1. Evaluating Aboveground Terrestrial Carbon Flux as Ecosystem Planning Tool (Mt. Kenya Ecosystem) Ochieng A. Adimo

2. Outline of the presentation Introduction Objectives Methods Results and discussions Acknowledgement

3. Introduction Presentation is an evaluation of : Terrestrial Carbon sink flux estimation using; Net Primary production (NPP) Production Efficiency Models (PEMs) CASA , GLOPEM , TURC , C-Fix , MODIS, BEAMS (McCallum et al., 2009) Vegetation remote sensing General circulation model downscaling SDSM GIS Interpolation of meteorological data

4. Net Primary Production (NPP) Carbon is removed from the atmosphere via photosynthesis by plants ecosystem – Gross Primary Production GPP ( g C M2 ) Autotrophic respiration (Ra) ( g C M2 ) NPP ( g C M2 ) = GPP - Ra Annual amount of vegetation produced on land in terms of elemental carbon.

5. Justification for using NPP – Demand and Supply NPP is the “Common currency” for Climate Change, Ecological, & Economic Assessment. (Imhoff et al. 2010) Demand for NPP strongly influences land use/land cover change and land management policy. Agricultural versus ‘natural systems’ - Conflicting needs; energy production versus conservation of biodiversity HUMAN APPROPRIATION OF NPP (HANPP)

6. Conceptual framework PEMs are based on the theory of light use efficiency (LUE) Which states that : “a relatively constant relationship exists between photosynthetic carbon uptake and radiation receipt at canopy level.” (McCallum et al., 2009) At leaf level the relation is not linear

7. Conceptual framework Cont’d PEMs usually require inputs of ; Meteorological data : radiation, temperature , moisture (VPD) Satellite- derived fraction of absorbed photosynthetically available radiation (FAPAR).

8. Objectives To assess Mt. Kenya vegetation carbon assimilation Potential by Net primary production Using CASA model To Quantify Human Appropriation of Net Primary Production in Mt. Kenya Ecosystem

9. Hypothesis CASA- can estimate Carbon flux variability in different vegetation using 30 M resolution Landsat 7 ETM

10. Study Area

12. NPP = Sr* EVI *emax*T*W

13. Where Sr = 12 hrs surface solar irradiance

14. EVI = enhanced vegetation index

15. T = stress scalar temperature 30 years

16. W = stress scalar water deficit 30 years

18. EVI modeling in ERDAS model maker RED BLUE NIR NIR RED

19. Thornewaite Monthly water balance model Assume that air temperature is correlated with the integrated effects of net radiation and other controls of evapotranspiration, Temperature water

21. Sample input data 30 years daily/monthly

22. RESULTS THORNTHWAITE MODEL

23. GIS MODELING

24. CREAT RELATIONAL GEODATABASE

25. Interpolate parameters to Raster-Spline

26. Temperature scalar

28. Land cover Data Acquisition and processing Landsat Thematic Mapper (TM) satellite (25/02/1987 & 14/02/2002) Aster extracted land cover maps SRTM data ERDAS ARCGIS FRAGSTAT

29. Land use / land cover assessment based on Landscape metrics The aim included; To classify land use / land cover, and quantify function and spatial data that defines initial conditions of the landscape.

30. Land cover/use

32. Distribution of Mount Kenya vegetation cover

33. Vegetation zonation

34. Tree plantation spatial distributions of Mt. Kenya

35. Mount Kenya Riverine networks of Mt. Kenya

36. Human appropriation of net primary productivityHANPP HANPP = NPP0-NPPt (1) NPPo = NPP before human NPPt = current management And, NPPt = NPPact – NPPh (2) Thus NPPact = NPPt + NPPh (3)

37. HANPP ΔNPPLC = NPP0 - NPPact ( 4) HANPP = NPPh+ΔNPPLC (5) Therefore, HANPP = NPPh+ΔNPPLC = NPP0-NPPt (6) and NPP = Sr EVI emaxTW (Karl-Heinz Erb et al., 2009)

38. Assumptions Assumption 1: The imagery data taken in the dry season when there are no crops in the field represents NPPt . Assumption 2:Above ground NPP can estimate HANPP used for approximating ecosystem energetic (energy flow).

42. RIVER BASIN PLANNING

43. Aggregate HANPP of indigenous vegetation per river basin

44. Land use /land cover in river basin 11

45. Key finding Satellite observed canopy greenness EVI is useful as a variable to help account for CO2 sink in Mt. Kenya Indigenous species are more resilient Agricultural land have great potential for sequestration CASA model captures landscape scale variability Thornewaite water balance model show consistent water deficit maps. CASA model can be used for annual Co2 fixation estimation in mount Kenya

46. Acknowledgement

47. THANK YOU