VCS REDD Methodology Module. Methods for monitoring forest cover changes in REDD project activities

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1 1 VCS REDD Methodology Module Methods for monitoring forest cover changes in REDD project activities Version 1.0 May 2009 I. SCOPE, APPLICABILITY, DATA REQUIREMENT AND OUTPUT PARAMETERS Scope This module provides methods for monitoring changes in land cover due to deforestation, forest degradation and carbon stock enhancement and to calculate activity data for each of these categories of change. Applicability conditions This methodology module is applicable for locating and measuring: a. The area of forest land converted to non-forest land; b. The area of forest land undergoing loss in carbon stock from degradation; c. The area of forest land undergoing carbon stock enhancement. Data requirements For the periodical revision of the validated baseline, a Regional Forest Cover Benchmark Map, showing the location of forest land within the reference region at the beginning of the crediting period, is needed. For periodical verifications, a Project Forest Cover Benchmark Map, showing the location of forest land within the project area at the beginning of each monitoring period, is required to apply this module. Furthermore, where leakage is monitored in a leakage belt, a Leakage Belt Forest Cover Benchmark Map showing the location of forest land within the leakage belt area at the beginning of each monitoring period is also required. Where forest land contains more than one forest class, the Forest Cover Benchmark Maps described above shall be stratified in forest classes representing each a different carbon density class. Output parameters This module provides methods to determine the following parameters: Parameter SI Unit Description A RR,ActivityData,i,t ha yr -1 Activity data of category i in the Reference Region at year t 1

2 2 A PA, ActivityData,i,t ha yr -1 Activity data of category i in the Project Area at year t A LK, ActivityData,i,t ha yr -1 Activity data of category i in the Leakage Belt at year t II. PROCEDURE The procedure is implemented by applying the following 6 steps: STEP 0. Selection of the procedure STEP 1. Selection of data sources STEP 2. Pre-processing STEP 3. Interpretation and classification STEP 4. Post-processing STEP 5. Accuracy assessment STEP 6. Documentation STEP 0. Selection of the procedure The REDD project activity may be located: 1. Scenario 1 in a region for which no forest monitoring program exist; or 2. Scenario 2 in a region for which a forest monitoring program exists and at a scale that is applicable to the project area. It is possible that the monitoring of forest cover change will be suitable but not the change in forest carbon stocks 1. If Scenario 1 applies: Steps 1 to 6 shall be applied. If Scenario 2 applies: If the third party that is monitoring the changes in forest cover and forest carbon stocks is approved or sanctioned by the national or regional government, then the data generated by the existing program shall be used, unless they are not applicable according to the criteria listed below. If the third party is not approved or sanctioned by the national or regional government, project participants can decide not to use the data generated by the existing program if they consider that such data do not adequately represent the changes in forest cover and/or carbon stocks that are occurring within the reference region, project area and, where applicable, leakage belt of the REDD project activity. In this case steps 1 to 5 of this module apply. 1 For example, a national forest monitoring system is not likely to be suitable for the project area as the scale is too coarse and will not provide enough sample ground plots to produce an estimated change in forest carbon stocks with high accuracy and precision. 2

3 3 An existing monitoring program is applicable under the following conditions: a) Monitoring occurs in the entire project area and, where applicable, leakage belt, of the proposed REDD project activity. If data from the existing monitoring program are used to periodically revise the baseline, monitoring shall occur in the entire reference region. b) Categories of change that are measured are the same of those reported in the validated baseline. c) Monitoring shall occur during the entire crediting period of the proposed REDD project activity (defined here as the period of time during which the validated project baseline is valid). If monitoring will occur for a fewer number of years than the crediting period, steps 1 to 5 of this module shall be used for the years of the crediting period for which the existing monitoring program will not operate. d) Monitoring methods are transparently documented and are similar to those used to determine the baseline of the REDD project activity. e) Monitoring must be either: independently verified by a VCS accredited verifier, or registered under a VCS-acknowledged system, or established by the national or regional government; or verified by an independent team or peer-reviewed. If the latter two requirements are not satisfied, VCS verifiers shall perform an independent verification of the existing monitoring program. STEP 1. Selection of data sources The same source of remotely sensed data and data analysis techniques shall be used within a crediting period. If remotely sensed data have become available from new or more accurate sources (e.g. from a different sensor system) these can only be used if they are available since the start date of the crediting period. The data collected and posterior analysis shall cover: The entire reference region: at least at the beginning and at the end of the crediting period (for periodical revision of the baseline). The entire project area: at least at the beginning and at the end of each monitoring period (for periodical verifications). The entire leakage belt, where required: at least at the beginning and at the end of each monitoring period (for periodical verifications). 3

4 4 STEP 2. Pre-processing The remotely sensed data collected shall be prepared for posterior analysis. A minimum pre-processing involves geometric correction and geo-referencing and cloud and shadow detection and removal (see Box 1). Guidance for interpretation of remote sensing imagery is given in the GOFC-GOLD Sourcebook for REDD. Box 1. Typical pre-processing tasks a) Geometric correction and geo-referencing: Images in a time series must overlay properly to each other and to other GIS maps used in the analysis (i.e. for postclassification stratification). The average geo-location error between two images shall be < 1 pixel. b) Cloud and shadow detection and removal: Areas obscured by clouds and shadows in the data set shall be removed. This can be done by simple visual interpretation. To analyze changes in forest cover and forest class in the cloud and shadow areas removed, apply Steps 1 to 6 using different sources of data, such as radar, aerial photographs, or field surveys. c) Radiometric corrections may be necessary to ensure that similar objects have the same spectral response in multi-temporal datasets (e.g. to reduce haze effects). For simple scene-by-scene analysis (i.e. visual interpretation), the radiometric effects of topography and atmosphere do not need to be digitally normalized. When multi-temporal data sets are analyzed together to detect changes, and digital and automated approaches are used, radiometric correction may be required to calibrate spectral values to the same reference objects in multi-temporal datasets. This can be done by identifying a dark object (e.g. a water body) and calibrating the other images to the first. However, direct change detection may not require atmospheric correction if the analyst makes sub-categories during the classification process to account for varying atmospheric and topographic conditions, which will keep the signature variances small. d) Topographic normalization is not necessary for simple scene-by-scene analysis (i.e. visual interpretation), but is recommended for hilly terrains when digital and automated approaches are used. However, where the terrain is topographically complex, visual interpretation techniques can be as accurate as digital and automated approaches. Tasks c to e may or may not be realized, depending on the interpretation and classification technique used (Step 3). 2 GOFC-GOLD, 2008, Reducing greenhouse gas emissions from deforestation and degradation in developing countries: a sourcebook of methods and procedures for monitoring, measuring and reporting, GOFC-GOLD Report version COP13-2, (GOFC-GOLD Project Office, Natural Resources Canada, Alberta, Canada) available at: 4

5 5 STEP 3. Interpretation and classification 3.1 Monitoring of deforestation Many methods exist to detect and map deforestation using remotely sensed data. The method selected shall be based on common good practice in the remote sensing field and will depend on available resources and the availability of image processing software. The key is that the method of analysis results in estimates of any deforestation that may occur in the project and leakage areas. See Box 2 and IPCC 2006 GL AFOLU, Chapter 3A.2.4 for additional guidance. Box 2. General guidance on change detection techniques Two main change detection techniques exist and may be used in REDD project activities: (1) Post-classification change detection: Two maps are generated for two different time points and then compared to detect changes in forest cover. (2) Pre-classification change detection (direct classification of change): Two data sets acquired at different dates are analyzed together to detect the locations where a change has occurred. As several methods are available to derive land use change maps from multitemporal data sets, no specific method are prescribed. As a general guidance: Automated classification methods are preferred because the interpretation is more efficient and repeatable than a visual interpretation. Independent interpretation of multi-temporal images should be avoided as it is less accurate (but is permitted). Interpretation is usually more accurate when it focuses on change detection with interdependent assessment of two multi-temporal images together. Any method (segmentation followed by supervised object classification, unsupervised followed by labeling, training followed by supervised, etc.) that is applied to classifying a single image date can also be used to classify two images at the same time by combining them into a single data file and classifying all the data channels together, specifying classes of change and no change. The following methodological procedure is recommended: - Obtain multi-temporal images, co-register the images and combine them into a single data file, and process the file to estimate change and nochange categories directly. - For each final category desired, make sub-categories as needed under different atmospheric and topographic conditions to lower the variances in signatures of each category and minimize inter-category spectral confusion. - Allow for iterations of classifications, as the first result will always have conspicuous errors (e.g. incorrect non-forest around cloud edges, etc.). Looking at these errors, modify or create new sub-categories. 5

6 6 3.2 Monitoring of areas undergoing degradation Remote sensing technology using optical sensors is not capable of direct measurements of biomass and changes thereof 3 but has some capability to identify forest strata that have undergone a change in biomass 4. Using remote sensing data to monitor areas of forest degradation by the extraction of wood for fuel will be practically impossible to achieve. In this case it will be important to design the project to ensure that no further extraction of wood occurs, and the monitoring system shall prove to the verifier that extraction is not happening in the project area. An option is to survey the project area on a 1 to 2-year basis using a line transect method (also known as a point quarter method), with many lines across the project area systematically surveyed to check whether new tree stumps are evident or not. If evident, then the carbon emissions associated with the tree removal shall be determined using the methods outlined in the baseline module BL-DFW, and reported as project emissions. 3.3 Monitoring of areas undergoing carbon stock enhancement If the project contains forest areas that are assumed to be accumulating carbon and these areas are included in the baseline, then their geographic boundaries shall be known this will be one or more of the strata. The system in place for monitoring the project area shall be used for monitoring any change in the area of this stratum (or strata). Ground measurements shall be used to monitor the change in carbon stocks through time as given in the carbon pool modules. STEP 4. Post-processing Post-processing is required to: 1. Map all relevant categories of area change (deforestation, degradation, carbon stock enhancement). 2. Calculate the area of each category of change (or activity data ) within the project area and, where required, the leakage belt. For periodical revision of the baseline, do this also for the reference region. For the calculation of each category of change: a) At the end of each monitoring period: Calculate the area of each category within the project area and, where required, the leakage belt. 3 4 However, technology is developing rapidly, including techniques such as RADAR, SAR, or LiDAR. For example, a multi-temporal set of remotely sensed data can be used to detect changes in the structure of the forest canopy. A variety of techniques, such as Spectral Mixture Analysis (Souza et al. 2005), SAR or LiDAR, can be used under this approach but no specific technology is prescribed here. Some of the newer technologies can estimate carbon contents of forest types, if supported by field information such as sample plots to calibrate the technology and fieldwork leading to allometric equations of key species. Project proponents shall use techniques that are suitable to their specific situation and that have been published in peer-reviewed papers. 6

7 7 Update the Forest Cover Benchmark Maps for the project area and leakage belt. b) At least every 10 years (when the project baseline must be revised): Calculate the area of each category within the reference region, project area and, where required, the leakage belt. Update the Forest Cover Benchmark Maps for the reference region, project area and leakage belt. c) Estimating activity data in cloud-obscured areas: Calculating the rate of deforestation, when maps have gaps due to cloud cover, is a challenge. If there are clouds in either date in question in the area for which the rate is being calculated, then the rate shall come from areas that were cloud free in both dates in question. This shall be estimated in % per year. Then, a maximum possible forest cover map shall be made for the most recent time period. The historical rate in % shall be multiplied by the maximum forest cover area at the start of the period for estimating the total area of deforestation during the period. STEP 5. Accuracy assessment The accuracy of the outcome of the previous step shall be assessed by estimating the following three types of errors: (1) Overall classification accuracy (2) Error of omission of each category of change (error of excluding an area from a category to which it does truly belongs, i.e. area underestimation) (3) Error of commission of each category of change (error of including an area in a category to which it does not truly belong, i.e. area overestimation) for each category of change, using statistical sampling. STEP 6. Documentation A consistent time-series of land use-change data must emerge from periodical monitoring. This is only possible if a consistent methodology is applied over time. The detailed methodological procedures used in pre-processing, classification, postclassification processing, and accuracy assessment of remotely sensed data, shall be carefully documented. In particular, the following information shall be provided for remotely sensed data: a) Data sources and pre-processing: Type, resolution, source and acquisition date of the remotely sensed data (and other data) used; geometric, radiometric and other corrections performed, if any; spectral bands and indexes used (such as NDVI); projection and parameters used to geo-reference the images; error estimate of the geometric correction; software and software version used to perform pre-processing tasks; etc.. 7

8 8 b) Data classification: Definition of the classes and categories; classification approach and classification algorithms; coordinates and description of the ground-truth data collected for training purposes; ancillary data used in the classification, if any; software and software version used to perform the classification; additional spatial data and analysis used for post-classification analysis, including class subdivisions using non-spectral criteria, if any; etc.. c) Classification accuracy assessment: Accuracy assessment technique used; coordinates and description of the ground-truth data collected for classification accuracy assessment; post-processing decisions made based on the preliminary classification accuracy assessment, if any; and final classification accuracy assessment. d) Methodological changes: If in subsequent periods changes will be made to the original methodology: Each change and its justification shall be explained and recorded; and When methods change, at the moment of change, the entire timeseries of past estimates that is needed to update the baseline shall be recalculated using the new method. For activity data collected through field measurements the methodological procedures applied shall be documented, including those used for stratification and uncertainly and accuracy assessment. 8

9 9 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 -1 A RR,ActivityData,i,t ha yr Activity data of category i in the Reference Region at year t A PA, ActivityData,i,t ha yr -1 Activity data of category i in the Project Area at year t A LK, ActivityData,i,t ha yr -1 Activity data of category i in the Leakage Belt at year t Remote sensing and field surveys Remote sensing and field surveys Remote sensing and field surveys 9

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