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

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

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

Forest Carbon, Markets and Communities (FCMC) Program
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Arlington, Virginia 22209 USA
Telephone: (703) 592-6388
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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


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

Applications and Technology GEF Global Environmental Facility GFIMS Global Fire Information Management System GFOI MGD Global Forest Observation Initiative Methods and Guidance Documentation GFW Global Forest Watch GHG Greenhouse gas GHGMI Greenhouse Gas Management Institute GIS Geographic Information System GLAS Geoscience Laser Altimeter System GOFC-GOLD Global Observation of Forest and Land Cover Dynamics GPG-LULUCF Good Practice Guidance for Land Use.0 vi . VERSION 2.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. Reporting and Verification N20 Nitrogen oxide NAMA Nationally Appropriate Mitigation Strategies NASA National Aeronautics and Space Agency REDD+ MEASUREMENT. Meteorology and Environmental Studies ILUA Integrated Land Use Assessment INPE Brazilian National Space Research Institute IPCC Intergovernmental Panel on Climate Change KCA Key Category Analysis LDCM Landsat Data Continuity Mission LEDS Low Emission Development Strategies LiDAR Light Detection and Ranging LUC Land-use Change MADS Colombian Ministry for Sustainable Development MCT Brazilian Ministry of Science. Technology and Innovation MMU Minimum-mapping unit MRV Measurement. Land-use Change and Forestry GPS Global Positioning System IDEAM Colombian Institute for Hydrology. REPORTING AND VERIFICATION (MRV) MANUAL.

sustainable forest management and enhancement of forest carbon stocks. accuracy.0 vii . 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. 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. plus the role of conservation. completeness.NCs National Communications NFMS National Forest Monitoring System NGGIP National Greenhouse Gas Inventories Program NGO Non-governmental organization NNs Neural Networks NRT Near-real Time PCA Principal components analysis PRODES Projeto De Estimativa De Desflorestamento da Amazoni (Brazilian Amazon deforestation monitoring program) QA/QC Quality Assurance and Quality Control QUICC Quarterly Indicator of Cover Change RADAR Radio Detection and Ranging REDD+ Reducing emissions from deforestation and forest degradation. comparability. REPORTING AND VERIFICATION (MRV) MANUAL. VERSION 2.

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

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

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

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

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

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

so that any piece of material fits in exactly one category. By convention. substantial amounts of soil carbon (described in 4. However. but measuring root biomass is time consuming and expensive. 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. To increase sampling efficiency.g. fine roots are often excluded. 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. Typically. or measured as part of the soil carbon pool due to the difficulties in Manually separating them from soil.2. As discussed in Chapter 3. includes standing and lying deadwood. The boundary between fine and coarse roots depends on the method used to estimate the belowground biomass. soil carbon loss resulting from conversion of forest to agricultural cropland can be greater than emissions from aboveground biomass. 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. 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 4.1 Aboveground biomass In a forest. If litter is measured. and is neither double-counted nor excluded. Therefore.3). If forest land is converted to agricultural use or developed use. in some systems. 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. 4. 4. REDD+ MRV MANUAL: CHAPTER 4. and the shrub/small tree category includes woody plants at least 10 cm. 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. Pieces that are not large enough to be classified as coarse debris are classed as fine debris. 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.All inventories should measure live trees above a modest size because aboveground biomass (described in Section 4.5) may be lost. and thus monitoring of soil organic carbon stocks may be warranted. To accurately estimate the forest carbon stock of live biomass. with smaller pieces being classified as litter. A common minimum size of fine debris pieces is 1 cm.2. 50 cm or 1 m tall.2.g.3 Dead wood Dead wood. the boundary definition must correspond to the smallest piece of woody debris. 4. multiply the aboveground biomass by 1 + root-to-shoot ratio. the inventory should include all tree species. peat swamp forests.2. If there is substantial disturbance of forests via degradation. The IPCC recommends 2 mm diameter. Forest inventories limited to commercial species or trees in commercial (e. a sub-component of dead organic matter.2.. aboveground biomass typically emits the most carbon upon conversion to non-forest.2.1) is typically the largest biotic carbon pool in a forest that would be lost via deforestation.4 of the IPCC 2006 Guidelines. tallying trees with diameters of at least 10 cm. smaller woody plants and non-woody plants are excluded or are measured separately from larger woody plants. To obtain the belowground biomass.0 – FIELD-BASED INVENTORIES 55 .2 Belowground biomass Belowground biomass is an important carbon pool that may equal 25 percent or more of the aboveground biomass in many forests. e. it may be important to measure dead wood carbon stocks (described in 4.

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

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

REDD+ MRV MANUAL: CHAPTER 4. and iv) two stage sampling. Figure 4.1 are: i) random sampling.1. ii) systematic sampling. Diagram of random (top)..0 – FIELD-BASED INVENTORIES 58 . illustrated and elaborated below in Figure 4. Four common approaches.technologies should be introduced incrementally and only after trial applications can foster confidence of responsible personnel. 4. There are many options available for developing a sampling design. and stratified random (bottom) sampling. iii) stratified (random or systematic) sampling.3. systematic (right).2 Sampling design selection Sampling must be unbiased to ensure that resulting inventories will be reliable.

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

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

For example. in which the sample sizes required to target 95 percent confidence intervals equal to ± 10 % of the mean are calculated for a dataset both with and without stratification. highly variable strata covering small areas.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. in that stratification.000 10.1 Standard deviation 38. as shown in Design 3. which ignores sources of variability within the population. This can be achieved using REDD+ MRV MANUAL: CHAPTER 4.7 117.0 – FIELD-BASED INVENTORIES 61 .3 100.500 7. as travel between sample units represents the largest time and labor investment in forest inventory.000 31.000 12.spectral and/or temporal information.6 CV 74% 32% 43% 42% 52% Design 1 Sample size 217 41 74 69 402 for 4 independent forest inventories Design 2 Sample size 110 (not stratified) Design 3 Sample size 6 16 20 25 67 (stratified with Neyman allocation among strata) Table 4.1). achieves the targeted precision at the population level. The value of stratification is illustrated with the following example (Table 4. and 110 plots for the unstratified design (Design 2).0 83. like strata 3 and 4. will have less influence on the total number of plots needed than those strata that cover larger areas. The advantage of stratification is illustrated in comparing Designs 2 and 3. Two stage sampling Sampling vast hard-to-access landscapes necessitates efficient deployment of field effort. yet require substantially different levels of field effort to accomplish them: 67 sample plots for the stratified design (Design 3). Discussions of these can be found in Chapter 5. not at the stratum level. such as greenness indices and seasonality indices.6 27. like stratum 1 in Table 4.500 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. This is an important consideration.1. such as global biomass maps produced by research programs. both of which are designed to generate estimates at the population level.6 43.0 37.5 63. They also include classifications or derived products. Stratum 1 Stratum 2 Stratum 3 Stratum 4 Total Area (ha) 2.3 41.

as well as the size of the plot.3 Plot design considerations Plot design considerations include the type of plot to be used. From Smith et al. This method can be used to calculate the volume of coarse woody debris. While two stage sampling introduces an added source of sampling error. REDD+ MRV MANUAL: CHAPTER 4. Often these plots are called "fixed area" plots because the size is fixed. Typically. A transect plot design. typical options for a plot design are: • Points (dimensionless): variable radius plots can be implemented using a relascope or wedge prism. fixed area plots are circular or rectangular (including transects). For example.2: Sampling design including nested plots and line samples to measure the different types of carbon pools. Figure 4.0 – FIELD-BASED INVENTORIES 62 . where a tree is determined to be in or out as a function of the ratio of its diameter to distance from plot center. it concentrates field effort and results in minimizing overall effort. 18 It is important to note that a sample line plot design isn’t the same as a transect plot design.3. and the variables that need to be measured.a two-stage sample. • Lines (one dimensional): on a sample line 18. involving two steps in sampling. the first step might involve selecting polygons to sample from a population of polygons. Each is discussed below. it can be observed how many features intersects the line. is an area (two-dimensional). Plot types In a forest inventory. even if long and narrow. Inventory developers will also need to consider whether plots will be permanent or temporary. (2007). • Areas (two dimensional): all the trees found on a determined area are measured. and the second step involve allocating sample points within selected polygons. 4.

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

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

On the other hand. At some point. larger plots may average out some of the fine-scale variations in forests. 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. If any of these more complex sampling systems are considered. These include stratified random cluster sampling. the inventory developers will have to consider the density of large trees in the forest and the range of sizes of gaps. 20 percent of plots would be measured each year. For example. The CV is a measure of how different plots are from each other. ranked set sampling.0 – FIELD-BASED INVENTORIES 65 . The CV can be estimated from prior surveys that use a similar plot design in similar forests. Thus. 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.2 Coefficient of +/-20 Acceptable +/.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 4.2 shows the final results of a hypothetical example of estimating sampling sizes needed to reach specified sampling errors. This is an example of a panel sample. Plot numbers in stratified sampling are dependent on the variability of the carbon stock in each stratum and the level of precision required. the number of plots required to meet an increasing level of precision increases by four to reduce the uncertainty by half. calculated as the coefficient of variation (CV). Technically. If no prior surveys exist. and choose a plot size that is large enough that with the clumped spacing of trees in the forest. but are not dependent on the spatial extent of the project. the CV can be well over 100 percent. giving less plot-to-plot variability than smaller plots. when sampling large areas.5 on calculating uncertainties.6. the CV can be less than 30 percent. two-stage sampling. a biometrician or statistician should be consulted to ensure that sampling intensity calculations and data analysis procedures are correct. In fully stocked plantations. For small plots in forest with gaps. 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. As explained previously. one must choose an estimate of variability between plots. most plots will have the required number of trees. In such a case. The significant level is 95 percent for a large area REDD+ MRV MANUAL: CHAPTER 4. with 100 percent of plots measured every five years. when choosing plot size. the CV is the standard deviation divided by the mean. There are other more complex sampling systems that may or may not give more power for a given level of effort. and for a given amount of money. and panel sampling. Many inventories aim to keep crews continuously employed but only re-measure plots once every five years.5 +/. the number of plots is relatively independent from the size of the area.The key input to estimating the number of plots needed to obtain a given level of precision is the variation between plots. Table 4.2: Example of the number of sample plots needed to achieve specified sampling errors with simple random sampling. +/. a pilot study should be undertaken to estimate the CV. travel costs can have more effect on total cost than the number of plots. However. The chosen variability implies a plot size. In this case. When calculating the number of plots needed. These statistics are discussed in Section 4. greater statistical precision might be obtained by using fewer and larger plots than the theoretical optimum calculated without considering costs.

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

or procedures where team members check each other. transport. in part because it cannot be readily calculated like sampling error. • Field Manual specifying how field work is done. On the other hand. and local knowledge. food. Where field measurements include tree heights. where a supervisor or another person spot checks data by re-taking measurements (while the team is still on the plot). because they tend to know access routes and other locally unique information. Supervision should include visiting crews as they are doing plot work. where a different team independently re-visits and re-measures a subset of plots.5 FIELD WORK AND ANALYSIS 4. and checking the accuracy of their measurements (quality assurance and quality control is addressed below). There must be a process for giving crews feedback on the quality of their work. • Provides information on the uses of important species found in the plot. an often over-looked source of error in forest inventories. 4. measurement error of around 4 percent is achievable. supplies. Measurement error results from a host of issues. and mis-implementation of boundary correction methods. lodging. At the plot level. and • Field data check procedures. measurement error may reach 8 percent. the training of local community members to collect forest inventory measurements may not be cost effective. misinterpretation of live versus dead. • Quality objectives for each measurement. and communications are all provided as needed. misinterpretation of border trees and strata boundaries. as the data is being measured and recorded. particularly in closed canopy forest. equipment. and an independent person compares the two sets of measurements to make sure the measurements are within the required accuracy and precision limits. species identification ability. Field crews will need training to apply the selected protocols of the inventory. Careful adherence to meticulous field measurement protocols serves to reduce measurement error. together. with well-trained field crews. REDD+ MRV MANUAL: CHAPTER 4.5. Community monitoring may be more practical for detecting and specifying locations of infrequent events. and how to address unusual cases. capture the necessary measurement skills.0 – FIELD-BASED INVENTORIES 67 . an experienced supervisor should keep in close contact with crews during their first month of work.1 Field work Preparation for field work requires more than writing a field protocol and choosing plot locations. 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. It is highly desirable to have local community members included in the field crews. measurement error is typically assessed through remeasurement of a sample of plots via the spot checks described above. Key components of successful field work are: • Logistics planning and implementation to ensure that training. Instead. including not only mis- measurement of DBH and height (especially the latter in closed canopy broadleaf forests). especially if data collection is not performed frequently. which may limit acceptable values of data entries if electronic data recorders are used. ideally with rewards for good quality work and additional training if deficiencies are found. After training. but also species misidentification. One solution is to have teams composed of individuals who. and check cruising. Quality assurance procedures should include both immediate checks. such as new logging or clearing.

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

and • Organic carbon content (percent). usually 30 cm). Factors used in calculations should be well documented as to their values. • Soil bulk density (in g/cm3). This approach involves oxidizing a small sample at very high temperatures and using infrared gas absorption or gas chromatography to measure the amount of CO2 emitted. and tracking any changes to data. 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. (2007) provide a discussion of wood density determination. Bulk density is calculated for each sample from the measured mass and measured volume of samples. a statistician should be consulted to give proper methods of calculating the total uncertainty for the land-cover REDD+ MRV MANUAL: CHAPTER 4. Exceptions are identification of unknown tree species and determination of wood densities. If carbon stocks (or stock changes) are calculated separately for different pools occurring at a particular site. 4. to ensure that all needed data will be collected. or from a separate sample taken at the same location as sampling for carbon. However. and why the particular values are used in particular situations. Having all plots on a per-hectare basis allows calculation of statistical confidence of measurements based on the variability across plots and the numbers of plots. version tracking. sources.6 CALCULATING CARBON STOCKS FROM FIELD DATA 4. Carbon content is determined by laboratory analysis. and default Tier 1 factors are used for other pools. and then summing the pools to get the per hectare carbon stock represented by each plot. Bulk density can be measured on samples from which a subsample is later removed for carbon measurement. Key components of soil carbon quantification are: • Soil depth to be measured (in cm. the non-measured pools should not be combined with the measured pools before the calculation of uncertainty.5.1 Data management for calculations Calculating carbon stocks from field data must be done in an organized manner or errors will occur. For UNFCCC reporting. if separate reporting of each pool is not required. Procedures should include specifying the sequence of calculations. Smith et al. Calculation procedures should be tested on pilot data prior to committing to a particular inventory design. there is the option of calculating the carbon stock of each carbon pool on a per hectare basis. If only some pools are measured. The most common techniques for analyzing the carbon proportion of soil are based on measuring emissions from dry combustion of the samples.4. limiting who can make changes to data.0 – FIELD-BASED INVENTORIES 69 . Soil carbon does require laboratory analysis. it may be necessary to separately calculate the stock (or stock change) of each reported carbon pool. However.6.2 Laboratory analysis of samples Generally. laboratory analysis of woody biomass samples is not needed. The depth of sampling is specified in the inventory design. 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.

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

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

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

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

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

delineation of sample population area). Un is the uncertainty in percent for pool n. professional data managers will be consulted in the design of data storage forms and use of data storage equipment. it increases the likelihood of institutional memory transfer.g. REDD+ MRV MANUAL: CHAPTER 4.2.7. in tons. step in an inventory as it improves the efficiency of future inventories. it is important to develop a plan for how data and metadata will be stored and protected from unauthorized changes or loss. for the pool n. and Tier 3 measurements of live trees) the combined uncertainty of the estimated change can be calculated using Equation 5. checking and accuracy standards Data quality is essential. Having multiple teams involved in data analysis maintains awareness of the data and appropriate uses of these data. data must be thoroughly checked before carbon stocks are calculated.7. and who controls access should be readily available. field technician training and management of field crews are the foundations of data quality. As discussed above. and any adjustments or calculations must be clearly specified to build confidence in later users. However. data from an inventory must be stored so it can be retrieved at a later date. Data should be archived in at least two locations. the entire inventory could be worthless. Correctly and consistently archiving data and associated metadata represents an important. 4. and necessary. and ensure that later measurements and calculations are comparable to earlier data. Methods for measurement. Land-Use Change and Forestry (GPG-LULUCF) (2003): (U1 + E1 )2 + (U2 + E2 )2 + ⋯ + (Un + En )2 UE = � |E1 + E2 + ⋯ +En | where UE is the combined uncertainty in percent for the sum of changes in all pools 1 to n.0 – FIELD-BASED INVENTORIES 75 . in tons. Key aspects of forest inventory metadata are the protocols used to direct field measurements.7 DATA CHECKING 4. Further.2 of the IPCC Good Practice Guidance for Land Use.2 Archiving data and metadata To recalculate changes in carbon stocks over time.6 Propagation of error If the uncertainty for a combined estimate of sinks or emissions from multiple. independent pools is being calculated.6. if no reliable correction of a data error can be achieved. the faulty plot should be excluded from carbon stock calculations. This is especially important given the long time periods involved in REDD+ MRV. This includes checking for missing data and implausible data values. limited time is often invested in these activities. This is also important for satisfying the IPCC principles of TACCC. sample design. Regardless of the strength of the foundation. and information about where data is stored. Metadata describe how data are collected and what they represent. If field data have substantial errors. data cleaning. Relatively frequent use of the data ensures that data will be transferred to new storage and retrieval media formats and processing systems. 4. what is included. Inventory design. as well as the underlying sampling methodologies applied (e. particularly if different pools are measured with different UNFCCC methodology tiers (such as Tier 1 factors for shrubs and forest floor.1 Data cleaning.4. At a minimum. as new equipment and software are adopted. and En is the emission or removal for the stratum. Ideally.

While it is not necessary to collect different datasets using the same sampling approaches.g. it may be necessary in the early stages of national REDD+ accounting. and permit aligning inventories to common minimum diameter thresholds. forest inventories become irreplaceable windows to the past.8 CONSOLIDATING INVENTORY DATASETS Given resource constraints.. from commercial forest inventories. environmental research). 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.e. A second critical consideration is that the candidate data must be screened to ensure a minimum acceptable level of data consistency and quality. as repeated measurements form an archive. or at least estimates of wood volume in live trees. often with reports by pool and stratum.3 Data analysis and reports Typical reports from forest carbon inventories include: • Calculations of biomass and carbon stocks. • Calculations of stock changes over time.g. prior to the implementation of a national forest inventory. REDD+ MRV MANUAL: CHAPTER 4.0 – FIELD-BASED INVENTORIES 76 . • All data must have been collected with minimum quality assurance and quality control (QA/QC) procedures. The initial use of an inventory might be only to measure timber volume or carbon stock. REDD+ projects. One cannot foresee what issues will become important in the future. However.7. and experiences show that a well-maintained inventory will likely have many valuable uses. Foremost among these considerations is that the population that a given inventory represents be clearly delineated. to ensure consistency in estimation procedures (e. and a well-documented historic data set can provide a window into past conditions or changes. to develop estimates from existing datasets (e. New needs and questions arise. i. 4.4. they may contribute to the study of additional dynamics as well. the value of these data will increase. This is just as important as the plot data itself. that the area within which samples were drawn (with a known probability) is mapped and documented. otherwise it is not clear what population (forest area) that data represents. ensuring minimal measurement error and sample bias (see discussion of QA/QC in this same chapter). or to start estimating changes in carbon stocks. Important assessments are: • Raw data (plot measurements with individual tree data) should be preferentially sourced. • Timber inventories. Depending on parameters measured. Over time. allometric equations used) across inventories. and • Reporting uncertainties across pools and strata. and are a way of evaluating changes over time without having to wait years or decades for a new set of measurements..

McRoberts et al. national forested area) is constructed as a sort of jigsaw puzzle. (1992).3). There will inevitably be gaps remaining that have not been sampled. a shorter allowable timeframe is recommended. e.000 150 10 B 5. using regression or nearest- neighbor approaches (see. accepting a certain level of unknown bias.g. satellite imagery). To derive a total estimate. Where substantial areas of forest are in an early successional stage. a 2014 estimate might be derived from data collected up to 5 or 10 years prior to the reporting date). 2011). • Delineated and sampled with a new targeted field effort to fill the gaps.g. • Minimum sample size of 10 to ensure stable. • Ignored (e. but strictly commercial inventories cannot be re-interpreted as all species total biomass). 2007). estimated from stand tables per Gillespie et al. The result is an area-weighted average. • All data must be collected from within a maximum time range (e. Inventory Sample population Estimate (mean t Standard error area (ha) CO2/ha aboveground (mean tCO2/ha live biomass) aboveground live biomass) A 10. including diameter thresholds for tree measurements (in some cases it may be possible to reconstruct diameter distributions 22. the entire population of interest (e.g..0 – FIELD-BASED INVENTORIES 77 .g. if they are an insignificant area)...g.000 200 20 X (un-sampled) 10..3: Example of an area-weighted average with each sampled area treated as an individual stratum 22 E... the individual inventory estimates are weighted by their respective (sampled) population areas. in the case of remote. or • Assigned estimates based on predictions derived from relationships of known estimates with ancillary data (e.g. e. with pieces represented by sampled areas. each with their own estimate. with each sampled area treated as an individual stratum (Table 4. spatially-explicit geo-physical data.000 130 (prediction) 60 (model prediction error) TOTAL 26.000 125 50 C 1.000 139 81 Table 4. un-biased estimates of mean and variance (Westfall et al. that can either be: • Delineated and excluded from MRV. thereby focusing MRV on managed lands. REDD+ MRV MANUAL: CHAPTER 4.g. for example. • All data must cover the same pools and use the same pool definitions. inaccessible areas where anthropogenic changes are unlikely to occur.. In this way.

g. and are particularly useful for monitoring forest growth). and • Emissions from wood removals due to logging and fuel wood collection. the error of the prediction should be included (e. in tandem with field-based approaches. requires targeted field-based sampling of areas subject to extraction activities. to target sampling and scale emission estimates. REDD+ MRV MANUAL: CHAPTER 4.Uncertainty in the overall estimate is derived by propagating inventory level errors. Importantly. Some recent advances have applied this approach to estimation of logging impacts by Griscom et al. diseases).e. and projects would become contributors to. Emerging technologies in remote sensing are discussed in Chapter 5.9 THE GAIN-LOSS METHOD Estimating comparatively “rare” and small changes in biomass distributed over a large landscape.. though they have achieved success in identifying areas subject to degradation (Souza et al.2). inventories of REDD projects) also facilitates participation from multiple stakeholders. like those produced from selective cut timber extraction activities. and participants in. as well as any error around the estimate of the independent variable(s) (Table 4. particularly with regard to subsistence and illegal logging. As discussed in Chapter 3. in addition to the stock change method.. it acknowledges the value of data collected via project-level MRV. 2005) and could be used. even with permanent plots (which permit greater resolution of change.. which understandably are difficult to locate and access (for targeted field sampling) or for which reliable and complete estimates of wood removals are unavailable. Typical fluxes (i. reliable information on wood removals from which harvest-related emissions can be estimated. (2014) and Pearson et al.g. Remote sensing-based approaches have not yet achieved sufficient resolution of stock change to be viable for direct estimation of emissions from degradation. referencing against a validation dataset). root mean squared error of predicted – observed. (2014). sinks and sources) tracked by a gain-loss type approach would include: • Sequestration from forest growth. Probability-based sampling approaches. would typically require prohibitively high sample intensities using the stock change method. particularly where the objective is to resolve those changes with even modest levels of statistical significance. the IPCC recognizes gain-loss as an alternate method to estimate carbon changes (IPCC. fire. or alternatively. 2006).. from anthropogenic wood removals. 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. this approach of building a national inventory from smaller scale datasets (e. Where an un-sampled area is assigned an estimate based on a prediction. Many challenges remain.e. The latter source of emissions. By utilizing information from multiple scale datasets. similar to a stratified sample.0 – FIELD-BASED INVENTORIES 78 . • Emissions from natural mortality (competition-related and senescence) and disturbance (mortality due to wind. national/jurisdictional MRV. is challenging. 4. the gain-loss method).

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