Introduction MODIS NDVI

Content MODIS NDVI Download/Pre-Processing MODIS Paddy Area Extraction Using MODIS NDVI Data

MODIS Spatial Resolution 250 m 500 m 1000 m Spectral Resolution 36 spectral bands – Hyper-spectral Radiometric Resolution 32 bit Temporal Resolution Daily MODIS stand for Moderate Resolution Imaging Spectroradiometer MODIS instrument provides high radiometric sensitivity in 36 spectral bands MODIS are viewing the entire Earth's surface every 1 to 2 days.

The Terra Satellite (EOS AM) on board in 1999 to present MODIS was launched into space aboard the NASA-EOS platform for studies of climate, vegetation, pollution, global and regional change, Environmental and Disaster Monitoring, The Terra Satellite (EOS AM) on board in 1999 to present The Aqua Satellite (EOS PM) on board in 2002 to present Orbit Terra: Orbit passes N  S Local equatorial crossing time is approximately 10.30 a.m. Aqua: Orbit passes S  N Local equatorial crossing time is approximately 1.30 p.m. * Differences in Terra and Aqua Orbits result in different viewing and cloud cover conditions for a given location. Terra (EOS-AM1) http://terra.nasa.gov MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Aqua (EOS-PM1) http://aqua.nasa.gov NASA MODIS Website: http://modis.gsfc.nasa.gov/index.php

Terra Satellite Path Sou1rce MODIS Rapid Response - LANCE (Feb,23,2012) Terra Satellite flight direction is shown by red arrow. The red dots show the overpass ti me of each 5-min granule. Similarly as the yellow dots.

Aqua Satellite Path Sou1rce MODIS Rapid Response - LANCE (Feb,23,2012) Aqua Satellite flight direction is shown by red arrow. The red dots show the overpass ti me of each 5-min.

1.Download MODIS Goto: https://earthdata.nasa.gov/ Data >>> Near Real Time Data Rapid Response >>> MODIS Subset >>> USDA Foreign Agricultural Service (FAS)

Download MODIS (Continued) Select FAS_Indochina Select Date Select Image [NDVI] Terra, 250 m.

Download MODIS (Continued) Download Geotiff file Right Click > Save file as.. ../Data/[leave file name as default]

2. Download MODIS - FTP Things to know before further download Data Version MODIS Product MODIS Tile Another way for Download MODIS Source: http://nsidc.org/data/modis/data_versions.htm

Data Version Version 1 (V001) Original, at-launch version. MODIS/Terra Snow Cover products originally processed as Version 001 (V001) were deleted from the NSIDC archive on 06 June 2003, due to the availability of reprocessed data with the improved V003 algorithm. Version 3 (V003) Contains processing refinements accommodating algorithm, instrument, and calibration stabilization. Version 5 (V005) Monthly snow products are available in a Climate Modeling Grid (CMG). Fractional snow cover was added to MOD10_L2 and MOD10A1. Sea Ice by Ice Surface Temperature (IST) and combined sea ice fields were removed from all sea ice products. Browse images are available for all products. V005 products use HDF compression making the file size much smaller. Source: http://nsidc.org/data/modis/data_versions.htm

MODIS Vegetation Products MOD13Q1 Terra Vegetation Indices Tile 250m 16 day MYD13C2 Aqua Vegetation Indices CMG 5600m Monthly MOD13C2 Terra Vegetation Indices CMG 5600m Monthly MYD13C1 Aqua Vegetation Indices CMG 5600m 16 day MOD13C1 Terra Vegetation Indices CMG 5600m 16 day MYD13A3 Aqua Vegetation Indices Tile 1000m Monthly MOD13A3 Terra Vegetation Indices Tile 1000m Monthly MYD13Q1 Aqua Vegetation Indices Tile 250m 16 day MYD13A2 Aqua Vegetation Indices Tile 1000m 16 day MOD13A2 Terra Vegetation Indices Tile 1000m 16 day MYD13A1 Aqua Vegetation Indices Tile 500m 16 day MOD13A1 Terra Vegetation Indices Tile 500m 16 day MOD13Q1 of Terra and MYD13Q1 Aqua that used in this analysis. The resolution is 250m and satellite passed the same location every 16 days. Products that used in this analysis MOD13Q1, MYD13Q1 Source: https://lpdaac.usgs.gov/products/modis_products_table

MODIS Tile Numbers MODIS Tile Number of Study Area - h28v06 Tiles are 10 degrees by 10 degrees at the equator. The tile coordinate system starts at (0,0) (horizontal tile number, vertical tile number) in the upper left corner and proceeds right (horizontal) and downward (vertical). The tile number of study area is (28,06) MODIS Tile Number of Study Area - h28v06

2. Download MODIS - FTP Go to ftp://ladsftp.nascom.nasa.gov Select allData Select 5 [Version 5] Select Data Product (MYD13Q1) Select Year (2007) Select Day (days of year; 201) Search Tile (H28V06) Save as *.hdf Another way for Download MODIS Source: http://nsidc.org/data/modis/data_versions.htm

Download MODIS Data Here Day is represented in Julian Date format – It is day count since the beginning of the year which is also known as Day of The Year (201) ftp://ladsftp.nascom.nasa.gov/allData/5/MYD13Q1/2014/201/ /allData/[Version]/[Product Code]/[Year]/[Day of the Year]/ MODIS Tile Number of Study Area - h28v06

MODIS filenames MOD13Q1.A2007225.h28v06.005.2007245083237.hdf -Product Short Name -Julian Date of Acquisition (A-YYYYDDD) -Tile Identifier (horizontal 29 vertical 07) -Collection Version -Julian Date of Production (YYYYDDDHHMMSS) -Data Format (HDF-Hierarchical Data Format) MODIS filenames follow a naming convention which gives useful information regarding the specific product. For example, the filename indicates such as:

Download MODIS Data Downloaded Data are in Folder 1_Pre_Processing_MODIS Input Downloaded Data are in Folder (Data\1_Pre_Processing_MODIS\Input)

Paddy Area Extraction Using MODIS NDVI Data The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements, and assess whether the target being observed contains live green vegetation or not.

Different Variations of Greenness (NDVI) in Different Land Cover Types Urban Forest Low NDVI, and positive values in Urban areas approximately 0.2 to 0.4 that Corresponding to Feature of grass In water areas the NDVI values as near -1 Paddy Field Water

NDVI Variation in Paddy Land NDVI data has distinguished patterns in paddy field which correspond to variation of greenness of paddy fields. More elaborately, comparably low NDVI value at the Start of the Season (SOS) and high NDVI value in middle of the season. Because of this unique pattern, paddy fields can be extracted. In Paddy Fields, NDVI 0.4  0.8 In Paddy Fields, the values of NDVI is 0.4 - 0.8 Start of the Season Peak NDVI

Paddy Land Extraction– Crop Calendar of Vietnam Based on Crop Calendar, images in June selected as Low NDVI images. Which means near start of the Dry season Based on Crop Calendar, images in August selected as High NDVI images. Which means near harvesting of Dry season

Paddy Land Extraction – Flow Chart Stack 1 of NDVI Images in Low NDVI period Stack 2 of NDVI Images in Max. NDVI period Calculate Pixel-Wise Mean Visualize as a False Color Composition Image ISODATA Classification Area Calculation This is a flow chart for main process for Paddy land map Stack1 as example image NDVI less than 0.4 Stack 2 as NDVI image higher than 0.4 It has error need to take mean to reduce error

Multispec Software Paddy Land Extraction MultiSpec developed by Purdue University, West Lafayette, Indiana, is a multispectral image data analysis software. Freely download https://engineering.purdue.edu/~biehl/MultiSpec/index.html OS Requirements Macintosh Windows

Overview Paddy Land Extraction Software – MultiSpec Import Data Stack Images Calculate Pixel-wise Average of NDVI data sets Visualizing Time Series Data as False Color Composite ISODATA Classification and Merging output classes

MODIS in Study Area High NDVI Low NDVI Stack 2 High NDVI period Yellow box represent for image in Start of the Season Blue box represent for MODIS image in March as Maximum NDVI period. Low NDVI High NDVI Stack 1 Low NDVI period Stack 2 High NDVI period

Open Images as Stack – (Start of the Season) 1. Click File Menu and Open Image 3. Browse location (Data\2_Paddy_Land_Extraction\Input) and Select one Image File (Philippines_NDVI_2010-01-01.tif) 4. Click Open 2. Select Multispectral Type

Open Images as Stack – (Start of the Season) 5. Click OK 6. Click OK

Open Images as Stack – (Start of the Season)

Open Images as Stack – (Start of the Season) 7. Click File Menu and Open Image 9. Browse location (Data\2_Paddy_Land_Extraction\Input) and Select "Philippines_NDVI_2010-01-17.tif" "Philippines_NDVI_2010-01-09.tif" images to make a composite of 3 images. 10. Click Open 8. Select “Link selected file(s) to Active Image Window”

Overview Paddy Land Extraction Software – MultiSpec Import Data Stack Images Calculate Pixel-wise Average of NDVI data sets Visualizing Time Series Data as False Color Composite ISODATA Classification and Merging output classes

Calculating Pixel-Wise Average 11. Click Process Menu and Reformat 12. Click Change Image Format

Calculating Pixel-Wise Average 18. Click OK 15. Select Average 14. Tick “New Channel from Function” 13. Turn On Transform Data 16. Enter 1,2,3 17. Click OK

Calculating Pixel-Wise Average 19. Save as Mean_NDVI_2010_01.tif in Data\2_Paddy_Land_Extraction\Output Folder 20. Click Save

Follow Same Procedure - 3 Images (Max. NDVI period) Follow Same Procedure for Images in 2007-August month (3 images). Summery of steps as following Open as Stack of 3 images Calculate Pixel Wise Average using “Click Change Image Format” tool and Save as

Overview Paddy Land Extraction Software – MultiSpec Import Data Stack Images Calculate Pixel-wise Average of NDVI data sets Visualizing Time Series Data as False Color Composite ISODATA Classification and Merging output classes False colour composite scheme allows vegetation to be detected readily in the image. In this type of false colour composite images, vegetation appears in different shades of red depending on the types and conditions of the vegetation, since it has a high reflectance in the NIR band

Flow Chart False Color Composition Stack 1 in Start of the Season Stack 2 in Max. NDVI period Mean_NDVI_2010_01.tif Mean_NDVI_2010_03.tif Stack Image Mean_NDVI_Stack.tif Now, there are 2 image that are mean of NDVI in Start of season as stack1 and mean of NDVI in Max period as stack2 Next process, we will Stack 2 images.

Open Above Result as False Color Composition (Mean_NDVI_2010_01.tif, Mean_NDVI_2010_03.tif) 22. Browse location (Data\2_Paddy_Land_Extraction\Output) and Select both Images as once, (Mean_NDVI_2010_01.tif, Mean_NDVI_2010_03.tif) 21. Close all existing Images and Click File Menu and Open Image 23. Click Open

Open Above Result as False Color Composition 24. Select “3-Channel Color” 25. Enter 2 as Red, 1 as Green, 1 as Blue 26. Click Ok

Open Above Result as False Color Composition - Output Here area in RED shows area that has high NDVI in 2nd image and low NDVI in 1st image which is correspond to paddy area Next Target is to Separate that Area using simple unsupervised classification method

Save Image this Stack Image 27. Click Process Menu and Reformat 28. Click Change Image Format 29. Select Header as GeoTIFF 30. Click OK

Save Image this Stack Image 31. Save as Mean_NDVI_Stack.tif in Data\2_Paddy_Land_Extraction\Output Folder 32. Click Save

Overview Paddy Land Extraction Software – MultiSpec Import Data Stack Images Calculate Pixel-wise Average of NDVI data sets Visualizing Time Series Data as False Color Composite ISODATA Classification and Merging output classes Next step is to Separate that Area using simple unsupervised classification method as ISODATA

Unsupervised classification ISODATA Iterative Self Organizing Data Analysis Technique More robust User specifies Iteration Clusters Merging and Splitting parameters ISODATA stand for Iterative Self Organizing Data Analysis Technique

Unsupervised classification ISODATA Assign each pixel to nearest cluster (mean spectral distance) Re-calculate cluster means and standard deviations If distance between two clusters < some threshold, merge them If standard deviation in any one dimension > some threshold, split into two clusters Delete clusters with small number of pixels Re-assign pixels, re-calculate cluster statistics etc. until changes of clusters < some fixed threshold

ISODATA Classification 34. Browse location (Data\2_Paddy_Land_Extraction\Output) and Select Stacked Images in Last Step (Mean_NDVI_Stack.tif) 33. Close all existing Images and Click File Menu and Open Image 35. Click Open

ISODATA Classification 36. Click Processor Menu and Cluster 37. Select ISODATA 38. Enter Number clusters (here 5) 39. Click OK 40. Select “Cluster Stats:” as Do Not Save 42. Click OK 41. Select “Cluster mask file:” as GeoTIFF

ISODATA Classification – Save Image 43. Save as Mean_NDVI_Stack_clMask.tif in Data\2_Paddy_Land_Extraction\Output Folder 44. Click Save

ISODATA Classification – Open Classified Image With Visual Inspection, we can distinguish, Cluster 2 as Paddy Land. Next step is to merge classed in order to classify to 2 classes (paddy and non-paddy) Usually, Starting from high number of classes and merging together gives better results. Optimum Number of Classes should be estimated by trial and error here.

ISODATA Classification – Merging Classes (Create Paddy/Non-Paddy Output) 46. Drag and Drop Clusters in to Groups Cluster 1 and Cluster 2 as shown in Figure 45. Select “”Groups/Classes”

ISODATA Classification – Merging Classes (Create Paddy/Non-Paddy Output) 47. Select “”Groups”

ISODATA Classification – Merging Classes -Save Image 48. Click Process Menu and Reformat 49. Click Change Image Format 50. Select Header as GeoTIFF 51. Click OK

ISODATA Classification – Merging Classes -Save Image 52. Save as Mean_NDVI_Stack_clMask_Merge.tif in Data\2_Paddy_Land_Extraction\Output Folder 53. Click Save

Non-Paddy Paddy

Limitation 1. Cloud – Cloud blocks the view of optical images making almost impossible to analyze data. Especially, this is prominent in Start of the Season which associated with Wet-Season (Especially in Rain fed paddy lands) 2. Thin Cloud – when Thin Cloud presents over highly vegetated area, NDVI values are reduced, leading to miss classification. (Even, associated Thin Cloud is not reflected well in standard MODIS vegetation quality data) 3. Flood Water – when flood water present in the ground NDVI value reduced producing wrong NDVI profile 4. Most NDVI time series variations are not a smooth curves due to Atmospheric effect, Flooding effects, Mix-pixel effect (small size paddy fields), etc. which leads to erroneous classifications. (Especially at the start of the season, NDVI variation is very vague and bumpy. Because of this, in above flow chart pixel-wise Mean calculates before thresh-holding)

Thank you

METADATA of MODIS NDVI Data Appendix: METADATA of MODIS NDVI Data

Donwload http://e4ftl01.cr.usgs.gov/MOLT/MOD13Q1.005/2010.01.01/ 0. METADATA file for MODIS images is also available for download by corresponding name with .xml extension http://e4ftl01.cr.usgs.gov/MOLT/MOD13Q1.005/2010.01.01/

Add Data 2. Browse location (Data\1_Pre_Processing_MODIS\Input) and Select Image, (MOD13Q1.A2010001.h29v07.005.2010027190113.hdf) 1. Add Raster Data 3. Select “[GDAL] Hierarchical Data . . “ 4. Click Open

Select Layer 6. Click OK 5.Select “MODIS_Grid_16Day_250m_500m_VI:250m_16_days_NDVI” This Widow shows all layers that available in selected HDF image including NDVI, EVI, VI Quality data, etc. (Total 11 layers). Here, we select only NDVI for the analysis

Open METADATA 7. Right Click on the Layer and Select Properties 8. Select “Metadata” tab and “Properties” section

Open METADATA – Part of Output METADATA File Driver GDAL provider HDF4Image HDF4 Dataset Dataset Description HDF4_EOS:EOS_GRID:"D:\Crop_Monitoring\RESTEC_Training\Phase_II\Material\Data\1_Pre_Processing_MODIS\Input\MOD13Q1.A2010001.h29v07.005.2010027190113.hdf":MODIS_Grid_16DAY_250m_500m_VI:250m 16 days NDVI _FillValue=-3000 add_offset=0 add_offset_err=0 ALGORITHMPACKAGEACCEPTANCEDATE=102004 ALGORITHMPACKAGEMATURITYCODE=Normal ALGORITHMPACKAGENAME=MOD_PR13A1 ALGORITHMPACKAGEVERSION=5 ASSOCIATEDINSTRUMENTSHORTNAME=MODIS ASSOCIATEDPLATFORMSHORTNAME=Terra ASSOCIATEDSENSORSHORTNAME=MODIS AUTOMATICQUALITYFLAG=Passed