Edith Archambault

Classification of Social economy organizations

For it is not a question of linking consequences, but of grouping and isolating, of analysing, of matching and pigeon-holing concrete contents; there is nothing more tentative, nothing more empirical (superficially, at least) than the process of establishing an order among things; nothing that demands a sharper eye or a surer, better-articulated language; nothing that more insistently requires that one allow oneself to be carried along by the proliferation of qualities and forms. And yet an eye not consciously prepared might well group together certain similar figures and distinguish between others on the basis of such and such a difference: in fact, there is no similitude and no distinction, even for the wholly untrained perception, that is not the result of a precise operation and of the application of a preliminary criterion.

Introduction

Standard classifications were built to simplify the complex world of the corporate sector, to analyse the international exchange of goods, more than services, and to make cross-country comparisons of production sectors and products feasible. They date back to the beginning of international exchange and are much older than the first system of national accounts [START_REF] Vanoli | Une histoire de la comptabilité nationale[END_REF]. On the mathematical point of view, classifications are embedded partitions. They introduce discontinuities in a continuous reality (from the smallest producer to the largest one, for example) under the following hypothesis:  There is much more similarity between the items inside a category than between the items of two neighbouring categories  The borderline or ambiguous cases have to be classified in one category and only one, in the same way anytime and anywhere  The classifications are able to be aggregated or broken up as Russian dolls  The standard classifications have to be revised periodically to include technical, institutional or organisational changes. These changes of the classifications introduce a discontinuity inside the time series Statistics. This is a great complication for historians or analysts of the long-term trends who have to link the successive time series. However classifications have not to be changed too often because the revisions break the time series statistics at the roots of economic and social policies evaluation and forecasts.

That is to say that standard classifications are artefacts and following their initial use, they fit the reality of corporate producers more than the reality of unincorporated enterprises owned by households; they also fit the reality of the government as a non market producer of collective or divisible services more than the production of services by the non-profit institutions.

However social economy entities are either market or non market producers and they must be included in the standard classifications (and they are, but not completely). In a first part we will examine where they are in national accounts and what are the advantages and drawbacks of these classifications. In a second part we will try to go beyond this standardisation to examine the role of social economy to create or repair social ties and their impact on the whole society.

Advantages and drawbacks of standard classifications

In this part we will expose and criticize the standard classifications applied to social economy units in the System of National Accounts (SNA 1993 andSNA 2008). We will privilege the national accounting framework and classifications despite the periodical resurgence of the critics 1 . which are common but, to a large extent, unfair. National accounting cannot do more than what it was built for [START_REF] Vanoli | Une histoire de la comptabilité nationale[END_REF]. But it has two precious and irreplaceable qualities: it is unifying and empowering. Unifying because it makes comparable different human activities by their purpose or their location, and allows guaging them together in common quantitative scales. Empowering, because national accounting acts as a kind of grammar, universally accepted, the control of which allows to formalize, to estimate and to make diverse complex realities or badly perceived interdependences understood. Suitably dominated and civilized by decades of use, the national accounting is an instrument open to complementary and qualitative data that humanize the monetary core framework [START_REF] Archambault | La longue marche vers un compte satellite de l'économie sociale[END_REF]. This openness relies in particular in the satellite accounts [START_REF] Joseph | Mesure des performances économiques et sociales[END_REF] 11 Where are Social economy units among the institutional sectors of the national accounts ?

A main classification of national accounts is the repartition of economic actors among institutional sectors. This classification relies firstly on the main economic function of the unit (to produce, to consume, to finance…) and secondly on its main resource (sales, taxes, wages or other incomes). The guidelines of the international System of National Accounts (SNA 1993) as well as the more recent one (SNA 2008) and their application to European countries, European System of Accounts ESA 1995 and 2010, break up social economy units among all the institutional sectors 2 of national accounts according to the following scheme,  Cooperatives 3 and non-profit institutions 4 (NPIs) the income of which comes mainly (more than 50% in ESA 1995) from the sales of the goods or services they produce at the market price are classified as "Non financial corporations" or "Financial corporations according to the kind of their product". These social economy organizations must "continue to be treated as market producers as long as their fees are determined mainly by 1 See in this book the chapter signed by Mertens and Marée.

2 An institutional unit is capable of owning assets and incurring liability on their own behalf. They are centers of legal responsibility and of decision making for all aspects of economic life. These institutional sectors are grouped together to form institutional sectors on the basis of their principal function and main resources (SNA 1993, 2.19-20) 3 Cooperatives are set up by producers for purposes of marketing their collective output. The profits of such cooperatives are distributed in accordance with their agreed rules and not necessarily in proportion to shares held, but effectively they operate like corporations (SNA 2008, 4,41). 4 According to SNA non-profit institutions are legal or social entities created for the purpose of producing goods and services but whose status does not permit them to be a source of income, profit or other financial gain for the units that establish, control or finance them (SNA 2008, 4.83 The "Non financial corporations" sector includes also NPIs serving the interests of businesses and/or funded by them such as chambers of commerce or trade associations (SNA 1993, 4.58, 4.59)  Insurance mutual societies are classified as "Financial corporations" providing life, accident, sickness and other form of insurance to institutional units or groups of units inside the subsector called "Insurance corporations and pensions funds" (SNA 1993, 4.97, 4.98) "

 NPIs providing goods or more often services free or at price that are not economically significant to individual households are classified in the Government sector if they are mainly (over 50% in ESA1995) financed and controlled by government sector's units.

These non market NPIs are classified inside the state government subsector if they are controlled and mainly financed by state ministries or public agencies or inside the local government subsectors if they are controlled and mainly financed by local government units (SNA 1993, 4.62-63). In SNA 2008," The determining factor is whether the unit is part of, or controlled by, government"and the criterium on the origin of the NPIs resources disappeared (SNA 2008 4.25). This major change between the two SNA will lower the NPIs classified inside the Government sector and broaden the NPISH sector.with independent NPIs mainly funded by the Government sector  Small NPIs run by volunteers without a paid staff are included in the Household sector and their current expenses are considered as final consumption while these expenses are considered as intermediate consumption for the NPIs allocated to other sectors (ESA 1995, 2.88)  Other non market NPIs -that is NPIs with at least one employee funded mainly by membership dues, donations or other earned income and/or mainly funded but not controlled by central or local government units -are classified in the Non profit institutions serving household sector (NPISH). This sector is composed on one hand of member serving organizations: professional or learned societies, political parties, labor unions, consumers associations, churches and religious societies, social, cultural, recreational or sports clubs…On the second hand another type of NPISH is composed of organizations created for a philanthropic or public purpose and not to serve the interests of their members; The resources of these charities relief or aid agencies are individual or corporate giving and public or international funding (SNA 1993, 4.65-67). NPISH is therefore a residual sector and many countries do not fill the NPISH accounts because of a lack of the basic statistical information; in this case, the operations of the NPISH units are either confused with those of households or simply overlooked The advantage of the SNA classification of institutional sectors is that all producers and consumers are inside one institutional sector and only one. All economic transactions are described in a sequence of current and accumulation accounts and balance sheets; with balancing items that form important aggregates such as value added, operating surplus, disposable income, saving and net worth.

The first obvious drawback of this classification is that the economic weight of social economy cannot be seized because of its fragmentation among the institutional sectors. The 2008 SNA affords a considerable improvement for NPIs: it recommends that both financial and non-financial corporations will be disaggregated to show non-profit institutions as separate subsectors to facilitate the derivation of a satellite account for NPIs (SNA 2008 4.35). A similar distinction is made for general government sector where NPIs may also be separately identified. Another more general drawback is that economic exchange is privileged over other functions of social economy units: Purely social ties, influence or political role are out of the scope but could be reintroduced by specific indicators in a Social economy satellite account.

To overcome these disadvantages and facilitate the building of a NPIs satellite account, the new SNA 2008 states: "Like the 1993 SNA, the 2008 SNA assigns non-profit institutions (NPIs) to different institutional sectors, regardless of motivation, tax status, type of employees or the activity they are engaged in. Recognizing the increasing interest in considering the full set of NPIs as evidence of "civil society", the 2008 SNA recommends that NPIs within the corporate and government sectors be identified in distinct subsectors so that supplementary tables summarizing all NPI activities can be separately derived" ( SNA, 2008, 4.35 and Annex 3. 17)

What do social economy units produce?

In the analysis of the economic functions of corporations or unincorporated enterprises two international classifications are in use; the International Standard Industrial Classification of economic activities (ISIC) and the Central Product classification (CPC). Generally a product matches with an activity but not always. There is not a one to one correspondence between activities and products: certain activities produce more than one product (join-products) and a product may be produced by using different techniques of production (2008 SNA, chapter 5).

Therefore CPC is more detailed than ISIC (five levels for CPC and four for ISIC).

The International Standard Industrial Classification of economic activities (ISIC)

Corporations and other enterprises including general government entities, NPISH and other social economy enterprises are classified according to their principal activity5 at the lowest level of the ISIC classification. These activities are grouped in industries; an industry gathers enterprises engaged in the same activity at the lowest level of the classification and in similar activities at most aggregated levels. The industries of ISIC (and its implementations to regional areas, NAICS for North America and NACE for Europe) are built according to three criteria, by declining importance:  The physical composition and the stage of fabrication of the good or service  The use of the good or service  The inputs, process and techniques of production Of course bullets 1 and 3 fit more to the production of goods and bullet 2 of services. ISIC and its twin, the Central Product Classification, are frequently revised because they becomes quickly obsolete with technical progress: for example many IT products or e-trade and on-line services were added to the last version ISIC rev 4, presented at the most aggregated level in Table 2. Social economy entities are not everywhere in ISIC. Their position in this classification varies according to countries, but in developed countries, they are found in few industries. In a nutshell, social economy units are nearly non-existent in the B to E; O and S to U industries. They are numerous in the agriculture forestry and fishing, financial and insurance, education, human health and social work and finally arts, entertainment and recreation industries. In other industries, they are rare, but present or absent according to countries.

The main interest of this classification is to compare cross-country the structure of social economy, because the detailed explanatory notes included in every classification are a guarantee that, roughly speaking6 ,all countries allocate the same activities to the same industries. It allows as well comparing the social economy entities with other companies, to calculate their "market share". Table 3 gives an example of this utilisation for France Another one is that the ISIC classification by main economic activity is little adapted to the NPIs the main object of which is of social or societal order as well as to those who defend causes more than they provide services. This classification was indeed designed to describe finely the market economy and they detail more the production of the goods than of theservices. Periodically revised to take into account the appearance of new goods and services under the influence of the technical progress, they remain badly adapted to the nonmarket production of government and the bulk of the NPIs services. The more recent classifications, ISIC Rev 4 and CPC, ver. 2, are in progress comparing to their predecessors, but they remain marked by their origin.

1.2.2. The Central Product Classification (CPC, ver 2)

As said before, CPC is consistent with ISIC, but more detailed, with a large span but not thorough one to one correspondence. CPC ver 2 offers especially an interesting breakdown of the community, social and personal services corresponding to the social work activities in the 932 to 935 codes, presented in table4 Another decisive improvement for the classification of NPIs relies in the breakdown at the finest level of the services provided by "other membership organizations non-elsewhere classified". This residual position (code 9599) is in most of the countries too often plethoric, reflecting as much the maladjustment of the classification as the difficulty of determining the main activity of a multiactive organization or still the laziness of the coder… This breakdown whose interest relies in the five digits subclasses is presented in Table 5. To fill the gaps of the ISIC rev 37 , the international research team of the Johns Hopkins Comparative Non-profit Sector Project was thus brought to test (and adopt because it was efficient) a specific classification in its object: the International Classification of not Profit Organizations (ICNPO). This ad hoc classification, which contains 12 groups and 30 subgroups, fits into ISIC and CPC. This embedment has a double advantage:  According to the uses and specificities of each country, the subgroups can receive titles which make sense in a country: the addition of subgroups of "popular education" or "social tourism" for example, within the group culture and leisure activities makes sense in Nordic and French-speaking countries.  The ICNPO classification is relevant only for the NPIs; if the scope is widened to the whole social economy, its join in ISIC allows to report specific activities of cooperatives and mutual companies: food-processing industries, trade, insurance and financial activities mainly. To experience, this classification mainly based on the nature of services provided showed itself well adapted to its object, because the residual category 12 was void in most of 36 countries which participated in the phase 2 of the project. Indeed the quality of a nomenclature is conversely proportional in the size of the residual category. That is why this classification was adopted, with minor modifications by the Handbook of Non-profit institutions in the System of National accounts published by the United Nations Organisation in 2003.

What kind of functions do social economy entities fulfil?

Functional classifications are proposed in the SNA to identify the purpose or the objectives of non-market producers (central and local governments and non-profit institutions serving households (NPISH), a major part of social economy entities. These classifications show in principle how government and NPISH cope with the needs of the population through the provision of collective goods or private goods with positive externalities.

For NPISH, the classification of the purposes of non-profit institutions serving households (COPNI) would describe the various outlays of NPISH splintered according to the social need fulfilled. Practically most countries do not collect such detailed information on these entities and COPNI is not really in use. It is a pity, because the COPNI of 2008 SNA is more detailed than its predecessor in 1993 SNA and much nearer to ICPNO. A potential advantage of COPNI is the inclusion of the R&D services into their object. Advocacy services have the same treatment. COPNI is also well fitted to the comparison of the respective role of government (through COFOG, the classification of total outlays of government by function8 ) and NPISH in providing collective or quasi-collective goods. It is especially significant when the government units cannot or do not desire to provide these collective or quasi-collective goods/services. The comparison of COPNI with the Classification of individual consumption according to purpose (COICOP), is also feasible and significant in terms of welfare and living conditions of households.

Disadvantages exist as well: a purpose is less objective than a product, even if it is a service product, less material than a good. A purpose has to be declared and the declared purpose can be different of the effective product. Finally, how to deal with multipurpose NPIs? The main activity of a multiactive company can be determined by the part of its value added or turnover (in default employment) devoted to this activity; it is impossible to do the same with multipurpose NPIs and that is why there would be an inflation of the residual category 09, services non elsewhere classified.

Beyond the standard classifications

Measuring the social ties that Social economy units create

The standard classifications are made to serve the national accounts that is to describe all the transactions which take place during one year in a country and contribute directly or indirectly (through intermediate production) to the gross domestic product (GDP) of the country. Therefore they overlook the social ties that market transactions and non-market cash or in kind transfers afford. For social economy entities, of course, these social ties come first because they associate persons contrary to stock companies and corporations. That is why social economy units are the building blocks of the social capital as Putnam showed it [START_REF] Robert | Bowling alone. The collapse and revival of American community[END_REF]. These social ties are rarely measured but if they are, specific classifications are needed according to the governance and the ownership of the social economy unit.

Measuring the membership of Social economy

Firstly, in producers' cooperatives members are generally owners and producers in the same time (but some shareholders are not producers). Secondly, in most cooperatives and mutual insurance companies (consumer cooperatives, cooperative or mutual banks, mutual damage, health or life insurance) and inside those NPIs who work in their members' interest, members are clients and owners at the same time. Thirdly in cooperatives who gather independent workers or unincorporated companies or corporations (such as farmers, craftsmen or traders), the members are either clients for intermediate products or sellers of their own product and they are owners of the cooperative in the same time.

In these three categories this double quality of the members generate social ties by frequent meetings including the annual general meeting of members where the governing body of the organisation is elected and accounts of its activity, results and management. Of course the social ties are weaker in large social economy organizations than in the small ones.

Fourthly and by contrast, the two qualities of member and beneficiary are dissociated in the associations who work for the public interest or the common wealth9 . However social ties are generated between members by the meetings, as in member oriented NPIs, but they are more intense when members become volunteers. Volunteering creates also social ties between volunteers and beneficiaries or clients.

This distinction between these four categories of members could be the beginning of a classification of members. In spite of the interest of having data on their members/owners, the knowledge on the membership is better for NPIs than for cooperatives and mutuals, with some exceptions The knowledge on the membership of NPIs is rather ancient in many developed countries, but the data are rarely comparable cross-country. However the data are comparable in the case of European Union owing to two surveys: the Survey on income and living conditions of households (SILC) or, less reliable, the European values surveyFigure 1.

The breakdown of membership is done according to the socio-demographic characteristics of the members and the industry of the NPI whose they are members. Unfortunately, the ad hoc classification adopted to identify the industry in these regular surveys is rather rough and incomplete or obsolete10

Measuring and classifying voluntary work

Voluntary work is not yet inside the scope of national accounts. As households' services mainly provided by the free of charge work of women mainly, of men in a less extent, the services provided by the volunteers to social economy organizations are outside the border of the production in use. However 2008 SNA gives a theoretical wider definition of production:

"Economic production may be defined as an activity carried out under the control and responsibility of an institutional unit that uses inputs of labour, capital and goods or services... Activities that are not productive in an economic sense include basic human activities such as eating, drinking, sleeping, taking exercise etc., that it is impossible for one person to employ another person to perform instead "(2008 SNA 6.24-25)

Now the production of services by volunteers is indeed carried under the control and responsibility of a social economy organization that uses labour, capital and intermediate goods or services. In addition voluntary work answers the criterion of the third party because we can generally substitute it some paid work [START_REF] Oli | Towards a definition of nonmarket activities[END_REF]. Volunteering is thus situated, as the household work, between the two borders, wide and narrow, of the production.

In addition, none of the following justifications of the SNA 2008 to exclude the own account production of services within households from the narrow borderline of the production is valid for the volunteer work of the social economy organizations:  the relative isolation and independence of these activities from markets,  the extreme difficulty of making economically meaningful estimates of their values,  the adverse effects it would have on the usefulness of the accounts for policy purposes and the analysis of markets and market disequilibria (2008 SNA, 6, 29-30) Indeed, the activities of the volunteers are independent neither from the market of goods and services nor from the labour market. It is not impossible to attribute an economic value in the time of volunteer work, because this work is socially organized and comparable more easily to paid work than the household unpaid work; its monetary valuation is thus less arbitrary [START_REF] Archambault | Mesurer le bénévolat pour en améliorer la connaissance et satisfaire à une recommandation internationale[END_REF]. Finally, the weight of volunteer time is much smaller than the household unpaid time, so it does not modify the labor market equilibrium. In addition, economic and social policy influences obviously the social economy organizations and the volunteers; sometimes even social policies are discussed with the social economy organizations and experimented by them.

Therefore there is a trend to measure voluntary work inside mutual societies and non-profit institutions. Volunteer work is no doubt a major part of the "added value" of these organizations, even if in the most professionalized it is reduced to the volunteering of board or other elected members. Volunteers are frequently members of the organization: one member over two fulfils some voluntary tasks, regularly or occasionally in most countries (SILC 2008) But membership is not a prerequisite: people can volunteer just a few times a year for special events through an organization they are not member of or even give time without pay regularly without being a member of the organization they work for.

As existing surveys on volunteering are neither regular nor comparable, the International Labor Office (ILO) asks in a recent Manual on the measurement of volunteer work to measure it according to the same methodology and the same classifications (ILO; 2011). We will present and criticize the definition, delimitation and classification of voluntary work included in the ILO Manual.

The definition proposed by the ILO Manual is the following:

"Unpaid non-compulsory work; that is, time individuals give without pay to activities performed either through an organization or directly for others outside their own household"11 . (2011 ILO, 3,5).

This international definition is more extensive than that of most of the existing surveys which concern only the organized volunteer work, while the definition of the ILO also includes the direct (or informal) volunteer work, the aid to other persons outside the volunteer's household. But these two forms of volunteer work are clearly distinguished and separated and obviously, only the organized one is of interest here. Volunteer work can be conducted to benefit an assortment of causes, including people, animals, the environment and the wider community. It provides divisible as well as collective goods and services. And volunteering can benefit to other organizations than social economy namely government agencies, local communities, and even private companies outside social economy.

The borderline cases with other activities must be obviously discussed and cut. It is a principle of all classification. The ILO definition specifies firstly that the volunteer work is not compulsory by law or by physical force. Not compulsory according to the law, what excludes for example community works accomplished in substitution of a penal punishment.

On the other hand a strong social pressure does not disqualify the volunteer work.

Secondly, the border between volunteering and leisure bases on the criterion of the third party [START_REF] Oli | Towards a definition of nonmarket activities[END_REF]. To give free lessons of tennis, it is volunteering, because the volunteer can be replaced by a paid coach. To play tennis is leisure, recreation, because nobody can be paid to play instead of the player. The divide is not so clear however still: the criterion of the third party works badly for the most militant and the most charismatic activities. May I pay somebody to demonstrate in the street to defend a cause or visit an ill person in a hospital instead of me? These activities are yet considered as traditional volunteer tasks.

Thirdly the border between volunteering and paid work is easy to make with paid employment because of the existence of a contract and of wage that makes the difference. It is more difficult with free-lance work especially when it begins and brings no earnings. But there are obviously intermediate situations between a wage-earner and a volunteer as well: the too generous reimbursement of expenses, the fringe benefits, the civic voluntary service of the younth indemnified below the minimum wage… It is doubtless necessary to put clearly the border in the simple defrayal on supporting documents.

Another border with own account service production is relevant mainly with direct (or informal) volunteering. But finally, for volunteering through an organization, the borderline with training or education has to be clarified as well. It excludes obviously the unpaid time spent to study because a student cannot pay somebody to study instead of her/him, but unpaid internships or students volunteer work in the social economy organizations or somewhere else is inside the scope of volunteering if it is carried out voluntarily but outside if it is compulsory, that is legally mandated to meet the examination requirement.

The ILO manual recommended approach to measure volunteering is to add a brief volunteer supplement to the Labour force survey, the most frequent and regular data collection program. Therefore, volunteer and paid work can be observed in the same industrial and occupational classification framework.

Because ISIC Rev. 4, and its national and multinational counterparts are the classifications used in labour force surveys, it is recommended as the classification structure to use for identifying the industry in which volunteer work occurs, particularly since ISIC Rev. 4 has incorporated at its finest level much of the detail originally available only in the ICNPO (cf above 1.2). But of course, ICNPO can be used as well and a detailed cross-walk between the two classifications is proposed in the ILO Manual.

The International Standard Classification of Occupations (ISCO-O8) or its national or regional equivalents is recommended by ILO 2011 to classify volunteer work activities. This classification contains four levels, but the two first ones are sufficient for volunteer work.

Hereafter, table 8 gives examples of volunteer occupations associated with ISCO major groups. ISCO-08 will make it possible to fulfil a reasonable degree of comparability in the data collected in different countries despite the various traditions of volunteering What are the advantages and the drawbacks of ISCO? On the positive side, comparability comes first. Secondly crosswalks between occupations commonly performed by volunteers and ISCO are given by the ILO manual and crosswalks with its national or multinational equivalents can be developed. Such indexes are thus key instruments for matching the verbatim responses of the person asked for in surveys to appropriate codes of the classification. Finally ISCO facilitates the task of assigning a monetary value to the volunteer work by making it possible to use the average wage of the performed occupation.

The drawbacks of this classification exist as well. Firstly some occupations commonly performed by volunteers are difficult to classify: it is the case for example of the volunteers of the organizations without paid staff who are fulfilling any task: they lead and manage their organizations and clean up after the meetings. This case is dominant in the bulk of countries. Other examples could be found in the most militant and expressive occupations. Secondly, when using the average wage of an occupation as the shadow wage of a volunteer, we suppose that the productivity of a volunteer and a paid employee is the same. That is obviously false (except the case of volunteering in the same occupation than one's job), because volunteers are generally less qualified than employees and they spend more time to socialize, to create social ties with the beneficiary of the service they provide.

Measuring the impact of SE entities needs to build ad hoc classifications

SE entities are more and more invited to account on their economic, social, environmental and societal impact to their stakeholders: owners, employees, volunteers, central and local public funders, donors… They have to evaluate their performance with other tools than the standard yardstick, the rate of profit. Accountability and evaluation lead to build multi-dimensional indicators included in reference classifications common to the organizations working in the same field, the same area or competing for the same bidding offer.

The useful classifications have to be built in partnership between the organizations and their partners. These ad hoc classifications of rules and targets can be embedded into standard classifications but it is not a necessary and sufficient condition, because cross-national comparison is no more an issue while it is benchmarking with organizations working in the same industry or towards the same public.. Of course these classifications are more normative than positive, but the indicators that observe the application of a rule or the completion of an action are positive. A good indicator has to be relevant to the purpose of the social economy organisation, simple to be understood by the stakeholders, including the volunteers, and calculated in the same way over a span of time long enough to present a reliable evolution.

Hereafter Table 9 shows how these indicators could be articulated to measure the social utility (or public interest) of social economy organizations (Gadrey, 2003) according to five multidimensional main themes, declined at two levels, the finest one consisting of one or more indicators.

Conclusion

Classifications structure our vision of the reality. As Giddens pointed out structures are rules and resources at the same time [START_REF] Anthony | The constitution of society, An outline of the theory of structuration[END_REF]. Social economy has to stick to standard classifications when cross-country comparison is at the agenda and has to be innovative in classifications as in other fields, especially when it has to report on its specificity, its values, its volunteering and its alternative way of governance. But one should not ask classifications more that they can give.

As Michel Foucault said it in the quotation in epigraph of this chapter, a classification is "an institution of an order among things". It is a way to put words on things (Foucault, 1966). As every institution it relies on the temporary agreement of the stakeholders (Desrosieres and Thevenot,2002) . But as Michel Foucault showed it in another book, an agreement or a rule has a self-disciplinary effect (Foucault, 1977) and the artefact may become a taken for granted truth.

The history of classifications [START_REF] Bernard | Essai sur les nomenclatures industrielles[END_REF][START_REF] Desrosières | La Politique des grands nombres : Histoire de la raison statistique[END_REF], Desrosières and Thevenot, 2002), and to a lesser extent this chapter, show how the classifications, these artefacts, shape our vision of the economic and social reality and overlook a part of this reality, namely social economy 12 . To study the history of classifications or to multiply the classifications existing in the same time, each one adapted to its purpose, is a way to show how classifications are discretionary, as three statisticians express it in the following quotation: "The economist is not interested, if we allow us this image, in the glasses through which he sees the economy: he is interested on the other hand strongly in what he sees. To see the glasses which we wear, it is necessary to remove them at first, and it blurs the view: also, the discussions about the classifications bring to consider as fragile, modifiable, finally rather doubtful aggregates from which the solidity was guaranteed up to there. Outlines, clear previously, become unpleasantly fuzzy » [START_REF] Bernard | Essai sur les nomenclatures industrielles[END_REF].



  A -Agriculture, forestry and fishing  B -Mining and quarrying  C -Manufacturing  D -Electricity, gas, steam and air conditioning supply  E -Water supply; sewerage, waste management and remediation activities  F -Construction  G -Wholesale and retail trade; repair of motor vehicles and motorcycles  H -Transportation and storage  I -Accommodation and food service activities  J -Information and communication  K -Financial and insurance activities  L -Real estate activities  M -Professional, scientific and technical activities  N -Administrative and support service activities  O -Public administration and defence; compulsory social security  P -Education  Q -Human health and social work activities  R -Arts, entertainment and recreation  S -Other service activities  T -Activities of households as employers; undifferentiated goods-and servicesproducing activities of households for own use  U -Activities of extraterritorial organizations and bodies Source: SNA 2008, Annex 1

Box 1 : social economy in national accounts Social economy organizations Institutional sector of SNA 1993

	Cooperatives and mutual banks, savings and loans
	and other SE financial organizations	Financial corporations, S12
	Insurance and health mutual societies	Financial corporations, S12
	Non market NPIs [mainly funded and] controlled
	by Government units	General Government, S13
	NPIs with no employee	Households, S14
	NPIs not elsewhere classified	Non-Profit	Institutions	Serving
		Households (NPISH), S 15
	Source: Archambault and Kaminski, 2009
	In national accounts, social economy is thus dispatched in the five institutional sectors, as
	summarized in Box 1
	Non financial cooperatives	Non financial corporations, S11
	Market Non-profit institutions	Non financial corporations, S11

Table 2 International Standard Industrial Classification of All Economic Activities, Rev

 2

. 4 http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=27&Lg=1

Table 3 Employment in social economy as percentage of total employment by industries, 2010

 3 In this table we can see that social economy mainly produce services in France as everywhere. The French production of goods by cooperatives is concentrated in agriculture and food manufacturing. Cooperatives and mutuals are very active in the financial and insurance industry while non-profit institutions have a significant share of the service industries connected to the welfare state : education, human health and mainly social work activities. Arts, entertainment and recreation services are mainly run by associations To produce services, the main input is work. Paid employment is clearly classified among labor intensive service industries, but volunteer work, a major input for associations, foundations (and of minor importance in mutuals and cooperatives) is overlooked. As volunteering is not proportional to paid employment among the diverse activities, the structure of activities of the NPIs and therefore of the social economy is biased. It is a first disadvantage of this classification.

	Industries

Source : INSEE-CLAP Tableaux harmonisés de l'économie sociale 2010

Table 4 Breakdown of community, social and personal services Hierarchy

 4

 Section: 9 -Community, social and personal services  Division:

93 -Human health and social care services Breakdown: This

Division is divided into the following Groups:  931 -Human health services  932 -Residential care services for the elderly and disabled  933 -Other social services with accommodation  934 -Social services without accommodation for the elderly and disabled  935 -Other social services without accommodation Source: SNA 2008, Annex 1

Table 5 Breakdown of the Services furnished by other membership organizations Hierarchy

 5 95991 -Services furnished by human rights organizations  95992 -Services furnished by environmental advocacy groups  95993 -Other special group advocacy services  95994 -Other civic betterment and community facility support services

 Section: 9 -Community, social and personal services  Division: 95 -Services of membership organizations  Group: 959 -Services furnished by other membership organizations  Class: 9599 -Services furnished by other membership organizations n.e.c. Breakdown:   95995 -Services provided by youth associations  95996 -Grant-giving services  95997 -Cultural and recreational associations (other than sports or games)  95998 -Other civic and social organizations  95999 -Other services provided by membership organizations Source: SNA 2008, Annex 1 1.2.3 The International Classification of Non Profit Organizations

Table 6 . International Classification of Non Profit Organizations (ICNPO)

 6

	1 Culture and recreation
	11 Culture (media and communication, arts, performing arts; museum, learned societies)
	12 Sports
	13 Recreation ( recreation , social tourism, service clubs)

Philanthropic intermediaries and voluntarism promotion 9 International activities (exchange programs

  , development assistance and relief) 10 Religion (religious congregations and associations) 11 Business

and professional associations and labor unions 12 Not elsewhere classified

	Source: Salamon, Sokolowski and associates, 2004

Table 7 . Classification Of the Purposes of Non-Profit Institutions Serving Households (2008 SNA)

 7

 01 -Housing

Table 8

 8 Examples of volunteer occupations associated with ISCO-08 major groups

	ISCO major group	Examples of volunteer occupations
	1. Legislators, senior officials	Lead or manage a non-profit organization,
	and managers	association, union, or similar organization.
		Serve on a board of directors or management
		committee of a social economy organization
		Policy and research managers
	2. Professionals	Develop emergency plans for a community
		Provide pro bono legal or dispute resolution services
		Manage a programme or organisation designed to
		collect and analyze data for public information
		Provide professional social work and counselling

Table 9 . Classification of a multidimensional social utility Theme 1 Global criteria Elementary criteria Social utility with strong economic component Created or saved economic wealth

 9 Crawford, summarised by Elbers and Schulpen in table10 (Ostrom and Crawford, 2005, Elbers and[START_REF] Willem | Corridors of power: the institutional Design of North-South NGO partnerships[END_REF]

			Lesser collective cost
			Indirect reduction of costs
			Contribution to the rate of activity
			Contribution to the economic
		Territory	dynamism Liveliness of the community, the
			district
	Theme 2	Global criteria	Elementary criteria
			Reduction of social inequalities
			Actions towards disadvantaged
	Equality, human development and sustainable development	Equality, development of "capabilities"	public Insertion of the long term unemployed in the employment
			Professional equality men/ women
			Modulated pricing for the services

Table 10 Classification of the rules and content of a partnership Type of rule Content Key questions Boundary Scope Position Choice Aggregation Information Pay-off

 10

	Entry and exit
	Outcomes
	Roles
	Actions
	Decision-
	Making

In case of multi activity, the principal activity is the determined by the largest part of value added (or by default the largest part of the turnover or the employees)

Car dans le cas de comparaison bilatérale ou multilatérale, il faut observer les interprétations des statisticiens des normes du SCN dans chaque pays.

ISIC rev 3 was in use before ISIC rev 4. The later benefitted of the critics addressed to the former

United Nations Statistics Division, Classification of the Functions of Government, online : http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=4&Top=2&Lg=1 (site visited on July 26 2013).

Foundations have no members.

For instance in SILC 2005: Health or social services organizations, charities; sports;culture; recreation; advocacy and lobbying associations; eldrly clubs; unions and professional associations(SILC 2008)

UN, 2003, 4, 45 gives the following definition: "work without monetary pay or legal obligation provided for persons living outside the volunteer's household"

See in this book the chapter signed by Artis, Bouchard and Rousselière.

Right in the housing Remedial courses for children in trouble Resumed self-confidence International solidarity, human development

Actions for the development and struggle against poverty Defense of human rights

Sustainable development

To improve the quality of the natural environment To protect natural resources

More democratic and alternative governance

Rules of internal democracy and joint participation

Free membership: free entrance and free exit Volunteers professinalism

Associative professionalism

Cooperative internal training Social and wage acknowledgement

Internal and external trainings Source: [START_REF] Edith | Connaissance des associations[END_REF], adapted from Gadrey, 2003. The partnerships between the central or local governments and social economy organizations are institutional arrangements regulated by rules, formal and informal, and procedures. Several authors developed typologies to analyse these institutional arrangements, the most famous of which is the classification of the rules governing the partnership by Ostrom and


Keywords: Remote sensing, large river floodplain forest, landscape, Congo River basin, PALSAR, MODIS, ICESat/GLAS

Wetlands represent 6% of the Earth's land cover surface. They are of crucial importance in the global water cycle and climatic dynamics. Nowadays, wetlands are the most threatened land cover type, nevertheless their spatial distribution and ecological functions are poorly documented. Despite the need for more detailed information, wetland mapping is a rarely activity. Few data are available mainly because of the complexity of obtaining good field data. We therefore propose a method based on multi sensor imagery analysis, to characterize land cover patterns of the second largest wetland area of the world (The 'Cuvette Centrale' of the Congo River basin). Time series of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) images are used to map land cover types based their phenological differences. Flooded areas in the Congo Basin have been mapped during different seasons using L-band synthetic aperture radar (PALSAR) imagery. The associated model has been improved upon by the addition of elevation data as well as mean canopy heights acquired with LIDAR (Light Detection and Ranging) data. The result of this study is the first detailed spatial distribution of four forested wetland types within the 'Cuvette Centrale' of the Congo River basin. This study demonstrates that the spatial organization of the floodplain landscape depends on the extent of flooding. The results also show that land cover phenology is closely related to the time period of flood and solar intensity for this region, similarly to what is observed in the extensive floodplain of the Amazon basin.

Wetlands and floodplains, which cover approximately 6% of the Earth's ice-free land surface (Matthews & Fung, 1987;Organisation for Economic Cooperation and Development (OECD), 1996), have many economic uses and provide an abundance of ecological services (Frazzier, 1999;Gosselink and Mitsch, 2000). Wetlands have a substantial impact on the alteration of flooding, flow rates, sediment stabilization, water quality, and groundwater recharge and discharge [START_REF] Maltby | Wetland management goals : wise use and conservation[END_REF][START_REF] Bullock | The role of wetlands in the hydrological cycle[END_REF]. Wetlands also play a key role in biogeochemical cycles, including the methane (CH 4 ) and carbon dioxide (CO 2 ) cycles in particular (Matthews, 2000;Shindell et al., 2004), and therefore exert influence on climate dynamics (RAMSAR convention, 1971). Wetlands are reservoirs of complex biological processes [START_REF] Arfi | Processus d'édification des ressources naturelles en zones inondables tropicales[END_REF], hot spots of biodiversity [START_REF] Keddy | Wet and wonderful: the world's largest wetlands are conservation priorities[END_REF], and they are also crucial in the life cycle regulation of many fauna and flora species [START_REF] Hey | Flood reduction through wetland restoration: the upper Mississippi river basin as a case history[END_REF]. Wetlands also provide habitats for fishing and hunting and may contain wood suitable for harvesting or exploitation (Callicott & Frodeman, 2009). Despite the provision of multiple ecosystem services, wetlands appear to be the most threatened of all landscape types (Callicott & Frodeman, 2009). The threat to wetlands is testified by the loss of half of the world's wetlands during the last century. This decrease was mainly due to anthropogenic pressure and climate change [START_REF] Mitsch | Wetlands 4th ed[END_REF][START_REF] Murdoch | Potential effects of climate change on surfacewater quality in north america[END_REF][START_REF] Maltby | Waterlogged wealth: why waste the world's wet places ?[END_REF]. International and local institutions promoted the conservation of wetlands since the RAMSAR convention ratified in 1971. The emphasis on nature conservation increased over the last twenty years (Frazzier, 1999;Convention for Biological Diversity, 1992).

Although many research efforts have attempted to characterize the large river floodplain of the Congo basin ecosystems so as to bolster their protection, the lack of knowledge regarding their spatial distribution and their ecological functions has hindered these efforts significantly ( , 2005). The forested wetlands within the 'Cuvette Centrale' of the Congo River basin, the second largest basin in the world after the Amazon basin, are poorly documented. Various projects at different levels of maturity proposed for example to transfer water from several tributaries of the Congo River into lake Chad (Bonifica, 1982(Bonifica, , 1984(Bonifica, and 1985;;[START_REF] Misser | RD Congo: economy[END_REF]Turton & Ashton, 2004;Biggs & Williams, 2001). These water transfers would produce substantial modifications to the hydrological regime in the 'Cuvette Centrale' and hence would cause a significant threat to the forested wetlands in the Congo River basin. The impacts of such a water transfer is also likely to be amplified by climatic change within the region [START_REF] Ipcc | Contribution of working groups I, II and III to the fourth assessment report of the intergovernmental panel on climate change[END_REF]. The risk of a degradation or even the destruction of forested wetlands resulting from a significant reduction in high water levels due to either natural or anthropogenic changes is particularly important. The lack of ecological as well as hydrological information render corroborated decision making difficult.

In this respect, remote sensing (RS) offers a unique opportunity to map and describe landscape units and forest types of areas where direct access is difficult. The spectral bands provided by RS sensors allows the investigation of different aspects of these tropical swamp ecosystems. Optical data are often used to characterize types of vegetation and deforestation (Vancustsem et al., 2009;[START_REF] Peixoto | Spatial and temporal dynamics of river channel migration and vegetation in central Amazonian white-water floodplains by remote-sensing techniques[END_REF]Achard et al., 2001;Wittmann et al., 2002;[START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF], and these have also been used in tropical areas even if dense cloud cover often makes characterization difficult. Radar RS observations complement optical data because they provide valuable information even when monitoring tropical land surfaces, which often have high cloud coverage. In addition, L-band SAR sensors are very useful for the monitoring inundated wetlands [START_REF] Rosenqvist | The use of spaceborne radar data for inundation modelling and subsequent estimations of trace gas emissions in tropical wetland areas[END_REF][START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels Remote[END_REF]. Light Detection and Ranging (LIDAR) has also been applied in remote areas for the investigation of topography and maximum canopy height in flat areas [START_REF] Lefsky | Carte géologique du Zaire 1/2000000[END_REF]. In 2010, Ballhorn et al. used ICESat/GLAS data (a space-borne LIDAR system) to estimate elevation and canopy height in a peat-land region of Kalimantan, Borneo. Various estimates of forests biomass were derived from these results. The combination of information provided by both classes of sensors allowed us to study many aspects of the tropical forest habitat, including not only its spatial distribution but also its structure and certain ecological functions and processes. Two previous studies using RS data aimed to map the wetland area within the 'Cuvette Centrale' of the Congo River basin. The first was conducted by De [START_REF] De Grandi | Flooded Forest Mapping at Regional Scale In the Central Africa Congo River Basin: First Thematic Results Derived by ERS-1 and JERS-1 Radar Mosaics, Second Retrieval of Bio-and Geo-physical Parameters from SAR data for Land Applications[END_REF] and used a single sensor approach and JERS-1 L-band SAR images to map flooded and non-flooded forest areas. The second study, was conducted by [START_REF] Bwangoy | Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices[END_REF] using a multi-sensor approach, to produce a 'wetland probability map' for the 'Cuvette Centrale' with a higher accuracy than the map created in the first study.

Though the forest types present in the 'Cuvette Centrale' have been well documented [START_REF] Evrard | Recherches écologiques sur le peuplement forestier des sols hydromorphes de la Cuvette Congolaise Centrale[END_REF], their spatial distributions have been poorly described due to difficulties of access to the floodplain area, lack of infrastructure and political instability. Hence, the accurate mapping of the spatial extension of the forest ecosystems within the 'Cuvette Centrale' is a strong requirement for enabling the monitoring of land cover changes related to modifications in rainfall and the associated hydrological regimes of the Congo River basin. Our objective is to document the spatial distribution and functional description of the wetland land cover types, using a multi-imagery remote sensing approach. This approach is fundamental for a continental water resources assessment for natural and anthropogenic interpretation.

Material and Methods

2.1.Study area

The Congo River basin harbors the second largest forest area in the world preceded only by the Amazon River basin. The study area under consideration, the 'Cuvette Centrale', is located in the center of the Congo River basin. It extends along the equator from 22° longitude west to 16° longitude east and from 3° latitude north to 3° latitude south (Fig. 1). 6 provided a map of this wetland area using a multi-sensor approach. From this study, a mask of the forested wetland was used to delineate the area of the current study (Fig. 1).

[Figure 1]

Physiography

The 'Cuvette Centrale' is surrounded by mountains and plateaus and is the remnant of a lake that occupied the area during the Tertiary geological period. It dried out as a consequence of the reduced Congo downstream riverbed through the Crystal Mountains towards the Atlantic Ocean [START_REF] Robert | Le Congo physique. Comité Spécial du Katanga[END_REF]. Sandy, lacustrine quaternary sediments cover the base of the area where forested wetlands are located (Lepersonne, 1974;Moukolo et al., 1993;[START_REF] Censier | Caractérisation de processus d'érosion régressive par analyse sédimentologique comparée des sables du chenal et des barres du cours inférieur de l'Oubangui (République Centrafricaine, Congo, Zaïre)[END_REF]. The topography is almost without height differences, and the average slope is less than 7 cm/km between Kisangani and Kinshasa [START_REF] Devroey | Annuaire hydrologique du Congo belge et du Ruanda -Urundi 1956[END_REF] over the last 50 km of the Kasai [START_REF] Devroey | Le Kasai et son bassin hydrographique[END_REF] and over the last 450 km of the Ubangi downstream of Dongo [START_REF] Yayer | Caractéristiques hydrographiques de l'Oubangui[END_REF]. Within the 'Cuvette Centrale', the Congo River and its tributaries are bordered by discontinued levees that have been covered with flooded forest [START_REF] Yayer | Caractéristiques hydrographiques de l'Oubangui[END_REF]. The floodplain is not limited laterally by these levees, and floods can extend over very large areas through networks of natural channels during high water seasons.

Rainfall

The average rainfall in the 'Cuvette Centrale' ranges between 1400 and 1800 mm. year -1 , with an average of 1655 mm.year -1 in Mbandaka (0.03 N, 18 E) (Fig. 2). Potential evapotranspiration is 1280 mm. year -1 (FAO, 2000). More than 20% of the rainfall is available for runoff, as observed in the discharge records in Brazzaville [START_REF] Briquet | Proceedings of "Quatrièmes journées hydrologiques de l[END_REF]Shahin, 2002). Because of the topographic barrier around the 'Cuvette Centrale', a large proportion of rainfall is believed to be of local origin; it is thought to follow a closed cycle consisting of evaporated rainfall, condensation, and then again, repeated rainfall [START_REF] Robert | Le Congo physique. Comité Spécial du Katanga[END_REF].

Hydrological regimes

At the outlet of the 'Cuvette Centrale', only 6% of the discharge measured in Kinshasa and Brazzaville originates from the upstream part of the Congo River basin, whereas 24% comes from the 'Cuvette Centrale' itself. 23% comes from the Kasai River sub-basin, and less than 20% comes from the Ubangi [START_REF] Briquet | Les écoulements du Congo à Brazzaville et la spatialisation des apports[END_REF]). In the 'Cuvette Centrale' between Kisangani and Mbandaka, the flow rates of the Congo River are characterized by a bimodal flooding pattern, consisting of high water in November and December, a secondary peak in April and May, low water in August and a secondary minimum in February and March. In other words, there is a 1 to 2 month time lag between flooding and rainfall seasonality (Fig. 2). Downstream of the Congo River, at Mbandaka, water is received from its two main tributaries, the Oubangi and the Kasai Rivers. The Oubangi extends its watershed over the two Congo tributaries and into the Central African Republic. The Oubangi's flow rate is characterized by high waters reaching their maximum in November while low water levels occur in March [START_REF] Callède | Le bassin de l'Oubangui. Coll. Monographies hydrologiques[END_REF]. The Kasai is the main tributary of the Congo River in the southern hemisphere. The watershed of the Kasai extends across the southern hemisphere into the Democratic Republic of the Congo and Angola. The Kasai reaches the Congo River at the downstream edge of the region where floodplain forest can be found. High water regimes generally occur in April, while low water regimes occur during August, although there is a small secondary high water period during December and January. In the downstream part of the river basin, waters are higher in December than in April due to the influence of the Congo River.

Within the downstream portion of the river, the waters are higher in December than in April due to the influence of the Congo's flow rate and the contribution of the Kasai tributaries that collect waters from Lake Mai-Ndombe in the equatorial domain [START_REF] Devroey | Le Kasai et son bassin hydrographique[END_REF]. The typical flood amplitude in the 'Cuvette Centrale' is between 3 and 5 m for the rivers [START_REF] Devroey | Annuaire hydrologique du Congo belge et du Ruanda -Urundi 1956[END_REF] [START_REF] Yayer | Caractéristiques hydrographiques de l'Oubangui[END_REF]. The volume of water that is potentially stored in the 'Cuvette Centrale' under the tree canopies can equals approximately half of the annual discharge from the Oubangi to the floodplain extending from the Congo River to the Likouala-aux-Herbes (Yayer,1951).

Vegetation

The 'Cuvette Centrale' is primarily covered by forested wetland subjected to the flood pulse of the Congo River basin (Junk et.al., 1989). The vegetation in this area is poorly studied (see [START_REF] Evrard | Recherches écologiques sur le peuplement forestier des sols hydromorphes de la Cuvette Congolaise Centrale[END_REF] for a review of the botanical inventories), as is the flood height and the flood length of each vegetation types. Indeed, the geographical conditions, the lack of infrastructure and the political instability make this area very difficult to access.

Four forest types have been identified, but not spatially mapped in detail. The typology of the four forest types was establish taking into account Evrard's inventories and the typology developed by [START_REF] Junk | A classification of major Naturally-Occuring Amazonian Lowland Wetlands[END_REF] for the Amazonian floodplain forest. 1) Forests subjected to relatively stable water levels (Entandrophragma palustre, Coelocaryon botryoide and Raphiales spp. palm forests);

2) Forests subjected to seasonal short lasting flood pulse of low amplitude (Guibourtia deumeusei and Oubanguia africana);

3) Non-flooded forests, characterized by higher and bigger trees [START_REF] Evrard | Recherches écologiques sur le peuplement forestier des sols hydromorphes de la Cuvette Congolaise Centrale[END_REF]) and a more important diversity of tree species than the other groups and; 4) Forest subjected to seasonal flood pulse, located along the river (Pachystela longepedicellata and Pseudospondias microcarpa) [START_REF] Léonard | Contribution a l'étude des formations ripicoles arbustives et arborescentes de la region d'Éala[END_REF].

These forest types can be distinguished according to their stand structure and floristic composition. (Huete et al., 2006). The values of EVI index have been derived from the multispectral measurements of the MODIS sensor onboard the NASA Terra satellite according to the following equation provided by [START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF]:

( ) ( ) L BLUE C RED C NIR RED NIR G EVI + × - × + - × = ρ ρ ρ ρ ρ 2 1 (Eq. 1)
Where ρNIR [841-876 nm], ρRED [620-670 nm] and ρBLUE [459-479 nm] are the reflectances of near infrared, red and blue bands respectively, G is the gain factor, L is the canopy background adjustment (that addresses non-linear, differential NIR and red radiant transfer through a canopy) and C1, C2 are the coefficients of the aerosol resistance terms (which use the blue band to correct aerosol influences in the red band). The coefficients used in the MODIS-EVI algorithm are: L=1, C1 = 6, C2 = 7.5, and G (gain factor) = 2.5. This dataset is available at http://reverb.echo.nasa.gov.

The EVI value is considered to be closely related to canopy structure and architecture. The EVI also provides improved sensitivity compared with other vegetation indices for high biomass regions, such as the tropical forest (Myneni et al., 2007;[START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF].

To reduce atmospheric bias and remaining 'contaminated' pixels after classical atmospheric corrections (see [START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF]), time-composited EVI imagery (2000[START_REF] Parolin | Submerged in darkness: adaptations to prolonged submergence by woody species of the Amazonian floodplains[END_REF] has been used.

According to Pennec et al. (2010), an average for each 16-day period is calculated over the ten-year time span examined in this study. An unsupervised k-means classification is then performed for yearly averaged EVI imagery. To determine the optimal number of land cover classes, we used the Thorndike index (Thorndike et al., 1953):

S k = ∑∑ ∑ * * max max max 1 1 2 1 1 k = k i = i ik k = k k σ n N (Eq. 2)
Here, N represents the total number of pixels, n k is the number of pixels by class, k is the class index varying between 1 and k max , i is the index of the 16-day period (from 1 to i max =23), and σ ik 2 is the variance of the class k for the period i. The optimum number of classes is obtained using the classes that correspond to the inflection point of the S k index. A comparison was established between two areas of the 'Cuvette Centrale', respectively the Eala and Lukulela areas, regarding the phenology of the EVI index and ancillary data (Fig . 1). The ancillary data include rainfall (mm), light intensity (cal g/cm²/day) [START_REF] Evrard | Recherches écologiques sur le peuplement forestier des sols hydromorphes de la Cuvette Congolaise Centrale[END_REF]) and the hydrology of the nearest river recorded with gauging data [START_REF] Devroey | Annuaire hydrologique du Congo belge et du Ruanda -Urundi 1956[END_REF]Bultot, 1971;[START_REF] Laraque | Banque de données hydrologiques des affluents du fleuve Congo-Zaïre et informations physiographiques[END_REF].

F o r R e v i e w O n l y 11

Estimation of tree heights using GLAS/ICESat L2 Global Land Surface Altimetry data

The Geoscience Laser Altimeter System (GLAS) instrument of the Ice, Cloud, and Land Elevation Satellite (ICESat) is a full waveform sensor consisting of a 1.064 µm laser at 40 Hz that has been operational from 2003 to 2010. The GLAS sensor acquired the returned energy of a footprint of 0.70 m and creates a waveform enabling the measurement of terrain elevation and the vertical distribution of vegetation density. This dataset includes laser footprint geo-location and reflectance as well as geodetic, instrumental, and atmospheric corrections for the range measurements. The dataset is available at http://reverb.echo.nasa.gov.

The LIDAR technique has been used to detect the topography [START_REF] Blair | Modeling laser altimeter return waveforms over complex vegetation using high-resolution elevation data[END_REF] of the "footprint" and to estimate the maximum height of the canopy for each forest type. Waveform analysis enables one to approximate the maximum height of the canopy for flat terrain [START_REF] Lefsky | Carte géologique du Zaire 1/2000000[END_REF][START_REF] Neuenschwander | Characterization of ICESat/GLAS waveforms over terrestrial ecosystems: Implications for vegetation mapping[END_REF] as well as topographical elevation (Ballhorn, et al., 2010).

The interpretation of these data is more complex for an area with a slope, where vegetation and ground reflectance are mixed in the signal return [START_REF] Chen | Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry[END_REF].

Two kinds of products have been used to study the waveform: 1) GLA01 as the raw product;

2) GLA14 which provides an approximation of beginning and end signals by fitting the complex waveform from GLA 1 into six Gaussian maximum peak distributions [START_REF] Zwally | Updated current year[END_REF]Harding & Carabajal, 2005).

From these Gaussian peaks, vegetation structure and topography could be extracted. All ICESat/GLAS data available for the 'Cuvette Centrale' from February 2003 to January 2009 were procured. The GLA14 data available were converted into shape vector files to enable overlays with EVI based classifications and ICESat tracks. This makes it possible to select corresponding MODIS footprints. SAR imagery is sensitive to the presence of water located under the vegetation canopy since the backscattered signal is significantly increased due to the radar double-bounce returns from the water and vegetation surfaces. The ScanSAR imagery has been pre-processed with respect to radiometric calibration, incidence angle normalization and speckle reduction. The imagery has first converted into sigma nought (σ 0 ) in dB [START_REF] Alos/Palsar | Level 1 product[END_REF]. The angle of incidence effect on the backscattering coefficients have been corrected using the procedure developed by [START_REF] Baghdadi | Evaluation of C-band Sar data for wetlands mapping[END_REF]. The normalized backscattering coefficient is given by:

σ n °= σ °( θ i ) F ( θ i ) (Eq. 3)
Here, σ°(θ i ) represents the backscattering coefficient (dB) at the incidence angle θ i .

In linear units, σ °( θ i ) is given by the relation β cos α θ (Ulaby et al., 1982;[START_REF] Beauchemin | Modelling forest stands with MIMICS: implications for caliration[END_REF]. Thus, F(θ) is of the form β cos α θ , and the normalized backscattering coefficient (β) is given [START_REF] Baghdadi | Evaluation of C-band Sar data for wetlands mapping[END_REF]. For each date of acquisition, the coefficient α is equal to the slope of the linear relationship between σ ° (dB) and 10 log [cos(θ )] , which has been established using ten homogeneous regions of interest (ROI's) chosen in the non-flooded forest and at an incidence angle between 18° and 43°. A Lee filter (Zhenghao & Fung, 1994) has subsequently

F o r R e v i e w O n l y 13 as σ °( θ i ) / cos α θ i
been applied to a window of 7*7 pixels to reduce speckle. Similar to EVI, an unsupervised k-means classification was performed for an optimum number of classes. This unsupervised classification was applied to a temporal series of PALSAR scenes, which represented different periods of flooding in the study area. To study the dynamics of inundation, each class was averaged.

A χ²-test was used to establish the relationship between an EVI class and a PALSAR class.

Results

EVI classification for forested wetland types

Based on the Thornlike index presented in Fig. 3, the optimum number of EVI-based forest classes has been determined using the elbow effect (Tibshirani et al., 2001). An unsupervised classification for the EVI average year was performed, and the results were clustered into four classes (Fig. 3).

[Figure 3]

The classification result is shown in [Figure 4]

The EVI classification has been compared and validated using two different datasets: the topographic map of Northern Congo (IGN, 1960) and the soil and tree elevations derived from the GLAS measurements. Thematic accuracy of the EVI vegetation map was assessed using a χ² test and a contingency table according to the IGN topographic map and the spatial distributions of the EVI classes. These results are shown in Fig. 5. EVI class footprints and transitions between EVI classes have been visually photo-interpreted using aerial photographs (shape, location and map).

[Figure 5] GLAS data allow for the acquisition of average elevations and maximum canopy heights for three EVI classes (Table 1). Class EVI-1 does not produce results due to the lack of ICESat-GLAS data for this type of forest. At least one EVI class elicits elevations significantly different from the others (ANOVA, p-value < 0.5). On average, the EVI-2 class is associates with the lowest elevations and is characterized by the lowest maximum canopy height. On average, the elevation of the EVI-4 class is higher than that of the other classes and demonstrates the largest maximum canopy height.

[Table 1] Time series analysis suggests that differences between EVI classes are not linked with phenological patterns (Fig. 6). Although the EVI classes demonstrate a similar temporal evolution, they display different magnitudes of EVI values. Each forested wetland type is characterized by a leaf phenology increasing as the class type changes from EVI-1 to EVI-4 (Fig. 6). These variations are mostly based on the intensity of the greenness related to the EVI index.

[Figure 6]

PALSAR-derived inundation classification

The optimum number of classes has been determined using the Thornlike index. For the series of PALSAR images of the 'Cuvette Centrale', the optimum number of classes is five (Fig. 7). One class represents the borders of the image, and its value is equal to zero for each of the PALSAR images examined. types have different greenness intensities but the same seasonality. Thus, our hypothesis is that the discrimination of forested wetland types according to the EVI index is based on the stand structure of each type. The density of the cover has also been shown to be significant, and the EVI index value was shown to be high (Huete et al., 2006). The structure is clearly identified on the stereo aerial photography and is related to the intensity of the EVI index [START_REF] Pennec | Characterization of tropical forests phenology in French Guiana using MODIS time-series[END_REF]. Class EVI-2 consists of sparse stands of forest canopy, whereas class EVI-4 has a closed stand forest structure composed of different understory strata. In general, a forest type with a multi-layered stratified canopy and a closed crown cover generates a greater EVI signal than a single-layered canopy with an open crown cover [START_REF] Gond | Broad scale patterns of forest landscape in Guiana Shield rain forests[END_REF].

The map obtained for this study offers a good spatial and thematic accuracy compared to the IGN topography map from 1960. Moreover, the vegetation exhibits a marked zonation related to the flooding regime (relationship with PALSAR dataset) as the floodplain vegetation of the Amazon and Okavango basins (Parolin et al., 2010). The GLAS data permit the evaluation of the average elevation and the maximum canopy height for three forested wetlands, which is in agreement with the Evrard and Leonard (1947) indications for emergent trees of seasonal flooded forest. However we notice that it is possible that GLAS data overestimate the maximum canopy height because in the Amazon floodplain, the forests subjected to relatively stable water level reaches an average height of 15-20 meters [START_REF] Scarano | Plant establishment on flooded and unflooded patches of a freshwater swamp forest in Southeastern Brazil[END_REF]. et al., 1995;[START_REF] Wang | Understanding the radar backscattering from flooded and non-flooded Amazonian forests: Results from canopy backscatter modeling[END_REF]. Because the backscattering coefficient has low temporal dynamics (less than 1.5 dB), this signature can be attributed to the forest subjected to relatively stable water levels. PALSAR-4 exhibits time variations similar to those of PALSAR-2 (non-flooded forests) during low water level periods (March/April) and variations similar to those of PALSAR-3 (forest subjected to relatively stable water level) during high water level periods (October/January). This class is associated with forests subjected to seasonal short lasting flood pulse, with low amplitude (<5m, [START_REF] Yayer | Caractéristiques hydrographiques de l'Oubangui[END_REF]. At the end of the year, the PALSAR-4 backscatter is lower than the PALSAR-3 return, which is likely to be due to the tree density of forests subjected to seasonal flood pulse compared to the low density of forests with stable water level.

The results of this study have been partially validated using the Congo River and Oubangui water levels in the 'Cuvette Centrale'. These water levels represent the increased water levels at the end of the year (November-December) and the lower water levels in March and April from the PALSAR-4 profile (Fig. 8). In terms of the dynamics and backscattering values, these results are consistent with those recently published for forest units, including flooded and non-flooded forests, using ALOS PALSAR data for the classification of different types of tropical and temperate forests (Thiel et al., 2009;[START_REF] Ardila | Angular backscatter variation in L-band ALOS ScanSAR images of tropical forest areas[END_REF][START_REF] Evans | Using ALOS/PALSAR and RADARSAT-2 to Map Land Cover and Seasonal Inundation in the Brazilian Pantanal[END_REF].

The comparison of the EVI and PALSAR classifications with the IGN topographic map demonstrates agreement for the spatial units obtained. However, we must consider that the 'Cuvette Centrale' of the Congo River basin is located on the equator and is therefore located in both the southern and the northern hemispheres, which each have distinct hydrologic systems [START_REF] Devroey | Annuaire hydrologique du Congo belge et du Ruanda -Urundi 1956[END_REF]. The PALSAR ScanSAR dataset currently available for this area only allows us to consider two seasons, the beginning and the end of the year. As a result, neither specific months nor the contribution of each tributary of the 'Cuvette Centrale' to the global inundation has been studied in

Classifications comparisons

We find similar forested wetland types using both approaches (PALSAR and EVI mapping (χ² test)). The spatial distribution of the EVI classes is driven by flooding status (stable water level, seasonal flood pulse, and no flooding). The EVI-4 class corresponds to the "no flooding" PALSAR class. Since this region is labeled on the topographic map as non-flooded forest, this is consistent with our results.

Given the relationship between EVI, GLAS, and PALSAR data and based on published data, we propose the following labels for EVI classes (Fig. 4):

-EVI-1 consists of forest subjected to seasonal flood pulse, located alongside rivers, as described by [START_REF] Evrard | Recherches écologiques sur le peuplement forestier des sols hydromorphes de la Cuvette Congolaise Centrale[END_REF] -EVI-2 consists of forests subjected to stable water level, dominated by Raphiales, occuring at an average elevation of 304 m, and with a 20 m maximal canopy height;

-EVI-3 consists of forests subjected to seasonal short lasting flood pulse, with low amplitude, as described by [START_REF] Evrard | Recherches écologiques sur le peuplement forestier des sols hydromorphes de la Cuvette Congolaise Centrale[END_REF], located at an average elevation of 306 m and with a 30 m maximum canopy height;

-EVI-4 consists of non-flooded forests located at an average elevation of 311 m and with a 40 m maximum canopy height.

To illustrate these classes, we show four field photographs (Fig. 10). The current study demonstrates also that in the 'Cuvette Centrale' of the Congo River basin, there is a strong relationship between high photosynthetic activity (EVI), a high level of light intensity [START_REF] Evrard | Recherches écologiques sur le peuplement forestier des sols hydromorphes de la Cuvette Congolaise Centrale[END_REF]), a low level of flooding and a high level of rainfall. The forested wetlands of the Congo River 'Cuvette' loop through four phenological phases (two rainy and two dry seasons; Fig. 11). Without the contribution of any field measurements, we hypothesize that some species may shed their leaves during the flooding periods (December to January and July to August), as the Amazonian floodplain trees (Schöngart et al., 2002;Haugaasen & Perez, 2005).

[Figure 11]

At the beginning of the year (February to March), the water level of the Congo River is at its minimum, and the light intensity and rainfall increase [START_REF] Devroey | Annuaire hydrologique du Congo belge et du Ruanda -Urundi 1956[END_REF]. The EVI demonstrates a similar increase likely corresponding with the growth of the vegetation. Rainfall and light intensity are relatively stable from March to May when Congo River flooding typically occurs (maximally in May). Then, the EVI index values increase until May, when they reach the first peak of activity, and then decrease until July. Each of the other variables also decreases until the minimum light intensities occurs in July that causes consequent photosynthetic activity decrease. This period also corresponds to large rainfall causing floods in the Congo.

Beginning in August and reaching a maximum in October, there are simultaneous increases in light intensity and photosynthetic activity, rainfall, Congo and Oubangui River floods. Additionally, these parameters reach secondary peaks of activity in October.

At the end of the year, light intensity, photosynthetic activity and rainfall decrease between November and January, which is concurrent with the maximum level of the Congo River flooding (December) and the high level of the Oubangui River. This period is also marked by lower Previous studies (Parolin et al., 2010;Schöngart, et al., 2002) have shown that the phenology of seasonal flooded forest (e.g., in várzea and igapó) is dependent on the maximum level of inundation. However, the phenology of terra firma forest in the Amazon Basin was shown to be driven by rainfall as well as light intensity [START_REF] Haugaasen | Tree phenology in adjacent Amazonian flooded and unflooded forests[END_REF]. Flooded and non-flooded forests react in a similar way; the main drivers of phenology are light and rainfall for non-flooded forest, and flooding and light intensity for flooded forests. Indeed, light intensity plays a key role. It is possible that light intensity could influence the two peaks of photosynthetic activity for each forest type because low levels of light intensity and a hydric stress occur during dry periods [START_REF] Farnsworth | Understanding the large scale driving mechanisms of rainfall variability over Central Africa[END_REF]Camberlin et al., 2001). Most likely many environmental parameters play key roles in the phenology of intrinsic factors related to the phylogenetic origin of the species or genus (Wright & van Shaik, 1994).

Conclusion

This paper explores the use of a multi-sensor approach for the thematic characterization of forested wetland types within the Congo River basin and demonstrates that each type of sensor provides complementary information for the investigation of the spatial distribution, inundation and leaf phenology of forested wetland types.

The unique feature of the 'Cuvette Centrale', compared to Amazonia, is that the rainy season occurs twice per year (April-May and September-October). The spatial distribution of forested wetland types is controlled by topography and also by the time and the intensity of the submersion. Any changes in this parameter can modify the organization and the functioning of the forested wetlands.

The phenology of forested wetlands is already modified from year to year according to the interannual variations of eco-climatic parameters.

  , Forest ecosystems goods and services, TA-C105, F-34398 Montpellier Cedex, (France) 2-Université de Toulouse; CNRS; IRD; OMP GET; 14 Avenue Edouard Belin, 31400 Toulouse, (France) 3-IRSTEA, UMR Tetis, 34196 Montpellier (France) 4-Département des sciences géomatiques, Pavillon L.-J.-Casault, Universite Laval, Quebec, G1K 7PK (Canada) 5-Joint Research Centre, European Commission, I-21027 Ispra (Italy)

  of forested wetlands by MODIS-Enhanced Vegetation Index (EVI) EVI, a "16-Day L3 Global 500 m MODIS product (MOD13A1 c5)", is a vegetation index. Data were acquired for the period from January 2001 till December 2009, and used to map the forested wetland types within the 'Cuvette Centrale' of the Congo River basin with respect to leaf phenology

  Aerial photographs(IGN, 1961) of the Northern part of the Democratic Republic of the Congo and a topographic map of the same Northern part (IGN -Institut Géographique National 1960) with forested wetland types mapped were used to validate the EVI data. The aerial photographs represent 15 000 km 2 of the 'Cuvette Centrale'; their references are AE-1960-61-NA33XI-XII (from 0 to 532) and AE-1961-62-NA33V-VI (from 248 to 524).

  footprints (issued from the unsupervised classification of MODIS data) were selected by EVI class to analyze the correlation between topography and spatial distribution for the different types of forested wetlands. The maximum height of the forested wetland types was determined by waveform analysis using the distance between the beginning of the signal and the centroid of the ground return(Ballhorn et al. 2010).2.4 Estimating inundation by ALOS PALSARThe Phased Array type L-band Synthetic Aperture Radar (PALSAR) sensors on board of the Lband Advanced Land Observing Satellite (ALOS) have been chosen to study the process of inundation. Six images, which were acquired on 1/25/2009, 3/15/2010, 4/27/2009, 09/07/2007, 10/25/2008, and 12/13/2009, have a horizontal/horizontal polarization and a 100 m spatial resolution at various viewing angles (from 18° to 43°).

Fig. 4 .

 4 The class EVI-1 of forest is located mainly alongside rivers and large lakes. This class is also present in the southwestern part of the Congo River basin and northwest of Lake Tele. EVI-1 covers an area of 24 000 km². The EVI-2 class is present in the central region of the 'Cuvette Centrale' and covers 85 000 km². The EVI-3 class covers 121 000 km² and is frequently associated with the EVI-2 class. The EVI-4 class covers a smaller area (56 and occurs near Lake Tele in the north of the 'Cuvette Centrale' along the Oubangui River as well as in the south near the lakes and tributaries of the Congo River.

  peaks of activity during May and October separated by a decrease of leaf density in December and August. Each EVI class exhibits similar characteristics, and an EVI mean value

[Figure 7 ]

 7 Fig.9presents the spatial distribution of the flooding area's derived from an unsupervised

  were identified using the PALSAR multi-temporal images that can be related to the vegetation map.In general, radar backscattering is lower for an open water body than for other surfaces. This is due to specular reflection from the water surface. This is a characteristic which permits the accurate determination of the extent of the flooding area for open water bodies, such as rivers and lakes not covered with vegetation. Moreover, the L-band has extensive higher canopy penetration depth, permitting the detection of water surfaces below the canopy. The multi-temporal use of L-band imagery allows the discrimination between forest subjected to relatively stable water level, forest subjected to seasonal flood pulse and non-flooded areas. As shown in Fig.8, there is no overlap of the mean values with their standard deviations indicated, for each class. The PALSAR imagery in the ScanSAR mode allows us to detect flooding over large areas (i.e., from 250 to 350 km, or three to five times the footprint of a conventional SAR scene).Regarding PALSAR image classification, the PALSAR-1 class elicits the lowest level of backscattering and can be used to identify non-covered water surfaces (rivers and lakes). Increasing backscattering coefficients during the low water season (March/April) are likely to be the consequence of the presence of islands and vegetation species found in the river channels and the open water of the Likouala-aux-Herbes and Moanda Rivers. PALSAR-2 exhibits very low backscatter variation over a year and a relatively low mean backscattering value. This is assumed to be originating from volume as well as multiple scattering mechanisms of non-flooded forests[START_REF] Martinez | Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data Remote[END_REF][START_REF] Evans | Using ALOS/PALSAR and RADARSAT-2 to Map Land Cover and Seasonal Inundation in the Brazilian Pantanal[END_REF]. PALSAR-3 has the highest backscattering coefficient values due to the double-bounce effect of the interactions of the radar pulse with the underlying water surface (Hess

  monitoring of inter-annual variation regarding the extent of flooding within this vegetation landscape is not currently accessible with ALOS radar data.

Figures

  Figures

Figure 1 .

 1 Figure 1. Location of the 'Cuvette Centrale' within the Congo River basin (Africa).

Figure 2 .

 2 Figure 2.Figure 2. The average rainfall (mm), evapotranspiration (mm), and average monthly

Figure 3 .

 3 Figure 3. The Thornlike index value vs. the number of clusters for unsupervised classification of the

Figure 4 .

 4 Figure 4. A map of the unsupervised classification using the EVI average year. The labeling takes

Figure 5 .

 5 Figure 5. Repartition percentage of each EVI class in each IGN class; the surface area examined

Figure 6 .

 6 Figure 6. Annual variation of the EVI index for each forest class.

Figure 7 .

 7 Figure 7. The Thornlike index vs. the number of clusters for the unsupervised classification of the

Figure 8 .

 8 Figure 8. Annual variation of the mean backscattering coefficient and standard deviation for each

Figure 9 .

 9 Figure 9. A map of the unsupervised classification results of 6 PALSAR products.

Figure 10 .Figure 11 .

 1011 Figure 10. Forested wetland photographs from field

Figure 1 .

 1 Figure 1. Location of the 'Cuvette Centrale' within the Congo River basin (Africa).

Figure 2 .

 2 Figure 2. The average rainfall (mm), evapotranspiration (mm), and average monthly components of water budget in Mbandaka (m), (sources: FAO, 2000; Devroey, 1957).

Figure 3 .

 3 Figure 3. The Thornlike index value vs. the number of clusters for unsupervised classification of the EVI index.

  Figure 4. A map of the unsupervised classification using the EVI average year. The labeling takes into account the Evrard and Junk typology, the PALSAR, the GLAS and the EVI information. The map corresponds to the EVI unsupervised classification with a masked inundated landscape (Bwangoy et al., 2010).

Figure 5 .

 5 Figure 5. Repartition percentage of each EVI class in each IGN class; the surface area examined corresponds to approximately to 50,000 km². IGN-1 corresponds to forests subjected to seasonal flood pulse, localised along river, IGN-2 corresponds to forests subjected to relatively stable water level, IGN-3 corresponds to forests subjected to relatively stable water level and IGN-4 corresponds to non flooded forests (IGN, 1960).

Figure 7 .

 7 Figure 7. The Thornlike index vs. the number of clusters for the unsupervised classification of the PALSAR images.

Figure 8 .

 8 Figure 8. Annual variation of the mean backscattering coefficient and standard deviation for each PALSAR-derived class.

Figure 9 .

 9 Figure 9. A map of the unsupervised classification results of 6 PALSAR products.

Figure 11 .

 11 Figure 10. Forested wetland photographs from field

  [START_REF] Bwangoy | Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices[END_REF]

	F o
	r
	R e v i e w
	O n
	l y

  This study investigates a new area of research regarding the 'Cuvette Centrale' floodplain in the Congo River basin. With the spatial distribution and the brief functional description provided, it is now possible to investigate this large ecosystem with more details. The documentation and the study of this huge remote area are fundamental for understanding continental natural resources involved in freshwater regulation, carbon stocks and basic chemical element exchanges at the tropical atmosphere-biosphere interface.(1998). A comparison of dispersal germination and establishment of woody plants subjected to distinct flooding regimes in Brazilian flood-prone forests and estuarine vegetation,

	for forest canopy height estimation from Geoscience Laser Altimeter System Waveforms, Journal of Applied Remote Sensing, 1,1-18. Scarano, F.R., Ecophysiological strategies of xerophytic and amphibious plants in the neotropics. Series Figures caption and Table
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	young Astrocaryum jauari Mart. (Arecaceae) in periodically inundated biotopes of Central
	Amazonia. Biotropica, 25, 384-396.
	Schöngart, J., Piedade, M. T. F., Ludwighausen, S., Horna, V. & Worbes, M. (2002). Phenology
	F o r F o r F o r F o r F o r F o r F o r F o r and stem-growth periodicity of tree species in Amazonian floodplain forests. Journal of Tropical F o F o Ecology, 18, 581-597. r r
	R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w
	O n O n O n O n O n O n O n O n O n O n
	l y l y l y l y l y l y l y l y l y l y
	Ulaby, F.T., Moore,R.K., & Fung, A.K. (1982). Microwave Remote Sensing, Active and Passive,
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	book program, Massachussetts, 609 pages.
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	23 28 30 32

Shahin M.

(2002)

. Hydrology and water resources of Africa. Springer Verlag, 659 p.

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, C.J., Thiel, C., Schmullius, C.C. (2009). Operational Large-Area Forest Monitoring in Siberia Using ALOS PALSAR Summer Intensities and Winter Coherence, IEEE Transaction on Geoscience and remote Sensing, 47, 3993-4000. Tibshirani, R., Walter, G., & Hastie, T. (2001). Estimating the number of clusters in a dataset via the gap statistic, Journal of Royal Statistic Society B., 63, 411-423. Thorndike, R., L. (1953). "Who Belong in the Family?". Psychometrika, 18. Turton A. & Ashton P. (2004). Southern african water cooperation -lessons from the Okavango basin. CSIR-environmentek, Pretoria 24 slides.

Table Table 1

 Table . EVI class structure characteristics documented with ICESAT-GLAS data.

Table 1 .

 1 ). EVI class structure characteristics documented with ICESAT-GLAS data.

	Class	Surface (km²)	Average	Canopy maximum Height
			elevation	(m)
			(m)
				Means	Std
	EVI-1	24 000	No Data	No Data	No Data
	EVI-2	85 000	304	20	4.04
	EVI-3	121 000	306	30	4.8
	EVI-4	56 000	311	41	3.7
		F o
		r
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				O n
				l y

Acknowledgments

The authors thank NASA, ESA and IRD for providing the remote sensing data. This study was supported by CIRAD funding. The authors also thank Frederique Seyler, Sylvie Gourley-Fleury, and Yves Laumonier for their help.