REDD Methodological Module. Location and quantification of the threat of unplanned baseline deforestation

Transcription

1 REDD Methodological Module Location and quantification of the threat of unplanned baseline deforestation Version 1.0 April 2009 I. SCOPE, APPLICABILITY, DATA REQUIREMENT AND OUTPUT PARAMETERS Scope This module provides methods for quantifying and locating the threat of unplanned deforestation in the baseline case. Applicability conditions This module is applicable for quantifying and locating the risk of unplanned conversion of forest land to non-forest land in the baseline case. The forest landscape configuration can be either mosaic or frontier. In case or the mosaic configuration, location analysis is not required if the carbon stock is homogeneous in more than 80% of the project area. The module Methods for stratifying the project area (X -STR) shall be used to determine whether this criterion is satisfied. Data requirements Spatial data on historical deforestation within the reference region 1 and data on spatial driver variables that either increase or reduce the risk of deforestation must be available to apply this module. This module calls upon the following other VCS-approved Modules and Tools: BL-UR Estimation of the baseline rate of unplanned deforestation Version 1.0 X -STR Methods for stratifying the project area Version 1.0 Output parameters This module provides methods to determine the following parameter: Parameter SI Unit Description 1 See the most recent version of the module Estimation of the baseline rate of unplanned deforestation (BL-UR) for criteria to determine the boundary of the reference region. 1

2 R Def,loc,t % Risk of deforestation at the location loc within the reference region at year t II. PROCEDURE The procedure is based on the assumption that deforestation is not random but a phenomenon that occurs at locations that have a combination of bio-geophysical and economic attributes that is particularly attractive to the agents of deforestation. For example, a forest located on fertile soil, flat land, and near roads and markets for agricultural commodities is likely to be at greater risk of deforestation than a forest located on poor soil, steep slope, and far from roads and markets. Locations at higher risk are assumed to be deforested first 2. This concept can be described empirically by analyzing the spatial correlation between historical deforestation and geo-referenced proxy driver variables. In the previous example, soil fertility, slope, distance to roads and distance to markets are the likely spatial proxy driver variables (SDV) or predisposing factors. These variables can be represented in a spatial data layer (or driver map ) and overlaid on a map showing historical deforestation using a Geographical Information System (GIS). From the combined spatial dataset, information is extracted and analyzed statistically to produce a map that shows the level of deforestation risk at each spatial location (= pixel or grid cell ). The risk at a given spatial location may change at the time when one or more of the spatial driver variables included in the model will change, e.g. when population density increases within a certain area or when infrastructure develops. The basic steps needed to perform the analysis described above are: STEP 0. Selection of the procedure or model STEP 1. Preparation of proxy driver maps STEP 2. Preparation of risk maps for deforestation STEP 3. Selection of the most accurate deforestation risk map using an acceptable validation metric STEP 4. Mapping of the locations of future deforestation STEP 0. Selection of the procedure The REDD project activity may be located: 1. in a region for which no regional deforestation baseline has been determined yet (Scenario 1); or 2 Several models and software have been proposed to analyze where deforestation is most likely to happen in a future period. This methodology is inspired by the GEOMOD model; because, landuse and land-cover change modeling is an active field, all models that implement at least the steps described in this module can be used. 2

3 2. in a region for which a regional deforestation baseline has already been assessed by a third party (Scenario 2). If Scenario 1 applies: Steps 1 to 4 must be applied. If Scenario 2 applies: If the third party that determined the regional deforestation baseline is approved or sanctioned by the national or regional government, the existing baseline must be used, unless it is not applicable according to the criteria listed below. If the third party is not the national or regional government, project participants can decide not to use the existing regional deforestation baseline if they consider that it does not reflect the baseline circumstances expected to occur in the project area during the crediting period. In this case Steps 1 to 4 of this module will apply. An existing regional deforestation baseline is applicable under the following conditions: a) The regional deforestation baseline has been projected for a reference region that includes the entire project area of the proposed REDD project activity. b) If the area for which the existing baseline rate has been projected is larger than the project area, the projected baseline must include the location of the expected baseline deforestation, so that areas subject to baseline deforestation can transparently be located within the project area. If no location analysis exists, Steps 1 and 4 of this module must be applied. c) If the area of the region is equal to the project area, and: 1. A location analysis exists: use the existing location analysis to identify the areas subject to baseline deforestation within the project area. 2. No location analysis exists, and: The landscape configuration is mosaic: assume that all locations have about the same risk to be deforested 3. The landscape configuration is frontier: apply Step 1 to 4 of this module d) The existing regional deforestation baseline is applicable to the entire period of time during which the project baseline must not be revisited (< 10 years), after which the deforestation baseline needs to be reassessed for its continued applicability. If it has been determined for a number of years fewer than the crediting period, Steps 1 to 4 of this module must be used 3 This assumption does not imply that the region cannot be divided in strata with different deforestation rates per stratum. 3

4 for the years of the crediting period for which the existing regional deforestation baseline is not applicable. e) Methods used to project the baseline deforestation rate are transparently documented so that assumptions and data used to do the projections can be verified. This provision does not apply in case of deforestation baselines established by the national or regional government having adopted a REDD scheme recognized by the UNFCCC or VCS. f) The existing regional deforestation baseline must either be: independently validated by a VCS accredited verifier, or is registered under a VCS acknowledged system, or has been established by the national or regional government having adopted a REDD scheme recognized by the UNFCCC or VCS, in which case it can be used; or it has been determined by an independent team and has been peerreviewed, in which case it can be used. If the previous two requirements are not satisfied, VCS verifiers shall do an independent validation of the existing regional deforestation baseline rate. STEP 1. Preparation of proxy driver maps Identify the spatial variables that most likely explain the pattern of deforestation in the reference region, such as: Landscape factors, e.g. vegetation type, soil fertility, slope, elevation, distance to navigable rivers and water bodies, etc. (as relevant). Human infrastructure, e.g. distance to roads, railroads, sawmills, settlements, etc. (as relevant); and Actual land tenure and management, e.g. private land, public land, protected land, logging concession, etc. (as relevant). Obtain spatial data for each variable identified and create digital maps representing the Spatial Features of each variable. Some models, such as Geomod, will require producing, for each of the digital maps, Distance Maps from the mapped features (e.g. distance to roads) or maps representing continuous variables (e.g. slope classes) and categorical variables (e.g. soil quality classes). For simplicity, let s call these maps Factor Maps. Other models do not require Factor Maps for each driver variable, and analyze all the driver variables and deforestation patterns together to produce a risk map. Where some of the spatial proxy driver variables are expected to change, collect information on the expected changes from credible and verifiable sources of information and prepare different Factor Maps for the same spatial driver variable, to represent the changes that will occur in different future periods. In case of planned infrastructure (e.g. roads, industrial facilities, settlements) provide documented evidence that the planned infrastructure will actually be constructed and 4

5 the time table of the construction. In case of new roads or road improvements, provide credible and verifiable information on the planned construction of different segments (e.g. how many kilometers will be constructed, where and when). Evidence includes: approved plans and budgets for the construction, signed construction contracts or at least an open bidding process with approved budgets and finance. If such evidence is not available use one of the two following options: Exclude the planned infrastructure from the driver variables considered in the analysis; or Adjust the baseline post facto, based on monitoring of actual infrastructure development during each monitoring period. To create the Factor Maps, use one of the following two approaches: Heuristic approach: Define value functions representing the likelihood of deforestation as a function of distance from point features (e.g. saw mills) or linear features (e.g. roads), or as a function of polygon features representing classes (e.g. of soil type, population density) based on local expert opinion or other sources of information. Specify and briefly explain each value function in the PD. A useful approach to estimate value functions is to sample spatially uncorrelated points in the Distance Maps and their corresponding location in the maps representing historical deforestation and to use regression techniques 4 to define the probability of deforestation as a function of distance. Empirical approach: Categorize each Distance Map in a number of predefined distance classes (e.g. class 1 = distance between 0 and 50 m; class 2 = distance between 50 and 100 m, etc.). In a table describe the rule used to build the classes and the deforestation likelihood assigned to each distance class 5. The deforestation likelihood is estimated as the percentage of pixels that were deforested during the period of analysis (i.e. the historical reference period). The empirical approach should be preferred over the heuristic approach. Use the heuristic approach only where there is insufficient information about the spatial location of historical deforestation or where the empirical approach does not produce accurate results when validated against a historical period. In the finalized Factor Maps, the value of a pixel must represent the deforestation risk or, as an approximation, the percentage of area that was deforested during the period of analysis in the distance class to which the pixel belongs. 4 e.g. logistic regression. 5 When building classes of continuous variables it is important to build classes that are meaningful in terms of deforestation risk. This implies the parameterization of a value function based on specific measurements. For instance, the criterion distance to roads might not have a linear response to assess the deforestation risk: a forest located at 50 km from the nearest road may be subject to the same deforestation risk of a forest located at 100 km, while at 0.5 km the risk may be twice as much as at 1.0 km. Data to model the value function and build meaningful classes can be obtained by analyzing the distribution of sample points taken from historically deforested areas. 5

6 STEP 2. Preparation of deforestation risk maps A Risk Map shows, for each pixel location l, the risk (or suitability ) of deforestation as a numerical scale (e.g. from 0 = minimum risk to some upper limit representing the maximum risk). Models use different techniques to produce Risk Maps. The Geomod model produces Risk Maps by calculating different weighted average combinations of the Factor Maps prepared with the previous step. Choose different combinations of Factor Maps and weights, taking into account expert opinion and the analysis performed in the previous steps. R Def Where: N N, loc, t = WSDV * PSDV, loc, t WSDV (1) SDV = 1 SDV = 1 R Def,loc,t Risk of deforestation at the location loc (pixel or grid cell) at year t; % yr -1 SDV N A particular factor; number Total number of factors; number W SDV Weight of the driver image SDV; % P SDV,loc,t Value of the grid cell of factor map SDV at location l and time t; number The weights (W SDV ) of each Factor Map can be determined heuristically through expert consultations or empirically using statistical analysis. For instance, Geomod-2 uses non-linear multiple-regression to weight each Factor Map. Other published models can also be used to produce Risk Maps. STEP 3. Selection of the most accurate deforestation risk map A model validation is needed to determine which of the deforestation risk maps is the most accurate. A good practice to validate a model (such as a risk map) is calibration and validation. Model calibration and validation: Two options are available to perform this task: a) calibration and validation using two historical sub-periods; and b) calibration and validation using tiles. To build tiles, divide the reference region in n equal-area subsets. Option (b) will be used when two historical sub-periods are not available for applying option (a). 6

7 a) Where two or more historical sub-periods are available, data from the most recent period can be used as the validation data set, and those from the previous periods as the calibration data set. Using only the data from the calibration period, prepare for each Risk Map a Prediction Map of the deforestation in the validation period. Overlay the predicted deforestation with locations that were actually deforested during the validation period. Select the Prediction Map with the best fit 6 and identify the Risk Map that was used to produce it. Prepare the final Risk Map using the data from the calibration and the validation period. b) Where only one historical sub-period is representative of what is likely to happen in the future, divide the reference region into tiles and randomly select half of the tiles for the calibration data set and the other half for the validation set. Perform the analysis explained above. Briefly report in the PD the procedures used to select the most suitable Risk Map. STEP 4. Mapping of the locations of future deforestation Future deforestation is assumed to happen first at the pixel locations with the highest deforestation risk value. To determine the locations of future deforestation do the following: Mask out all current non-forest land from the selected Deforestation Risk Map 7. In the transformed Deforestation Risk Map select the pixels with the highest value whose total area is equal to the area expected to be deforested in project year one (or first monitoring period). The result is the Map of Baseline Deforestation for Year 1 (or first monitoring period, respectively). Repeat the above pixel selection procedure for each successive project year (or monitoring period) to produce a Map of Baseline Deforestation for each future project year (or monitoring period). Do this at least for the upcoming crediting period and, optionally, for the entire project term. Add all yearly (or periodical) baseline deforestation maps in one single map showing the expected Baseline Deforestation for the Crediting Period and, optionally, Project Term. 6 7 The map with the best fit will be the map that best reproduced actual deforestation in the validation period. Parameters such as % of area of correct prediction, % of area of omitted prediction (area that was actually deforested but not predicted), and % of area of commission (area that was predicted as deforested but that was actually not deforested) can be used to identify the map with the best fit. The GEOMOD model refers to these maps as Potential for Land Use Change (PLUC). 7

8 III. Data and parameters used and generated in this module Data/parameter Unit Used in equations Descripiton Source of data Measurement procedure (if any) Comments N number 1 Total number of factors P SDV,loc,t number 1 Value of the grid cell of factor map SDV at location l and time t R Def,loc,t % 1 Risk of deforestation at the location l (pixel or grid cell) SDV number 1 A particular factor W SDV % 1 Weight of the driver image SDV 8