Narration: In this presentation you will learn about various monitoring methods for carbon accounting.

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2 Narration: In this presentation you will learn about various monitoring methods for carbon accounting. 2

3 Narration:The presentation is divided into four sections. 3

4 Narration: USAID s standard climate change CO 2 indicator is defined as the Quantity of greenhouse gas emissions, measured in metric tons of CO2 equivalent, reduced or sequestered as a result of assistance from the United States government in natural resources management, agriculture, and/or biodiversity sectors. This indicator can be used at the project level with USAID s Forest Carbon Calculator (see Topic 4 Section D). If the project is to influence national level policy, the USAID indicator will be a policy indicator, not CO 2. If USAID engages in large-scale attempts to change a country s emissions trajectory, then the national greenhouse gas inventory done with host government provides CO 2 impact measures. 4

5 Narration: The data needed for project monitoring with USAID s Forest Carbon Calculator includes: 1. The locations of projects according to administrative unit, such as state or district 2. How many hectares are affected by the activity, such as area of forest protected, reforested, regenerated, or under agroforestry 3. The measure of project effectiveness, including the percentage of reduction in deforestation, percentage of trees that survive at end of the year, the percentage of logging stopped or the percentage of logging that is being done with reduced impact 4. Documentation of how you estimated project effectiveness measures 5

6 Narration: Estimating and monitoring anthropogenic changes in carbon stocks at the project level involve several challenges and specific circumstances. Methods developed for national inventories do not capture these challenges and circumstances. At the project level, each project requires a detailed measuring and monitoring plan. In some cases, the accumulation of carbon in soils is so slow that the time to demonstrate the change is so long that it lies outside the normal project cycle. In those cases, the project activities should be adapted and modified according to the situation. More accurate and precise higher-tier methods, based on field measurements sometimes in combination with allometricor simulation models, should be applied. 6

7 Narration:There are several useful manuals and guidebooks you can use to help you design a forest carbon measuring and monitoring system. A Guide to Monitoring Carbon Storage in Forestry and AgroforestryProjects is oriented towards designing a field plan for measuring and monitoring of forest carbon pools. The IPCC s Good Practice Guidance for Land Use, Land-use Change and Forestry. Most carbon measuring and monitoring systems under the United Nations Framework Convention on Climate Change use approved IPCC methodologies. Reducing Greenhouse Gas Emissions from Deforestation and Degradation in Developing Countries: A Sourcebook of Methods and Procedures for Monitoring, Measuring and Reporting. This is the most comprehensive guidebook for carbon measuring and monitoring system in REDD schemes. The Sourcebook for Land Use, Land-use Change and Forestry projects is a useful general guidebook for measuring and monitoring forest carbon pools. 7

8 Narration: At the project level, monitoring includes several components: 1. Measure and monitor forest carbon pools and the changes in them. Depending on the case, these pools may include living above- and below-ground biomass, dead organic matter, and soil carbon. 2. Measure and monitor greenhouse gas emissions caused by project activities, such as burning biomass or using fossil fuels. 3. Define project boundaries, both area and activities. 4. Measure and monitor emissions of non-co 2 greenhouse gases, such as nitrous oxide from the use of fertilisers. 5. Estimate emissions caused by leakage, such as the displacement of activities from the project area into other areas. At project level, the monitoring includes the following components: 1) Measurement and monitoring of forest carbon pools and changes in them. These pools may include depending on the case living biomass (above and below ground), dead organic matter, and soil carbon 2) Greenhouse gas emissions caused by project activities e.g. from biomass burning or from the use of fossil fuels 3) Definition of project boundaries both in terms of area and activates i.e. de the definition of activities that are considered as project activities 4) Emissions of non-co 2 greenhouse gases, e.g. emissions of nitrous oxide from the use of fertilizers 5) Estimation of emissions caused by leakage, i.e. by the displacement of activities from the project are into other areas 8

9 Narration: There are five steps involved in monitoring: 1. Define monitoring boundaries 2. Stratify the area to be monitored into homogeneous sub-areas 3. Decide which carbon pools to monitor 4. Determine the type and number of plots to use 5. Determination of the monitoring frequency In the following slides you will learn about these steps in detail. 9

10 Narration: Measuring and monitoring plans for carbon sequestration projects describe methods and procedures for: 1. Stratifying project area 2. Selecting the carbon pools and non-co 2 gases to be monitored 3. Sampling design 4. Estimating the changes in carbon pools and non-co 2 gas emissions 5. Taking field measurements, both frequency and the methods used 6. Estimating non-co 2 gas removals 7. Monitoring the changes in emissions and removals from project activities 8. Measuring the frequency of carbon monitoring 9. Developing monitoring assurance and quality control procedures and plans 10

11 Narration: National monitoring systems must measure changes throughout all forested areas, use consistent methodologies at repeated intervals to obtain accurate results, and verify results with ground-based or very high-quality observations. Monitoring should produce estimates of carbon stocks that are both precise and accurate, which in turn will affect the monitoring costs. The figure below illustrates the concepts of accuracy and precision. Before initiating a programmeto monitor the carbon stocks of land cover classes, certain decisions must be made concerning the following key factors that directly impact the cost of implementing a monitoring system (GOFC-GOLD, 2009): Which level of accuracy and precision is to be attained the higher the targeted accuracy and precision, (or lower uncertainty) of estimates of carbon stocks, the higher the cost to monitor How to stratify forest lands stratification into relatively homogeneous units of land with respect to carbon stocks lowers the cost as it reduces the number of sample plots Which carbon pools to include the more carbon pools included, the higher the cost At what time intervals should carbon stocks in specific areas be monitored over time; the shorter the time interval, the higher the cost and specific areas targeted for REDD implementation activities may require more frequent measurements The most important concepts needed are (GOFC-GOLD, 2009): Biasis a systematic error that can occur, for example, due to flaws in the measurements or sampling methods or due to the use of an emission factor which is not suitable for the case to which it is applied. Bias means lack of accuracy. Random error describes the random variation above or below a mean value, and is inversely proportional to precision. Random error cannot be fully avoided, but can be reduced by, for example, increasing the sample size. Accuracyis the agreement between the true value and repeated measured observations or estimations of a quantity. Accuracy means lack of bias. Precisionillustrates the level of agreement among repeated measurements of the same quantity. This is represented by how closely grouped the results from the various sampling points or plots are. Precision is inversely proportional to random error. The above mentioned concepts of bias, accuracy, random error and precision can be illustrated by an analogy with a bulls eye on a target. In this analogy, how tightly the darts are grouped is the precision, and how close they are to the center is the accuracy. In the figure (A), the points are close to the center and are therefore accurate (lacking bias), but they are widely spaced and therefore are imprecise. In (B), the points are closely grouped and therefore are precise (lacking random error) and but are far from the center and so are inaccurate (i.ebiased). Finally, in (C), the points are close to the center and tightly grouped and are both accurate and precise (GOFC-GOLD, 2009). 11

12 Narration: You can calculate the sample size, or number of plots, by using this equation. To calculate number of plots without the tool mentioned above, use the following steps: Step 1: Identify the desired precision level. Step 2: Identify an area from which to collect preliminary data. For example, if the activity is to afforest agricultural lands and will last for 20 years, then an estimation of the carbon stocks in the trees of about six to 10 plots within an existing 15 to 20-year-old forest would be enough. Step 3: Estimate carbon stock, standard deviation and variance from the preliminary data. Step 4: Calculate the required number of plots. An online tool for calculating number of plots is available at: www. winrock.org/what/docs/plot_calculator_for_multi-strata_lands.xls 12

13 Narration:The accuracy and precision of ground-based measurements depend on which methods are employed and the frequency of collection. For carbon accumulation, the frequency of measurements should be defined in accordance with the rate of change of the carbon stock. Forest processes are generally measured over periods of five-year intervals; Carbon pools that respond more slowly, such as soil, are measured every 10 or even every 20 years. As verification and certification must occur every five years for CDM project activities, it is reasonable that at least the dominant biomass pool, which is trees, should be measured at the same frequency. It may not be possible to claim credit for pools not measured with a five-year frequency. For pools accumulating carbon more slowly, such as dead wood or soil, it would be logical to measure at time zero and again at the end of the project activity, and to claim credit at this time for all sequestration that has occurred in these pools. Source: Pearson et al.,

14 Narration:Detecting the difference in the carbon pool measured twice (time 1 and time 2) depends on two factors: the standard deviation of the mean and the length of the time interval between time 1 and time 2. The objective is to estimate the number of plots needed to establish the minimum change in the mean carbon stocks, with 95 per cent confidence, that has taken place from one monitoring event to the next, rather than to estimate the number of plots needed to establish that the two means are significantly different from each other. For the RME approach, the monitoring results from plots are pooled to derive a mean for the sample population at Time 1 and Time 2. Change in soil carbon is estimated by subtracting the maximum estimate of the population mean at Time 1 (mean at Time 1 plus half the 95 per cent confidence interval at Time 1) from the minimum mean estimate at Time 2 (mean at Time 2 minus half the 95 per cent confidence interval at Time 2). The resulting difference represents, with 95 per cent confidence, the minimum reliable change in mean soil carbon from Time 1 to Time 2 (Penman et al., 2003 ). 14

15 Narration: Here is an example of a forest carbon measuring and monitoring system in the Noel Kempf project in Bolivia. A total of 625 permanent sample plots were measured in an area of hectares. A fixed-area, nested plot design was used and in each plot, measurements were taken for various carbon pools, including all trees with a diameter at breast height larger than 5 centimetres understory fine litter standing standing dead wood soil to a depth of 30 centimetre The plot design was a fixed area with small and intermediate nested plots. The plots were a 4-metre radius for trees with a diameter at breast height of 5 to 20 centimetres and 14metre radius for trees with a diameter at breast height greater than 20 centimetres. 15

16 Narration:These are the results of the carbon inventory in the Noel Kempff Climate Action Project, Bolivia. The total amount of carbon in the project pools is about 118 Tg C ±4 per cent (95 per cent CI). Most of the carbon, 64 per cent, is in above-ground biomass of trees, followed by soil to 30 centimetres depth at 19 per cent, and roots at 16 per cent. The understory and fine litter account for about 3 per cent of the total. Source: Brown et al.,

17 Narration:Some projects will be successful in sequestering carbon within the project area, but the project activities may change activities or behaviourselsewhere. These changes may lead to reduced sequestration or increased emissions outside the project boundary, negating some of the benefits of the project. This is called leakage. A simple example is a project where reducing emissions from deforestation in the project area leads to increased deforestation in another area. Another example is a project that reforests an area of poor-quality grazing land, but leads to the owners of the displaced livestock to clear land outside the project boundaries to establish new pastures. It is often difficult to quantify leakage. In the Noel Kempffproject, leakage was caused by increased deforestation among local communities, which was hoped to be transitory, when new land-use systems were created. For the stop-logging component, the leakage was thoroughly screened and found to be in the 2 to 42 per cent range. The types of activities that might result in leakage vary with the type of projects, but both LULUCF and non-lulucf projects are subject to leakage. Leakage can often be minimised by good project design such as in the example above by including improved pasture management around the plantation so that displaced livestock can be accommodated without further clearing. Source: Brown et al., 2000; Pearson et al.,

18 Narration:To produce verifiable and certifiable measurements of changes in carbon stocks, quality assurance and quality control measures must be implemented. A quality assurance, quality control plan provides confidence to all stakeholders that the reported carbon credits are reliable and meet minimum measurement standards. The plan should become part of project documentation and cover procedures for: Collecting reliable field measurements Verifying laboratory procedures Verifying data entry and analysis techniques Maintenance and archiving data To ensure these procedures can be repeated, a set of Standard Operating Procedures should be prepared for each step. Source: Pearson et al.,

19 Narration: India s national forest carbon inventory is based on data obtained from the forest inventory. The objective of the forest inventory is to estimate growing stock of forests and trees outside forests on a two-year basis and improve the estimate in subsequent cycles. However, all the districts of the entire country will be covered in 20 years. For this purpose, the country has been stratified into 14 physiographic zones representing similar conditions and vegetation characteristics. Ten per cent of districts, or 60 districts, are covered in every two-year cycle. The districts are selected randomly within each zone with probability proportion to size. Along with the traditional forest inventory (measurement of trees), a vegetation survey of herbs and shrubs is also carried out. In addition, measurement of soil and litter carbon is also carried out. Revised method: Topographic sheets of 1:50,000 scale forms the base map for the inventory The topographic map is divided into grids of 2½ 2 ½ Within each grid, four sub-grids of size 1 ¼ 1 ¼ are laid Two sub-grids are randomly selected Sample plots are then laid in each sub-grid at the intersection of the diagonals At grid centre a square plot of 0.1 ha is laid out Measurements of various parameters like dbh, species name, crown-diameter etc. for all trees above 10 cm dbh are carried out For litter and humus and soil carbon, two sub plots of 1 sq. m are laid out on opposite corners of the inventory plot (0.1 ha) Samples of litter and humus and soil are then collected from all the sub-plots Further, from the centre of the plot measurements are done along the diagonals in all the 4 directions and points are marked at a distance of 30 meters Nested quadrants of 3m 3m for shrubs and 1m 1m for herbs are then laid out Regeneration Survey is also carried out in 3m x 3m plots Besides regeneration status it provides information about plant biodiversity, distribution & abundance Data collection, data entry and data checking is done by the four zonal offices located in different part of the country Partial data checking, data processing, analysis and output production is done in the headquarters at Dehradun More than 250 volume equations developed covering most of the tree species growing in different physiographic zones are used for estimating growing stock. The estimated cost of inventory and data processing of a sample plot is about US$ per plot of which about US$ is spent on travel to sample plot, field measurement including checking by supervisors and the rest on field preparation, equipment, designing, data entry, processing etc 19

20 Narration:Australia s National Carbon Accounting System accounts for greenhouse gas emissions from land-based sectors. It accounts for these activities through a highly integrated system that combines: remotely sensed land cover change, including mapped information from thousands of satellite images; land-use and management data; climate and soil data; greenhouse gas accounting tools; and spatial and temporal ecosystem modelling. 20

21 Narration:The United States Department of Agriculture Forest Service, Forest Inventory and Analysis Program conducts a continuous inventory and assessment of U.S. forests since the 1950s, covering forests on all private lands and most public lands. This national inventory provides periodic estimates of area, timber volume, tree biomass, and wood products. The programmealso reports on, among other variables, the status and trends in forest area and location; individual tree measurements of diameter, species, and health; and growing stock volume, increment, mortality, and removals by harvest. The forest inventory data are the basis for reporting statistics about carbon in U.S. forests. 21

22 Narration: A network of permanent sample plots, about one plot per 2400 hectares of forest land across the landscape, make up the Forest Inventory and Analysis and the Forest Health Monitoring programmes. More information can be obtained from 22

23 Narration: Until very recently, individual states were surveyed sequentially within regions, on a cycle averaging about 10 years. National statistics were compiled from these inventories every five years. For long-term trend monitoring and projections, five-year intervals are sufficient and consistent with requirements to report greenhouse gas emissions and sinks by five-year periods. Within the last few years, the implementation of state inventories has begun changing to a more continuous process. Data are collected in many states every year, with all of the sample plots scheduled for measurement over a 5-to 10-year period. These changes will facilitate annual reporting of basic inventory statistics, which in turn will facilitate reporting of carbon stock changes on an annual basis. 23

24 Narration:The US Environmental Protection Agency has compiled annual national statistics and reports on greenhouse gas emissions in the USA since the 1990s. The reports can be obtained from EPA s web site at 24

25 During this final discussion we will cover two topics: 1) How should a USAID project set up its monitoring? What fits within its timescale and funding? 2) Is the accuracy of good measures worth the cost? 25

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