Tutorials

Applying a Join between Features in Google Earth Engine.

Applying a Join between Features in Google Earth Engine.

Joins are used to combine elements from different collections (e.g. Image Collection or Feature Collection) based on a condition specified by an ee.Filter. The filter is constructed with arguments for the properties in each collection that are related to each other. Specifically, left Field specifies the property in the primary collection that is related to…

Computing a Buffer in Google Earth Engine.

Computing a Buffer in Google Earth Engine.

Google Earth Engine supports a wide variety of geometric operations. These include operations on individual geometries such as computing a buffer, centroid, bounding box, perimeter, convex hull, etc. A buffer is a zone that is drawn around a point, line or polygon that includes all the area within a specified distance of the geometric feature….

Preview of an Image Collection Using a Time Series Animation.

Preview of an Image Collection Using a Time Series Animation.

Images composing an ImageCollection can be visualized as either an animation or a series of thumbnails referred to as a filmstrip. These methods provide a quick assessment of the contents of an ImageCollection and an effective medium for detecting spatiotemporal change. This article focuses on how to prepare an Image Collection for visualization, provide example…

Performing Pan-Sharpening on Landsat 8 image in Google Earth Engine.

Performing Pan-Sharpening on Landsat 8 image in Google Earth Engine.

Pan-sharpening is the process of enhancing a low resolution multiband image by fusing it with a corresponding high spatial resolution panchromatic (single band) image. The resulting multiband image has a resolution similar to the panchromatic image since the two rasters fully overlap. In Google earth engine, pan-sharpening process is made possible by using two methods;…

Burn Severity Mapping Using Landsat and  Sentinel 2 Imagery.

Burn Severity Mapping Using Landsat and Sentinel 2 Imagery.

Burn severity refers to the effects that fire intensity has on the functioning of an ecosystem in the area that has been burnt.  It is the degree to which an ecosystem has been disturbed due to fire. The degree to which an area has been affected by fire can be measured using the Normalized Burn…

Air Quality Monitoring Using Sentinel 5 Precursor TROPOMI.

Air Quality Monitoring Using Sentinel 5 Precursor TROPOMI.

Air pollution is the presence of substances suspended in the atmosphere that are considered harmful to the health of human beings and other living things. Some of the pollutants include harmful gases such as nitrogen dioxide, Sulphur dioxide and carbon monoxide. These gases may be released into the atmosphere through either natural sources or anthropogenic…

Accuracy Assessment in Image Classification on GEE

Accuracy Assessment in Image Classification on GEE

This post covers accuracy assessment on a sentinel image in the Google Earth Engine. Selection of the area of interest and the classification classes This is done using the geometry tool as shown below. One can use the point tool or the polygon tool in the selection of the classes. Selection of the Image collection…

Computation of NDVI using Google Earth Engine

Computation of NDVI using Google Earth Engine

Normalized Difference Vegetation Index (NDVI) is a spectral index used to quantify the greenness of vegetation. It is used to monitor the health of plants and understand vegetation density. Calculating NDVI is done using two bands: the red band and the near-infrared band. This computation can be easily adapted using the Google earth engine. This…

Unsupervised Classification in the Google Earth Engine

Unsupervised Classification in the Google Earth Engine

This blog covers unsupervised classification in the Google earth engine. Source: Google Earth Engine The process of unsupervised classification in GEE is carried out using  the ee.Clusterer package. The package has been adopted from Weka Loading of the image The image used in classification is from the landsat program. A landsat 7 image is used…

Supervised classification in GEE

Supervised classification in GEE

In this post, we will cover the use of machine learning algorithms to carry out supervised classification. Source: Google earth engine developers Supervised classification is enabled through the use of classifiers, which include: Random Forest, Naïve-Bayes, cart, and support vector machines. The procedure for supervised classification is as follows: Selection of the image The first…

GeoDjango Tutorial Guide

GeoDjango Tutorial Guide

Some months ago, I was requested to develop a written guide on web-mapping system development using GeoDjango as published on the playlist on My Channel . After great efforts, I have released a complete GeoDjango guide that serves as a written replica of the YouTube Playlist. The guide illustrates the possibilities of developing a fully-fledged…

Using Photos in QGIS Projects

Using Photos in QGIS Projects

In QGIS, there are many formats of data-sets that can be used. Joins and relates help users get the best out of their data depending on the users’ needs. These tools exist out of-the-box for use by everyone. However, there are some tools or functionalities that are tricky to use as they don’t appear straightforward…

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