Mapping Assessment of Coastal Erosion Using LiDAR and Optical Data

Background

The world’s coastlines are ever changing due to the constant erosion and redistribution of sediment by waves and wind. However, weather events like hurricanes can cause drastic changes in a short period of time. The resulting changes in dune and beach composition can increase the risk of flooding, damage property and transportation infrastructure, and can also impact the ecological stability within a region.

Hurricane Sandy

Hurricane Sandy tore its way up the eastern seaboard of the United States in late October of 2012. It is the second costliest hurricane in US history, causing an estimated 65 billion dollars worth of damage. Fire Island, a small coastal barrier island along the southern edge of Long Island, New York, experienced extremely severe erosion due to the high tides and winds from Sandy. It was important in the storm’s aftermath to access the damage to be able to budget and plan for restoration, as well as look for ways to mitigate damage from future storms.

Using ENVI^® Products to Assess the Damage

It is estimated that hurricane Sandy did the equivalent of 30 years of normal wear and tear damage to the Fire Island dunes in the course of a week. Fifty percent of the dunes were overwashed, and an average of 22 meters of erosion was measured along dunes.

Analyzing satellite imagery and LiDAR point cloud data with ENVI image analysis tools, we can begin to get an idea of just how drastic the erosion was in this area.

Assessment of Hurricane Sandy Coastal Erosion Using High Resolution Imagery

By using high resolution data gathered over Fire Island before and after Hurricane Sandy it is possible to assess costal changes that were a result of the storm. This was accomplished by running a change detection on two DigitalGlobe images to identify areas of major change along the coastline.

This WorldView 2 image is of an area on Fire Island before the hurricane hit.

After pulling the data into ENVI for visualization of change, it becomes evident where a new inlet was cut through the dunes from the storm.

Visualization and Change Detection

Using the ENVI Change Detection workflow, it is easy to plug in the two datasets from above to get an accurate two-dimensional assessment of major changes that occurred in the area. Since the vegetative coverage of the dunes changed so drastically, it was necessary to highlight only areas of major change so as not to confuse the results.

In this image, ENVI has identified major areas of change (in green), clearly mapping the creation of the new inlet and the erosion of the shoreline.

Coastal Change Assessment Using LiDAR

After analyzing the DigitalGlobe data, LiDAR point clouds were retrieved from USGS in an effort to assess volumetric loss of sediment from the dunes over the same area on Fire Island. Next we see a three-dimensional view of the analyzed point cloud showing the same area in before and after shots. ENVI LiDAR was used to automatically extract Digital Elevation Models (DEMs) from the point cloud to be pushed into ENVI for further analysis.

Before hurricane.

After hurricane. (Image courtesy of NOAA.)

Push the Elevation Data to ENVI

Once the DEM’s were extracted, a single click pushes the elevation data over to ENVI where we can view the elevation models, classify them.

We can then do some more three dimensional visualization by draping the DigitalGlobe image over the extracted elevation data.

Classify the DEM and Run Band Math to Assess Change

The next step is to classify the elevation data sets by height to assist in visualization of the different datasets, and to run change detection. Here we see the two individual data sets as they were classified, and finally we can see an assessment of the change in elevation achieved by running band math difference on the elevation values.

In this image, areas of red and yellow depict areas of sediment loss, or erosion, while areas in green depict areas of sediment gain, or accretion.

The results (below) show how high-resolution data and LiDAR can be used to depict coastal change on a small area of interest. In situations where the resolution of data such as Landsat is too coarse to accurately assess change, high resolution data is a great way to be able to understand two dimensional change. LiDAR is a very useful data type when assessing coastal change, as the elevation information that can be extracted from the point clouds is extremely accurate and can be used to depict volumetric, or three-dimensional change. In each of these cases, the data was of better resolution than the Landsat data, and yielded more accurate results. However, when attempting to analyze change on a larger scale, the size of the datasets and the processing power of your computer may be prohibitive.