Flood Monitoring: a Tutorial Using the Semi-Automatic Classification Plugin

Flooding is the 'overflowing of the normal confines of a stream or other body of water, or the accumulation of water over areas that are not normally submerged' (IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change).

Unfortunately, a severe flooding has affected Pakistan recently, inundating more than one thousand villages as reported in this article by NASA. Flood monitoring is fundamental in these emergency situations, and for planning actions of prevention and adaptation to flooding.

This tutorial illustrates how to monitor floods performing the supervised classification of Landsat images (however the same methodology could be applied to other sensors such as MODIS images)In particular, we are going to classify two images acquired in 2013 in Cambodia; in October, the heavy seasonal rains were followed by the Typhoon Nari causing the flood along the Mekong and Tonlé Sap rivers, affecting more than a half-million people (for more information about this and other flooding events, read the article by NASA here). You can see the study area before and after the flood in this image by NASA.

Study area, Cambodia
(image available from the U.S. Geological Survey)


Accuracy Assessment Using Random Points and the Semi-Automatic Classification Plugin for QGIS

This post is a brief tutorial about how to perform the accuracy assessment of a land cover classification using the Semi-Automatic Classification Plugin (SCP) for QGIS.
In particular, we are going to create ROIs using random points over the image (a new function of  SCP 3.1.0), which will be photo-interpreted and used as reference for the accuracy assessment.

This tutorial assumes that we have already performed the classification of a Landsat image following the instructions of this previous tutorial. The land cover classes of this classification are:
  • Class 1 = Water (e.g. surface water);
  • Class 2 = Vegetation (e.g. grassland or trees);
  • Class 3 = Built-up (e.g. artificial areas, buildings and asphalt);
  • Class 4 = Bare soil (e.g. soil without vegetation).

The following are the main phases:
  1. Automatic creation of ROIs at random points;
  2. Photo-interpretation of created ROIs;
  3. Calculation of classification accuracy using created ROIs as reference.
This is the video tutorial, and following the tutorial phases are described in detail.

Major Update: Semi-Automatic Classification Plugin v. 3.1.0

This post is about a major update for the Semi-Automatic Classification Plugin for QGIS, version 3.1.0.

Following the changelog:
-new function for the creation of random points and ROIs
-bug fixing

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I am working in the fields of GIS and Remote Sensing.