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This publication was produced for review by the United States Agency for
International Development. It was prepared by Tetra Tech.

This publication was produced for review by the United States Agency for International Development by Tetra Tech ARD,
through a Task Order under the Prosperity, Livelihoods, and Conserving Ecosystems (PLACE) Indefinite Quantity
Contract Core Task Order (USAID Contract No. EPP-I-00-06-00008-00, Order Number AID-OAA-TO-11-00022).

Tetra Tech
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Tetra Tech Contacts:
Ian Deshmukh, Senior Technical Advisor/Manager

Forest Carbon, Markets and Communities (FCMC) Program
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Stephen Kelleher, Chief of Party

Olaf Zerbock, USAID Contracting Officer’s Representative

• Jennifer Hewson, Conservation International
• Marc Steininger, FCMC & Conservation International
• Stelios Pesmajoglou, Greenhouse Gas Management Institute

Contributing authors:
• Angel Parra, Consultant; GHG inventory & LULUCF sector expert
• Gordon Smith, Greenhouse Gas Management Institute
• David Shoch, TerraCarbon, LLC
• John Musinsky, National Ecological Observatory Network
• Fred Stolle, World Resources Institute
• Kemen Austin, World Resources Institute
• Irene Angeletti, Greenhouse Gas Management Institute

The US Agency for International Development (USAID) has launched the Forest Carbon, Markets and Communities
(FCMC) Program to provide its missions, partner governments, local and international stakeholders with assistance in
developing and implementing REDD+ initiatives. FCMC services include analysis, evaluation, tools and guidance for
program design support; training materials; and meeting and workshop development and facilitation that support US
Government contributions to international REDD+ architecture.

Please cite this report as:
Hewson, J., M.K. Steininger and S. Pesmajoglou, eds. 2014. REDD+ Measurement, Reporting and Verification (MRV) Manual,
Version 2.0. USAID-supported Forest Carbon, Markets and Communities Program. Washington, DC, USA.





The author’s views expressed in this publication do not necessarily reflect the
views of the United States Agency for International Development or the United
States Government.


TABLE OF CONTENTS....................................................................................... III
ACRONYMS AND ABBREVIATIONS ...................................................................... V
ACKNOWLEDGEMENTS ................................................................................... VIII
1.0 INTRODUCTION....................................................................................... 1
1.1 PURPOSE, SCOPE AND STRUCTURE ...................................................................................... 1
1.2 BACKGROUND ............................................................................................................................... 4
1.3 REFERENCES...................................................................................................................................... 8
2.0 INSTITUTIONAL ARRANGEMENTS.............................................................. 9
2.1 INTRODUCTION ............................................................................................................................ 9
2.2 ELEMENTS OF A MRV SYSTEM FOR REDD+ .......................................................................13
2.4 STEPS IN ESTABLISHING INSTITUTIONAL ARRANGEMENTS.....................................19
2.5 EXAMPLES ........................................................................................................................................22
2.6 EPA NATIONAL SYSTEM TEMPLATES ...................................................................................26
2.7 REFERENCES....................................................................................................................................30
3.1 INTRODUCTION ..........................................................................................................................31
3.2 IPCC GUIDANCE...........................................................................................................................34
3.3 INVENTORY AND REPORTING STEPS .................................................................................38
3.4 DEFINITIONS OF CARBON POOLS AND LAND USES ..................................................41
3.6 REFERENCES....................................................................................................................................49
4.0 FIELD-BASED INVENTORIES .................................................................... 50
4.1 INTRODUCTION ..........................................................................................................................50
4.2 CARBON POOLS AND THEIR MEASUREMENT ................................................................54
4.3 CONCEPTS AND CONSIDERATIONS IN INVENTORY DESIGN ................................ 57
4.4 THE FOREST CARBON INVENTORY TEAM .......................................................................66
4.5 FIELD WORK AND ANALYSIS .................................................................................................67
4.6 CALCULATING CARBON STOCKS FROM FIELD DATA ...............................................69


............................................................................3 VERIFICATION ...........................................................................................................................................................................0 THEMATIC REVIEWS ....7 COMMON SATELLITE DATA SOURCES FOR LAND-USE MONITORING .........................................2 REPORTING....................................................8 SELECTED RESOURCES......2 LAND USES AND CATEGORIES IN THE UNFCCC ................................................1 INTRODUCTION ............6 REFERENCES. 152 7..... 4................. 141 6........................................................................................10 REFERENCES .....................................................................................................................3 NEAR-REAL TIME MONITORING AND ALERT SYSTEMS .......................................................9 THE GAIN-LOSS METHOD ............................. REPORTING AND VERIFICATION (MRV) MANUAL...............93 5............................................................................................................... 165 7............11 SELECTED RESOURCES .......................................................................... 125 6..................... 127 6............................................ 82 5......................... 152 7..................................................1 HISTORY OF REDD+ UNDER THE UNFCCC ..........................2 COMMUNITY-BASED MONITORING ................................7 DATA CHECKING .............0 REMOTE SENSING OF LAND COVER CHANGE ............81 5......................................... 121 6........................................................................................84 5............................ 109 5... 181 REDD+ MEASUREMENT.....................................................0 iv ...........................................76 4........... 151 7......82 5..................................................................1 INTRODUCTION .....................4 REMOTE SENSING OVERVIEW ............90 5....................................3 OVERALL STEPS AND NEEDS.......... VERSION 2.................75 4............................................................................................4 REFERENCES............................................. 114 5......5 EMERGING AREAS OF RESEARCH.79 4............... 125 6........................................................................................................0 REPORTING AND VERIFICATION: ELEMENTS AND GUIDANCE ....................................... 118 5...................78 4.....................................................................................................................................................................8 CONSOLIDATING INVENTORY DATASETS ....................

VERSION 2. Markets and Communities Program FCPF Forest Carbon Partnership Facility REDD+ MEASUREMENT. REPORTING AND VERIFICATION (MRV) MANUAL. ACRONYMS AND ABBREVIATIONS ACR American Carbon Registry AD Activity Data AFOLU Agriculture. Forestry and Other Land Use AGB Aboveground biomass BCEFs Biomass conversion and expansion factors BRDF Bi-directional reflectance distribution function BURs Biennial Update Reports CH4 Methane CI Conservation International CMP Conference of the Parties serving as the Meeting of the Parties to the Kyoto Protocol CO2 Carbon dioxide COP Conference of the Parties CV Coefficient of Variation DBH Diameter at Breast Height DEM Digital Elevation Model DTs Decision Trees EFDB Emissions Factor Database EFs Emissions Factors EM Electromagnetic EOS Earth Observation System EPA Environmental Protection Agency FAO Food and Agriculture Organization FAS Fire Alert System FCMC Forest Carbon.0 v .

FIRMS Fire Information and Resource Management System FREL Forest Reference Emission Level FRL Forest Reference Level FSI Forest Survey of India FUNCATE Foundation of Space Science. VERSION 2.0 vi . Technology and Innovation MMU Minimum-mapping unit MRV Measurement. REPORTING AND VERIFICATION (MRV) MANUAL. Reporting and Verification N20 Nitrogen oxide NAMA Nationally Appropriate Mitigation Strategies NASA National Aeronautics and Space Agency REDD+ MEASUREMENT. Meteorology and Environmental Studies ILUA Integrated Land Use Assessment INPE Brazilian National Space Research Institute IPCC Intergovernmental Panel on Climate Change KCA Key Category Analysis LDCM Landsat Data Continuity Mission LEDS Low Emission Development Strategies LiDAR Light Detection and Ranging LUC Land-use Change MADS Colombian Ministry for Sustainable Development MCT Brazilian Ministry of Science. Land-use Change and Forestry GPS Global Positioning System IDEAM Colombian Institute for Hydrology. Applications and Technology GEF Global Environmental Facility GFIMS Global Fire Information Management System GFOI MGD Global Forest Observation Initiative Methods and Guidance Documentation GFW Global Forest Watch GHG Greenhouse gas GHGMI Greenhouse Gas Management Institute GIS Geographic Information System GLAS Geoscience Laser Altimeter System GOFC-GOLD Global Observation of Forest and Land Cover Dynamics GPG-LULUCF Good Practice Guidance for Land Use.

sustainable forest management and enhancement of forest carbon stocks.0 vii . REPORTING AND VERIFICATION (MRV) MANUAL. comparability. and consistency TOA Top-of-atmosphere UMD University of Maryland UNDP United Nations Development Programme UNEP United Nations Environment Programme UNFCCC United Nations Framework Convention on Climate Change USAID United States Agency for International Development USGS United States Geological Survey VCS Verified Carbon Standard WGs Working Groups WMO World Meteorological Organization WRI World Resources Institute REDD+ MEASUREMENT. accuracy. completeness. plus the role of conservation.NCs National Communications NFMS National Forest Monitoring System NGGIP National Greenhouse Gas Inventories Program NGO Non-governmental organization NNs Neural Networks NRT Near-real Time PCA Principal components analysis PRODES Projeto De Estimativa De Desflorestamento da Amazoni (Brazilian Amazon deforestation monitoring program) QA/QC Quality Assurance and Quality Control QUICC Quarterly Indicator of Cover Change RADAR Radio Detection and Ranging REDD+ Reducing emissions from deforestation and forest degradation. SBSTA Subsidiary Body on Scientific and Technical Advice SES Social and Environmental Soundness SINA Colombian National Environmental System SLR Side Looking RADAR SRTM Shuttle Radar Topography Mission TACCC IPCC principles of transparency. VERSION 2.

including Ned Horning of the American Museum of Natural History. Brice Mora of the Global Observation of Forest Cover and Land Dynamics. Axel Penndorf of BlackBridge. the International Panel on Climate Change and the Landsat program. Karyn Tabor. REPORTING AND VERIFICATION (MRV) MANUAL. Frank Martin Seifert of the European Space Agency's Centre for Earth Observation ESRIN. Andrew Lister. Maggie Roth. and Leif Kindberg of FCMC. Michael Gillenwater of the Greenhouse Gas Management Institute. Asim Banskota of the University of Minnesota. and members of the USAID Climate Change office. Chris Potter of the US National Aeronautics and Space Administration’s Ames Research Center. SilvaCarbon Consultant.ACKNOWLEDGEMENTS The authors thank the various colleagues in our organizations who have commented on the text. Deborah Lawrence of the University of Virginia. The authors also thank those who have made graphics available for use in this Manual. Additional figures are from the websites of the United Nations Framework Convention on Climate Change. and Jamie Eaton of TerraCarbon LLC. REDD+ MEASUREMENT. Megan McGroddy. and Charles Scott of the US Forest Service. Mario Chacon and Johnson Cerda of Conservation International. Ronald McRoberts. including Colin Silver. VERSION 2. Christine Dragisic of the US State Department. John Rogan of Boston University. Carly Green and Jim Penman of the Global Forest Observations Initiative. Rishi Das.0 viii .

Irene Angeletti. 4. This chapter is relevant to the activities highlighted on the following page.0 – FIELD-BASED INVENTORIES 50 . of this Manual outlines the sequence of steps required for generating a national GHG inventory. David Scoch 4.1 INTRODUCTION Section 3.0 FIELD-BASED INVENTORIES Authors: Gordon Smith. Inventory and Reporting Steps. REDD+ MRV MANUAL: CHAPTER 4.3.

0 – FIELD-BASED INVENTORIES 51 . STEP 6: Report emissions and removals estimates. appropriate to the tier level identified. estimating the uncertainty in each estimate. if using the gain-loss method. STEP 5: Quantify emissions and removals. ensuring that the requirements in terms of emission and removal factors are met. and worksheets where appropriate. using the reporting tables. carbon pool and non-CO2 source.STEP 0: Establish Institutional Arrangements. through stratification and other methods. STEP 1: Estimate the land areas in each land-use category. for representing areas in the GPG-LULUCF. REDD+ MRV MANUAL: CHAPTER 4. Within the categories designated as key. AD represents the extent over which a human activity occurs. STEP 2: Conduct key category analysis (KCA) for the relevant categories. including expert peer review of the emission estimates following specific guidance under each land-use category. pool or non-CO2 gas. STEP 7: Perform verification and implement quality control checks. for the time period required. Emissions and removals estimates represent the product of the AD by the associated EFs. EFs represent coefficients that quantify the emissions/removals per unit STEP 4: Generate Activity Data (AD). STEP 3: Design a forest carbon inventory to generate Emissions Factors (EFs). assess significant non-CO2 gases and carbon pools and prioritize such pools in terms of methodological choice. Document and archive information used to produce the national emissions and removals estimates following specific instructions under each land- use category.

facilitating national communication of carbon emissions and removals from land use.1 provides an illustrative inventory design and implementation work plan. countries will have to establish National Forest Monitoring Systems (NFMS) that quantify changes in land cover and terrestrial carbon stocks using a combination of ground-based forest carbon inventory approaches for estimating. and consistency (TACCC).0 – FIELD-BASED INVENTORIES 52 . Box 4. plus the role of conservation. comparability. When a forest carbon inventory can serve multiple needs. forest carbon stocks and forest-area changes. sustainable forest management and enhancement of forest carbon stocks (REDD+). REDD+ MRV MANUAL: CHAPTER 4. completeness.According to United Nations Framework Convention on Climate Change (UNFCCC) guidance for reducing emissions from deforestation and forest degradation. accuracy. it will likely be easier to obtain resources to prepare the inventory and maintain support for continued work over time. anthropogenic forest-related greenhouse gas (GHG) emissions by sources and removals by sinks. Countries will need to perform these processes according to the Intergovernmental Panel on Climate Change (IPCC) principles of transparency. including providing accurate input into a national GHG inventory. A field-based forest carbon inventory has multiple purposes. as appropriate. and supporting the generation of GHG offset credits or national programs to mitigate emissions.

g. Develop sample design a. Select and acquire measurement technologies/equipment 3. permanent or temporary) and sampling intensity. Produce reports and summary results 4.g. nested fixed area. Consider scale/resolution desired and whether focus is on estimation of stocks or stock change. maps and relevant GIS coverages. or extend the geographic range of a forest carbon inventory and thereby jointly serve carbon inventory and other resource management needs. two- stage) d. It may be possible to extend the use of collected data. desired precision.1: Illustrative inventory design and implementation work plan • Establish Institutional Conduct: a capacity and needs assessment through a consultative process engaging a wide range of potential users of data (beyond REDD+ MRV end uses) 1. cluster. and land-use management needs. since multiple information users can advocate for its continued funding. Data checking/quality control 2. wildlife. Define budget and personnel and capacity building needs • Design inventory 1. The national forest inventories supported by the Food and Agricultural Organization (FAO) National Forest Monitoring and Assessment REDD+ MRV MANUAL: CHAPTER 4. forest heterogeneity and plot size e. Document design and rationale • Prepare for inventory implementation 1. Allocate samples and produce maps for inventory implementation f. 2. systematic. specifying end uses and desired outputs. and can serve other land management. Select and validate allometric equations and define required measurements 3. Define forest carbon pools (informed by KCA analysis) and other environmental parameters to measure 2. variable radius. Define objective. For example. Set up inventory database 5. Organize and train field and administrative personnel 6. Select desired precision /allowable uncertainty 3. Analyze data and calculate estimates and uncertainty 3. desired scale/resolution. Define sample unit (e. This type of data sharing can make the inventory more cost effective and ensure financing from more sources.g. Submit results to external technical expert verification (as part of larger UNFCCC REDD+ verification process) Forest carbon inventory data have substantial overlap with timber inventory data.0 – FIELD-BASED INVENTORIES 53 . Box 4. Develop field measurement and data management protocols 4. fixed area. aerial photographs and remote sensing data) b. Produce tactical plans to guide deployment of field teams • Implement inventory 1. Define population/forest area c. Select sampling approach (e. the latter with consideration to budget constraints. pilot plot data from existing inventories. simple random. stratified. Conduct periodic internal audits (via plot re-measurements) through implementation to confirm adherence to protocols and identify and correct any measurement errors • Analysis and reporting 1. Collect field measurement data and enter to database 2. Define inventory organization/administration 2. Collect data to develop sample design (e. the Mexican National Forest and Soil Inventory carried out an effective consultation process to identify the information that various users of the inventory would require.

program provide another example. This chapter focuses on implementation of the stock change method. hold promise to complement. The gain-loss method. Tier 1 methods rely on the use of default values. mortality). non-timber forest products and socio-economic indicators 13. new model-based approaches in development. and facilitate mapping. and implementation of sampling schemes. For perspective. “traditional” field-based measurement practices and probability-based approaches. Sampling and measurement guidance presented in this chapter is relevant to both the stock change and gain-loss methods. relating biomass to remote sensing data (see Chapter 5. and may eventually encompass an entire country.2 CARBON POOLS AND THEIR MEASUREMENT REDD+ forest carbon inventories should quantify stocks of carbon in pools that might change significantly under the REDD+ mechanism or under the REDD+ reference level. diameter increment) and losses (e. these inventories collect data on carbon stock. the stock change method does not produce emission factors and activity data.g. In the latter case. or to estimate gains (e. Besides the collection of information regarding timber species and volume. and best suited for landscapes where the area of forest undergoing transitions in structure is either difficult to delineate or relatively evenly distributed across large areas. measurements are used to produce emission factors associated with specified transitions in forest structure (e. This chapter also focuses on the derivation of country or region-specific data that could be used with a Tier 2 or Tier 3 method. is discussed in Section 4. In comparison. derived by delineating the area of those transitions. the information below highlights considerations specific to forest inventories. There has been no decision for non-Annex I Parties regarding use of the 2006 Guidelines. 2006). which is then expanded using activity data.g. and Measurement. while the Guidance from 2006 uses stock ddifference (IPCC. Field-based measurements are critical elements in both traditional probability-based sampling and in model- based estimation approaches involving interpretation of remote sensing imagery. In contrast. but not replace. REDD+ MRV MANUAL: CHAPTER 4. While the different carbon pools are described in Chapter 3.fao. but instead produces overall estimates of stocks for the entire area under MRV (held constant) for two points in time and estimates emissions as the difference between the two estimates. 13 See http://www. 4. A further discussion of gain-loss and stock change methods is included in section 2. gain-loss. As discussed in Chapter 3. the stock change method is less targeted than the gain-loss method. the IPCC recognizes two methods to estimate carbon changes: the gain-loss method and the stock change method 14 and establishes hierarchical Tiers of data specificity (IPCC. intact to degraded forest). and thus in this Manual we use the former term throughout. stratification. Repeated measure from sample plots can be used to estimate stocks at two points in time. which focuses on identifying and measuring fluxes. meaning that inventories covering more area become less expensive when the cost is calculated on a per-hectare basis.0 – FIELD-BASED INVENTORIES 54 . Other resource management goals may be addressed by having teams collect some additional types of data while doing their carbon inventory work. In this 14 The GPG-LULUCF (IPCC. forest inventory is most accurately conceived of as a collection of procedures and technologies that encompass a system. even though the 2006 Guidelines are more up-to-date and use the latter..9 below. emerging technologies). REDD+ activities may encompass vast areas of land. Reporting and Verification (MRV) should be at a corresponding scale. Geographic Information Systems (GIS) and remote sensing approaches (see Chapter 5) are part of this system. 2003) uses the term stock change. There are economies of scale in forest carbon inventories.g. 2006).1 of the Global Forest Observation Initiative (GFOI) Methods and Guidance Document (GFOI 2013).

the inventory should include all tree species. Therefore. the boundary definition must correspond to the smallest piece of woody debris.g. so that any piece of material fits in exactly one category. aboveground biomass typically emits the most carbon upon conversion to non-forest.0 – FIELD-BASED INVENTORIES 55 . If litter is measured. The boundary between fine and coarse roots depends on the method used to estimate the belowground biomass. substantial amounts of soil carbon (described in 4.3). To accurately estimate the forest carbon stock of live biomass.2.2. To obtain the belowground biomass. 4. A common minimum size of fine debris pieces is 1 cm. in some systems. As discussed in Chapter 3. The IPCC recommends 2 mm diameter. dead woody stems where the long axis of the stem is within 45 degrees of vertical are classified as standing dead and stems where the long axis is more than 45 degrees off vertical are classified as lying dead wood. over 30 cm DBH) diameter classes often exclude important components of total aboveground biomass and consequently offer limited reliability in estimation of carbon stocks and stock changes. peat swamp forests.2.1) is typically the largest biotic carbon pool in a forest that would be lost via deforestation. Pieces that are not large enough to be classified as coarse debris are classed as fine debris. Standing dead wood is typically measured along with living tree biomass (see aboveground biomass) and recorded as deadwood because its density often differs from live trees.1 Aboveground biomass In a forest. A typical minimum piece size for coarse woody debris is 10 cm in diameter and sometimes there is also a minimum length requirement that pieces be at least 1 m long. it may be important to measure dead wood carbon stocks (described in 4..2 Belowground biomass Belowground biomass is an important carbon pool that may equal 25 percent or more of the aboveground biomass in many forests.3 Dead wood Dead wood.4 of the IPCC 2006 Guidelines. smaller woody plants and non-woody plants are excluded or are measured separately from larger woody plants. multiply the aboveground biomass by 1 + root-to-shoot ratio. 50 cm or 1 m tall. includes standing and lying deadwood. 4. or measured as part of the soil carbon pool due to the difficulties in Manually separating them from soil.2.g. An example of a default set of size categories is one where the tree category has a diameter at breast height (DBH) of at least 10 cm. If forest land is converted to agricultural use or developed use. 4. a sub-component of dead organic matter. Forest inventories limited to commercial species or trees in commercial (e. soil carbon loss resulting from conversion of forest to agricultural cropland can be greater than emissions from aboveground biomass. and the shrub/small tree category includes woody plants at least 10 cm.5) may be lost. REDD+ inventory developers may choose to apply a Tier 1 approach for belowground biomass which uses the default root-to- shoot ratios provided in Table 4. e. tallying trees with diameters of at least 10 cm. there may be sub-categories such as small trees that are 10-40 cm DBH and large trees that are greater than 40 cm DBH. However.All inventories should measure live trees above a modest size because aboveground biomass (described in Section 4.2. with smaller pieces being classified as litter. but measuring root biomass is time consuming and expensive. By convention. Typically. and is neither double-counted nor excluded. To increase sampling efficiency. fine roots are often excluded.2. and thus monitoring of soil organic carbon stocks may be warranted. If there is substantial disturbance of forests via degradation. REDD+ MRV MANUAL: CHAPTER 4.

but some projects have measured soil carbon to 1 m depth or more. and changes in the stock are slow. A humic layer of organic soil is the decomposed remnants of vegetative material and is typically not included in the litter pool. litter tends to decompose easily. soil carbon stocks may have large changes and should be measured. At depths of more than one to three meters. decomposed organic material. material less than 1 cm in diameter are defined as litter. Whatever boundary is chosen. moisture saturation. may not be worth measuring since the pool is not typically large. if decomposition is slowed by factors such as cold temperatures. but the total amounts can be significant because the mass of soil is so large.2. or nutrient limitation. In undisturbed systems. there is more soil carbon per centimeter of depth at the surface than there is at 40 or 100 cm below. it should be measured separately from the litter and the mineral soil carbon pools. in more arid woodlands. However. By convention.” includes fine woody debris. If a significant decomposed organic layer is present between the litter and the mineral soil. the density of soil carbon is low. an organic layer may form. However. laboratory tests that do not differentiate organic from inorganic carbon should be avoided. Total soil carbon stocks are often as large as.4. particularly if the agricultural activities include plowing. woody biomass carbon stocks. the litter pool may be defined as including fine woody debris up to 10 cm in diameter. and wet forests. When switching from plowing to trees REDD+ MRV MANUAL: CHAPTER 4. or larger than. particularly if there is no fine woody debris pool. and the amount of effort required to sample soil carbon increases with depth. If there are small or modest degrees of disturbance of the forest. a key decision is the depth to which soil will be measured. Examples of this include peat and muck soils. typically 40-50% of this carbon is lost. substantial carbon loss can occur in the deeper soil. better described as the “forest floor. low pH. there is no inorganic carbon in soils. but typically the boundary is defined as a specific piece size or degree to which pieces are buried. In closed. foliage and twigs that are on the ground and not attached to a plant stem. For some forest types. the same boundary must be used for the maximum size of pieces in the litter pool and the minimum size of pieces in the woody debris pool. at depths up to one meter or more.0 – FIELD-BASED INVENTORIES 56 .4 Litter Litter. There is less standardization of the definitional boundary between soil carbon and woody debris. By convention.5 Soil organic matter As discussed in Chapter 3. and as a result. 4. However. except for sites that are so arid that few trees are likely to grow and sites with carbonaceous soils such as limestone. Fine woody debris is small pieces of dead wood. many projects that maintain existing forest do not measure soil carbon stocks because the stocks are assumed to be constant. peat is a buildup of minimally decomposed plant material. carbon is generally counted if it is in the top 20 or 30 cm of soil. Although there are often measurable amounts of soil organic carbon down to depths of several meters. As a result. moist. live roots less than 2 mm in diameter are often classified as part of the soil carbon pool. most of the change in soil carbon happens near the surface in the top 20-30cm. The threshold size of roots and dead wood to be included in the soil carbon category must correspond to definitions used in the live belowground biomass and dead wood categories. In the years to decades following conversion to plowed fields. It is typically included in the soil pool. and live roots of 2 mm or greater are classified as belowground live biomass. but if this pool occurs infrequently it may be included in the litter pool. If measuring soil carbon. soil carbon stocks are unlikely to change much. in the case of forest clear-cutting and conversion to agriculture. if inorganic soil carbon is likely to be present. Guidance provided by the IPCC specifies separate reporting of changes in organic soil carbon stocks and inorganic soil carbon stocks (but change in inorganic soil carbon stocks is assumed to be zero unless Tier 3 methods are used) (IPCC 2006). As a result. this category includes all organic carbon in mineral and organic soils to a specified depth.2. while muck is black. However. Typically. The density of soil carbon decreases with depth. as well as live fine roots that are above the mineral or organic soil. .

To capture much of the carbon stock change that results from land management changes.0 – FIELD-BASED INVENTORIES 57 . even when measuring to significant depths. such as to a depth of 1 m. Larger changes in percentage terms are easier to detect with sampling. NFMS “should build on existing systems. an entire country. Initially. As a result. Much of the gain in soil carbon in the first few years of conversion from crops to forest is in the top few centimeters of soil. many inventories sample only the top 20 or 30 cm of soil. dry forest. Consideration of inventory design should also take account of. or something in between.19 paragraph 4(a)). the percentage change in carbon stock may be large. 4. When measuring soil carbon loss upon conversion of forest or grassland to cropland with plowing. shrubland. and grassland areas where there is conversion to agriculture. and seek to build on. soil carbon losses are usually large enough to justify the cost and difficulty of measuring soil carbon loss to 1 m depth. The goal is to choose an efficient design that achieves the desired level of precision at a minimum cost. and over what geographic areas. In moist forest. it is possible that half the soil carbon gain in the first five to 10 years will be in the top 10 cm of soil. In conversion of tilled cropland to grassland there can be significant carbon gains to more than 1 m of depth and it may be worth conducting deep sampling. per COP19 (Decision 11/CP. existing capacities and experience of personnel that will be involved in both field measurement and analysis. it can be efficient to only measure soil carbon to 20 or 30 cm depth. to be able to claim credit for avoiding emission of deeper soil carbon.3 CONCEPTS AND CONSIDERATIONS IN INVENTORY DESIGN Many possible inventory designs can be used to estimate forest carbon stocks. REDD+ monitoring activities may also only focus on lands that are classified as managed forest. This requires choosing what is to be estimated. such as 50 or 100 cm. In wet forests and coarse texture soils that have little carbon. while limiting sampling effort. the gain in percentage terms is greatest when only the top few centimeters are measured. This shallow sampling is especially common for inventories focused on detecting carbon stock increases. or forest subject to anthropogenic change.” Radically new approaches or REDD+ MRV MANUAL: CHAPTER 4. Producing accurate estimates of stocks and changes for all the various strata will significantly increase costs and may not prove the most efficient approach. A country should aim to understand trends for specific regions or forest types.3.or no-till cropping. The geographic scope might be a particular block of land amounting to only a few dozen hectares.1 Needs assessment The first decision in designing an inventory is to assess what needs to be known as a result of the inventory. avoided deforestation projects may sample soil much deeper than 30 cm. In general. Therefore. The next decision is whether there are sub-divisions within the total area where extra information is needed. the process of designing an inventory involves a sequence of steps: 1) Needs assessment 2) Sample design selection 3) Plot design considerations 4) Cost assessment and specification of sampling intensity 4.

2 Sampling design selection Sampling must be unbiased to ensure that resulting inventories will be reliable. Four common approaches. systematic (right).0 – FIELD-BASED INVENTORIES 58 . Diagram of random (top).. ii) systematic sampling.1 are: i) random sampling. iii) stratified (random or systematic) sampling.technologies should be introduced incrementally and only after trial applications can foster confidence of responsible personnel. There are many options available for developing a sampling design. REDD+ MRV MANUAL: CHAPTER 4. and iv) two stage sampling. illustrated and elaborated below in Figure 4.3.1. Figure 4. and stratified random (bottom) sampling. 4.

000 geo-referenced permanent points. and that quantifying this variability is a key part of quantifying how reliably the samples reflect the forest as a whole. Forest Service Forest Inventory and Analysis program). it is common to stratify by ecotype or forest type. While many approaches to stratification exist. random allocation of plots often presents challenges to navigation in the field and produces plot distributions that invariably under- sample some areas and over-sample others. The ILUA set up 221 tracks (each track has 4 sampling plots) systematically across the country at 50 km distances. From 2008 onward. An example of a recent national forest inventory performed using systematic sampling is the Integrated Land Use Assessment (ILUA) carried out by the Zambia Forestry Department (2005-2008). This approach to stratification increases statistical power. while un-biased.Random Sampling A random sample approach locates plots within a study area at random. where the National Forest and Soil inventory established a systematic sample grid of 25. 2013). where regularly spaced plots are measured. Mexico provides another example. and separately sampling each sub area. Within each stratum. Stratification increases efficiency of sampling. with the first intersection point located at random. Typically. may appear to lack credible coverage. Carbon stock (or stock change) is estimated for each stratum. giving a more precise estimate of carbon stocks for a REDD+ MRV MANUAL: CHAPTER 4. non- specialists may not fully understand bias and may have difficulty accepting random sampling and protocols that result in plots being located in sites that the non-specialist views as not representative of the forest. In practice. A variation on systematic sampling is to randomly locate one plot within each cell defined by the grid lines (as is done in the U. then the stocks of the strata are summed to estimate the stock (or stock change) of the entire area. Many people find systematic sampling attractive because it gives equal emphasis to all parts of the area being sampled. though the calculation of uncertainty for systematic samples is less straightforward than for random samples. and is especially useful if little is known about forest conditions or dynamics. systematic sampling involves laying a regular grid over the geographic area to be inventoried. such that all points are monitored once every five years (GOFC-GOLD. The primary advantage of random sampling is that the calculation of means and uncertainty is simple. giving more precise estimates for the same or less effort. Each point contains four sites of 400 m2. inventories must seriously consider perceptions of both specialists and non-specialists. The spacing of the grid lines is calculated so that the desired number of plots can be placed in the area. Its intuitive layout also facilitates navigation in the field. For example. and locating plot centers at the grid intersection points.S. A systematic sample ensures that all geographic areas are equally represented. Systematic Sampling Many national forest inventories instead use a systematic sampling design. which. Systematic sampling offers significant improvement over simple random sampling in terms of reduced uncertainty. Stratified Sampling Stratified sampling is accomplished by dividing the sampling area into relatively homogenous sub-areas.0 – FIELD-BASED INVENTORIES 59 . It is important to understand that stratified sample designs do not produce estimates at the stratum level with equal precision as for the overall population – the goal of stratification is to distribute sampling effort more efficiently to produce a population-level estimate. a systematic sample or simple random sample is conducted. about 20 percent of the points have been re-measured. Training should include communication of the importance of measuring the various conditions that occur over the geographic extent of the inventory.

and FAO ecological zones 17.When choosing – FIELD-BASED INVENTORIES 60 . and may be essential if net changes in stocks are small compared to total stocks. The IPCC recommends stratifying by climate.g. This would mean stratifying by the expected future change in carbon stock. therefore.given number of plots of a given design and also increases the likelihood that plots within a particular stratum will be similar to other plots in that stratum. Thus. To stratify a country’s forest. If no previous information on forest types exists in the country. 4. combining forest types with small areas and sample sizes). but strata can also be redefined ex post via post-stratification and plots re-allocated in a changing landscape.2. 2011) is maintained within each stratum. the stratification can be done initially using global ecological datasets. developers should consider what is known about the forest and the dynamics of carbon stock change. These include indices based on 15 http://geodata. or forest with the same carbon stock dynamics.worldwildlife. If the goal of an inventory is to precisely quantify changes in forest carbon stocks. one can use various GIS data on elevation. Westfall et al. having lower variance between plots gives a higher probability that the total carbon stock will be close to the carbon stock estimated from the sampling. areas with different sampling intensities constitute different strata. soils or other parameters. ecological zone.cfm 17 http://www.home REDD+ MRV MANUAL: CHAPTER 4.1). but for this forest to be different from other strata. and management practices (Vol. Post-stratification involves re-allocating sample plots among strata. section 1. This may be a national forest benchmark map or some other valid map source. through an original systematic sample). Stratification approaches are further discussed in the GFOI Methods and Guidance.4. after the original plot allocation has taken 16 http://www. particularly where proportional allocation can be roughly maintained (e. Various remote-sensing-based data can also be used.3. the goal is to have relatively homogeneous forest (in terms of structure). it is first necessary to have a current map of the dimension being used to stratify.. through monitoring of forest cover and change classes with remotely sensing). Stratifying by expected change in carbon stock can be desirable. allocation of plots should be weighted toward areas where carbon stocks are susceptible to decrease from degradation or deforestation. While this post-stratification approach is straightforward in terms of estimation of the mean. In this case. the selected sampling intensities in these areas may be less than in accessible areas. or by deploying supplemental samples. soil. and such areas should be identified and delineated as separate priority strata. For a given number of plots. The latter can be ensured by consideration in the definition of strata (e.5 (GFOI 2013). and where sufficient sample size (of 10 or more plots.grid. Chapter 3. Stratification is generally performed ex ante. The added uncertainty produced by post-stratification tends to be small. To stratify within each land-cover type. The goal is to "block" differences into different strata.fao. and allocating more sampling effort to strata that are expected to have greater change over time (decrease or increase).g. as is often the case with degradation.. World Wildlife Fund Ecoregions 16. sampling efforts can be focused towards more variable ecotypes or forest types. Of equal or more importance. Homogeneous strata need few plots to precisely estimate their carbon stocks and. or increase from forest growth and regeneration. Calculation of variance for a post-stratified sample is presented in Cochran (1977). and requires that the new strata areas are known exactly and that the existing plots can be unambiguously re-assigned to the new strata (e. using the same calculation as a pre-stratified sample introduces some additional uncertainty due to the random and changing sample sizes within strata. Within each such as maps of Holdridge life zones 15.g. the cost of placing large numbers of plots in remote inaccessible forest areas may be prohibitively expensive and/or logistically impossible.

6 27. like strata 3 and 4.spectral and/or temporal information.1. Discussions of these can be found in Chapter 5.0 – FIELD-BASED INVENTORIES 61 . such as greenness indices and seasonality indices.0 37. achieves the targeted precision at the population level. This can be achieved using REDD+ MRV MANUAL: CHAPTER 4.500 7. will have less influence on the total number of plots needed than those strata that cover larger areas. For example. and 110 plots for the unstratified design (Design 2). The value of stratification is illustrated with the following example (Table 4.1). like stratum 1 in Table 4. They also include classifications or derived products. in that stratification.6 43. in which the sample sizes required to target 95 percent confidence intervals equal to ± 10 % of the mean are calculated for a dataset both with and without stratification. highly variable strata covering small areas. as travel between sample units represents the largest time and labor investment in forest inventory. The advantage of stratification is illustrated in comparing Designs 2 and 3.1 Standard deviation 38.0 83. as shown in Design 3.5 63.500 Pilot observations 20 90 65 50 30 110 25 85 70 180 95 120 120 140 40 75 25 75 90 170 45 110 65 100 Mean 51.3 41. Two stage sampling Sampling vast hard-to-access landscapes necessitates efficient deployment of field effort. such as global biomass maps produced by research programs.1: Comparison of required sampling intensities for stratified and unstratified sample designs Design 1 allows for reporting with the targeted precision at the individual stratum level by implementing an independent sampling effort in each forest stratum.3 100. yet require substantially different levels of field effort to accomplish them: 67 sample plots for the stratified design (Design 3). not at the stratum level. Stratum 1 Stratum 2 Stratum 3 Stratum 4 Total Area (ha) 2.6 CV 74% 32% 43% 42% 52% Design 1 Sample size 217 41 74 69 402 for 4 independent forest inventories Design 2 Sample size 110 (not stratified) Design 3 Sample size 6 16 20 25 67 (stratified with Neyman allocation among strata) Table 4.000 31.000 10.7 117.000 12. This is an important consideration. both of which are designed to generate estimates at the population level. which ignores sources of variability within the population.

18 It is important to note that a sample line plot design isn’t the same as a transect plot design. Each is discussed below. 4. it concentrates field effort and results in minimizing overall effort. (2007). Typically. REDD+ MRV MANUAL: CHAPTER 4. From Smith et al. where a tree is determined to be in or out as a function of the ratio of its diameter to distance from plot center.a two-stage sample. For example. A transect plot design. as well as the size of the plot. fixed area plots are circular or rectangular (including transects). and the variables that need to be measured. This method can be used to calculate the volume of coarse woody debris.3. involving two steps in sampling. the first step might involve selecting polygons to sample from a population of polygons. is an area (two-dimensional). Often these plots are called "fixed area" plots because the size is fixed. typical options for a plot design are: • Points (dimensionless): variable radius plots can be implemented using a relascope or wedge prism. and the second step involve allocating sample points within selected polygons. Plot types In a forest inventory. it can be observed how many features intersects the line.0 – FIELD-BASED INVENTORIES 62 .2: Sampling design including nested plots and line samples to measure the different types of carbon pools. While two stage sampling introduces an added source of sampling error. Inventory developers will also need to consider whether plots will be permanent or temporary. • Lines (one dimensional): on a sample line 18. • Areas (two dimensional): all the trees found on a determined area are measured. even if long and narrow. Figure 4.3 Plot design considerations Plot design considerations include the type of plot to be used.

and consequently have lower inter-plot variability. which is the ratio of diameter to distance from plot center that determines whether or not a tree is measured. using variable radius plots. can account for the fact that distances measured along a slope are smaller when projected into a horizontal map plane. The same efficiencies are achieved. Plot size Plot “size” is assessed differently for different plot types. plot size is simply the area encompassed by the plot. Fixed area and variable radius plots are equally valid choices for a forest inventory (Grosenbaugh and Stover. Data is analyzed using cluster means. not the values observed on individual plots. For area plots.2). square. For prism plots. in the cardinal directions. Larger plots capture more variation in forest structure. When designing an inventory to achieve a target level of precision. reducing the amount of border trees. different plot types may be used in different strata to accommodate different forest structures and logistics. increasing the variability within plots. the key issue is the prism factor. in forests where visibility and penetrability is limited. dis-aggregated plot. which sample trees with probability proportional to size. the sample unit is the cluster. Forest inventories report measurements over horizontal areas. Fixed area plots can have different shapes: circular. required sampling intensities (see below) can be reduced by increasing plot size. a very high number of small trees and fewer larger trees). plots may need to be adjusted to account for slope and boundary considerations. Nesting designs that incorporate fixed area subplots of different sizes. These typically need be applied only if the slope is greater than 10 percent. not the plot. Also. such as combining lines with fixed area or point plots within which to sample lying dead wood and aboveground biomass. thus. For line plots. 1957. Therefore. targeting different diameter classes. they are difficult to use in dense stands of small trees (e.. and how many trees are included. or rectangular. In the field.. respectively (Figure 4.Plots of different types may also be combined to achieve efficiencies in measuring different forms of forest biomass. Plot size affects the variability of carbon stocks observed on different plots. it is preferable to have circular plots because they have the smallest perimeter for the same area.e. and achieves the same reduction in inter-plot variability as larger plot sizes. However. It is often advisable to keep with tradition to avoid extended learning periods and consequent measurement errors. While variable radius plots are more effective in directing sampling effort to the most influential elements in a population (large trees). An example of a cluster pattern would be five plots per cluster where one plot is centered on the central point of the cluster. effectively. the cluster is simply a larger. early successional forest). transects tend to cover more site conditions.0 – FIELD-BASED INVENTORIES 63 . and the variability used in calculations of plots needed for the inventory will imply an approximate plot design. These issues determine the “size” of the plot. allows for increased efficiency in plot measurements as they distribute measurement effort more evenly across diameter distributions.. Most importantly. The typical diameter distribution in a mature forest has a negative J shape (i. Schreuder et al. 1987). and surpassed. the choice of fixed area versus variable radius plots depends on the level of comfort and familiarity of personnel. Plot types and sizes should be kept constant within strata. Where plots overlap the REDD+ MRV MANUAL: CHAPTER 4. Slope corrections. Defining the sample unit as a cluster of plots may also be used as a means of reducing inter-sample variability. In contrast. and the remaining four plots are located with plot centers 200m away from the cluster center. transects tend to be preferable to facilitate accessibility to the entire plot and ensure that trees do not remain uncounted. where plot size is increased. Effectively.g. Normally. the key issue is length. it is recommended to analyze actual plot data to estimate the variability that will result if different plot sizes are selected for different sizes or types of trees.

the more plots will be needed to obtain a given level of precision. then considering the analysis steps. Ducey et al. There is a risk that plots. which are re-measured periodically (e. allow for estimating the stand growth and disturbances with more precision (for a given number of plots) and can therefore quantify small increases or decreases in stocks.0 – FIELD-BASED INVENTORIES 64 . Thus.4 Cost considerations and specification of sampling intensity The sampling intensity (i. with variable radius plots.g. for the baseline number of plots needed. To reduce uncertainty by half can require four times as many plots. When establishing permanent plots. REDD+ MRV MANUAL: CHAPTER 4. therefore. Second. when forest carbon is being measured. and attempting to quantify annual changes in forest carbon stocks resulting from human activities can be confounded by weather and wildfire. a metal stake may be placed completely in the ground for re-identification with a metal detector (Smith et al. back to the plot data. forest variability and plot size employed.e. Parameters to measure The parameters that will be measured in the sample plots depend on the carbon pools of interest and the allometric equations that will be used to convert tree measurements into biomass.. An advantage of temporary plots is that both stratum boundaries and the intensity of sampling can be easily changed over time. every five years). providing a cushion in case some permanent plots cannot be relocated or land cover changes. Inventory costs are driven by variable costs that are a function of field effort. 2007. more plots yield lower sampling errors. First. 2011). the number of samples measured) predicates the precision and resolution of estimates that can be achieved by a forest inventory. Note that weather and disturbance events can cause annual changes in forest carbon stock that are larger than anthropogenic changes. using a mirage method) or. It is strongly recommended to work in reverse. Temporary plots are often used in timber inventories.boundary of an inventory area. corrections are applied to produce equivalent measurements for a complete sample (e. the statistical precision of a biomass estimate depends on the variability of the forest. not by measurements at the plots. getting extremely precise estimates may become expensive. desired precision. 2004)... 4.. The greater the variability of the forest. it is good practice to increase the minimum number of plots. and thus determine what data should be collected. it is necessary to detect the magnitude of change in carbon stocks over a short period of time such as five years or less. Typically. The majority of field effort is represented by travel between plots. For example. it may be desirable to mark plot centers with monuments that are not visible to the human eye.. Selecting the sampling intensity depends on a range of factors. Consequently. approaches that minimize travel time. including budget constraints. Diaz. desired scale/resolution of estimates. The goal of sampling is to reach a desired precision of the estimate of carbon stocks for an acceptable cost.g. when visibly marked.3. can improve efficiency in field effort. may be treated differently by forest users or plantation managers and. first considering what needs to be known as a result of the inventory. by 5 to 20 percent. including two stage samples and cluster sample designs. Permanent plots versus temporary plots Permanent plots. A range of inventory designs should be compared with representative cost data to find a design that meets the specified needs at an acceptable cost. a walk through method (Avery and Burkhart 1994. Some general principles dictating required sampling intensity must be considered.

However. The CV can be estimated from prior surveys that use a similar plot design in similar forests.2: Example of the number of sample plots needed to achieve specified sampling errors with simple random sampling. +/. These statistics are discussed in Section 4. greater statistical precision might be obtained by using fewer and larger plots than the theoretical optimum calculated without considering costs. Plot numbers in stratified sampling are dependent on the variability of the carbon stock in each stratum and the level of precision required. 20 percent of plots would be measured each year. the cost of increasing the size of existing plots no longer yields a significant reduction of variance when compared to that which could be achieved by increasing the number of plots to the sample. For example. a pilot study should be undertaken to estimate the CV. On the other hand. Table 4. one must choose an estimate of variability between plots. when sampling large areas. This is an example of a panel sample.10 Acceptable Acceptable Variation Error Acceptable Error Error Error 100% 98 392 1568 9801 50% 25 98 392 2450 20% 4 16 63 392 15% 2 9 35 221 Table 4. two-stage sampling. most plots will have the required number of trees. The significant level is 95 percent for a large area REDD+ MRV MANUAL: CHAPTER 4. giving less plot-to-plot variability than smaller plots. larger plots may average out some of the fine-scale variations in forests.2 Coefficient of +/-20 Acceptable +/.5 on calculating uncertainties. a biometrician or statistician should be consulted to ensure that sampling intensity calculations and data analysis procedures are correct.The key input to estimating the number of plots needed to obtain a given level of precision is the variation between plots. As explained previously. but are not dependent on the spatial extent of the project. the CV can be less than 30 percent. In this case. If any of these more complex sampling systems are considered. When calculating the number of plots needed. and for a given amount of money. The CV is a measure of how different plots are from each other. when choosing plot size. the CV can be well over 100 percent. travel costs can have more effect on total cost than the number of plots. At some point. Technically. and choose a plot size that is large enough that with the clumped spacing of trees in the forest. In such a case. Many inventories aim to keep crews continuously employed but only re-measure plots once every five years. In fully stocked plantations. the number of plots is relatively independent from the size of the area. with 100 percent of plots measured every five years. and panel sampling.0 – FIELD-BASED INVENTORIES 65 . If no prior surveys exist. For small plots in forest with gaps. calculated as the coefficient of variation (CV).6. the CV is the standard deviation divided by the mean. The chosen variability implies a plot size. There are other more complex sampling systems that may or may not give more power for a given level of effort.2 shows the final results of a hypothetical example of estimating sampling sizes needed to reach specified sampling errors. the inventory developers will have to consider the density of large trees in the forest and the range of sizes of gaps. the number of plots required to meet an increasing level of precision increases by four to reduce the uncertainty by half. There is a theoretical optimal balance between plot size and number of plots that can be achieved through some combination of field experiments or prior knowledge. ranked set sampling. a level of variability might assume that almost all plots contain at least four large trees and that very few plots will contain gaps with few or no medium or large trees.5 +/. These include stratified random cluster sampling. Thus.

A key issue is how field crews will be staffed. The entity responsible for the entire inventory may be governmental or may be part of a university or some other non-governmental organization with appropriate expertise and ability to continue operation. o Entering data (including translating local species names into scientific names). and o Coordinating with regions and users of inventory results. • Field crews that are responsible for data collection.0 – FIELD-BASED INVENTORIES 66 .4. This entity must also coordinate closely with the single national entity designated with the overall responsibility for the GHG inventory.2. A well-established national inventory where measurements are repeated regularly should have its own staff. and o Transmitting data to the central national office. o Performing quality checks on data collection performed by field teams. The entity is responsible for developing the inventory. o Coordinating with the land-cover mapping team. • Regional offices that are responsible for: o Organizing and training field teams.4 THE FOREST CARBON INVENTORY TEAM A national forest inventory team should be comprised of: • An entity with overall responsibility for the entire inventory and the ability to make decisions that are binding to regions (if regions are used). should be one goal for national inventories. • Provides information on the local names of species measured. o Data processing and analysis. and REDD+ MRV MANUAL: CHAPTER 4. Ideally. it may be efficient to have different staff in different regions. Community-based monitoring. field crews should be a combination of technicians with measurement skills accompanied by local community members. o Providing backstopping support to field teams. as discussed in Section 7. including the training and incorporation of local community members into the inventory. o Setting up protocols for data collection. If the inventory covers a very large area. which includes: o Selecting the sampling and plot design. o Organizing the procurement of data collecting equipment. The inclusion of members of the local community is crucial for the following reasons: • Allows access to the plots.

and an independent person compares the two sets of measurements to make sure the measurements are within the required accuracy and precision limits. which may limit acceptable values of data entries if electronic data recorders are used. Careful adherence to meticulous field measurement protocols serves to reduce measurement error. species identification ability. including not only mis- measurement of DBH and height (especially the latter in closed canopy broadleaf forests). Supervision should include visiting crews as they are doing plot work. misinterpretation of border trees and strata boundaries. and how to address unusual cases. as the data is being measured and recorded. an experienced supervisor should keep in close contact with crews during their first month of work.1 Field work Preparation for field work requires more than writing a field protocol and choosing plot locations. and check cruising. Where field measurements include tree heights. in part because it cannot be readily calculated like sampling error. On the other hand. transport. One solution is to have teams composed of individuals who. lodging. Community monitoring may be more practical for detecting and specifying locations of infrequent events. or procedures where team members check each other. together. where a different team independently re-visits and re-measures a subset of plots. and local knowledge. At the plot level. capture the necessary measurement skills. and checking the accuracy of their measurements (quality assurance and quality control is addressed below). particularly in closed canopy forest. equipment.5 FIELD WORK AND ANALYSIS 4. Measurement error results from a host of issues. and communications are all provided as needed. misinterpretation of live versus dead. especially if data collection is not performed frequently. and • Field data check procedures. Field crews will need training to apply the selected protocols of the inventory. but also species misidentification. measurement error may reach 8 percent. the training of local community members to collect forest inventory measurements may not be cost effective. supplies. Quality assurance procedures should include both immediate checks.0 – FIELD-BASED INVENTORIES 67 . • Field Manual specifying how field work is done. Key components of successful field work are: • Logistics planning and implementation to ensure that training. This might mean having technicians with measurement skills who travel around large areas and temporary crew members who know local terrain and assist in measurements. where a supervisor or another person spot checks data by re-taking measurements (while the team is still on the plot). measurement error of around 4 percent is achievable. REDD+ MRV MANUAL: CHAPTER 4. There must be a process for giving crews feedback on the quality of their work. an often over-looked source of error in forest inventories. with well-trained field crews. • Quality objectives for each measurement. and mis-implementation of boundary correction methods. • Provides information on the uses of important species found in the plot. food. measurement error is typically assessed through remeasurement of a sample of plots via the spot checks described above. such as new logging or clearing. 4. It is highly desirable to have local community members included in the field crews.5. Instead. ideally with rewards for good quality work and additional training if deficiencies are found. because they tend to know access routes and other locally unique information. After training.

easy for field technicians to use. Typically. Ecuador. Many inventories use monuments to mark plot centers. avoiding considerable time spent sorting out spelling errors in species names. using laser hypsometers is much faster than tape measures and clinometers for measuring tree heights.0 – FIELD-BASED INVENTORIES 68 . there must be a method for relocating plots that does not only rely on consumer-level GPS readings. as long as the tags are not commonly removed by people or animals. Tree species can also be specified using a menu. Further. Including specific location details or complementary types of data can assist in checking and correcting errors and other problems. Often. For example. While commercial timber cruising software is readily available. best practices when collecting Global Positioning System (GPS) data should be used. Electronic forms can be designed to prompt users to fill in missing values and can question or reject implausible values. Manual instruments such as diameter tapes. taking into account the published accuracy of the receiver type. and field test procedures before adopting them. Locating plots To ensure the integrity of the sample design and avoid bias. Recording the distance and direction from the plot center to each tree is very useful in later relocating plot centers and facilitating check cruises. Canada. individual trees within a plot helps check for trees missing from the measurement. recording the location of. plots must be located prior to fieldwork.All inventories should have written standards specifying the maximum inaccuracy allowed for each piece of data. Over time. Maximum allowable errors should be developed in consultation with experts in both field work and data analysis. However. There are a variety of textbooks and Manuals available that describe how to perform field work. Mexico. and do not fail due to dead batteries or mechanical problems. It is recommended that countries carefully review multiple Manuals when developing their own field Manual. The United States. Aligning tree tags or painted markings on trees toward the plot center can assist in relocating plot centers. Particular attention should be given to errors that would have a large effect on final carbon stock or stock change estimates. the greatest challenge in estimating costs is the number of plots per day that a field crew can measure. Laser rangefinders may be needed to estimate the heights of tall trees in dense forests. based on a desktop GIS analysis. and re-find the rebar with a metal detector. and may be more durable than electronic measuring instruments. tape measures. it may not be adaptable to record the data that an inventory needs to record. For example. Electronic data recorders also require substantial skill to set up. Many projects record field data on paper data sheets. and data should be removed from field recorders daily which can be difficult if teams go into the forest for a week or two at a time. The monument must be something that is unlikely to be removed over time. this depends more on the amount of time it takes to get from one plot to the next than the amount of time spent at each plot. Paper data sheets are both low cost and familiar. many inventories drive a section of steel rebar completely into the ground at the plot center. And since errors can occur with GPS locations. REDD+ MRV MANUAL: CHAPTER 4. however. and tagging. and as long as the markings do not cause the trees in the plots to be treated differently from the trees outside the plots. check cruising the accuracy of measurements. Electronic data recorders can be hard to keep charged through multiday periods in the field. data recorders can save considerable costs in terms of reducing the need to print data sheets and copy data from paper sheets to electronic form. Russia and others have detailed field Manuals that provide useful examples when designing inventories. and relocating plot centers. and clinometers may be easier for field technicians to learn to use.

and why the particular values are used in particular situations. or from a separate sample taken at the same location as sampling for carbon. If only some pools are measured.5. • Soil bulk density (in g/cm3). This approach involves oxidizing a small sample at very high temperatures and using infrared gas absorption or gas chromatography to measure the amount of CO2 emitted. and tracking any changes to data. sources. The most common techniques for analyzing the carbon proportion of soil are based on measuring emissions from dry combustion of the samples. Exceptions are identification of unknown tree species and determination of wood densities. The depth of sampling is specified in the inventory design. Calculation procedures should be tested on pilot data prior to committing to a particular inventory design.0 – FIELD-BASED INVENTORIES 69 . Carbon content is determined by laboratory analysis. there is often interest in knowing the change in stocks of a particular pool – especially the live tree pool – and it is often desirable to separately calculate stocks for different pools or groups of pools. However. Soil carbon does require laboratory analysis. a statistician should be consulted to give proper methods of calculating the total uncertainty for the land-cover REDD+ MRV MANUAL: CHAPTER 4. usually 30 cm). and then summing the pools to get the per hectare carbon stock represented by each plot. Combining all pools is statistically appropriate and tends to give somewhat lower plot-to-plot variability than separately calculating the stock of each carbon pool. Procedures should include specifying the sequence of calculations. version tracking. Smith et al. (2007) provide a discussion of wood density determination. it may be necessary to separately calculate the stock (or stock change) of each reported carbon pool. Bulk density is calculated for each sample from the measured mass and measured volume of samples. However. Factors used in calculations should be well documented as to their values. Key components of soil carbon quantification are: • Soil depth to be measured (in cm. Having all plots on a per-hectare basis allows calculation of statistical confidence of measurements based on the variability across plots and the numbers of plots.1 Data management for calculations Calculating carbon stocks from field data must be done in an organized manner or errors will occur. and default Tier 1 factors are used for other pools. limiting who can make changes to data. to ensure that all needed data will be collected. the non-measured pools should not be combined with the measured pools before the calculation of uncertainty.6 CALCULATING CARBON STOCKS FROM FIELD DATA 4. if separate reporting of each pool is not required.2 Laboratory analysis of samples Generally. Bulk density can be measured on samples from which a subsample is later removed for carbon measurement. 4.4. For UNFCCC reporting. If carbon stocks (or stock changes) are calculated separately for different pools occurring at a particular site. laboratory analysis of woody biomass samples is not needed.6. and • Organic carbon content (percent). there is the option of calculating the carbon stock of each carbon pool on a per hectare basis.

from this. If correction is not possible. Alternatively. unless the biomass estimates for these larger trees are compared to measured biomass of other large trees and REDD+ MRV MANUAL: CHAPTER 4. the different pools do not count as different samples when calculating uncertainty. recording the reason why the data was dropped. Significant errors are also likely to occur if equations are applied to trees larger than the range from which the equation was developed. all data should be compiled into a single file for each carbon pool. The sample size is n = 100. population level estimates of the total amount of change can be calculated. For a given number of plots. Data should be examined for missing and implausible values. and the same plots are not measured at the two different time periods. deletions of uncorrectable data. Before calculations commence. it is impossible to check the quality and accuracy of calculations and resulting carbon stock estimates. otherwise scale dependent variability in allometric relationships will be lost. the change in stock is often calculated as the difference of means between the two time periods.2 Allometric equations Selection of allometric equations Carbon equations usually take two forms: allometric equations or biomass conversion and expansion factors (BCEFs).type. and there will be no way to determine the size of the errors. 2011). As a result. and ideally should be drawn from a comparable geographic scale. drop the data from the data set. which would be the case if each observation of each pool counted as a different sample. or weighing of harvested trees and relating one or more structural variables — typically DBH and tree height — to a variable of interest. and this information is key in the subsequent verification phase of MRV. if permanent plots are measured. If temporary plots are used. such as tree volume or biomass (Diaz and Delaney. For example. biomass equations should not be used for trees larger than the largest tree used to develop the equation in question. dead tree. and the reason for each calculation. 4. Methods for calculating uncertainty in simple situations are described below. Problems should be checked against plot sheets or earlier forms of the data. pan-tropical equations are applied to derive estimates for smaller geographic areas. Total carbon stock is then calculated by summing the stocks of the different strata. the change can be calculated for each plot. and corrected where possible. including the sources of those factors and equations. if 100 plots are measured and there are live tree. should be representative of the population of trees to which it will be applied. The latter is a particularly important issue where.6. shrub. The sample dataset from which the allometric equation is derived. Calculation of carbon stocks from field data requires good organization to ensure that the data are efficiently sorted and the resulting calculations are correctly generated.0 – FIELD-BASED INVENTORIES 70 . including: corrections of errors in the data. Allometric equations are regressions derived from detailed measurements of volume of trees. Data should not be removed from the analysis only because values are outliers. The carbon stock for a particular stratum is obtained by calculating the average carbon stock per hectare of all the plots within a stratum and multiplying by the area of the stratum to get the stratum carbon stock. not n = 600. the difference of the means must be used. factors and equations used. for example. this approach usually gives greater statistical confidence (as long as plots with significant disturbance – such as logging or fire – are not mixed with plots without disturbance) and statistical uncertainty is calculated from the set of changes observed on the different plots. Allometric model selection is often one of the largest sources of error in forest inventories. the sequence of calculations. herbaceous and litter pools. and. It is important to record the details of data manipulations performed. If the carbon stock is calculated separately for each inventory date. Without robust records. coarse woody debris. which can usually be identified from source documents.

one should consider both the availability of analysts who can identify the species and the additional cost and uncertainty of tree-height measurements.. the species (or combination of species) or growth form and potential biomass should be similar. Dietz and Kuyah (2011) prepared guidelines for establishing regional allometric equations through destructive sampling21.the estimates are documented to be reasonable. or group of species 20. BCEFs are dimensionless factors that convert the merchantable volume of trees into their total aboveground biomass. and adjusted for the difference in wood density. and if estimates are adjusted for differences in wood densities. Developing new and testing existing equations If no information is found regarding a certain species.globallometree. tend to have less error when applied to trees larger than the trees from which the equation was developed. single-stemmed trees. Unless the allometric equation was developed using measurements of trees in the area where the equation will be used. New allometric equations can be developed with a relatively small sampling of approximately 30 trees for a particular species or group of species. and from stands with similar trajectories of development as the stands to which the equation will be applied. multiple-stemmed trees. Various sources can be useful when seeking allometric equations. equations for a similar species may be used. it is critical to apply the same definition of merchantable volume (or growing stock) that was used in the derivation of the BCEF. the wood-density adjustment factor is calculated by dividing the specific gravity of the species to which the equation will be applied by the specific gravity of the species used to develop the 20 The most useful groupings may be by morphology class (e. or equations that use height.2 of the IPCC Guidelines (2006). In this case. GlobAllomeTree19 and published literature. 21 More guidance on developing biomass tables is found in MacDicken (1997) Annex 4. either the species should be grouped so that each group has a similar wood density. REDD+ projects may have to develop new allometric equations. it is preferable to use allometric equations that are developed from trees similar to those being studied. Under the Global Environmental Facility’s (GEF) Carbon Benefit Project. In particular. This unreliability is particularly great with equations that are simple exponential models. such as local forestry institutions. Most allometric equations give unrealistic results when applied to trees larger than the trees from which the equation was developed. When considering taxa-specific equations. Logistic equations.g. shrubs) (MacDicken. BCEFs are used for rough estimates of biomass when a timber inventory is available but resources are not available to measure carbon stocks in forests. General equations are provided in Annex 4A.0 – FIELD-BASED INVENTORIES 71 . if the growth forms of the species are all similar. If an equation will be applied to a wide variety of species. 1997). and 19 A database developed by FAO. Wood density may need measuring. equations that use diameter only should be considered useful for rough estimates of biomass. When using BCEFs. Alternatively. Section C. where the rate of increase in predicted biomass declines as the diameter increases. the French Research Centre CIRAD and Tuscia University. Another guide for developing allometric equations is Aldred and Alemdag (1988). but a larger sample is desirable. REDD+ MRV MANUAL: CHAPTER 4. Therefore. BCEFs are unreliable when applied to forests of different structure from the forest where the BCEF was developed. or wood density should be incorporated into the biomass estimation. Equations that use height and diameter but not wood density can be adapted to estimate the biomass of species different from the species from which the equation was developed. http://www.

Results are plotted against equation predictions to assess bias and either validate the equation or re-parameterize the equation to produce a better fit. belowground biomass is often estimated using general equations that estimate it as a function of aboveground biomass. or dead herbaceous vegetation).0 – FIELD-BASED INVENTORIES 72 . 4. The carbon proportion of dry biomass weight can be estimated either by laboratory analysis. For large projects on dry sites. Destructive sampling involves cutting down and weighing a sample of trees. and also taking measurements on a sample of branches. stem wood. Care should be taken to achieve a representative sample of tree species across a range of diameter classes (> 15cm dbh. green wood volumes must be used to calculate wood density. In general. REDD+ MRV MANUAL: CHAPTER 4. Methods are described in Bledsoe et al.3 Non-tree pools Scaling up from samples to a per-hectare mass is straight-forward. Processing of litter samples and calculations of litter biomass are similar to the methods used for herbaceous vegetation. where more energy is allocated to root growth. Volume is measured by dividing the tree trunk into segments and measuring the two end diameters and the length of each segment.. or growing conditions from where the equations were developed. because roots of different trees and shrubs intertwine. (1999). non-commercial species). and calculating a weighted average carbon fraction. The volume of coarse woody debris per hectare is calculated for each density class for each stratum: Volume of coarse wood debris (m3/m2) = π2 * [(d12 + d22 + … + dn2)/8L] where d1. to achieve this. climate conditions. weighing them in the field and drying them to develop a dry-to-field weight ratio for the weights of the whole trees. and cutting a subsample of tree parts.because forest measurements are made on live green trees. the ratio of belowground to aboveground biomass is higher for dry and nutrient-deficient sites.g.. and L = the length of the line (100 m. Dry wood volume cannot be used. Belowground biomass can be measured by digging and weighing root balls. As a result.g. Harmon and Sexton. or by examining samples to see what plant parts compose the litter (e.6. d2. because these measurements may give significantly greater biomass than default ratios for sites of any productivity. significantly reducing cost and effort required for this labor intensive activity. Volume is converted to mass using the appropriate density factor. destructive sampling may be worthwhile to measure the biomass of roots in the project area. below which there are rarely substantial inaccuracies in equations). Limited destructive sampling for this purpose could be conducted in coordination with an active harvest operation in the area of interest. undertaking supplemental harvest (e. and coring a sample of locations between the stems. 1996). Belowground biomass Belowground biomass is extremely difficult to measure for an individual tree. foliage versus branch wood. The field measurements are transformed to dry weight and scaled to per hectare basis. This can be checked by destructive sampling or measuring the volumes of a few trees of different sizes. as necessary. and for young successionals forests. dn = diameter (m) of each of the n pieces intersecting the line. Checking the fit of published allometric equations is good practice when equations are applied to sites with different productivities. Samples are dried and weighed and the dry to field weight ratio is calculated. finding the carbon contents of each component in the literature.

6. measurements taken during a dry season of November 2011 through February 2012 could be taken to represent the carbon stock present as of February 1 of 2012. For example. and calculations to detect and fix human errors. and are based on statistical theory. Technically. Confidence intervals can be calculated for different “confidence levels”. in this example. For example.5 Quantifying uncertainty The reliability of carbon stock estimates is reported in the form of statistical confidence intervals that quantify the chance that the sample plots used to calculate carbon stocks might be different from the actual conditions that exist throughout the entire forest. the population value would be the carbon stock measured if every tree was measured. In this example.4 Combining carbon pools The per-hectare carbon stock of each pool in each plot is summed with the other pools in that plot to give the per hectare carbon stock for each plot. All these potential errors mean that two independent measurements of the same tree.6. The true population value is the value that would be found if every individual in the population was measured. measurements are taken to represent a particular date. one can say that if a similar inventory was conducted many times in the exact same way but choosing a different set of plots. In reality. then approximately 2/3 of the plots will have carbon stocks within 50 tons per hectare of the average carbon stock. between 90 and 110 tons per hectare. However. these uncertainties are reporting the chance that the sample is different from the actual total population. Typical confidence levels are 90 percent and 95 percent. for example. To interpret. There are many other kinds of errors that could lead to false numbers. 4. The standard error of the estimate is a measurement of uncertainty of the estimate of the mean value. and independent checking of measurements. first the standard deviation and standard error of the estimate must be calculated. for the sake of reporting and change over time. typically the mean—that is. a 95 percent confidence interval of +/. The total carbon stock is calculated by multiplying the average value per hectare times the number of hectares. most likely. A common index of uncertainty associated with an estimate from an inventory is the confidence interval. Nonetheless. The standard deviation is a property of the population. There are several mechanisms that can be used to limit errors other than sampling errors. People often interpret that to mean that one can be 95 percent confident that the true value lies within the confidence interval. and thus that they increase the confidence interval and do not bias the stock estimates. The standard error is a property of the sample and can be reduced by REDD+ MRV MANUAL: CHAPTER 4. These methods are beyond the scope of this Manual. might differ by a few millimeters.0 – FIELD-BASED INVENTORIES 73 . To calculate a confidence interval. data. The width of the confidence interval conveys to the data consumer a sense of confidence in the accuracy of the estimate. Consult an appropriate textbook for guidance on how to use these methods. However. because some carbon stocks vary between seasons. made by different people. These include quality standards. The confidence interval represents a range of values surrounding an estimate. or models should be used to normalize the data to a single year. either a panel design should be used to calculate the average and changes. The standard deviation is a measurement of how different individual samples are from each other. it is better to assign a date that the measurements represent. Some inventories only specify the year that measurements represent. If inventories are taken over multiple years. most assume that these non-sampling errors are random and not biased. measurements are taken over a period of weeks to months. 95 percent of the confidence intervals generated would contain the true population value.4. The technical name for this chance difference is sampling error. if the standard deviation of a set of plots is 50 tons per hectare of carbon.10 percent surrounding an estimate of 100 tons per hectare of carbon.

This is for a two-tailed test.measuring a larger sample. t is the tcritical point from a table of student t test values. should be combined to estimate the biomass carbon stock. the uncertainty is calculated for each stratum and the uncertainties are weighted and combined. Also. If calculating the change in carbon stock from one simple random sample to another measured at a later date. The confidence interval is: CI = ±t × SE where CI is the confidence interval.. For guidance. The confidence interval for each carbon pool within each stratum is then calculated.e. i. and the degrees of freedom is typically the number of plots minus one. if doing paired sampling. SE is the standard error for the particular stratum and pool. and the degree of congruence of sampling methods used in the different pools or strata.0 – FIELD-BASED INVENTORIES 74 . in tons per stratum. The details of calculating the uncertainty of a stratified inventory are beyond the scope of this Manual.025 of the probability in each tail of the distribution. the covariance term is subtracted in the estimation of standard error. i.e. S is the standard deviation of the estimated mean carbon stock per hectare of the particular carbon pool and stratum. the change is calculated as the difference of means. and X hectare. Permanent plots achieve greater precision in the estimation of change than temporary plots – for permanent plots. and n is the number of plots in the stratum. such as live trees and dead trees. Note that typical uncertainties in forest inventories are generally weighted by the number of sample units observed in each stratum or by area. There are multiple acceptable methods for combining uncertainties across multiple pools or strata. and are presented in many statistics textbooks. on-site biomass and carbon stored in wood products are separate pools. To calculate the uncertainty of a stratified inventory. the uncertainty is reduced relative to the same number of plots in a simple random sample. Pools should be independent and pools should technically be spatially separated.. rather than by the number of tons in each stratum. REDD+ MRV MANUAL: CHAPTER 4. The methods differ depending on the degree of difference of type between the pools or strata. depending on whether temporary or permanent plots are used. a 95 percent confidence would leave 0. If the inventory is stratified. CI is the confidence interval for that pool and � is the average estimated carbon stock of that pool and stratum. For example. Methods differ for calculating the difference of means confidence. measuring more plots. in tons per hectare. consult a forest measurement textbook such as Avery and Burkhart (1994) or a statistics textbook. consult a statistics textbook for guidance. Separate classes of biomass. for the appropriate confidence level and degrees of freedom. The standard error of the estimated mean for each carbon pool within each stratum is: SE = √(S/n) where SE is the standard error of the estimated mean carbon stock per hectare for the particular carbon pool and stratum. The confidence interval can be expressed as a percentage of the mean: Un = (CI/ � X) where: Un is the uncertainty in percent for pool and stratum n.

g. if no reliable correction of a data error can be achieved.1 Data cleaning. professional data managers will be consulted in the design of data storage forms and use of data storage equipment. Land-Use Change and Forestry (GPG-LULUCF) (2003): (U1 + E1 )2 + (U2 + E2 )2 + ⋯ + (Un + En )2 UE = � |E1 + E2 + ⋯ +En | where UE is the combined uncertainty in percent for the sum of changes in all pools 1 to n. This includes checking for missing data and implausible data values. and ensure that later measurements and calculations are comparable to earlier data. in tons. 4. Un is the uncertainty in percent for pool n.2. the entire inventory could be worthless.6.7 DATA CHECKING 4. checking and accuracy standards Data quality is essential.7. As discussed above. Regardless of the strength of the foundation. it increases the likelihood of institutional memory transfer. as well as the underlying sampling methodologies applied (e. Further.2 of the IPCC Good Practice Guidance for Land Use. 4. At a minimum. and who controls access should be readily available. This is especially important given the long time periods involved in REDD+ MRV.7. particularly if different pools are measured with different UNFCCC methodology tiers (such as Tier 1 factors for shrubs and forest floor.2 Archiving data and metadata To recalculate changes in carbon stocks over time. and necessary. However. data cleaning. as new equipment and software are adopted. it is important to develop a plan for how data and metadata will be stored and protected from unauthorized changes or loss. and information about where data is stored. Inventory design. REDD+ MRV MANUAL: CHAPTER 4. data must be thoroughly checked before carbon stocks are calculated. sample design. for the pool n. Relatively frequent use of the data ensures that data will be transferred to new storage and retrieval media formats and processing systems. Having multiple teams involved in data analysis maintains awareness of the data and appropriate uses of these data. Key aspects of forest inventory metadata are the protocols used to direct field measurements. This is also important for satisfying the IPCC principles of TACCC. Ideally. what is included. in tons.6 Propagation of error If the uncertainty for a combined estimate of sinks or emissions from multiple. If field data have substantial errors. Methods for measurement.4. and En is the emission or removal for the stratum. Correctly and consistently archiving data and associated metadata represents an important. and Tier 3 measurements of live trees) the combined uncertainty of the estimated change can be calculated using Equation 5. the faulty plot should be excluded from carbon stock calculations. delineation of sample population area). and any adjustments or calculations must be clearly specified to build confidence in later users. field technician training and management of field crews are the foundations of data quality. Data should be archived in at least two locations. independent pools is being calculated. step in an inventory as it improves the efficiency of future inventories. Metadata describe how data are collected and what they represent. data from an inventory must be stored so it can be retrieved at a later date. limited time is often invested in these activities.0 – FIELD-BASED INVENTORIES 75 .

and a well-documented historic data set can provide a window into past conditions or changes. A second critical consideration is that the candidate data must be screened to ensure a minimum acceptable level of data consistency and quality.0 – FIELD-BASED INVENTORIES 76 . i. 4. New needs and questions arise.. Over time. and permit aligning inventories to common minimum diameter thresholds. environmental research).g. or at least estimates of wood volume in live trees. While it is not necessary to collect different datasets using the same sampling approaches.e. to ensure consistency in estimation procedures (e.g. allometric equations used) across inventories. to develop estimates from existing datasets (e.. Foremost among these considerations is that the population that a given inventory represents be clearly delineated. often with reports by pool and stratum. Depending on parameters measured. that the area within which samples were drawn (with a known probability) is mapped and documented. and • Reporting uncertainties across pools and strata. and experiences show that a well-maintained inventory will likely have many valuable uses. Important assessments are: • Raw data (plot measurements with individual tree data) should be preferentially sourced.4. REDD+ projects. and are a way of evaluating changes over time without having to wait years or decades for a new set of measurements.3 Data analysis and reports Typical reports from forest carbon inventories include: • Calculations of biomass and carbon stocks. REDD+ MRV MANUAL: CHAPTER 4.8 CONSOLIDATING INVENTORY DATASETS Given resource constraints. • Timber inventories. The initial use of an inventory might be only to measure timber volume or carbon stock. otherwise it is not clear what population (forest area) that data represents.7. One cannot foresee what issues will become important in the future. • All data must have been collected with minimum quality assurance and quality control (QA/QC) procedures. the value of these data will increase. they may contribute to the study of additional dynamics as well. However. This is just as important as the plot data itself. prior to the implementation of a national forest inventory. from commercial forest inventories. • Calculations of stock changes over time. or to start estimating changes in carbon stocks. it may be necessary in the early stages of national REDD+ accounting. as repeated measurements form an archive. forest inventories become irreplaceable windows to the past. there are a number of critical considerations to ensure that data are consolidated to avoid or minimize bias in the derivation of an overall stock estimate. ensuring minimal measurement error and sample bias (see discussion of QA/QC in this same chapter).

for example. spatially-explicit geo-physical data.g. that can either be: • Delineated and excluded from MRV. or • Assigned estimates based on predictions derived from relationships of known estimates with ancillary data (e.g. • Ignored (e. thereby focusing MRV on managed lands.. The result is an area-weighted average. 2011).g.. estimated from stand tables per Gillespie et al. satellite imagery). (1992).000 150 10 B 5.g. with each sampled area treated as an individual stratum (Table 4.3).000 200 20 X (un-sampled) 10. • All data must cover the same pools and use the same pool definitions.3: Example of an area-weighted average with each sampled area treated as an individual stratum 22 E.. but strictly commercial inventories cannot be re-interpreted as all species total biomass).000 130 (prediction) 60 (model prediction error) TOTAL 26. There will inevitably be gaps remaining that have not been sampled. Where substantial areas of forest are in an early successional stage. e. un-biased estimates of mean and variance (Westfall et al. McRoberts et al. the entire population of interest (e. In this way. REDD+ MRV MANUAL: CHAPTER 4..000 125 50 C 1. • Minimum sample size of 10 to ensure stable. a 2014 estimate might be derived from data collected up to 5 or 10 years prior to the reporting date). 2007). inaccessible areas where anthropogenic changes are unlikely to occur.g. • All data must be collected from within a maximum time range (e. accepting a certain level of unknown bias.g. using regression or nearest- neighbor approaches (see. each with their own estimate. in the case of remote. with pieces represented by sampled areas.. e.g. • Delineated and sampled with a new targeted field effort to fill the gaps. national forested area) is constructed as a sort of jigsaw puzzle. if they are an insignificant area). including diameter thresholds for tree measurements (in some cases it may be possible to reconstruct diameter distributions 22. a shorter allowable timeframe is recommended. the individual inventory estimates are weighted by their respective (sampled) population areas. Inventory Sample population Estimate (mean t Standard error area (ha) CO2/ha aboveground (mean tCO2/ha live biomass) aboveground live biomass) A 10.000 139 81 Table 4...0 – FIELD-BASED INVENTORIES 77 . To derive a total estimate.

Uncertainty in the overall estimate is derived by propagating inventory level errors. Importantly. as well as any error around the estimate of the independent variable(s) (Table 4.2). to target sampling and scale emission estimates. Typical fluxes (i. the IPCC recognizes gain-loss as an alternate method to estimate carbon changes (IPCC. inventories of REDD projects) also facilitates participation from multiple stakeholders. root mean squared error of predicted – observed. Emerging technologies in remote sensing are discussed in Chapter 5. like those produced from selective cut timber extraction activities.g. By utilizing information from multiple scale datasets. the gain-loss method). this approach of building a national inventory from smaller scale datasets (e. national/jurisdictional MRV. and participants in. diseases).. particularly where the objective is to resolve those changes with even modest levels of statistical significance. though they have achieved success in identifying areas subject to degradation (Souza et al. 2006).g. Where an un-sampled area is assigned an estimate based on a prediction. referencing against a validation dataset). and projects would become contributors to. fire. would typically require prohibitively high sample intensities using the stock change method. or alternatively. similar to a stratified sample. in addition to the stock change method. which understandably are difficult to locate and access (for targeted field sampling) or for which reliable and complete estimates of wood removals are unavailable. Some recent advances have applied this approach to estimation of logging impacts by Griscom et al. Probability-based sampling approaches. (2014). in tandem with field-based approaches.0 – FIELD-BASED INVENTORIES 78 . and • Emissions from wood removals due to logging and fuel wood collection. from anthropogenic wood removals. The latter source of emissions.. Remote sensing-based approaches have not yet achieved sufficient resolution of stock change to be viable for direct estimation of emissions from degradation. and are particularly useful for monitoring forest growth).e. As discussed in Chapter 3. the error of the prediction should be included (e. particularly with regard to subsistence and illegal logging. Many challenges remain. 4. is challenging. (2014) and Pearson et al. it acknowledges the value of data collected via project-level MRV. 2005) and could be used.9 THE GAIN-LOSS METHOD Estimating comparatively “rare” and small changes in biomass distributed over a large landscape. A more efficient approach to estimate small changes in comparison to the stock size is to focus directly on measurement of fluxes to and from the stock (i.. REDD+ MRV MANUAL: CHAPTER 4. sinks and sources) tracked by a gain-loss type approach would include: • Sequestration from forest growth. • Emissions from natural mortality (competition-related and senescence) and disturbance (mortality due to wind. reliable information on wood removals from which harvest-related emissions can be estimated. even with permanent plots (which permit greater resolution of change.. requires targeted field-based sampling of areas subject to extraction activities.e.

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