IJERD – International Journal of Environmental and Rural Development (2017) 8-2

Research articleerd

Application of Integrated Modeling System to Ecosystem
Services Evaluation
MAKOTO OOBA*National Institute for Environmental Studies, Fukushima Branch, Fukushima, Japan
Email: ooba.makoto@nies.go.jp

KIICHIRO HAYASHI
Nagoya University, Nagoya, Japan
Received 14 December 2016

Accepted 11 November 2017(*Corresponding Author)

Abstract In recent years, many case studies have been conducted on ecosystem service (ES)evaluation in both developed and developing regions. A method to integrate the ES evaluation
has hitherto been undeveloped and a few systems been proposed for the same. Based on ourprevious studies on estimations of the potential supply of ES in both rural and urban areas, amodeling system capable of performing an integrated evaluation of ES is automaticallyapplied into our study. A geographical and ecological information database of the ecosystem
in Japan was connected, and an interface of an estimation model of the ecosystem services
was connected to an integrated system. A system with a visible interface and clearly definedconcepts and concept relationships, is helpful not only to researchers who are unfamiliar with
GIS and modeling, but also to stakeholders and decision makers.
Keywords environmental planning, ecosystem services (ES), Geographical Information
System (GIS), meta-modeling, semantic modeling

INTRODUCTION
The concept of ecosystem services (ES) refers to a variety of services ranging from highly related
ecological functions (supporting and regulating services, e.g., material cycles inside ecosystems) tohighly related human society functions (provisioning and cultural services, e.g. food production,
logging, and recreation space provision).
Recently, many quantitative and spatial case studies on ES have been conducted using thegeographical information system (GIS, e.g., Verhagen et al., 2015). Large projects on the assessment
framework of ES, such as InVEST (Jackson et al., 2016), Oppla (Verweij et al., 2015), TESSA (Pehet al., 2013), and the Artificial Intelligence for Ecosystem Services (ARIES project, 2016), are also
being conducted.
Rapid emergence of overwhelming volumes of data in recent years, for example the increasing
number of elevation, landuse, and species occurrence databases, make choosing a suitable dataset forour study difficult. Many portal sites and meta-database projects for efficient database location are
now being developed, e.g., KNB (The Knowledge Network for Biocomplexity), and OBIS (OceanBiogeographic Information System). Despite great efforts to digitize data location, many researchers
still search for files using browsers and download them individually, unzip, and convert them to asuitable format (file type, encodings, geographical projection, etc.), manually.
The existence of diverse methods of ES assessment in terms of aims and scopes presents aserious barrier to the sharing of ES studies, especially to the integration of various ES evaluations.
For example, various methods of quantifying ES have been proposed: lookup table, statistical models
explained by environmental variables, and mechanistic models based on ecology and earth sciences(Verhagen et al., 2015). The lookup table is fairly straightforward and transparent. However, itsaccuracy and its application are limited by its high dependency on location and disregard for the
temporal and spatial continuity of ecosystems. Mechanistic models can perform highly accurate andⒸ ISERD100

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detailed simulations; however, models developed by researchers or projects cannot be exchanged
because highly specific ecosystem and ES concept terminologies are assigned.
Semantic modeling and semantic meta-modeling (Villa et al., 2014) are attractive methods inthe big-data and divergence-model era. Based on machine-readable knowledge bases, such as unit
systems, physical constants, formulae, and relationships between ES and landuse, a distributed
database and model are automatically assembled to the requirements of a user.
OBJECTIVESIn this paper, the authors applied semantic modeling to our previous estimation method of urban ES(Ooba et al., 2015). A detailed description of the current situation of data-model rich ecology is givenin the former section, where the needs of semantic modeling are discussed. In the next section, detailsof a trial application of the semantic modeling on urban ES assessment are given.
BACKGROUND OF SEMANTIC METHODOLOGY
Before the downsizing of digital electronic devices, measuring and observing field data such as
geographical and ecological data was rather difficult in terms of accessing study areas, recording
outside, and security. However, acquiring field data from various electric sensors, data loggers,
wireless internet, remote sensing, global positioning system, etc. is much easier. Growing memory
and bands of the networks governed by Moore's law results in relatively low-cost storage andtransport of geographical and ecological data. Although the explosive availability of the data
provides equal accessibility, new problems that have already been pointed out in the introduction
section, still occur.
One of the solutions to this problem is the development of meta-databases. These systems needdata annotations such as author names, data type, location and time of observation, and so on. For
description method for the annotation, Dublin Core is commonly used and a related search engine
for ecology has been developed, e.g., Morpho by the KNB project.
Statistical methods of computer modeling, including spatial analysis, have made marked
progress with the growth of data availability. Free and commercial software for statistical modelingare well developed as they are not required to list software names. In addition, distributed dataanalysis frameworks are also available (e.g., Spark). It also mentions that the advancement ofmachine learning in this decade must be focused on.
It seems that the abundant data and models are not enough to address the concerns of policy
makers and stakeholders regarding biodiversity, ecosystem, and ES. In addition to the diverse modelsof ES that are difficult to integrate, these models that assess and interpret ES do not meet the morepractical needs of policy makers, stakeholders, and governmental and private planners (Urban andrural design, environmental planning, and ecosystem conservation).
Machine readable knowledge can solve this problem through machine reasoning (Villa, 2009;
Villa et al., 2009, 2014). Ontology in computer science suggests an inference system based on setsof concepts and relationships among the concepts in a domain (e.g. disciplines of science or industrial
sectors). For example, if a system holds an ontological engine and can access a knowledge base of
the unit system, it appropriately converts from a unit (e.g., area, m2) to another unit (e.g., ha).
A system that operates databases and models using such ontological inference provides anintegrated platform for the assessment of ES for specialists, as well as non-specialists, e.g., for aid indecision making regarding ecosystem conservation to keep ES from development. Non-specialists
may not always understand the significance of the contribution of data and models to the success ofa project, such as an environmental problem, in terms of accuracy and effectiveness as well as aspecialist. However, the understanding of some concepts related to the environmental problem issimple enough that they can be shared by both non-specialists and specialists. Once an agreement on
the ontology and the semantic modeling system to be used is established, discussions on the problem
may benefit the data and models based on the system.

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k.LAB software provides an integrated environment for the development of knowledge bases
and data-model complexes, including automatic model selection (meta-modeling). This technology
has been developed in conjunction with the ARIES project. Many studies have been conducted in
the San Pedro River Basin, USA (Bagstad et al., 2016a), Puget Sound, Washington (Zank et al.,2016), the Southern Rocky Mountains (Bagstad et al., 2016b), and so on.
CASE STYDY
A fairly simple and transparent method was proposed for the spatial assessment of ES supply
potential for a rapidly growing city in a developing country (Ooba et al., 2015, 2016; Kay et al.,2015). Integrated potential supply of ES can be estimated from a limited dataset (e.g. digital elevation
map, DEM, and remote sensing image) and it can suggest the relative importance of green spaceinside a study area for the purpose of conserving ES supply. First, elevation and land cover map are
obtained from remote sensing images or geographical maps. Indices related to each type of ES(supporting, regulating, provisioning, cultural, and habitat) are calculated using primary units or
simple models related to a target type of land cover on a km-level grid. In the previous study (Oobaet al., 2014), these indices were collinear due to limited data-sources; one index that was notcorrelated to other indices was assigned to one type of ES category (Table 1). Finally, the ES indices
are aggregated to an integrated index by ES weights estimated from results of internet surveys (Oobaet al., 2016).
Table 1 Methods used to estimate ecosystem services (see also Ooba et al., 2015)
Service category
Supporting
Provisioning
Regulating
CultureHabitat

Proxy variable
Carbon sequestration
Food supply
Inverse of soil erosion
coefficient
Economic value of green
space
Continuity of green
space

Basic units, method details*
3.09(W)
2.98(A)S = 65.41sin2T+ 4.56sinT+ 0.065
C = 1 (R), 0.33(A), 0.0085(W)
Value per unit area as green belt
area A(ha) V = 3.0184A-0.437
ArgGIS tool (Focal statistics)
proximity as 2 km radius

Unitt/y hat/y ha106
JPY/y-* Land-use codes—R: Residential Area, A: Agricultural field, W: Vegetation mainly covered by wood plantFig. 1 Concepts and related attributes for the simple evaluation of ES supply potentialⒸ ISERD

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These manual operations by GIS were transplanted into a semantic modeling environment(k.LAB, ver. 0.9.8) based on ontology. Because there was no common knowledge base (ontology)
that suited our purpose, we created a small, toy-level, ontology based on our method, for a trial
assessment (Fig. 1). Two main concepts of land area and potential ES supply were asserted on a
project in the system as "LandArea" and "ESPotential", and these concepts had their types describedby the following attributes. "LandAreaType": WaterSurface, ResidentialArea, AgriculturalField,VegatationWP (Wood Plant); "ESPotentialType": Provisioning, Regulating, Supporting, Cultural,Habitat. In the next step, models that mention a multiple line statement are defined for file loading
and calculating on the project. A study area is Nagoya City, Japan as the same as in the previous
study, and then spatial context must be defined on the integrated system before model running.
In this study, due to somewhat complex spatial assessment calculation for Cultural and Habitat
ESPotential, these raster files that had been prepared by GIS were loaded into the projects. DEM thatan index of regulating services needs is obtained automatically from the standard knowledge base ofk.LAB accessed to the default repository.
Results that is the same as the results of previous studies (Ooba el al., 2015). Relatively highpotential supplies were exhibited in the area surrounding the city (agricultural fields and secondaryforests).
The source code and data in this study will be open, and the k.LAB software can be used freely
after a simple application in the integrated modeling project site (ARIES project, 2016).
CONCLUSION
Solving environmental problems requires collaborations between, not only biologists and ecologists,
but also other scientists, engineers, environmental planners, and individuals from many other sectors.
Vihervaara (2013) stressed the role of international and ecological monitoring networks, such asILTER, for the assessment of biodiversity and ES, and the usefulness of the acquired data in themitigation and adaptation of climate change. Integrated platforms for semantic modeling may
enhance the potential of the data. Moreover, ES tools suitable for non-specialists under consensus on
ecological concepts may provide new insight into regional planning from the perspectives ofbiodiversity and ES. Bagstad et al. (2014) mentioned that the semantic framework using hypothesis
driven approaches or trusted process-based models is more confidential than statistical modelingfrom a reason of being able to validate of reasoning.
We have reported on the results of our toy program for the popups of popularization of semantic
modeling in the Asia region; however, we can elaborated on more complex functions of the wellestablished k.LAB system. Many researchers have indicated more complicated usage of the systemsuch as baysian model, watershed model, mechanistic model of ecosystem and so on, which havebeen researched in the ARIES project.
ACKNOWLEDGEMENTS
This study was supported by the Environment Research and Technology Development Fund (1-1401,2-1404, MOE, Japan). The authors thank Prof. F. Villa and Dr. K.J. Bagstad for their valuable advice
regarding ARIES project and k.LAB system.
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