Selection of an appropriate classification technique for coastal biomass mapping using high and low resolution dataset Uro...
<ul><li>Presentation Outlines </li></ul><ul><li>Research Questions </li></ul><ul><li>Study Area </li></ul><ul><li>Methodol...
Research Questions <ul><li>Maximum Likelihood Classification (MLC) or Sub Pixel Classification (SPC)- which is time and co...
Study Area <ul><li>Sandspit  is located at the west of Liyari river, near Hawks Bay along the Karachi coast line.  </li></...
Satellite Data Used <ul><li>Landsat image of 30 m resolution </li></ul><ul><ul><li>Spatial resolution 30m </li></ul></ul><...
<ul><li>Data acquisition & preprocessing </li></ul><ul><li>Field Survey </li></ul><ul><li>Image Classification </li></ul><...
<ul><li>Field Visit </li></ul><ul><ul><li>Avicennia marina  – dominant mangrove species, height varies from 5 to 20 ft. </...
Maximum Likelihood Classification (MLC)  <ul><li>MLC calculates different statistical parameters from the inputs known as ...
 
MLC results – Landsat  MLC results – QuickBird
Tabular comparison of MLC results Land cover Classes QuickBird (ha) Landsat (ha) Tall Mangroves 57.45 87.83 Medium Mangrov...
Sub Pixel Classification (SPC) <ul><li>SPC is used to map the landcover classes which are smaller than the pixel size </li...
 
SPC results for Mangroves SPC results for Saltbush SPC results for Algae
Association map of Mangroves, Saltbush and Algae M A S
Tabular comparison of SPC and MLC Landcover Classes QuickBird (ha) Landsat (ha) Difference (ha) MLC MLC SPC Landsat (MLC) ...
Conclusions and Recommendations MLC when applied on Landsat failed to separate a very important vegetation component into ...
 
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Selection of an appropriate classification technique for coastal biomass mapping using high and low resolution dataset

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Selection of an appropriate classification technique for coastal biomass mapping using high and low resolution dataset

  1. 1. Selection of an appropriate classification technique for coastal biomass mapping using high and low resolution dataset Urooj Saeed, GIS Coordinator World Wide Fund for Nature – Pakistan [email_address] Presented at 2 nd International Conference on Advances in Space Technologies 29-30 November, 2008; Islamabad, Pakistan
  2. 2. <ul><li>Presentation Outlines </li></ul><ul><li>Research Questions </li></ul><ul><li>Study Area </li></ul><ul><li>Methodology </li></ul><ul><li>Analysis and Results </li></ul><ul><li>Conclusions and recommendations </li></ul>
  3. 3. Research Questions <ul><li>Maximum Likelihood Classification (MLC) or Sub Pixel Classification (SPC)- which is time and cost efficient technique for coastal biomass mapping? </li></ul><ul><li>Can medium resolution satellite data be used as an alternate of better-resolution satellite data? </li></ul>
  4. 4. Study Area <ul><li>Sandspit is located at the west of Liyari river, near Hawks Bay along the Karachi coast line. </li></ul><ul><li>Area = 1618 ha </li></ul><ul><li>Major vegetation types : </li></ul><ul><ul><li>Mangrove </li></ul></ul><ul><ul><li>Saltbush </li></ul></ul><ul><ul><li>Algae </li></ul></ul>
  5. 5. Satellite Data Used <ul><li>Landsat image of 30 m resolution </li></ul><ul><ul><li>Spatial resolution 30m </li></ul></ul><ul><ul><li>Acquisition date 6 th October, 2001 </li></ul></ul><ul><ul><li>Tide Height Value 2m </li></ul></ul><ul><li>Quickbird image </li></ul><ul><ul><li>Spatial resolution 2.4m </li></ul></ul><ul><ul><li>Acquisition date 27 th April, 2003 </li></ul></ul><ul><ul><li>Tide Height Value 2m </li></ul></ul>
  6. 6. <ul><li>Data acquisition & preprocessing </li></ul><ul><li>Field Survey </li></ul><ul><li>Image Classification </li></ul><ul><ul><li>Maximum Likelihood – Landsat and QuickBird </li></ul></ul><ul><ul><li>Sub Pixel – Landsat </li></ul></ul><ul><li>Comparison and Analysis </li></ul>Methodology
  7. 7. <ul><li>Field Visit </li></ul><ul><ul><li>Avicennia marina – dominant mangrove species, height varies from 5 to 20 ft. </li></ul></ul><ul><ul><li>More the distance from the creeks less is the density/height of the mangroves </li></ul></ul><ul><ul><li>Floating algae was also observed </li></ul></ul><ul><ul><li>GPS coordinates of different forest density classes were recorded </li></ul></ul>
  8. 8. Maximum Likelihood Classification (MLC) <ul><li>MLC calculates different statistical parameters from the inputs known as training areas and on the basis of these parameters it assigns a specific class to certain pixel </li></ul><ul><li>In this study MLC technique was used to develop output maps of both the datasets i.e Quick Bird and Landsat </li></ul>
  9. 10. MLC results – Landsat MLC results – QuickBird
  10. 11. Tabular comparison of MLC results Land cover Classes QuickBird (ha) Landsat (ha) Tall Mangroves 57.45 87.83 Medium Mangroves 125.9 216.41 Small Mangroves 180.89 113.68 Regeneration 5.35 Nil Sub-Total for Mangroves 370 417.92 Salt Bushes 38.57 Nil Floating Algae 131.03 Nil Algae on mud 42.82 Nil Sludge/wet soil 11.18 66.11 Water 364.04 378.91 Settlements 45.86 119.35 Salt pans 25.61 116.56 Mudflats 149.52 155.39 Land soil 444.66 398.04
  11. 12. Sub Pixel Classification (SPC) <ul><li>SPC is used to map the landcover classes which are smaller than the pixel size </li></ul><ul><li>Five major modules were used to run this algorithm </li></ul><ul><li>In this study SPC was applied only on Landsat satellite image </li></ul>
  12. 14. SPC results for Mangroves SPC results for Saltbush SPC results for Algae
  13. 15. Association map of Mangroves, Saltbush and Algae M A S
  14. 16. Tabular comparison of SPC and MLC Landcover Classes QuickBird (ha) Landsat (ha) Difference (ha) MLC MLC SPC Landsat (MLC) – QuickBird (MLC) Landsat (SPC) – QuickBird (MLC) Mangroves 376.65 417.92 375.56 41.27 - 1.09 Saltbush 26.05 Nil 26.33 - 0.28 Algae 42.82 Nil 67.65 - 24.83
  15. 17. Conclusions and Recommendations MLC when applied on Landsat failed to separate a very important vegetation component into a different landcover classes. By using SPC, mixed pixel problem was satisfactorily overcome by classifying high spatial frequency vegetation classes i.e. saltbush and algae SPC proved to be a time effective technique as less training areas for Material of Interest (MOI) were required Due to the time and cost effectiveness, it is highly recommended to use SPC on medium resolution data for coastal biomass mapping at large area. It is also suggested to use selective patches of high resolution satellite image for the ground truthing and MOI definition.
  16. 19. Thank you

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