Satellite Image Example

Satellite Image Example WIFIRE: An Integrated System for Wildfire Analysis Wildfires can cause significant damage to property and the environment. To avoid the loss of wealth and life, it is crucial to find them as soon as possible. Satellite picture data is one of the best resources for forecasting the spread of wildfires. In this article, we will analyze a sample satellite image data set from one of our research projects called WIFIRE and use the numpy library in this analysis. WIFIRE is an integrated system for wildfire analysis that is capable of handling changing urban dynamics and climate. The system integrates networked observations from a variety of sources, including heterogeneous satellite data and real-time remote sensor data. It then processes all this data using computational techniques in signal processing, visualization, predictive fire modeling, and data assimilation in a scalable way. The end result is a system that can track weather patterns and other variables to gauge how quickly a wildfire will spread. You can read more about WIFIRE at wifire.ucsd.edu. Understanding Satellite Image Data Before we dive deeper into WIFIRE, let's first understand what satellite image data is and how it helps in fighting wildfires. Simply put, satellite image data is the image of the earth's surface taken from a satellite orbiting the earth. These images are usually in the form of maps, photos, or satellite imagery. In the context of WIFIRE, these images are used to monitor weather patterns, terrain, vegetation, and other factors that can contribute to the spread of wildfires. How Computers Store Images Now that we know what satellite image data is let's understand how computers store images. Computers store images as a mosaic of tiny squares, and each square is called a pixel. Each pixel is only one color, defined by a set of numbers. A simple way to describe each pixel is using a combination of three colors, namely red, green, and blue. This is what we call an RGB image. In an RGB image, each pixel is represented by three 8-bit numbers associated with the values for red, green, and blue, respectively. The combination of these three will, in turn, give us a specific shade of the pixel color. Since each number is an 8-bit number, the values range from 0 to 255. For example, a yellow value can be identified by 255, 255, and 0 for RGB, as we see in this diagram. If all three values are at full intensity, that means they are 255, it shows as white. If all three colors are muted or have the value of 0, the color shows as black. Since each value can have 256 different intensity or brightness values, it makes 16.8 million total shades. In Python, RGB images are ndarrays of shape height times width times three for each RGB layer. Image Processing in Python Now that we have a basic background in how color images are stored in Python, let's look at how we can use WIFIRE to analyze satellite image data. We will mainly use numpy functions, filtering techniques, and some domain-specific knowledge to analyze the image data. Numpy is a powerful library for numerical computations in Python, and it provides a host of functions for image processing. The Notebook We'll go over a notebook that makes use of a satellite image from landfire.gov that may be used to analyze the Earth's surface in this lesson. Every Earth science application will frequently contain a study of visual graphs, whether they be maps, photographs, or satellite imagery like this one. A type of image processing will also be used in numerous other data science applications. We need to have a basic knowledge of image formats and how to work with images in Python to get started in this vast