APRIL Project.Landsat 5/7/8 - Technical process using ENVI 5.3

By: Ida Bagus Ketut Wedastra

1. Import or download LANDSAT 8 Landsat 8 downloaded from USGS official website. The website name is http://earthexplorer.usgs.gov/.Find the area of interest and choose Landsat achieved data and than choose result tab.

Download images that you need.

2. Image band stackingLandsat 8 consist of 11 band with 1 panchromatic band and we only use seven low resolution band (band 1-7) and one high resolution band (band 8).Staking process needs seven low resolution bands (band 1-7) into one raster file with seven layer. This is process using ENVI software.

3. Radiometric calibrationCorrections related to the sensitivity of the remote sensor, topography and sun angle, and atmospheric scattering and absorption. Calibrating image data to radiance, reflectance, or brightness temperatures. ENVI/Radiometric Correction/Radiometric Calibration.

4. Edit HeaderIf using data from tiff file, you should change wavelength value using header editor in raster management edit ENVI header. Below is value of wavelength that you can use to full fill.

5. Masking ImageMasking is a process to crop an satellite image based on boundary of study area. The boundary is vector spatial data (*.shp) that use as focused area.Open shapefile data, File/open/

a. Vector to ROI/Regions of Interest/Vector to ROI

Result:

b. Subset Data from ROIs/Regions of Interest/Subset Data from ROIs

Result:

Do same step on panchromatic band (band 8) for Landsat 7 or Landsat 8

6. Cloud removalTo reduce cloud and cloud shadow, we will mask original image with cloud or cloud shadow, and here some step to masking the imagery.Using ERDAS Imagine/Ermapper:a. Convert or save as the imagery to Ermapper formatb. Open imagery in Erdas Ermapperc. Using formula

Pilih: YES

d. For Landsat 5 can use syntax below;if SIGMA(I1..I3 | I? * PC_COV(I1..I3 | , R1, I?, 1)) > TotalBand*mean (,r1,i1)+mean (,r1,i2)+mean (,r1,i3)+mean (,r1,i5)+mean (,r1,i6) then null elseif (I1 - I4) / (i1 + i4) > 0 then null else if (I2 - I5) / (i2 + i5) > mean (,r1,i2) + mean (,r1,i5) + mean (,r1,i6) then null else if ((i1-mean(,r1,i1)) * stdev(,r1,i1))+ (i1-mean(,r1,i1)) > mean(,r1,i1) + mean(,r1,i4) then null else 255TotalBand = 6

e. For Landsat 8 can use syntax belowif (((i1 - i2)/(i1+i2)) < 0.05 and ((i3 - i4)/(i3+i4)) < 0) and ((i5 - ((i5+i6+i7)/3))/(i5+i6+i7)/3) > 0.02 then null else if (I2 - I5) / (i2 + i5) > 0 then null else if (I3 - I6) / (i3 + i6) > TotalBand*mean (,r1,i3) + mean (,r1,i6) + mean (,r1,i4) then null else if ((i2-mean(,r1,i2)) * stdev(,r1,i2))+ (i2-mean(,r1,i2)) > mean(,r1,i2) + mean(,r1,i5)*TotalBand then null else if I1 / I2 < mean(,r1,i1)/mean(,r1,i2) then null else 255

f. Use filter “Median 5 x 5”g. Save the image in ers format.h. Open image on ENVI

Hitam = 255 Putih = 0

i. Build Masking/Raster Management/Masking/Build Mask

Option, use cloud data,

put output name. contoh: cloud_mask

j. Apply Masking/Raster Management/Masking/Apply Mask

Put output name

7. Atmospheric correctionAtmospheric correction using Dark Pixel Subtraction, this process aims to adjust spectral value from all band to minimum value (0).

8. Image MosaicsMosaic multiple imageries into single imagery./Mosaicking/Seamless MosaicFirst open reference image than second image as adjust image.

Result:

Save as Imagine format

9. Image pan-sharping (Landsat 7/8)Image pan-sharping is combine low resolution image with high resolution image. This process result is high resolution image with seven bands./Image Sharpening/Gram-Schmidt Pan Sharpening

10. Visual interpretationWe have to prepare shapefile data (polygon) before start digitizing process. Digitizing process depending on image combination, interpretation key, and experiences on image interpretation.Image combination is combination of band with color composition on Red layer, Green layer and Blue layer. Based on function of each band, the combination layer using band 5, band 4 and band 3 (Landsat 5) or band 6, band 5 and band 3 in Landsat 8.Combination band 543 will show vegetation in green color and soil with red color. We also can use combination 453 to show vegetation differences (crop, mangrove, wet shrub, etc.) and other combination to help user to distinguish object in the images. Below is step to start the process:a. Open data in Arcgis

b. Create new shapefile (polygon)

c. Start Editing, Using cut polygon to separate polygon

d. Save editing.