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
159 Bank Street, Suite 300
Burlington, Vermont 05401 USA
Telephone: (802) 658-3890
Fax: (802) 658-4247

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

Forest Carbon, Markets and Communities (FCMC) Program
1611 North Kent Street
Suite 805
Arlington, Virginia 22209 USA
Telephone: (703) 592-6388
Fax: (866) 795-6462

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


............................................................................................... 109 5..............................5 EMERGING AREAS OF RESEARCH....................................................................................................9 THE GAIN-LOSS METHOD .................. 114 5.................4 REMOTE SENSING OVERVIEW ...................................................................... 152 7.......4 REFERENCES...................8 SELECTED RESOURCES........82 5.........................................75 4... 121 6............... 4...................................................................................................................................... 165 7...................................... 141 6....................................................................................................................................................1 HISTORY OF REDD+ UNDER THE UNFCCC .....1 INTRODUCTION ....................3 NEAR-REAL TIME MONITORING AND ALERT SYSTEMS ............................................76 4.........................................10 REFERENCES .......0 REMOTE SENSING OF LAND COVER CHANGE .................................7 COMMON SATELLITE DATA SOURCES FOR LAND-USE MONITORING ..............2 REPORTING.............2 COMMUNITY-BASED MONITORING ...11 SELECTED RESOURCES .....................81 5.2 LAND USES AND CATEGORIES IN THE UNFCCC .............................................................................79 4..3 OVERALL STEPS AND NEEDS...............................................................................7 DATA CHECKING .......................................93 5............................................ 82 5......................................... 125 6............. VERSION 2................................................. 125 6...................84 5..................................0 REPORTING AND VERIFICATION: ELEMENTS AND GUIDANCE ..........................................0 THEMATIC REVIEWS ..................3 VERIFICATION ............ REPORTING AND VERIFICATION (MRV) MANUAL......... 152 7..........................1 INTRODUCTION .............8 CONSOLIDATING INVENTORY DATASETS ..................................................................... 151 7...................................................................0 iv ................................................................................................... 118 5........................................................................78 4.............................90 5......................................................................................................................................................................................................... 181 REDD+ MEASUREMENT............ 127 6.............6 REFERENCES..........................................................................

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. Markets and Communities Program FCPF Forest Carbon Partnership Facility REDD+ MEASUREMENT.0 v . ACRONYMS AND ABBREVIATIONS ACR American Carbon Registry AD Activity Data AFOLU Agriculture. REPORTING AND VERIFICATION (MRV) MANUAL. VERSION 2.

REPORTING AND VERIFICATION (MRV) MANUAL.0 vi . 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.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. Land-use Change and Forestry GPS Global Positioning System IDEAM Colombian Institute for Hydrology. Reporting and Verification N20 Nitrogen oxide NAMA Nationally Appropriate Mitigation Strategies NASA National Aeronautics and Space Agency REDD+ MEASUREMENT. Meteorology and Environmental Studies ILUA Integrated Land Use Assessment INPE Brazilian National Space Research Institute IPCC Intergovernmental Panel on Climate Change KCA Key Category Analysis LDCM Landsat Data Continuity Mission LEDS Low Emission Development Strategies LiDAR Light Detection and Ranging LUC Land-use Change MADS Colombian Ministry for Sustainable Development MCT Brazilian Ministry of Science. Technology and Innovation MMU Minimum-mapping unit MRV Measurement.

REPORTING AND VERIFICATION (MRV) MANUAL. comparability. and consistency TOA Top-of-atmosphere UMD University of Maryland UNDP United Nations Development Programme UNEP United Nations Environment Programme UNFCCC United Nations Framework Convention on Climate Change USAID United States Agency for International Development USGS United States Geological Survey VCS Verified Carbon Standard WGs Working Groups WMO World Meteorological Organization WRI World Resources Institute REDD+ MEASUREMENT. completeness. plus the role of conservation. VERSION 2.0 vii . accuracy.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. sustainable forest management and enhancement of forest carbon stocks. 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.

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

1 INTRODUCTION Section 3. This chapter is relevant to the activities highlighted on the following page. REDD+ MRV MANUAL: CHAPTER 4.3. Inventory and Reporting Steps. 4.0 – FIELD-BASED INVENTORIES 50 . David Scoch 4. Irene Angeletti.0 FIELD-BASED INVENTORIES Authors: Gordon Smith. of this Manual outlines the sequence of steps required for generating a national GHG inventory.

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

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

permanent or temporary) and sampling intensity. Develop field measurement and data management protocols 4.1: 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. and land-use management needs. This type of data sharing can make the inventory more cost effective and ensure financing from more sources. variable radius. Produce tactical plans to guide deployment of field teams • Implement inventory 1. Allocate samples and produce maps for inventory implementation f. simple random. Consider scale/resolution desired and whether focus is on estimation of stocks or stock change. desired precision. Select and acquire measurement technologies/equipment 3. pilot plot data from existing inventories. or extend the geographic range of a forest carbon inventory and thereby jointly serve carbon inventory and other resource management needs. aerial photographs and remote sensing data) b. Document design and rationale • Prepare for inventory implementation 1. and can serve other land management. Data checking/quality control 2. wildlife. forest heterogeneity and plot size e. two- stage) d. Select sampling approach (e. The national forest inventories supported by the Food and Agricultural Organization (FAO) National Forest Monitoring and Assessment REDD+ MRV MANUAL: CHAPTER 4.g. Define population/forest area c. 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. specifying end uses and desired outputs. fixed area. Define budget and personnel and capacity building needs • Design inventory 1. Select and validate allometric equations and define required measurements 3. Produce reports and summary results 4. Define objective. stratified.0 – FIELD-BASED INVENTORIES 53 . Select desired precision /allowable uncertainty 3. Collect data to develop sample design (e. 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. 2. maps and relevant GIS coverages. Define inventory organization/administration 2. 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. For example. systematic. Define sample unit (e. Box 4. Analyze data and calculate estimates and uncertainty 3.g. cluster. Develop sample design a. since multiple information users can advocate for its continued funding. It may be possible to extend the use of collected data. the latter with consideration to budget constraints. nested fixed area.g. desired scale/resolution. Organize and train field and administrative personnel 6. Collect field measurement data and enter to database 2. Set up inventory database 5.

which is then expanded using activity data. relating biomass to remote sensing data (see Chapter 5.9 below. 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.g. Field-based measurements are critical elements in both traditional probability-based sampling and in model- based estimation approaches involving interpretation of remote sensing imagery. hold promise to complement. REDD+ activities may encompass vast areas of land. 2006).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 gain-loss 14 The GPG-LULUCF (IPCC.0 – FIELD-BASED INVENTORIES 54 . and thus in this Manual we use the former term throughout. In comparison. diameter increment) and losses (e. For perspective. stratification. and facilitate mapping. derived by delineating the area of those transitions. is discussed in Section 4.. This chapter focuses on implementation of the stock change method. Sampling and measurement guidance presented in this chapter is relevant to both the stock change and gain-loss methods. As discussed in Chapter 3. even though the 2006 Guidelines are more up-to-date and use the latter. There has been no decision for non-Annex I Parties regarding use of the 2006 Guidelines. A further discussion of gain-loss and stock change methods is included in section 2. In the latter case. 2006). 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. these inventories collect data on carbon stock.program provide another example. Besides the collection of information regarding timber species and volume. REDD+ MRV MANUAL: CHAPTER 4. gain-loss. “traditional” field-based measurement practices and probability-based approaches. the information below highlights considerations specific to forest inventories. and Measurement. and implementation of sampling schemes. the stock change method does not produce emission factors and activity data. measurements are used to produce emission factors associated with specified transitions in forest structure (e. Reporting and Verification (MRV) should be at a corresponding scale. 13 See http://www. 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. Geographic Information Systems (GIS) and remote sensing approaches (see Chapter 5) are part of this system. mortality). 2003) uses the term stock change. intact to degraded forest). In contrast.fao. There are economies of scale in forest carbon inventories. which focuses on identifying and measuring fluxes. forest inventory is most accurately conceived of as a collection of procedures and technologies that encompass a system. 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. In this context. While the different carbon pools are described in Chapter 3. emerging technologies). while the Guidance from 2006 uses stock ddifference (IPCC. or to estimate gains (e. non-timber forest products and socio-economic indicators 13.1 of the Global Forest Observation Initiative (GFOI) Methods and Guidance Document (GFOI 2013). the stock change method is less targeted than the gain-loss method.g. 4. Repeated measure from sample plots can be used to estimate stocks at two points in time. Other resource management goals may be addressed by having teams collect some additional types of data while doing their carbon inventory work. Tier 1 methods rely on the use of default values. new model-based approaches in development. but not replace.g. and may eventually encompass an entire country. meaning that inventories covering more area become less expensive when the cost is calculated on a per-hectare basis.

but measuring root biomass is time consuming and expensive. tallying trees with diameters of at least 10 cm. REDD+ MRV MANUAL: CHAPTER 4.4 of the IPCC 2006 Guidelines.1) is typically the largest biotic carbon pool in a forest that would be lost via deforestation.g. 50 cm or 1 m tall. the inventory should include all tree species. smaller woody plants and non-woody plants are excluded or are measured separately from larger woody plants. or measured as part of the soil carbon pool due to the difficulties in Manually separating them from soil. with smaller pieces being classified as litter. By convention.5) may be lost. aboveground biomass typically emits the most carbon upon conversion to non-forest.2. Therefore.2. If litter is measured. fine roots are often excluded.2. To obtain the belowground biomass. The IPCC recommends 2 mm diameter.g.. As discussed in Chapter 3. it may be important to measure dead wood carbon stocks (described in 4. 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. so that any piece of material fits in exactly one category. and is neither double-counted nor excluded. and the shrub/small tree category includes woody plants at least 10 cm.2.0 – FIELD-BASED INVENTORIES 55 . multiply the aboveground biomass by 1 + root-to-shoot ratio. 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. 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.All inventories should measure live trees above a modest size because aboveground biomass (described in Section 4. However. 4.3 Dead wood Dead wood.3).2.2 Belowground biomass Belowground biomass is an important carbon pool that may equal 25 percent or more of the aboveground biomass in many forests. and thus monitoring of soil organic carbon stocks may be warranted. includes standing and lying deadwood. substantial amounts of soil carbon (described in 4. A common minimum size of fine debris pieces is 1 cm.1 Aboveground biomass In a forest. A typical minimum piece size for coarse woody debris is 10 cm in diameter and sometimes there is also a minimum length requirement that pieces be at least 1 m long. Pieces that are not large enough to be classified as coarse debris are classed as fine debris. Typically. peat swamp forests. To increase sampling efficiency. e.2. Forest inventories limited to commercial species or trees in commercial (e. soil carbon loss resulting from conversion of forest to agricultural cropland can be greater than emissions from aboveground biomass. If there is substantial disturbance of forests via degradation. The boundary between fine and coarse roots depends on the method used to estimate the belowground biomass. the boundary definition must correspond to the smallest piece of woody debris. 4. 4. 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. 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. If forest land is converted to agricultural use or developed use. a sub-component of dead organic matter. 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. in some systems. To accurately estimate the forest carbon stock of live biomass.

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

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

and stratified random (bottom) sampling..2 Sampling design selection Sampling must be unbiased to ensure that resulting inventories will be reliable.0 – FIELD-BASED INVENTORIES 58 .technologies should be introduced incrementally and only after trial applications can foster confidence of responsible personnel. Diagram of random (top). Figure 4. systematic (right).3. ii) systematic sampling.1. Four common approaches. REDD+ MRV MANUAL: CHAPTER 4. and iv) two stage sampling. 4. iii) stratified (random or systematic) sampling.1 are: i) random sampling. illustrated and elaborated below in Figure 4. There are many options available for developing a sampling design.

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

Stratification is generally performed ex ante. therefore. particularly where proportional allocation can be roughly maintained (e. the stratification can be done initially using global ecological datasets. This would mean stratifying by the expected future change in carbon stock. Of equal or more importance. section 1. To stratify within each land-cover type.2. To stratify a country’s forest.grid. World Wildlife Fund Ecoregions 16. soils or other parameters. using the same calculation as a pre-stratified sample introduces some additional uncertainty due to the random and changing sample sizes within strata. and FAO ecological zones 17.g. one can use various GIS data on elevation. 2011) is maintained within each stratum. and such areas should be identified and delineated as separate priority strata. Thus. the goal is to have relatively homogeneous forest (in terms of structure). Post-stratification involves re-allocating sample plots among strata. the selected sampling intensities in these areas may be less than in accessible areas. Homogeneous strata need few plots to precisely estimate their carbon stocks and. Stratification approaches are further discussed in the GFOI Methods and Guidance. through an original systematic sample). ecological zone. and where sufficient sample size (of 10 or more plots. The IPCC recommends stratifying by climate.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.. If the goal of an inventory is to precisely quantify changes in forest carbon stocks. combining forest types with small areas and sample sizes). In this Westfall et al.0 – FIELD-BASED INVENTORIES 60 . and may be essential if net changes in stocks are small compared to total stocks. This may be a national forest benchmark map or some other valid map source. after the original plot allocation has taken place. or increase from forest growth and regeneration. Within each stratum.When choosing strata.g.. The latter can be ensured by consideration in the definition of strata ( 16 http://www.fao. 4. developers should consider what is known about the forest and the dynamics of carbon stock change. but strata can also be redefined ex post via post-stratification and plots re-allocated in a changing landscape. and allocating more sampling effort to strata that are expected to have greater change over time (decrease or increase).1). as is often the case with degradation. it is first necessary to have a current map of the dimension being used to stratify. Various remote-sensing-based data can also be used. The goal is to "block" differences into different strata. such as maps of Holdridge life zones 15. soil.unep. While this post-stratification approach is straightforward in terms of estimation of the mean. Calculation of variance for a post-stratified sample is presented in Cochran (1977). but for this forest to be different from other strata. areas with different sampling intensities constitute different strata. 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.. allocation of plots should be weighted toward areas where carbon stocks are susceptible to decrease from degradation or deforestation. through monitoring of forest cover and change classes with remotely sensing).home REDD+ MRV MANUAL: CHAPTER 4. or by deploying supplemental samples. Chapter 3.5 (GFOI 2013). These include indices based on 15 http://geodata. The added uncertainty produced by post-stratification tends to be small.g.4. sampling efforts can be focused towards more variable ecotypes or forest types. the cost of placing large numbers of plots in remote inaccessible forest areas may be prohibitively expensive and/or logistically impossible. and requires that the new strata areas are known exactly and that the existing plots can be unambiguously re-assigned to the new strata ( or forest with the same carbon stock dynamics. and management practices (Vol. For a given number of plots.3. Stratifying by expected change in carbon stock can be desirable.cfm 17 http://www. If no previous information on forest types exists in the country.

as travel between sample units represents the largest time and labor investment in forest inventory. will have less influence on the total number of plots needed than those strata that cover larger areas.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 4.1. They also include classifications or derived products. For example. achieves the targeted precision at the population level. This is an important consideration.7 117. The value of stratification is illustrated with the following example (Table 4.3 41.3 100.0 37. as shown in Design 3.000 31.spectral and/or temporal information.6 43.1). both of which are designed to generate estimates at the population level.1 Standard deviation 38.0 83. 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.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. and 110 plots for the unstratified design (Design 2). The advantage of stratification is illustrated in comparing Designs 2 and 3.5 63. This can be achieved using REDD+ MRV MANUAL: CHAPTER 4. like strata 3 and 4.500 7. like stratum 1 in Table 4.6 27.1: 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.0 – FIELD-BASED INVENTORIES 61 . in that stratification. Two stage sampling Sampling vast hard-to-access landscapes necessitates efficient deployment of field effort. not at the stratum level. Stratum 1 Stratum 2 Stratum 3 Stratum 4 Total Area (ha) 2. highly variable strata covering small areas. which ignores sources of variability within the population.000 12. such as global biomass maps produced by research programs. Discussions of these can be found in Chapter 5. such as greenness indices and seasonality indices. yet require substantially different levels of field effort to accomplish them: 67 sample plots for the stratified design (Design 3).000 10.

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

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

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

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

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

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

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

However. 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. Smith et al. and • Organic carbon content (percent). 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. version tracking. it may be necessary to separately calculate the stock (or stock change) of each reported carbon pool. or from a separate sample taken at the same location as sampling for carbon.1 Data management for calculations Calculating carbon stocks from field data must be done in an organized manner or errors will occur.4. If carbon stocks (or stock changes) are calculated separately for different pools occurring at a particular site.6 CALCULATING CARBON STOCKS FROM FIELD DATA 4.0 – FIELD-BASED INVENTORIES 69 . Exceptions are identification of unknown tree species and determination of wood densities. Calculation procedures should be tested on pilot data prior to committing to a particular inventory design. (2007) provide a discussion of wood density determination. the non-measured pools should not be combined with the measured pools before the calculation of uncertainty. and tracking any changes to data. 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. If only some pools are measured. Key components of soil carbon quantification are: • Soil depth to be measured (in cm. However. there is the option of calculating the carbon stock of each carbon pool on a per hectare basis.6. usually 30 cm). Procedures should include specifying the sequence of calculations. and default Tier 1 factors are used for other pools. a statistician should be consulted to give proper methods of calculating the total uncertainty for the land-cover REDD+ MRV MANUAL: CHAPTER 4. Factors used in calculations should be well documented as to their values.2 Laboratory analysis of samples Generally. to ensure that all needed data will be collected. Bulk density can be measured on samples from which a subsample is later removed for carbon measurement. Soil carbon does require laboratory analysis. 4. sources. and then summing the pools to get the per hectare carbon stock represented by each plot. Carbon content is determined by laboratory analysis. The most common techniques for analyzing the carbon proportion of soil are based on measuring emissions from dry combustion of the samples. 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. Bulk density is calculated for each sample from the measured mass and measured volume of samples. The depth of sampling is specified in the inventory design. if separate reporting of each pool is not required.5. For UNFCCC reporting. limiting who can make changes to data. laboratory analysis of woody biomass samples is not needed. • Soil bulk density (in g/cm3). and why the particular values are used in particular situations.

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

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

Processing of litter samples and calculations of litter biomass are similar to the methods used for herbaceous vegetation. or by examining samples to see what plant parts compose the litter (e.. belowground biomass is often estimated using general equations that estimate it as a function of aboveground biomass. 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. Volume is converted to mass using the appropriate density factor. Limited destructive sampling for this purpose could be conducted in coordination with an active harvest operation in the area of interest.g. non-commercial species). The field measurements are transformed to dry weight and scaled to per hectare basis. the ratio of belowground to aboveground biomass is higher for dry and nutrient-deficient sites. 1996). This can be checked by destructive sampling or measuring the volumes of a few trees of different sizes. Samples are dried and weighed and the dry to field weight ratio is calculated. and coring a sample of locations between the stems. For large projects on dry sites. below which there are rarely substantial inaccuracies in equations). and for young successionals forests. because roots of different trees and shrubs intertwine. and also taking measurements on a sample of branches. Results are plotted against equation predictions to assess bias and either validate the equation or re-parameterize the equation to produce a better fit. and cutting a subsample of tree parts.6. Dry wood volume cannot be used. and L = the length of the line (100 m. dn = diameter (m) of each of the n pieces intersecting the line. destructive sampling may be worthwhile to measure the biomass of roots in the project area. Care should be taken to achieve a representative sample of tree species across a range of diameter classes (> 15cm dbh. stem wood. weighing them in the field and drying them to develop a dry-to-field weight ratio for the weights of the whole trees. Methods are described in Bledsoe et al. climate conditions.g. as necessary. As a result.because forest measurements are made on live green trees. Harmon and Sexton. foliage versus branch wood. Volume is measured by dividing the tree trunk into segments and measuring the two end diameters and the length of each segment. undertaking supplemental harvest (e. because these measurements may give significantly greater biomass than default ratios for sites of any productivity. finding the carbon contents of each component in the literature. In general. Belowground biomass Belowground biomass is extremely difficult to measure for an individual tree. The carbon proportion of dry biomass weight can be estimated either by laboratory analysis. significantly reducing cost and effort required for this labor intensive activity.3 Non-tree pools Scaling up from samples to a per-hectare mass is straight-forward. or growing conditions from where the equations were developed. to achieve this. (1999). Destructive sampling involves cutting down and weighing a sample of trees. green wood volumes must be used to calculate wood density. or dead herbaceous vegetation). and calculating a weighted average carbon fraction. REDD+ MRV MANUAL: CHAPTER 4.0 – FIELD-BASED INVENTORIES 72 . Checking the fit of published allometric equations is good practice when equations are applied to sites with different productivities.. where more energy is allocated to root growth. 4. d2. Belowground biomass can be measured by digging and weighing root balls.

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

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

REDD+ MRV MANUAL: CHAPTER 4. Relatively frequent use of the data ensures that data will be transferred to new storage and retrieval media formats and processing systems. 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. field technician training and management of field crews are the foundations of data quality. limited time is often invested in these activities. However. as new equipment and software are adopted. professional data managers will be consulted in the design of data storage forms and use of data storage equipment. This is especially important given the long time periods involved in REDD+ MRV. in tons. what is included. and Tier 3 measurements of live trees) the combined uncertainty of the estimated change can be calculated using Equation 5. 4. sample design.6 Propagation of error If the uncertainty for a combined estimate of sinks or emissions from multiple.6. and who controls access should be readily available.7. Metadata describe how data are collected and what they represent. for the pool n. the entire inventory could be worthless. This is also important for satisfying the IPCC principles of TACCC.7. as well as the underlying sampling methodologies applied (e.2. Having multiple teams involved in data analysis maintains awareness of the data and appropriate uses of these data. Key aspects of forest inventory metadata are the protocols used to direct field measurements. step in an inventory as it improves the efficiency of future inventories. If field data have substantial errors. and necessary. in tons.7 DATA CHECKING 4.g. and any adjustments or calculations must be clearly specified to build confidence in later users. particularly if different pools are measured with different UNFCCC methodology tiers (such as Tier 1 factors for shrubs and forest floor.0 – FIELD-BASED INVENTORIES 75 . Regardless of the strength of the foundation. independent pools is being calculated.2 of the IPCC Good Practice Guidance for Land Use. Further. the faulty plot should be excluded from carbon stock calculations.4.1 Data cleaning. it increases the likelihood of institutional memory transfer. data from an inventory must be stored so it can be retrieved at a later date. Data should be archived in at least two locations.2 Archiving data and metadata To recalculate changes in carbon stocks over time. Methods for measurement. delineation of sample population area). checking and accuracy standards Data quality is essential. if no reliable correction of a data error can be achieved. As discussed above. At a minimum. and information about where data is stored. and En is the emission or removal for the stratum. Ideally. and ensure that later measurements and calculations are comparable to earlier data. data must be thoroughly checked before carbon stocks are calculated. 4. Inventory design. Correctly and consistently archiving data and associated metadata represents an important. it is important to develop a plan for how data and metadata will be stored and protected from unauthorized changes or loss. This includes checking for missing data and implausible data values. data cleaning. Un is the uncertainty in percent for pool n.

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

There will inevitably be gaps remaining that have not been sampled.. that can either be: • Delineated and excluded from MRV. the individual inventory estimates are weighted by their respective (sampled) population areas. the entire population of interest (e. satellite imagery).. • Minimum sample size of 10 to ensure stable. national forested area) is constructed as a sort of jigsaw puzzle.g. for example. including diameter thresholds for tree measurements (in some cases it may be possible to reconstruct diameter distributions 22.000 139 81 Table 4. McRoberts et al. Inventory Sample population Estimate (mean t Standard error area (ha) CO2/ha aboveground (mean tCO2/ha live biomass) aboveground live biomass) A 10. spatially-explicit geo-physical data. each with their own estimate..g.g.000 200 20 X (un-sampled) 10.g. • All data must cover the same pools and use the same pool definitions. a 2014 estimate might be derived from data collected up to 5 or 10 years prior to the reporting date).0 – FIELD-BASED INVENTORIES 77 .. 2007).3). a shorter allowable timeframe is recommended. The result is an area-weighted average. Where substantial areas of forest are in an early successional stage. • All data must be collected from within a maximum time range (e.g. e. un-biased estimates of mean and variance (Westfall et al. In this way.3: Example of an area-weighted average with each sampled area treated as an individual stratum 22 E. or • Assigned estimates based on predictions derived from relationships of known estimates with ancillary data (e.000 125 50 C 1.g. accepting a certain level of unknown bias.000 130 (prediction) 60 (model prediction error) TOTAL 26. inaccessible areas where anthropogenic changes are unlikely to occur.000 150 10 B 5. estimated from stand tables per Gillespie et al. • Delineated and sampled with a new targeted field effort to fill the gaps. REDD+ MRV MANUAL: CHAPTER 4.. if they are an insignificant area).. • Ignored (e. with pieces represented by sampled areas.. To derive a total estimate. using regression or nearest- neighbor approaches (see. with each sampled area treated as an individual stratum (Table 4. but strictly commercial inventories cannot be re-interpreted as all species total biomass).g. in the case of remote. thereby focusing MRV on managed lands. 2011). e. (1992).

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

.pdf. Washington.H. Delaney. ed. K. Shiver. B.4. 464 pp. Finley and J. 1996. J. Ramirez-Maldonado.. Germany. H. USA. Tech. and H. USA. E. M. Forest Inventory. 4th edition.10 REFERENCES Forest inventory. Dietz. H. pp. Estimating areal means and variances of forest attributes using the k-nearest neighbors technique and satellite imagery. World Agroforestry Center (ICRAF). Forest Trends. 456 pp. and W. 2002. Chapter 5 in Pancel. John Wiley & Sons.E.msu. T. 1988. Schreuder. Sampling Methods for Multiresource Forest Inventory. Berlin. Tomppo. 1977. Canadian Forestry Service. 2003.J. J. Springer-Verlag. W. 2004.T. Alemdag. Brink. and J. 134 pp Cochran. Heikkinen. Sampling Techniques for Forest Resource Inventory. John Wiley & Sons.goes. E. Aldred. D.. 2002. Petawawa National Forestry Institute.. H. 1993. M. REDD+ MRV MANUAL: CHAPTER 4.T. Gregoire and G. Inc. Available at: http://www. sampling design and statistics Avery. Forest mensuration. USA. Gen. Kenya.E. Measurement and Monitoring. Husch. Comparison of three sampling methods in estimating stand parameters for a tropical forest. A. and B.O. Forest Ecology and Management 21:119-127. Kim Iles and Associates.O. eds. Ernst and H. 2011.0 – FIELD-BASED INVENTORIES 79 . New York. Schreuder. NY.G. Burkhart. Valentine. Carbon Benefits Project: Modelling. Ltd. Forest Science 50: 427-435 Grosenbaugh. 2011. A Walkthrough Solution to the Boundary Overlap Problem. Carbon Stock Assessment Guidance: Inventory and Monitoring Procedures. Remote Sensing Environment 111(4): 466–480 Schreuder. RMRS-GTR-126. John Wiley & Sons. 111 p. DC. Beers and J. In: Building Forest Carbon Projects. and S. Forest Service. Global Environment Facility.. Forest Science 3: 2-14. Guidelines for establishing regional allometric equations for biomass estimation through destructive sampling. 3rd ed. 356 pp. 2004. A Sampler of Inventory Topics. John Wiley and Sons. Point-sampling compared with plot-sampling in southeast Texas. Ducey. and M.. Olander.... Rep. NY. Sampling techniques. New York. Department of Agriculture. Borders. Iles. Tropical forestry handbook. 2007. Diaz.S. Statistical techniques for sampling and monitoring natural resources. A. 1993. Gove. and I. Wood. Johannes. B. Kershaw. and H. Kuyah. Forest Measurements. U. Stover. Kohl. E. T. S. 1987. CO. Rocky Mountain Research Station. R. L. McRoberts R.S. Inc. Information Report PI-X-77. Fort Collins. McGraw-Hill. Banyard and G. Fifth edition. Nairobi. L. T. 1957.H. Inc. Guidelines for Forest Biomass Inventory. 243-332.

0 – FIELD-BASED INVENTORIES 80 . GOFC-GOLD. 1996. WA. G. Carlos M. H. Sass. 1997. Winrock. Post-stratified estimation: within-strata and total sample size recommendations.. The line intersect method in forest fuel sampling. Forest Resources Assessment Programme Working Paper 94/E. J. Guidelines for measurements of wood detritus in forest ecosystems. R. IPCC. Geneva. gains and losses of carbon stocks in forests remaining forests. Reynolds. P. Parton. Olsen. General technical report INT-16. measure.E. Ogden. Forest Ecology and Management 48:69-88. Intergovernmental Panel on Climate Change. USA. S. 2011. Warren. IPCC. A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals associated with deforestation. Roberts and M. R. Synthesis documents on forest carbon inventory and REDD+ field-based sampling MRV Gillespie A. G. K.W. US LTER Network Office. FAO. Coulston.R. 1992. Remote Sensing Environment 98: 329-343 Westfall. Ogle. K. Rome. S. 2003. Canadian Journal of Forest Research 41: 1130-1139. 2013. L.J. Harmon. R. A line intersect technique for assessing logging waste. Holtkamp. 2013. and P.. Sexton. Paustian. J. Duke University Press. 2004. USA. Cochrane. NC.L. W. UT: US Department of Agriculture. Integrating remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests: Methods and Guidance from the Global Forest Observations Initiative: Pub: Group on Earth Observations.. Van Wagner. K. US LTER Publication No. Combining spectral and spatial information to map canopy damage from selective logging and forest fires. 2005. IPCC Guidelines for National Greenhouse Gas Inventories. Souza. Japan. Good Practice Guidance for Land Use. GFOI. 2006. Japan.A. Edited by Zach Willey and Bill Chameides. Hayama. Switzerland. The Netherlands). National Forest Inventory Field Manual Template. Sampling and measurement of downed dead wood Brown. Changsheng. A Guide to Monitoring Carbon Storage in Forestry and Agroforestry Projects.Smith. A. J. Land-Use Change and Forestry. GOFC-GOLD Report version COP19-2. D. L. 1974. 1964. and verify greenhouse gas offsets. Forest Science 14: 20-26. and J. Brown and A. Hayama. C. Bruce. M. M. Nicholas Institute for Environmental Policy Solutions. Hammerschlag. 229p.E.. 20. Raleigh. Barbour. Forest Science 10: 267-276. REDD+ MRV MANUAL: CHAPTER 4. Forest Service. Tropical forest biomass estimation from truncated stand tables. Seattle. Handbook for Inventorying Downed Woody Material. Wageningen University. 2007. W.G.A. and W. University of Washington. (GOFC-GOLD Land Cover Project Office. and forestation. Harnessing farms and forests in the low-carbon economy: how to create. Jr. Patterson and J. Intergovernmental Panel on Climate Change. Lugo.F. J.E. 1968. MacDicken. J.A. Intermountain Forest and Range Experiment Station. McCarl. Barbour. M. and W. S. Duke University Press. Raleigh. Global Change Biology. Casarim.0 – FIELD-BASED INVENTORIES 81 . T..winrock.. Brown and R. Birdsey. measure. 229 p. B.M.nrs. W.. 2014.fed. Carbon emissions performance of commercial logging in East Kalimantan. Available at: www.. 2007. United States Department of Agriculture. S. J. Brown and F. L. Holtkamp. NC.leafasia. Walker and S. S. Sass. Brown. 2005. Paustian.. A. Parton. Sourcebook for Land Use. K. 2005. Changsheng. Ellis and F. S. Harnessing farms and forests in the low-carbon economy: how to create.E. Nicholas Institute for Environmental Policy Solutions. Winrock International and the World Bank Biocarbon Fund. P. Pearson. R. Land-Use Change and Forestry Projects. Putz. and verify greenhouse gas offsets. 20: 923–937. Hammerschlag. R. Ogle. Available at: http://www. Bruce. Measurement Guidelines for the Sequestration of Forest Carbon. Indonesia. Pearson. General Technical Report NRS-18. 2014. Available at: http://www. Reynolds. McCarl. J.H.R. T. R. Smith. H. Environmental Research Letters 9: 034017-034028.Pearson.11 SELECTED RESOURCES Carbon Measurement and Monitoring (CMM) Module of the USAID-supported Lowering Emissions in Asia’s Forests (LEAF) program. Gain-Loss approaches to estimation of logging impacts Griscom. Carbon emissions from tropical forest degradation caused by logging. T. Edited by Zach Willey and Bill Chameides. 47 pp. L. J. REDD+ MRV MANUAL: CHAPTER 4. 57pp.

gov .U.S. Agency for International Development 1300 Pennsylvania Avenue.usaid. DC 20523 Tel: (202) 712-0000 Fax: (202) 216-3524 www. NW Washington.