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

Module 2: Shenandoah National Park-DEM/DSM, Tree Height and Tree Density LIDAR Analysis

-
Image
LiDAR, or light detection and ranging, data is a very useful tool for foresters to understand the forest canopy and terrain, which is useful information for monitoring forest health, calculating forest biomass, classifying terrain, identifying watersheds, etc. In this lab, ArcGIS Pro was used to analyze USGS Lidar data in the form of a .las file to create a digital elevation model, which shows bare earth elevations, and a digital surface model, which shows both natural and man-made features, like buildings and trees. The LAS DATASET TO RASTER tool was used to create both the DEM and DSM. However, the DEM appearance was set to filter for Ground points before using the tool, while the DSM appearance was set to filter for Non Ground points. The Minus tool can calculate the difference in height from the ground and the tree canopy surface, or essentially the tree height. The negative values are located  along roads or clearings. This likely means there was some return issue along the fo...
Read more

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 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