Monitoring Changes in the Amazon Rainforest: a Tutorial Using the Semi-Automatic Classification Plugin


Forest is defined as 'land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use'  (FAO 2010, Global Forest Resources Assessment 2010 Main report).
This is a tutorial about the land cover monitoring of forests, using the Semi-Automatic Classification Plugin for QGIS. In particular, we are going to classify two Landsat images acquired over the Amazon Rainforest, in RondĂ´nia (Brazil), in 1985 and 2013.

Before the tutorial, please watch the following video that illustrates the study area and provides very useful information about the deforestation (by ESA Earth from Space which broadcasts new videos every Friday). Also, a description and an animation of the area that we are going to classify is available here.



Supervised Classification for Wildfire Monitoring: Assessment of the Burnt Area


 Updated tutorial at 
https://fromgistors.blogspot.com/2017/01/wildfire-monitoring.html


Particularly after the summer, wildfires occur all over the world causing several damages and burning hectares of forest. Although sometimes fires are natural, it is important to monitor their environmental impacts.
Remote sensing can provide valuable information for monitoring wildfires. Watch the following footage by ESA Earth from Space (new videos are available every Friday) which illustrates a very interesting example of wildfire detected by satellite imagery.


This tutorial is about wildfire monitoring through the use of semi-automatic supervised classifications of remote sensing images.
We are going to classify a Landsat 8 image acquired over Alaska in 2013, during the Castle Rocks fire in Denali National Park and Preserve. Before starting the tutorial, please read this article about fires in Denali National Park and Preserve by NASA Landsat Science.
Study area, Alaska
(image available from the U.S. Geological Survey)

We are going to estimate the burnt area following these steps:
  1. Conversion of raster bands from DN to Reflectance;
  2. Creation of the ROIs and spectral signatures;
  3. Classification of the image using a threshold;
  4. Estimation of the burnt area.

The classification is focused on burnt surfaces; therefore we are going to identify only this class by using the Spectral Angle Mapping algorithm with the definition of a threshold.

Supervised Classification for Flood Monitoring Using the Semi-Automatic Classification Plugin


 Updated tutorial at 
https://fromgistors.blogspot.com/2017/01/flood-monitoring.html





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 TonlĂ© 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




    

AN UPDATED VERSION OF THIS TUTORIAL IS AVAILABLE HERE 










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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