Continuing our series on the use of RapidEye data in geospatial projects, for this edition of The Geospatial Times we demonstrate the value of RapidEye satellite imagery for automated extraction of water bodies across large areas.
The RapidEye Constellation
RapidEye is a constellation of five 5-meter medium resolution satellites each offering five spectral bands of information. The RapidEye constellation offers a daily revisit time to every location on the planet with a huge footprint that is 40-km wide. The data is priced extremely competitively with a base price of $1.28 per square kilometer for all five spectral bands – academics receive a discount on this price. RapidEye adds a fifth band, the red edge, to the ‘traditional’ multispectral set of blue, green, red and near-infrared (NIR). The additional spectral data available in the red edge band allows users to extract more useful land ‘information’ than can be extracted from traditional 4-band imagery sources.
Water Body Extraction
While RapidEye has lower resolution than data offered by DigitalGlobe and GeoEye, its wide area coverage and five spectral bands make it an ideal solution for automated extraction of water bodies over a large area. To create the animation below, we used ENVI to extract water bodies in an automated fashion from a scene of RapidEye data collected over Harrison County, Ohio during 2011. To complete this extraction, we first built a training set of medium size ponds utilizing the Region of Interest (ROI) tool in ENVI 4.8. This training set contained four polygons (approximately 360 pixels) of ponds evenly dispersed throughout the Ohio RapidEye scene. Next, we ran a Parallelepiped Supervised Classification filter set to identify pixels in the imagery that were within three standard deviations of the mean spectral value of the ROI.
An inspection of the results revealed we had room for improvement with extremely dark ponds; so an extra polygon over a missed pond was added into the ROI. This brought the total number of pixels in the ROI to 420. The same Parallelepiped filter was run again on the entire RapidEye scene yielding the results you see below. We were rather surprised by the accuracy of the results with only five polygons included in the ROI – well above 95% accuracy. In fact, the filter picked up even very tiny ponds (sometimes only four pixels in total area) that the human eye would have had a hard time detecting. Water is a particularly strong candidate for automated feature extraction given its distinct spectral signature with decreasing reflectance as you move from Blue to NIR across the five bands.
If you would like to find out more about using RapidEye for your academic, engineering or any landscape study, let us know at firstname.lastname@example.org or (303) 993-3863.