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Monitoring Coastal Change With Multispectral Imagery

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Driven by tides, powerful sea currents and overall climate change, coastal change threatens shore communities and local economies. Accurate detection and measurement of coastal change can inform scientific investigations and facilitate flooding disaster preparedness and mitigation.

What do we mean by coastal change detection and measurement? We want to find where water has replaced land and vice versa, as well as the extent of these phenomena. So we developed an end-to-end GBDX workflow for coastal change detection and measurement at the native resolution (<2 m) of our 8-band multispectral imagery.

Let’s consider a sample area of interest that you may be familiar with: Cape Cod, a region well known for extreme changes in the coastal landscape. The image boundaries and their intersection are shown in the following figure.

Coastal Change - Cape Cod

The workflow takes two roughly collocated images of Cape Cod, captured in 2010 and 2016 by WorldView-2 and WorldView-3, and computes coastal change on the entire images, roughly an area of 1500 km2 in less than 30 minutes. Change is detected by aligning the two images, computing a water mask in each one, and then overlaying the two masks to compute the difference.

Water masks, before and after. White is water and black is land.

Results

This a close-up of an area where water has retreated, most likely due to extreme tidal effects.

Before close-up

After close-up

And here is the change heat map:

Coastal change heatmap

The colors represent the degree of water retreat. Note that in some areas the water has retreated by 1km!

Here is a snapshot of the Chatham area. Red indicates water loss and green indicates water gain. Note that water loss is due to tidal effects, while water gain is most likely due to shifting sand bars.

Chatham area

And here’s a snapshot of the Marconi transatlantic wireless station area. The red blob on the left indicates the presence of a tidal marsh.

coastal-change-marconi

Have these dramatic results and images caught your attention? You can find the full story at gbdxstories.digitalglobe.com/coastal-change, complete with Python code and a full resolution coastal change map!

https://platform.digitalglobe.com/detecting-measuring-coastal-change/?utm_source=facebook&utm_medium=display&utm_term=geospatial&utm_campaign=gbdx+content+marketing&utm_content=coastal+change

Creating training inputs and spectral signatures for image classification in QGIS

http://monde-geospatial.com/creating-training-inputs-and-spectral-signatures-for-image-classification-in-qgis/

Moving forward in this mini-series on image processing with the Semi-automatic Classification (SCP) plugin in QGIS, we will see how to carry out the creation of training areas required for supervised image classification.

In addition we will see how to create and edit the spectral signatures and how to preview the result of the classification based on the algorithm and the signatures collected.

Six best Earth images from NASA’s ASTER project

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been taking stunning photos of the earth’s surface since 1999.

Aster is a joint project between NASA and Japan’s Ministry of Economy, Trade and Industry, and has been taking high resolution images of life on earth for 17 years.

For the first time, the 2.95 million satellite images are now available to the public via the ASTER Gallery Map.

http://monde-geospatial.com/six-best-earth-images-from-nasas-aster-project/