Lab 8: Spectral Signature Analysis and Resource Monitoring

Goals and Background

  Remote sensing can be used the monitor and interpret the spectral reflectance of earth's surface features across different bands of the electromagnetic spectrum. This is done through the creation of spectral signatures. In this lab 12 spectral signatures will be created for the the following features:
        1. Standing Water
        2. Moving Water
        3. Deciduous Forest
        4. Evergreen Forest
        5. Riparian Vegetation
        6. Crops
        7. Dry Soil (Uncultivated)
        8. Moist Soil (Uncultivated)
        9. Rock
        10. Asphalt Highway
        11. Airport Runway
        12. Concrete Surface
    Remote sensing can also be used to monitor the health of vegetation and soils. This will be done by calculating the NDVI index, and the ferrous soils ratio for Eau Claire and Chippewa counties and then creating a map of the results.

Methods

Part 1: Analyzing Spectral Signatures

This part consisted of creating the 12 spectral signatures in Erdas. There are a few steps in creating a spectral signature for a given feature. First, one must load the image which the user wants to derive the spectral signiture from. In this lab, its a ETM ++ image of Eau Claire and Chippewa counties in Wisconsin. Then, one navigates to Drawing → Polygon, and draws and AOI for the region which the spectral signature will be created. To help identify the 12 features, the image view was linked to Google Earth, which can be done in the Google Earth tab. After creating the AOI for the feature, one then activates the Raster tab and then navigates to Supervised → Signature Editor → Create new Signature from AOI. Figure 5.0 shows the AOI for the Moving Water feature.

Fig 5.0: AOI for Moving Water

  Most of the AOIs created were fairly small because many of the features didn't cover a very large area. After the 12 signatures were created, some analysis was done on them to see how similar and different features are in terms of the reflectivness of electromagnetic energy across different wavelengths. Figure 5.1 shows what the Signature Window looked like after all twelve signatures were collected.

Fig 5.1: Signature Window

Part 2: Monitoring Vegetation and Soil Health

  This part consisted of using the Indicies tool to calculate NDVI and Ferrous Soils ratio for the two counties. NDVI is calculated by using the equation: NDVI = (NIR - Red) / (NIR + Red). NIR and Red refer to their respective spectral bands in the ETM++ image. The Ferrous Soils ratio is calculated using the equation: Ferrous Mineral = (MIR) / (NIR). Once again, MIR and NIR refer to their respective spectral bands in the ETM++ image. Neither of these were calculated manually, to get the results quick for the entire image, the Indicies tool was used. The Indicies tool and its parameters, input, and output can be seen below in figure 2.1.

Fig 5.2: Calculating the NDVI Index

  Then the Indicies tool was used again, but this time, the ferrous soils index was chosen. This can be seen below in figure 5.3. After the two output images were created, they were brought into ArcMap, and maps were created to help interpret the data.

Fig 5.3: Calculating the Ferrous Mineral Ratio

Results

  Figure 5.4 below on the right shows the standing water signature while figure 5.5 shows the moving water signature. For all of the signatures, layer one is band one, layer two is band two, layer three is band three, layer four is band four, layer five is band five, and layer six is band seven in the ETM++ image. The standing water and moving water have very similar spectral signatures, the main difference occurs in band 4 where moving water has greater reflectance.

Fig 5.4: Standing Water Spectral Signature

Fig 5.5: Moving Water Spectral Signature

  Figure 5.6 shows the spectral signature for a deciduous forest while figure 5.7 shows the spectral signature for an evergreen forest. The spectral signatures for the deciduous forest and for the evergreen forests are similar as well. The main difference is that overall the deciduous forest has a higher reflectance. This is because deciduous forests usually contain more vegetation and leaves can reflect electromagnetic energy more than pine needles can.

Figure 5.6: Deciduous Forest Spectral Signature

Figure 5.7: Evergreen Forest Spectral Signature

  Figure 5.8 shows the spectral signature for riparian vegetation, and figure 5.9 is a spectral signature for crops. The main difference between these signatures is that crops overall reflect much more than riparian vegetation. This is because crops are constantly being irrigated and therefore are healthy, where as riparian vegetation occurs along stream edges and the farther away the riparian vegetation extends from the stream, the less healthy it will be.

Fig 5.8: Crops Spectral Signature

Fig 5.9: Riparian Vegetation Spectral Signature

  Figure 5.10 shows the spectral signature for dry soil, and figure 5.11 shows the spectral signature for moist soil. There is one main difference between the dry soils and moist soils spectral signature. This difference occurs in bands 4, 5, and 6. The dry soil reflects more in these bands while the moist soil reflects less.

Fig 5.10: Dry Soil Spectral Signature

Fig 5.11: Moist Soil Spectral Signature

  Figure 5.12 is the spectral signature for rock, and figure 5.13 is the spectral signature for an asphalt highway. Surprisingly, there are very little similarities between the rock and asphalt highway spectral signatures. Overall, the rock reflects much less. This is perhaps because the rock chosen for this spectral signature was located at big falls on the Eau Claire River where perhaps water was included in the AOI which then contaminated the rock spectral signature.

Fig 5.12: Rock Spectral Signature

Fig 5.13: Asphalt Highway Spectral Signature

  Figure 5.14 is a spectral signature for an airport runway, and figure 5.15 is a spectral signature for a concrete surface. In both signatures, the maximum reflectance can be seen in band 5. However, the lowest reflectance for the airport runway occurs in band 4 while the lowest reflectance for the concrete surface occurs in band 7.

Fig 5.14: Airport Runway Spectral Signature

Fig 5.15: Concrete Spectral Signature

  Figure 5.16 is what the spectral signature looks like for all the features when they are plotted on the same graph. The band that sticks out the most is band 4. For features which contain chlorophyll, their spectral reflectance increases in this band, for features which don't contain chlorophyll, their spectral reflectance decreases in this band.

Fig 5.16: All of the Spectral Signatures Plotted Together

  Figure 5.17 is a map of the NDVI index. Much of the county as a high vegetation index. This is because, there is more farmland in the eastern portions of these counties. Also, there is more forest in the eastern portion as well. The areas where this is no vegetation in the western portion of the counties is where there are fallow crop fields.

Fig 5.17: NDVI Index Map

  Figure 5.18 is a map of the ferrous soils ratio. Looking at the ferrous minerals map, most of the ferrous minerals are primarily located in the western portion of Eau Claire and Chippewa counties. Ferrous minerals are generally less present in the eastern portion of the map. There is a fine boundary that runs northwest to southeast in the map. To the west of this boundary, ferrous minerals are present, and to the east of this boundary, ferrous minerals are low or absent. another spatial pattern is tat many of the ferrous minerals are concentrated near the Chippewa River.

Fig 5.18: Ferrous Minerals Map

Sources

United States Geological Survey, Earth Resources Observation and Science Center. ETM++ Satellite image