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Benthic Habitat Classification with Planetscope Imagery in Earth Engine
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=r7bypx06scc
Welcome to this tutorial on how to classify benthic habitats such as seagrass, sand, and coral using Planetscope imagery in Google Earth Engine. In this video, I will walk you through the step-by-step process of creating a classification map of benthic habitats using a machine learning algorithm in Google Earth Engine. • Script: https://code.earthengine.google.com/3... • Benthic habitats are critical components of coastal ecosystems that provide food and shelter to a diverse range of marine organisms. Accurate mapping of these habitats is essential for monitoring changes in their distribution and abundance over time. This tutorial will demonstrate how to use high-resolution Planetscope imagery and Google Earth Engine to classify benthic habitats with high accuracy. • In this tutorial, I assume that you have basic knowledge of remote sensing and Google Earth Engine. I will cover the following topics: • 1. Data preprocessing: This section will cover how to import and preprocess the Planetscope imagery to prepare it for classification. • 2. Classification: We will show you how to train a machine learning algorithm using a set of labeled data to classify benthic habitats. • 3. Accuracy assessment: Once the classification is complete, we will show you how to evaluate the accuracy of the classification using validation data. • This tutorial is designed for anyone who is interested in learning how to classify benthic habitats using remote sensing data. By the end of this video, you will have gained the skills and knowledge necessary to apply this technique to your own research or conservation projects. • So, whether you are a marine biologist, conservationist, or remote sensing enthusiast, join us in this exciting journey of mapping benthic habitats with high accuracy using Google Earth Engine and Planetscope imagery. Don't forget to subscribe to our channel for more exciting tutorials like this. Let's get started!
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