Pub Date : 2023-12-07DOI: 10.1080/07038992.2023.2289022
Kaushik Gupta, Jens K. Ehn
Numerous studies have been conducted to enhance our understanding of how climate change impacts landfast ice and its break-up in spring or summer. Yet, predictions of break-up timing have proven el...
{"title":"Terrestrial Snowmelt as a Precursor to Landfast Sea Ice Break-up in Hudson Bay and James Bay","authors":"Kaushik Gupta, Jens K. Ehn","doi":"10.1080/07038992.2023.2289022","DOIUrl":"https://doi.org/10.1080/07038992.2023.2289022","url":null,"abstract":"Numerous studies have been conducted to enhance our understanding of how climate change impacts landfast ice and its break-up in spring or summer. Yet, predictions of break-up timing have proven el...","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"14 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138556673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-27DOI: 10.1080/07038992.2023.2285778
Hamdy Elsayed, Ahmed Shaker
This paper discusses a comprehensive methodology for transforming a static LiDAR (Light Detection and Ranging) system into a mobile mapping system. The initial step involves integrating various sen...
{"title":"From Stationary to Mobile: Unleashing the Full Potential of Terrestrial LiDAR through Sensor Integration","authors":"Hamdy Elsayed, Ahmed Shaker","doi":"10.1080/07038992.2023.2285778","DOIUrl":"https://doi.org/10.1080/07038992.2023.2285778","url":null,"abstract":"This paper discusses a comprehensive methodology for transforming a static LiDAR (Light Detection and Ranging) system into a mobile mapping system. The initial step involves integrating various sen...","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"104 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138530599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully polarimetric (FP) and simulated compact polarimetric (CP) C- (RADARSAT-2) and L- (ALOS-2 PALSAR-2) band SAR, in the 2018 melt season in the Canadian Arctic Archipelago, for stage-wise separation of first year ice (FYI) and multiyear ice (MYI). SAR scenes at both near- (19.1–28.3°) and far- (35.8–42.1°) range incidence angles and coincident high-resolution optical scenes were used to assess the impact of surface melt ponds on separability within a landfast ice zone of diverse ice thickness. C-band provided better separability between FYI and MYI during pond onset, while L-band was superior during pond drainage due to MYI volumetric scattering. CP parameters matched FP performance across the melt season. HH and HV, commonly offered in ScanSAR mode for both frequencies, presented good separability during pond onset and drainage. Using both C-band and L-band SAR along with constraining incidence angle ranges, enhances sea ice type identification and separability. Our results can support ice type classification and seasonal stage detection for climate studies and enhance existing frameworks for ice motion vector retrievals.
{"title":"Melt Season Arctic Sea Ice Type Separability Using Fully and Compact Polarimetric C- and L-Band Synthetic Aperture Radar","authors":"Aikaterini Tavri, Randall Scharien, Torsten Geldsetzer","doi":"10.1080/07038992.2023.2271578","DOIUrl":"https://doi.org/10.1080/07038992.2023.2271578","url":null,"abstract":"Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully polarimetric (FP) and simulated compact polarimetric (CP) C- (RADARSAT-2) and L- (ALOS-2 PALSAR-2) band SAR, in the 2018 melt season in the Canadian Arctic Archipelago, for stage-wise separation of first year ice (FYI) and multiyear ice (MYI). SAR scenes at both near- (19.1–28.3°) and far- (35.8–42.1°) range incidence angles and coincident high-resolution optical scenes were used to assess the impact of surface melt ponds on separability within a landfast ice zone of diverse ice thickness. C-band provided better separability between FYI and MYI during pond onset, while L-band was superior during pond drainage due to MYI volumetric scattering. CP parameters matched FP performance across the melt season. HH and HV, commonly offered in ScanSAR mode for both frequencies, presented good separability during pond onset and drainage. Using both C-band and L-band SAR along with constraining incidence angle ranges, enhances sea ice type identification and separability. Our results can support ice type classification and seasonal stage detection for climate studies and enhance existing frameworks for ice motion vector retrievals.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"71 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.1080/07038992.2023.2259504
Clovis Cechim Junior, Hideo Araki, Rodrigo de Campos Macedo
The purpose of this research was to distinguish and estimate natural forest areas at Paraná, Brazil. Forest plantations (Silviculture) and natural forests have high annual vegetative vigor, as well as agricultural areas in the periods of agricultural harvests, which can bring classification errors between these classes of Land Use and Land Cover (LULC), these classes have similar spectral signatures, but have a distinct texture that can be separated in the supervised classification process, with the joining of object and pixel-to-pixel classification method approaches. Thus, image segmentation techniques through Object-Based Image Analysis (OBIA) and Machine Learning (ML) made forest mapping possible over a large territorial extension. The Google Earth Engine (GEE) platform was used to calculate the vegetation indices (VIs) and Spectral Mixture Analysis (SMA) fraction spectral from Sentinel-2 images, and the creation of homogeneous spectrally shaped regions under supervised classification of phytoecological regions and mesoregions. The overall precision obtained in the mappings resulted in 0.94 Kappa Index (KI) and 96% of Overall Accuracy (OA), which indicates a high performance in large-scale forest mapping. The proposed dataset, source codes and trained models are available on Github (https://github.com/Cechim/simepar-brazil/), creating opportunities for further ad vances in the field.
{"title":"Object-Based Image Analysis (OBIA) and Machine Learning (ML) Applied to Tropical Forest Mapping Using Sentinel-2","authors":"Clovis Cechim Junior, Hideo Araki, Rodrigo de Campos Macedo","doi":"10.1080/07038992.2023.2259504","DOIUrl":"https://doi.org/10.1080/07038992.2023.2259504","url":null,"abstract":"The purpose of this research was to distinguish and estimate natural forest areas at Paraná, Brazil. Forest plantations (Silviculture) and natural forests have high annual vegetative vigor, as well as agricultural areas in the periods of agricultural harvests, which can bring classification errors between these classes of Land Use and Land Cover (LULC), these classes have similar spectral signatures, but have a distinct texture that can be separated in the supervised classification process, with the joining of object and pixel-to-pixel classification method approaches. Thus, image segmentation techniques through Object-Based Image Analysis (OBIA) and Machine Learning (ML) made forest mapping possible over a large territorial extension. The Google Earth Engine (GEE) platform was used to calculate the vegetation indices (VIs) and Spectral Mixture Analysis (SMA) fraction spectral from Sentinel-2 images, and the creation of homogeneous spectrally shaped regions under supervised classification of phytoecological regions and mesoregions. The overall precision obtained in the mappings resulted in 0.94 Kappa Index (KI) and 96% of Overall Accuracy (OA), which indicates a high performance in large-scale forest mapping. The proposed dataset, source codes and trained models are available on Github (https://github.com/Cechim/simepar-brazil/), creating opportunities for further ad vances in the field.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135081525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1080/07038992.2023.2259505
Karima Mazirh, Said El Goumi, Mounsif Ibnoussina, Omar Witam, Mohamed Nocairi, Rachida Kasimi, Salah Er-Raki
Climate change and rapid urbanization have significant impact on green spaces and natural resources in African countries. To investigate this impact in the city of Marrakech, this study develops remote-sensing data to monitor changes in land cover and land use from 1990 to 2020. Results show almost 35% diminution of vegetation cover over the investigation period. In 1990, the city of Marrakech had a vegetation cover of 4.2 km2, which fell to 2.7 km2 in 2020. The main change occurred between 1990 and 2000 with a decrease of 13.7%, which is essentially due to the increase in build-up areas, related to the rapid growth of the city’s population. This evolution in land cover affects the urban environment negatively including air quality and temperature regulation. This research provides a better understanding of changing trends, confirms the importance of using satellite imagery to monitor vegetation cover in urban settings, helps determine efficient environmental management, and affects successful green infrastructure policy and planning, thereby allowing for improved adaptation and mitigation to climate change.
{"title":"Using Remote Sensing to Address Green Heritage of the City of Marrakech, Morocco","authors":"Karima Mazirh, Said El Goumi, Mounsif Ibnoussina, Omar Witam, Mohamed Nocairi, Rachida Kasimi, Salah Er-Raki","doi":"10.1080/07038992.2023.2259505","DOIUrl":"https://doi.org/10.1080/07038992.2023.2259505","url":null,"abstract":"Climate change and rapid urbanization have significant impact on green spaces and natural resources in African countries. To investigate this impact in the city of Marrakech, this study develops remote-sensing data to monitor changes in land cover and land use from 1990 to 2020. Results show almost 35% diminution of vegetation cover over the investigation period. In 1990, the city of Marrakech had a vegetation cover of 4.2 km2, which fell to 2.7 km2 in 2020. The main change occurred between 1990 and 2000 with a decrease of 13.7%, which is essentially due to the increase in build-up areas, related to the rapid growth of the city’s population. This evolution in land cover affects the urban environment negatively including air quality and temperature regulation. This research provides a better understanding of changing trends, confirms the importance of using satellite imagery to monitor vegetation cover in urban settings, helps determine efficient environmental management, and affects successful green infrastructure policy and planning, thereby allowing for improved adaptation and mitigation to climate change.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135486838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The images captured from SAR sensors are inherently weakened by speckle noise. The SAR image processing community targeted this problem with many feature-based filters. Since SAR images are low-contrast images, edge retention is the most crucial aspect to consider. This helps in the efficient retrieval of information. This paper provides a two-step edge-preserving homomorphic SAR image despeckling technique that implements a guided filter as the first step, and a modified method of noise thresholding using the bivariate shrinkage rule and canny edge operator in the Discrete Orthonormal Stockwell Transform (DOST) domain as the second step. The use of a canny edge operator improves overall edge preservation after despeckling. The use of noise thresholding delivers the highest level of speckle reduction in the DOST domain. The detected edges are added to the residual part obtained after removing the noise to produce more informative content. According to several qualitative and quantitative criteria, the suggested approach is compared to some of the newest despeckling methods. The execution time of the proposed method is around 7.2679 seconds. Upon conducting qualitative and quantitative analysis, it has been determined that the proposed method surpasses all other despeckling methods that were compared.
{"title":"An Algorithmic Approach towards Remote Sensing Imagery Data Restoration Using Guided Filters in Real-Time Applications","authors":"Prabhishek Singh, Manoj Diwakar, Debjani Ghosh, Ankit Vidyarthi, Deepak Gupta, Punit Gupta","doi":"10.1080/07038992.2023.2257323","DOIUrl":"https://doi.org/10.1080/07038992.2023.2257323","url":null,"abstract":"The images captured from SAR sensors are inherently weakened by speckle noise. The SAR image processing community targeted this problem with many feature-based filters. Since SAR images are low-contrast images, edge retention is the most crucial aspect to consider. This helps in the efficient retrieval of information. This paper provides a two-step edge-preserving homomorphic SAR image despeckling technique that implements a guided filter as the first step, and a modified method of noise thresholding using the bivariate shrinkage rule and canny edge operator in the Discrete Orthonormal Stockwell Transform (DOST) domain as the second step. The use of a canny edge operator improves overall edge preservation after despeckling. The use of noise thresholding delivers the highest level of speckle reduction in the DOST domain. The detected edges are added to the residual part obtained after removing the noise to produce more informative content. According to several qualitative and quantitative criteria, the suggested approach is compared to some of the newest despeckling methods. The execution time of the proposed method is around 7.2679 seconds. Upon conducting qualitative and quantitative analysis, it has been determined that the proposed method surpasses all other despeckling methods that were compared.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136363828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04DOI: 10.1080/07038992.2023.2252926
Jumi Gogoi, Nathaniel K. Newlands, Z. Mehrabi, N. Coops, N. Ramankutty
{"title":"Assessing the Performance of Satellite-Based Models for Crop Yield Estimation in the Canadian Prairies","authors":"Jumi Gogoi, Nathaniel K. Newlands, Z. Mehrabi, N. Coops, N. Ramankutty","doi":"10.1080/07038992.2023.2252926","DOIUrl":"https://doi.org/10.1080/07038992.2023.2252926","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49406588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Novel Approach to Wind Retrieval from Sentinel-1 SAR in Tropical Cyclones","authors":"Xianbin Zhao, Weizeng Shao, Mengyu Hao, Xingwei Jiang","doi":"10.1080/07038992.2023.2254839","DOIUrl":"https://doi.org/10.1080/07038992.2023.2254839","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48449794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-21DOI: 10.1080/07038992.2023.2248270
Dhirendra Prasad Yadav, Deepak Kumar, Anand Singh Jalal, Ankit Kumar, Surbhi Bhatia Khan, T. Gadekallu, Arwa A. Mashat, A. Malibari
{"title":"Spectral–Spatial Features Exploitation Using Lightweight HResNeXt Model for Hyperspectral Image Classification","authors":"Dhirendra Prasad Yadav, Deepak Kumar, Anand Singh Jalal, Ankit Kumar, Surbhi Bhatia Khan, T. Gadekallu, Arwa A. Mashat, A. Malibari","doi":"10.1080/07038992.2023.2248270","DOIUrl":"https://doi.org/10.1080/07038992.2023.2248270","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46474837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.1080/07038992.2023.2247095
Sergey V. Samsonov, Wanpeng Feng
A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using DInSAR processing technique was developed at the Canada Center for Remote Sensing. Ground deformation rates from 2017 to 2023 were computed over a large territory of North America and Eurasia from more than 220,000 readily available Sentinel-1 images, and the performance and shortcomings of the developed processing system were analyzed. Here, we present the processing methodology and several examples of deformation rate maps and time series produced with this automated system. Examples include the deformation of slow- moving deep-seated landslides in two regions of Canada, subsidence at the Komsomolskoe oil field in the Russian Arctic, the Tengiz oil field in Kazakhstan, multiple large subsiding regions and landslides in northwestern Iran, and two large subsiding regions in the Yellow River Delta and Xinjiang, China. Many deformation processes observed in these deformation rate maps, including large landslides, have previously been unknown to the research community. Systematic radar penetration depth changes were observed in multiple regions and were investigate in detail for 1 Eurasian region. Computed deformation rates for North America and Eurasia are available to the research community and can be downloaded from the data repository.
{"title":"Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges","authors":"Sergey V. Samsonov, Wanpeng Feng","doi":"10.1080/07038992.2023.2247095","DOIUrl":"https://doi.org/10.1080/07038992.2023.2247095","url":null,"abstract":"A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using DInSAR processing technique was developed at the Canada Center for Remote Sensing. Ground deformation rates from 2017 to 2023 were computed over a large territory of North America and Eurasia from more than 220,000 readily available Sentinel-1 images, and the performance and shortcomings of the developed processing system were analyzed. Here, we present the processing methodology and several examples of deformation rate maps and time series produced with this automated system. Examples include the deformation of slow- moving deep-seated landslides in two regions of Canada, subsidence at the Komsomolskoe oil field in the Russian Arctic, the Tengiz oil field in Kazakhstan, multiple large subsiding regions and landslides in northwestern Iran, and two large subsiding regions in the Yellow River Delta and Xinjiang, China. Many deformation processes observed in these deformation rate maps, including large landslides, have previously been unknown to the research community. Systematic radar penetration depth changes were observed in multiple regions and were investigate in detail for 1 Eurasian region. Computed deformation rates for North America and Eurasia are available to the research community and can be downloaded from the data repository.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135597818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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