The Gain setting for Landsat 7 (High or Low Gain) depends on: Sensor Calibration - Application. the surface cover types of the earth and the sun angle

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1 Sensor Calibration - Application Station Identifier ASN Scene Center atitude (34 3'0.64"N) Day Night DAY Scene Center ongitude (33 0'7.72"E) WRS Path WRS Row Corner Upper eft atitude (3 32'47.40"N) Date Acquired 2/04/2004 Corner Upper eft ongitude (32 3'34.32"E) Start Time 2004:03:08:: Corner Upper Right atitude (3 4'44.2"N) Stop Time 2004:03:08:2:0.0 Corner Upper Right ongitude (34 7'.72"E) Image Quality VCID Corner ower eft atitude (33 4'6.2"N) Image Quality VCID 2 Cloud Cover 0.00 Corner ower eft ongitude (3 47'.00"E) Sun Elevation Corner ower Right atitude (33 37'4.2"N) Sun Azimuth Corner ower Right ongitude (33 4'0.84"E) Raw data are converted from digital numbers (DN) to units of radiance using the following equation: Where: Radiance = min max QCA max = spectral radiance at the minimum quantized and calibrated data digital number = spectral radiance at the maximum quantized and calibrated data digital number = 2 ( max min) QCA + min QCA max The Gain setting for andsat 7 (High or ow Gain) depends on: the surface cover types of the earth and the sun angle QCA = DN

2 Gain Band Table.2 ETM+ Spectral Radiance Range watts/(meter squared * ster * µm) Before July, 2000 After July, 2000 Gain Band 2 ow Gain High Gain ow Gain High Gain Gain Band 3 Band Number MIN MAX MIN MAX MIN MAX MIN MAX Gain Band Gain Band Gain Band 6 VCID Gain Band 6 VCID 2 H Gain Band Gain Band 8 Consider in our example that DN = 00 Radiance ts = (W/m -2 ster - µm - ) A reduction in between-scene variability is achieved through normalization for solar irradiance by converting spectral radiance to reflectance using the following equation: ( π ts ) d ρts = 2 E *cos( θ )* ESUNλ d Where: ρ ts ts d ESUN λ θ = reflectance at the top of the atmosphere = spectral radiance at the sensor s aperture = Earth-Sun distance in Astronomical Units = solar irradiance at the top of the atmosphere = solar zenith angle Station Identifier Day Night WRS Path WRS Row Date Acquired Start Time Stop Time Image Quality VCID Image Quality VCID 2 Cloud Cover Sun Elevation Sun Azimuth ASN DAY /04/ :03:08:: :03:08:2:

3 Table d.4 Earth-Sun Distance in Astronomical Units Julian day = The number of days elapsed since the start of the year Julian Day Dist. Julian Day Dist. Julian Day Dist. Julian Day Dist. Julian Day Dist Jan. 3 Febr. 2 March 3 April 2 = d =,002 A reduction in between-scene variability is achieved through normalization for solar irradiance by converting spectral radiance to reflectance using the following equation: ( π ts ) d ρts = 2 E *cos( θ )* ESUNλ d Where: 0 0 ρ ts ts d ESUN λ = reflectance at the top of the atmosphere = spectral radiance at the sensor s aperture = Earth-Sun distance interpolated from Table (based on Julian day) in Astronomical Units = solar irradiance at the top of the atmosphere θ 0 = solar zenith angle The elevation angle is the angular height of the sun in the sky measured from the horizontal. The elevation is 0 at sunrise and 0 when the sun is directly overhead (which occurs for example at the equator on the spring and fall equinoxes). The zenith angle is similar to the elevation angle but it is measured from the vertical rather than from the horizontal, thus making the zenith angle = 0 - elevation. 3

4 Station Identifier Day Night WRS Path WRS Row Date Acquired Start Time Stop Time Image Quality VCID Image Quality VCID 2 Cloud Cover Sun Elevation ASN DAY /04/ :03:08:: :03:08:2: o Solar zenith angle = 0 o solar elevation angle = 0 o 3 o = o A reduction in between-scene variability is achieved through normalization for solar irradiance by converting Table.3 ETM+ Solar Spectral Irradiances spectral radiance to reflectance using the following equation: Band ESUN λ (Watts x m -2 x µm -) (Watts x cm -2 x µm - ) ( π ) d ts ρts = E0*cos( θ0)* ESUNλ d Where: ρ ts = reflectance at the top of the atmosphere = spectral radiance at the sensor s aperture ts d = Earth-Sun distance interpolated from Table (based on Julian day) in Astronomical Units ESUN λ = solar irradiance at the top of the atmosphere θ 0 = solar zenith angle The thermal band of andsat 7 ETM+ (band 6) is converted from spectral radiance to temperature. The conversion equation is: Reflectance r = K 2 T = K ln( + ) λ Where: T = effective at-satellite temperature in Kelvin K = calibration constant = ( 2 W* m * ster µ m ) K 2 = calibration constant = ( o Kelvin) 2 λ = spectral radiance ( W* m * ster µ m ) 4

5 K = ( W* m * ster µ m ) In our example consider DN=00 K2 = (Kelvin) Table.2 ETM+ Spectral Radiance Range watts/(meter squared * ster * µm) The thermal band of andsat 7 satellite has both ow Gain and High Gain Band Number Before July, 2000 ow Gain High Gain MIN MAX MIN MAX After July, 2000 ow Gain High Gain MIN MAX MIN MAX Atmospheric Effects Temperature K = =.3 o C

6 7 th May 200: Prior to atmospheric correction 7 th May 200: After atmospheric correction 4 th July: Prior to atmospheric correction 4 th July: After atmospheric correction 2 st August: Prior to atmospheric correction 2 st August: After atmospheric correction 6

7 Atmospheric effects Image acquisition Areas of water (hectares) 7 th May 200 Prior to atmospheric correction 8. After atmospheric correction 43.6 Image Processing 4 th July August September Difference between areas classified as water before and after atmospheric correction Spectral characteristics of different materials Image Classification Reflectance (%) Vegetation Dry bare soil Visual Interpretation Unsupervised Classification Supervised Classification 20 Water (Clear) Wavelength (µm) Visual interpretation Class identification Identification of the classes of ground cover to be detected is essential prior to image classification (from desk study or other sources) Visual interpretation consists of: the visual identification of image features and their assignment into classes. This is enabled by the use of time series images where crops can be discriminated based on changes during their growth stage. 7

8 Visual interpretation of the images is initially carried out to identify broad classes such as water, soil and vegetation 24 th February 2000 False colour presentation. Band 6 (thermal) projected on red, Band 4 (near-infrared) projected on green and band 2 (green) projected on blue The images are presented in false colour (Bands 2,4,6): the red colour indicates high temperature areas, the green vegetation and the black water. Field pattern In the following images, vegetation, soil and water are easily discriminated and marked. Also Field Patterns are clearly identified. Water Soil Field pattern Water Field pattern 7 th July

9 Water 7 th July 2000 Soil Water Spilled water Soil False colour presentation of 7th July 2000 image. Band 6 (thermal) projected on red, Band 4 (near-infrared) projected on green and band 2 (green) projected on blue th September 2000 Water False colour presentation of th September 2000 image. Band 6 (thermal) projected on red, Band 4 (near-infrared) projected on green and band 2 (green) projected on blue Soil th September 2000 Classification is the process of sorting pixels into a finite number of individual classes, or categories of data, based on their data file values. Salty soil Winter wheat followed by alfalfa Summer wheat followed by alfalfa Alfalfa Early-cultivated rice Rice Water irrigation system Open water Unclassified There are two classification methods, unsupervised classification and supervised classification.

10 Unsupervised Classification Unsupervised classification is a computer-automated process where pixels are assigned to classes based on their spectral characteristics. It is used when little is known about the data and giving meaning to the classified results depends on the analyst. The way the pixels are assigned into classes depends on the clustering algorithm used. The most popular unsupervised classification algorithm is ISODATA (Iterative Self-Organising Data Analysis Technique). ISODATA assigns pixels into clusters based on the minimum Euclidean spectral distance. In the first iteration, the means of the clusters are arbitrarily determined. New cluster means are determined after each iteration. The pixels are assigned to the nearest cluster centre every iteration. This is repeated until the number of iterations or the threshold specified by the user is reached. Euclidean Spectral Distance Euclidean Spectral Distance Y p ( x,y ) Using Pythagora s Theorem, in 2-dimensional space: Z p ( x,y,z ) Similarly, in 3-dimensional space, the equation becomes: d qp= (p x q x ) 2 + (p y q y ) 2 q ( x,y,z) d qp = (p x q x ) 2 + (p y q y ) 2 + (p z q z ) 2 q ( x,y) X X Y Deep clear water body: W Very low reflectance in the near infrared waveband Vigorous vegetation: V Very high reflectance in the near infrared waveband The red and near infrared wavebands might therefore be selected as the features on which the classification is to be based. ow reflectance in the visible red ow reflectance in the visible red 0

11 Visible red (band in andsat 7 ETM+) W a c Points a, b, c, d represent pixel values (reflectance values after the sensor calibration). V d We can choose a decision rule such as points will be labelled as members of the group whose centre is closest in feature space to the point concerned To do this, we should consider each point separately (e.g. point a) and determine the Euclidean distance from that point (point a) to each of the centres V and W. b Near infrared (band 4 in andsat 7ETM+ Points that are closer to V will be labelled as vegetation V while the points closer to W will be labelled as water W. The results of the classification described, is a matrix. Its elements are numerical pixel labels. For example, the pixels that are classified as water are given the number 2. The pixels that are classified as vegetation are given the number. Then, the analyst can choose colours to associate each class, i.e., he can choose green colour to associate all pixels that are labelled, and the colour blue for all pixels labelled as 2. In this way, we produce a colour-coded thematic map of the image. In the first iteration, the means of the clusters are arbitrarily determined. New cluster means are determined after each iteration. Apart from the decision rule shortest distance to centre we used for this example, there are other ones. The pixels are assigned to the nearest cluster centre every iteration. This is repeated until the number of iterations or the threshold specified by the user is reached.

12 Unsupervised Classification. Number of Classes used Four class unsupervised classification on 7 th May 200 Class : water Class 2: vegetation Class 3: sparse vegetation and soil Class 4: soil and salty soil Six class unsupervised classification on 7 th May 200 Class : water Class 2: vegetation stage Class 3: vegetation stage 2 Class 4: vegetation stage 3 Class : sparse vegetation and soil Class 6: soil and salty soil Four class unsupervised classification on 4 th July 200 2

13 Water is classified as the first class independent of the number of classes specified Six class unsupervised classification on 4 th July 200 Unsupervised Classification. Number of Classes used Unsupervised Classification 2. Single or Time-series image? The number of classes specified significantly affects the classification % of pixels changing class % of pixels changing class Change of class Change of class Percentage of pixels changing class (jumping or dropping) as a result of six class unsupervised classification on 4 th July 200 and 2 st August 200 Percentage of pixels changing class (jumping or dropping) as a result of six class unsupervised classification on 7 th May 200 and 4 th July 200 3

14 Unsupervised Classification 2. Single or Time-series image? The use of a single image for land use classification can result in a non-representative land use map, especially when crop types are being identified. Crops growing at different times will not show in a single image and different crops at similar growth stages and under similar conditions (e.g. both in soil or both in water) will have similar reflectance and may not be distinguishable. Unsupervised Classification Unsupervised classification is not a precise method for image classification. The number of classes specified significantly affects the classification. The labelling of the classes depends on the analyst and is therefore subjective. Unsupervised Classification Unsupervised classification cannot effectively be used for discriminating crops since it assigns pixels into classes only based on their radiometric characteristics and crops have similar radiometric characteristics when are at similar growing stages. On the other hand, unsupervised classification showed to be effective in identifying water. 4