Updated: Feb 10, 2020
This was posted as part of an assignment requirement in a remote sensing course at York University. Please be careful if you are planning on using this content.
The United States Geological Survey (USGS) EarthExplorer (EE) tool is probably the easiest tool for extracting satellite imagery such as Sentinel or Landsat data. The USGS website itself provides data about earthquakes, water, remote sensing and Landsat, volcanoes and landslides. You can also find publications, software, maps and much more. This page explores the this website as well as Landsat data.
Before we proceed, it is important to ask yourself what data you need and what you are trying to accomplish. For example, Landsat 8 has multispectral bands of 30-metres resolution with a panchromatic band of 15-metre resolution, this satellite imagery may not be the best resolution for your purposes. Here is the breakdown of what I recommend using, but please visit the USGS website here for all products.
Tier 1: Contains the highest quality Level-1 Precision Terrain (L1TP) data considered suitable for time-series analysis. The georegistration is consistent and within prescribed tolerances [<12m root mean square error (RMSE)].
Landsat 8 - Tier 1: According to the USGS (2019), 'the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are instruments onboard the Landsat 8 satellite, which was launched in February of 2013. The satellite collects images of the Earth with a 16-day repeat cycle, referenced to the Worldwide Reference System-2. The satellite’s acquisitions are in an 8-day offset to Landsat 7 (see Landsat Acquisition). The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). The spectral bands of the OLI sensor, while similar to Landsat 7’s ETM+ sensor, provide enhancement from prior Landsat instruments, with the addition of two new spectral bands: a deep blue visible channel (band 1) specifically designed for water resources and coastal zone investigation, and a new infrared channel (band 9) for the detection of cirrus clouds. Two thermal bands (TIRS) capture data with a minimum of 100 meter resolution, but are registered to and delivered with the 30-meter OLI data product. (See Landsat satellite band designations for more information.) Landsat 8 file sizes are larger than Landsat 7 data, due to additional bands and improved 16-bit data product'.
Landsat 4-5 - Tier 1: According to the USGS (2019), 'the Landsat Thematic Mapper (TM) sensor was carried onboard Landsats 4 and 5 from July 1982 to May 2012 with a 16-day repeat cycle, referenced to the Worldwide Reference System-2. Very few images were acquired from November 2011 to May 2012. The satellite began decommissioning activities in January 2013. Landsat 4-5 TM image data files consist of seven spectral bands (See band designations). The resolution is 30 meters for bands 1 to 7. (Thermal infrared band 6 was collected at 120 meters, but was resampled to 30 meters.) The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi)'.
Sentinel-2: According to USGS (2019), 'the Sentinel fleet of satellites is designed to deliver land remote sensing data that are central to the European Commission’s Copernicus program. The Sentinel-2 mission is the result of close collaboration between the European Space Agency (ESA), the European Commission, industry, service providers, and data users. The mission has been designed and built by a consortium of around 60 companies led by Airbus Defence and Space, and supported by the CNES French space agency to optimize image quality and by the DLR German Aerospace Centre to improve data recovery using optical communications'.
For this project, I will be using the Landsat 5 imagery. Below I go through the steps to download the data, which is freely available. Please note that some bands may be limited-use only.
Downloading Satellite Imagery
Go to the EarthExplorer website here: https://earthexplorer.usgs.gov/
Search criteria: here you enter which date/days you want to look at. You can type in an address or town, or you can simply drag the map with your mouse to select a domain. Click results;
Next is the data sets: here you can select which data you want to work with. We will select the Landsat drop-down menu -> Landsat collection 1 level 1 and select Landsat 4-5 TM C1 Level-1. You can also select multiple Landsats and the Sentinel-2 if you want;
You should now have something like this:
5. On each available image, you can click on the little image (highlighted in green above), to see what the image looks like.
6. Download the image and you will need to login/create an account to download the multiple files:
You can get away with just grabbing the natural colour image, but it depends what you are trying to do. The natural colour image has 3 bands (RGB).
For this assignment, I decided to investigate the Red River Flooding of 2011 and 2010 during the month of April in southern Manitoba. To do so, I acquired one imagery from April 6, 2010 and one imagery from April 25, 2011 from Landsat 5. These were the two closest image to compare during the spring thaw. The Red River is a south-north flowing river, flowing through Fargo, Grand Forks, Winnipeg and dumping into Lake Winnipeg. Due to the flat topographic nature of the Red River Valley, the Red River floods every year with different severity depending on a multitude of factors. A first-of-its-kind Red River Floodway was constructed in 1968, costing $63 million dollars. After a major flood in 1997 and proving its effectiveness, the floodway was expanded for a one in 700-year flood. The purpose of the floodway is to divert the Red River floodwaters from the City of Winnipeg. More information can be found here.
If you are not familiar with the area, below is a Google Maps locator for Pembina, MB, which is the border crossing between North Dakota and Manitoba. Just north of this point is the town of Morris, which sustained major flooding in 2011 (as well as other years).
Here is another view of the extracted Landsat 5 imagery for April 6, 2010 and its location:
Manipulating the data
Now is the time to import your downloaded images into PCI Geomatica, ArcMap or other programs for data analysis. I wanted to compare the 2011 flood extent with the 2010 flood extent. Below you can see the files, which extend roughly from St. Jean Baptist, MB to south of Grand Forks, ND for 2010 (left) and 2011 (right):
This is the natural colour imagery or true colour. We can apply a linear enhancement to discern more easily between land, vegetation and water. The view below is again 2010 (left) and 2011 (right). The floodwaters easily 'pop-up' and it enables us to quickly see that the flooding in 2011 was much more expansive than 2010.
We can take this a little further. Remember the picture earlier from the flooding in Morris, MB? The extent of the 2011 flooding becomes much more obvious when zooming into that specific location. Morris is surrounded by a dike. The whole city basically shuts down and closes its 'walls'.
Investigating image pixels
Keeping in mind the above locations (Morris, MB), we can look at the histograms for each bands. This information will tell us how many red, green, blue pixels are present for each bands. For our purposes, we are looking at if there are any changes in the blue pixels. Let us take a look at both of the histograms for the images above for Morris, MB and how they have changed:
So as we can expect, we see pixel counts in the 25 000 range for 2010 and in the 35 000 range for 2011. As expected, we have more blue pixels in 2011 and therefore more water in the image.
We could certainly continue our analysis and compare every town along the highway 75 (in Manitoba) and I-29 (in North Dakota), which shows the most extensive flooding during our study period of April 2010 and April 2011. While the flooding may not look significantly different while looking at a large scale image, once you get into the smaller scale you can really see how significant the extent of the flooding was in 2011 versus 2010. When you dig a little deeper into the data and compare each bands, specifically the blue bands, you can see a significant amount of overland water throughout the Red River Valley in Manitoba and North Dakota.
USGS. (2019). USGS EROS Archive - Landsat Archives - Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) Level-1 Data Products. Retrieved from https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-8-oli-operational-land-imager-and?qt-science_center_objects=0#qt-science_center_objects
USGS. (2019). USGS EROS Archive - Landsat Archives - Landsat 4-5 Thematic Mapper (TM) Level-1 Data Products. Retrieved from https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-tm-level-1-data?qt-science_center_objects=0#qt-science_center_objects
USGS. (2019). USGS EROS Archive - Sentinel-2. Retrieved from https://www.usgs.gov/centers/eros/science/usgs-eros-archive-sentinel-2?qt-science_center_objects=0#qt-science_center_objects