Shelby's Remote Sensing Class Blog

  Shelby Short Goal and Background The goal of this lab exercise was to gain introductory knowledge on LiDAR data structure and processing. Specific objectives revolve around processing and retrieval of various surface and terrain models, and then processing and creating intense images and other derivative products from point cloud. LiDAR is one of the recently expanding areas of remote sensing with significant growth in new job creation. To successfully complete this lab, students will work with Lidar point clouds in LAS file format. Part 1: Point cloud visualization in Erdas Imagine  To start this lab we added the LiDAR .las files to our image viewer in ERDAS. After examining our image, we looked at the density of the points. The back-scattered points have x,y, and z coordinates. Bodies of water have low point density because backscattering is only 5%, and the other energy is absorbed which is shown in Figure 1.  Figure 1: Backscattering of surface features by the footb...

  Shelby Short  Goal and Background:      The goal of this lab was to gain understanding in image preprocessing, specifically by using geometric correction. This lab explores two major types of geometric correction such as image-to-map rectification. These methods are normally performed on satellite images as a method of preprocessing before looking at the biophysical and sociocultural data that can be extracted from the given satellite images(Wilson, 2020).      To complete this lab we  used a United States Geological Survey (USGS) 7.5-minute digital raster graphic (DRG) image of the Chicago Metropolitan Statistical Area and adjacent regions to correct a Landsat TM image of the same AOI. Next, we collected ground control points (GCPs) from the USGS 7.5 minutes DRG and used it to rectify the TM image. Finally, we used a corrected Landsat TM image for eastern Sierra Leone to rectify a geometrically distorted image of the same AOI(Wilson, 2020)...

Shelby Short Goal and Background:   The main goal of this lab was for us as students to look at multiple Earth Surface and Near Surface features on Earth to better understand their spectral reflectance measurements and then use that data to make remote sensing inferences. These near surface and surface features were captured with satellite imaging which allowed us to collect spatial band data that was then graphed, and analyzed(Wilson, 2020).  Methods and Analysis:                 The first part of the lab we looked at analyzing spectral images from a LandsatETM+ image of the Eau Claire Area. We measured and plotted the spectral reflectance of 12 materials and surfaces from the image. To do this we created a polygon on the image we were analyzing (Figure 1). Then you use the Signature editor tool(Figure 2) to create an Area of Interest that can then be plotted(Figure3). By plotting the spectral ...

Goal and Background: The goals of this lab were to (1) delineate a study area from a larger satellite image scene, (2) demonstrate how spatial resolution of images can be increased, (3) introduce some radiometric enhancement techniques in optical images, (4) link a satellite image to Google Earth which is used as a source of additional material, (5) dive into a variety of resampling methods used on satellite images, (6) explore image mosaicking, and (7) look at binary change detection through graphical modeling(Wilson, 2020). Methods and Analysis:             The first part of the lab had to do with image subsetting and the creation of an Area of Interest or AOI. A subset in this instance is a small section that is taken from a larger image creating an AOI polygon. To do this you take a shapefile and lay it over the raster data and then create a raster image that matches the shapefile. This is shown in Figure 1. a b Figure...