Remote Sensing - Lab 8: Spectral Signature Analysis & Resource Monitoring
Goal and Objectives
The goal of this lab exercise is to gain experience measuring and interpreting spectral reflectance signatures of various Earth surface and near-surface materials captured by satellite images. In addition, basic monitoring of the health of Earth surface resources (vegetation and soil) using remote sensing band ratio techniques will be practiced.
To gain experience with spectral reflectance signatures, the spectral reflectance of 12 materials will be collected, graphed, and analyzed to distinguish spectral separability between surface features. The steps taken to complete this analysis are outlined in the Methods section of this blog post.
Methods
There are two objectives that this lab exercise seeks to achieve; (1) Collect and analyze the spectral reflectance signatures of 12 materials and surfaces and (2) Monitor the health of Earth's vegetation and soil resources. This section will outline the processes used in order to complete the analysis of each of these objectives.
Part 1: Spectral Signature Analysis
In this part of the lab exercise, the objective is to collect and analyze the spectral reflectance signatures of 12 materials and surfaces.
- Standing Water
- Moving Water
- Deciduous Forest
- Evergreen Forest
- Riparian Vegetation
- Crops
- Dry Soil (uncultivated)
- Moist Soil (uncultivated)
- Rock
- Asphalt Highway
- Airport Runway
- Concrete Surface (bridge, parking lot, etc.)
The first spectral signature will be collected from Lake Wissota, near Chippewa Falls, WI. Use the Polygon tool in the 'Area of Interest' (AOI) tools > 'Drawing' tab to take a spectral reflectance sample from the middle of the lake, as seen in Image 1.1.
 |
| Image 1.1. Spectral Reflectance sample taken from Lake Wissota using the Polylgon tool |
To plot the spectral signature, click on the 'Raster' tab and then 'Supervised' > 'Signature Editor' in the Classification group. Once the Signature Editor is open, click on the 'Create New Signature(s) from AOI' icon to add the signature from the sample polygon. To show the spectral curve of the signature, click on the 'Display Mean Plot Window' icon in the Signature Editor tool. Image 1.2 shows the Signature Editor tool and the corresponding signature plot for the area of interest in Lake Wissota.
 |
| Image 1.2. Signature Editor tool used to collect and display spectral signature of standing water in Lake Wissota |
Proceed to collect samples of the remaining 12 features and analyze their spectral reflectance signatures using the same procedure that was used for standing water. To aid in locating each of the 12 surfaces, link Erdas Imagine to Google Earth in order to better identify the surfaces.
Part 2: Resource Monitoring
In this part of the lab exercise, the objective is to perform a simple band ratio to monitor the health of vegetation and soils.
Section 1: Vegetation Health Monitoring
The first step is to click on the Raster tab > Unsupervised > NDVI to open the Indices interface. The calculation used for the band ratio can be seen highlighted in the Indices interface in Image 2.1.
The output will be a black and white image where the lightest shades represent the healthy vegetation in the area.
Section 2: Soil Health Monitoring
Soil health can be monitored by using another simple band ratio. The ratio that will be implemented is the Ferrous Mineral ratio, which monitors the spatial distribution of iron contents in soils. Image 2.2 highlights the band ratio that is used. To simplify what is shown, the Ferrous Mineral ratio = MIR/NIR.
The output will be a black and white image where the lightest shades represent the area with high ferrous minerals.
Section 1: Vegetation Health Monitoring
The first step is to click on the Raster tab > Unsupervised > NDVI to open the Indices interface. The calculation used for the band ratio can be seen highlighted in the Indices interface in Image 2.1.
 |
| Image 2.1. Indices interface with band ratio formula highlighted |
The output will be a black and white image where the lightest shades represent the healthy vegetation in the area.
Section 2: Soil Health Monitoring
Soil health can be monitored by using another simple band ratio. The ratio that will be implemented is the Ferrous Mineral ratio, which monitors the spatial distribution of iron contents in soils. Image 2.2 highlights the band ratio that is used. To simplify what is shown, the Ferrous Mineral ratio = MIR/NIR.
 |
| Image 2.2. Indices interface for Ferrous Mineral ratio |
The output will be a black and white image where the lightest shades represent the area with high ferrous minerals.
Results
The final outputs and analysis of this lab exercise are detailed below.
Part 1: Spectral Signature Analysis
The spectral signature is used to distinguish classes of surface features when analyzing spectral reflectance in remote sensing.
The first surface material analyzed in this lab exercise was Standing Water. For the sample polygon that was taken from Lake Wissota, the blue band between 0.4 and 0.5 micrometers had the highest reflectance and the red band between 0.6 and 0.7 micrometers had the lowest reflectance. Beyond 0.9 micrometers, absorption reaches 100%. This can be seen in the Spectral Signature Plot in Image 3.1. Almost all incident energy that contacts water is absorbed. Absorption is very strong for the red and NIR spectral bands, and weaker for the blue band, which is why it reflects the most electromagnetic energy.
Looking at another Spectral Signature Plot in Image 3.2, the deciduous forest reflects the highest at the Red band because Red and NIR energy destroys a plant’s protein cells and in order to protect itself, the plants must reflect this energy. Reflectance is the lowest at the Green band because Green light is where a plant gets its energy during the photosynthesis process.
Viewing spectral signatures for multiple surfaces is helpful in distinguishing different reflectance characteristics. For example, the Spectral Signature Plot in Image 3.3 shows soil, but the difference is that one signature is for dry soil and the other for moist soil. Based on the surface samples that were collected in this lab exercise, the largest variance between the Dry and Moist soils comes at the NIR band (0.7 – 0.8 micrometers). This is the band where the difference in moisture levels makes the largest impact. Dry surfaces have higher reflection in the NIR band, which is where the biggest difference can be seen in the signature graph.
Finally, spectral signatures can be analyzed to see the difference between different classes of surface materials. The Spectral Signature Plot in Image 3.4 shows all 12 surface materials that were collected and analyzed in this lab exercise.
Two signatures that are very similar in shape are the Standing Water and Moving Water signatures. This is expected because these are both water features. Even though they are part of the same river system, there is still some slight variation in reflectance between the two. The Standing Water has slightly higher reflectance in the Green band, which might indicate that the site of the sample collected for the Moving Water might have some algae present, which would account for higher absorption of Green energy.
Deciduous Forest and Riparian Vegetation are another pair that have very similar signatures, based on the samples that were collected. Riparian Vegetation grows along water and is especially present near river banks. In this study area, the Riparian Vegetation that grows is very similar in characteristics to the Deciduous Forests that are also in the area.
Spectral signatures are also useful in showing how certain materials differ from one another. For example, the Deciduous Forest and Riparian Vegetation surfaces are the only ones where reflectance peaked at the Red band. These surfaces peak here because they have the most plant material that harvests Green energy from the sun for photosynthesis. Many of the other surfaces identified in this exercise are urban/man-made features (concrete, asphalt, runway) that absorb the most in the Red band.
The first surface material analyzed in this lab exercise was Standing Water. For the sample polygon that was taken from Lake Wissota, the blue band between 0.4 and 0.5 micrometers had the highest reflectance and the red band between 0.6 and 0.7 micrometers had the lowest reflectance. Beyond 0.9 micrometers, absorption reaches 100%. This can be seen in the Spectral Signature Plot in Image 3.1. Almost all incident energy that contacts water is absorbed. Absorption is very strong for the red and NIR spectral bands, and weaker for the blue band, which is why it reflects the most electromagnetic energy.
 |
| Image 3.1. Spectral Signature Plot of standing water surface material |
Looking at another Spectral Signature Plot in Image 3.2, the deciduous forest reflects the highest at the Red band because Red and NIR energy destroys a plant’s protein cells and in order to protect itself, the plants must reflect this energy. Reflectance is the lowest at the Green band because Green light is where a plant gets its energy during the photosynthesis process.
 |
| Image 3.2. Spectral Signature Plot of deciduous forest surface material |
Viewing spectral signatures for multiple surfaces is helpful in distinguishing different reflectance characteristics. For example, the Spectral Signature Plot in Image 3.3 shows soil, but the difference is that one signature is for dry soil and the other for moist soil. Based on the surface samples that were collected in this lab exercise, the largest variance between the Dry and Moist soils comes at the NIR band (0.7 – 0.8 micrometers). This is the band where the difference in moisture levels makes the largest impact. Dry surfaces have higher reflection in the NIR band, which is where the biggest difference can be seen in the signature graph.
 |
| Image 3.3. Spectral Signature Plot of moist and dry soils |
Finally, spectral signatures can be analyzed to see the difference between different classes of surface materials. The Spectral Signature Plot in Image 3.4 shows all 12 surface materials that were collected and analyzed in this lab exercise.
 |
| Image 3.4. Spectral Signature Plot of 12 unique surface materials |
Two signatures that are very similar in shape are the Standing Water and Moving Water signatures. This is expected because these are both water features. Even though they are part of the same river system, there is still some slight variation in reflectance between the two. The Standing Water has slightly higher reflectance in the Green band, which might indicate that the site of the sample collected for the Moving Water might have some algae present, which would account for higher absorption of Green energy.
Deciduous Forest and Riparian Vegetation are another pair that have very similar signatures, based on the samples that were collected. Riparian Vegetation grows along water and is especially present near river banks. In this study area, the Riparian Vegetation that grows is very similar in characteristics to the Deciduous Forests that are also in the area.
Spectral signatures are also useful in showing how certain materials differ from one another. For example, the Deciduous Forest and Riparian Vegetation surfaces are the only ones where reflectance peaked at the Red band. These surfaces peak here because they have the most plant material that harvests Green energy from the sun for photosynthesis. Many of the other surfaces identified in this exercise are urban/man-made features (concrete, asphalt, runway) that absorb the most in the Red band.
Part 2: Resource Monitoring
Simple band ratios can be used to monitor the conditions of the land in the satellite images. The two band ratios in this lab are used for vegetation health monitoring and soil health monitoring.
Section 1: Vegetation Health Monitoring
The areas in the NDVI image below (Image 4.1) that are dark green represent healthy vegetation. Large swaths of this dark green designate a healthy forest area that is densely populated with healthy trees and other vegetation.
Areas of the image that are medium to light-green are areas that have an absence of vegetation. The light color in this image can be associated with water or possibly even bare agriculture fields that only contain soil or brown, dried grasses at the time that this image was taken. The middle green areas continue around the developed areas of Eau Claire and Chippewa falls, which further evidences that there is less vegetation in this area compared to the dark green areas that are seen to the north and east of these urban areas.
Section 2: Soil Health Monitoring
The spatial distribution of ferrous minerals in Chippewa and Eau Claire counties is mostly to the west of the Chippewa River, as seen in Image 4.2, where most of the agricultural land in the area is located. Iron is important for healthy plants, which is why it makes sense that agricultural operations would be located on iron-rich soils.
The areas in the NDVI image below (Image 4.1) that are dark green represent healthy vegetation. Large swaths of this dark green designate a healthy forest area that is densely populated with healthy trees and other vegetation.
 |
| Image 4.1. Vegetation Presence map of Eau Claire and Chippewa Counties |
Areas of the image that are medium to light-green are areas that have an absence of vegetation. The light color in this image can be associated with water or possibly even bare agriculture fields that only contain soil or brown, dried grasses at the time that this image was taken. The middle green areas continue around the developed areas of Eau Claire and Chippewa falls, which further evidences that there is less vegetation in this area compared to the dark green areas that are seen to the north and east of these urban areas.
Section 2: Soil Health Monitoring
The spatial distribution of ferrous minerals in Chippewa and Eau Claire counties is mostly to the west of the Chippewa River, as seen in Image 4.2, where most of the agricultural land in the area is located. Iron is important for healthy plants, which is why it makes sense that agricultural operations would be located on iron-rich soils.
 |
| Image 4.2. Ferrous Mineral Distribution map of Eau Claire and Chippewa Counties |
Comments
Post a Comment