Canadian Journal of Remote Sensing最新文献_第3页

Terrestrial Snowmelt as a Precursor to Landfast Sea Ice Break-up in Hudson Bay and James Bay 陆地融雪是哈得逊湾和詹姆斯湾陆地海冰破裂的前兆

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-12-07 DOI: 10.1080/07038992.2023.2289022

Kaushik Gupta, Jens K. Ehn

引用次数: 0

From Stationary to Mobile: Unleashing the Full Potential of Terrestrial LiDAR through Sensor Integration 从固定到移动:通过传感器集成释放地面激光雷达的全部潜力

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-11-27 DOI: 10.1080/07038992.2023.2285778

Hamdy Elsayed, Ahmed Shaker

引用次数: 0

Melt Season Arctic Sea Ice Type Separability Using Fully and Compact Polarimetric C- and L-Band Synthetic Aperture Radar 利用全和紧凑型偏振C波段和l波段合成孔径雷达的融冰季节北极海冰类型可分离性

4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-10-31 DOI: 10.1080/07038992.2023.2271578

Aikaterini Tavri, Randall Scharien, Torsten Geldsetzer

{"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}

引用次数: 0

Object-Based Image Analysis (OBIA) and Machine Learning (ML) Applied to Tropical Forest Mapping Using Sentinel-2 基于对象的图像分析(OBIA)和机器学习(ML)在Sentinel-2热带森林制图中的应用

4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-09-30 DOI: 10.1080/07038992.2023.2259504

Clovis Cechim Junior, Hideo Araki, Rodrigo de Campos Macedo

{"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}

引用次数: 0

Using Remote Sensing to Address Green Heritage of the City of Marrakech, Morocco 利用遥感技术处理摩洛哥马拉喀什市的绿色遗产

4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-09-15 DOI: 10.1080/07038992.2023.2259505

Karima Mazirh, Said El Goumi, Mounsif Ibnoussina, Omar Witam, Mohamed Nocairi, Rachida Kasimi, Salah Er-Raki

{"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}

引用次数: 0

An Algorithmic Approach towards Remote Sensing Imagery Data Restoration Using Guided Filters in Real-Time Applications 一种实时应用中基于引导滤波器的遥感图像数据恢复算法

4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-09-08 DOI: 10.1080/07038992.2023.2257323

Prabhishek Singh, Manoj Diwakar, Debjani Ghosh, Ankit Vidyarthi, Deepak Gupta, Punit Gupta

{"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}

引用次数: 1

Assessing the Performance of Satellite-Based Models for Crop Yield Estimation in the Canadian Prairies 评估加拿大大草原作物产量估算卫星模型的性能

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-09-04 DOI: 10.1080/07038992.2023.2252926

Jumi Gogoi, Nathaniel K. Newlands, Z. Mehrabi, N. Coops, N. Ramankutty

引用次数: 1

Novel Approach to Wind Retrieval from Sentinel-1 SAR in Tropical Cyclones 热带气旋中Sentinel-1 SAR反演风的新方法

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-08-31 DOI: 10.1080/07038992.2023.2254839

Xianbin Zhao, Weizeng Shao, Mengyu Hao, Xingwei Jiang

引用次数: 0

Spectral–Spatial Features Exploitation Using Lightweight HResNeXt Model for Hyperspectral Image Classification 基于轻量级HResNeXt模型的高光谱图像分类光谱空间特征挖掘

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-08-21 DOI: 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

引用次数: 0

Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges 基于Sentinel-1 DInSAR的北美和欧亚大陆形变反演:大数据方法、处理方法和挑战

4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2023-08-10 DOI: 10.1080/07038992.2023.2247095

Sergey V. Samsonov, Wanpeng Feng

{"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}

引用次数: 0