This first lab covered visual interpretation of aerial imagery and contained three sections. The first section required use to identify and delineate various tones and textures from a USGS aerial image. The second part leveraged more contextual attributes like shadows, shape and size, patters, and association to correctly identify features in another USGS aerial photo. The the final exercise use a true color and false color images to compare differing colors for the same selected features.
My internship is motoring along well but has been bogged down by the resent series of hurricanes. The time set aside to perform my the task have been getting interrupted by these recent events. The items in my work plan which have been accomplished are:
The list above is the foundation for the software, which is Python based using the Flask framework. Once that is installed the next step is installing the Angular single-page application which provides the user interface to GIS information store in the database.
An interesting thing with the above techstack is that all of the components are free, opensource software (FOSS). This means that any person may use, alter, and further develop them at no cost. Contrary many of the solutions provided by Esri and Mapbox which require some type of subscriptions or licensing fee. When searching for employment related to GIS developers the majority are seeking those with knowledge of Esri rather than open-source solutions. Esri has made many things in the GIS world easier but many of those same things can be freely done with comparable opensource software.
For anyone interested in FOSS geospatial software consider checking out The Open Source Geospatial Foundation (OSGeo) at https://www.osgeo.org/
Michael Lucas - https://www.linkedin.com/in/nasumilu/
The lab provided insight into interpolation methods which include:
Thiessen polygon is proximity based interpolation which partitions an area by corresponding point's influence. This method of interpolation provides the means to better understand the distribution or the "points" area of influence.
The IDW interpolation operates using the principle that the closer surrounding points are to a given location the more influence (weight) they have on the estimated value. The IDW is best used when the geographic phenomenon is spatially measured uniformly (i.e. relatively uniform distribution).
The spline method like linear interpolation pass through the know points but applies a smoothing effect. The smoothing (tension) is adjusted to best fit a natural phenomena which is expected to smoothy transition over distance.
Kriging interpolation is a computational intense geostatistical technique which estimates unknown values at specific location based surrounding point values. There are different types of Kriging but generally is a powerful method which provide a prediction and insight into the reliability of the prediction.
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| Spline Interpolation - Water Quality Tamp Bay (instructional purpose only) |
