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

10. MLC results – Landsat MLC results – QuickBird

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

14. SPC results for Mangroves SPC results for Saltbush SPC results for Algae

15. Association map of Mangroves, Saltbush and Algae M A S

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

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.

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