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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


...........6 REFERENCES..........................2 LAND USES AND CATEGORIES IN THE UNFCCC ................................................................... 125 6..............78 4.......... 118 5.....................................2 REPORTING.......81 5............7 COMMON SATELLITE DATA SOURCES FOR LAND-USE MONITORING ................................3 NEAR-REAL TIME MONITORING AND ALERT SYSTEMS ....................................................................................0 REPORTING AND VERIFICATION: ELEMENTS AND GUIDANCE ........................75 4..............84 5............ 165 7..........2 COMMUNITY-BASED MONITORING ................... 121 6...........................................................................................8 SELECTED RESOURCES..................................4 REMOTE SENSING OVERVIEW .....................................76 4.......................................5 EMERGING AREAS OF RESEARCH............ 151 7...........................0 THEMATIC REVIEWS ................................... REPORTING AND VERIFICATION (MRV) MANUAL..................................1 INTRODUCTION ............................................ 141 6................................................................................................. 181 REDD+ MEASUREMENT........................................................................1 HISTORY OF REDD+ UNDER THE UNFCCC ................................................................... VERSION 2.3 VERIFICATION ..............................................................10 REFERENCES ............................ 127 6......................... 152 7..0 REMOTE SENSING OF LAND COVER CHANGE ................................................................................................................1 INTRODUCTION .................................................................................................................................................................... 109 5....................... 125 6.........................................................................0 iv .8 CONSOLIDATING INVENTORY DATASETS ..............................4 REFERENCES........................................................................................................................................................ 114 5....79 4.................................................. 152 7....................................................................................93 5........................................3 OVERALL STEPS AND NEEDS...........90 5.....................................................................................................................................................82 5.......................................................... 82 5.............. 4......7 DATA CHECKING ...11 SELECTED RESOURCES ............9 THE GAIN-LOSS METHOD ..........................

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 . Markets and Communities Program FCPF Forest Carbon Partnership Facility REDD+ MEASUREMENT. ACRONYMS AND ABBREVIATIONS ACR American Carbon Registry AD Activity Data AFOLU Agriculture. REPORTING AND VERIFICATION (MRV) MANUAL. VERSION 2.

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. Technology and Innovation MMU Minimum-mapping unit MRV Measurement.0 vi . Reporting and Verification N20 Nitrogen oxide NAMA Nationally Appropriate Mitigation Strategies NASA National Aeronautics and Space Agency REDD+ MEASUREMENT. REPORTING AND VERIFICATION (MRV) MANUAL.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. 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. VERSION 2.

VERSION 2. plus the role of conservation. accuracy. sustainable forest management and enhancement of forest carbon stocks. REPORTING AND VERIFICATION (MRV) MANUAL. 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. completeness.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. 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.0 vii .

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

1 INTRODUCTION Section 3.0 – FIELD-BASED INVENTORIES 50 . Irene Angeletti.0 FIELD-BASED INVENTORIES Authors: Gordon Smith.3. David Scoch 4. 4. of this Manual outlines the sequence of steps required for generating a national GHG inventory. Inventory and Reporting Steps. This chapter is relevant to the activities highlighted on the following page. REDD+ MRV MANUAL: CHAPTER 4.

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

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

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

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

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. 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. 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. it may be important to measure dead wood carbon stocks (described in 4.2. 4. To accurately estimate the forest carbon stock of live biomass.g.2. e. in some systems.5) may be lost.4 of the IPCC 2006 Guidelines. The boundary between fine and coarse roots depends on the method used to estimate the belowground biomass.2. with smaller pieces being classified as litter. 4. 4. Typically.. and the shrub/small tree category includes woody plants at least 10 cm. multiply the aboveground biomass by 1 + root-to-shoot ratio. and thus monitoring of soil organic carbon stocks may be warranted. 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.3 Dead wood Dead wood. and is neither double-counted nor excluded. Forest inventories limited to commercial species or trees in commercial (e. or measured as part of the soil carbon pool due to the difficulties in Manually separating them from soil. By convention. If litter is measured. Therefore. tallying trees with diameters of at least 10 cm. the boundary definition must correspond to the smallest piece of woody debris.3). but measuring root biomass is time consuming and expensive. peat swamp forests.1 Aboveground biomass In a forest. the inventory should include all tree species. a sub-component of dead organic matter. If forest land is converted to agricultural use or developed use.2. fine roots are often excluded.0 – FIELD-BASED INVENTORIES 55 . 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.2 Belowground biomass Belowground biomass is an important carbon pool that may equal 25 percent or more of the aboveground biomass in many forests. 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. To obtain the belowground biomass.2.2. soil carbon loss resulting from conversion of forest to agricultural cropland can be greater than emissions from aboveground biomass.g. so that any piece of material fits in exactly one category. The IPCC recommends 2 mm diameter.All inventories should measure live trees above a modest size because aboveground biomass (described in Section 4. Pieces that are not large enough to be classified as coarse debris are classed as fine debris. substantial amounts of soil carbon (described in 4. 50 cm or 1 m tall. includes standing and lying deadwood.1) is typically the largest biotic carbon pool in a forest that would be lost via deforestation. REDD+ MRV MANUAL: CHAPTER 4. aboveground biomass typically emits the most carbon upon conversion to non-forest. However. smaller woody plants and non-woody plants are excluded or are measured separately from larger woody plants. 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. A common minimum size of fine debris pieces is 1 cm. If there is substantial disturbance of forests via degradation. As discussed in Chapter 3. To increase sampling efficiency.

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

When measuring soil carbon loss upon conversion of forest or grassland to cropland with plowing. REDD+ monitoring activities may also only focus on lands that are classified as managed forest. Therefore. 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. and seek to build on. This requires choosing what is to be estimated. 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 4.3 CONCEPTS AND CONSIDERATIONS IN INVENTORY DESIGN Many possible inventory designs can be used to estimate forest carbon stocks. while limiting sampling effort. 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. Producing accurate estimates of stocks and changes for all the various strata will significantly increase costs and may not prove the most efficient approach. many inventories sample only the top 20 or 30 cm of soil. the percentage change in carbon stock may be large. The goal is to choose an efficient design that achieves the desired level of precision at a minimum cost.0 – FIELD-BASED INVENTORIES 57 . Larger changes in percentage terms are easier to detect with sampling.” Radically new approaches or REDD+ MRV MANUAL: CHAPTER 4. Consideration of inventory design should also take account of. such as 50 or 100 cm. This shallow sampling is especially common for inventories focused on detecting carbon stock increases.or no-till cropping. and grassland areas where there is conversion to agriculture. 4. Initially. or forest subject to anthropogenic change. avoided deforestation projects may sample soil much deeper than 30 cm. and over what geographic areas. The geographic scope might be a particular block of land amounting to only a few dozen hectares. soil carbon losses are usually large enough to justify the cost and difficulty of measuring soil carbon loss to 1 m depth. In moist forest. The next decision is whether there are sub-divisions within the total area where extra information is needed. In wet forests and coarse texture soils that have little carbon. As a result. it can be efficient to only measure soil carbon to 20 or 30 cm depth. or something in between. the gain in percentage terms is greatest when only the top few centimeters are measured. A country should aim to understand trends for specific regions or forest types.1 Needs assessment The first decision in designing an inventory is to assess what needs to be known as a result of the inventory. shrubland. existing capacities and experience of personnel that will be involved in both field measurement and analysis. even when measuring to significant depths.19 paragraph 4(a)). an entire country. In general. To capture much of the carbon stock change that results from land management changes. dry forest. 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. to be able to claim credit for avoiding emission of deeper soil carbon. such as to a depth of 1 m. NFMS “should build on existing systems. per COP19 (Decision 11/CP.3.

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. ii) systematic sampling.1. and iv) two stage sampling.1 are: i) random sampling. 4.. Figure 4.2 Sampling design selection Sampling must be unbiased to ensure that resulting inventories will be reliable. systematic (right). Four common approaches. REDD+ MRV MANUAL: CHAPTER 4.0 – FIELD-BASED INVENTORIES 58 . iii) stratified (random or systematic) sampling.3. and stratified random (bottom) sampling. illustrated and elaborated below in Figure 4. Diagram of random (top).

though the calculation of uncertainty for systematic samples is less straightforward than for random samples. with the first intersection point located at random. 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. and that quantifying this variability is a key part of quantifying how reliably the samples reflect the forest as a whole. Within each stratum. and is especially useful if little is known about forest conditions or dynamics. 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. Stratified Sampling Stratified sampling is accomplished by dividing the sampling area into relatively homogenous sub-areas. giving a more precise estimate of carbon stocks for a REDD+ MRV MANUAL: CHAPTER 4. 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). Each point contains four sites of 400 m2. systematic sampling involves laying a regular grid over the geographic area to be inventoried. Stratification increases efficiency of sampling. may appear to lack credible coverage. From 2008 onward. such that all points are monitored once every five years (GOFC-GOLD. In practice. Its intuitive layout also facilitates navigation in the field.0 – FIELD-BASED INVENTORIES 59 . Typically. and separately sampling each sub area.000 geo-referenced permanent points. Forest Service Forest Inventory and Analysis program). Carbon stock (or stock change) is estimated for each stratum. The ILUA set up 221 tracks (each track has 4 sampling plots) systematically across the country at 50 km distances. Many people find systematic sampling attractive because it gives equal emphasis to all parts of the area being sampled. it is common to stratify by ecotype or forest type. 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. Systematic sampling offers significant improvement over simple random sampling in terms of reduced uncertainty. While many approaches to stratification exist.S. The primary advantage of random sampling is that the calculation of means and uncertainty is simple. then the stocks of the strata are summed to estimate the stock (or stock change) of the entire area. about 20 percent of the points have been re-measured. A systematic sample ensures that all geographic areas are equally represented. Training should include communication of the importance of measuring the various conditions that occur over the geographic extent of the inventory. The spacing of the grid lines is calculated so that the desired number of plots can be placed in the area. which. 2013). This approach to stratification increases statistical power. Mexico provides another example.Random Sampling A random sample approach locates plots within a study area at random. inventories must seriously consider perceptions of both specialists and non-specialists. where regularly spaced plots are measured. giving more precise estimates for the same or less effort. and locating plot centers at the grid intersection points. while un-biased. For example. 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. Systematic Sampling Many national forest inventories instead use a systematic sampling design. where the National Forest and Soil inventory established a systematic sample grid of 25. a systematic sample or simple random sample is conducted.

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. or increase from forest growth and regeneration. the goal is to have relatively homogeneous forest (in terms of structure).. through monitoring of forest cover and change classes with remotely sensing). ecological zone.4. therefore.unep. These include indices based on 15 http://geodata.3. If the goal of an inventory is to precisely quantify changes in forest carbon stocks. Thus. Stratifying by expected change in carbon stock can be desirable. it is first necessary to have a current map of the dimension being used to stratify.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. and allocating more sampling effort to strata that are expected to have greater change over time (decrease or increase). For a given number of plots. and management practices (Vol. soils or other parameters. 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. Post-stratification involves re-allocating sample plots among strata. and such areas should be identified and delineated as separate priority strata. Homogeneous strata need few plots to precisely estimate their carbon stocks and. and where sufficient sample size (of 10 or more plots. or forest with the same carbon stock dynamics. such as maps of Holdridge life zones 15. through an original systematic sample).. after the original plot allocation has taken place. 2011) is maintained within each stratum. To stratify within each land-cover type. Of equal or more importance. World Wildlife Fund Ecoregions 16. The added uncertainty produced by post-stratification tends to be small. areas with different sampling intensities constitute different strata. Stratification approaches are further discussed in the GFOI Methods and Guidance. but strata can also be redefined ex post via post-stratification and plots re-allocated in a changing landscape. or by deploying supplemental samples. using the same calculation as a pre-stratified sample introduces some additional uncertainty due to the random and changing sample sizes within strata.g. To stratify a country’s forest. Various remote-sensing-based data can also be used. as is often the case with degradation.0 – FIELD-BASED INVENTORIES 60 .org/geonetwork/srv/en/main. soil.home REDD+ MRV MANUAL: CHAPTER 4. the cost of placing large numbers of plots in remote inaccessible forest areas may be prohibitively expensive and/or logistically impossible.1).g. In this case. The latter can be ensured by consideration in the definition of strata (e. If no previous information on forest types exists in the country. and FAO ecological zones 17. and may be essential if net changes in stocks are small compared to total stocks.cfm 17 http://www.grid. the selected sampling intensities in these areas may be less than in accessible areas. This would mean stratifying by the expected future change in carbon stock.2. section 1. The IPCC recommends stratifying by climate. sampling efforts can be focused towards more variable ecotypes or forest types. Within each stratum.When choosing strata. 4. Chapter 3. developers should consider what is known about the forest and the dynamics of carbon stock change. but for this forest to be different from other strata.5 (GFOI 2013). While this post-stratification approach is straightforward in terms of estimation of the mean. Stratification is generally performed ex ante.worldwildlife. one can use various GIS data on elevation. This may be a national forest benchmark map or some other valid map source. allocation of plots should be weighted toward areas where carbon stocks are susceptible to decrease from degradation or deforestation. the stratification can be done initially using global ecological particularly where proportional allocation can be roughly maintained ( 16 http://www. Westfall et al.g. combining forest types with small areas and sample sizes).. Calculation of variance for a post-stratified sample is presented in Cochran (1977). The goal is to "block" differences into different strata.fao.

For example. Two stage sampling Sampling vast hard-to-access landscapes necessitates efficient deployment of field effort.000 12.7 117.6 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. as shown in Design 3. will have less influence on the total number of plots needed than those strata that cover larger areas. This is an important consideration.3 41.spectral and/or temporal information. like stratum 1 in Table 4. both of which are designed to generate estimates at the population level. and 110 plots for the unstratified design (Design 2). which ignores sources of variability within the population. like strata 3 and 4. as travel between sample units represents the largest time and labor investment in forest inventory.500 7.000 31.0 – FIELD-BASED INVENTORIES 61 . They also include classifications or derived products. highly variable strata covering small areas.1.1 Standard deviation 38. The value of stratification is illustrated with the following example (Table 4.3 100. such as global biomass maps produced by research programs. Stratum 1 Stratum 2 Stratum 3 Stratum 4 Total Area (ha) 2. in that stratification.0 83. achieves the targeted precision at the population level. The advantage of stratification is illustrated in comparing Designs 2 and 3. such as greenness indices and seasonality indices.000 10. 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. not at the stratum level.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 51.6 27.6 43. yet require substantially different levels of field effort to accomplish them: 67 sample plots for the stratified design (Design 3).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. This can be achieved using REDD+ MRV MANUAL: CHAPTER 4.1). Discussions of these can be found in Chapter 5.5 63.0 37.

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

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

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

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

including the training and incorporation of local community members into the inventory.0 – FIELD-BASED INVENTORIES 66 . should be one goal for national inventories. o Setting up protocols for data collection. and REDD+ MRV MANUAL: CHAPTER 4. • Regional offices that are responsible for: o Organizing and training field teams. If the inventory covers a very large area. o Coordinating with the land-cover mapping team. as discussed in Section 7. which includes: o Selecting the sampling and plot design. o Organizing the procurement of data collecting equipment. A well-established national inventory where measurements are repeated regularly should have its own staff. 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. A key issue is how field crews will be staffed. Ideally. o Providing backstopping support to field teams. The inclusion of members of the local community is crucial for the following reasons: • Allows access to the plots. o Entering data (including translating local species names into scientific names).4. o Performing quality checks on data collection performed by field teams. field crews should be a combination of technicians with measurement skills accompanied by local community members.2.4 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). it may be efficient to have different staff in different regions. o Data processing and analysis. The entity is responsible for developing the inventory. and o Transmitting data to the central national office. Community-based monitoring. • Field crews that are responsible for data collection. and o Coordinating with regions and users of inventory results. • Provides information on the local names of species measured. This entity must also coordinate closely with the single national entity designated with the overall responsibility for the GHG inventory.

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

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

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

Without robust records. and the reason for each calculation. It is important to record the details of data manipulations performed. Significant errors are also likely to occur if equations are applied to trees larger than the range from which the equation was developed. The sample dataset from which the allometric equation is derived. Allometric model selection is often one of the largest sources of error in forest inventories. not n = 600. Before calculations commence.type. unless the biomass estimates for these larger trees are compared to measured biomass of other large trees and REDD+ MRV MANUAL: CHAPTER 4. deletions of uncorrectable data. the different pools do not count as different samples when calculating uncertainty. recording the reason why the data was dropped. the change can be calculated for each plot.0 – FIELD-BASED INVENTORIES 70 . which would be the case if each observation of each pool counted as a different sample. population level estimates of the total amount of change can be calculated. factors and equations used. For a given number of plots. biomass equations should not be used for trees larger than the largest tree used to develop the equation in question. If correction is not possible. from this. or weighing of harvested trees and relating one or more structural variables — typically DBH and tree height — to a variable of interest. and ideally should be drawn from a comparable geographic scale. As a result. all data should be compiled into a single file for each carbon pool. the change in stock is often calculated as the difference of means between the two time periods. including: corrections of errors in the data. including the sources of those factors and equations. should be representative of the population of trees to which it will be applied. and corrected where possible. 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. if permanent plots are measured. If temporary plots are used. 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. herbaceous and litter pools. dead tree. for example. Data should not be removed from the analysis only because values are outliers. such as tree volume or biomass (Diaz and Delaney. it is impossible to check the quality and accuracy of calculations and resulting carbon stock estimates. pan-tropical equations are applied to derive estimates for smaller geographic areas. and there will be no way to determine the size of the errors. The latter is a particularly important issue where. Total carbon stock is then calculated by summing the stocks of the different strata. 4.6. Data should be examined for missing and implausible values. and this information is key in the subsequent verification phase of MRV. drop the data from the data set. Alternatively. The sample size is n = 100. coarse woody debris. and the same plots are not measured at the two different time periods. Methods for calculating uncertainty in simple situations are described below. 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. otherwise scale dependent variability in allometric relationships will be lost. If the carbon stock is calculated separately for each inventory date. which can usually be identified from source documents. Allometric equations are regressions derived from detailed measurements of volume of trees. 2011). if 100 plots are measured and there are live tree. shrub. the sequence of calculations.2 Allometric equations Selection of allometric equations Carbon equations usually take two forms: allometric equations or biomass conversion and expansion factors (BCEFs). and. the difference of the means must be used. Problems should be checked against plot sheets or earlier forms of the data. For example.

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

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

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

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

Correctly and consistently archiving data and associated metadata represents an important. professional data managers will be consulted in the design of data storage forms and use of data storage equipment.2 Archiving data and metadata To recalculate changes in carbon stocks over time. the faulty plot should be excluded from carbon stock calculations. field technician training and management of field crews are the foundations of data quality.6 Propagation of error If the uncertainty for a combined estimate of sinks or emissions from multiple.2. This includes checking for missing data and implausible data values. 4. Further. Regardless of the strength of the foundation. sample design. However. Having multiple teams involved in data analysis maintains awareness of the data and appropriate uses of these data. and ensure that later measurements and calculations are comparable to earlier data. If field data have substantial errors. As discussed above.g. This is especially important given the long time periods involved in REDD+ MRV. Inventory design. Relatively frequent use of the data ensures that data will be transferred to new storage and retrieval media formats and processing systems. Metadata describe how data are collected and what they represent. if no reliable correction of a data error can be achieved.7 DATA CHECKING 4. limited time is often invested in these activities. data cleaning. as new equipment and software are adopted. data from an inventory must be stored so it can be retrieved at a later date. the entire inventory could be worthless. At a minimum. it increases the likelihood of institutional memory transfer. Key aspects of forest inventory metadata are the protocols used to direct field measurements. Ideally. particularly if different pools are measured with different UNFCCC methodology tiers (such as Tier 1 factors for shrubs and forest floor.7.1 Data cleaning. and Tier 3 measurements of live trees) the combined uncertainty of the estimated change can be calculated using Equation 5. Methods for measurement. for the pool n. in tons. as well as the underlying sampling methodologies applied (e.7. and any adjustments or calculations must be clearly specified to build confidence in later users. checking and accuracy standards Data quality is essential. and necessary. what is included. Data should be archived in at least two locations. and who controls access should be readily available. delineation of sample population area). data must be thoroughly checked before carbon stocks are calculated. REDD+ MRV MANUAL: CHAPTER 4. and En is the emission or removal for the stratum. 4. step in an inventory as it improves the efficiency of future inventories.2 of the IPCC Good Practice Guidance for Land Use. in tons. 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.0 – FIELD-BASED INVENTORIES 75 . independent pools is being calculated. Un is the uncertainty in percent for pool n. and information about where data is stored.6. it is important to develop a plan for how data and metadata will be stored and protected from unauthorized changes or loss. This is also important for satisfying the IPCC principles of TACCC.4.

and are a way of evaluating changes over time without having to wait years or decades for a new set of measurements.8 CONSOLIDATING INVENTORY DATASETS Given resource constraints. New needs and questions arise. that the area within which samples were drawn (with a known probability) is mapped and documented.0 – FIELD-BASED INVENTORIES 76 . and • Reporting uncertainties across pools and strata.4. Depending on parameters measured. • All data must have been collected with minimum quality assurance and quality control (QA/QC) procedures.3 Data analysis and reports Typical reports from forest carbon inventories include: • Calculations of biomass and carbon stocks. as repeated measurements form an archive. prior to the implementation of a national forest inventory. • Timber inventories. One cannot foresee what issues will become important in the future. and permit aligning inventories to common minimum diameter thresholds. or to start estimating changes in carbon stocks. otherwise it is not clear what population (forest area) that data represents. from commercial forest inventories. or at least estimates of wood volume in live trees. to ensure consistency in estimation procedures (e. they may contribute to the study of additional dynamics as well. i. and a well-documented historic data set can provide a window into past conditions or changes. • Calculations of stock changes over time. it may be necessary in the early stages of national REDD+ accounting.. forest inventories become irreplaceable windows to the past.7.g. to develop estimates from existing datasets (e. ensuring minimal measurement error and sample bias (see discussion of QA/QC in this same chapter). However. While it is not necessary to collect different datasets using the same sampling approaches. environmental research). Foremost among these considerations is that the population that a given inventory represents be clearly delineated. REDD+ MRV MANUAL: CHAPTER 4.g. This is just as important as the plot data itself. A second critical consideration is that the candidate data must be screened to ensure a minimum acceptable level of data consistency and quality. and experiences show that a well-maintained inventory will likely have many valuable uses. REDD+ projects.. Important assessments are: • Raw data (plot measurements with individual tree data) should be preferentially sourced. allometric equations used) across inventories.e. the value of these data will increase. 4. 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. The initial use of an inventory might be only to measure timber volume or carbon stock. often with reports by pool and stratum. Over time.

McRoberts et al.000 150 10 B 5. estimated from stand tables per Gillespie et al..g. spatially-explicit geo-physical data.g.g. un-biased estimates of mean and variance (Westfall et al.000 130 (prediction) 60 (model prediction error) TOTAL 26. or • Assigned estimates based on predictions derived from relationships of known estimates with ancillary data (e. for example.. • All data must be collected from within a maximum time range (e. In this way. accepting a certain level of unknown bias. To derive a total estimate.g. 2007). satellite imagery).g. Where substantial areas of forest are in an early successional stage. the entire population of interest (e. 2011).3: Example of an area-weighted average with each sampled area treated as an individual stratum 22 E. e. a 2014 estimate might be derived from data collected up to 5 or 10 years prior to the reporting date). • Ignored (e. with each sampled area treated as an individual stratum (Table 4.. but strictly commercial inventories cannot be re-interpreted as all species total biomass). a shorter allowable timeframe is recommended.. the individual inventory estimates are weighted by their respective (sampled) population areas. with pieces represented by sampled areas.3). using regression or nearest- neighbor approaches (see. in the case of remote. e. Inventory Sample population Estimate (mean t Standard error area (ha) CO2/ha aboveground (mean tCO2/ha live biomass) aboveground live biomass) A 10. The result is an area-weighted average. if they are an insignificant area).g..g. • Minimum sample size of 10 to ensure stable. including diameter thresholds for tree measurements (in some cases it may be possible to reconstruct diameter distributions 22. each with their own estimate.000 125 50 C 1.0 – FIELD-BASED INVENTORIES 77 . that can either be: • Delineated and excluded from MRV.000 139 81 Table 4. There will inevitably be gaps remaining that have not been sampled.. REDD+ MRV MANUAL: CHAPTER 4. inaccessible areas where anthropogenic changes are unlikely to occur. • Delineated and sampled with a new targeted field effort to fill the gaps..000 200 20 X (un-sampled) 10. • All data must cover the same pools and use the same pool definitions. national forested area) is constructed as a sort of jigsaw puzzle. (1992). thereby focusing MRV on managed lands.

the error of the prediction should be included (e. referencing against a validation dataset). sinks and sources) tracked by a gain-loss type approach would include: • Sequestration from forest growth. particularly with regard to subsistence and illegal logging.. 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. and • Emissions from wood removals due to logging and fuel wood collection.e. national/jurisdictional MRV.Uncertainty in the overall estimate is derived by propagating inventory level errors. as well as any error around the estimate of the independent variable(s) (Table 4.. and projects would become contributors to. particularly where the objective is to resolve those changes with even modest levels of statistical significance. Where an un-sampled area is assigned an estimate based on a prediction. the gain-loss method). like those produced from selective cut timber extraction activities. root mean squared error of predicted – observed. in tandem with field-based approaches.2). which understandably are difficult to locate and access (for targeted field sampling) or for which reliable and complete estimates of wood removals are unavailable. By utilizing information from multiple scale datasets. inventories of REDD projects) also facilitates participation from multiple stakeholders.g. is challenging.g. the IPCC recognizes gain-loss as an alternate method to estimate carbon changes (IPCC. and participants in. Remote sensing-based approaches have not yet achieved sufficient resolution of stock change to be viable for direct estimation of emissions from degradation. Importantly. fire. (2014) and Pearson et al. even with permanent plots (which permit greater resolution of change. this approach of building a national inventory from smaller scale datasets (e. requires targeted field-based sampling of areas subject to extraction activities. it acknowledges the value of data collected via project-level MRV. or alternatively.. (2014).. 4. would typically require prohibitively high sample intensities using the stock change method. similar to a stratified sample. • Emissions from natural mortality (competition-related and senescence) and disturbance (mortality due to wind. The latter source of emissions. Many challenges remain. As discussed in Chapter 3. 2005) and could be used.9 THE GAIN-LOSS METHOD Estimating comparatively “rare” and small changes in biomass distributed over a large landscape.e. in addition to the stock change method. Some recent advances have applied this approach to estimation of logging impacts by Griscom et al. from anthropogenic wood removals. Emerging technologies in remote sensing are discussed in Chapter 5. reliable information on wood removals from which harvest-related emissions can be estimated. diseases). 2006). though they have achieved success in identifying areas subject to degradation (Souza et al. Probability-based sampling approaches. to target sampling and scale emission estimates.0 – FIELD-BASED INVENTORIES 78 . REDD+ MRV MANUAL: CHAPTER 4. and are particularly useful for monitoring forest growth). Typical fluxes (i.

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