ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

COMPARING ASTER AND LANDSAT 7 ETM+ FOR CHANGE DETECTION

Taufique Mahmood, Graduate student Greg Easson, Associate Professor

Department of Geology and Geological Engineering, The University of Mississippi.

thmahmoo@olemiss.edu, geasson@olemiss.edu.

ABSTRACT The limited radiometric resolution and expiring life expectancy of the Landsat sensors have prompted this study to investigate the potential of using ASTER imagery in conjunction with Landsat imagery. This project developed methods to calibrate processed ASTER imagery and Landsat 7 ETM+ imagery to equivalent measurements for change detection studies. Landsat 7 ETM+ and ASTER imagery of southwestern Bangladesh were acquired on the same date for this study, with a thirty three minute temporal difference between the acquisition times. The methods were developed on a training site and verified on a test site. The methods developed for calibration were regression with Discrete Fourier Transform and cross-calibration approach using digital number ratios. The results, using both regression with Discrete Fourier Transform and cross-calibration approach show that more than sixty-five percent pixels of the test site are within 3 DN difference range for green, red and SWIR I band. However, only approximately forty-five percent of the pixels in the near infrared band are within the 3 DN difference range for test site using both of these methods. The variances of the DN differences for the green, red and SWIR I bands are low, whereas the variances of DN differences are high for the near infrared band in the test site. The variance of DN differences using the regression with Discrete Fourier Transform method is lower than the variance using cross-calibration approach for each spectral band in the test site. The lower variance indicates that regression with Discrete Fourier Transform more precisely calibrated both sensors rather than the cross-calibration approach using DN ratio. The results of this study suggest that the ASTER can be used for change detection in conjunction with Landsat 7 ETM+ using developed calibration methods.

INTRODUCTION

The Landsat programs have served the terrestrial mapping community by providing consistent and continuous imagery of earth surface for last three decades. The Landsat data archives are very important for global and local change detection studies. However, the existing Landsat sensors, Landsat 7 ETM+ and Landsat 5 TM, are operating with limited radiometric resolution which reduces and restricts their use for the end user. The recent malfunction of Scan Line Corrector in Landsat 7 ETM+ causes individual scan line to alternately overlap, resulting in missing data. The incomplete data of Landsat 7 ETM+ are not providing enough information to terrestrial mapping community for some global and local change detection studies. These limitations of Landsat 7 ETM+ and the need to detect change prompted this study to develop and test methods of using ASTER imagery as an alternative to Landsat 7 ETM+ imagery. To be able to use ASTER with Landsat 7 ETM+, the calibration methods are required to ensure that the same radiometric scale is used for both datasets. Even though both datasets have the same radiometric resolution (8 bit), large DN (Digital Number) differences were observed between the near-simultaneous imagery of both sensors for the same ground target (Figure 1). This study attempted to develop and test calibration methods for processed data (Digital Number) of both sensors for the similar spectral bands found on both sensors. The similar spectral bands between two sensors are green, red, near infrared and SWIR I band (Figure 2). Previous studies in radiometric calibration methods using two different sensors include cross-calibration techniques (Dinguirard and Slater, 1999) and vicarious calibration (Teillet et al., 2001). However, these two techniques are applicable to un-processed or raw imagery data. A majority of end users prefer processed imagery data (Digital Number) which is commonly easier to use and has been geometrically and radiometrically corrected. The study used L1 B level data of ASTER and L1G level data of Landsat 7 ETM+ in the development and testing of calibration methods.

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

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90.00

Green Red Near Infrared SWIR ILandsat bands

DN

Landsat 7 ETM+ DN ASTER DN

STUDY AREA

The study site for this study is the mangrove forest (The Sundarban) of south-western Bangladesh. The Sundarban, the largest block of productive and tidal halophytic mangrove forest in the world, is only world heritage site for mangrove forest. This site was selected due to its ecological heritage, availability of near-simultaneous imagery for both sensors and high surface variability.

Figure 1. DN difference between Landsat 7 ETM+ and ASTER for mangrove forest.

Figure 2. Spectral bands of ASTER and Landsat 7 ETM+.

0.45

0.51

0.57

0.63

0.69

0.75

0.81

0.87

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1.61

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1.73

2.15

2.21

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2.33

2.39

1 2 3 4 5 7

1 2 3 4 5 6 7 8 9

ASTER

L ANDSAT

Visible and NIR Short wave IR

Near Infrared

Red Blue SWIR I SWIR II Green

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

METHODS

The goal of this research is to develop methods to use ASTER as an alternate to Landsat 7 ETM+ for change detection research in mangrove forest. The major components of this study were data collection, geometric correction, selection of training and test site within mangrove forest for development and test of calibration methods in the context of change detection of mangrove forest. This study used regression with DFT (Discrete Fourier Transform) and cross-calibration using DN ratio for calibration. The tasks required before calibration are data collection, geometric correction, radiometric correction and selection of training and test sites.

Pre-calibration Tasks

Data collection. ASTER and Landsat 7 ETM+ imagery of southwestern Bangladesh for the same date, November 29, 2001 with a thirty-three minute temporal difference between the two acquisition times was acquired for this study. This study assumed that surface and atmospheric condition did not change significantly during the thirty-three temporal difference. The same date of acquisition of imagery also provided a good opportunity to compare the spectral response of ASTER and Landsat 7 ETM+.

Geometric correction. The ASTER imagery was geo-rectified and registered to the Landsat 7 ETM+ imagery because the ASTER image obtained did not contain projection and geometric information. The re-sampling method used in geo-rectification was the nearest neighbor. The projection of rectified imagery is UTM zone 46, with an average RMS error is 5 meters. This study applied and tested the calibration methods with both 15 m and 30 m spatial resolution, therefore, the VNIR bands of ASTER were resampled into both spatial resolutions during the geometric correction process. The green, red and near infrared bands of Landsat 7 ETM+ were also resampled into 15 m spatial resolution.

Radiometric correction. The image provider commonly radiometrically corrects the image, for instrumental errors, to a radiometric accuracy of 5% (Abrams et al., 2002) for ASTER and 3% for Landsat 7 ETM+ (Baker et al., 2000). Atmospheric correction was not necessary in this investigation because the close acquisition times of the imagery.

Selection of training and test sites. The training and testing sites were selected to develop and test calibration methods (Figure 3). In this research, one training site was selected to develop calibration method and one test site was selected to test the accuracy of the developed methods. In the training and testing sites, approximately 80% pixels are mangrove forest and 20% pixels are tidal water. As this study focused on the calibration methods in mangrove forest, the water pixels were ignored during the analyses. Moreover, water is one of the most dynamic physical features that can dramatically change during the temporal difference in image acquisitions. This dynamic nature of the water is also contradictory to one of the basic premises of this study that was that the atmospheric and ground condition did not change between the acquisition of the images. Calibration Methods

Two methods, regression with DFT and cross-calibration using DN ratio, were developed and tested for calibration of both sensors. The methods were developed on a training site and then tested. Regression with DFT. The regression with Discrete Fourier Transform (DFT) approach was developed using both structural regression and DFT based calibration. This method is a hybrid approach which uses a combination of structural regression (reduced major axis) and Discrete Fourier Transform based calibration. Using the structural regression, the predicted ASTER DN was calculated. Although, the structural regression reduced the pre-regression DN difference between ASTER DN and Landsat 7 ETM+, there were still DN differences observed between the predicted ASTER DN and Landsat 7 ETM+ DN in a large number of pixels. Using the phase angle matrix of Landsat 7 ETM+, the remaining differences between the predicted ASTER DN and the Landsat 7 ETM+ were significantly reduced. The use of phase angle of reference data (Landsat 7 ETM+) is the part of Discrete Fourier Transform based calibration which was proposed by Peleg (1998).

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

(1) SS

bx

y1 =

Figure 3. Location of training site and test site within the mangrove forest of south-western Bangladesh.

Structural regression. The structural regression predicts the data which is supposed to exist in between a noise-

free reference dataset and an input dataset (Woodhouse, 2003). The structural regression adjusts the uncertainty in both reference and input dataset and predicts the underlying, theoretical, perfect-world relationship between the two datasets and is more concerned with variation in the both datasets. The high variability in a dataset is dealt with by considering the standard deviation (reduced major axis) and the variance covariance matrix (major axis) in slope estimation (Davis, 2002). Two approaches are available for structural regression; the major axis (MA) and the reduced major axis (RMA). The major axis approach minimizes the squared deviation from the regression line in both the X and Y directions concurrently. The reduced major axis (RMS) approach minimizes the product of the deviation in both directions (Davis, 2002). In this research both approaches were tested, with the reduced major axis method yielding a more accurate result based on the variance of the difference between the predicted value and the reference value.

In the reduced major axis (RMS) procedure, the slope is defined as the ratio of the standard deviation of the two variables, x and y,

where, b1= slope, Sy = standard deviation of the dataset y, and Sx = standard deviation of the dataset x. The intercept is defined in the equation 2.

where, b0 = intercept, Y = arithmetic mean of the dataset y, and X = arithmetic mean of the dataset x. In this study, coefficient b1 and b0 were calculated from the Landsat 7 ETM+ and ASTER data set of the training site. These coefficients were used to calculate the predicted ASTER value in the testing dataset.

Discrete Fourier Transform. In image processing, the two dimensional Discrete Fourier Transform (DFT) is frequently used to filter image spectrally in the complex domain. The Discrete Fourier Transform decomposes both the ASTER and Landsat 7 ETM+ images into the spatial frequency domain (Jensen, 1996). It is an important image

(2) XbYb 10 ×−=

Test site Training site

ASTER scene boundary

20 km0

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

processing tool which is used to decompose an image into its sine and cosine components. The output of the transformation represents the image in the frequency domain, while the input image is the spatial domain equivalent. In the Fourier domain image, each point represents a particular frequency contained in the spatial domain image. For an (M x N) image matrix, the DFT of the image is calculated using following equation,

∑ ∑−

=

−

=+=

1N

0x

1M

0y (3) ))

Nvy

Muxj(2-y)exp(f(x,

NM1v)F(u, π

where, N is the number of pixels in x direction, M is the number of pixels in y direction, f(x,y) is the M x N image data, F(u,v) = Discrete Fourier Transform of f(x,y), j= 1− , n = 1….N, m = 1….M, u = the spatial location in x direction of frequency domain and v = the location in y direction of frequency domain. The Discrete Fourier Transform (DFT) yields a matrix of complex numbers where each complex number contains a real part and an imaginary part. The amplitude is calculated using following equation (Brigham, 1988),

( )( ) ( )( ) ( )( ) (4) vu,FIvu,FRevu,FA 22 +=

where, A(F(u,v)) = amplitude of Fourier Transform, Re(F(u,v)) = real part of complex number, I (F(u,v)) = imaginary part of the complex number. The phase angle is calculated using the following equation (Brigham, 1988),

( )( ) ( )( )( )( ) (5)

vu,FRevu,FItanvu,Fθ 1−=

where, ( )( )vu,Fθ = Phase angle of the complex number. The amplitude indicates the amount of a certain frequency component at certain pixel location and the phase indicates the position of frequency component in the image.

Calibration procedure. The DFT of the ASTER DN, the predicted ASTER DN and the Landsat DN of the test area were calculated. The amplitude and phase angle of respective DFT were calculated. It was observed that phase angle did not change or changed very little after structural regression whereas, the amplitude changed significantly. The phase angle difference between the DFT of the predicted ASTER DN and the DFT of the corresponding Landsat 7 ETM+ DN is one of the reasons for difference between predicted ASTER DN and Landsat 7 ETM+ DN. The cross-plot between phase angles of the ASTER and Landsat 7 ETM+ images does not show any trend in the data which limits the application of any statistical prediction (Peleg, 1998). The phase angle is significantly related to position of particular DN in the matrix because phase angle is calculated using the location in the frequency domain and the original DN value. At this stage of the analysis, this study assumed that the phase angle of the Landsat 7 ETM+ DN is noiseless. Therefore, this study disregarded the phase angle matrix of DFT of the predicted ASTER DN and used the phase angle of the DFT of the Landsat 7 ETM+ DN of the test area. The DFT of the calibrated ASTER DN of the test area was calculated using amplitude of the DFT of predicted ASTER DN and the phase angle of the DFT of Landsat 7 ETM+ (Equation 6).

( )( ) ( )( ) (6) vu,θL1-expvu,PAAFAXA ××=

where, FAXA = DFT of the calibrated ASTER DN, ( )( )vu,PAA = amplitude of DFT of the predicted ASTER DN,

( )vu,θL = phase angle of the DFT of Landsat DN. By taking the inverse Fast Fourier Transform of FAXA, the calibrated ASTER DN was calculated (Equation 7).

(7) )ICFFT(FAA XAXA =

AXA = Calibrated ASTER DN. Cross-calibration Approach Using DN Ratio

This study developed a cross-calibration method for calibrating processed ASTER and Landsat 7 ETM+ data using a DN ratio. This method assumes that the surface and atmospheric conditions did not change significantly

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

between the two image acquisition times and that both sensors received an approximately equal amounts of energy reflected from the surface features. As both sensors receives received equal amount of reflected energy, calibrated ASTER DN that would be equal to Landsat 7 ETM+ DN (Equation 8).

(8) DNDN LXA =

where, DNXA= Calibrated ASTER DN and DNL= Landsat DN. Equation 9 is obtained by multiplying the right hand side of equation 8 by DNA/ DNA.

(9) DNDNDNDN

A

ALXA ×=

where, DNXA= Calibrated DN value of ASTER image, DNL= DN value of Landsat image and DNA= DN value of ASTER image.

Landsat 7 ETM+ Digital Number calculation. The spectral radiance for Landsat 7 ETM+ at the sensor aperture for Landsat 7 ETM+ is calculated using following formulas (Chander and Markham, 2003).

(10) βDNαL LLλ +×=

where LLλ= Spectral radiance at sensor’s aperture in W×m-2×sr-1×μm-1 ,α = Rescaled gain (W×m-2×sr-1×μm-1)/DN for Landsat 7 ETM+, and β = Rescaled bias in W×m-2×sr-1×μm-1 for Landsat 7 ETM+.

For relatively clear cloud free Landsat scenes, a reduction in between-scene variability can be achieved through normalization for solar irradiance by converting spectral radiance to planetary reflectance or albedo (Chander and Markham, 2003). This combined surface and atmospheric reflectance (planetary reflectance) of the Earth is computed with the following formula (Equation 11).

(11) CosθESUNdLπ

ρLLλ

2Lλ

LP ×××

=

where, LPρ = unitless planetary reflectance, θL= Solar zenith angle for ETM sensor, EUNLλ = mean solar exo-atmospheric irradiance in W×m-2×μm-1 for band i and d = Earth-sun distance in astronomical unit.

In equation 11, LLλ is the amount of energy reflected back to the sensor and (ESUNLλCosθL)/d2 is the amount of solar energy coming from the sun. Equation 12 has been developed by substituting the LLλ (spectral radiance at aperture of sensor), from equation 10 into equation 11. The solution for DNL yields the equation 12.

(12) dπα

dπβCosθESUNρDN

2

2LLλLP

L××

××−××=

ASTER Digital Number calculation. The spectral radiance for ASTER at the sensor aperture is calculated using following equation (Abrams et al., 2002).

(13) )1DN(cL AAλ −×=

where LAλ = spectral radiance at the sensor aperture, c = coefficient of conversion in W×m-2×sr-1×μm-1/DN. These values of c are given in the telescope specific metadata file (Abrams et al., 2002). Abrams et al., (2002) recommended using the high gain for green and red for ASTER and to use normal gain for rest of the seven bands. The planetary reflectance for ASTER is calculated using the following equation (Dinguirard and Slater, 1999).

(14) CosθESUN

dLπ

AAλ

2Aλ

APρ×

××=

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

where, APρ = unitless planetary reflectance, θA= solar zenith angle for ASTER, ESUNAλ = mean solar exo-atmospheric irradiance in W×m-2×μm-1 for band i and d = Earth-sun distance in astronomical unit.

The solar exo-atmospheric irradiance for ASTER was reported by Thome et al., (2001). Equation 15 has been developed by substituting the LAλ (spectral radiance at aperture of sensor), from equation 13 into equation 14. The solution for DNA yields the equation 15.

The ratio of equation 12 to equation 15 gives the ratio of DNL to DNA (Equation 16).

(16) )2dπcCosθESUN(ρα

)2dπβCosθESUN(ρcDNDN

AAλAP

LLλLP

A

L

××+×××

××−×××=

The calibrated ASTER data was calculated using equation 17. This equation was developed by substituting DN

ratio of Landsat 7 ETM+ to ASTER in equation 16 from equation 9. The c/α is the ratio of the coefficient derived from gain of ASTER to gain of Landsat 7 ETM+.

In the equation 17, the planetary reflectance of both sensors varies due to the differences in the physical

properties, such as offset and gain, of the sensors. The variation in planetary reflectance depends on the parameters of sensors, primarily rescaled offset and gain for Landsat 7 ETM+ and coefficient of conversion from DN to radiance for ASTER. Applying the DN ratio of each pixel of the training site to the respective pixel of the test site would create errors because both training and test sites are located in different geographic areas. If nth pixel is water in training site and nth pixel is mangrove forest in test site, then DN ratio of that pixel is trained to predict water instead of mangrove forest at nth pixel location of test site. As a result, the DN differences will be higher between calibrated ASTER DN and Landsat 7 ETM+ DN. Therefore, in this research, the arithmetic mean of the DN ratios of training site was used to calculate the calibrated ASTER DN of the test site. The arithmetic mean gave a representative DN ratio of training area which would be applicable to all features of the test area.

RESULTS

The DN differences between calibrated ASTER DN and Landsat 7 ETM+ DN were calculated for the test site. The DN differences were categorized into three categories; 3 DN difference, 6 DN difference and more than 6 DN difference. Regression with DFT

The results show that regression with Discrete Fourier Transform method significantly reduced the pre-calibration DN differences between the original ASTER DN and the Landsat 7 ETM+ DN for the mangrove forest pixels in the green and red band for the test site (Figure 4 and Table 1). In the green and red band, most of the mangrove forest pixels are in 3 DN difference range. However, the percentage of pixels with a DN difference of 3 is approximately 55% for near infrared band. The lower number of pixels in the 3 DN difference range is also indicated by the higher variance in near infrared band (Table 1). For the SWIR I band, approximately 75% of the pixels are in the 3 DN difference range. The spatial distribution of DN difference for the green and red bands with a 3 DN difference range in the test site are (where are they, you tell that the others are evenly distributed) is shown in Figure 4A and 4B. However, pixels with a DN difference range of 3 and pixels with a 6 DN difference range for near infrared band were evenly distributed across the test site (Figure 4C). The spatial DN difference map for SWIR

(17) )2dπcCosθESUN(ρα

DN)dπβCosθESUN(ρcDNAAλAP

A2

LLλLPXA

××+×××

×××−×××=

(15) 2dπc

2dπcθ CosESUNρDN AAλAP

A××

××+××=

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

I band is dominated by pixels with a 3 DN difference with some pixels of 6 DN difference along the bank of tidal stream (Figure 4D).

Table 1. Pixel (%) of different DN difference ranges and variance of DN difference for test site

using regression with DFT

Test site

Pixel (%) at 15 m Pixel (%) at 30 m

Spectral bands

3 6 >6 OR <-6 Water

Variance

3 6 >6 OR <-6 Water

Variance

Green 83 0 0 17 0.749 83 0 0 17 0.651Red 83 0 0 17 1.342 83 0 0 17 1.238

Near IR 58 21 4 17 7.707 64 18 1 17 6.921SWIR I 78 4 1 17 2.475

Cross-calibration Approach Using DN Ratio

The DN values of the ASTER and Landsat 7 ETM+ for training site were calculated using the equation 12 and 15. The rescaled gain and offset are given in the metadata file of the Landsat 7 ETM+ and the coefficients of conversion from DN to radiance are given in the ASTER user handbook (Abrams et al., 2002). The DN ratio of Landsat to ASTER for the training site was calculated using equation 17. The arithmetic mean of the DN ratio is given in Table 2.

Table 2. DN ratio of the test site with 15 m and 30 m spatial resolution.

Spectral bands and Sensors 15 m 30 m Spectral bands Landsat 7

ETM+ ASTER DNL/ DNA DNL/ DNA

Green Band 2 Band 1 0.901 0.901 Red Band 3 Band 2 1.298 1.297 Near Infra red Band 4 Band 3 1.458 1.454 SWIR I Band 5 Band 4 Not applicable 1.712

Figure 4. Spatial distribution (30 m) of the DN differences in the test site for (A) Green band (B) Red band, (C) Near infrared band and (D) SWIR I band using regression with DFT.

A

B

C

D

5 0 6 > 6 OR < 3

Water

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

The cross-calibration approach using DN ratio also reduced the pre-calibration DN difference between ASTER DN and Landsat 7 ETM+ DN for at least 74% of the pixels ( 3 DN difference) for the green and red spectral bands (Table 3). However, this method reduced the pre-calibration difference for only fifty percent of the pixels ( 3 DN difference) in the near infrared band (Table 3). The table of DN differences for SWIR I band shows that approximately 70% of the pixels have a DN difference of 3. The variance of the DN difference is low for the green, red and SWIR I bands whereas it is high for near infrared band (Table 3). The high variance in near infrared band is also indicated by the presence of the least number of pixels in 3 DN difference range among the all spectral bands. The spatial distributions of DN difference for four spectral bands are shown in figure 5. The spatial distributions of DN difference for green, red and SWIR (Figure 5) are characterized by low ( 3 DN difference) DN difference pixels whereas the spatial distribution of DN difference for near infrared band is typified by both low ( 3 DN difference) and high ( 6 DN difference and more 6 DN difference) DN difference pixels.

Figure 5. Spatial distribution (30 m) of the DN differences in the test site for (A) Green band (B) Red band, (C) Near infrared band and (D) SWIR I band using cross-calibration approach using DN ratio.

Table 3. Pixel (%) of different DN difference ranges and variance of DN difference for test site using cross-calibration

Test site

15 m 30 m Pixel (%) Pixel (%)

Spectral bands

3 6 >6 OR <-6 Water

Variance

3 6 >6 OR <-6 Water

Variance

Green 77 6 0 17 2.085 78 5 0 17 2.198Red 74 8 1 17 3.368 74 9 0 17 3.718

Near IR 55 23 5 17 14.42 55 22 6 17 16.12SWIR I 78 4 1 17 4.58

DISCUSSION AND CONCLUSIONS The objective of this research was to eliminate the DN differences between the ASTER DN and Landsat 7

ETM+ DN for the same features. The regression with Discrete Fourier Transform (DFT) and cross-calibration approach using DN ratio were applied to calibrate the DN of ASTER imagery to the DN of Landsat 7 ETM+ imagery. Both methods were able to eliminate pre-calibration DN difference considerably between two sensors for

A

B

C

5 km 0 Water 3

6 > 6 OR < -6

D

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

green, red and SWIR I band. However, pixels with high DN differences were observed for near infrared band. The comparison of variances between two methods show that regression with DFT yields lower variance than cross-calibration approach for each spectral band in the test site (Table 4). The lower variance of regression with DFT indicates that regression with DFT method predicted more efficiently and accurately than the cross-calibration approach using DN ratio. For all spectral bands, the percentage of pixels with a 3 DN difference range using regression with DFT is higher than the cross-calibration approach using DN ratio (Table 4). This indicates that the regression with DFT successfully reduced the pre-calibration DN difference for higher percentage of pixels than the cross-calibration approach using DN ratio. The percentage of pixels with a 3 DN difference range (Table 4) and the variance of the DN difference (Table 4) indicate that the regression with DFT produced better result than cross-calibration approach using DN ratio.

Table 4. Variance of the DN difference and the percentage of 3 DN difference pixels using cross-calibration approach and regression with DFT (Discrete Fourier Transform)

Cross-calibration approach Regression with DFT

15 m 30 m 15 m 30 m

Spectral bands

3 DN difference

(%)

Variance 3 DN difference

(%)

Variance 3 DN difference

(%)

Variance 3 DN difference

(%)

Variance

Green 76.30 2.285 78.24 2.192 82.93 0.749 84.52 0.651 Red 76.20 3.368 73.24 3.718 82.77 1.342 83.50 1.238

Near Infrared 55.07 14.422 55.86 16.15 58.03 7.707 64.13 6.921 SWIR I 68.78 5.21 78.94 2.475

. The methods used in this study were regression with Discrete Fourier Transform and cross-calibration using

DN ration. The regression with Discrete Fourier Transform method consisted of two phases. The first phase was designed to estimate the predicted ASTER DN using structural regression (Reduced major axis method). The second phase was performed in complex domain and used the amplitude of the predicted ASTER DN and phase angle of Landsat 7 ETM+ DN to calculate the calibrated ASTER DN. The second phase analysis significantly improved the accuracy of the analyses and reduced the DN differences between predicted ASTER DN and Landsat 7 ETM+ DN. The results show that the DN difference is low ( 3 DN difference) for most of the mangrove forest pixels in green, red and SWIR band. However, the results for near infrared band show pixels with both low ( 3 DN difference) and high DN difference ( 6 DN difference and more than 6 DN difference). The procedures for calibration of ASTER imagery with this method are given below:

Estimate the slope and intercept using equation 1 and 2 respectively from ASTER and Landsat 7 ETM+ imagery of the training site.

Calculate the predicted ASTER DN for the test site using the original ASTER DN of the test site as an input.

Calculate the Discrete Fourier Transform (Equation 3) of the predicted ASTER DN and the Discrete Fourier Transform of Landsat 7 ETM+ DN of the test site

Calculate the amplitude matrix (Equation 4) and phase angle matrix (Equation 5) from the Discrete Fourier Transform of the predicted ASTER DN and the Discrete Fourier Transform of Landsat 7 ETM+ DN.

Calculate the Discrete Fourier Transform of calibrated ASTER DN (Equation 6) using the amplitude matrix of the DFT for the predicted ASTER DN and the phase angle matrix of the DFT for Landsat 7 ETM+.

Calculate the inverse of the DFT of the calibrated ASTER to estimate the calibrated ASTER DN (real number.)

These procedures were performed using Mathcad and ERDAS Imagine software. Mathcad was used to perform

the structural regression (Reduced major axis method) and for calculating the Discrete Fourier Transform and its amplitude and the phase angle. ERDAS Imagine was used to convert the calibrated ASTER DN matrix into image matrix and represent the spatial distribution of the calibrated ASTER DN.

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

The cross-calibration approach using DN ratio used the arithmetic average of DN ratio (Equation 17) of Landsat 7 ETM+ DN to ASTER DN. This arithmetic mean of DN ratio was calculated from the datasets of the training site. The calibrated ASTER DN of the test site was calculated by multiplying the original ASTER DN of the test site with the arithmetic average of the DN ratio of the training site. The results using this method also show low ( 3 DN difference) DN difference for most of the mangrove forest pixels in green, red and SWIR I band. However, the DN difference for near infrared band is characterized by the pixels with both low DN difference and pixels having high DN differences. The procedures for this method were performed using the Spatial Modeler module of ERDAS Imagine.

Both calibration methods reveal the high difference in DN between the two sensors in the near infrared band. Possible explanations for high difference in near infrared band are given below:

The near infrared is very sensitive and responsive to the biomass, chlorophyll and partially to plant’s water content of mangrove species (Jensen, 1986). This high reflectivity of this band enhances the difference between the reflected energy of two sensor system. Even the slight difference between the sensors in the NIR band is amplified due to high sensitivity to vegetation.

The scattering of the energy is proportional to the wavelength of the electromagnetic spectrum (Richards and Jia, 1999). The scattering increases with the increase in wavelength which indicates more scattering in near-infrared band than green and red bands. The high scattering causes loss of energy during reflection. This high scattering in near infrared band may be another possible explanation for high DN difference. However, the SWIR I band has a longer wavelength than near infrared band but yields better results than near infrared band, possibly due to the lower sensitivity to vegetation in the SWIR I band.

Mangrove forests are one of the most variable surfaces on the earth. This high variation in the surface results in large number of mixed pixels that commonly occur at the transitional zone between forest and tidal water body. These mixed pixels are another source of error in these analyses. Due to the mixed pixels, the high DN differences are prominent along the bank of the tidal stream.

Based on the analyses in the test site, the cross-calibration approaches using DN ratio and the regression with

DFT method are the recommended methods to calibrate ASTER and Landsat 7 ETM+ imagery in green, red and SWIR I bands. Both methods also significantly reduced the DN difference in near infrared band. The conclusions can be made in the light of present study can summarized as follows:

The DN difference between calibrated ASTER DN and Landsat 7 ETM+ DN was low ( 3 DN difference) for the majority of the mangrove forest pixels in the green, red and SWIR I bands using both the cross-calibration method and regression with DFT.

The use of the phase angle of Landsat 7 ETM+ produced the best results in calibration analyses. After structural regression, the phase angle did not change. Instead of using the phase of the DFT of predicted value, the use of phase angle of Landsat 7 ETM+ significantly improved the result and reduced the variance of the DN difference.

High DN difference was found for large number of mangrove forest pixels in near infrared band for both calibration methods.

The regression with DFT calibrated more effectively and efficiently than cross-calibration approach using DN ratio.

The further studies for near infrared bands will be helpful to reduce the observed high DN difference using

developed methods. The scope of the current study is confined to the same date imagery and processed image data. To make these calibration methods more useful for change detection research, the further studies using imagery of different date are recommended.

REFERENCES

Abrams, M., S. Hook, and B. Ramachandran (2002). ASTER user handbook, Jet Propulsion Laboratory, California, 135p.

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