Bertram Ostendorf

Environmental decision support systems: Current issues, methods and tools

Abstract Development of environmental decision support systems (EDSS) is rapidly progressing. The sustainable management of natural resources has a growing research focus as the awareness of the complexity of interactions between socio-cultural, economical and biophysical system components is increasingly acknowledged. As better data and methods become available, the complexity of the system representation is augmenting. At the same time realism and relevance are increasing and allowing direct support for management and policy development. This article gives the background of recent developments in EDSS and summarises a selected set of papers that were presented at the 2nd Biennial Conference of the International Society of Environmental Modelling and Software (IEMSS 2004). Recent developments show a continuum between integrated assessment modelling and EDSS with varying levels of stakeholder participation in both EDSS development and application. There is a general tendency towards better utilisation of interdisciplinary data, integration and visualisation of temporal and spatial results. Future developments appear directed towards better representation of reality in models, improving user-friendliness and use in a negotiation or group discussion context.

The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates

A feedforward artificial neural network, coupled with a regional GIS (geographic information system), is described that is being used to assess the potential impacts of climate change on a complex landscape of tropical forests. The model quantifies the relative suitability of environments for 15 forests classes using the best information that is available: a structural-environmental classification of forest types, vegetation maps and spatial estimates of environmental variables. Inputs to the model include climate variables, soil parent material classes and terrain variables. The model is highly successful at distinguishing the relative suitability of environments for the forest classes with 75% of the forest mosaic accurately predicted by the model at a one hectare resolution over more than two million hectares. The model was used to estimate potential forest distributions in several climates occurring since the end of the last glacial period. These distributions shift dramatically in response to scenarios representing past climates. Certain locations are occupied by a forest class in only some climates while others are always occupied by the same class despite large changes in regional mean annual temperature and precipitation. Using the model to assess the possible impacts of future climate change and estimating the pre-settlement distribution of forest types in the region is also discussed. The coupling of neural networks with a cellular automata model is also described as a means to assess the importance of spatial constraints on the potential redistribution of forest types in the future. The usefulness of artificial neural networks when applied to vegetation change studies in our region suggests that this approach could be applied in many tropical regions, where floristic diversity is high and mechanistic understanding is comparatively low.

A model of arctic tundra vegetation derived from topographic gradients

We present a topographically-derived vegetation model (TVM) that predicts the landscape patterns of arctic vegetation types in the foothills of the Brooks Range in northern Alaska. In the Arctic there is a strong relationship between water and plant structure and function and TVM is based on the relationships between vegetation types and slope (tan β) and discharge (δ), two independent variables that can be easily derived from digital terrain data. Both slope and discharge relate to hydrological similarity within a landscape: slope determines the gravitational hydrological gradient and hence influences flow velocity, whereas discharge patterns are computed based on upslope area and quantify lateral flow amount. TVM was developed and parameterized based on vegetation data from a small 2.2 km2 watershed and its application was tested in a larger 22km2 region. For the watershed, TVM performed quite well, having a high spatial resolution and a goodness-of-fit ranging from 71–78%, depending on the functions used. For the larger region, the strength of the vegetation types predictions drops somewhat to between 56–59%. We discuss the various sources of error and limitations of the model for purposes of extrapolation.

GIS-based modelling of spatial pattern of snow cover duration in an alpine area

Snow cover duration patterns of an alpine hillslope (approximately 2 km 2 ) were derived using daily terrestrial photographic remote sensing. We have developed a suite of quantitative models in order to investigate the relative controls of topographic factors, the degree of non-linearity, the effect of seasonal differences and a possible influence of further systematic influences. We have only used data that are relatively easily available to ensure applicability beyond the site. Elevation, slope angle and aspect, and potential irradiation for the winter period can be directly derived from a digital elevation model. The number of days with temperature 0°C was included using a regression with elevation. Furthermore, a coarse vegetation classification (forested/not forested) was included. To estimate the necessary degree of non-linearity for such modelling without forming exact assumption about the functional interrelations, results from a linear regression analysis are compared with an artificial neural network (ANN). The results show that a R 2 of 71% can be achieved by means of a linear approach, whereas a non-linear approach (ANN) leads to 81%. An indirect estimation demonstrates that a further 6% can be explained without considering data on annually specific weather conditions. The analysis of the residuals shows a clear spatial pattern. This indicates that additional spatial variables may allow a further improvement of the model.

Systematic landscape restoration in the rural–urban fringe: meeting conservation planning and policy goals

Many landscapes that straddle the rural/urban divide suffer from low levels of species diversity following extensive clearing and fragmentation of native vegetation communities and conversion of land to agriculture. Further pressures are placed on remnant vegetation by encroaching urban expansion. These landscapes now exhibit a mosaic of small, patchy vegetation remnants that are under considerable pressure from housing and light-industrial development. Furthermore, agriculture in these landscapes tends to be of high economic value from uses such as intensive horticulture. Concerted and well-planned efforts are needed to balance the many conflicts of interest and competing demands for land with the need to restore landscapes for the protection of biodiversity. There has been a recent move in Australia toward regional biodiversity planning and goal setting, however specific detail on how to plan for achieving targets in complex landscapes is lacking. This paper applies a systematic landscape restoration model to a mixed-use, peri-urban landscape on the northern fringes of Adelaide, South Australia. The region contains fragments of remnant vegetation amongst a mosaic of high-value horticulture, light industry and urban development. Models produce maximally efficient solutions that meet comprehensive, adequate and representative conservation targets. Further constraints are added to the model to take into account the value of agricultural output, the biodiversity value of remnants, and property size and tenure. The effects on solution efficiencies as the number of constraints increase are investigated. This paper demonstrates the flexibility found in applying a systematic landscape restoration methodology. The process we present can be transferred to any rural–urban fringe region.

An introduction to patterns of fire in arid and semi-arid Australia, 1998–2004

Fire is a crucial element in shaping our world, whether of natural or anthropogenic origin. These fires can have both positive and negative consequences and impacts on our natural environment, society and its economics, not to mention global climate. Previous analyses of fire regimes in arid and semi-arid Australia have been of limited spatial or temporal extent. This lack of knowledge has hampered attempts at effective fire management. Satellite imagery allows the continuous detection, monitoring and mapping of fires. Active fires can be detected as fire hotspots, and burned areas mapped as patches from the change of surface reflectance properties in successive images. Data from NOAA’s advanced very high resolution radiometer (AVHRR) were used to assess the distribution, seasonality, frequency, number and extent of fire hotspots (FHS) and fire affected areas (FAA) across the entire arid and semi-arid country of Australia from 1998 to 2004. Utilising both of these fire datasets is important, as they complement each other and provide a more robust analysis of fire patterns. Between 1998 and 2004 almost 27% of arid and semi-arid Australia burnt at least once. The main trends in fire distribution follow latitudinal rainfall gradients. Regression analysis also shows a strong relationship with the pattern of antecedent rainfall. The seasonality of fire events varies between climate zones in accordance with the varying distribution of precipitation and temperature, which influence fuel accumulation and curing. For the first time we have a picture of fire patterns across the entire arid and semi-arid regions of the country. This includes several high fire years in certain areas following above-average rainfall. This analysis highlights similarities and differences between regions, giving policy makers and managers a basis from which to make more informed decisions in the present, and with which to compare future regimes.

Relationships between a terrain-based hydrologic model and patch-scale vegetation patterns in an arctic tundra landscape

Implicit in the relationship between vegetation patterns and landforms is the influence of topography on the water regime at the patch scale. Hence, based on the numerous process-based studies linking plant structure and function to water in the arctic, we hypothesize that the general pattern of arctic landscapes can be explained by a mesotopographic variable such as water drainage. In this paper, we test this hypothesis by examining the spatial relationship between patterns of vegetation and the water regime in a small watershed in northern Alaska. Using gridded elevation data, we develop a model (T-HYDRO) to generate a 2-dimensional water flow field for the watershed and compare this to vegetation patterns as given by 1) a vegetation map developed from aerial photographs in conjunction with extensive field sampling; and 2) a normalized difference vegetation index (NDVI). Our results show that it is possible to account for about 43% of the spatial variance in NDVI, which supports our hypothesis. In spite of its limitations, the correspondence of patterns presented in this paper provides encouraging evidence that we can find simple approaches to stratify landscapes and that it is possible to overcome the frequently made assumption of spatial homogeneity in ecosystems modeling.

CREDOS: A Conservation Reserve Evaluation And Design Optimisation System

Abstract A number of spatial decision support systems (SDSSs) are already available for the systematic planning of conservation reserves. These existing systems offer varying levels of integration and interactivity. However, these systems generate solutions that are sub-optimal. Integer programming (IP) optimisation techniques guarantee optimal solutions but have been criticised for excessively long or intractable solution times. Modern IP software has addressed this criticism by finding solutions in quick time. The aim in using IP techniques in this paper is to design conservation reserves that are of the minimum possible area to satisfy defined conservation targets, thereby producing reserve systems of greatest efficiency. The Conservation Reserve Evaluation and Design Optimisation System (CREDOS) is a fully integrated SDSS that calls on a third-party optimiser to generate solutions. We attempt to provide a foundation for a viable alternative to existing systems by tightly coupling CREDOS with a proprietary geographic information system (GIS) and IP analytical software by means of an interactive interface. It provides conservation planners with a fully featured planning system for both terrestrial and marine conservation reserves that is easy to use, offers high levels of interactivity and flexibility, and produces practical and efficient solutions that can be used to support their experience and judgement. This paper describes the methodology used in the design and construction of CREDOS, and discusses the effectiveness of the system in facilitating the planning of conservation reserves. The functionality of CREDOS is assessed in the context of designing Marine Protected Areas (MPAs). The results are maximally efficient, practical, and manageable arrangements of potential MPA sites that satisfy conservation targets.

Landscape patterns of carbon dioxide exchange in tundra ecosystems

Ecosystem carbon dioxide (CO2) exchange is important because it is an indicator of energy captured by the system, it is related via decomposition to nutrient turnover, and long-term carbon storage and release affect atmospheric CO2 concentrations and the global carbon budget. The large amount of carbon stored in northern soils — and the potential for its release to the atmosphere with climate warming (Miller 1981) — has stimulated much research on ecosystem gas exchange in the Arctic. This work has focused primarily on the potential response of these systems to shifts in climate factors (Oechel and Billings 1992). Because of its stature, tundra is one of the few natural ecosystems for which whole-system CO2 fluxes have been determined (e.g., Grulke et al. 1990; Oechel et al. 1992, 1993; Tenhunen et al. 1995), because relatively small chambers can be placed over “representative patches” of tundra vegetation (Grulke et al. 1990; Vourlitis et al. 1993).

Real options analysis for land use management: Methods, application, and implications for policy

Discounted cash flow analysis, including net present value is an established way to value land use and management investments which accounts for the time-value of money. However, it provides a static view and assumes passive commitment to an investment strategy when real world land use and management investment decisions are characterised by uncertainty, irreversibility, change, and adaptation. Real options analysis has been proposed as a better valuation method under uncertainty and where the opportunity exists to delay investment decisions, pending more information. We briefly review the use of discounted cash flow methods in land use and management and discuss their benefits and limitations. We then provide an overview of real options analysis, describe the main analytical methods, and summarize its application to land use investment decisions. Real options analysis is largely underutilized in evaluating land use decisions, despite uncertainty in policy and economic drivers, the irreversibility and sunk costs involved. New simulation methods offer the potential for overcoming current technical challenges to implementation as demonstrated with a real options simulation model used to evaluate an agricultural land use decision in South Australia. We conclude that considering option values in future policy design will provide a more realistic assessment of landholder investment decision making and provide insights for improved policy performance.