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Ecosystem Provisioning Services Automated Valuation Process Model for Sustainable Land Management
Available online at www.sciencedirect.com
ScienceDirect Procedia Computer Science 104 (2017) 65 – 72
ICTE 2016, December 2016, Riga, Latvia
Ecosystem Provisioning Services Automated Valuation Process Model for Sustainable Land Management Jurijs Holmsa,*, Irina Arhipovaa, Ildiko Tulbureb, Gatis Vitolsa b
a Latvia University of Agriculture, Liela iela 2, Jelgava, LV-3001, Latvia University “1 Decembrie 1918”, Gabriel Bethlen Nr.5, Alba Iulia, 510009, Romania
Abstract For effective management of natural capital, it is necessary to identify all ecosystems provisioning services (supply of food, wood, etc.), evaluating each of them. The article presents ecosystem provisioning services’ automated valuation process model. As data sources are proposed to use information from the European Union member states institution’s data registers, integrating it using classical data base, XML schema definition and geographic information systems technologies. The proposed model provides industry professionals with the opportunity for online decision making, that is based on actual data, and as well fuzzy logic based assessment method for sustainable land management. The article defines the data sources that are available for modeling and describes the specific problems with data integrating. As a result of the study information system architecture for ecosystem provisioning services' valuation for land management is developed. © Published by by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license © 2017 2016The TheAuthors. Authors. Published Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the scientific committee of the international conference; ICTE 2016. Peer-review under responsibility of organizing committee of the scientific committee of the international conference; ICTE 2016 Keywords: Ecosystem, provisioning, Fuzzy Logic based assessment method, Sustainable land management
1. Introduction At present there is no consensus about terms “ecosystem services” and “ecosystem”, so in this article is used terminology adopted from “Ecosystems and Human Well-being: Synthesis”1. Ecosystem services (ES), they are potential gains or losses which a person can receive from ecosystems, while the ecosystem – is a plant, animal and
* Corresponding author. Tel.: +371-26474338. E-mail address: [email protected]
1877-0509 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the scientific committee of the international conference; ICTE 2016 doi:10.1016/j.procs.2017.01.063
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micro-organism dynamic interaction with inanimate objects (for example, soil, terrain, weather conditions). Ecosystems can be divided in two major categories: subsistence ecosystems (not affected or almost not affected by human) and modified ecosystems, which are intensively managed by human (for example, agricultural land and urban areas) and four sub-categories: provisioning services, regulating services, cultural services and supporting services so-called as ecosystem functions1. After work “The Limits to Growth”2 publication in 1972, it became clear that humanity at closed or localized area cannot grow indefinitely without negative consequences to the environment and future generations. In 1987 at “Brundtland’s”3 report appears idea about sustainable development strategy, where “Humanity has the ability to make development sustainable to ensure that it meets the needs of the present without compromising the ability of future generations to meet their own needs3”. There are economic and ecological concepts for assessing ES4. The article dealt with provisioning ES valuation models, based on digitally stored datasets. The evaluation accuracy is primary depended on the concept, as well on the level of detail of valuation model and on data quality and actuality. Building the correct model’s architecture is very time-consuming and expensive process. Evaluation model should be based on a significant amount of data, where for each separate dataset are responsible mostly individual organization. No one organization has all necessary for modeling data in one place. Harmonization of classifications will greatly facilitate data interoperability between the institutions, thus facilitate the construction of valuation model’s architecture for all European Union (EU) Member States (MS) national territory. The data in institutions registers overlaps. For example, “Rural blocks” (polygons) that accumulated in the Rural Support Service may be overlapped by “forest land plot” from State Forest register. In this case coordination (harmonization) of data registration process between the two institutions can solve overlapping and gaps problems in data. Currently, the Latvian cadastre data on land use, for the MS territory, is available only as a text. And new data continues to be collected only as text. But the data in “Rural Support Service” and the “National Forest Register” are also available as a graphics. The need to combine text and graphic information also complicates the assessment model. Implementation of unified data specifications for all registers can greatly facilitate data processing. Despite the global trends on data opening, so called open data approach, in Latvia data from many registers are still not publicly available. This makes difficult data inventory for non-industry professionals. For example, the data from many state registers or institutions are not available for universities or is available for a fee, which significantly complicating research activities. EU MS data register’s financing from the state budget could improve this situation. The aim of the article is to define provisioning ecosystem services automated valuation process model for sustainable land management. The following tasks are defined: 1) to perform provisioning ecosystem services assessment tools analysis; 2) to define land management tasks; 3) to develop the information system’s (IS) architecture for land development; 4) to develop an automated process for land management to maximize provisioning ecosystem services monetary value (based on cadastral value). 2. Materials and methods For data monetary value determination many methodologies can be applied, for example direct market pricing, production function, avoided cost, hedonic pricing, travel cost, contingent valuation and other methods and combinations5. Robert Costanza claims, the problem is that the valuation is implicit in the decision and hidden from view6. Improved transparency about the valuation of ES (while recognizing the uncertainties and limitations) can only help to make better decisions6. Despite that Rudolf de Groot found that provisioning services are more often values through direct market pricing methods5. Most people understand values expressed in monetary units, this is often a convenient denominator for expressing the relative contributions of the other forms of capital, including natural capital6. Estimating of aggregate accounting value for ES in monetary units have a critical role to play in heightening awareness and estimating the overall level of importance of ES relative to and in combination with other contributors to sustainable human wellbeing7. As a result, spatial development specialists and the land consolidation specialists, creating spatial development documents, will be able to apply this model for planning and decision making, including tasks like maximizing the cadastral value of land, according to the cadastral valuation methodology. If planning specialists as
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well will follow the ecosystem environmental assessment methodologies, like Fuzzy Logic based assessment method, they will provide to society a sustainable land management. Economic human activities have the direct goal to increase the quality of life of the population, but at the same time they could have unthinkable unintended effects on the environment and society. With the goal of assuring the sustainable development of our human society it is therefore necessary to evaluate economic activities not only from economic and technological points of view but from environmental and social ones as well. For such an interdisciplinary evaluation there is a need to develop an integrative assessment method, which is simultaneously considering in a modular way on local level specific indicators from different fields, such as economic, environmental and social ones8. 2.1. Fuzzy Logic based assessment method Regarding this subject several materials and books are available at present, trying to clarify the great potential of Fuzzy Logic to be used in the assessment field, but there is still a need to demonstrate its concrete application possibilities9. Fuzzy Logic is based on the knowledge that the reality is rather inexact than precise, because all made human affirmations have a certain free interpretation domain. As a special case the traditional binary logic is part of fuzzy logic, but operating only with two values of interpretation, 0 or 1, yes or no. In contrast to the well-defined sets of the Set Theory, real existing sets are rather fuzzy limited, essentially due to the uncertainties in the used language. A set is fuzzy limited if the assignment of one is not given to all the members of the set. A fuzzy set is defined by the so-called membership function, that can take any values on the interval [0, 1], not only 0 or 1. The key notion when modelling with Fuzzy Logic is the linguistic variable10. The mathematical description of processes requires a precise quantitative presentation of the influences considered. In opposition to this, verbal rules of behavior contain fuzzy formulated knowledge, which is generally more intelligible for human beings. Beyond that, linguistically formulated variables have higher aggregated information content, and therefore it is more difficult to quantify them. The concept of linguistic variables connects the description of verbal and therefore fuzzy information with mathematical precision. The values of a linguistic variable are verbal expressions, called linguistic terms, for instance small. The content of each linguistic term is identified with one fuzzy set and assigned to the related numerical scale of the basic variable by a particular membership function. Thus, the fuzzy sets build the connection between linguistic expression and numerical information10, 11. To process fuzzy formulated knowledge several linguistic variables must be linked by linguistic operators. The connecting rules represent the knowledge, which is stored in a rule base or knowledge base, similar to expert systems. The procedure consists of the following steps: fuzzification, inference and defuzzification (see Fig. 1).
Fig. 1. General operational diagram by fuzzy logic applications.
The fuzzification step is the linguistic interpretation of any crisp input value of a basic variable. For this purpose, the basic numerical interval, the number of the linguistic terms and the according verbal expressions of the linguistic variable have to be previously fixed. After fuzzification, the inference has to draw conclusions from the propositions with regard to the knowledge base. The knowledge formulated as IF-THEN-rules has to be applied to the new fuzzy statements. Inference consisting of aggregation of the IF-parts of each rule, implication and accumulation of the results of the rules THEN-parts, causes a weighting of each single rule on the total result. The result of the implication itself is the assignment of a proposition of a rule to a linguistic term of the output variable. Running all rules generates several different images of the output variable because of the different parts of the output linguistic terms. On the other hand, a crisp output value could be drawn from the resulting membership distribution by several
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procedures in the defuzzification step. The most familiar one is to determine the center of gravity of the area representing the resulting membership distribution of the participating linguistic terms. The abscissa value represents then the crisp output value. Such a knowledge based approach means the methodical attempt to substitute missing algorithmic procedures by using human knowledge. Thus, even partially fulfilled conditions result in partially fulfilled conclusions, so these conditions are considered also in the result. Therefore, the possibility to consider uncertain information and interdisciplinary knowledge in systems modelling is given. This fact encourages applications not only for the technological field, but also for the field of environmental assessments as well as for sustainability assessments9, 12. For this goal several assessment criteria have to be taken into account by simultaneously considering several indicators from different fields, as it is the specific case in the field of sustainable land management13, 14. 2.2. Provisioning ES assessment tools analysis In general, assessment tools can be divided in two categories: decision making tools and planning tools1, 5, 6, 15-30. There many tools available, that for data source use information not from national registries or in offline mode. There are indicators dashboards, electronic and paper review tools, atlases, screening tools, model building for assessing some indicators. Advanced development prospects have tools, what for data input use official EU MS data registers. Most prospective tools for data input should accept data in INSPIRE (Infrastructure for spatial information in Europe) specification31. This makes possible to build huge models for all EU territory using united data specification and easily applicable in every EU state. In European Commission Technical Report “Mapping and Assessment of Ecosystems and their Services”32 the attention is focused on EU mapping, as also there are described classification and harmonization aspects. Attention is drawn to geographic information system (GIS) technologies; we can make very effective spatial analysis using it32. Currently in Latvia and in EU is actively implementing INSPIRE Directive33 and till 2020 there are many harmonized spatial data sets should be available. For example, datasets like: hydrography, protected sites, land cover, agricultural and aquaculture facilities, area management, atmospheric conditions, bio-geographical regions, energy resources, habitats and biotopes, land use, soil, etc. 2.3. Information system’s architecture for land development Land and Land parcels have four categories of characteristics: 1) physical phenomena (soil, ground water, irrigation/drainage conditions, land use and etc.); 2) infrastructure (roads, facilities and etc.); 3) location and environment; 4) land as property characteristics34. Thus Land management tasks are to administer and to control land management process (as physical phenomena and as property) and to plan land development35. The following land development planning concepts exist: sustainable development, social oriented development and market oriented development. All planning concepts works good only if development aims are clearly defined. In each planning iteration current situation is compared with predefined indicators, thus allows making decisions to correct trends. Can be divided three land development levels: national, regional and municipality level. Basically sustainable development concept is adopted at national level first and gradually is distributed to regional and municipality level with time. The main idea of the theory is - the maintenance of environment in order to do not worsen the situation in nature as such and to do not make certain territories population’s socio-economic situation poorer in long term. In “Latvian sustainable development strategy till 2030”35 has defined, that for effective natural capital management, it is necessary to calculate the National natural capital amount. For a community to be able successfully maintain, increase and sustainably use natural capital, it is necessary to calculate its value. Calculating the value of natural capital, the total economic value concept should be used, which deals with both: direct and indirect use values and non-use value30. INSPIRE directive will provide a wide range of harmonized datasets by 2020. Latvia is also implementing the INSPIRE directive and the INSPIRE data themes will serve as an excellent source of data for EU harmonized mapping and capturing. To define provisioning ES automated valuation process model and to develop IS architectures, we should clearly understand with what kind of input/output data we should work with. We
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understand that data specifications in countries and organizations may differ. But to make this data interoperable, some international standards and European directive can guide way to us. Taking into account that basic data unit in our model will be land parcel and information about land, Land Administration Domain Model (LADM) was analyzed36. But some following aspects (necessary for us) are outside the scope of this standard: these are construction of external databases with party data, valuation data, land use data, land cover data, physical utility network data and modelling of land administration processes. This means, that we cannot use this international standard as the basis for creating valuation process model. At the following step INSPIRE directive, some implementing rules and some technical guidance are analyzed. INSPIRE defines data specifications for 34 themes like cadastral parcels, land use, land cover, agricultural and aqua cultural facilities and etc. But some aspects like valuation data and data replication are stile out of scope. So we decided to build IS architecture and process’s model focusing on INSPIRE directive and on classical approach like using non-spatial information, Simple Object Access Protocol (SOAP) web services (WS), data base (DB) links and replicas to get access to country/institution specific unharmonized data. Developed IS architecture (see Fig. 2) provides possibility to work with text and spatial data from national data holders / National Mapping and Cadastral Agency (NMCA). Data Exchange is ensured using DB links and replications for big datasets, Web Feature Service (WFS) for spatial data sets and feature types without geometry, SOAP for text data exchange. To make data interoperable - comprehensive data transformation/reclassification module must be implemented where data from unharmonized datasets will be harmonized to allow data processing in the harmonized environment. We must not underestimate the data transformation / data interoperability problem, because for some datasets this problem cannot be solved just using only technical means, for example, classification and normative act harmonization also could be necessary. To ensure sustainable development simplified Banfield’s37 rational planning model are adopted as infinite loop. Loop steps: 1) data collection, 2) analysis of data (in planning or decision making process), 3) forecasting the future (planning), establishing goals (planning), design of alternatives (planning), 4) assessment, indicator screening, comparing wit goals, 5) reaction to the land development trends. If necessary, using alternative development plans as input for step 1.
Fig. 2. Information system’s architecture for land development.
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2.4. Automated process for valuation of the land management Based on developed IS architecture and analyzing evaluation process for land management tasks, automated valuation process’s model for land sustainable management was developed (see Fig.3). The process also can be used for maximizing provisioning EU monetary value (based on cadastral value), if this will be defined as process goal. Taking into account specifics of NMCA valuation process models can be used as Regular process for operations on massive datasets and as Irregular process for online decision making.
Fig. 3. Automated process for valuation of the land management.
Regular process deals with huge datasets and on a regular basis. For example, it can be integrated in national land valuation processes or national territorial planning processes. For data input (see Fig. 3; steps: 1, 2, 3, 4) and output (see Fig. 3; steps: 12, 13, 14) national registers are used. It can be data in national specifications or in more interoperable way - in INSPIRE specification. In each next valuation process loop output data becomes as input. For huge datasets is desirable to use DB links or replicas (see Fig. 3; steps: 5, 11) for information exchange processes. On regular basis (for example yearly), Business Process Execution Language (BPEL) process (see Fig. 3; step: 6) starts to harvest information from EU MS registers and, using predefined rules, transform data to interoperable condition. At this step most of the issues can be solved using GIS technologies, such as geoprocessing and table joining. Further planning specialists (using, for example, Fuzzy Logic based assessment method) create land
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development plans (see Fig. 3, step: 7), then going mass land valuation process (see Fig. 3, step: 8) (using predefined rules) and BPEL (see Fig. 3, step: 9), compare planning and valuation results with predefined goals and if goals are not reached return land management project for redevelopment (see Fig. 3; step: 7), if goals are reached transform information to each national register’s format (see Fig. 3; step: 10) and writes loop results to MS national registers. Banfield’s37 Rational planning model is implemented as loop from steps 7, 8, 9 in Fig. 3. Irregular process serves for online decision making on land parcel level. For information exchange are used WS (see Fig. 3; steps: 5.1’, 5.2’, 11.1’, 11.2’) based solutions, that allow not to download huge amount of information, but work on specific object level. All WS should be developed using united XML schema repository to improve possibility of data interoperability. Banfield’s37 rational planning model is implemented as loop from steps 7’, 8’, 9’ on Fig. 3. Other irregular process steps are similar to regular process’s steps. 3. Results and discussion After analyzing literature and internet resources about provisioning ES assessment tools, it can be concluded, that most perspective assessment tools are built using GIS technologies. A new complex approach should enter into practice soon, where for provisioning ES assessment will be used complex solutions based on spatial data infrastructure (SDI) concept and as data source - authoritative, interoperable, up-to-date data will be used. This makes possible real time data processing including online decision making. The Land management tasks are to administer and to control land management process (as physical phenomena and as property) and to plan land development. Many approaches can be used to realize this tasks, the most farsighted is sustainable development concept, where depending on goals always is possible to make decisions (or to plan changes) to correct development trends. To bring closer the automated implementation of these tasks, the IS architecture for land development is defined. IS architecture is focused to use interoperable data (like INSPIRE datasets), but not only. The fragile place of architecture is data transformation module, because for some datasets interoperability or data reclassification problem cannot be solved just using only technical means, for example, classification or normative act harmonization also could be necessary. To implement IS architecture for land development, an automated general process model for land management was developed. Process model uses modern information technologies and provides regular and irregular process runtime capabilities. Regular process model is oriented on huge amount of data processing for planning purposes. Irregular process is oriented on interactive or on demand – online decision making and provides a capability to operate with data at feature/object level, what can be useful in emergency situations and for small land areas high accuracy sustainable development. 4. Conclusion For assuring local sustainability in the form of shaping sustainable human settlements an appropriate sustainable management on a local level is necessary to be carried out. In the process of operationalization of sustainable development an important part is represented by developing innovative models for sustainable land management. A fuzzy logic based method has been discussed offering new possibilities by its potential to integrate complexity in the systematic and exact mathematical approach and assuring a transparent assessment. Provisioning ES automated valuation general process model for sustainable land management was defined and published at this article, which makes possible to continue to develop automated process models for sustainable land development, focused on the use of SDI technologies and fuzzy logic based assessment method. References 1. 2. 3. 4.
Millennium Ecosystem Assessment. Ecosystems and Human Well-being: Synthesis. Washington DC: Island Press; 2005. Meadows DH. et al. The Limits to Growth. New York: Universe Books; 1972. Report of the World Commission on Environment and Development. United Nations. General Assembly; 1987. Farber SC. et al. Economic and ecological concepts for valuing ecosystem services. Ecological Economics. 41; 2002. p. 375–392.
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de Groot R. et al. Global estimates of the value of ecosystems and thei rservices in monetary units. Ecosystem Services. 1; 2012. p. 5061. Costanza R. et.al. Changes in the global value of ecosystem services. Elsevier: Global Environmental Changes. 26; 2014. p. 152-158. Luisetti T. et al. Valuing the European 'coastal blue carbon' storage benefit. Mar. Pollut. Bull. 71; 2013. p. 101-106. Tulbure I. Local Sustainability Implementing Strategies. Albena: SGEM2016 proceedings. 6, Vol. II; 2016. p.621-628. Jischa MF. Herausforderung Zukunft. 2nd edition. Elsevier, Spektrum Publishing House, Heidelberg; 2005. Tulbure I. Technology Assessment. Lecture Notes. Course at the Clausthal University of Technology. Clausthal-Zellerfeld; 2013. Ludwig B. Methoden zur Modellbildung in der Technikbewertung. CUTEC-Schriftenreihe No. 18. Clausthal-Zellerfeld; 1995. Tulbure I. Integrative Modellierung zur Beschreibung von Transformations-prozessen. VDI-Fortschrittsberichte. Reihe 16, No. 154. VDI-Verlag, Düsseldorf; 2003. Enemark S. Integrated Land-Use Management for Sustainable Development. International Federation of Surveyors; 2007. Palamariu M, Tulbure I. Land Use Planning Strategies for Shaping a Sustainable City. SGEM2016 Proceedings. Book3, Vol. I; 2016. p.317-322. Nature service „Conservation Tools & Services”. Available: http://www.natureserve.org/conservation-tools/ecosystem-basedmanagement-tools-network/about_ebm_tools/toolkits.html. Model for Assessing Habitat Vulnerability to Climate Change in the Tropical Ande. Available: http://www.natureserve.org/conservation-tools/model-assessing-habitat-vulnerability-climate-change-tropical-andes. Community-Based Risk Screening Tool – Adaptation and Livelihoods. Available: https://www.iisd.org/cristaltool/. Spatial planning tools for ecosystem approach to aquaculture and fisheries development. Available: https://www.researchgate.net/post/What_are_the_spatial_planning_tools_for_ecosystem_approach_to_aquaculture_and_fisheries_dev elopment. Community Planning Toolkit Community Planning. Available: http://www.communityplanningtoolkit.org/sites/default/files/CommunityPlanningUpdate.pdf. Biodiversity Mainstreaming Toolbox. Available: http://biodiversityadvisor.sanbi.org/wp-content/uploads/2015/02/BiodiversityMainstreaming-Toolbox_Senior-Managers-Summary.pdf. European Spatial Planning: Adapting to Climate Events. Available: http://www.espace-project.org/. The Adaptation Support Tool. Available: http://climate-adapt.eea.europa.eu/adaptation-support-tool. Biodiversity Planning Toolkit. Available: http://alge.org.uk/projects/files/draftool.pdf. E-Learning Tool “Planning for community based adaptation to climate change“. Available: http://www.fao.org/climatechange/24444-0ed5117aa4b7b6355bca46e0cf60eb161.pdf. Planning for Climate Change – Toolkit. Available: http://unhabitat.org/books/planning-for-climate-change-toolkit/. Between structures and norms: assessing tax increment financing for the Dutch spatial planning toolkit. Available: http://online.liverpooluniversitypress.co.uk/doi/10.3828/tpr.2015.20. Spatial planning and environmental assessments. Available: https://www.environment.gov.za/sites/default/files/docs/economicsof_ecosystems_spatialplanning_enviroassessment.pdf. la Notte A. et al. Biophysical Assessment and Monetary Valuation of Ecosystem Services: Scenario analysis for the case of water purification in Europe. European Commission. Institute for Environment and Sustainability; 2012. p. 72. Klein TM. et al. Ecosystem services visualization and communication: A demand analysis for designing information and conceptualizing decision support systems. Ecosystem Services. 13; 2015. p. 173-183. Tinch R, Mathieu L. Marine and coastal ecosystem services: Valuation methods and their application and etc. UNEP-WCMC: Biodiversity Series. No. 33; 2011. p. 46. INSPIRE data specifications. European Commission. Joint Research Center. Available: http://inspire.ec.europa.eu/data-specifications. Technical Report (2016-095) “Mapping and Assessment of Ecosystems and their Services”. European Commission; 2016. INSPIRE Directive. European Commission: Official Journal of the European Union, L 108; 2007. Available: http://eurlex.europa.eu/legal-content/EN/ALL/?uri=OJ:L:2007:108:TOC. Auzins A. Land Management Basics. Riga: RTU; 2008. (in Latvian). Sustainable Development Strategy of Latvia until 2030. Available: http://www.varam.gov.lv/lat/pol/ppd/?doc=13857. Land Administration Domain Model. ISO standart; 2012. Available: https://www.iso.org/obp/ui/#iso:std:iso:19152:ed-1:v1:en. Meyerson M, Banfield EC. Politics, planning, and the public interest. New York: Free Press; 1955.
Jurijs Holms is Phd student of Information Technology faculty of Latvia University of Agriculture. He scientific interests are Spatial Data Infrastructure, Geomatics, Web Map and Feature Services. His main research is about application solutions of spatial data infrastructure. Contact him at [email protected].
ScienceDirect Procedia Computer Science 104 (2017) 65 – 72
ICTE 2016, December 2016, Riga, Latvia
Ecosystem Provisioning Services Automated Valuation Process Model for Sustainable Land Management Jurijs Holmsa,*, Irina Arhipovaa, Ildiko Tulbureb, Gatis Vitolsa b
a Latvia University of Agriculture, Liela iela 2, Jelgava, LV-3001, Latvia University “1 Decembrie 1918”, Gabriel Bethlen Nr.5, Alba Iulia, 510009, Romania
Abstract For effective management of natural capital, it is necessary to identify all ecosystems provisioning services (supply of food, wood, etc.), evaluating each of them. The article presents ecosystem provisioning services’ automated valuation process model. As data sources are proposed to use information from the European Union member states institution’s data registers, integrating it using classical data base, XML schema definition and geographic information systems technologies. The proposed model provides industry professionals with the opportunity for online decision making, that is based on actual data, and as well fuzzy logic based assessment method for sustainable land management. The article defines the data sources that are available for modeling and describes the specific problems with data integrating. As a result of the study information system architecture for ecosystem provisioning services' valuation for land management is developed. © Published by by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license © 2017 2016The TheAuthors. Authors. Published Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the scientific committee of the international conference; ICTE 2016. Peer-review under responsibility of organizing committee of the scientific committee of the international conference; ICTE 2016 Keywords: Ecosystem, provisioning, Fuzzy Logic based assessment method, Sustainable land management
1. Introduction At present there is no consensus about terms “ecosystem services” and “ecosystem”, so in this article is used terminology adopted from “Ecosystems and Human Well-being: Synthesis”1. Ecosystem services (ES), they are potential gains or losses which a person can receive from ecosystems, while the ecosystem – is a plant, animal and
* Corresponding author. Tel.: +371-26474338. E-mail address: [email protected]
1877-0509 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the scientific committee of the international conference; ICTE 2016 doi:10.1016/j.procs.2017.01.063
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micro-organism dynamic interaction with inanimate objects (for example, soil, terrain, weather conditions). Ecosystems can be divided in two major categories: subsistence ecosystems (not affected or almost not affected by human) and modified ecosystems, which are intensively managed by human (for example, agricultural land and urban areas) and four sub-categories: provisioning services, regulating services, cultural services and supporting services so-called as ecosystem functions1. After work “The Limits to Growth”2 publication in 1972, it became clear that humanity at closed or localized area cannot grow indefinitely without negative consequences to the environment and future generations. In 1987 at “Brundtland’s”3 report appears idea about sustainable development strategy, where “Humanity has the ability to make development sustainable to ensure that it meets the needs of the present without compromising the ability of future generations to meet their own needs3”. There are economic and ecological concepts for assessing ES4. The article dealt with provisioning ES valuation models, based on digitally stored datasets. The evaluation accuracy is primary depended on the concept, as well on the level of detail of valuation model and on data quality and actuality. Building the correct model’s architecture is very time-consuming and expensive process. Evaluation model should be based on a significant amount of data, where for each separate dataset are responsible mostly individual organization. No one organization has all necessary for modeling data in one place. Harmonization of classifications will greatly facilitate data interoperability between the institutions, thus facilitate the construction of valuation model’s architecture for all European Union (EU) Member States (MS) national territory. The data in institutions registers overlaps. For example, “Rural blocks” (polygons) that accumulated in the Rural Support Service may be overlapped by “forest land plot” from State Forest register. In this case coordination (harmonization) of data registration process between the two institutions can solve overlapping and gaps problems in data. Currently, the Latvian cadastre data on land use, for the MS territory, is available only as a text. And new data continues to be collected only as text. But the data in “Rural Support Service” and the “National Forest Register” are also available as a graphics. The need to combine text and graphic information also complicates the assessment model. Implementation of unified data specifications for all registers can greatly facilitate data processing. Despite the global trends on data opening, so called open data approach, in Latvia data from many registers are still not publicly available. This makes difficult data inventory for non-industry professionals. For example, the data from many state registers or institutions are not available for universities or is available for a fee, which significantly complicating research activities. EU MS data register’s financing from the state budget could improve this situation. The aim of the article is to define provisioning ecosystem services automated valuation process model for sustainable land management. The following tasks are defined: 1) to perform provisioning ecosystem services assessment tools analysis; 2) to define land management tasks; 3) to develop the information system’s (IS) architecture for land development; 4) to develop an automated process for land management to maximize provisioning ecosystem services monetary value (based on cadastral value). 2. Materials and methods For data monetary value determination many methodologies can be applied, for example direct market pricing, production function, avoided cost, hedonic pricing, travel cost, contingent valuation and other methods and combinations5. Robert Costanza claims, the problem is that the valuation is implicit in the decision and hidden from view6. Improved transparency about the valuation of ES (while recognizing the uncertainties and limitations) can only help to make better decisions6. Despite that Rudolf de Groot found that provisioning services are more often values through direct market pricing methods5. Most people understand values expressed in monetary units, this is often a convenient denominator for expressing the relative contributions of the other forms of capital, including natural capital6. Estimating of aggregate accounting value for ES in monetary units have a critical role to play in heightening awareness and estimating the overall level of importance of ES relative to and in combination with other contributors to sustainable human wellbeing7. As a result, spatial development specialists and the land consolidation specialists, creating spatial development documents, will be able to apply this model for planning and decision making, including tasks like maximizing the cadastral value of land, according to the cadastral valuation methodology. If planning specialists as
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well will follow the ecosystem environmental assessment methodologies, like Fuzzy Logic based assessment method, they will provide to society a sustainable land management. Economic human activities have the direct goal to increase the quality of life of the population, but at the same time they could have unthinkable unintended effects on the environment and society. With the goal of assuring the sustainable development of our human society it is therefore necessary to evaluate economic activities not only from economic and technological points of view but from environmental and social ones as well. For such an interdisciplinary evaluation there is a need to develop an integrative assessment method, which is simultaneously considering in a modular way on local level specific indicators from different fields, such as economic, environmental and social ones8. 2.1. Fuzzy Logic based assessment method Regarding this subject several materials and books are available at present, trying to clarify the great potential of Fuzzy Logic to be used in the assessment field, but there is still a need to demonstrate its concrete application possibilities9. Fuzzy Logic is based on the knowledge that the reality is rather inexact than precise, because all made human affirmations have a certain free interpretation domain. As a special case the traditional binary logic is part of fuzzy logic, but operating only with two values of interpretation, 0 or 1, yes or no. In contrast to the well-defined sets of the Set Theory, real existing sets are rather fuzzy limited, essentially due to the uncertainties in the used language. A set is fuzzy limited if the assignment of one is not given to all the members of the set. A fuzzy set is defined by the so-called membership function, that can take any values on the interval [0, 1], not only 0 or 1. The key notion when modelling with Fuzzy Logic is the linguistic variable10. The mathematical description of processes requires a precise quantitative presentation of the influences considered. In opposition to this, verbal rules of behavior contain fuzzy formulated knowledge, which is generally more intelligible for human beings. Beyond that, linguistically formulated variables have higher aggregated information content, and therefore it is more difficult to quantify them. The concept of linguistic variables connects the description of verbal and therefore fuzzy information with mathematical precision. The values of a linguistic variable are verbal expressions, called linguistic terms, for instance small. The content of each linguistic term is identified with one fuzzy set and assigned to the related numerical scale of the basic variable by a particular membership function. Thus, the fuzzy sets build the connection between linguistic expression and numerical information10, 11. To process fuzzy formulated knowledge several linguistic variables must be linked by linguistic operators. The connecting rules represent the knowledge, which is stored in a rule base or knowledge base, similar to expert systems. The procedure consists of the following steps: fuzzification, inference and defuzzification (see Fig. 1).
Fig. 1. General operational diagram by fuzzy logic applications.
The fuzzification step is the linguistic interpretation of any crisp input value of a basic variable. For this purpose, the basic numerical interval, the number of the linguistic terms and the according verbal expressions of the linguistic variable have to be previously fixed. After fuzzification, the inference has to draw conclusions from the propositions with regard to the knowledge base. The knowledge formulated as IF-THEN-rules has to be applied to the new fuzzy statements. Inference consisting of aggregation of the IF-parts of each rule, implication and accumulation of the results of the rules THEN-parts, causes a weighting of each single rule on the total result. The result of the implication itself is the assignment of a proposition of a rule to a linguistic term of the output variable. Running all rules generates several different images of the output variable because of the different parts of the output linguistic terms. On the other hand, a crisp output value could be drawn from the resulting membership distribution by several
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procedures in the defuzzification step. The most familiar one is to determine the center of gravity of the area representing the resulting membership distribution of the participating linguistic terms. The abscissa value represents then the crisp output value. Such a knowledge based approach means the methodical attempt to substitute missing algorithmic procedures by using human knowledge. Thus, even partially fulfilled conditions result in partially fulfilled conclusions, so these conditions are considered also in the result. Therefore, the possibility to consider uncertain information and interdisciplinary knowledge in systems modelling is given. This fact encourages applications not only for the technological field, but also for the field of environmental assessments as well as for sustainability assessments9, 12. For this goal several assessment criteria have to be taken into account by simultaneously considering several indicators from different fields, as it is the specific case in the field of sustainable land management13, 14. 2.2. Provisioning ES assessment tools analysis In general, assessment tools can be divided in two categories: decision making tools and planning tools1, 5, 6, 15-30. There many tools available, that for data source use information not from national registries or in offline mode. There are indicators dashboards, electronic and paper review tools, atlases, screening tools, model building for assessing some indicators. Advanced development prospects have tools, what for data input use official EU MS data registers. Most prospective tools for data input should accept data in INSPIRE (Infrastructure for spatial information in Europe) specification31. This makes possible to build huge models for all EU territory using united data specification and easily applicable in every EU state. In European Commission Technical Report “Mapping and Assessment of Ecosystems and their Services”32 the attention is focused on EU mapping, as also there are described classification and harmonization aspects. Attention is drawn to geographic information system (GIS) technologies; we can make very effective spatial analysis using it32. Currently in Latvia and in EU is actively implementing INSPIRE Directive33 and till 2020 there are many harmonized spatial data sets should be available. For example, datasets like: hydrography, protected sites, land cover, agricultural and aquaculture facilities, area management, atmospheric conditions, bio-geographical regions, energy resources, habitats and biotopes, land use, soil, etc. 2.3. Information system’s architecture for land development Land and Land parcels have four categories of characteristics: 1) physical phenomena (soil, ground water, irrigation/drainage conditions, land use and etc.); 2) infrastructure (roads, facilities and etc.); 3) location and environment; 4) land as property characteristics34. Thus Land management tasks are to administer and to control land management process (as physical phenomena and as property) and to plan land development35. The following land development planning concepts exist: sustainable development, social oriented development and market oriented development. All planning concepts works good only if development aims are clearly defined. In each planning iteration current situation is compared with predefined indicators, thus allows making decisions to correct trends. Can be divided three land development levels: national, regional and municipality level. Basically sustainable development concept is adopted at national level first and gradually is distributed to regional and municipality level with time. The main idea of the theory is - the maintenance of environment in order to do not worsen the situation in nature as such and to do not make certain territories population’s socio-economic situation poorer in long term. In “Latvian sustainable development strategy till 2030”35 has defined, that for effective natural capital management, it is necessary to calculate the National natural capital amount. For a community to be able successfully maintain, increase and sustainably use natural capital, it is necessary to calculate its value. Calculating the value of natural capital, the total economic value concept should be used, which deals with both: direct and indirect use values and non-use value30. INSPIRE directive will provide a wide range of harmonized datasets by 2020. Latvia is also implementing the INSPIRE directive and the INSPIRE data themes will serve as an excellent source of data for EU harmonized mapping and capturing. To define provisioning ES automated valuation process model and to develop IS architectures, we should clearly understand with what kind of input/output data we should work with. We
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understand that data specifications in countries and organizations may differ. But to make this data interoperable, some international standards and European directive can guide way to us. Taking into account that basic data unit in our model will be land parcel and information about land, Land Administration Domain Model (LADM) was analyzed36. But some following aspects (necessary for us) are outside the scope of this standard: these are construction of external databases with party data, valuation data, land use data, land cover data, physical utility network data and modelling of land administration processes. This means, that we cannot use this international standard as the basis for creating valuation process model. At the following step INSPIRE directive, some implementing rules and some technical guidance are analyzed. INSPIRE defines data specifications for 34 themes like cadastral parcels, land use, land cover, agricultural and aqua cultural facilities and etc. But some aspects like valuation data and data replication are stile out of scope. So we decided to build IS architecture and process’s model focusing on INSPIRE directive and on classical approach like using non-spatial information, Simple Object Access Protocol (SOAP) web services (WS), data base (DB) links and replicas to get access to country/institution specific unharmonized data. Developed IS architecture (see Fig. 2) provides possibility to work with text and spatial data from national data holders / National Mapping and Cadastral Agency (NMCA). Data Exchange is ensured using DB links and replications for big datasets, Web Feature Service (WFS) for spatial data sets and feature types without geometry, SOAP for text data exchange. To make data interoperable - comprehensive data transformation/reclassification module must be implemented where data from unharmonized datasets will be harmonized to allow data processing in the harmonized environment. We must not underestimate the data transformation / data interoperability problem, because for some datasets this problem cannot be solved just using only technical means, for example, classification and normative act harmonization also could be necessary. To ensure sustainable development simplified Banfield’s37 rational planning model are adopted as infinite loop. Loop steps: 1) data collection, 2) analysis of data (in planning or decision making process), 3) forecasting the future (planning), establishing goals (planning), design of alternatives (planning), 4) assessment, indicator screening, comparing wit goals, 5) reaction to the land development trends. If necessary, using alternative development plans as input for step 1.
Fig. 2. Information system’s architecture for land development.
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2.4. Automated process for valuation of the land management Based on developed IS architecture and analyzing evaluation process for land management tasks, automated valuation process’s model for land sustainable management was developed (see Fig.3). The process also can be used for maximizing provisioning EU monetary value (based on cadastral value), if this will be defined as process goal. Taking into account specifics of NMCA valuation process models can be used as Regular process for operations on massive datasets and as Irregular process for online decision making.
Fig. 3. Automated process for valuation of the land management.
Regular process deals with huge datasets and on a regular basis. For example, it can be integrated in national land valuation processes or national territorial planning processes. For data input (see Fig. 3; steps: 1, 2, 3, 4) and output (see Fig. 3; steps: 12, 13, 14) national registers are used. It can be data in national specifications or in more interoperable way - in INSPIRE specification. In each next valuation process loop output data becomes as input. For huge datasets is desirable to use DB links or replicas (see Fig. 3; steps: 5, 11) for information exchange processes. On regular basis (for example yearly), Business Process Execution Language (BPEL) process (see Fig. 3; step: 6) starts to harvest information from EU MS registers and, using predefined rules, transform data to interoperable condition. At this step most of the issues can be solved using GIS technologies, such as geoprocessing and table joining. Further planning specialists (using, for example, Fuzzy Logic based assessment method) create land
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development plans (see Fig. 3, step: 7), then going mass land valuation process (see Fig. 3, step: 8) (using predefined rules) and BPEL (see Fig. 3, step: 9), compare planning and valuation results with predefined goals and if goals are not reached return land management project for redevelopment (see Fig. 3; step: 7), if goals are reached transform information to each national register’s format (see Fig. 3; step: 10) and writes loop results to MS national registers. Banfield’s37 Rational planning model is implemented as loop from steps 7, 8, 9 in Fig. 3. Irregular process serves for online decision making on land parcel level. For information exchange are used WS (see Fig. 3; steps: 5.1’, 5.2’, 11.1’, 11.2’) based solutions, that allow not to download huge amount of information, but work on specific object level. All WS should be developed using united XML schema repository to improve possibility of data interoperability. Banfield’s37 rational planning model is implemented as loop from steps 7’, 8’, 9’ on Fig. 3. Other irregular process steps are similar to regular process’s steps. 3. Results and discussion After analyzing literature and internet resources about provisioning ES assessment tools, it can be concluded, that most perspective assessment tools are built using GIS technologies. A new complex approach should enter into practice soon, where for provisioning ES assessment will be used complex solutions based on spatial data infrastructure (SDI) concept and as data source - authoritative, interoperable, up-to-date data will be used. This makes possible real time data processing including online decision making. The Land management tasks are to administer and to control land management process (as physical phenomena and as property) and to plan land development. Many approaches can be used to realize this tasks, the most farsighted is sustainable development concept, where depending on goals always is possible to make decisions (or to plan changes) to correct development trends. To bring closer the automated implementation of these tasks, the IS architecture for land development is defined. IS architecture is focused to use interoperable data (like INSPIRE datasets), but not only. The fragile place of architecture is data transformation module, because for some datasets interoperability or data reclassification problem cannot be solved just using only technical means, for example, classification or normative act harmonization also could be necessary. To implement IS architecture for land development, an automated general process model for land management was developed. Process model uses modern information technologies and provides regular and irregular process runtime capabilities. Regular process model is oriented on huge amount of data processing for planning purposes. Irregular process is oriented on interactive or on demand – online decision making and provides a capability to operate with data at feature/object level, what can be useful in emergency situations and for small land areas high accuracy sustainable development. 4. Conclusion For assuring local sustainability in the form of shaping sustainable human settlements an appropriate sustainable management on a local level is necessary to be carried out. In the process of operationalization of sustainable development an important part is represented by developing innovative models for sustainable land management. A fuzzy logic based method has been discussed offering new possibilities by its potential to integrate complexity in the systematic and exact mathematical approach and assuring a transparent assessment. Provisioning ES automated valuation general process model for sustainable land management was defined and published at this article, which makes possible to continue to develop automated process models for sustainable land development, focused on the use of SDI technologies and fuzzy logic based assessment method. References 1. 2. 3. 4.
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Jurijs Holms is Phd student of Information Technology faculty of Latvia University of Agriculture. He scientific interests are Spatial Data Infrastructure, Geomatics, Web Map and Feature Services. His main research is about application solutions of spatial data infrastructure. Contact him at [email protected].