1. Using satellite observations to measure the direct climate impacts of oil palm expansion in Indonesia Natalie Schultz Heat budget group meeting June 13, 2013

2. Background Deforestation Agricultural Expansion Biogeochemical EffectsBiophysical Effects CO2 emissions Other GHGs N2O CH4 Evapotranspiration Albedo Surface roughness Net climate impact Climate policy Land management

3. Background Global demand for food and fuel is driving agricultural expansion in the tropics Dramatic loss of forests in recent years in Indonesia, caused in large part by the expansion of oil palm plantations World Resources Institute

4. Background Previous studies in the Amazon have shown that the conversion of rainforest to cropland or pasture can influence regional and global climate through changes in the surface energy and water budgets – Higher temperatures, changes in cloud cover and precipitation in addition to increased CO2 emissions – This research has focused on annual crops such as soybean, sugarcane, etc. – Indonesia LUC unique because oil palm lifespan is ~25 yrs and grows to ~20 m tall What are the direct climate effects of oil palm expansion in Indonesia?

5. Data Sources Landsat TM from January 2010 and reference image from 2000 (path 119, row 62). – Subset to ~3000 km 2 region MODIS Products – LST 8day 1km (MOD11A2) – NDVI 16 day 1km (MOD13A2) – ET annual 1km (MOD16A3) – Albedo 16 day 1km (MCD43B3)

6. Uneven-aged stands Use annual minimum NDVI to determine year of field conversion Landsat TM Jan 16, 2010

7. Oil palm expansion from NDVI 20012002 2010200920082007 2006 200520042003

8. Site selection 4km 2 palm field 4km 2 forest

9. Temperature YearMeanStd Dev -80.1410.000 -70.5300.124 -60.5590.473 -50.7410.630 -40.8080.435 -30.7230.632 -21.3120.614 1.3070.925 03.2860.452 13.7290.649 22.4720.541 32.8551.022 41.0530.749 50.2880.000

10. Evapotranspiration YearMeanStd Dev -826.6500.000 -7-37.80019.304 -6-63.53364.515 -5-24.63156.941 -4-29.15065.451 -3-32.61976.293 -2-57.94472.128 -111.456133.121 0-424.706126.958 1-208.219171.490 2-73.94494.899 339.70046.426 43.912141.863 554.2000.000

11. NDVI YearMeanStd Dev -8-0.0520.000 -7-0.0040.039 -6-0.0540.021 -5-0.0440.042 -4-0.0220.033 -3-0.0610.029 -2-0.0600.009 -0.0530.027 0-0.2010.069 1-0.1150.060 2-0.0530.038 30.0000.055 40.0380.079 50.0850.000

12. Albedo YearMeanStd Dev -80.0130.000 -70.0050.009 -60.0170.004 -50.0070.004 -40.0130.006 -30.0080.004 -20.0030.008 0.009 00.0170.009 10.0270.012 20.0270.012 30.0420.000 40.0350.000 50.0300.000

13. TemperatureET

14. NDVIAlbedo

15. Surface energy budget ET has stronger influence on surface temperature than albedo – 425 mm/year (1.16 mm/day) ET reduction  average reduction in latent heat flux of 30 W/m 2 – 0.017 increase in albedo  approx 3.5 W/m 2 reflected Roughness effect not quantified

16. Additional considerations Results valid for clear-sky conditions only – Preliminary examination of NDVI “pixel reliability” index Uncertainties in MODIS data– validation Limited in space and time

17. Conclusions Changes in LST, ET, NDVI, and albedo were observed following the conversion of forest to oil palm field. Is the spatial extent and rate of change occurring in ways that could trigger larger scale climate feedbacks (e.g precip, clouds, veg changes)? Should this type of information be incorporated into a metric of the total land use change effect on climate? How to quantify?