التفاصيل البيبلوغرافية
| العنوان: |
An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery |
| المؤلفون: |
Dominique Chabot, Christopher Dillon, Adam Shemrock, Nicholas Weissflog, Eric P. S. Sager |
| المصدر: |
ISPRS International Journal of Geo-Information, Vol 7, Iss 8, p 294 (2018) |
| بيانات النشر: |
MDPI AG, 2018. |
| سنة النشر: |
2018 |
| المجموعة: |
LCC:Geography (General) |
| مصطلحات موضوعية: |
environmental monitoring, freshwater ecosystems, OBIA, random forests, remote sensing, rivers, unmanned aircraft, UAS, UAV, wetlands, Geography (General), G1-922 |
| الوصف: |
High-resolution drone aerial surveys combined with object-based image analysis are transforming our capacity to monitor and manage aquatic vegetation in an era of invasive species. To better exploit the potential of these technologies, there is a need to develop more efficient and accessible analysis workflows and focus more efforts on the distinct challenge of mapping submerged vegetation. We present a straightforward workflow developed to monitor emergent and submerged invasive water soldier (Stratiotes aloides) in shallow waters of the Trent-Severn Waterway in Ontario, Canada. The main elements of the workflow are: (1) collection of radiometrically calibrated multispectral imagery including a near-infrared band; (2) multistage segmentation of the imagery involving an initial separation of above-water from submerged features; and (3) automated classification of features with a supervised machine-learning classifier. The approach yielded excellent classification accuracy for emergent features (overall accuracy = 92%; kappa = 88%; water soldier producer’s accuracy = 92%; user’s accuracy = 91%) and good accuracy for submerged features (overall accuracy = 84%; kappa = 75%; water soldier producer’s accuracy = 71%; user’s accuracy = 84%). The workflow employs off-the-shelf graphical software tools requiring no programming or coding, and could therefore be used by anyone with basic GIS and image analysis skills for a potentially wide variety of aquatic vegetation monitoring operations. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2220-9964 |
| Relation: |
http://www.mdpi.com/2220-9964/7/8/294; https://doaj.org/toc/2220-9964 |
| DOI: |
10.3390/ijgi7080294 |
| URL الوصول: |
https://doaj.org/article/5e5557acd9ab4e199f03811462b03d51 |
| رقم الانضمام: |
edsdoj.5e5557acd9ab4e199f03811462b03d51 |
| قاعدة البيانات: |
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