|Title||Automated surface water extraction from RapidEye imagery including cloud and cloud shadow detection|
|Source||Geomatics Canada, Open File 52, 2019, 20 pages, https://doi.org/10.4095/315176 (Open Access)|
|Publisher||Natural Resources Canada|
|File format||pdf (Adobe® Reader®)|
|Subjects||hydrogeology; geophysics; Science and Technology; surface waters; hydrography; floods; remote sensing; satellite imagery; mapping techniques; statistical analyses; soils; soil moisture; National
Hydrographic Network; methodology; feature extraction; automation; RapidEye; geographic data; cloud detection; cloud shadow detection; WorldView-2; WorldView-3; data processing|
|Illustrations||location maps; tables; satellite images; plots; frequency distribution diagrams|
|Program||Methodology, Remote Sensing Science|
|Released||2019 09 19|
|Abstract||Mapped surface water extents represent fundamental geospatial information required by a large number of public and private sector stakeholders in Canada. Currently available surface water maps from
Canada's National Hydrographic Network (NHN) are out-of-date in many locations in part due to the vintage of the maps themselves, and also because water extents are dynamic and changing. Previously, a significant amount of Canada's NHN base
geospatial data was manually interpreted from airphotos and other sources of high-resolution imagery, which enabled detailed mapping but required significant human and financial resources. Satellite imagery can provide a cheaper alternative due to
the free availability of certain data and potential for automated feature extraction. Freely available medium resolution data from sensors such as Landsat can provide information at a scale of 1:50 k while more detail is needed to map smaller water
courses that can only be resolved from high resolution sensors (< 5 m) such as RapidEye. This Open File describes a robust, fully automated procedure to extract surface water from RapidEye imagery including cloud and cloud shadow screening, as a
potential method to enhance future NHN updating. The method is applied to RapidEye imagery residing in the Government of Canada's image archive over the St-John, Red and Richelieu Rivers. Qualitative assessment of the Richelieu product shows high
overall agreement with water features in Google Earth imagery and NHN, with errors stemming from spectral confusion with dark soil and urban shadow. Canada's RapidEye archive currently covers approximately 30 % of provinces while its 5 m spatial
resolution provides a good compromise between detail and data volume that can be processed on modern workstations. Similar spectral bands available in other higher resolution satellite sensors (< 2 m) such as WorldView suggest more detailed surface
water extents may become available using methods developed here and adapted to these sensors.|
|Summary||(Plain Language Summary, not published)|
This open file describes a new technique to extract open surface water from high resolution optical imagery from the RapidEye satellite constellation.
The Government of Canada has purchased and maintains a significant coverage of RapidEye imagery over our landmass, covering approximately 30 % of southern provinces. The National Hydrographic Network (NHN) needs updating while previous methods
relying on airphotos is expensive and time consuming and therefore, partners in Sherbrooke Geobase are seeking alternative solutions. We expect this work will form part of a new, more cost effective and efficient solution to help with NHN updating.
Developing this capacity also helps the Emergency Geomatics Services in case similar high-resolution imagery becomes available during a flood event, as it did in 2017 when the Disasters Charter was activated over Ottawa-Montreal.