Radiometric and Atmospheric Correction

Introduction:

Before analysis can be performed on satellite imagery, it needs to be atmospherically corrected. In this lab we will be using three different methods: Empirical Line Calibration, Dark Object Subtraction, and Multidate Image Normalization.

Methods:

Empirical Line Calibration (ELC) was performed by developing regression equations between in situ reflectance measurements and those recorded by the sensor for the same target. In order to perform ELC, I selected 5 areas within the image, then found in situ reflectance information from spectral libraries within Erdas Imagine.



The next method used was Dark Object Subtraction (DOS). The satellite image is converted to at-satellite spectral radiance, before it is converted to true surface reflectance. The radiance conversion is performed by analyzing the image metadata in order to determine the original and re-scaled pixel values. The conversion to reflectance is done by calibrating the radiance image based on: the distance between the earth and the sun, atmospheric transmittance between the ground and sensor, the sun zenith angle, atmospheric transmittance from the sun to ground, and the mean atmospheric spectral irradiance.

Image 1: Comparing the ground control points.

The final correction method used was Multidate Image Normalization (MIN). The first step was to  collect radiometric ground control points from the base image and subsequent image (Image 1). The points were used to build regression equations in Excel. The sample points were collected from static, non-vegetated areas throughout the image scene.

Results:

ELC didn’t really seem to change any pixel values by major amounts (Image 2).

Image 2: The original image (left), and ELC corrected image (right).

DOS increased the visible contrast between features, and eliminated atmospheric haze (Image 3).

Image 3: The original image (left) and DOS corrected image (right) viewed using a 7,5,3 band combination.

The MIN method greatly reduced the visible haze on the imagery, and produced more vivid values overall.

Image 4: The Chicago 2000 (left) and Chicago 2009 MIN (right) viewed using a 7,5,3 band combination.

In the future, I would be more likely use Dark Object Subtraction than the other methods, as it produced a more accurate result. However, Multidate Image Normalization may also be used if I am performing analysis over multiple time periods.

Lab 2: Surface Temperature Extraction from Thermal Remote Sensing Data

Introduction:

Thermal imagery holds incredibly useful information, but requires a very different workflow than reflective imagery. In this lab, I first performed visual image interpretation on thermal imagery, then progressed into constructing models in order to quantitatively estimate land surface temperature.

Methods:

In order to become comfortable with thermal imagery, I first examined thermal imagery captured over the Eau Claire, Wisconsin. I used my knowledge of existing features to interpret the images' brightness values, in order to further develop my understanding of the properties of thermal images and the features they capture.

After gaining some understanding of the imagery, I created a model to convert ETM+ imagery's values from digital numbers to the satellite's original radiance values. The model used the following equation, "spectral radiance = Grescale * digital number value + Brescale". In relation to the slope equation (y=mx+b), Grescale is the value the radiance was divided by in order to create the digital number. Brescale is the lowest radiance value the satellite recorded. In order to calculate the Grescale, I used the following equation, "Grescale = (LMAX - LMIN)/(QCALMAX - QCALMIN)". The LMAX and LMIN values are the highest and lowest radiance values originally recorded by the satellite, respectively. QCALMAX and QCALMIN are the highest and lowest calibrated pixel values, respectively. As the thermal imagery is recorded in 8-bits, QCALMAX is 255 and QCALMIN is 1. After calculating the Grescale, I was able to use the Grescale and Brescale to calculate the at-satellite radiance values (Figure 1).

Figure 1: The model for calculating at-satellite radiance.

Unfortunately, radiance is only the satellite's recorded value, and not the actual surface temperature. In order to determine the actual surface temperature, I needed to implement an additional equation. The equation to convert radiance to surface temperature is, "Temperature = k2 / ln((k1/radiance)+1)". The values k1 and k2 are calibration constants for the satellite that were recorded before it was launched into orbit, so implementing the equation only required me to identify the values for k1 and k2. I created a new model, with the output radiance raster as the input value for the new equation. Running the new model generated a temperature surface raster, showing temperature in degrees Kelvin.

Next, I performed the same series of equations to calculate radiance and surface temperature, only using Landsat TM imagery instead of ETM+ imagery. Calculating surface temperature required different values for k1 and k2, as Landsat TM was calibrated slightly differently than Landsat ETM+

The final step of this lab was to calculate land surface temperature for Chippewa and Eau Claire counties from a Landsat 8 thermal image. First, I performed an image subset using an area of interest file of the counties' boundaries. Next, I followed similar procedures as the previous two temperature calculations, only using different k1 and k2 values. After generating a surface temperature raster with degrees recorded in Kelvin, I added an additional operation to convert the temperature into degrees Fahrenheit.

Results:

Each of the calculations produced a useful output, but by converting the temperature from Kelvin into Fahrenheit, the usability of the Landsat 8 output image was greatly increased (Figure 2).

Figure 2: Fahrenheit surface temperature extracted from Landsat 8's thermal band.

Sources:

Landsat satellite image is from Earth Resources Observation and Science Center, United States Geological Survey. Area of interest (AOI) file is derived from ESRI counties vector features. 

Lab 1: Image quality assessment and statistical analysis

Introduction:

In order to properly use multispectral data for land use/land cover classification the data must first be preprocessed to identify and remove sources of redundancy. I used two different methods to identify problematic band combinations, feature space imaging and correlation analysis.

Methods:

Feature space imaging illustrates the pixel values of two images by plotting them on a scatterplot graph. Band combinations with high association will appear as a cohesive line across the graph (Figure 1). Band combinations with low association will display points widely spread across the surface of the scatterplot graph (Figure 2). I used feature space imaging to assess the covariance values of Landsat imagery of Eau Claire, WI.

Figure 1: High covariance between bands 2 and 3

Figure 2: Low covariance between bands 4 and 5

Correlation analysis compares bands with one another and assesses the extent of association between each band combination. The output of correlation analysis is typically displayed as a correlation matrix. The cell values indicate the extent of interrelationship, with higher values (-1 and +1) indicating extremely high association, and lower values (~0) indicating low association. If two bands are highly correlated, one of them should be removed, to preserve the integrity of the analysis. I used correlation analysis to assess covariance of bands in Landsat imagery for Eau Claire, WI, and Quickbird imagery for the Florida Keys and Sundarbans, Bangladesh.

Results:

After creating the feature space plots, I deemed the removal of bands 2 and 7 necessary in order for proper analysis to be performed. Band 2 had high covariance with both band 1 and band 3 (Figures 1,3). Band 7 had high covariance with band 5, indicating one of them needed to be removed. Band 5 and band 4 had the lowest covariance of any band combination, which convinced me to eliminate band 7 rather than band 5.

Figure 3: Bands 1 and 2 have high covariance

The Eau Claire correlation matrix verified my band removal assessment from the feature space plots, as it indicated bands 2 and 7 have the highest correlation values (Table 1).   The correlation matrix for the Florida Keys revealed high correlations between the bands 1&2 … suggesting the removal of band 2 (Table 2). The correlation matrix for Sundarbans, Bangladesh revealed a similar pattern, with band combinations 1&2 having high correlations (Table 3).

Table 1: Eau Claire Correlation Matrix

Table 2: Florida Keys Correlation Matrix

Table 3: Sundarbans Correlation Matrix

Sources

Landsat satellite image is from Earth Resources Observation and Science Center, United States Geological Survey. Quickbird high resolution images are from Global Land Cover Facility at www.landcover.org