Prioritising areas for forest restoration

Transcription

1 Prioritising areas for forest restoration Mathew Williams Jeff Exbrayat, Luke Smallman, David Milodowski University of Edinburgh and National Centre for Earth Observation

2 Challenge Significant forest cover losses, particularly in the tropics Major international effort to restore forests Where should these efforts be focused? What is their likely effect and over what timescale? How should efforts be monitored, reported and verified?

3 Rich era of biomass observations BIOMASS NASA Conae ALOS-2 - JAXA +Landsat, Sentinels, MODIS etc

4 Observed Potential Saatchi map Built from optical and lidar satellite data Baccini map How much biomass could the Amazon hold? Amazon C stocks

5 Correlate intact forest biomass with physical variables Diurnal temperature range Elevation Days with ground frost Annual precipitation Number of wet days Mean relative humidity Hatching shows disturbed forest

6 Importance of physical variables to explain biomass in intact forest landscapes Elevation, rainfall, latitude Wet days, wind, temperature range

7 Observed Potential Saatchi map Baccini map Amazon C stocks

8 Reforestation potential for the Amazon Deficit correlation R Pg C Observed Potential Deficit SA BA Exbrayat and Williams 2015

9 Vast area of opportunity in the tropics

10 Machine learning Tropical application Trained using historical land use map (Hurtt et al) and CRU climate Avitabile biomass map optimises Ba and Sa maps

11 Historical impact of LULCC on pantropical AGB stocks Current biomass Pg C ( ) Biomass with LU from 1860s Pg C ( ) Difference 75.6 Pg C ( ) Exbrayat et al. (under review)

12 Current tropical biomass deficit Current biomass Pg C ( ) Biomass with LU from 1860s Pg C ( ) Difference 75.6 Pg C ( ) Exbrayat et al. (under review)

13 Outlook Exbrayat et al. (under review) 75.6 Pg C of emissions corresponds to 17.8 ppm of CO 2 ~8 years of fossil-fuel emissions at current level Potential for carbon sequestration through reforestation

14 Tropical biomass deficit Dominated by 22% reduction in tropical forest cover Biomass deficit Change in Forest cover Americas Africa Asia 62% 20% 18% 54% 12% 34% 6.5M km 2 loss of non-forested primary land (e.g. savannas) is an order of magnitude greater than the 0.6M km 2 loss of forests

15 Local test: Kenyan forests Locally calibrated biomass map from Rodriguez Veiga (Uni Leicester) Local biomass data from Kenya Forest Service, KEFRI Land cover from the ESA-CCI Climate data

16 Restoration opportunities in Kenya

17 Biomass deficit of Kenyan forests Using places that remained forested from 1992 to 2015 (according to ESA-CCI) 1km x 1km spatial resolution, more relevant to policy makers Current biomass = 0.35 Pg C ( ) Potential Biomass = 0.80 Pg C ( ) Exbrayat et al. (in prep.)

18 Bonn Challenge Restoring 3.5 million km 2 of forests, corresponding to the successful implementation of the Bonn Challenge, can potentially lead to the long-term sequestration of 23.7 Pg C ( ) Gains with plantations, or agroforestry will be much lower

19 Future forests How well can we estimate forest growth and dynamics?

20 DALEC f(gsi) 7 Initial conditions 31 parameters 6 climate drivers

21 UK forestry model-data fusion framework Observations HWSD C som prior 400 MODIS LAI time series (m 2 /m 2 ) 6 RS Biomass (gc m -2 ) Drivers Forest Clearance Intact Outputs e.g. Wood stocks R C LAI a labile GPP Ai r C te O 2 m p Radiati on Update C foliar f(g CSI) root C CWD C wood C litter C SOM R h Plant carbon flow Influences on Photosynthesis Air Temperature MDF algorithm

22 Results Wood stocks in 2010 Wood stock increment MgC ha -1 MgC ha -1 yr -1

23 How well can we estimate stocks and fluxes? Stocks (MgC ha -1 ) 2010 Fluxes (MgC ha -1 yr -1 ) NFI CARDAMOM Biomass (38/168) DeadOrg (9.1/39.3) Soil (91/389) UNFCCC UK (CARBINE) DBiomass 1.3 (0.9/1.6) 0.4 (-4.3/2.3) DDeadOrg 0.04 (0.03/0.05) 0.9 (-0.1/2.2) DSoil 0.03 (0.02/0.04) -0.1 (-0.9/0.7) Harvest 0.3 (0.2/0.4) 0.7 (0.2/3.9)

24 To what extent does information constrain analytical uncertainty? No info Age known No additional information Reduction in uncertainty from single biomass assimilation: RS RS+Age Biomass 70% CWD 14% Soil C 0% D Biomass 7% D CWD 11%

25 Monitoring, reporting and verification (MRV) Key requirement for results based payments, e.g. REDD+, International Climate Fund MRV must be transparent, consistent, complete and accurate Massive challenge for 350 M ha Model-satellite integration can help

26 Conclusions We can map the ecological potential for biomass Other factors will shape the restoration success We can map the potential for biomass accumulation Highly dependent on satellite accuracy Model-data integration can support MRV

27 Acknowledgements: Anthony Bloom NERC, ESA Kenya Forest Service UK Space Agency, Ecometrica

28 Data-driven estimates of past above-ground biomass AGB present = f(climate, land use, topography) in the present Train a Random Forest algorithm to derive this relationship and predict biomass under past land use ΔAGB LULCC = AGB present AGB past Isolate impact of LU by using current climate (CRU) and atmospheric CO 2 concentrations Fig. 2 in Jung et al. (2009) BG

29 Out-of-sample validation in the present R 2 = 0.93 n = R 2 = 0.93 n = No loss of predictive capability between calibration and validation Final prediction is trained on full dataset (R 2 = 0.93)

30 Restoration opportunities in Kenya