Remote Sensing Week 4: Spatial Enhancement and Multispectral Data

For this week's lab, we performed spatial enhancements using low pass and high pass filters in ERDAS Imagine and ArcGIS Pro, looked at image histograms, and used the Inquire Cursor to explore values in multiple bands at once. For an aerial image of part of Washington state, I identified distinctive features and created the following maps with band combinations that emphasizes those features and contrasts with the surroundings.



Comments

Post a Comment

Popular posts from this blog

Week 4: Vector Analysis

Image
In Part 1 of this week's lab, we explored geodatabases, queries, and spatial analysis using a variety of spatial data about infrastructure, environmental features, and parcels in Escambia County. I forgot to check the metadata and had to go back at the end of the lab once I looked back over the process summary. I couldn't find the metadata at first--it came up with no information when I clicked on the layers in the Contents pane. Eventually I figured out that I had to click on the geodatabase files in the Catalog pane to view the metadata. I then got a message saying the metadata needed to be updated to ArcGIS metadata format to be viewed for the Pensacola_streets_08 and escambia_cellular feature classes, so I had to Google how to upgrade it. It's ironic that I completely forgot about metadata after reading a chapter about how important it is to evaluate metadata early in a project. I've gotten so used to having every step of a lab spelled out in exact detail that I jus...
Read more

Remote Sensing Final Project: Change in NDVI Over Time

Image
Change in NDVI by Neighborhood in Richmond, VA from 2018-2021 Introduction For my final project, I was interested in analyzing vegetation change in urban areas in Richmond, VA. I was inspired by the article “Urban environmental stewardship and changes in vegetative cover and building footprint in New York City neighborhoods (2000–2010),” in which Locke et al. (2014) assess whether there is a correlation between the number of environmental stewardship groups in New York City neighborhoods and whether that neighborhood gained or lost vegetation between 2000 and 2010. This study used Spectral Mixture Analysis on Landsat 5 imagery from 2000 and 2010 to determine the percent vegetation change for each neighborhood. For my project, I decided to measure vegetation change for each neighborhood in Richmond using the simpler method of NDVI. Remote Sensing Imagery I downloaded imagery from the National Agriculture Imagery Program (NAIP), provided by the USDA-FSA Aerial Photography Field Office. T...
Read more