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GIS- and RS-based spatial decisionsupport: structure of a spatialenvironmental information system(SEIS)G. Bareth aa University of Cologne, Geography Department , GIS & RSAlbertus-Magnus-Platz , Cologne, NRW, 50923, GermanyPublished online: 18 May 2009.
To cite this article: G. Bareth (2009) GIS- and RS-based spatial decision support: structure ofa spatial environmental information system (SEIS), International Journal of Digital Earth, 2:2,134-154, DOI: 10.1080/17538940902736315
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GIS- and RS-based spatial decision support: structure of a spatialenvironmental information system (SEIS)
G. Bareth*
University of Cologne, Geography Department, GIS & RS Albertus-Magnus-Platz, Cologne,NRW 50923, Germany
(Received 19 March 2008; final version received 8 January 2009)
The development of spatial decision support for environmental resource manage-ment, e.g. forest and agroecosystem management, biodiversity conservation, orhydrological planning, started in the 1980s and was the focus of many researchgroups in the 1990s. The combined availability of spatial data and communica-tion, computing, positioning, geographic information system (GIS)- and remotesensing (RS)-technologies has been responsible for the implementationof complex SDSS since the late 1990s. The regional GIS-based modellingof environmental resources, and therefore ecosystems in general, requiressetting-up an extensive geo and model database. Spatial data on topography,soil, climate, land use, hydrology, flora, fauna and anthropogenic activities haveto be available. Therefore, GIS- and RS-technologies are of central importance forspatial data handling and analysis. In this context, the structure of spatialenvironmental information systems (SEIS) is introduced. In SEIS, the input datafor environmental resource management are organised in at least seven sub-information systems: base geodata information system (BGDIS), climateinformation system (CIS), soil information system (SIS), land use informationsystem (LUIS), hydrological information system (HIS), spatial/temporal biodi-versity information system (STBIS), forest/agricultural management informationsystem (FAMIS). The major tasks of a SEIS are to (i) provide environmentalresource information on a regional level, (ii) analyse the impact of anthropogenicactivities and (iii) simulate scenarios of different impacts.
Keywords: environmental information system; GIS; regional modelling; remotesensing; resource management; spatial decision support system
1. Introduction
A spatial decision support system (SDSS) is defined as a system which comprises a
decision support system (DSS), a geographic information system (GIS), and a model
base management system (MBMS). The latter, as well as the knowledge analysis, is
part of DSSs. In Figure 1, the general architecture of a SDSS is shown according to
Leung (1997) and Malczewski (1999). The idea of such a system is the overall SDSS
development environment. This development combines the state of the art in
software and knowledge engineering and in spatial data analysis. The latest
approaches are, for example, described by Laudien and Bareth (2007) using Java
and ESRI’s ArcGIS Engine for SDSS programming. The centre of the SDSS is
*Email: [email protected]
ISSN 1753-8947 print/ISSN 1753-8955 online
# 2009 Taylor & Francis
DOI: 10.1080/17538940902736315
http://www.informaworld.com
International Journal of Digital Earth,
Vol. 2, No. 2, June 2009, 134�154
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the expert system shell, which coordinates the whole system. It is responsible for
the information flows and directs control flows. The communication between the
DBMS, the MBMS, the knowledge base, and the spatial data handling unit is
implemented by interfaces. The interface for a decision maker is usually a graphical
user interface (GUI) which provides access to the system for spatial decision support
(SDS).
For environmental resource management, models and the availability of spatial
data play an important role. Consequently, the development of such models is of
central importance for the whole approach. Therefore, the focus in this contribution
will be on (i) the structure of an adequate spatial database for SDSSs that focus on
environmental resource management, (ii) the integration of models into the SDS,
and (iii) the interfaces which are necessary for the whole system to ensure the
communication between the SDSS components as shown in Figure 1.In the last two decades, public interest in environmental issues owing to human
activities in forest and agricultural ecosystems has increased significantly. Therefore,
these topics became part of public policy. For policy decision making, information
for decision support is essential and derives from research activities (McCloy 2006,
Sharma et al. 2006). For generating information about related environmental
problems and resource management, the development of complex ecosystem models
during the last 30 years can be regarded as one of ‘the biggest revolutions in the
study of soil C/N cycling’ and related processes (Shaffer et al. 2001), which are
closely related to global and climate change research. Traditionally, these models
were developed and used for point or site-specific applications (Hartkamp et al.
1999). Independent from these studies, regional estimates were calculated on the
basis of the ecosystem approach, which multiplies the area of a defined ecosystem
with e.g. N2O-emissions (Matson and Vitousek 1990, Jungkunst et al. 2006).For complex regional modelling or decision support for agriculture and/or
forestry, the latter approach is not sufficient (Beauchamp 1997, Jones et al. 2003).
Therefore, using a GIS is necessary. Although, in some DSS approaches, spatial
Expert SystemShell
UserGUI
Experts
DBMS MBMS
KnowledgeDatabase
ModelBase
Attribute-Database
DecisionMakers:Demandfor SDS
Spatial Data Handling
Geo-Database
GIS-and RS-Analyses
linkagesplausible linkages
UserGUI
Experts
DBMS MBMS
KnowledgeDatabase
ModelBase
Attribute-Database
DecisionMakers:Demandfor SDS
Spatial Data Handling
Geo-Database
GIS-and RS-Analyses
Figure 1. Architecture of a spatial decision support system (SDSS) (modified from Leung,
1997).
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analysis has been considered (Matthews and Knox 1999), it has been used less
frequently in ecosystem scenario simulation. Shaffer and Ma (2001) state that
process-based agro-ecosystem models interfaced with GIS will be the next ecosystem
model generation.
In general, a GIS provides methods for spatial data capture, storage, analysis,
and presentation (Bill 1999). The elements of a GIS are hardware, software, data,application, and user. GISs were introduced in the late 1960s (Burrough and
McDonnell 1998) and worked only on mainframes. The fast development of
computer hardware and software facilitated the rapid development of GIS in the
1980s for application on workstations. In the early 1990s, powerful GIS-software was
available for MS-Windows operating systems and supported the wide distribution of
applications of GIS-technologies. Nowadays, GIS-applications and -users are
increasing rapidly, facilitated by WebGIS applications such as VirtualEarth or
GoogleEarth and the GIS developments for personal location-based services,
routing and navigation. Latest developments are based on spatial web portal
(SWP) technologies introduced by Yang et al. (2007).
Until the late 1980s, the development of process-based ecosystem models and
GIS were separated. This changed significantly in the beginning of the 1990s. The
interfacing of such models with GIS was part of several research activities to satisfy
the increased demand for regional information for decision support (Engel et al.
1993, Hoogenboom et al. 1999). Still, owing to the limited data availability forregional applications, these applications were very restricted. In the late 1980s,
governmental bureaus and/or commercial companies started offering more and more
digital spatial data products. Interfaces for the import of these data into commercial
GIS software were implemented (Maidment 1996). Consequently, the increased
availability of spatial data lead to more regional and national GIS applications
interfaced with process-based ecosystem models (Falloon et al. 1998, Li et al. 2001,
Brown et al. 2002, Jones et al. 2003). Recently, the establishment of national
geodata infrastructure has become the focus of many research activities (Bilo and
Bernard 2005).
Nowadays, the limitation of process-based environmental decision support is still
the availability of spatial data. This problem derives from the fact that the process-
based models were developed for site or plot scales and input data for this scale are
not available on regional or national levels. There are three ways to solve this
problem: (i) to sample the necessary input data, e.g. management data, detailed land
use information etc., which is usually not possible for large areas, (ii) to aggregate
and generalise the inputs from available sources (Li 2000), which is sometimes not asatisfying approach, or (iii) to use GIS-, remote sensing (RS)-technologies and data
generation methodologies, and data mining techniques to create the lacking spatial
information for the regional applications (Bock and Kothe 2005, Eastman 2005,
Hansen 2005).
The availability of and the access to digital geodata is a key issue in macro, meso
and micro scale GIS modelling of environmental issues (Bareth and Yu 2002,
Bambacus et al. 2008) and consequently for SDS for resource management as well.
While it is still possible to collect necessary geodata with a limited amount of time
and money for micro scale modelling, it is essential to have secondary sources for
meso and macro scale modelling. Owing to the limitations of data availability and
data detail, the GIS- and RS-based production of new data becomes essential in
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regional resource management (McCloy 2006). The generation of new data has to
satisfy the demand of the inputs for available process-based ecosystem models.
Therefore, to facilitate the technologies for data generation and data mining, setting-
up an extensive geodata infrastructure for serving regional ecosystem modelling
approaches is a precondition for spatial modelling in general (Goodchild 1996) and
this counts for resource management, too. Bill and Fritsch (1994) call a GIS for
environmental applications an environmental information system (EIS). However,the term EIS is often also used in a non-GIS context. Consequently, a GIS for
environmental applications in a spatial context can be regarded as a spatial
environmental information system (SEIS).
A SEIS is considered as an extensive geodatabase or rather a spatial data
infrastructure, which includes all data for modelling purposes, the model itself, as
well as the functions for data generation and data mining. Additionally, metadata
must be included in the SEIS for considering international standards defined by ISO
and OpenGIS (Guptill 1999, Bernhardsen 2002). Another important point is the
information about data quality in the decision making process (Veregin 1999). In
general, information about the data quality is lacking in many GIS applications
(Heuvelink 1999). However, knowledge of data quality is essential. ‘If data quality is
an important property of almost all geographical data, then it must affect the
decisions made with those data. In general, the poorer the quality of the data, the
poorer the decision. Bad decisions can have severe consequences’ (Longley et al.
1999). This is a very important issue because the SEIS serves as a SDSS for
environmental resource policies in managed and unmanaged ecosystems.
2. Spatial decision support systems
The development of SDS for environmental resource management, e.g. forest and
agro-ecosystem management, biodiversity conservation, or hydrological planning,
started in the 1980s and was the focus of many research groups in the 1990s (Wright
et al. 1993, Leung 1997, Naesset 1997, Crist et al. 2000). While the first developments
derived from the early enthusiastic attempts to develop decision models in the 1960s
and 1970s (Davis and McDonald 1993), the technological progress in electronical
computing, GIS- and RS-software development, and communication infrastructure
in the 1990s enabled the design and implementation of more complex SDSSs such as
NELUP, ArcForest, SARA, or RELMdss (O’Callaghan 1995, Mowrer 1997).
NELUP, the NERC-ESRC Land Use Programme, was developed for rural land
use planning on the basis of watershed modelling approaches (O’Callaghan 1996).Hydrological, agricultural, economical, and ecological sciences were included. It
comprises socio-economic and ecosystem modelling approaches. ArcForest was
developed by ESRI Canada to support forest management and planning (Mowrer
1997). It was developed using the software tools ArcInfo and Oracle for an UNIX
environment. SARA, the Spreadsheet Assisted Resource Analysis, was also
developed for forest management and includes linear programming models for
economic analysis to determine economic-ecological tradeoffs, too (Mowrer 1997).
It was interfaced with GIS on the input and output side. The Regional Ecosystem
and Land Management decision Support System (RELMdss) was developed by
Prof. Richard Church (NCGIA, UCSB) for a Windows environment (Church et al.
2000). The system was developed to generate and implement forest and land use
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plans. Some of the systems of that period included complex economic-ecological
spatial modelling approaches for resource management (Dabbert et al. 1999,
Bouman et al. 2000). Bouman et al. (2000) describe ‘Tools for Land Use Analysis
on Different Scales’. In this paper, comprehensive economic-ecological modelling
approaches are described and interfaced with GIS for regional scenario creation for
Costa Rica. Statistical regression, linear programming, process-based and expert
knowledge modelling approaches were applied in this framework. The same is truefor the regional modelling approach for sustainable and environmentally compa-
tible land use in the state of Baden-Wurttemberg, Germany (Dabbert et al. 1999).
The hardcopy of this contribution comes with a CD-ROM which provides
a windows-based user interface for the regional modelling framework. The GIS
part is programmed with ESRI’s MapObjects and VBA. Several interfaces were
programmed to link the socio-economic and environmental models.
Simultaneously, governmental bureaus, e.g. for surveying and mapping, made
enormous efforts in the 1980s to establish digital databases for topography, geology,
soil, climate, etc. (Longley et al. 2005). These developments actually started in the
early 1970s and in this first stage, the focus was on pure spatial data (Bill and Fritsch
1994). At the same time, official and/or commercial companies started to provide
digital spatial data, e.g. remote sensing data. The launch of Landsat-1, formerly
known as ERTS-1 in 1972, was the start to involve satellite analysis in resource
management (McCloy 2006). Further milestones in remote sensing sensor develop-
ment were the development of radar systems (e.g. Seasat, ERS-1, JERS-1), thelaunch of SPOT-1 in 1986 with a linear array sensor, of IKONOS in 1999 with a
1 m resolution, and the latest launch of the radar sensor TerraSAR-X with a 1 m
resolution (http://www.infoterra.de) (Lillesand et al. 2004).
Finally, the availability of GPS technologies since 1988 (NAVSTAR-GPS) and
the full availability since 1995 can also be considered a milestone for decision support
of environmental resources. Accurate and mobile positioning and navigation
measurements are a precondition for spatial data capture and analysis. Especially
for environments with poor topographic data coverage, the positioning technologies
are a key method to capture environmental and socio economic data. Before the turn
off on 1 May 2000 of the SA (selective availability), the use of correction systems
in positioning was mandatory. Global DGPS providers such as Omnistar (www.
omnistar.com) were more important in that period. Nowadays, WAAS services
and/or regional DGPS services such as SAPOS in Germany (http://www.sapos.de)
enable cm accuracies in positioning with L1- and L2-GPS receivers.
Since the new millennium, a new generation of SDSSs is in the focus of theresearch community considering the latest developments in remote sensing, GIS, and
geodata infrastructure. The realisation of software developments, which are
independent of operating systems, but dependent on high-performance network
capabilities and partly grid computing environments, are in progress. The GLOWA
DANUBE (http://www.glowa-danube.de) and the GLOWA IMPETUS (http://
www.impetus.uni-koeln.de) projects for example represent such spatial modelling
approaches for water management decision support with extensive knowledge,
model and geo databases. The SDSSs are developed in Java including different
approaches of GIS and RS analyses for a network-based client-server environment.
Similar SDSS frameworks were introduced in the early 2000s. For example, David
et al. (2004) presented the GEOLEM approach as an interoperable modelling
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framework. GEOLEM (Geospatial Object Library for Environmental Modelling)
aims for the elimination of GIS-specific knowledge in a modelling framework and
vice versa (David et al. 2004). The above mentioned GLOWA projects exactly follow
this new paradigm for SDS including a modelling framework. Additionally, it is
possible to integrate expert knowledge by using expert systems. Malczewski (1999)
describes expert systems in a spatial context as spatial expert systems (SES).
Integrated systems of SDSS combined with SES are considered ‘intelligent SDSSs
(ISDSSs) or spatial expert support systems (SESSs)’.
Finally, the combined availability of spatial data and communication, computing,positioning, GIS- and RS-technologies were utilised for the implementation of
complex SDSS since the late 1990s. The regional GIS-based modelling of
environmental resources and therefore of ecosystems in general requires setting-up
extensive geo and model databases. Spatial data about topography, soil, climate, land
use, hydrology, flora, fauna and anthropogenic activities have to be available. The
lack of adequate spatial databases is still a limiting factor in SDS and accordingly in
regional ecosystem modelling. Therefore, GIS- and RS-technologies are of central
importance for spatial data handling and analysis to implement the required
database. As introduced above, a GIS-based EIS is defined as a SEIS. It uses the
latest technology developments and can be considered as a spatial data infrastructure
for decision support that is related to questions and problems of environmental
resource management.
3. Structure of SEIS
The establishment of SEIS requires setting-up of a extensive geo- and attribute-
database. Especially modelling of C- and N-cycles of ecosystems requires numerous
input parameters e.g. pH, soil texture, fertilizer N-Input, animal waste input, use ofirrigation water, dates of sowing and harvest, yield, etc. (Charles-Edwards et al.
1996, Grant 2001, Seppelt 2003). In general, data about climate/weather, hydrology,
soils, land use, and management are essential and must be available in SEIS.
Furthermore, methods and models for data analysis and data presentation have to be
integrated.
The structure and elements of a SEIS are visualised in Figure 2. Owing to the
definition and the defined task of a SEIS, this computer based system is understood
as a GIS for environmental resource applications and includes seven different
information systems which are:
. Base Geo Data Information System (BGDIS)
. Soil Information System (SIS)
. Hydrological Information System (HIS)
. Climate Information System (CIS)
. Land Use Information System (LUIS)
. Spatial/Temporal Biodiversity Information System (STBIS)
. Forest/Agricultural Management Information System (FAMIS)
Most important for spatial matching and for georeference of all data in a SEIS is
the integration of framework datasets within a Base Geo Data Information System
(BGDIS) (Furst et al. 1996). The use of an available information system provided by
official sources, such as official bureaus for surveying and mapping, is recommended
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to meet this requirement. For example in Germany, there is an official digital
topographic database in vector format for different scales available (1:25,000;
1:250,000; 1:1,000,000) (http://www.atkis.de). The use of unique base geodata
enables the exchange of data between independent projects and applications. The
BGDIS needs to provide topographical data, elevation lines or a digital elevation
model (DEM), a set of ground control points (GCPs), and an administrative
boundary data set. The latter are important because statistical data for adminis-
trative units can be linked to GIS for the implementation of spatial information
systems (Bareth and Yu 2002). The integration of a DEM is important for relief
S-T Biodiv GDB
Spatio-Temp. Biodiv. Information System (STBIS)
F/A Managm.. GDB
F/A Management Information System (FAMIS)
• Plant communities• Div. invertebrates• Div higher plants• Div. protozoans• Expert knowledge
• Crop rotation maps (LUIS)• Agric. Mgmt. Maps • Agric. Statistics• Agric. Mgmt. Surveys• Expert Knowledge
SEIS GDB
ModelsINPUTS
Web-GISUser Interface
Soil GDB
Soil Information System (SIS)
Climate GDB
Climate Information System (CIS)
Land Use GDB
Land Use Information System (LUIS)
Hydrological GDB
Hydrological Information System (HIS)
Base GDB
Base Geo Data Information System (BGDIS)
• Soil maps (analog)• Soil maps (digital)• Soil surveys• Radar remote sensing• Expert knowledge
• Land Use maps (Analog)• Land Use maps (Digital)• Land cover change• Land use scenarios• DGPS Surveys
• Climate maps (analog)• Climate maps (digital)• Official weather data• Climate scenarios• Project Weather Stations
• Hydrology maps (analog)• Hydrology. Maps (digital) • Contamination/Quality• Recharge• Expert Knowledge
• Topographic maps (analog)• Topographic maps (digital)• Administrative boundaries • DEM• DGPS/tachymeter surveys
SEIS Maps
Base Maps
Soil Maps
Climate Maps
Land Use Maps
Hydro. Maps
S-T. Biodiv. Maps
F/A Managm.. Maps
Figure 2. Structure of a spatial environmental information system (SEIS).
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analyses (e.g. computing aspect, inclination, etc.) and 3-D-visualisations. If digital
base geodata are not available or accessible, the establishment of a BGDIS is
necessary. For this task, aerial or satellite-based survey campaigns, digitisation
of topographical maps, and/or GPS/tachymeter surveys are standard methods
(Bill 1999).
The soil information system (SIS) is essential for providing soil parameters forthe agro-ecosystem modelling. Many applications of regional modelling of agricul-
tural issues use soil maps to derive spatial soil parameters for model inputs (e.g.
Falloon et al. 1998, Matthews and Knox 1999, Brown et al. 2002). Therefore, the SIS
has to include (i) spatial soil information and (ii) a detailed description of the soil
types including soil genesis, physical and chemical soil properties (see Figure 3)
(Le Bas et al. 1996). In case soil maps are not available, surveys have to be carried out
or methods of computed soil map generation (McBratney et al. 2003, Scull et al.
2003, Bock and Kothe 2005) have to be applied to create a SIS. If soil maps are
available but lack detailed descriptions of soil types, expert knowledge can be used to
generalise typical soil properties. Usually, soil data are not fully available for scales
ranging between 1:10,000 and 1:200,000. If these data exist, the information level
often does not fit to the model requirements. Therefore, methods of soil map
generation need to be considered for the establishment of a SIS. Examples of GIS-
based soil mapping are described e.g. by Bareth (2001a), Carre and Girard (2002)
and Zhu et al. (1997 2001). Bareth (2001a) describes a knowledge-based approach to
disaggregate available soil information on the basis of a computed relief analysis of aDEM. Carre and Girard (2002, Bock and Kothe 2005) use regression kriging for soil
mapping. In their analysis, they consider landform and land cover, derived from a
DEM and satellite image analysis. An expert knowledge-based fuzzy soil inference
scheme (soil�land inference model, SoLIM) was introduced by Zhu et al. (1997
2001). The authors combine techniques of GIS, fuzzy logic and inference for digital
soil mapping. Reviews on the state of digital soil mapping are given by McBratney
et al. (2003) and Scull et al. (2003). Finally, radar remote sensing can be additionally
applied to derive soil parameters such as soil moisture or soil bulk density (Dobson
and Ulaby 1998, Woodhouse 2005).
The climate information system (CIS) provides the necessary climate/weather
data (compare Figure 2). Available climate maps can be digitised if there are no
digital data available. Usually, weather data is obtainable from official meteoro-
logical bureaus and weather services (e.g. http://www.dwd.de). For the generation of
detailed weather maps from point data, GIS-interpolation methods can be applied
(Running and Thornton 1996, Matthews et al. 1999, Thomas 2002). Again, the basis
for using topography to interpolate weather data is a DEM, which should beavailable in the BGDIS, and land use information which should be available in the
LUIS. Additionally, weather data can be collected from weather stations of research
projects. In general, the availability and accessibility of daily weather data from
official and commercial sources are guaranteed. An important issue is the availability
of various climate scenario data in the CIS which is important to simulate the impact
agricultural systems in the future (Hansen 2005, Sarkar and Kar 2006).
Land use data also have to be available in a SEIS for regional modelling. These
data should be organised in a land use information system (LUIS) (compare
Figure 2). Usually, land use maps are available, but they lack the necessary
information detail. In official land use maps, agricultural land use is generally
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Soil-ID Soil Type1 Cambisol2 Luvisol
Land Use-ID Land Use1 Grassland2 Arable Land
12
Land Use
12
Soil
1.
13
24
Soil - Land Use
Polygon Overlay2.
Result-ID Land Use-ID Land Use Soil-ID Soil Type
1 1 Grassland 1 Cambisol
2 1 Grassland 2 Luvisol
3 2 Arable Land 2 Luvisol
4 2 Arable Land 2 Cambisol
Tabular Overlay
Knowledge-Based Production Rules or Process-based Models (e.g. DNDC) 3.
13
24
Emission
Regional Farm ModelVisual Presentation4.
Result-ID Land Use-ID Land Use Soil-ID Soil Type
kg pro ha-1yr-1N 2
kg N 2 O-N ha-1yr-1
1 1 Grassland 1 Cambisol 150 2
2 1 Grassland 2 Luvisol 150 2
3 2 Arable Land 2 Luvisol 250 3
4 2 Arable Land 2 Cambisol 250 3
N-Fertilization O-Emission in
2 Luvisol
1
2 Luvisol
2
1.1.
13
24
Soil - Land Use
2.
4 2 2 Cambisol4 24 2
3.
13
24
Emission13
24
Emission
4.4.
Result-ID Land Use-ID Land Use Soil-ID Soil Type
kg pro ha-1yr-1N 2
kg N2 O-N ha-1yr-1
150 2
Grassland 2 Luvisol 150 2
3 2 Arable Land 2 Luvisol 250 3
Arable Land 2 Cambisol 250 3
N-Fertilization O-Emission in
kg pro ha-1yr-1N2
-1
150 2
150 2
250 3
250 3
N-Fertilization O-Emission in
Figure 3. Model integration into the GIS-based soil-land use-system approach (modified from Bareth 2005).
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differentiated between arable land, grassland, orchards and some special land use
classes such as paddy fields. For detailed agro-ecosystem modelling, this information
resolution is rather poor. Detailed land use maps which provide information about
the major crops and crop rotations are necessary. The analysis of multispectral,
hyperspectral and/or radar data from satellite or airborne sensors is a standard
method to retrieve such information with remote sensing methodologies (Sommer
et al. 1998, Lillesand et al. 2004). By using a multidata approach, the retrievedinformation from multitemporal and multiannual remote sensing analysis can be
integrated into official land use maps to enhance both the information level (e.g. crop
rotation) of existing land use data and the quality of the land use classification
(Bareth 2001b, Rohierse and Bareth 2004, Bareth 2008). Besides the remote sensing
methods, digital land use data can be created by field surveys or by digitisation
of available land use maps. Additionally, land use data from agricultural statistics
have to be integrated into a SEIS. Information such as arable land per administrative
unit, total sown area, etc. can be retrieved from these sources (Bareth and Yu 2002).
In addition for the simulation of scenarios, the results of land-cover change models
have to be incorporated, which are reviewed by Eastman (2005). Another method to
provide land use change data for agricultural systems is the economic modelling of
regional farms considering environmental or political conditions (Bareth and
Angenendt 2003, Neufeldt et al. 2006).
Given the important controlling influence of water upon ecosystems, it is
essential that ecosystem models provide detailed knowledge of hydrologicalconditions. Therefore, data describing the dominant water cycle components have
to be integrated as a hydrological information system (HIS). These include
precipitation, evaporation, evapotranspiration, soil moisture, groundwater, and
runoff / river discharge. If such data are not available, they can be acquired through
a combination of approaches including field monitoring at reference sites and the use
of remote sensing techniques (e.g. for soil moisture, evapotranspiration, ground-
water), and/or modelling. The application and integration of hydrological models
into the MBMS is also an important task for the SEIS.
The spatio-temporal biodiversity information system (STBIS) finally forms the
key component for the establishment of the multi-trophic diversity models. As such,
the STBIS combines all multi-trophic diversity datasets in a central spatial sub-
information system, and should include high-resolution habitat-specific data
combining terrestrial and aquatic ecosystems. Data from all the other sub-
information systems have to be evaluated for correlations between biodiversity
patterns and the environmental parameters. It is obviously of high importance for
the creation of the SDSS focusing on sustainable resource management ande.g. biodiversity conservation.
Setting-up an agricultural management information system (FAMIS) (compare
Figure 2) is a crucial part of the implementation of a SEIS. For regional agro-
ecosystem modelling, farm management data on regional level e.g. fertilizer N-Input,
animal waste input, use of irrigation water, dates of sowing and harvest, yield, etc.
are a must. The regional availability of this kind of spatial information is rather poor
and research about how to regionalise management data has not yet been intensively
investigated, e.g. how to distribute animal waste and mineral fertilizer inputs in a
region. The application e.g. of average N-fertilizer input derived from agricultural
county statistics is not satisfying for the regional application of agro-ecosystem
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models (Li et al. 2002). Working on a regional scale, it is usually not possible to set
up a spatial database which provides this information for all fields of the farmers in
the region. Therefore, this kind of information has to be generated using results from
farm surveys, from agricultural statistics and from expert knowledge. Using all these
sources, it is possible to define typical farm management for distinct crops and crop
rotations in a special climate for a region which can be linked to the detailed land use
information in the LUIS and the spatial weather data of the CIS (Rohierse 2003).
Consequently, the LUIS combined with the CIS become the basis for the FAMIS
and so for the regionalisation of agricultural management within a region.
Finally, a SEIS is the sum of the described information systems. They are linked
to each other for further analysis and data mining using GIS technologies.
Additionally, models and methods for agricultural environmental modelling have
to be integrated in the SEIS (compare Figure 3). In the context of a SDSS, models
are organised in a Modelbase Management System (MBMS) (Leung 1997) and
interact with the spatial data by defined interfaces (Figure 4). Therefore, it is possible
SEIS GDB
Soil GDB
Agric. Mgmt. GDB
Climate GDB
Land Use GDB
Basis GDB
MBMS
User Interface:
WWW-Browser
Interface forModel Inputs
Web GISInterface
SEIS Maps
SEIS Maps
Interfacefor Simulation
Scenarios
Data flow in one direction
Data flow in two directions
Result n GDB
Result 2 GDB
Result 1 GDB
Interface forModel Outputs
SEIS Tables
S-T Biodiv. GDB
Hyrological GDB
SEIS Maps
Figure 4. Interfaces and data flow for model integration into a SEIS.
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to link or even integrate complex agro-ecosystem models into GIS (Hartkamp et al.
1999, Seppelt 2003) and consequently into a SEIS as well. The selection of the
methods and models which are integrated into a SEIS has a further important
impact on the set-up of the geo- and attribute-database. The methods and models
require distinct input parameters. Consequently, the first step of setting-up a SEIS is
the selection of the methods and models which are integrated in the SEIS later. The
methods and models define the demand of the data and of the required interfaces of
a SEIS.
4. Integration of models in a SEIS
Traditionally, process based agro-ecosystem models or agronomic models are
generally developed and used for site or field scales (Ma and Shaffer 2001,
McGechan and Wu 2001). Therefore, interfacing GIS and remote sensing with
agro-ecosystem models is becoming more important (Schneider 2003). Shaffer et al.
(2001) state that ‘the linkage of process-level models to GIS will be the next
generation of model application for spatially distributed fields or watersheds’.
Hartkamp et al. (1999) describe four different ways to interface GIS with
agronomic models. They also introduce definitions for the different ways of
interfacing:
. Interface: the place at which diverse (independent) systems meet and act on or
communicate with each other.
. Link: to connect.
. Combine: to unite, to merge.
. Integrate: to unite, to combine, or incorporate into a larger unit; to endsegregation.
Linking GIS with models is basically just an exchange of files or data. In this
case, the model is independent from the GIS and vice versa. Only the results of each
system are exchanged. Often it is also described as loosely coupled (Longley et al.
2005). Combining GIS with models, also described as closely coupled (Longley et al.
2005), involves processing data and automatically exchanging data. Finally,
integrating GIS and models describes the real incorporation of one system into
the other. For the user, only one GUI is provided and the model is programmed in a
GIS framework (Laudien et al. 2007). This approach is also defined as embedded(Longley et al. 2005).
Especially for regional modelling of C-and N-dynamics in (agro-)ecosystems on a
regional scale, the integration of such models is important and can be regarded as a
key issue (Shaffer et al. 2001). Available approaches of GIS-model interfaces for
agro-ecosystem models hardly use the GIS-analysis capabilities. For example, the
denitrification and decomposition model (DNDC) (Li et al. 2001) has, in its latest
versions, a regional model part included. Spatial parameters such as land use and
soil are not considered in their spatial relation. GIS-functionalities are only used to
display the results on county levels using a county map. Plant and Bouman (1999)
described a GIS-DNDC-interface of the linking type for the Atlantic zone of Costa
Rica. The introduced approach is not automated and consequently very difficult to
use for other regions. Other examples of linking GIS and models are given e.g. by
Engel (1997), Falloon et al. (1998), Garnier et al. (1998), Ma and Shaffer (2001),
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McGechan and Wu (2001). These studies clearly show the importance of interfacing
agro-ecosystem models with GIS. Therefore, a method for integrating process-based
agro-ecosystem models into a GIS is described in Figure 3.
The method for the integration of agro-ecosystem models into GIS is based on
the soil-land use-system approach (SLUSA) (Bareth et al. 2001). SLUSA is based
on the ecosystem approach described by Matson and Vitousek (1990). Theecosystem approach was improved in order to estimate and visualise greenhouse
gas emissions (CH4, CO2, N2O) from agricultural soils for a distinct region (Bareth
2003). The improvement of the ecosystem approach described by Matson and
Vitousek (1990) was undertaken by using a GIS and available digital spatial data.
SLUSA is a GIS- and knowledge-based approach for environmental modelling. In
Figure 3, the methodology of SLUSA is shown. The first step consists of setting-up a
GIS which contains relevant data and represents the necessary geodata infrastruc-
ture for the modelling task. In the second step, GIS tools are used to overlay climate,
soil, land use, topography, farm management, and other data such as preserved areas
or biotopes (if available). This procedure is the basis for the spatially related
identification of different soil-land-use-systems which represents unique systems
similar to the introduced ecotopes or hydrotopes (Seppelt 2003). In the third step,
measurement data and process knowledge of the region of interest as well as from
literature are linked to these systems. For the linkage, knowledge based production
rules are programmed. The latter ones are commonly used to generate new
knowledge from expertise in knowledge based systems such as expert systems(Wright et al. 1993).
Based on SLUSA, Figure 3 describes the integration of agro-ecosystem models
into GIS. While the steps 1 2 and 4 remain in the method, the third step in SLUSA is
changed. The implementation of the knowledge based production rules of SLUSA is
replaced by the integration of an agro-ecosystem model. All input parameters for the
model are derived from the GIS-database. The advantage of this method is the
automatic spatial modelling. Huber et al. (2002) adapted e.g. the DNDC model
program code for that purpose and necessary interfaces were programmed using
COM (Microsoft component object model), which is also used for GIS-based spatial
data analysis by Ungerer and Goodchild (2002).
In Figure 4, the essential interfaces for model incorporation of and the dataflow
in SEIS are shown. In a SEIS geo-database (GDB), the described BGDIS, SIS, CIS,
LUIS, and AMIS are integrated. In the example of Figure 4, an agro-ecosystem
model can now retrieve the input parameters for a model run from the SEIS. For this
step, an interface for the communication between the model and the SEIS has to bedeveloped e.g. by Laudien et al. (2007) and Huber et al. (2002). Data flow between
the model and the SEIS is in both directions, because the information of what kind
of data the model needs for a model run goes to the SEIS and then the model
receives the input parameters. The modelling results are then stored via an interface
for model output in a new geo-databases of the SEIS. Data flow is in one way from
the model to the SEIS. Finally, it is then possible to use GIS-tools for data export
and visualisation, e.g. for map, table or graphic layout productions. An interface for
user defined creation of simulation scenarios should be implemented as well
(Laudien et al. 2007). This interface would enable the user to create new simulation
scenarios, for example for SDS to develop sustainable strategies in agriculture. Here,
the data flow is in one direction from the user interface via the interface for
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simulation scenarios to the models. Finally, full or limited SEIS data access should
be provided to users via a WebGIS. A WebGIS represents the interface for users who
use standard browsers to access the SEIS.
All interfaces can be implemented by available technologies for a service oriented
architecture (SOA) (Stollberg and Zipf 2008). Latest approaches are based on
programming JAVA and/or AJAX interfaces (Baaser et al. 2006, Laudien and Bareth2007) or should be based on a spatial web portal (SMP) using portlet technologies,
web services for remote portlets, Java specification request, and OGC patial web
services (Yang et al. 2007). Additionally, the communication between the different
databases of the sub-information systems and the different components of a SDSS
can be solved using PHP, CGI and/or XML, too. Additionally, latest OGC standards
such as Web Processing Service (WPS) should be implemented. Thereby, it is possible
to link and integrate complex socio-economic biodiversity/ecosystem models into the
GIS with various methods (Hartkamp et al. 1999, Seppelt 2003) and consequently
into the proposed structure of a SEIS.
5. Discussion and conclusions
The importance of tools for spatial data handling in the framework of decisionmaking and regional resource management for managed and unmanaged forest and
agricultural ecosystems is obvious. Therefore, McCloy (2006) describes regional
resource management information systems (RMIS) in the context of spatial data
handling and analysis tools. The combined application of GIS, remote sensing and
DSS technologies are the keys for such systems (McCloy 2006). According to
Malczewski (1999), the establishment of a SESS integrates the methods of SDSS and
SES. Consequently, this also takes into account for the SDS for resource manage-
ment (Jones et al. 2003). GIS and remote sensing can be used to bridge the demands
from site-specific applications to regional and national ones (e.g. Plant and Bouman
1999, Brown et al. 2002, Chowdary et al. 2005). Shaffer et al. (2001) state the
importance of GIS interfaced agro-ecosystem models. The technical issues of how to
interface models with GIS are widely discussed in literature, e.g. by Hartkamp
(1999), Longley et al. (2005), Miller et al. (2005) or Seppelt (2003). In addition,
numerous solutions and applications are described (e.g. Ma and Shaffer 2001,
McGechan and Wu 2001, Jones et al. 2003), also in a spatial decision context (e.g.
Dabbert et al. 1999, Laudien et al. 2007).
While there is immense knowledge available about SOA and SWP technologies(Bambacus et al. 2007), SDSS implementation (Laudien et al. 2007, Stollberg and
Zipf 2008), model development, calibration and evaluation, there is still a need for
improvement to simulate e.g. the whole C- and N-cycle in forest and agro-ecosystems
on a regional level (Shaffer et al. 2001, Benbi and Richter 2002). McCloy (2006)
points out that the key level of management in terms of resource sustainability is the
regional level. For regional applications of GIS interfaced models, the problems are
more on the side of data availability and quality as well as on extrapolation of the
site-specific models (Heuvelink 1999, Bareth 2005). On the one hand, quality and
accuracy is a big issue for the development of models and the evaluation and
calibration is always discussed. On the other hand, regional applications of agro-
ecosystem models rarely discuss the quality of the regional input parameters and
their impact on simulation results. In particular, the quality of available soil type
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parameters, land use data, and agricultural management does not fit to the
philosophy of site specific process-based models which simulates what crop is grown
where, on which soil, in which climate, and with what kind of management (Bareth
2005). This is also stated by Kersebaum et al. (2007). By comparing different agro-
ecosystem models, the authors conclude that ‘applications of agro-ecosystem models
on a field or regional level are mostly characterised by a high uncertainty of input
data, especially regarding soil and management information’. Focusing on the latter
problem, the SEIS approach proposes a solution, the multidata approach (MDA) for
enhanced land use mapping (Bareth 2008), in the context of the LUIS and the
FAMIS. Here, the SEIS can significantly enhance the spatial modelling framework
by providing adequate spatial data. Consequently, the quality of the regional input
data is a key issue in spatial resource management and affects the decisions made
with those data: ‘the poorer the quality of the data, the poorer the decision’ (Longley
et al. 1999).
Regional modelling with agro-ecosystem models means the handling of an
extensive geodata infrastructure. Hence, GIS and remote sensing technologies have
to be applied. Those tools also provide measures for data quality for the
evaluation of the spatial input data. Additionally, concepts and standards for the
description of the data, the metadata, are integrated in many commercial GIS and
remote sensing software. Therefore, the regional modelling of agricultural-
environmental issues should be based on a strict architecture: the GIS- and
RS-based SEIS, which enables
. the data capture (also of spatial data),
. the data storage and management (also of spatial data),
. the data analysis and manipulation (also of spatial data),
. the generation of new data (for model input),
. the interfacing of GIS and models,
. the application of metadata standards, and
. the presentation (maps, tables, etc.) of the SEIS (including the modelling
results).
The proposed structure of the SEIS provides the necessary database
functionalities combined with the capability to deal with spatial data. Addition-
ally, the implementaion of open GIS standards such as Web Map Service (WMS),
Web Feature Service (WFS), Geography Markup Language (GML), Styled Layer
Descriptor (SLD), Web Coverage Service (WCS), Catalogue Service for the Web
(CSW), Web Coordinate Transformation Service (WCTS), and Web processing
Service (WPS) are important for future developments of object oriented modelling
approaches and the related data inputs in a SOA.
By combining this structure of a SEIS with modern software development tools,
such as Java and ArcGIS Engine or GeoTools (both for spatial data analysis and
handling), it is possible to implement powerful SDSSs for environmental resource
management including appropriate models and expert knowledge. With this
development framework, a SDSS developer can satisfy the requirements of a
modern SDSS according to Figure 1.
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Notes on contributor
Georg Bareth is a Professor of Geography at the University of Cologne, Germany, and theHead of the GIS & RS Group of the Geography Department. He received his Diploma degreefrom Technical University Stuttgart in Geography in 1995 and his PhD in AgriculturalInformatics from University of Hohenheim-Stuttgart in 2000. His habilitation was also donein Agricultural Informatics at the University of Hohenheim-Stuttgart in 2004. He is a co-chairof the ISPRS WG VII/5 ‘Methods for change detection and process modelling’. His currentresearch focuses on geographic information science, remote sensing, and 3D-analyses.
References
Baaser, U., Gnyp, M.L., Hennig, S., Hoffmeister, D., Kohn N., and Bareth, G., 2006. OnlineCampusGIS for the University of Cologne. Proc. AGIT’2006, Heidelberg: WichmannVerlag, 24�31. In German.
Bambacus, M., Yang, C., Evans, J. Li, Z., and Li, W., 2008. Sharing earth science informationto support the Global Earth Observing System of Systems (GEOSS). In: Proceedings ofIGARSS 2008, 6�11 July 2008, Boston, CD-ROM, IEEE International Geoscience andRemote Sensing Symposium.
Bambacus, M., Yang, P., Evans, J., Cole, M., Alameh, N., and Marley, S., 2007. Aninteroperable portal supporting prototyping geospatial applications. URISA Journal, 19 (2),15�21.
Bareth, G., 2008. Multi-data approach (MDA) for enhanced land use and land cover mapping.The International Archives of the Photogrammetry, Remote Sensing and Spatial InformationScience, Vol. XXXVI. Part B, 1059�1065.
Bareth, G., 2005. Uncertainty of regionalization with agro-ecosystem models and GIS, Proc.of the 4th International Symposium on Spatial Data Quality ’05, 212�220.
Bareth, G. and Angenendt E., 2003. Economic-ecological modeling of greenhouse gases fromagriculture at the regional level. Berichte uber Landwirtschaft, 81 (1), 29�56. In German withEnglish summary.
Bareth, G., 2001a. Disaggregation of a soil map 1.200,000 by using a relief analysis of a 50 mDEM. GIS, Journal for Spatial Information and Decision Making, 12/2001. Heidelberg:Wichmann Verlag, 33�40. In German with English abstract.
Bareth, G., 2001b. Integration of an IRS-1C land use classification in the officialtopographical information system (ATKIS) to enhance the quality of the information ofarable land and grassland for a dairy farm region in Southern Germany (In German withEnglish abstract). GIS, Journal for Spatial Information and Decision Making, 6/2001.Heidelberg: Wichmann Verlag, Heidelberg, 40�45.
Bareth, G. and Doluschitz, R., 2003. Wissens- und GIS-basierte regionale Abschatzung vonKohlendioxid-, Methan- und Lachgas-Emissionen aus landwirtschaftlicher Landnutzung.Z. Agrarinformatik, 4, 52�64.
Bareth, G., Heincke, M., and Glatzel, S., 2001. Soil-Land-use-system approach to estimatenitrous oxide emissions from agricultural soils. Nutrient Cycling in Agroecosystems, 60 (1�3),219�234.
Bareth, G. and Yu, Z., 2002. Agri-enviro-information-system for the North China Plain.Proceedings of International Society for Photogrammetry and Remote Sensing (ISPRS)Commission II Symposium of Integrated Systems for Spatial Data Production, Custodian andDecision Support, ISPRS, 23�29.
Beauchamp, E.G., 1997. Nitrous oxide emission from agricultural soils. Canadian Journal ofSoil Science, 77, 113�123.
Benbi, D.K. and Richter, J., 2002. A critical review of some approaches to modeling nitrogenmineralization. Biology and Fertility of Soils, 35, 168�183.
Bernhardsen, T., 2002. Geographic information systems: an introduction. New York: Wiley.Bill, R., 1999. Heidelberg: Wichmann Verlag.Bill, R. and Fritsch, D., 1994. Grundlagen der Geo-Informationssysteme, Band 1: Hardware,
Software und Daten. Heidelberg: Wichmann Verlag.
International Journal of Digital Earth 149
Dow
nloa
ded
by [
Flor
ida
Atla
ntic
Uni
vers
ity]
at 1
1:07
22
Nov
embe
r 20
14
Bilo, M. and Bernard, L., 2005. INSPIRE � Aufbau einer Infrastruktur fur raumbezogeneInformationen in Europa. In: L. Bernard, J. Fitzke and R.M. Wagner, eds. Geodatenin-frastruktur � Grundlagen und Anwendungen. Heidelberg: Wichmann Verlag, 18�28.
Bock, M. and Kothe, R., 2005. Regionalisierung von Bodenkennwerten zur Ableitung vonfunktionalen Bodenkonzeptkarten. In: M. Moller and H. Helbig, eds. GIS-gestutzteBewertung von Bodenfunktionen. Heidelberg: Wichmann Verlag, 35�44.
Bouman, B.A.M., Jansen, H.G.P., Schipper, R.A., Hengsdijk, H., and Nieuwenhuyse, A.,2000. Tools for Land Use Analysis on Different Scales, With Case Studies for Costa Rica.Dordrecht, Boston, London: Kluwer Academic Publishers.
Brown, L., Syed, B., Jarvis, C., Sneath, W., Phillips, R., Goulding, W.T., and Li, C., 2002.Development and application of a mechanistic model to estimate emission of nitrous oxidefrom UK agriculture. Atmospheric Environment, 36, 917�928.
Burkart, M.R. and James, D.E., 1999. Agricultural nitrogen contributions to hypoxia in theGulf of Mexico. Journal of Environmental Quality, 28, 850�859.
Burrough, P.A. and McDonnell, R.A., 1998. Principles of geographic information systems.Oxford University Press, Oxford, 1998.
Carre, F. and Girard, M.C., 2002. Quantitative mapping of soil types based on regressionkriging of taxonomic distances with landform and land cover attributes. Geoderma, 110(3/4), 241�263.
Charles-Edwards, D.A., Doley, D., and Rimmington, G.M., 1986. Modelling plant growth anddevelopment. Sydney: Academic Press.
Chowdary, V.M., Rao, N.H., and Sarma, P.B.S., 2005. Decision support framework forassessment of non-point-source pollution of groundwater in large irrigation projects.Agricultural Water Management, 75 (3), 194�225.
Church, L.R., Murray, A.T., Figueroa, M.A., and Barber, K.H., 2000. Support systemdevelopment for forest ecosystem management. European Journal of Operational Research,121, 247�258.
Crist, P.J., Kohley, T.W., and Oakleaf, J., 2000. Assessing land-use impacts on biodiversityusing an expert tool. Landscape Ecology, 15 (1), 47�62.
Czeranka, M., 1996. Spatial Decision Support Systems in Naturschutz und Landschaftsp-flege? Umsetzungsaspekte fur die raumbezogene Planung. In: F. Dollinger and J. Strobl,Angewandte Geographische Informationsverarbeitung VIII�Salzburger GeographischeMaterialien, 24, 21�27.
Dabbert, S., Herrmann, S., Kaule, G., and Sommer, M., 1999. Landschaftsmodellierung fur dieUmweltplanung, Methodik, Anwendung und Ubertragbarkeit am Beispiel von Agrarlandschaf-ten. Berlin Heidelberg New York: Springer.
David, O., Viger, R.J., Schneider, I.W. and Garcia, L., 2004. Geospatial Interoperability inModeling Frameworks � The’ GOLEM’ Approach [online]. International EnvironmentalModelling and Software Society iEMSs 2004 International Conference, June 2004.Available from: http://www.iemss.org/iemss2004/pdf/integratedmodelling/davigeos.pdf[Accessed 18 January 2008]
Davis, J.R. and McDonald, G., 1993. Applying a Rule Based Decision Support System toLocal Government Planning. In: J.R. Wright, L.L. Wiggins, R.K. Jain and T.J. Kim, eds.Expert Systems in Environmental Planning. Berlin Heidelberg New York: Springer, 23�46.
Dobson, M.C. and Ulaby, F.T., 1998. Mapping soil moisture distribution with imaging radar.In: F.M. Henderson and A.J. Lewis, eds. Principles & applications of imaging radar.New York: Wiley, 407�433.
Eastman, J.R., 2005. Transition potential modeling for landcover change. In: D.J. Maguire,M. Batty and M.F. Goodchild, eds. GIS, spatial analysis, and modelling. Redlands: ESRIPress, 357�385.
Engel, B.A., Srinivasan, R., and Rewerts, C., 1993. A spatial decision support system formodeling and managing agricultural non-point source pollution. In: M.F. Goodchild, B.O.Parks and L.T. Steyaert, eds. Environmental Modeling with GIS. New York: OxfordUniversity Press, 231�237.
Engel, T., 1997. Nutzung von Informatik und Elektronik zur Systemanalyse und Unterstutzungeiner nachhaltigen Landbewirtschaftung., Professorial Dissertation. Fakultat fur Land-wirtschaft und Gartenbau, Technische Universitat Munchen.
150 G. Bareth
Dow
nloa
ded
by [
Flor
ida
Atla
ntic
Uni
vers
ity]
at 1
1:07
22
Nov
embe
r 20
14
Falloon, P.D., Smith, P., Smith, J.U., Szabo, J., Coleman, K., and Marshall, S., 1998. Regionalestimates of carbon sequestration potential: linking the Rothamsted carbon model to GIS.Biology and Fertility of Soils, 27, 236�241.
Furst, D., Roggendorf, W., Scholles, F. and Stahl, R., 1996. Umweltinformationssyteme -Problemlosungskapazitaten fur den vorsorgenden Umweltschutz und politische Funktion.Beitrage zur raumlichen Planung, 46. Forschungsbericht, Haanover 1996, ISBN 3-923517-33-5.
Foody, G.M., 2003. Uncertainty, knowledge discovery and data mining in GIS. Progress inPhysical Geography, 27 (1), 113�121.
Garnier, M., Lo Porto, A., Marini, R., and Leone, A., 1998. Integrated use of GLEAMS andGIS to prevent groundwater pollution caused by agricultural disposal of animal waste.Environmental Management, 22 (5), 747�756.
Goodchild, M.F., 2005. GIS and modelling overview. In: D.J. Maguire, M. Batty and M.F.Goodchild, eds. GIS, spatial analysis, and modelling. Redlands: ESRI Press, 1�17.
Goodchild, M.F., 1996. The spatial data infrastructure of environmental modelling. In: M.F.Goodchild, D. Maidment, M. Crane and S. Glendinning, eds., 1996. GIS and environmentalmodelling: progress and research issues. Fort Collins: GIS World Books, 11�15.
Grant, R.F., 2001. A review of the Canadian ecosystem model � ecosys. In: M.J. Shaffer, L. Maand S. Hansen, eds. Modeling carbon and nitrogen dynamics for soil management. BocaRaton: Lewis Publishers, 173�264.
Guptill, S.C., 1999. Metadata and data catalogues. In: P.A. Longley, M.F. Goodchild,D.J. Maguire and D.W. Rhind, eds. , GIS, Vol. 2. New York: Wiley, 677�692.
Hansen, J.W., 2005. Integrating seasonal climate prediction and agricultural models forinsights into agricultural practice. Philosophical Transactions of the Royal Society B, 360(1463), 2037�2047.
Hartkamp, A.D., White, J.W., and Hoogenboom, G., 1999. Interfacing GIS with agronomicmodeling: A review. Agronomy Journal, 91, 761�772.
Heuvelink, G.B.M., 1999. Propagation of error in spatial modeling with GIS. In: P.A. Longley,M.F. Goodchild, D.J. Maguire and D.W. Rhind, eds., GIS, Vol. 1: New York: Wiley, 207�217.
Hoogenboom, G., Wilkens, P.W, Thornton, P.K., Jones, J.W., Hunt, L.A. and Imamura, D.T.,1999. Decision support system for agrotechnology transfer v3.5. In: G. Hoogenboom, P.W.Wilkens and G.Y. Tsuji, eds., DSSAT version 3, Vol. 4. Honolulu: University of Hawaii, 1�3.
Huber, S., Bareth, G., Doluschitz, R., 2002. Integrating the process-based simulation modelDNDC into GIS. In: W. Pillman and K. Tochtermann, eds., Environmental communicationin the information society, Proc. EnviroInfo Vienna 2002. Vienna: International Society forEnvironmental protection, 649�654.
Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens,P.W., Singh, U., Gijsman, A.J., and Ritchie, J.T., 2003. The DSSAT cropping system model,European. Journal of Agronomy, 18, 235�265.
Jungkunst, H.F., Freibauer, A., Neufeldt, H., and Bareth, G., 2006. A review on nitrous oxideemissions from agricultural land use in Germany. Journal of Plant Nutrition and SoilScience, 169 (3), 341�351.
Kersebaum, K.CH., Hecker, J.-M., Mirschel, W., and Wegehenkel, M., 2007. Modelling Waterand Nutrient Dynamics in Soil�Crop Systems. Berlin Heidelberg New York: Springer.
Lal, H., Hoogenboom, G., Calixte, J.-P., Jones, J.W., and Beinroth, F.H., 1993. Using cropsimulation models and GIS for regional productivity analysis. Transactions of the ASAE, 36,175�184.
Laudien, R. and Bareth, G., 2007. Developing and programming Spatial Decision SupportSystems with Java and ArcGIS Engine (ESRI) (in German). GIS, 04/2007, 16�21.
Laudien, R., Rohrig, J., Bareth, G. and Menz, G., 2007. Spatial Decision Support System zurModellierung der agrarischen Marginalitat in Benin (Westafrika). Proc. AGIT’2007.Heidelberg: Wichmann.
Le Bas, C., King, D., Jamagne, M. and Daroussin, J., 1996. The European soil informationsystem. European Soil Bureau, Research Report (4), 33�42.
Leung, Y., 1997. Intelligent Spatial Decision Support Systems. Berlin Heidelberg New York:Springer.
International Journal of Digital Earth 151
Dow
nloa
ded
by [
Flor
ida
Atla
ntic
Uni
vers
ity]
at 1
1:07
22
Nov
embe
r 20
14
Li, C., 2000. Modeling trace gas emissions from agricultural ecosystems. Nutrient Cycling inAgroecosystems, 58, 259�276.
Li, C., Zhuang, Y., Cao, M., Crill, P., Dai, Z., Frolking, S., Moore, B., Salas, W., Song, W., andWang, X., 2001. Comparing a process based agro-ecosystem model to the IPCCmethodology for developing a national inventory of N2O emissions from arable lands inChina. Nutrient Cycling Agroecosystems, 60, 159�175.
Li, C., Qiu, J., Frolking, S., Xiao, X., Salas, W., Moore, B., Boles, S., Huang, Y. and Sass, R.,2002. Reduced methane emissions from large-scale changes in water management of China’srice paddies during 1980�2000. Geophysical Research Letters, 29 (20) DOI: 10.1019/2002GL015370.
Lillesand, T.M., Kiefer, R.W., and Chipman, J.W., 2004. Remote sensing and imageinterpretation. New York: Wiley.
Longley, P.A., Goodchild, M.F., Maguire, D.J., and Rhind, D.W., 2005. GeographicInformation Systems and Science. New York: Wiley.
Longley, P.A., Goodchild, M.F., Maguire, D.J. and Rhind, D.W., 1999. Data quality �introduction. In: P.A. Longley M.F. Goodchild D.J. Maguire and D.W. Rhind, eds., GIS,Vol. 1. New York: Wiley, 175�176.
Ma, L. and Shaffer, M.J, 2001. A review of carbon and nitrogen processes in nine U.S. soilnitrogen dynamic models. In: M.J. Shaffer, L. Ma and S. Hansen, eds. Modeling carbon andnitrogen dynamics for soil management. Boca Raton: Lewis Publishers, 55�102.
Maguire, D.J., 2005. Towards a GIS platform for spatial analysis and modeling. In: D.J.Maguire, M. Batty and M.F. Goodchild, eds. GIS, spatial analysis, and modelling. Redlands:ESRI Press, 19�39.
Maidment, D.R., 1996. Environmental modeling within GIS. In: M.F. Goodchild, D.Maidment, M. Crane and S. Glendinning, eds. GIS and environmental modelling: progressand research issues. Fort Collins: GIS World Books, 315�323.
Maidment, D.R., Robayo, O., and Merwade, V., 2005. Hydrologic modeling. In: D.J. Maguire,M. Batty and M.F. Goodchild, eds. GIS, spatial analysis, and modelling. Redlands: ESRIPress, Redlands, 319�332.
Malczewski, M., 1999. GIS and multicriteria decision analysis. New York: John Wiley & SonsInc.
Matson, P.A. and Vitousek, P.M., 1990. Ecosystem approach to a global nitrous oxide budget.Bioscience, 40 (9), 667�672.
Matthews, K.B., Sibbald, A.R., and Craw, S., 1999. Implementation of a spatial decisionsupport system for rural land use planning: integrating geographic information system andenvironmental models with search and optimisation algorithms. Computers and Electronicsin Agriculture, 23 (1), 9�26.
Matthews, R. and Knox, J., 1999. Up-scaling of methane emission-experimental results: finalreport. Department of Natural Resources Management. Silsoe, UK: Cranfield University.
McBratney, A.B., Mendonca Santos, M.L., and Minasny, B., 2003. On digital soil mapping.Geoderma, 117 (1�2), 3�52.
McCloy, K.R., 2006. Resource management information systems: remote sensing, GIS andmodeling. Boca Raton: Taylor & Francis.
McGechan, M.B. and Wu, L., 2001. A Review of carbon and nitrogen processes in Europeansoil nitrogen dynamic models. In: M.J. Shaffer, L. Ma and S. Hansen, eds. Modeling carbonand nitrogen dynamics for soil management. Boca Raton: Lewis Publishers, 103�172.
Miller, D.R., 1996. Knowledge-based systems for coupling GIS and process-based ecologicalmodels. In: M.F. Goodchild, D. Maidment, M. Crane and S. Glendinning, eds. GIS andenvironmental modelling: progress and research issues. Fort Collins: GIS World Books,231�234.
Miller, I., Knopf, S., and Kossik, R., 2005. Linking general-purpose dynamic simulationmodels with GIS. In: D.J. Maguire, M. Batty and M.F. Goodchild, eds. GIS, spatialanalysis, and modelling. Redlands: ESRI Press, 113�129.
Moller, M., 2005. Disaggregierung von Bodeninformationen auf der Grundlage digitalerReliefdaten. In: M. Moller und and H. Helbig, eds. GIS-gestutzte Bewertung vonBodenfunktionen. Heidelberg: Wichmann Verlag, 67�89.
152 G. Bareth
Dow
nloa
ded
by [
Flor
ida
Atla
ntic
Uni
vers
ity]
at 1
1:07
22
Nov
embe
r 20
14
Mowrer, T., 1998. Decision support systems for ecosystem management: An evaluation ofexisting systems. Gen. Tech. Rep. RM-GTR-296. Fort Collins, CO: U.S. Department ofAgriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station.
Neufeldt, H., Schafer, M., Angenendt, E., Li, C., Kaltschmitt, M., and Zeddies, J., 2006.Disaggregated greenhouse gas emission inventories from agriculture via a coupledeconomic-ecosystem model. Agriculture, Ecosystems and Environment, 112, 233�240.
Naesset, E., 1997. Geographical information system in long-term forest management andplanning with special reference to preservation of biological diversity: A review. ForestEcology and Management, 93 (1�2), 121�136.
O’Callaghan, J.R., 1996. Land Use: The Interaction of Economics, Ecology and Hydrology.Berlin Heidelberg New York: Springer.
O’Callaghan, J.R., 1995. NELUP: An Introduction. Journal of Environmental Planning andManagement, 38 (1), 5�20.
Plant, R.A.J. and Bouman, B.A.M., 1999. Modeling nitrogen oxide emissions from currentand alternative pastures in Costa Rica. Journal of Environmental Quality, 28, 866�872.
Rohierse, A., 2003. Regionale Darstellung der Umweltbelastungen durch klimarelevante Gase inder Agrarlandschaft Kraichgau: Das Boden-Landnutzungs-Informations-System fur Treib-hausgasemissionen. Dissertation. Inst. Landw. Betriebslehre, Universitat Hohenheim.
Rohierse, A. and Bareth, G., 2004. Integration einer multitemporalen Satellitenbildklassifika-tion in ATKIS zur weiteren Differenzierung der Objektart Ackerland, GIS, 03/2004,abcverlag, Heidelberg, 35�41.
Running, S.W. and Thornton, P.E., 1996. Generating daily surfaces of temperature andprecipitation map using the PRISM model. In: M.F. Goodchild, L.T. Steyaert andB.O. Parks, eds. GIS and Environmental Modeling: Progress and Research Issues. FortCollins: GIS World Books, 93�98.
Sarkar, R. and Kar, S., 2006. Evaluation of management strategies for sustainable rice wheatcropping system, using DSSAT seasonal analysis. J. Agric. Sc., 144, 421�434.
Schneider, K., 2003. Assimilating remote sensing data into a land surface process model.International Journal of Remote Sensing, 24, 2959�2980.
Scull, P., Franklin, J., Chadwick, O.A., and McArthur, D., 2003. Predictive soil mapping: areview. Progress in Physical Geography, 27 (2), 171�197.
Seppelt, K., 2003. Computer-based environmental management. Weinheim: Wiley-VCH.Shaffer, M.J., Ma, L., and Hansen, S., 2001. Introduction to simulation of carbon and
nitrogen dynamics in soils. In: M.J. Shaffer., L. Ma and S. Hansen, eds. Modeling carbonand nitrogen dynamics for soil management. Boca Raton: Lewis Publishers, 1�10.
Shaffer, M.J. and Ma, L., 2001. Carbon and nitrogen dynamics in upland soils. In: M.J.Shaffer, L. Ma and S. Hansen, eds. Modeling carbon and nitrogen dynamics for soilmanagement. Boca Raton: Lewis Publishers, 11�26.
Sharma, T., Carmichael, J., and Klinkenberg, B., 2006. Integrated modeling for exploringsustainable agriculture futures. Futures, 38 (1), 93�113.
Sommer, S., Hill, J., and Megier, J., 1998. The potential of remote sensing for monitoring ruralland use changes and their effects on soil conditions. Agriculture, Ecosystems &Environment, 67 (2), 197�209.
Stollenberg, B. and Zipf, A., 2008. Geoprocessing services for spatial decision support in thedomain of housing market analyses. Proc. 11th AGILE Intern. Conf. GIS, Girona, Online-Proc. (http://plone.itc.nl/agile_old/Conference/2008-Girona/index.htm).
Tang, H.J., 2006. Estimations of soil organic carbon storage in cropland of China based onDNDC model. Geoderma, 134 (1�2), 200�206.
Thomas, A., 2002. Integration von Methoden der Geoinformatik fur die Klimaforschung:Relieforientierte Regionalisierung von Klimadaten Ostasiens. GIS Zeitschrift fur raumbe-zogene Informationen und Entscheidungen, 12/2002, 16�21.
Ungerer, M.J. and Goodchild, M.F., 2002. Integrating spatial data analysis and GIS: a newimplementation using the Component Object Model (COM). International Journal ofGeographical Information Science, 16 (1), 41�53.
Veregin, H., 1999. Data quality parameters. In: P.A. Longley, M.F. Gooodchild, D.J. Maguireand D.W. Rhind, eds., GIS, Vol. 1. New York. Wiley, 177�189.
International Journal of Digital Earth 153
Dow
nloa
ded
by [
Flor
ida
Atla
ntic
Uni
vers
ity]
at 1
1:07
22
Nov
embe
r 20
14
Woodhouse, I.H., 2005. Introduction to microwave remote sensing. Boca Raton: Taylor &Francis.
Wright, J.R., 1993. GIS and spatial modeling. In: J.R. Wright, L.L. Wiggins, R.K. Jain and T.J.Kim, eds. Expert systems in environmental planning. Berlin: Springer, 83�84.
Wright, J.R., Wiggins, L.L., Jain, R.K., and Kim, T.J., 1993. Expert Systems in EnvironmentalPlanning. Berlin Heidelberg New York: Springer.
Wright, J.R. and Buehler, K.A., 1993. Probabilistic Inferencing and Spatial Decision SupportSystems. In: J.R. Wright, L.L. Wiggins, R.K. Jain and T.J. Kim, eds. Expert Systems inEnvironmental Planning. Berlin, Heidelberg, New York: Springer, 119�144.
Wu, Q., Li, H.Q., Wang, R.S., Paulussen, J., He, Y., Wang, M., Wang, B.H., and Wang, Z.,2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS.Landscape and Urban Planning, 78 (4), 322�333.
Yang, P., Evans, J., Cole, M., Marley, S., Alameh, N., and Bambacus, M., 2007. The emergingconcepts and applications of the Spatial Web Portal. PE&RS, 73 (6), 691�698.
Zhu, A.X., Band, L., Vertessy, R., and Dutton, B., 1997. Derivation of soil properties using asoil land inference model (SoLIM). Soil Science Society of America Journal, 61 (2), 523�533.
Zhu, A.X., Hudson, B., Burt, J., Lubich, K., and Simonson, D., 2001. Soil mapping using GIS,expert knowledge, and fuzzy logic. Soil Science Society of America Journal, 65, 1463�1472.
154 G. Bareth
Dow
nloa
ded
by [
Flor
ida
Atla
ntic
Uni
vers
ity]
at 1
1:07
22
Nov
embe
r 20
14