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).

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Tetra Tech Contacts:
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Stephen Kelleher, Chief of Party

Olaf Zerbock, USAID Contracting Officer’s Representative

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

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

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

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





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


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


............... 121 6................................................................................84 5...........................................10 REFERENCES ...3 OVERALL STEPS AND NEEDS...................................................7 DATA CHECKING ....79 4..............................................................................5 EMERGING AREAS OF RESEARCH..............................................................................................................................................75 4........................................................8 SELECTED RESOURCES......8 CONSOLIDATING INVENTORY DATASETS ..............................................................0 REPORTING AND VERIFICATION: ELEMENTS AND GUIDANCE ..........1 INTRODUCTION ............................................ 118 5............................................................. REPORTING AND VERIFICATION (MRV) MANUAL..........................................................81 5.................................................... 82 5........................1 HISTORY OF REDD+ UNDER THE UNFCCC .....................................................................................3 VERIFICATION .........................................................................................................................................................................0 REMOTE SENSING OF LAND COVER CHANGE ........................................................90 5........................................................................................................................ 152 7....... 125 6............4 REFERENCES............9 THE GAIN-LOSS METHOD ................................76 4........4 REMOTE SENSING OVERVIEW .. 127 6............................................................................................. 109 5....................................................................................................................................... 151 7.0 THEMATIC REVIEWS .......93 5...............................11 SELECTED RESOURCES .......................................................................... 152 7..............82 5.....78 4............................................................0 iv .................................... 141 6................7 COMMON SATELLITE DATA SOURCES FOR LAND-USE MONITORING ........................... 165 7........................2 COMMUNITY-BASED MONITORING ....6 REFERENCES......................2 LAND USES AND CATEGORIES IN THE UNFCCC ..... 181 REDD+ MEASUREMENT........................................................................................................ 4...............................................1 INTRODUCTION ................ VERSION 2..... 125 6................3 NEAR-REAL TIME MONITORING AND ALERT SYSTEMS .................................2 REPORTING.............................. 114 5.

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

Reporting and Verification N20 Nitrogen oxide NAMA Nationally Appropriate Mitigation Strategies NASA National Aeronautics and Space Agency REDD+ MEASUREMENT. Technology and Innovation MMU Minimum-mapping unit MRV Measurement.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.0 vi . REPORTING AND VERIFICATION (MRV) MANUAL. 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. 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. Land-use Change and Forestry GPS Global Positioning System IDEAM Colombian Institute for Hydrology. VERSION 2.

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

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

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

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

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

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

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

4.2.3). tallying trees with diameters of at least 10 cm.4 of the IPCC 2006 Guidelines. To accurately estimate the forest carbon stock of live biomass. To obtain the belowground biomass. so that any piece of material fits in exactly one category. The IPCC recommends 2 mm diameter. but measuring root biomass is time consuming and expensive. the boundary definition must correspond to the smallest piece of woody debris. As discussed in Chapter 3. 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. and is neither double-counted nor excluded. or measured as part of the soil carbon pool due to the difficulties in Manually separating them from soil.0 – FIELD-BASED INVENTORIES 55 .1) is typically the largest biotic carbon pool in a forest that would be lost via deforestation. If litter is measured. Therefore. 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. and the shrub/small tree category includes woody plants at least 10 cm. However. the inventory should include all tree species. REDD+ MRV MANUAL: CHAPTER 4. 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.3 Dead wood Dead wood.2 Belowground biomass Belowground biomass is an important carbon pool that may equal 25 percent or more of the aboveground biomass in many forests. To increase sampling efficiency. with smaller pieces being classified as litter. in some systems. Typically.2.2. 50 cm or 1 m tall. peat swamp forests. a sub-component of dead organic matter.2. substantial amounts of soil carbon (described in 4. multiply the aboveground biomass by 1 + root-to-shoot ratio. fine roots are often excluded. aboveground biomass typically emits the most carbon upon conversion to non-forest. Forest inventories limited to commercial species or trees in commercial (e.All inventories should measure live trees above a modest size because aboveground biomass (described in Section 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. The boundary between fine and coarse roots depends on the method used to estimate the belowground biomass. smaller woody plants and non-woody plants are excluded or are measured separately from larger woody plants. By convention. If there is substantial disturbance of forests via degradation. Pieces that are not large enough to be classified as coarse debris are classed as fine debris. 4.1 Aboveground biomass In a forest. soil carbon loss resulting from conversion of forest to agricultural cropland can be greater than emissions from aboveground biomass. A common minimum size of fine debris pieces is 1 cm. 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. If forest land is converted to agricultural use or developed use. 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.5) may be lost.2.g. and thus monitoring of soil organic carbon stocks may be warranted.g.2. e. includes standing and lying deadwood.. it may be important to measure dead wood carbon stocks (described in 4. 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.

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

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

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

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

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

as travel between sample units represents the largest time and labor investment in forest inventory.1 Standard deviation 38. such as global biomass maps produced by research programs.5 63. highly variable strata covering small areas. This can be achieved using REDD+ MRV MANUAL: CHAPTER 4. not at the stratum level.0 – FIELD-BASED INVENTORIES 61 .spectral and/or temporal information. like stratum 1 in Table 4.1: 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.000 10. in that stratification.0 37. both of which are designed to generate estimates at the population level.0 83.6 27. yet require substantially different levels of field effort to accomplish them: 67 sample plots for the stratified design (Design 3). achieves the targeted precision at the population level.7 117.1). Two stage sampling Sampling vast hard-to-access landscapes necessitates efficient deployment of field effort.1. like strata 3 and 4. such as greenness indices and seasonality indices. and 110 plots for the unstratified design (Design 2). The advantage of stratification is illustrated in comparing Designs 2 and 3.500 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 51.000 31. Stratum 1 Stratum 2 Stratum 3 Stratum 4 Total Area (ha) 2. This is an important consideration. as shown in Design 3. They also include classifications or derived products.000 12. which ignores sources of variability within the population. 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. Discussions of these can be found in Chapter 5.6 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 4.3 100.3 41. The value of stratification is illustrated with the following example (Table 4. For example.6 43. will have less influence on the total number of plots needed than those strata that cover larger areas.500 7.

Plot types In a forest inventory. it can be observed how many features intersects the line.3 Plot design considerations Plot design considerations include the type of plot to be used. involving two steps in sampling. • Areas (two dimensional): all the trees found on a determined area are measured. fixed area plots are circular or rectangular (including transects). For example. and the variables that need to be measured. REDD+ MRV MANUAL: CHAPTER 4.2: Sampling design including nested plots and line samples to measure the different types of carbon pools. 4. A transect plot design.a two-stage sample. This method can be used to calculate the volume of coarse woody debris. even if long and narrow. it concentrates field effort and results in minimizing overall effort. While two stage sampling introduces an added source of sampling error. as well as the size of the plot. Each is discussed below. and the second step involve allocating sample points within selected polygons. where a tree is determined to be in or out as a function of the ratio of its diameter to distance from plot center. From Smith et al. • Lines (one dimensional): on a sample line 18. typical options for a plot design are: • Points (dimensionless): variable radius plots can be implemented using a relascope or wedge prism. is an area (two-dimensional). 18 It is important to note that a sample line plot design isn’t the same as a transect plot design. the first step might involve selecting polygons to sample from a population of polygons. Typically.0 – FIELD-BASED INVENTORIES 62 . Often these plots are called "fixed area" plots because the size is fixed.3. Figure 4. Inventory developers will also need to consider whether plots will be permanent or temporary. (2007).

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

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

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

o Setting up protocols for data collection.2. as discussed in Section 7. o Entering data (including translating local species names into scientific names). o Data processing and analysis.0 – FIELD-BASED INVENTORIES 66 . • Provides information on the local names of species measured. and REDD+ MRV MANUAL: CHAPTER 4. it may be efficient to have different staff in different regions. which includes: o Selecting the sampling and plot design. 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. The inclusion of members of the local community is crucial for the following reasons: • Allows access to the plots. If the inventory covers a very large area.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). • Regional offices that are responsible for: o Organizing and training field teams. The entity is responsible for developing the inventory. and o Transmitting data to the central national office. o Providing backstopping support to field teams. o Organizing the procurement of data collecting equipment. Community-based monitoring. and o Coordinating with regions and users of inventory results. o Performing quality checks on data collection performed by field teams. • Field crews that are responsible for data collection. 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.4. A key issue is how field crews will be staffed. Ideally. o Coordinating with the land-cover mapping team. should be one goal for national inventories. including the training and incorporation of local community members into the inventory. field crews should be a combination of technicians with measurement skills accompanied by local community members.

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

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

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.2 Laboratory analysis of samples Generally. a statistician should be consulted to give proper methods of calculating the total uncertainty for the land-cover REDD+ MRV MANUAL: CHAPTER 4. it may be necessary to separately calculate the stock (or stock change) of each reported carbon pool. and • Organic carbon content (percent). usually 30 cm). Carbon content is determined by laboratory analysis. Bulk density can be measured on samples from which a subsample is later removed for carbon measurement. For UNFCCC reporting. and default Tier 1 factors are used for other pools. Key components of soil carbon quantification are: • Soil depth to be measured (in cm. However. Bulk density is calculated for each sample from the measured mass and measured volume of samples. Factors used in calculations should be well documented as to their values.5. and why the particular values are used in particular situations. version tracking. the non-measured pools should not be combined with the measured pools before the calculation of uncertainty. or from a separate sample taken at the same location as sampling for carbon. 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. Exceptions are identification of unknown tree species and determination of wood densities. 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. Calculation procedures should be tested on pilot data prior to committing to a particular inventory design. laboratory analysis of woody biomass samples is not needed. 4. However. (2007) provide a discussion of wood density determination. if separate reporting of each pool is not required. and then summing the pools to get the per hectare carbon stock represented by each plot.6 CALCULATING CARBON STOCKS FROM FIELD DATA 4. and tracking any changes to data. to ensure that all needed data will be collected. 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. sources.0 – FIELD-BASED INVENTORIES 69 .4.1 Data management for calculations Calculating carbon stocks from field data must be done in an organized manner or errors will occur. Soil carbon does require laboratory analysis. Smith et al. If carbon stocks (or stock changes) are calculated separately for different pools occurring at a particular site. The depth of sampling is specified in the inventory design. there is the option of calculating the carbon stock of each carbon pool on a per hectare basis.6. • Soil bulk density (in g/cm3). limiting who can make changes to data. Procedures should include specifying the sequence of calculations.

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

or equations that use height. or group of species 20. In particular. it is critical to apply the same definition of merchantable volume (or growing stock) that was used in the derivation of the BCEF. Developing new and testing existing equations If no information is found regarding a certain species. and if estimates are adjusted for differences in wood densities. 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. When considering taxa-specific equations. New allometric equations can be developed with a relatively small sampling of approximately 30 trees for a particular species or group of species. and from stands with similar trajectories of development as the stands to which the equation will be applied. such as local forestry institutions.g. Logistic equations. Section C. tend to have less error when applied to trees larger than the trees from which the equation was developed. BCEFs are dimensionless factors that convert the merchantable volume of trees into their total aboveground biomass. Under the Global Environmental Facility’s (GEF) Carbon Benefit Project. or wood density should be incorporated into the biomass estimation.globallometree. General equations are provided in Annex 4A. shrubs) (MacDicken. and 19 A database developed by FAO. 1997). BCEFs are unreliable when applied to forests of different structure from the forest where the BCEF was developed. Wood density may need measuring. Various sources can be useful when seeking allometric 20 The most useful groupings may be by morphology class (e. equations that use diameter only should be considered useful for rough estimates of biomass. REDD+ MRV MANUAL: CHAPTER 4. Another guide for developing allometric equations is Aldred and Alemdag (1988). it is preferable to use allometric equations that are developed from trees similar to those being studied. where the rate of increase in predicted biomass declines as the diameter increases. REDD+ projects may have to develop new allometric equations. When using BCEFs.2 of the IPCC Guidelines (2006). 21 More guidance on developing biomass tables is found in MacDicken (1997) Annex 4. 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. one should consider both the availability of analysts who can identify the species and the additional cost and uncertainty of tree-height measurements.the estimates are documented to be reasonable. single-stemmed trees. If an equation will be applied to a wide variety of species. 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. http://www. but a larger sample is desirable. and adjusted for the difference in wood density. This unreliability is particularly great with equations that are simple exponential models. GlobAllomeTree19 and published literature. the species (or combination of species) or growth form and potential biomass should be similar. if the growth forms of the species are all similar. either the species should be grouped so that each group has a similar wood density. equations for a similar species may be used. the French Research Centre CIRAD and Tuscia University. multiple-stemmed trees. Unless the allometric equation was developed using measurements of trees in the area where the equation will be used. Therefore. Most allometric equations give unrealistic results when applied to trees larger than the trees from which the equation was developed.. Alternatively. In this case. Dietz and Kuyah (2011) prepared guidelines for establishing regional allometric equations through destructive sampling21.0 – FIELD-BASED INVENTORIES 71 .

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

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 2012.4. and thus that they increase the confidence interval and do not bias the stock estimates. The width of the confidence interval conveys to the data consumer a sense of confidence in the accuracy of the estimate. Nonetheless. Technically. All these potential errors mean that two independent measurements of the same tree. A common index of uncertainty associated with an estimate from an inventory is the confidence interval. measurements are taken over a period of weeks to months. if the standard deviation of a set of plots is 50 tons per hectare of carbon. first the standard deviation and standard error of the estimate must be calculated.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. However. Typical confidence levels are 90 percent and 95 percent. People often interpret that to mean that one can be 95 percent confident that the true value lies within the confidence interval. There are several mechanisms that can be used to limit errors other than sampling errors. between 90 and 110 tons per hectare. and are based on statistical theory. There are many other kinds of errors that could lead to false numbers. 4. data. For example. The standard deviation is a property of the population. To interpret. However. In reality. In this example. The true population value is the value that would be found if every individual in the population was measured. in this example. these uncertainties are reporting the chance that the sample is different from the actual total population. a 95 percent confidence interval of +/.0 – FIELD-BASED INVENTORIES 73 . because some carbon stocks vary between seasons. and calculations to detect and fix human errors. typically the mean—that is. Some inventories only specify the year that measurements represent. Consult an appropriate textbook for guidance on how to use these methods. might differ by a few millimeters. The technical name for this chance difference is sampling error. made by different people. The standard deviation is a measurement of how different individual samples are from each other. measurements are taken to represent a particular date. the population value would be the carbon stock measured if every tree was measured.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.6. These include quality standards. for example. it is better to assign a date that the measurements represent. for the sake of reporting and change over time.6. These methods are beyond the scope of this Manual. The total carbon stock is calculated by multiplying the average value per hectare times the number of hectares. The standard error of the estimate is a measurement of uncertainty of the estimate of the mean value. If inventories are taken over multiple years. and independent checking of measurements. either a panel design should be used to calculate the average and changes. one can say that if a similar inventory was conducted many times in the exact same way but choosing a different set of plots. The standard error is a property of the sample and can be reduced by REDD+ MRV MANUAL: CHAPTER 4. 95 percent of the confidence intervals generated would contain the true population value. The confidence interval represents a range of values surrounding an estimate. Confidence intervals can be calculated for different “confidence levels”. most assume that these non-sampling errors are random and not biased. most likely. To calculate a confidence interval. For example.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. or models should be used to normalize the data to a single year.

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

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

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

in the case of remote. REDD+ MRV MANUAL: CHAPTER 4. In this way.g. with each sampled area treated as an individual stratum (Table 4. • Minimum sample size of 10 to ensure stable..g. un-biased estimates of mean and variance (Westfall et al.. a 2014 estimate might be derived from data collected up to 5 or 10 years prior to the reporting date). using regression or nearest- neighbor approaches (see. McRoberts et al. the entire population of interest (e. • Delineated and sampled with a new targeted field effort to fill the gaps. accepting a certain level of unknown bias. (1992). e.. including diameter thresholds for tree measurements (in some cases it may be possible to reconstruct diameter distributions 22. a shorter allowable timeframe is recommended. • Ignored (e. but strictly commercial inventories cannot be re-interpreted as all species total biomass). To derive a total estimate. for example. satellite imagery).000 150 10 B 5.0 – FIELD-BASED INVENTORIES 77 . 2007). with pieces represented by sampled areas. or • Assigned estimates based on predictions derived from relationships of known estimates with ancillary data (e.g.000 125 50 C 1.000 130 (prediction) 60 (model prediction error) TOTAL 26. The result is an area-weighted average. thereby focusing MRV on managed lands...3: Example of an area-weighted average with each sampled area treated as an individual stratum 22 E. if they are an insignificant area)..3). the individual inventory estimates are weighted by their respective (sampled) population areas. inaccessible areas where anthropogenic changes are unlikely to occur.000 200 20 X (un-sampled) 10. There will inevitably be gaps remaining that have not been sampled.000 139 81 Table 4.. that can either be: • Delineated and excluded from MRV. estimated from stand tables per Gillespie 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. Where substantial areas of forest are in an early successional stage.g. • All data must cover the same pools and use the same pool definitions. • All data must be collected from within a maximum time range (e. e. spatially-explicit geo-physical data. 2011).g. each with their own estimate.g.g. national forested area) is constructed as a sort of jigsaw puzzle.

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

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