamental Research Funds for the Central Universities,China under contract Nos 2042024kf0037 and 2042022dx0001the Natural Science Foundation of Wuhan under cocntract No.2024040701010030.
文摘Loss of multiyear ice(MYI)is of great importance for Arctic climate and marine systems and can be monitored using active and passive microwave satellite data.In this paper,we describe an upgraded classification algorithm using the data from the scatterometer and radiometer sensors onboard the Chinese Haiyang-2B(HY-2B)satellite to identify MYI and first-year ice(FYI).The proposed method was established based on K-means and fuzzy clustering(K-means+FC)and was used to focus on the transition zone where the ice condition is complex due to the highly commixing of MYI and FYI,leading to the high challenge for accurate classification of sea ice.The K-means algorithm was applied to preliminarily classify MYI using the combination of scatterometer and radiometer data,followed by applying fuzzy clustering to reclassify MYI in the transition zone.The HY-2B K-means+FC results were compared with the ice type products[including the Ocean and Sea Ice Satellite Application Facility(OSI SAF)sea ice type product and the Equal-Area Scalable Earth-Grid sea ice age dataset],and showed agreement in the time series of MYI extent.Intercomparisons in the transition zone indicated that the HY-2B K-means+FC results can identify more old ice than the OSI SAF product,but with an underestimation in identifying second-year ice.Comparisons between K-means and Kmeans+FC results were performed using regional ice charts and Sentinel-1 synthetic aperture radar(SAR)data.By adding fuzzy clustering,the MYI is more consistent with the ice charts,with the overall accuracy(OA)increasing by 0.9%–6.5%.Comparing against SAR images,it is suggested that more scattered MYI floes can be identified by fuzzy clustering,and the OA is increased by about 3%in middle freezing season and 7%–20%in early and late freezing season.
基金Under the auspices of National Natural Science Foundation of China(No.31971639)National Natural Science Foundation of Fujian Province,China(No.2023J01225)。
文摘The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus making them invaluable for monit-oring forest health.To compare the performance of these two satellites’red-edge bands in monitoring forest health,this study selected forests in Liuyang City,Hunan Province and Tonggu County,Jiangxi Province and Hanzhong City,Shaanxi Province in China as study areas and used three commonly used red-edge indices and the Random Forest(RF)algorithm for the comparison.The three selected red-edge indices were the Normalized Difference Red-Edge Index 1(NDRE1),the Missouri emergency resource information system Ter-restrial Chlorophyll Index(MTCI),and the Inverted Red-Edge Chlorophyll Index(IRECI).Through training of sample regions,this study determined the spectral differences among three forest health levels and established classification criteria for these levels.The res-ults showed that GF-6 imagery provided higher accuracy in distinguishing forest health levels than Sentinel-2A,with an average accur-acy of 90.22%versus 76.55%.This difference is attributed to variations in the wavelengths used to construct the red-edge indices between GF-6 and Sentinel-2A.In the RF algorithm,this study employed three distinct band combinations for classification:all bands including red-edge bands,excluding red-edge bands,and only red-edge bands.The results indicated that GF-6 outperformed Sentinel-2A when using the first and second band combinations,yet slightly underperforming with the third.This outcome was closely associ-ated with the importance of each band’s contribution to classification accuracy reveled by the Gini importance score,their sensitivity in detecting forest health conditions,and the total number of bands employed in the classification process.Overall,the NDRE1 derived from GF-6 achieved the highest average accuracy(90.22%).This study provides a scientific basis for selecting appropriate remote sens-ing data and techniques for forest health monitoring,which is of significant importance for the future ecological protection of forests.
