1 - Food and Agriculture Organization
A Review of Methods to Measure and Monitor Historical Forest Degradation
Martin Herold1, Yasumasa Hirata2, Patrick Van Laake3, Gregory Asner4, Victoria Heymell5, Rosa María Román-Cuesta6
1. Wageningen University. Building 101. Droevendaalsesteeg 3, 6708 PB Wageningen. The Netherlands. Tel. +31 317 481276; Fax: +31 317 419000. Martin.Herold@wur.nl
2. Shikoku Research Center, FFPRI. Forestry and Forest Products Research Institute 2-915 Asakuranishi, Kochi, Kochi, 780-8077. Japan. Tel. +81-88-844-1121; begin_of_the_skype_highlighting Fax. +81-88-844-1130. hirat09@affrc.go.jp
3. UN REDD Vietnam Programme. 172 Ngoc Khanh, #805. Ba Dinh, Ha Noi Vietnam. patrick.van.laake@
4. Carnegie Institution. 260 Panama Street. Stanford, CA 94305. USA. Tel.+1-462-1047 200 gasner@globalecology.stanford.edu
5. FAO. Viale delle Terme di Caracalla 15. 00100 Rome, Italy. Tel. +39 06 570 54451 Fax: +39 06 570 55137, victoria.heymell@
6. UN-REDD Programme. FAO MRV team. Viale delle Terme di Caracalla 15. 00100 Rome. Tel. +39 06 570 52044; Fax: +39 06 570 55137. rosa.roman@
Abstract
There are currently more than fifty definitions of forest degradation but none of them is accepted in the international negotiations as a univocal, operational and multipurpose definition (i.e. for the use in national-level reporting. While forest degradation is a broad topic, the review presented here is addressing the degradation issue from a climate change and forest carbon stock change perspective; in particular considering the current discussions on REDD+. The IPCC 4th Assessment Report sustained that the world’s degraded forests reached ca. 100 million of hectares per year. This represents almost 10 times more global area affected by degradation than by deforestation (i.e. ca. 100 million degraded ha.yr-1 versus ca. 13 million deforested ha.yr-1 during 2000-2005). For this reason, forest degradation rates must necessarily be reported together with deforestation rates to guarantee integrated and coherent climate mitigation actions. The REDD+ mechanism addresses the evident role of reducing deforestation and forest degradation as global climate mitigation tools, but it also considers the role of conservation, sustainable management of forests and the enhancement of forest carbon stocks. While the final rules for REDD+ are still under development, non-Annex I countries will have to evaluate their historic rates of deforestation and degradation to estimate their Reference Emission Levels. There is not one method to monitor forest degradation that fits all circumstances and the methodological choice depends on a number of factors including the type of degradation, available data, capacities and resources and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field based data (i.e. Multi-date national forest inventories and permanent sample plot data, commercial forestry datasets, proxy data from domestic markets, etc) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of them both providing the strongest alternative. Historic degradation assessments for non-Annex I countries frequently lack consistent historic field data, forcing countries to rely strongly on remote sensing approaches mixed with current field assessments of carbon stock changes. The current paper describes methodologies for assessing current and historical rates of forest degradation to support developing countries interested in implementing the REDD+ mechanism.
1. Introduction
1. Definitions of forest degradation in relation to forest carbon stocks
There are currently more than fifty definitions of forest degradation (Lund 2009, Simula 2009) but none of them is accepted in the international negotiations[1] as a univocal, operational, multipurpose definition.
Forest degradation is generically defined as the reduced capacity of a forest to provide goods and services (FAO 2002). However, this definition is too broad to be operational. In the context of climate change, the IPCC (2003) developed a definition of forest degradation that focuses on human-induced changes in the carbon cycle in the long run:
“A direct human-induced long-term loss (persisting for X years or more) of at least Y% of forest carbon stocks [and forest values] since time T and not qualifying as deforestation or an elected activity under Article 3.4 of the Kyoto Protocol[2]”.
In order to operationalise this definition, i.e. for the use in national-level reporting, it would be necessary to specify an area threshold, as well as time and carbon loss thresholds.
Forest degradation, from the point of view of climate change policy and the IPCC national estimation and reporting guidelines, refers to a loss of carbon stock within forests that remain forests (IPCC 2003). More specifically, degradation represents a human-induced negative impact on carbon stocks, with measured forest variables (i.e. canopy cover) remaining above the threshold for the definition of a forest. Moreover, to be distinguished from (sustainable) forestry activities, the decrease should be persistent. The IPCC 2003 definition faces several challenges if used for monitoring purposes: i) it lacks a clear definition of a temporal threshold considered as “long term”; ii) it lacks a suggestion or identification of minimum thresholds of carbon stock change associated with degradation to distinguish it from natural forest disturbances; and iii) it is challenged by the identification and isolation of human-induced degradation from other degradation factors, which may well be interlinked. The persistence could be evaluated by monitoring carbon stock changes either over time (i.e. a net decrease during a given period, e.g. 20 years) or along space (e.g. a net decrease over a large area where all the successional stages of a managed forest are present) (GOFC-GOLD 2009).
Considering that, at national level, sustainable forest management may lead to national gross losses of carbon stocks (e.g. through harvesting) which are lower than (or equal to) national gross gains (in particular through forest growth), consequently a net decrease of forest carbon stocks at national level during a reporting period would be due to forest degradation within the country. Conversely, a net increase of forest carbon stocks at national level would correspond to forest enhancement. Therefore, it is also possible that no specific definition is needed, and that any net emission will be reported simply as a net decrease of carbon stock in the category “Forest land remaining forest land” (GOFC-GOLD 2009) – a perspective that is also shared by an expert group convened by the UNFCCC SBSTA[3] (UNFCCC 2008).
For the purpose of this paper, which is aims to provide a review of different assessment methods, the above mentioned assumption will be used. However, under other circumstances, a specific definition may be required. In this case, Simula (2009) summarizes the elements that an operational definition of forest degradation should provide: 1) identification of forest goods and services, 2) a spatial context of assessment, 3) a reference point, 4) coverage of both the process and state (degradation/degraded forests), 5) relevant threshold values, 6) specification of reasons for degradation (human induced/natural), 7) an agreed set of variables, and 8) indicators to measure the change of a forest. Additional elements could be added or singled out, depending on the particular interests related to the purpose of the definition.
While forest degradation is broad topic, the review presented here is addressing the degradation issue from a climate change, carbon and REDD+ perspective. The authors have collated and critically reviewed case studies, articles, guidelines, manuals and other documents describing methodologies for assessing current and historical rates of forest degradation to support developing countries interested in implementing the REDD+ mechanism.
2. Main causes of forest degradation affecting changes in forest carbon stocks
Forest degradation can have any number of causes, dependent on resource condition, environmental factors, socio-economic and demographic pressure and “incidents” – e.g. pests, disease, fire, natural disasters. The understanding and separation of different degradation processes is important for the definition of suitable methods for measuring and monitoring. Various types of degradation will have different effects on the forest (carbon) and result in different types of indicators that can be used for monitoring degradation using in situ and remote methods (i.e. trees being removed, canopy damaged etc.).
For the purpose of this review the emphasis is on those forms of forest degradation that are caused by direct human impacts on the forests (i.e wood removal) or indirect human impacts (i.e. long term forest management that favours fire presence and impacts) on the forest. The reduction of forest degradation by human influenced causes is eligible under the REDD+ mechanism (4/CP.15[4]; Draft Decision/CP.16[5]).
1. Extraction of forest products for subsistence
Privately or communally managed forests are often subject to extraction of forest products for immediate use by local households. Extractions are for such uses as fuelwood for cooking, collection of fruits, roots and other edible tree organs, collection of fodder for livestock, and harvesting of timber and thatch for construction. In more established and stable cultures and communities such extractions can be sustainable (e.g. tribal groups in Papua New Guinea and elsewhere, community managed forests in Nepal and India, the ejido system in Mexico), but in many other cases the increasing population of the last few decades has put so much pressure on the forest that the extraction is no longer sustainable.
The following sub-sections describe activities causing forest degradation and reductions in carbon stocks in forests that are not under other land uses; reflecting the requirements of the definition used for FAO’s global forest resources assessments. It should be noted that expansion of agriculture into forests (i.e. forest grazing, shifting cultivation, agroforestry) also cause losses in forest carbon stocks and should also be considered and monitored, and depending on the forest definition reported as either emissions on forest or non-forest land.
2. Extraction of forest products for local markets
Most developing countries have seen rapid urbanization in recent decades and this has created a market for forest-based products which, in some cases, has resulted in forest degradation. Particularly the production of charcoal has led to forest degradation, in dry forest ecosystems such as the miombo of southern Africa. Other products that are harvested include timber, bamboo and rattan for construction and furniture making and minor products such as raffia, vines and leaves for making household utensils and rope.
Serving local and national markets for forest-based products has been facilitated by the expansion of road infrastructure in most developing countries. Better infrastructure has decreased the cost and expanded the options of transportation of forest products. Since the cost of forest products – e.g. charcoal – is often far lower than “urban” alternatives – e.g. kerosene – the cost of transportation is easily recouped.
3. Industrial extraction of forest products
International scrutiny of harvesting operations in developing countries has led to the development of alternative harvesting schemes to replace the total removal of commercially interesting tree species of specimens above a minimal girth and with little or no consideration for the remaining stock during harvesting. While the management is fostering regeneration of the forest – this is an explicit requirement under all international certification schemes for selective harvesting – the forest will have noticeably lower carbon stocks for many years.
4. Natural disturbances such as wildfire
All forms of excessive forest product extraction leading to degradation, as described in this section, impact the resilience of the forest to withstand external impacts, such as fire, pests and drought. These impacts can be positive, although most are negative. Most impacts are driven by changes in the local hydrology: through extraction of trees or tree products more solar radiation is transferred to the soil which leads to drying of the soil and ultimately stress for the trees. For forests out of human influence, natural fires and degradation due to insect outbreaks are not reported under the Convention (i.e. remote Siberian boreal forests ignited by lightning). However, very few developing countries have non-human influenced forest, so fires as well as insect outbreaks and any other forest disturbances, must be reported and will reduce a country’s REDD+ emission gains.
Different degradation processes are usually active within the same country. Some may affect large areas, some not, and it is common that they are not equally distributed among the country’s territory. Thus, forest degradation activities are often focused in specific areas and this should be considered in national measurement and monitoring efforts.
1.3 Forest degradation as key source category in the context of REDD+
Disturbances that lead to degradation such as forest fires, pests (insects and diseases) and climatic events including drought, wind, snow, ice, and floods have been reported to roughly affect 100 million of hectares globally per year (FAO 2006a, in the IPCC 4th Assessment Report (Nabuurs et al. 2007)). Globally, this value represents almost 10 times more area affected by forest degradation than by deforestation (i.e. 12.9 million ha.yr-1 (2000-2005), FAO (2006b); MEA (2005), indicating the scale and importance of global forest disturbances that lead to degradation. While these values are a compilation of areas affected by forest disturbances around the world, tropical regions are well known for large scale disturbances that lead to forest degradation: fire activity has been repeatedly reported to affect the tropic and subtropical region more than other latitudes (Dwyer et al. 1999, Giglio et al. 2006) and severe storms and wind blows are also well known large scale degradation factors in tropical South America (Negrón-Juárez et al. 2010). For this reason, in the context of REDD+, forest degradation rates must necessarily be reported together with deforestation rates to guarantee integrated and coherent climate mitigation actions.
Forest degradation often has different driving forces than deforestation. The emission levels of degradation are lower than for deforestation (per unit area); but cumulative and secondary effects result in significant carbon emission and degradation is often a precursor to deforestation. Addressing deforestation does not automatically reduce rates of degradation. Failing to include degradation in a REDD+ agreement could leave considerable amounts of forest-based emissions unaccounted for (Murdiyarso et al. 2009). In case reducing deforestation measures are taken, monitoring forest degradation is important to avoid displacement of emissions from reduced deforestation. The evident role of reducing deforestation and forest degradation as global mitigation tools led to the petition by the Coalition for Rainforest Nations in Montreal 2005, at COP11, to reinforce Article 2 of the Kyoto Protocol regarding the protection and enhancement of sinks and reservoirs of greenhouse gases not controlled by the Montreal Protocol.
As a result of this petition, in December 2007, COP13 in Bali adopted 2 decisions:
1. The Bali Action Plan Decision 1/CP.13,
Where the Intergovernmental Panel on Climate Change decided to address:
“Policy approaches and positive incentives on issues relating to reducing emissions from deforestation and degradation in developing countries; and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries”
1. Reducing emissions from deforestation in developing countries: approaches to stimulate action Decision 2/CP.13. This decision provides a mandate for several elements and actions by Parties relating to reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries.
A methodological decision followed in COP15, Copenhagen 2009:
2. Methodological guidance for activities relating to reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries 4/CP.15.
This decision requests developing country Parties to take certain guidance into account for the 5 REDD+ activities, in particular those relating to measurement and reporting:
“To establish, according to national circumstances and capabilities, robust and transparent national forest monitoring systems and, if appropriate, sub-national systems as part of national monitoring systems that:
i) Use a combination of remote sensing and ground-based forest carbon inventory approaches for estimating, as appropriate, anthropogenic forest-related greenhouse gas emissions by sources and removals by sinks, forest carbon stocks and forest area changes;
ii) Provide estimates that are transparent, consistent, as far as possible accurate, and that reduce uncertainties, taking into account national capabilities and capacities;
iii) Are transparent and their results are available and suitable for review as agreed by the Conference of the Parties.”
IPCC guidance
As stated in paragraph 6 of Decision 2/CP.13, as the basis for reporting greenhouse gas emissions from deforestation and forest degradation under the Convention, non-Annex I Parties are encouraged to use the most recently adopted or encouraged reporting guidelines, which include the application of the Good Practice Guidance for Land Use, Land-Use Change and Forestry (GPG-LULUCF, IPCC 2003, 2006). This guidance will promote the elaboration of country forest reports on emissions by sources and absorptions by sinks that are transparently measured, consistently calculated over time, comparable with other countries methodologies, and as complete and accurate as possible. Unlike previous Guidelines, the LULUCF 2003 Good Practice Guidelines is “land use based”, meaning that countries need to report degradation under the category: “Forest land use that remain forest land use”.
To estimate the emissions associated with forest degradation, countries need to evaluate two aspects (IPCC 2003):
1) Changes in forest carbon stocks due to the degradation processes per unit area (Emission Factors = EF). How much carbon is lost from the forests and released to the atmosphere due to the degradation process (i.e. commonly measured through forest field sampling and through repeated forest inventories) (reported as MgC.ha-1-yr-1). Emission factors should be calculated for each of the 5 forest pools requested under the UNFCCC: aboveground, belowground, deadwood, litter, and soil organic carbon.
2) Area of forest land areas that remains forest land effected (Activity Data = AD); ideally for different disturbances or degradation types. How much and where forest area is or undergoing degradation changes? (i.e. statistics calculated through forest inventories or through remote sensing) (reported in ha).
The estimation of the carbon emissions from forests on the national level caused to degradation will be done by multiplying the EF by the AD and sum it up for all forest and degradation types.
A few extra aspects are required to estimate emissions associated with forest degradation:
1) Selection of the main carbon pools to measure and monitor forest degradation: the IPCC (2003) defines five carbon pools to be measured and monitored: aboveground biomass, below-ground biomass, litter, dead wood and soil organic carbon. Key stock sources must be selected. In the tropics, the most generalized approach is to monitor only above-ground biomass, even though soil stocks in peatlands also require attention and can contain more carbon stock than the AGB. In this working paper we will mainly focus on methodologies to monitor changes in aboveground forest biomass.
2) Reporting Tiers: the IPCC (2003) provides three tiers for estimating emissions, with increasing levels of data requirements and analytical complexity and increasing accuracy:
• Tier 1 uses default values for forest biomass and forest biomass mean annual increment (MAI) which are obtained from the IPCC Emission Factor Data Base (EFDB), corresponding to broad continental forest types (e.g. African tropical rainforest). Tier 1 also uses simplified assumptions to calculate emissions.
• Tier 2 uses country-specific data (i.e. collected within the national boundary), and by resolving forest biomass at finer scales through the delineation of more detailed strata. For degradation, in the absence of repeated measures from a representative inventory, Tier 2 uses the gain-loss method using locally-derived data on mean annual increment.
• Tier 3 uses actual inventories with repeated measures of permanent plots to directly measure changes in forest biomass and/or uses well parameterized models in combination with plot data.
Tier 3 often focuses on measurements of trees only, and uses region/forest specific default data and modelling for the other pools. The Tier 3 approach requires long-term commitments of resources and personnel, generally involving the establishment of a permanent organization to house the programme.
3) Methods to estimate carbon stock changes (emission factors): There are two fundamentally different, but equally valid default approaches to estimate carbon stock changes, under the IPCC:
1) The stock-based or stock-difference approach
2) The process-based or gain-loss approach.
These approaches can be used to estimate stock changes in any carbon pool, although their applicability to soil carbon stocks is limited. The stock-based approach estimates the difference in carbon stocks in a particular pool at two points in time. This method can be used when carbon stocks in relevant pools have been measured and estimated over time, such as in national forest inventories. The process-based or gain-loss approach estimates the net balance of additions to and removals from a carbon pool. This type of method is used when annual data such as biomass growth rates and wood harvests are available. In reality, a mix of the stock difference and gain-loss approaches can be used (GOFC-GOLD 2009).
Most non-Annex I countries are only now starting to develop their national forest inventories and cannot count on two or more measurements in time to apply the Stock-Difference method at a national level. Most non-Annex I countries will therefore have to rely on the Gain-Loss method to calculate their emission factors.
4. Identifying and promoting the use of effective and cost efficient methodologies and tools to monitor forest degradation
The latest request by the Climate Convention to measure, monitor, and report human-induced forest greenhouse gas emissions and removals opens new methodological challenges. Developing countries will have to identify and promote the use of effective and cost efficient methodologies and tools to monitor forests and related degradation in terms of changes in forest carbon stocks and sequestration rates in “forests remaining forests”, with an initial focus on historical periods, consistency over time, and the need to establish capacities to continue monitoring in future periods.
This will be particularly important for forest carbon rich countries such as those in tropical regions, where forest degradation can easily be identified as a key source category. A key source category is “an emission or sink category that is prioritised within the national inventory system because its estimate has a significant influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, or both” (IPCC 2003). Key source categories should be estimated using higher tiers if sufficient resources are available. Higher tiers refer to lower level of uncertainty associated with the data and therefore higher accuracies.
The key category concept is important since it helps focusing country monitoring efforts to estimate the most relevant components of the GHG budget. Since direct emissions from degradation are smaller than those from deforestation (per unit area/emission factor), a country may have the flexibility to put more efforts in monitoring deforestation and can consider which types of degradation are significant on the national level and should be measured using high tiers. These considerations help to make the monitoring efforts more efficient in terms of monitoring cost versus measured carbon stock changes (per hectare).
There is not one method to monitor forest degradation. The choice of different approaches depends on a number of factors including the type of degradation, available data, capacities and resources and the potentials and limitations of various measurement and monitoring approaches. Methodological challenges associated with measuring forest degradation are diverse, among others:
1) Consideration of temporal degradation thresholds and spatial scales. The effect of forest degradation on forest carbon stocks depends on time. To avoid mixing the effects of short-term carbon stock reductions using sustainable forest management practices with long-term effects of unsustainable practices leading to forest degradation, a temporal threshold can be selected for each ecosystem type. Temporal thresholds of degradation that guarantee “long-term” disturbance depend on ecosystem resilience and the intensity of the disturbance, and are complex to establish. Spatially, there is a general dominant perception that a forest stand is the basic unit of decision-making in conserving or enhancing forest carbon. However, forest management decisions are based on land planning which concerns larger forest management units (for example, watershed, landscapes and forest concessions). Minimum mapping units also have key methodological implications.
2) Integration of field and satellite datasets: Monitoring changes in carbon stocks due to forest degradation relies heavily on field surveys but can benefit from integration of remotely sensed data with site-specific biophysical field attributes. Which biophysical parameters to measure and which time thresholds are appropriate for relating both data approaches are key issues to consider. Moreover, it is best to calibrate remote sensing variables with local forest biomass instead of using regional means. However, limited historical data exists for forest degradation.
3) Spatial impact and intensity: Different forest degradation processes activities are often focused in specific areas within a country and this should be considered in national measurement and monitoring efforts to track the most important activities and impacts and to use the available monitoring resources most efficiently.
4) Identification of key forest carbon stocks affected by degradation: Methods for calculating carbon stock changes vary for each relevant carbon pool (above ground biomass, below ground biomass, litter, dead wood and soil organic carbon), as well as for emissions of non-CO2 greenhouse gases. In boreal forests such as Siberian Taigas or in tropical mountain regions, degradation of carbon rich peatlands will require different methodologies from above ground measurements of forest degradation in tropical evergreen rainforests.
5. Current challenges to measuring historical forest degradation
Historical forest degradation is a critical element in quantifying a country’s potential reduction in emissions and, therefore, its potential future benefits through a potential post-Kyoto mechanism. Ex-ante evaluations of forest degradation are required to estimate a country´s reference emissions level, which will condition its future access to carbon crediting. The estimation of historical forest degradation faces however, further complications besides the previously mentioned general methodological challenges:
1) Lack of data: many countries, in particular those in tropical regions lack historical data on forest degradation and its impacts on forest carbon stocks. Historical national data are often limited to satellite archives while remote sensing itself has limitations in detecting degradation activities.
2) Insufficient capacity: while many developing countries have some level of experiences and data for monitoring commercial forestry activities, these capacities are often not sufficient to implement a national survey to assess historical deforestation and forest degradation.
3) Temporal considerations: there is currently no agreement regarding the temporal threshold associated with “long-term carbon stock loss”. If the forest degradation process in the field implies a cumulative long-term gradual carbon stock loss, methodological approaches such as direct measurements through remote sensing and fieldwork could still be usefully applied. However, if forest carbon stock losses occur in shorter time periods, field validation and remote sensing measurements are challenged by the quick recovery of the forest, jeopardizing its measurement and monitoring. Moreover, in areas with high cloud persistence satellite data might be unavailable to track short-term degradation disturbances.
4) Integration of different data sources: Historical datasets on forest degradation are rare. The integration of remotely sensed data with site-specific biophysical field attributes for past assessments and other sources (i.e. forest management data) is challenging. The magnitude of historical forest degradation may have to be estimated through indirect approaches: modelling and/or other indirect methods to minimize the risk of overestimating avoided emissions under REDD+.
5) Inconsistencies when linking historic and present degradation datasets and methodologies: Data limitations for historical periods, in particular those of consistent multi-date field data or the availability of high-quality remote sensing data, are often less prominent in current or future monitoring efforts and assessments are expected to be more accurate and verifiable. Nevertheless, the need for consistency between historical, current and future estimates is important and should be a primary target of related surveys.
2. Overview of methods for estimating emissions from forest degradation
To estimate the emissions attributed to forest degradation, countries need to evaluate changes in forest carbon stocks due to the degradation processes (Emission Factors = EF), and changes in forest land areas that remain forest land (Activity Data = AD); ideally for different disturbances or degradation types, since different degradation processes imply different levels of carbon loss.
Since Parties need to offer country specific data with uncertainties (Tier 2 reporting), the estimation of country specific EF factors relies heavily on field sampling, frequently through National Forest Inventories (NFI), while the estimation of annual estimates of AD is more operatively done through national wall-to-wall remote sensing approaches.
There exists, of course, a clear need to merge remote sensing to support field data collection, and field validation is needed to ground-truth remote sensing approaches. To the other extreme are those initiatives where remote sensing is being tested to estimate local EF (i.e. LIDAR and Radar technologies as a way to derive forest structure and above ground biomass), and field sampling is used as a way to estimate AD. These approaches are, however, not exempt from complications and limitations for reporting under the UNFCCC. Hence, EF derived from Remote Sensing rarely include uncertainty estimations that relate directly to forest biomass variability and their estimates do not include any other pool outside the above-ground biomass. Conversely, AD reporting through field data such as National Forest Inventories can only offer adequate levels of uncertainty if there is a sufficient number of ground sampling units (e.g. several dozen thousand per country)[6] (Stach et al. 2009). Moreover, NFI would not allow for annual AD reporting – needed for REDD+ reporting, since they are only re-measured over longer time periods (i.e. 5-10 year recensus).
Table 2 shows a summary of the relevancy of different forest degradation approaches (i.e. field data collection versus remote sensing) developed by Acharya and Dangi (2009) for Nepal.
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Table 2: Relevancy of different forest degradation assessment approaches in Nepal (based on aerial photographs 1:12000 to 1:60000) and Landsat TM image experiences) Source: Acharya and Dangi (2009)
2.1 Field observations and surveys to assess Emission Factors: changes in forest carbon stocks
A critical step to estimate forest degradation is a well designed and implemented sampling scheme to collect carbon stock data on the ground to measure carbon stock changes due to degradation. Frequent field methods to evaluate carbon stock changes include (GOFC-GOLD 2009):
➢ Inventory based approaches (national, sub-national) (i.e. Mexico);
➢ Data from targeted field surveys (including interviews), research and permanent sample plots (i.e. India; Russia);
➢ Commercial forestry data (i.e. logging concessions and harvest estimates) (i.e. Democratic Republic of Congo);
➢ Proxy data from domestic markets (charcoal, subsistence).
In the case of most Annex I countries, the collection of forest data through periodic forest inventories since the 1980s allows them to estimate the EF associated with historical and current forest degradation processes. For most non-Annex I countries, however, these long-term forest datasets are almost non-existent, or are focused on specific field assessments for commercial timber. In these cases, the time variable has to be substituted by space (e.g. evaluating net carbon stock decreases over a large area where all the successional stages of managed and unmanaged forests are present) (GOFC-GOLD 2009). This latter approach would consider the carbon stocks of the unmanaged forests as the reference value and would estimate the EF of the degraded forests by comparison. Some non-Annex I countries have some information available based on ground field data collection.
1. Sampling strategy and forest stratification
Field data collection can easily overwhelm any forest management organization wishing to determine forest degradation with reasonable accuracy levels (i.e. this value is a country decision, however an example of good accuracy would be a 10% maximum error at 90% confidence interval).
Most forest inventories are multipurpose. This means that several forest attributes are assessed and the inventory design frequently tries to optimize more than one forest attribute. When designing the sampling scheme of a Forest Inventory, there are at least two ways to reduce the sampling effort and to reduce uncertainties as far as practicable:
The first is a stratification of the territory and the forest land into more homogeneous units, under the assumption that the estimates of the variable of interest will be more similar within a stratum than among strata. Homogeneity here refers to a variable of interest: in this case carbon stocks. Stratification can consider as many levels as necessary. For carbon stock measurement it should include, at least:
1. Forest ecology, forest type: This determines the maximum biomass content and general properties of growth dynamics. Professional forest inventory at this level should determine general quantities of the forest – tree associations and density, basic wood density, average height . – and allometric equations of the forest type.
2. Human practices that alter forests and supposedly result in similar carbon stocks:
i) Degradation status: Understood as human induced activities that reduce the carbon stock, forest dynamic and forest composition for a selected time threshold (i.e. excessive fuelwood removal, wood charcoal production, fires, insect outbreaks, (see Section 2.2).
ii) Forest management for timber extraction (i.e. low impact logging activities, illegal logging, plantations).
iii) Conservation activities: Active management to either avoid human encroachment and/or to restore degraded areas.
A second consideration would be the cost-effectiveness principle, which not only takes into account the statistical requirements to obtain accurate estimates of forest parameters but also the costs associated with the implementation and logistics of the inventory. The designing and implementation of a Pilot Phase before the complete development of the inventory will offer clues about both statistical and cost requirements.
2. Survey methods
Professional forestry organizations have typically used permanent sampling plots (PSP) to inventory forest resources and temporal dynamics. When historical records exist, it is worthwhile to continue the sampling on this basis. For assessment of forest degradation, however, it is advisable to work with sampling plots at random locations (in the forest stratum) in order to avoid bias in the estimates – the local population may avoid PSPs knowing that they are under special scrutiny of the forestry organizations.
Circular or square plots at random locations in the forest or transect surveys are the primary forms of survey organization. Circular plots have the advantage of being non-directional, while transect surveys are especially useful to detect gradients in some forest property. The distribution of the plots or transects has to be representative of the environmental conditions within the forest stratum, particularly exposure to the sun, elevation and soil hydrology.
Consistent and repeated measurements over time that cover all forest types in the entire country are key to offer reliable country-specific estimates of forest degradation.
A major issue affecting the reporting of forest degradation emissions is the estimation of its uncertainty. Among the REDD+ activities, both forest degradation and deforestation will require high levels of accuracy and certainty since they are major contributors of countries´ GHG forest budgets. Current degradation estimates can be used to reduce uncertainties but historic degradation will necessarily come with large uncertainties due to lack of available data to determine its accuracy.
3. Data collection strategy
The data that needs to be collected can be broken down into two distinct types:
1. Ecosystem or forest type properties. Typical data in this category are basic wood density, average tree height, free branch height, taper, biomass expansion factor, description of tree associations, species identification to report on the incidence of minor species such as bamboo, rattan, lianas and vines, litter and dead wood, root biomass and soil properties. These kind of properties require specialized skills for accurate assessment, but they tend to be stable over time and space and thus need only periodic surveying on a sampling basis. The sampling is best performed by professional foresters.
2. Forest dynamics data. An essential property that has to be collected on a regular basis and distributed over the entire forest (stratum) is diameter at breast height (DBH). A follow up on other ecosystem gains and losses such as litter-fall, root growth, tree mortality, coarse woody debris, decomposition and changes in species composition would also be required for balancing the forest carbon cycle. With the properties from the first category, biomass can accurately be determined from DBH. DBH is also very easily measured and with little training people without formal knowledge of forest survey can be employed to collect DBH, making large-scale inventories feasible. When DBH is collected on a plot basis, the statistical distribution of individual tree DBH can be used as a proxy indicator for forest degradation, if the DBH distribution of undisturbed forest is used as a reference.
The data indicated here is routinely collected by forestry organizations in many countries and is not specific to the assessment of forest degradation. However, older inventories that have emphasized merchantable volumes of commercially interesting species – as was the case in most colonial forestry systems – can be correlated to similar inventories in the present era, supplemented with forest properties that allow for the assessment of biomass, thus enabling an estimate of historical biomass content.
2. Remote sensing methods to measure Activity Data: changes in forest land uses that remain forest land uses
While most Annex I countries have been reporting their changes in forest area affected by degradation based on their National Forest Inventories, the measurement and monitoring of AD through remote sensing offers a series of advantages: i) it represents an operational, consistent, coherent, transparent and fairly accurate way of reporting on AD, which allows for near-real reporting on land use changes, ii) it is cost and time effective, iii) it offers data over remote and logistically complicated regions, iv) it offers a high frequency of data that help minimize seasonality problems, v) it is the only approach that objectively offers information on historical trends, and iii) it favours the control of leakage and permanence issues.
However, it also has several disadvantages: i) it is hampered by clouds, ii) it is limited by the technical capacity to sense and record the change in canopy cover with small changes likely not to be apparent unless they produce a systematic pattern in the imagery, iii) optical remote sensing is not useful to identify sub-canopy changes and therefore it is insensitive to under-canopy forest degradation (i.e. certain fire types, certain overgrazing, certain logging activities, and iv) not all degradation processes can be monitored with high certainty using remote sensing data. Table 3 offers a list of degradation processes that are best detected through remote sensing. Of course, a mixed approach would be desirable.
|Highly Detectable |Detection limited & increasing data/effort |Detection very limited |
|Deforestation |Selective logging |Harvesting of most non-timber plants products |
|Forest fragmentation |Forest surface fires |Old-mechanized selective logging |
|Recent slash-and-burn agriculture |A range of edge-effects |Narrow sub-canopy roads ( ................
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