التفاصيل البيبلوغرافية
| العنوان: |
River Eelgrass Detection using Very High-Resolution Satellite Imagery |
| المؤلفون: |
Meisam Amani, S. Mohammad Mirmazloumi, Sahel Mahdavi |
| بيانات النشر: |
Zenodo, 2022. |
| سنة النشر: |
2022 |
| مصطلحات موضوعية: |
Vegetation mapping, Image classification, Iterative methods, Satellite imagery, Remote sensing, Very high resolution satellite imagery, Coastal ecosystems, Remote-sensing, Bridge constructions, Ecosystems, Rivers, Worldview-3, Machine learning, Human activities, Accurate mapping, Climate change, Anthropogenic activity, Machine-learning, Aquatic vegetation |
| الوصف: |
River eelgrass has a vital role in the coastal ecosystem, which can be threatened by human activities and climate change. Accurate mapping of eelgrass habitats is important for sustainable eelgrass conservation and management. A large portion of the Shediac river in the province of New Brunswick, Canada is covered by eelgrass, which can be threatened by different natural and anthropogenic activities, such as bridge construction. Thus, mapping and monitoring eelgrass in this river are of interest to the province. In this study, a very high-resolution Worldview-3 image along with an Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification algorithm was applied to map eelgrass over a portion of Shediac river. The results indicated a reasonable classification accuracy, and it was observed that 90,416.43 m2 of the study area was covered by river eelgrass. © 2022 IEEE. |
| URL الوصول: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ce2a4758f190e17a8440a92b00588aa
https://zenodo.org/record/7276610 |
| Rights: |
OPEN |
| رقم الانضمام: |
edsair.doi.dedup.....6ce2a4758f190e17a8440a92b00588aa |
| قاعدة البيانات: |
OpenAIRE |