In this lesson you will learn how to deal with clouds in your remote sensing data. Extreme cloud cover and shadows can make the data in those areas, un-usable given reflectance values are either washed out (too bright - as the clouds scatter all light back to the sensor) or are too dark (shadows which represent blocked or absorbed light). Work with Clouds, Shadows and Bad Pixels in Remote Sensing DataĬlouds and atmospheric conditions present a significant challenge when working with multispectral remote sensing data. The biggest problem was a large cloud and associated shadow that covered your study area of interest - the Cold Springs fire burn scar. You ran into some challenges when you began to work with the data. The scene was further cropped to reduce the file size for the class. Each geotiff file was a part of a Landsat scene, that had been downloaded for this class by your instructor. In the previous lessons, you learned how to import a set of geotiffs that made up the bands of a Landsat raster. wikipedia Challenges Working with Landsat Remote Sensing Data Each Landsat scene is about 115 miles long and 115 miles wide (or 100 nautical miles long and 100 nautical miles wide, or 185 kilometers long and 185 kilometers wide). Landsat images are usually divided into scenes for easy downloading. These images, are divided into smaller regions - known as scenes. Landsat satellites orbit the earth continuously collecting images of the Earth’s surface. Define mask / describe how a mask can be useful when working with remote sensing data.Use a mask to remove portions of an spectral dataset (image) that is covered by clouds / shadows.Describe the impacts of cloud cover on analysis of remote sensing data.

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Use Data for Earth and Environmental Science in Open Source Python Home.

Chapter 12: Design and Automate Data Workflows.

SECTION 7 INTRODUCTION TO API DATA ACCESS IN OPEN SOURCE PYTHON.

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Chapter 11 Calculate vegetation indices in python.Chapter 7 Intro to multispectral remote sensing data.

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