Outline. Motivation 11/21/2012. Carbon and Biodiversity Loss due to Forest Degradation a Cambodian Case Study

Transcription

1 Synthesis workshop on options for monitoring forest degradation at sub national levels in the Mekong November, Marriott Courtyard Hotel, Bangkok Carbon and Biodiversity Loss due to Forest Degradation a Cambodian Case Study Nophea Sasaki, PhD Associate Professor University of Hyogo, Kobe, Japan Kimsun Chheng, PhD Director General, Forestry Administration Phnom Penh, Cambodia Nobuya Mizoue, PhD Associate Professor Kyushu University, Fukuoka, Japan Outline Motivation Deforestation vs Forest Degradation Study Methods and Materials Forest Inventory Data Stand Structures and Tree Grades Results and Discussions Challenging for Monitoring Forest Degradation Concluding Remarks Motivation Selective logging is commonly used for managing tropical forests but its practice caused great concerns over the loss of biodiversity, carbon stocks, and logging induced fires Unplanned logging results in indiscriminate logging starting with highly expensive trees with significant logging damages and wastes Monitoring such degradation is still challenging unless the process of degradation is known to some degree Here, this degradation process is discussed using the data available in Cambodia Study Methods and Materials Inventory data: 179 plots from ex forest concessions in three provinces in Cambodia (Kimsun 2012) Forest stand structures Forest Health Grades by DBH Classes: Good trees => harvest first Tree Grades by DBH Classes: Expensive trees => harvest first 1

2 Study sites and plots Results and Discussions Health Forest Health Assessment Grades Defect/Diseases Canopy Cover (%) A No defective >50% B Some defective disease 20 50% C defective disease <20% Density (trees) Density and Stand Volume by Health Grades (179 plots) MgCO2/ha Once big trees are gone: Forest still remain forest but big trees and carbon stocks are reduced. About 45.6% of carbon stocks are reduced (degraded) Quality MgCO2 % A % B % C % Total % Target of loggers, and thus cause degradation Lost 45.6% of carbon stocks. At least >50% will be emitted immediately. The rest depends on how harvested wood is used (short vs long life carbon) 2

3 Density and C. Stocks by Timber Grades (59 plots in Kratie province) Forest Degradation Process Illegal timber of luxury species Density (trees) Timber Grade C Stocks (MgCO2) Management? Trees are likely to be harvested first if proper planning and enforcement are not implemented Timber Prices (US$/m3) Luxury: , Grade I: ; Grade II: ; Grade III: ; Out of grade (OTHR): 100 Carbon Sinks Forest Degradation Status of Individual Trees in the IUCN List Most trees in Luxury Grade are listed as Critically Endangered Species Some trees in Grade I and Grade II are listed as Threatened Species Some species of luxury species are being traded in terms of weight i.e., kilogram and their trunks are uprooted. This causes great concern over the disappearance of such species Challenging for Monitoring Can we estimate canopy loss by tree species? New technology can detect minor changes of canopy cover (Asner et al. 2006) but not by tree species New data for forest monitoring became gradually and freely available (Asner et al. 2010) but how can such data be used in developing countries remains a challenge 3

4 Concluding Remarks Timber prices determine the trees to be felled Large trees with good commercial values are likely to be harvested first Monitoring such loss requires technologies that can link the loss of canopy cover to individual species Allometric equations using remote sensing technology could help monitor large scale tropical forest degradation While inventory data can help, capacity building in developing countries could ensure long term success of the monitring activities Acknowledgement Participation in this workshop are covered by the USAID funded program, the LEAF Research project is funded by KAKENHI ( ) for Research Grant Thank You for Your Kind Attention Biodiversity => Unplanned Tree Felling: Expensive Trees are likely to be cut (all DBH>30 cm) Species Code Botanical Name Density Volume Grades Price IUCN List & Grade (trees/ha) (m3/ha) (US$/m3) Luxury Grade LUXURY 3,400 11,000 Critically Endangered CHKM Dispyros spp. Grade I KRKO Sindora conchinchinnensis 2.7 I Threatened Species DCSP Tarrietia javanica 1.4 I Endangered Species SRLO Lagerstroemia sp. 0.6 I KRLA Dialium sp. 0.6 I Others Grade II II CHBG Dipterocarpus costatus 7.53 II Threatened Species CRMS Vatica astrotricha 3.90 II PHDK Anisoptera glabra 6.64 II Threatened Species SRKM Payena elliptica 1.33 II Others 1.33 II Grade III III PHON Callophyllum sp III PRNG Eugenia spp III TLOK Parinarium annamensis 2.86 III TRTM Crypteronia sp III Others 8.27 UNKNOWN unkn TOTAL HARVESTING (30% cut) 51.1 Note: This calculation is based on the assumption that trees with DBH> 30 cm canbe harvested. In practice, suchdbh limitsfromonespecies toanother Based on data from 207 plots in Sandan, Cambodia 4

5 Local involvement in measuring and governing carbon stocks Forest inventory Case study I REDD+ Presentation by Michael Køie Poulsen Nordisk Fond for Miljø og Udvikling / Nordeco I REDD+ Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks The aim is to ensure that the implementation of a future REDD+ mechanism is based on the highest level of knowledge on carbon storage in landscapes monitoring technology potentially negative impacts on local livelihoods governance structures for managing payments Funded by the 7th Framework Programme of the European Commission The I REDD+ project is divided into 7 work packages WP1 Project management, coordination of outreach and dissemination WP2 Green house gas emissions from vegetation and soil WP3 Remote sensing based monitoring of change in land use and biomass WP4 Community based monitoring of change in land use and biomass WP5 Local livelihoods and food security WP6 Governance and institutions WP7 Monitoring, reporting and verification (MRV) and capacity development redd.eu WP4 Deliverables Theoretical framework for community monitoring Participatory land use maps Evaluation of accuracy/ costs of community monitoring Policy brief 1

6 Community member will: Identify forest strata Establish permanent vegetation plots Community member will: Measure girth Count trees Measure sloops (?) Identify wood hardness (?) I REDD+ field sites and field form Status of work on Evaluation of accuracy and costs of community monitoring Villagers in 7 villages in 4 countries established 289 permanent forest vegetation plots All have been re measured by forester Altitude gradient: lowland to montane forest Disturbance gradient: largely undisturbed to heavily disturbed 2

7 Measurements of above ground woody biomass by community members and foresters Number of permanent vegetation plots established by community members and re measured by foresters Indonesia Kalimantan (1) 64 China Manlin (2) 30 China Manlin (3) 30 Vietnam Diem (4) 30 Vietnam Moi (5) 27 Vietnam Moi (6) 28 Vietnam Moi (7) 18 Laos Sakok (8) 32 Laos Sakok (9) 30 Measurements of above ground woody biomass by community members and foresters Comparison of each plot above ground carbon by community and forester Indonesia (site 1), China (site 2 3), Laos (site 4 5) and Vietnam (site 6 9) 3

8 Comparison of each plot above ground tc/ha by community and forester (Sakok 2, Laos) Comparison of no. of trees of each size class 350 Laos y = x R² = Sakok Community Linear (Sakok 2) Forester Community can collect large volumes of data at low cost 4

9 Added value? The local people themselves: Can reduce the risk that REDD+ undermines local forest control Individual nation: National REDD+ implementation can become linked to local decision making World community: Can promote sharing of benefits Challenges Individual trees not marked in all areas (enable direct comparison) Wood density estimation (greatly affects biomass estimation, both for communities and foresters, propose in 2nd field season to ask identify species and divide into 3 categories by both com and foresters) Slope (propose calculation in 2nd field season) Trees with huge and difficult to measure boles (propose that both communities and foresters have particular attention to this in the refresher training) 5

10 Find our publications via Monitoring leading to management interventions Effectiveness of participatory and conventional scientific biodiversity monitoring methods in generating conservation management interventions intended to improve the way local people (black), outsiders (white), and both (gray) manage Philippine protected area resources. Community monitoring leads to quick decisions at the oprational level of forest management 6

11 Synthesis Workshop on Options for Monitoring Forest Degradation at Sub National Scales in the Mekong Bangkok, Thailand, November 13 14, 2012 Methods for monitoring emissions and removals from forest harvesting for timber and fuelwood Sandra Brown How to Estimate Emissions for Forest Harvesting Practices? Two methodologies: 1. Remote sensing using medium resolution imagery for activity data and stock change method for EF 2. Combination of timber extraction rates, management plans, and high resolution imagery for activity data and gain loss for EF 1 2 Method 1 Commercial Logging Operations Emission Factors from Sample Plots Emissions: Activity data =change in area of non logged versus logged forest caused by logging Would need data on expansion of non intact forests monitored on shorter time frames GOFC GOLD Sourcebook Emission factors based on stock change method = average C stock of unlogged forest average C stock logged forest C stock of logged forests likely to be extremely variable and hard to meet a reasonable precision level without intensive sampling Would need to stratify by year of harvest and extraction rates field intensive, sampling issues at low extraction rates (<30 40 m3/ha) Results in estimates of net change in C stocks in gaps and skid trails C stocks of unlogged forest can be combined with area of roads and landing to estimate emissions from deforestation 3 4 1

12 5 Method 2 Estimate emissions directly in gaps change detection Use gain loss method for estimating net emissions Roads Skid trails Carbon dioxide Landing decks Collateral damage dead wood Wood products Estimating Emissions from Selective Logging Method allows for estimating emissions by components Emissions, t C/yr = [V x WD x CF x (1 LTP)] +[V x LDF ] +[A LI xlif] Where: V = volume timber extracted over bark per logging block (m3) AD A LI = Area of roads, skids, log landings per logging block (ha) AD WD = wood density (t/m3) CF = carbon fraction LTP = proportion of extracted wood in long term products LDF = logging damage factor (t C/m3) dead biomass left in gap from felled tree and collateral damage LIF = logging infrastructure factor (t C/ha) dead biomass produced by construction of roads, skids, log landings Volume reported<volume felled so additional effort must be made to make sure felled but not extracted trees are included 6 Quantify Changes in Live and Dead C Stocks Which Pools?: Select those directly impacted by tree felling Logging gaps: Aboveground biomass (AGB) Below ground biomass (BGB) Estimate AGB by allometry and BGB by root:shoot ratios Infrastructure EF: Use results from unlogged forests Extracted Volumes Estimate carbon in log based on estimated volume from field measurements and wood density Logging Damage Factor Measure area of gap Measure DBH of damaged trees (snapped, uprooted) and estimate carbon stock from application of allometric equations Carbon in tree top/stump= total tree C minus C in logs 7 8 2

13 Damage Caused by Infrastructure Option 1 Use Landsat type imagery to obtain deforested areas of roads and landing decks For skid trails Direct measurements of a sample of trails including length, width, and DBH & species of damaged trees (snapped, broken, uprooted), or Length of skid trails planned and mapped in management plan plus field measures of width and damaged trees of a sample of trails Damage Caused by Infrastructure Option 2 Logging decks Roads Obtain very high resolution satellite imagery (e.g GeoEye<60 cm resolution) covering active logging areas Digitize length and area of structures Skid trails Damage Caused by Infrastructure Option 3 Use high resolution digital stereo aerial imagery to digitize and estimate area of roads and logging decks, and skid trails; express as proportion of sample area Estimate number and proportion of total sample area covered by gaps for a measure of trees felled Estimating Gains in Selective Logging Areas Gains, t C/yr = A G x CA Where: A G = Total area of gaps in logging blocks (ha ) = gap area/m 3 extracted *total extraction in m 3 CA = net carbon accumulation of trees in gaps (t C ha 1 yr 1 ) accounting for delayed mortality Can be estimated from: Measurements in a chronosequence of logging gaps Establish plots in gaps just after logging and re measured through time Or from locally calibrated models Uncertainty if how long accelerated growth occurs until gaps close? 12 3

14 Case Study Guyana Total Emissions for Logging in Guyana Emissions (tco 2) Thousands 3,000 2,800 2,600 2,400 2,200 2, , Year Impact of Timber Extraction on Canopy Cover in Guyana MOU with Norway required Guyana to deduct 50% of C stocks within 500 m buffer around all deforestation related to infrastructure Estimated Uncertainty The simple error propagation equation is: (Eq. 4) Where U total = percentage uncertainty in the product of the quantities (half the 90% confidence interval divided by the total and expressed as a percentage); U i = percentage uncertainties associated with each of the quantities, i= 1, Given many sources of error, Monte Carlo analysis will be used in next version

15 Estimated Uncertainty Summary Source of Error Uncertainty Origin Activity data 0 Total timber produced per year, unknown, assume zero Logging damage factor 7.3% Based on logging sample plots (LDF) Field measurements 0 Unknown, likely less than 5% of plots, assumed to be zero at this time Wood density of extracted timber Factors used in estimating carbon in long-term products 0.9% Based on field plots average of timber species extracted (does not account for misidentification) 20% Unknown, assumed to be at least 20% at this time Regrowth factor 17.3 Based on the 90% CI around the preliminary regrowth field plots and on the average gap area per m3 extracted from the logging plots Skid trail: Width C stock Overall Total 3% 12% 30.1% Based on average of field measurements Based on field measurements in biomass plots Based on Eq. 4 Several options to choose from for measuring carbon impacts of timber harvesting Experience indicates gain loss method is efficient as: EF can be used for repeated monitoring events for a given practice emissions per gap vary little Data on emissions can be obtained cost effectively and used with reliable extraction rates and existing management plans High resolution imagery or aerial imagery needed to monitor gap creation if extraction rates not known well Methods for estimating gains can be more time costly depending on option selected Thank You! Thanks to the WI Ecosystem Services team, particularly Felipe Casarim and Katie Goslee Also thanks to Pradeepa Bholanath, Nasheta Dewnath, Hans Sukhdeo, and Carey Bhojedat of the Guyana Forestry Commission Support from US AID, The World Bank, GFC, and TNC For more information see: Or contact me: sbrown@winrock.org 19 5