Link to Lauren's GIS Portfolio 2024

-

To see my official portfolio, please see the link below: 

https://docs.google.com/document/d/16GaLTTi5j0aiEpk0gDzoSS3IbkwO24_96i_VH5hEkIw/edit?usp=sharing 

Thank you, 

Lauren Chaphe

Comments

Post a Comment

Popular posts from this blog

Lab 4: Spatial Enhancement, Multispectral Data, and Band Indices

-
Image
 This week's lab continued with ERDAS Imagine to add various filters to raster imagery data, as well as editing the band color combinations. Available filters that can be applied include high pass, low pass, mean, range, etc. Each filter produces different variation to the photo and can be useful to highlight certain features in an image. For example, the high pass filter created a photo with more detail and more defined edges, while the low pass filter created an image with softer, smoother edges. Band combinations can also be changed. For the images above, we were given clues on what pixel values to look for when identifying an unknown feature. The first feature was a river with had pixel values between 12 and 18 on layer 4 and I chose to use a band combo of 4, 2, 1 which made the forested areas appear red and the water appear black. The second feature was the snow capped mountains. A band combo of 5, 4, 3 showed the snow as bright blue and the forested areas as a bright green. T...
Read more

Module 2.2: Interpolation Methods- Tampa Bay Water Quality

-
Image
Interpolation involves estimating unknown data based on measured or known collection data points that are located nearby. There are multiple methods of interpolation, but this module evaluated the following methods: Thiessen Spline Inverse Distance Weighing (IDW)  In this scenario, we wanted to interpolate water quality data for the entire Tampa Bay area. Water quality data included measured biochemical oxygen demand (BOD) in milligrams per liter at 41 sampling points distributed within Tampa Bay. This same dataset was used in all three methods and the results were compared.  The CREATE THIESSEN POLYGONS tool was used to create the Thiessen method interpolation results. This method essentially assigns a value based on the closest sample point. This is a simple method and easy to interpret, but the boundaries are too abrupt to accurately estimate the natural flow of water in Tampa Bay.  The IDW method assumes that values that are closer have more "weight" or influence than...
Read more

Module 4: Coastal Flooding and Storm Surge Impact Modeling

-
Image
This week applied ArcGIS Pro towards analyzing hurricane impacts to buildings using LIDAR and USGS DEM raster data. Part one of the assignment included building data and USGS .las data collected pre and post hurricane Sandy in New Jersey. By utilizing the RASTER CALCULATOR tool to subtract the pre-hurricane data from the post hurricane data, I was able to create a raster dataset that shows areas with negative and positive changes in elevation.  As shown in the map above, red areas are areas that experiences a loss in elevation. Specifically the red areas in the center of the map can be assumed to be buildings/structures demolished during the storm. The blue areas are most likely areas were sediment was deposited and resulted in a gain in elevation. One other thing to note is the change in elevation running parallel to the shoreline, shown as a linear orange area. This is most likely a loss of elevation due to the erosion of the sand dunes during the storm. The black building outlin...
Read more