Remote Sensing Lab 8

Introduction

Determining land use for parcels of land is important in a variety of areas such as real estate and construction. Often the cost to send out a team to survey the area and determine the land use is too time consuming or the cost is too high. Remote sensing allows analysts to determine the land use from satellite data and provide that information to clients. In this lab, the goal is to gain experience in measuring and interpreting spectral reflectance signatures of various surfaces using satellite imagery. After collecting the signatures they are analyzed to determine whether their spectral signatures verify the land use type identified on a reference image. The second portion of the lab involves monitoring the health of vegetation and soils using band ratio techniques. Vegetation monitoring is useful for a wide variety of professions such as farmers and conservation scientists.

Methods

Part 1: Spectral Signature Analysis

The first part of this lab involved collecting spectral signatures of several different surfaces from a satellite image. Google Earth was used as a reference to identify the specific land cover type of areas on the satellite image (figure 1).

Figure 1. Google Earth and satellite image used to collect spectral signatures.

 Once the area of interest was determined, a polygon was drawn within the area. Then the mean pixel values within the polygon were inputted into the signature editor where plots of each surface’s spectral signature could be obtained (figure 2).

Figure 2. Spectral signatures obtained using polygons on the satellite image.

Once all 12 signatures were obtained, the highest and lowest reflectance bands were recorded for each surface shown in figure 3.

Figure 3. Spectral signatures with highest and lowest reflectance.

Part 2: Vegetation Health Monitoring

In this part of the lab the health of vegetation was determined using the normalized difference vegetation index (NDVI).

NDVI = NIR – Red/NIR + Red

The NDVI tool was opened in Erdas Imagine with the specified input image, the sensor set as Landsat 7 Multispectral and NDVI set as the function (figure 4).

Figure 4. NDVI function.

Finally, a band ratio using the ferrous mineral ratio was implemented to monitor the spatial distribution of iron contents in soils within Eau Claire and Chippewa Counties.

Results

From the signature tool spectral signature curves for each of the twelve features were obtained.

Figure 5. Standing water spectral reflectance curve.

Figure 6. Moving water spectral reflectance curve.

Figure 7. Deciduous forest spectral reflectance curve.

Figure 8. Evergreen forest spectral reflectance curve.

Figure 9. Riparian vegetation spectral reflectance curve.

Figure 10. Crops spectral reflectance curve.

Figure 11. Dry soil spectral reflectance curve.

Figure 12. Wet soil spectral reflectance curve.

Figure 13. Rock spectral reflectance curve.

Figure 14. Asphalt highway spectral reflectance curve.

Figure 15. Airport runway spectral reflectance curve.

Figure 16. Concrete surface spectral reflectance curve.

Figure 17. Spectral reflectance curves for all 12 surfaces.

1.       While all the curves are slightly different in figure 17, there are a couple of trends that are apparent in the curves. There are six curves that follow the same general trend but at different respective reflectance levels: airport runway, dry soil, wet soil, rock, asphalt, and concrete surface. Rock had the highest reflectance out of the five and the asphalt highway had the lowest overall reflectance. All these surface have the same general trend because they are all similar surfaces (flat and made from minerals). The rock had the highest reflectance because the minerals were light colored which increased the reflectance. The asphalt had the lowest reflectance because of the dark coloring of the surface which absorbs more wavelengths. The second trend was the two water curves which were similar to each other because they both depicted water, but the standing water had a higher reflectance because it had a flatter surface that reflected energy better. Finally, the Crops, deciduous forest, riparian vegetation, and evergreen forest all had similar curves. These are all similar in overall shape because they all have some form of vegetation in them and therefore they will exhibit the same overall curve trend even though the level of reflectance may vary. The two water signatures varied the most in trend from the other surface because its properties are so different. In addition, the vegetation surfaces and the ground surfaces are both located on land and thus share commonalities that the water surfaces do not. The vegetation spectral curves differed in the NIR or band 4 from the 6 hard surfaces because plants reflect more energy to avoid protein deformation while the hard surfaces absorbed this wavelength.

Figure 18. Vegetation cover of Eau Claire and Chippewa Counties.

 Figure 18 is a map of the vegetation cover of Eau Claire and Chippewa Counties. This map was based on the NDVI function which measures the health of vegetation using the red band and NIR band. The northeastern portion of the map is dark green which indicates high amounts of vegetation. The eastern half of the map is lighter in color which indicates lesser amounts of vegetation. The lack of vegetation in these areas is related to cities such as Eau Claire, roads, and crop fields that may not have crops currently growing.

Figure 19. Ferrous mineral distribution in Eau Claire and Chippewa Counties.

Figure 19 shows the distribution of ferrous minerals in Eau Claire and Chippewa Counties.  In the map overall, there is a higher concentration of ferrous minerals in the eastern half compared to the western and northwestern portion. Dark orange sections of the image denote high concentrations of ferrous minerals, which would appear in the image where there is exposed soil. Upon closer inspection, it is apparent that higher ferrous mineral concentrations coincide with exposed crop fields and exposed soil surfaces. This is more common in urban areas such as Eau Claire and Chippewa Falls. The light orange areas correspond to heavily vegetated areas that block the ground surface which covers the ferrous minerals. This is more common in the eastern portion of the map where there is more vegetation cover. 

Both portions of this lab utilized spectral signature curves to determine land cover characteristics such as land cover type, vegetation cover, and ferrous mineral concentrations. The ability to use remote sensing to determine land characteristics is a useful tool that can save time, money, and resources. Remote sensing can also analyze areas that are inaccessible to ground studies. Upon completion of this lab basic skills in spectral signature analysis were obtained. 

Sources

Satellite image is from Earth Resources Observation and Science Center, United States Geological Survey