Lab 4: Spatial Enhancement & Multispectral Analysis – GIS 4035

This week’s lab introduced several important remote sensing skills that helped me better understand how satellite imagery is structured and how different filters, band combinations, and indices can bring out features that aren’t obvious at first glance. Module 4 focused on working in both ArcGIS Pro and ERDAS IMAGINE, and I definitely noticed that each program has its strengths depending on the task.

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Getting Started: Image Enhancements

We began by learning about spatial enhancement techniques, which modify pixel values to highlight patterns, edges, or generalize areas of uniform texture. I applied both low pass and high pass filters to see how they changed the appearance of land cover. The low pass filter softened the image, while the high pass filter amplified edges—especially along roads, buildings, and ridgelines. These enhancements turned out to be important for understanding how different kernels and spatial frequencies affect interpretation.

Working With Multispectral Data

A major part of the lab involved exploring the individual spectral bands and learning what each wavelength reveals. Opening each layer in ERDAS IMAGINE helped me identify which bands were most useful for vegetation, soil, water, and built features.

I used grayscale views and the Inquire Cursor to evaluate DN values, which became very important once we reached the feature identification portion of the exercise.

Creating NDVI

Next, I created an NDVI raster using the built-in tool in ERDAS. NDVI’s formula (Band 4 – Band 3) / (Band 4 + Band 3) made it easy to separate healthy vegetation from clearcuts, soil, water, and other features.

Using the swipe tool to compare NDVI with the original image showed me how vegetation completely dominated the scene, which explained why so much of the NDVI surface looked “washed out.”

Exercise 7: Feature Identification and Subsetting

The final exercise brought together everything from earlier in the lab. I used the image histogram, grayscale views, multispectral combinations, and the Inquire Cursor to identify three distinct features in the scene. Once each feature was located, I created subsets and mapped them in ArcGIS Pro using different RGB band combinations.

Feature 1: River (Band Combo 4-3-2)

The river was identified by its very low NIR reflectance, visible as a spike between DN 12–18 in Layer 4. Water absorbs NIR strongly, so it appeared extremely dark in the grayscale display. The 4-3-2 composite (false color) made the river stand out sharply against bright red vegetation.

Feature 2: Barren Land (Band Combo 5-4-3)

This area showed a unique spectral signature—bright in visible bands (DN ~200) but very dark in SWIR (DN 9–11). This is typical of exposed soil or dry ground. Using the 5-4-3 combination highlighted these moisture differences and made barren areas easy to distinguish from vegetated surroundings.

Feature 3: Brackish or Shallow Water (Band Combo 3-2-1)

The last feature was a patch of water with unusually high reflectance in the visible bands but no change in SWIR. This indicated shallow or sediment-filled water. Using the 3-2-1 true-color display made this tonal variation in the water much more visible.

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Reflection

This lab required a lot of trial and error, especially when navigating ERDAS IMAGINE’s tools and interpreting histograms. However, by the end I felt much more comfortable reading DN values, selecting effective band combinations, and understanding how different wavelengths help reveal different types of features. This was also my first time creating multiple image subsets for mapping, and it really helped reinforce how multispectral analysis supports real-world feature identification.