FOREST MONITORING AND BIOMASS ESTIMATION FOR REDD+ WITH INSAR

Transcription

1 FOREST MONITORING AND BIOMASS ESTIMATION FOR REDD+ WITH INSAR Svein Solberg, Johannes May, Belachew Gizachew, Wiley Bogren, Johannes Breidenbach Norwegian Institute of Bioeconomy Research GFOI R&D and GOFC-GOLD Land Cover Science Meeting THE HAGUE, 31. Oct. 4. Nov. 2016

2 InSAR data 2000: SRTM C and X ~2012: TanDEM-X

3 Step 1: ΔH 1 = DEM TDX DEM SRTM_C

4 STEP 2: REMOVING ARTEFACTS IN SRTM C-BAND using ANOVA model ΔH 1 = DEM TDX DEM SRTM_C = C artifact + ΔH time + ΔH penetration + e 1 = b 0 + L1 + L2 + B1 + B2 + e

5 ΔH 1 ANOVA RESULTS: R 2 = 0.18 Source DF SS MS F Value Pr > F Belts B <.0001 Belts B <.0001 Lines L <.0001 Lines L <.0001 Error 2.07* Corrected Total 2.07* ΔH corr1 = ΔH 1 - C artifact C artifact

6 PENETRATION DIFFERENCE DEPENDING ON FOREST COVER AND LAND COVER TYPE X-band C-band

7 STEP 3: REMOVING PENETRATION DIFFERENCES AND ARTIFACTS OF SRTM-X using GLM model ΔH 2 = DEM SRTM_X - DEM SRTM_C_corr1 + e 3 = X artifact + ΔH penetration + e 3 = b 0 + XL i + FC*b j + e

8 GLM RESULTS: ΔH 2 R 2 = 0.53 Source DF SS MS F Pr > F XL <.0001 forest_cover * Land_cover <.0001 Error 8.84E Corrected Total 8.84E Residual e 3 GLM model: X artifact + ΔH penetration ΔH 2 = b0 + XL i + FC* j + e 3, L1 = 1 km lines XLi = X-band errors as lines FC* j = penetration difference per forest

9 Penetration difference X-C, m, C TO X PENETRATION CORRECTION MODEL Forest cover % Evergreen Broadleaf Forest Woody Savanna Savannas Grasslands Permanent Wetlands Croplands Cropland/Natural Vegetation Mosaic others / mixed

10 MAKING A SIMULATED SRTM X-BAND DEM DEM SRTM_X_sim = DEM SRTM_C - C artifact X artifact Global Forest Cover 2000 (Hansen et al.) MODIS land cover lifting of c-band dem

11 STEP 4: CHECKING FOR REMAINING BIAS OR RAMP ERRORS cells systematically distributed Zero forest cover No forest cover changeno-forest points systematically distributed over Uganda: Average North-South slope East-West slope ΔH 0.9 mm 8 mm 16 mm

12 STEP 5: VOID FILLING IN STEP TERRAIN (> 30 DEGREES), 1% OF AREA

13 MEAN HEIGHT CHANGE FOR LANDSAT CHANGE CATEGORIES Land cover type loss no change gain Evergreen Broadleaf Forest Woody Savanna Savannas Grasslands Permanent Wetlands Croplands Cropland/Natural Vegetation Mosaic others / mixed For example: Evergreen Broadleaf Forest loss category with Landsat corresponds to 8.8 * 18.4 t/ha/m = 162 t/ha in AGB loss = ca 162 t/ha CO 2 emission Table x. Height change estimates from the ANOVA used for filling of void areas and pixels having unreliable height change estimates

14 COMPARISON AND SYNERGY WITH LANDSAT: FOREST GROWTH IN PROTECTED AREAS InSAR Landsat

15 FOREST GROWTH IN PROTECTED AREAS (2)

16 STEP 6: FROM ΔH TO ΔAGB TO ΔB TO ΔC BIOMASS AGAINST INSAR HEIGHT

17 Savannahs: Noisy relationships due to differences in stem taper

18 STEP 7: UNCERTAINTY ESTIMATION WITH MONTE CARLO 45 random samples; each containing approximately 4 million pixels (1% of the data) 5 times processing of each sample = 225 processing batches In each processing we varied the correction factors randomly according to their uncertainty Sequential processing aggregating errors through the 5 steps: 1. error removal of C-band SRTM, 2. correction from C to X-band SRTM, 3. replacing voids and extreme, illegal values with values specific the given land cover and forest change category, 4. recalculating ΔH to ΔAGB, 5. expansion of ΔAGB to ΔB,

19 UGANDA: CHANGE Forest height decrease : ΔH = 33 cm Corresponding CO 2 emission ΔCO 2 = 27 mill t/year 95% confidence interval = ± 10.5 mill t/year

20 A NOVEL METHOD FOR DIRECT ESTIMATION OF FOREST CARBON CHANGES: Conventional method E = A EF InSAR method E = A H EF H

21 CONCLUSIONS SRTM and Tandem-X can be used for estimating 12 year changes as a Reference Emission Level in REDD+, and for forest C stocks at large scale