Flood Monitoring Tutorial with Sentinel 2 and Landsat 8 Images in QGIS 3

Flood1.png

 

With current technology and the availability of remote sensing tools through different servers makes it possible to determine or estimate the areas that are flooded or could be, the focus of this tutorial in which a methodology to determine flood zones will be described from the calculation of the NDVI and compare the results with the use of two servers, Sentinel 2 and Landsat 8.

 

Determination of the study area

The study area corresponds to a stretch of the Ucayali River near the city of Iquitos in the department of Loreto. Information on satellite images was downloaded from two satellites Landsat 8 and Sentinel 2 for the month of December 2017.

The analysis area is presented in the following figure.

Flood2.png

 

 Tutorial

 

Processing of satellite images

The data of the study area were obtained from two Landsat 8 and Sentinel 2 servers,

For the Sentinel 2 server, bands 2, 3, 4, 5, 6, 7, 8, 8A, 11 and 12 are used. The following image shows the downloaded band 8 with a background of the Google Satellite server for the zone of study. To download Sentinel 2 satellite images you can review this tutorial:

https://www.hatarilabs.com/ih-en/how-to-download-esa-sentinel-2-satellite-images

Flood3.png

 

For the Landsat 8 server, bands 2, 3, 4, 5, 6 and 7 are used. In the following image, band 5 downloaded with a background of the Google Satellite server for the study area is observed. 

Flood4.png

 

NDVI Calculation

The normalized vegetation differential index (NDVI) is a normalized index that allows you to generate an image that shows the greenery (the relative biomass). This index takes advantage of the contrast of the characteristics of two bands of a multispectral raster dataset: the chlorophyll pigment absorptions in the red band and the high reflectivity of the plant material in the near infrared band (NIR).

The green leaves usually present a better reflection in the range of wavelengths near the infrared than in the range of visible wavelengths. On the other hand, clouds, water and snow have a better reflection in the visible range than in the range of near-infrared wavelengths, while the difference is almost zero for rocks and bare ground. The calculation of the NDVI creates a single band dataset that basically represents greenery. Negative values represent clouds, water and snow, while values close to zero represent rocks and bare earth.

The equation of the NDVI documented and predetermined is the following:

NDVI = (IR-R) / (IR + R)

Where: IR = pixel values of the infrared band; R = pixel values of the red band

 NDVI obtained from the Sentinel 2 bands.

NDVI obtained from the Sentinel 2 bands.

 

In the previous image, the NDVI obtained from the Sentinel 2 bands is presented, the blue gradient represents zones with presence of water and the green gradient represents areas with presence of vegetation.

 NDVI obtained from the Landsat 8 bands

NDVI obtained from the Landsat 8 bands

In the same way as presented for the Sentinel 2 bands, the NDVI obtained from Landsat 8 was classified, where the blue gradient represents zones with presence of water and the green gradient represents areas with presence of vegetation.

 

Determination of flood zones

The flood zone was obtained from the NDVI raster, as mentioned in the theoretical part of the NDVI, values close to zero or negative represent zones with presence of water, for which a raster classification is made, with the help of the calculator SCP bands.

Flood8.png

 

The following figure shows the flood zones obtained from the Sentinel 2 bands.

Flood9.png

 

As you can see in the image above the flood zones describe the course of the river as well as show areas near it; in addition to this it is worth mentioning that it is necessary to carry out a correction of cloudiness since this interferes in the results as it is observed there are zones that are confused as flood zones but in reality they are representing the clouds present in the place.

 

The following figure shows the flood zones obtained from the Landsat 8 bands.

Flood10.png

 

It can be seen in the image that the dark blue areas that indicate the presence of water describe the path of the river, as well as are represented in areas near the river that would be the ones that would be flooded. In addition, as described in the previous results, the presence of cloudiness influences the results as is also observed in this image.

 

From the results obtained before and when observing the images you can say that you get better results from the Sentinel 2 bands where one of the reasons may be because they have better horizontal resolution (10 meters) compared to the Landsat 8 bands that have resolution of (30 meters), in addition to present less cloudiness.

Saul Montoya

Saul Montoya es Ingeniero Civil graduado de la Pontificia Universidad Católica del Perú en Lima con estudios de postgrado en Manejo e Ingeniería de Recursos Hídricos (Programa WAREM) de la Universidad de Stuttgart con mención en Ingeniería de Aguas Subterráneas y Hidroinformática.

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