Forestry and Agricultural Greenhouse Gas Modeling Forum # Shepherdstown, WV

Transcription

1 Forestry and Agricultural Greenhouse Gas Modeling Forum #4 March 6-8, Shepherdstown, WV Overview of scientific, technical and methodological issues related to reducing emissions from deforestation in developing countries (RED-DC) DC) Sandra Brown sbrown@winrock.org 1

2 Status of RED-DC DC discussions within UNFCCC Referred to as reduced emissions from deforestation and not averted deforestation Two groups: Market based approach (e.g. CfRFN) Project/program based (CDM like) versus national approach Non-market based (e.g( Brazil) with $$ from developed countries and new and additional to ODA Program of voluntary reductions and taking into account different national circumstances 2

3 General elements of program under discussion Use term reference scenario or reference emission rate instead of baseline Monitoring and accounting done at national scale No forward baseline scenarios instead instead attempt to reduce reference emission rate by X amount (akin to Annex 1 countries estimating their GHG reductions under Kyoto based on knowledge of trends within country) Country submissions to SBSTA special meeting at Cairns available at s/3896.php 3

4 Drivers of deforestation and degradation as reported in national communications to the UNFCCC Driver Number of Parties Forest conversion to agricultural uses 33 Harvesting for fuelwood and charcoal 25 Improper forest management, including selective 17 logging and overexploitation Fires and biomass burning 13 Population pressure 13 Development pressure, such as expanding 11 urbanization, settlements and new infrastructure (e.g., electricity lines, roads) Illegal logging 8 Policies and laws that drive land use conversions 7 Exploitation of mineral resources, mining 4 4

5 Where should a country focus efforts to reduce emissions from deforestation? Spatial modeling approaches can help countries plan where to focus efforts to reduce deforestation to maximize return on investments 5

6 Deforestation projections at regional scales Experience in the development of land cover change projections exist (designed to explore the development of baseline projections for pilot projects): Methods exist to map threat for deforestation based on spatial analyses using remote sensing data, key proxy drivers of deforestation, and GEOMOD at the regional scale Tested and applied to eight regions (Brazil, Bolivia, Belize, Mexico, ROC, Indonesia) in the tropics (Brown et al. 2007, in press; supported by US EPA and US AID; available at U=9086) Such an approach could also be used by countries in planning where to focus efforts to reduce deforestation 6

7 Steps used to simulate changes in land use/land cover using GEOMOD. Step 1 simulates the existing landscape and validates it with a reference land use map Step 2 simulates future land uses based on projected rates of deforestation and the Potential Land Use Change map (PLUC). Step 3 creates a carbon emission map over time based on a carbon map and simulated land use maps 7

8 East Kalimantan, Indonesia as a case study Acknowledge: Silvia Petrova WI Fred Stolle WRI 8

9 Main proxy drivers of deforestation Tested 102 combinations of drivers to explain deforestation in the region Kappa for location statistic Cities (HR) Driver maps combinations used to create suitability maps Accessibility driver maps Deforested area (HR) Sawmills (HR) Rivers (HR) Roads (HR) Topography Elevation (HR) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Accessibility is modeled as distance from.. 9

10 Suitability for change (SFC) map Created based on the most important drivers of deforestation for the period of 1997 to 2003 distance from already deforested area distance from sawmills elevation Suitability for change map Reference (left) and simulated (right) map for 2003 Reference 2003 Simulated

11 Threat map of future deforestation Combines suitability for change map with projected rates of deforestation over 10 year period generates a potential land use change map for period Rescaled based on equal interval of full range (1-210) into three classes 11

12 Combine with map of carbon stocks in above- and below- ground biomass pools Brown, S., L. R. Iverson, A. Prasad, A. L. Brenkert, T. W. Beaty, and R. M. Cushman Geographical Distribution of Biomass Carbon in Tropical Southeast Asian Forests: 12 A Database. ORNL/CDIAC-119, NDP-068.

13 Identifies areas with potential high pay-off if well protected 13

14 Can the science, technology, and accounting methods available meet the needs to? estimate GHG emissions for the historical reference scenario monitor emissions through time into future develop methods that are transparent and scientifically credible and sensitive enough to detect change through time develop methods that can be implemented by in-country experts 14

15 Much progress in M&M of CO 2 emissions from deforestation in the last 10 years. Remote sensing data at various scales readily available Change in land cover for most tropical regions can be measured from space with confidence using RS data since the 1990s Peer reviewed tools and methods available to estimate carbon stocks in forests at national and regional scales with high confidence. Methods for estimating net and gross emissions from deforestation are available in existing IPCC reports (1996, 2003, 2006) 15

16 Steps involved in a monitoring system for deforestation DeFries et al Forest inventories In-situ/plot data-projects Targeted remote surveys e.g. Lidar and aerial imagery FAO statistics 16 IPCC-GPG / AFOLU

17 Monitoring change in forest cover Remote sensing data available for many land cover changes and many developing countries since 1990s and deforestation can be measured from space, however Not all areas covered; cloud cover issues for some key tropical countries Identification of secondary forests not easy Identification of degraded forests developing Identification of selectively logged forest developing Development of new technology and new analytical methods in RS field progressing for addressing these challenges and likely to be available for future monitoring 17

18 Monitoring carbon stocks Need to match estimates of carbon stocks with changes in land cover to improve accuracy and precision of emission estimates Current operational optical satellites cannot remotely sense biomass carbon Optical satellites have difficulty in distinguishing secondary from mature forests, yet carbon stocks can differ greatly because of effects of age and ecological zone 18

19 How are forest biomass C stocks in the tropics presently estimated? Robust tools exist for converting traditional, statistically designed forest inventory data to carbon stocks in trees; defaults used for other pools (IPCC GPG Ch. 3; FAO) Many tropical countries have no recent national or even regional forest inventories of growing stock volume Research plots generally of limited use as not from population of interest and designed for other purposes Biomass Expansion Factor Tropical Temperate Growing Stock Volume (m^3/ha)

20 Country Biomass Estimation Year Brazil Malaysia Indonesia Peru Bolivia Dem Rep. of Congo Cameroon Central African Republic Republic of Congo Tanzania Scientific studies with small, non-stratified plots : Volume to biomass using GPG (2003) and expansion factors from Brown (1997); extrapolated to Sabah and Sarawak Volume (year 2000 estimated for various species) to biomass using expansion factor from Brown (1997) 1993, , 1998, 2000 Volume inventories, not converted to biomass 1995,2000 Volume (humid forest only) to biomass using expansion factors Brown 1997), default values for roots & dead wood from IPCC Volume to biomass using average wood density for Africa and BEF from Brown (1997) 1990 volume (assumed constant for 2000 and 2005) to biomass using density and expansion factors (sources not listed) Partial inventory only. Volume (constant from ) to biomass using average wood density for Africa and BEF from Brown (1997) and FAO (2004) Directives Volume (assumed constant for ) to biomass using average wood density for Africa and BEF from IPCC GPG Volume (by vegetation classes) to biomass using average density for Africa and expansion factors (sources not listed) ,

21 How to measure carbon stocks? Traditional inventory approach: Can be done in smaller countries and at project scale Requires large resources at national level Cost-prohibitive for large countries and not practical Need remote means that are: Cost-effective Low uncertainty (high precision) Transparent and repeatable Acceptable to policy makers 21

22 Future trends in measuring and monitoring forest carbon stocks Build on existing techniques regular inventories done by sampling Need remote means Not necessary for wall-to to-wall mapping but statistical sampling approach New remote technology developing Lidar already shown to measure changes in forest structure height is a good indicator of forest biomass change High resolution digital imagery combined with new field data on key metrics of forest carbon-crown crown area and tree height 22

23 Mean carbon density (t C/ha) Example of application of high resolution imagery for estimating C stocks Fly parallel transects and establish image plots Measure tree crowns and heights Convert to C with allometric equations 0 Ground Estimate M3DADI Estimate Ground Imagery 116 plots 25 plots 23

24 Conclusions Analysis of airborne or satellite remotely sensed data is the only practical approach to measure changes in forest cover at national and international scales. Since the early 1990s, tools and methods exist to measure changes in forest area from space with confidence. There are no accepted standard practices for measuring forest carbon stocks using RS data; instead they are estimated from traditional forest inventories or from default data. Investments are required to expand inventories of forest carbon stocks so that reliable carbon estimates can be applied to deforested and degraded areas interpreted from RS imagery. New technologies and approaches are developing for measuring carbon stocks over large areas with confidence using a combination of satellite and airborne imagery. 24

25 Conclusions (cont.) Methods for estimating net and gross emissions from areas with measurable deforestation are available in existing IPCC reports Reliable and transparent results from application of these methods are hampered by capacity, availability, and access to data on both change in forest cover and, more critically, by change in carbon stocks NEXT STEPS Development of standard protocols for interpreting and analyzing remote sensing data at various scales, including which data to collect and use, how to analyze the data, and acceptable levels of accuracy to attain, etc (akin to GPG for C stocks for LULUCF) Development of standard protocols for estimating carbon stocks of forests undergoing change at national scales, building on existing methods given in IPCC reports, and decisions on acceptable levels of accuracy and precision to attain. 25