Venessa BennettVenessa Bennett w0279429@nscc.caw0279429@nscc.caCHANGE DETECTION STUDY - ARCTIC SUMMER SNOW, ICE and SEA ICE MONITORING NW GREENLANDCHANGE DETECTION STUDY - ARCTIC SUMMER SNOW, ICE and SEA ICE MONITORING NW GREENLAND

INTRODUCTIONINTRODUCTION

Figure 2a Figure 2b

LANDSAT 7 - 2006LANDSAT 5 - 2002

NN NN

CANADA

GREENLAND

Qaanaaq

Nuuk

CANADA

GREENLAND

NN

Figure 1

The purpose of this study is to conduct change detection and a land cover classification to highlight significant areas of difference in the distribution of snow and ice in northwestern Greenland between 2002 and 2006 using Landsat TM 5 and Landsat TM 7 data (Fig. 1). The two Landsat scenes were acquired in early July for the two years evaluated, across a region encompassing the Kennedy Channel that separates NW Greenland from NE Canada (Fig. 2a, b). The closest town of noteworthy size is Qaanaaq, approximately 340 km to the SW of the study area (Fig. 1). Land cover types are limited to snow, ice, sea ice, water (ocean, lakes, rivers), glaciers, bare rock and other quaternary glacial features. No significant anthropogenic influence is notes in the land cover types present in the Area of Interest (AOI).

Several techniques are employed after image pre-processing to identify increase or decreases in snow and ice landcover units in the a subset of the main Landsat scene study area, including:

Image processing was completed using both the ERDAS IMAGINE software packaged (Version 14.00.0100, build 715; Intergraph Corporation) and PCI Geomatica 2014. Presentation maps presented were created in ArcGIS 10.2.2. Metadata describing both Landsat scenes and the associated DEM used in image pre-processing are provided in the 'Data Sources' section. The Landsat data are in the geographic co-ordinate system WGS 84 and projected to UTM zone 20.

KEN

NED

Y CH

AN

NEL

KEN

NED

Y CH

AN

NEL

Petermann Glacier

Petermann Glacier

The purpose of this study is to conduct and a to highlight significant areas of difference in the distribution of snow and ice in northwestern Greenland between 2002 and 2006 using Landsat TM 5 and Landsat TM 7 data ( ). The two Landsat scenes were acquired in early July for the two years evaluated, across a region encompassing the Kennedy Channel that separates NW Greenland from NE Canada ( ). The closest town of noteworthy size is Qaanaaq, approximately 340 km to the SW of the study area ( ). Land cover types are limited to snow, ice, sea ice, water (ocean, lakes, rivers), glaciers, bare rock and other quaternary glacial features. No significant anthropogenic influence is notes in the land cover types present in the Area of Interest ( ).

change detection land cover classification

Fig. 1

Fig. 2a, bFig. 1

AOI

Several techniques are employed after image pre-processing to identify increase or decreases in snow and ice landcover units in the a subset of the main Landsat scene study area, including:

1) Visualization of false colour composites (relative changes), 1) (relative changes), Visualization of false colour composites

2) Individual band subtractions (quantitative) and 2) (quantitative) and Individual band subtractions

3) Discriminant Function Change Detection (quantitative estimate of the probability of change per pixel).3) (quantitative estimate of the probability of change per pixel).

Discriminant Function Change Detection

Image processing was completed using both the ERDAS IMAGINE software packaged (Version 14.00.0100, build 715; Intergraph Corporation) and PCI Geomatica 2014. Presentation maps presented were created in ArcGIS 10.2.2. Metadata describing both Landsat scenes and the associated DEM used in image pre-processing are provided in the 'Data Sources' section. The Landsat data are in the geographic co-ordinate system . WGS 84 and projected to UTM zone 20

WORKFLOWWORKFLOW

CHANGE DETECTION WORKFLOW

ObtainLANDSATscenes &

layer stacking

Download fromhttp://earth

explorer.usgs.gov/

ATMOSPHERICCORRECTION

Ground Reflectance

ATCOR3 correctionto convert radianceto ground spectral

reflectance

Requires DEM to applyterrain effect due to

sun angle corrections

PRE PROCESSING

FALSE COLOURCOMPOSITE

VISUALIZATION

Visual examinationof multi-temporal

scenes usingband false colour

composites

FINAL MAP PRODUCT

Recoding of classifiedimage to highlight

regions of significantchange

Conversion of histogramof Landsat 5 to matchhistogram of Landsat 7

Radiometric Normalization

(Histogram Matching)

IMAGE DIFFERENCEIndividual band

subtraction

Use of Image difference tool to conduct individual

band subtraction ofbands most sensitive to

snow and ice change

CHANGE DETECTION

ObtainETOPO1 DEM

Download fromhttp://www.ngdc.

noaa.gov/mgg/global/global.html

NO SRTM data available for AOI

DISCRIMINANTFUNCTION CHANGE

DETECTION

Unsupervised classification - pixel to

pixel analysis thatdetermines the probability

that each pixel has changed

Figure 3

A schematic illustration of the simplified change detection and classification workflow is provided in Figure 3. Landsat data were downloaded for two time intervals for which acquisition dates were as close as possible to reduce seasonal variations effects.

The two images were imported into ERDAS Imagine 2014 and layer stacked to create two multispectral image files. The ETOPO1 DEM data was downloaded from National Geophysical Data Center (NGDC) and used during atmospheric correction procedures. An atmospheric correction was completed on both the Landsat 5 and 7 multispectral scenes to convert DN to ground reflectance values. Care was needed when apply cloud masking parameters to ensure cloud was delineated from the surrounding snow and ice. Figure 4 illustrates the results of the ATCOR procedure for the Landsat 7 scene for a true colour composite. The PCI .pix files were translated into .img files and re-imported into ERDAS Imagine and subset to an AOI (100 x 70 km).

The Histogram Matching tool was used to carry out radiometric normalization. The tool was used to match the overall scene brightness and contrast from the Landsat 5 scene with the Landsat 7 scene. The Landsat 5 histogram was forced to match the Landsat 7 histogram. Figure 5 illustrates the results of histogram matching for the Landsat 5 scene using a 4-5-6 false colour composite.

PRE - PROCESSING

A schematic illustration of the is provided in . Landsat data were downloaded for two time intervals for which acquisition dates were as close as possible to reduce seasonal variations effects.

simplified change detection and classification workflow Figure 3

The two images were imported into ERDAS Imagine 2014 and layer stacked to create two multispectral image files. The ETOPO1 DEM data was downloaded from National Geophysical Data Center (NGDC) and used during atmospheric correction procedures. An atmospheric correction was completed on both the Landsat 5 and 7 multispectral scenes to convert DN to ground reflectance values. Care was needed when apply cloud masking parameters to ensure cloud was delineated from the surrounding snow and ice. illustrates the results of the procedure for the Landsat 7 scene for a true colour composite. The PCI .pix files were translated into .img files and re-imported into ERDAS Imagine and subset to an .

The tool was used to carry out radiometric normalization. The tool was used to match the overall scene brightness and contrast from the Landsat 5 scene with the Landsat 7 scene. The Landsat 5 histogram was forced to match the Landsat 7 histogram. illustrates the results of histogram matching for the Landsat 5 scene using a 4-5-6 false colour composite.

Figure 4

Figure 5

ATCORAOI (100 x 70 km)

Histogram Matching

PRE - PROCESSING

DN VALUES - LANDSAT 7 SCENEATMOSPHERIC CORRECTION -

LANDSAT 7 SCENE

Figure 4

ATMOSPHERICALLY CORRECTED LANDSAT5 SUBSET SCENE

ATMOSPHERICALLY CORRECTED + HISTOGRAM MATCHED LANDSAT5

SUBSET SCENE

Figure 5

Three steps were involved to analyze snow and ice variations within the Landsat 5 and 7 Imagery between 2002 and 2006. A preliminary review of various band composites that were most sensitive to the different landcover features was carried out. This was done to identify which bands would be most useful for subsequent image difference calculations. The Image Difference tool was then used on each band most applicable for delineating variations and snow and ice cover. Finally, an unsupervised classification was carried out to that calculated the probability that the before and after pixels had undergone change (within various confidence levels). The resultant imageis a greyscale image with pixel values between 0.0 and 1.0 that were subsequently recodedinto information classes representing spectral change classes.

CHANGE DETECTION & CLASSIFICATIONCHANGE DETECTION & CLASSIFICATION Three steps were involved to analyze snow and ice variations within the Landsat 5 and 7 Imagery between . A preliminary review of various that were most sensitive to the different landcover features was carried out. This was done to identify which bands would be most useful for subsequent . The Image Difference tool was then used on each band most applicable for delineating variations and snow and ice cover. Finally, an

was carried out to that calculated the probability that the before and after pixels had undergone change (within various confidence levels). The resultant imageis a greyscale image with pixel values between 0.0 and 1.0 that were subsequently recodedinto information classes representing spectral change classes.

2002 and 2006

image difference calculations

band composites

unsupervised classification

FALSE COLOUR COMPOSITE - VISUALIZATIONFALSE COLOUR COMPOSITE - VISUALIZATION

False colour composite Landsat 5 and 7 composites were assessed to see which combinations were more effective at highlighting differences in snow and ice land cover types. Cloud free areas were selected for the visualization process. Visible wavelengths (bands 1-3) were much better for imagery sea ice than the mid and near –infrared wavelengths, but provided poor resolution of ice fields. Mid and short wave infrared wavelengths (bands 5 and 6) provide no resolution of sea ice cover, but sharply defined the edge of snow and ice areas. Near infra-red wavelengths (band 4) provide detailed resolution of internal variations with the snow and ice cover areas in addition to sea ice. Figure 6 illustrates single band imagery for bands 1, 4 and 6 used for final false colour composite 1-4-6 shown in Figure 7. These bands were selected for individual band subtraction algorithms (Image Difference).

Landsat 7: BAND 1 Landsat 7: BAND 6Landsat 7: BAND 4

Figure 6

LANDSAT7 - 2002: 1-4-6 FALSE COLOUR COMPOSITE LANDSAT 5 - 2006: 1-4-6 FALSE COLOUR COMPOSITE

Figure 7

False colour composite Landsat 5 and 7 composites were assessed to see which combinations were . Cloud free areas were selected for the visualization process. Visible wavelengths (bands 1-3) were much better for imagery sea ice than the mid and near –infrared wavelengths, but provided poor resolution of ice fields. Mid and short wave infrared wavelengths (bands 5 and 6) provide no resolution of sea ice cover, but sharply defined the edge of snow and ice areas. Near infra-red wavelengths (band 4) provide detailed resolution of internal variations with the snow and ice cover areas in addition to sea ice. illustrates single band imagery for bands 1, 4 and 6 used for final false colour composite 1-4-6 shown in . These bands were selected for individual band subtraction algorithms ( .

more effective at highlighting differences in snow and ice land cover types

Figure 6Figure 7 Image Difference)

Change detection was carried out using the Image Difference tool for bands 1, 4 and 6. The tool involves a band subtraction between the radiometric balanced Landsat 5 imagery (2006) and the ground reflectance data in the Landsat 7 Imagery (2002). Two images are produced from the Image Difference tool including (i) a continuous, single band greyscale dataset represent the results of the before and after scene subtraction process, and (ii) a Highlight Change file which allows for simple thresholds to be established showing zones of intense change (increase or decrease).

IMAGE DIFFERENCE - BAND SUBTRACTIONIMAGE DIFFERENCE - BAND SUBTRACTION

Very bright pixels in the image difference files represent zones of additive change (i.e. increased snow and ice cover). Conversely, very dark pixels have illustrate zones of net loss of snow and ice between the two time intervals examined. Figures 8, 9 and 10 illustrate the results of band subtraction for bands 1, 4 and 6. A 10 % threshold was set for the highlight change file. Areas in red represent zones of loss or decrease and areas in green represent zones of increase or net gain.

IMAGE DIFFERENCE BAND 1 IMAGE DIFFERENCE BAND 4 IMAGE DIFFERENCE BAND 6

Figure 8 Figure 9 Figure 10

was carried out using the for . The tool involves a band subtraction between the radiometric balanced Landsat 5 imagery (2006) and the ground reflectance data in the Landsat 7 Imagery (2002). Two images are produced from the Image Difference tool including (i) a dataset represent the results of the before and after scene subtraction process, and (ii) a file which allows for simple thresholds to be established showing zones of intense change (increase or decrease).

Change detectioncontinuous, single band greyscale

Image Difference toolHighlight Change

bands 1, 4 and 6

Very bright pixels in the image difference files represent zones of additive change ( ). Conversely, very dark pixels have illustrate zones of between the two time intervals examined. illustrate the results of band subtraction for bands 1, 4 and 6. A 10 % threshold was set for the highlight change file. Areas in red represent zones of loss or decrease and areas in green represent zones of increase or net gain.

i.e. increased snow and ice cover net loss of snow and iceFigures 8, 9 and 10

DISCRIMINANT FUNCTION CHANGE DETECTIONDISCRIMINANT FUNCTION CHANGE DETECTION

The Discriminant function change detection is an algorithm that utilizes the Mahalanobis Distance calculation to determine change between two multi-temporal co-registered images *2. The tool characterizes the natural distribution of spectral clusters in the data space of one image, then uses a discriminant function to measure probability of change of the pixels in the other image *2. The method represents a form of pixel to pixel change detection. A simplified procedure involves the following steps *2:

The is an algorithm that utilizes the to determine change between two multi-temporal co-registered images *2. The tool characterizes the natural distribution of spectral clusters in the data space of one image, then uses a discriminant function to measure probability of change of the pixels in the other image *2. The method represents a form of . A simplified procedure involves the following steps *2:

Discriminant function change detection Mahalanobis Distance calculation

pixel to pixel change detection

Figure 11 illustrates the results of the discriminant function change detection tool. Recoding of the image into important spectral change classes was completed on this dataset (Figure 12) and the Band 1 image difference file (Figure 13). The discriminant function contains 1 main information class representing increase in unspecified snow and ice. The image difference tool was recoded into 4 information classes included sea ice decrease, thin snow cover, medium snow cover and glacial ice and snow (mixed class).

illustrates the results of the discriminant function change detection tool. was completed on this dataset ( ) and the Band 1 image difference file ( ). The discriminant function contains class representing increase in unspecified snow and ice. The image difference tool was included sea ice decrease, thin snow cover, medium snow cover and glacial ice and snow (mixed class).

Figure 11Figure 12 Figure 13

Recoding of the image into important spectral change classes1 main information

recoded into 4 information classes

NN NN

Spectral Change ClassIncreased Snow and

Ice

Figure 11 Figure 12

DISCRIMINANT FUNCTION GREY SCALE CLASSIFICATION RECODED DISCRIMINANT FUNCTION RESULTS

1. Identify the base image (the image representing the starting time frame after which change occurs) – Landsat 7 – 2002 dataset. 2. Perform an unsupervised classification on the base image into reasonable number of spectral classes. 3. Thematic output from base unsupervised classification is used as a zonal mask to extract multivariate signatures from the second image (Landsat 5 – 2006).4. For each pixel in the second image, the Mahalanobis Distance is calculated using the signature corresponding the class to which it is assigned (determined in step 3)5. Mahalanobis Distance is converted to a probability metric, which represents the likelihood that the pixel in the second image has changed from the base starting image.6. This value is written to each pixel and assigned to a grey scale.

1. Identify the base image (the image representing the starting time frame after which change occurs) – Landsat 7 – 2002 dataset. 2. Perform an unsupervised classification on the base image into reasonable number of spectral classes. 3. Thematic output from base unsupervised classification is used as a zonal mask to extract multivariate signatures from the second image (Landsat 5 – 2006).4. For each pixel in the second image, the Mahalanobis Distance is calculated using the signature corresponding the class to which it is assigned (determined in step 3)5. Mahalanobis Distance is converted to a probability metric, which represents the likelihood that the pixel in the second image has changed from the base starting image.6. This value is written to each pixel and assigned to a grey scale.

Figure 13

BAND 1 - IMAGE DIFFERENCE RECODED SCENE

DISCUSSIONDISCUSSION

The purpose of this study was to examine the efficacy of change detection tools available within ERDAS imagine to discern changes in the distribution of snow and ice in NW Greenland between 2002 and 2006. A visual inspection of several band composites but most noteable the 1-4-6 false colour composite reveals that decrease and increase in snow and ice are apparent throughout the AOI. The extent of sea ice significant decreases and snow cover increases during the 4 year time frame. Additionally, the mouth of the Peterman glacier feeding into the Kennedy Channel, shows an increase in area. The results produced from both the Image Difference and Discriminant Function tool are highly sensitive to the pre-processing the datasets undergo. Slight radiometric imbalances can lead to either false positives or false negatives.

The Image Difference tool was very effective in identifying these main changes in snow and ice cover in the AOI when the greyscale image was used for analysis. The highlight change data proved less reliable and yielded threshold values that were misleading. The poor results from the highlight change tool are likely due to the thresholding parameters and also radiometric imbalances between images that are particularly evident over the ice sheets.

The Discriminant function tool proved less useful than the image difference tool. The main areas of additional snow and glacier ice were identified, however the algorithm had difficulties identifying the decrease in sea ice in the 2006 image. A single information class was derived from the unsupervised classification in contrast to four information classes derived from the image difference results for band 1.

The increase in snow cover in 2006 compared to 2002 is likely due to a summer snowfall event. The increase in area of ice at the mouth of the Petermann Glacier between 2002 and 2006 is likely due to the constant state of flux glaciers undergo close to their outlets (Kennedy Channel in this case). Calving off of the glacier front typically occurs at these localities. The reduction in sea ice however is most definitely because of anthropogenic global warming and all those nasty fossil fuels heating the planet

The purpose of this study was to examine the to discern changes in the in NW Greenland between 2002 and 2006. A visual inspection of several band composites but most noteable the reveals that decrease and increase in snow and ice are apparent throughout the AOI. The extent of sea ice significant decreases and snow cover increases during the 4 year time frame. Additionally, the mouth of the Peterman glacier feeding into the Kennedy Channel, shows an increase in area. The results produced from both the Image Difference and Discriminant Function tool are highly sensitive to the pre-processing the datasets undergo. Slight radiometric imbalances can lead to either false positives or false negatives.

efficacy of change detection tools available within ERDAS imagine distribution of snow and ice 1-4-6 false colour composite

The tool was very effective in identifying these main changes in snow and ice cover in the AOI when the greyscale image was used for analysis. The highlight change data proved less reliable and yielded threshold values that were misleading. The poor results from the highlight change tool are likely due to the thresholding parameters and also radiometric imbalances between images that are particularly evident over the ice sheets.

Image Difference

The tool proved less useful than the image difference tool. The main areas of additional snow and glacier ice were identified, however the algorithm had difficulties identifying the decrease in sea ice in the 2006 image. A was derived from the unsupervised classification in contrast to derived from the image difference results for band 1.

Discriminant function four information classessingle information class

The increase in snow cover in 2006 compared to 2002 is likely due to a summer snowfall event. The increase in area of ice at the mouth of the Petermann Glacier between 2002 and 2006 is likely due to the constant state of flux glaciers undergo close to their outlets (Kennedy Channel in this case). Calving off of the glacier front typically occurs at these localities. The reduction in sea ice however is most definitely because of anthropogenic global warming and all those nasty fossil fuels heating the planet

DATA SOURCESDATA SOURCES

Three main datasources were used for this study including - (1) Landsat 5 TM - (LT50332482006186KIS00), (2) Landsat 7 ETM – (LE70332482002183EDC00) & (3) Etopo1 Global DEM

Landsat 5 and 7 multispectral data were acquired from the USGS Earth Explorer website, http://earthexplorer.usgs.gov/. Data acquisition parameters are summarised in Tables 1 and 2. Landsat 5 consisted of two sensors including a Multispectral Scanner (MSS) and a Thematic Mapper (TM). The satellite had a 705 km, sun synchronous, near polar orbit with a 16 day repeat cycle and a 99 minute orbit period. Seven bands are available in the data that include a blue and thermal band (Table 1) *1. The data have a 30 x 30 m ground resolution and an 8 bit dynamic range (allowed for 256 DN values for each spectral band). Landsat TM consisted of a scan angle of 15.4o yielding an equivalent swath width (185 km) to the MSS *1. Scanning occurred in both forward and backward oscillations that permitted improved spatial resolution. A scan line corrector was implemented to remove gaps in acquired data derived from forward and backward scanning direction *1.

Landsat 7 carries the Enhanced Thermal Mapper that includes a 15 m panchromatic band and improved radiometric sensitivity and improved spatial resolution of the thermal band (60 m) *1. The orbital specifications are as for Landsat 5. Temporal resolution is 16 days. Data acquired after May 31 2003 are affected by a failed scan line corrector *1. Spectral resolution is summarized in Table 2. The Landsat 5 scene was acquired on the 5th July, 2006 and the Landsat 7 scene was acquired on the 2nd of July 2002 prior to the problem with the scan line corrector.

The ETOPO1 data was downloaded from http://www.ngdc.noaa.gov/mgg/global/global.html in TIFF file format. The data represents a global (terrestrial+bathymetric) digital elevation dataset at a spatial resolution of 1 arc minute. The horizontal datum is WGS 84 and the vertical datum. All source elevation data used in building ETOPO1 were transformed to WGS 84 geographic. The vertical datum of ETOPO1 is “sea level”. Source elevation data were not converted to a common vertical datum due to the large cell size of ETOPO1 (1 arc-minute; ~2 km). This means that the vertical uncertainty of ETOPO1 elevations (greater than 10 meters) exceeds the differences between vertical datums near sea level (usually less than a meter). Elevation measurement units are in meters.

LANDSAT 7 - ETM

Landsat Scene Identifier Landsat Scene Identifier LE70332482002183EDC00LE70332482002183EDC00WRS Path WRS Path 033033WRS Row WRS Row 248248

Date Acquired Date Acquired 2002/07/022002/07/02Start Time Start Time 2002:183:18:51:39.93412502002:183:18:51:39.9341250Stop Time Stop Time 2002:183:18:52:07.00568742002:183:18:52:07.0056874Scene Cloud Cover Scene Cloud Cover 0.530.53Center Latitude Center Latitude 81°21'43.20"N81°21'43.20"NCenter Longitude Center Longitude 62°35'57.12"W62°35'57.12"W

Sensor Identifier Sensor Identifier ETMETM

Nadir Off Nadir Nadir Off Nadir NADIRNADIRFull or Partial Scene Full or Partial Scene FULLFULL

Acquisition ParameterAcquisition Parameter DetailsDetails

B1B2B3B4B5B6B7B8

0.45-0.520.52-0.600.63-0.690.77-0.901.55-1.7510.4-12.52.09-2.350.52-0.90

30303030

15306030

BAND Wavelength (micrometer) Resolution (m)

B1B2B3B4B5B6B7B8

0.45-0.520.52-0.600.63-0.690.77-0.901.55-1.7510.4-12.52.09-2.350.52-0.90

30303030

15306030

BAND Wavelength (micrometer) Resolution (m)

Table 2

LANDSAT 5 - TM

Landsat Scene Identifier Landsat Scene Identifier LT50332482006186KIS00LT50332482006186KIS00WRS Path WRS Path 033033WRS Row WRS Row 248248

Sensor Identifier Sensor Identifier TMTM

Nadir Off Nadir Nadir Off Nadir NADIRNADIR

Full or Partial Scene Full or Partial Scene FULLFULL

Date Acquired Date Acquired 2006-07-052006-07-05

Start Time Start Time 2006:186:18:56:08.420812006:186:18:56:08.42081

Scene Cloud Cover Scene Cloud Cover 10.0010.00Center Latitude Center Latitude 81°19'38.93"N81°19'38.93"N

Center Longitude Center Longitude 62°13'35.15"W 62°13'35.15"W

Stop Time Stop Time 2006:186:18:56:35.033692006:186:18:56:35.03369

Acquisition ParameterAcquisition Parameter DetailsDetails

B1B2B3B4B5B6B7

0.45-0.520.52-0.600.63-0.690.77-0.901.55-1.7510.4-12.52.08-2.35

30303030

3012030

BAND Wavelength (micrometer) Resolution (m)

B1B2B3B4B5B6B7

0.45-0.520.52-0.600.63-0.690.77-0.901.55-1.7510.4-12.52.08-2.35

30303030

3012030

BAND Wavelength (micrometer) Resolution (m)

Table 1

Three main datasources were used for this study including - (1) Landsat 5 TM - (LT50332482006186KIS00), (2) Landsat 7 ETM – (LE70332482002183EDC00) & (3) Etopo1 Global DEM

Landsat 5 and 7 multispectral data were acquired from the USGS Earth Explorer website, http://earthexplorer.usgs.gov/. Data acquisition parameters are summarised in Tables 1 and 2. Landsat 5 consisted of two sensors including a Multispectral Scanner (MSS) and a Thematic Mapper (TM). The satellite had a 705 km, sun synchronous, near polar orbit with a 16 day repeat cycle and a 99 minute orbit period. Seven bands are available in the data that include a blue and thermal band (Table 1) *1. The data have a 30 x 30 m ground resolution and an 8 bit dynamic range (allowed for 256 DN values for each spectral band). Landsat TM consisted of a scan angle of 15.4o yielding an equivalent swath width (185 km) to the MSS *1. Scanning occurred in both forward and backward oscillations that permitted improved spatial resolution. A scan line corrector was implemented to remove gaps in acquired data derived from forward and backward scanning direction *1.

Landsat 7 carries the Enhanced Thermal Mapper that includes a 15 m panchromatic band and improved radiometric sensitivity and improved spatial resolution of the thermal band (60 m) *1. The orbital specifications are as for Landsat 5. Temporal resolution is 16 days. Data acquired after May 31 2003 are affected by a failed scan line corrector *1. Spectral resolution is summarized in Table 2. The Landsat 5 scene was acquired on the 5th July, 2006 and the Landsat 7 scene was acquired on the 2nd of July 2002 prior to the problem with the scan line corrector.

The ETOPO1 data was downloaded from http://www.ngdc.noaa.gov/mgg/global/global.html in TIFF file format. The data represents a global (terrestrial+bathymetric) digital elevation dataset at a spatial resolution of 1 arc minute. The horizontal datum is WGS 84 and the vertical datum. All source elevation data used in building ETOPO1 were transformed to WGS 84 geographic. The vertical datum of ETOPO1 is “sea level”. Source elevation data were not converted to a common vertical datum due to the large cell size of ETOPO1 (1 arc-minute; ~2 km). This means that the vertical uncertainty of ETOPO1 elevations (greater than 10 meters) exceeds the differences between vertical datums near sea level (usually less than a meter). Elevation measurement units are in meters.

This project is a student project and was completed for education purposes. The poster should not be reproduced or distributed in any format.This project is a student project and was completed for education purposes. The poster should not be reproduced or distributed in any format.DISCLAIMERDISCLAIMER

REFERENCESREFERENCES

*1 http://landsat.gsfc.nasa.gov/?page_id=2290*1 http://landsat.gsfc.nasa.gov/?page_id=2290 *2 https://intergraphgovsolutions.com/assets/white-paper/Discriminant_Function_Change_in_ERDAS_IMAGINE.sflb.pdf*2 https://intergraphgovsolutions.com/assets/white-paper/Discriminant_Function_Change_in_ERDAS_IMAGINE.sflb.pdf