The uncertain impact of climate change on forest ecosystems – How qualitative modelling can guide future research for quantitative model development

Climate change could significantly alter forest productivity and climax states. Hence modelling productivity under climate change will need to account for many alternative ecosystem states. We apply qualitative modelling to identify the most likely ecosystem representations for a well-researched Tasmanian forest. Its main ecosystem is a tiered forest with rainforest, wet sclerophyll and myrtaceae components. Interactions between these components are uncertain, especially under additional pressures from climate change. Qualitative modelling is a structured method to analyse these uncertainties. We identify the most appropriate models and research efforts for model development. Further, we identify research needs for interactions between root pathogens and forest components, with research on some impacts of system components on fire being ruled out. The qualitative modelling approach applied here was useful in identifying research priorities for modelling complex ecosystems, even under uncertain system understanding or deficiencies in quantitative data.     

The simulation of land-cover change using a distributed computing environment

Computer models are used in landscape ecology to simulate the effects of human land-use decisions on the environment. Many socioeconomic as well as ecological factors must be considered, requiring the integration of spatially explicit multidisciplinary data. The Land-Use Change Analysis System or LUCAS has been developed to study the effects of land-use on landscape structure in such areas as the Little Tennessee river basin in western North Carolina and the Olympic Peninsula of Washington state. These effects include land-cover change and species habitat suitability. The map layers used by LUCAS are derived from remotely sensed images, census and ownership maps, topological maps and output from econometric models. A public-domain geographic information system (GIS) is used to store, display and analyze these map layers. A parallel version of LUCAS (pLUCAS) was developed using the parallel virtual machine (PVM) on a network of workstations giving a speedup factor of 10.77 with 20 nodes. A parallel model is necessary for simulations on larger domains or for maps with a much higher resolution.     

Improving ecosystem services modelling: Insights from a Bayesian network tools review

Numerous studies attempt to unravel the role played by Biodiversity in ecosystems and ES reliance on Biodiversity. Achieving this aim is difficult given: the multi-layered Biodiversity-ES relationship; the temporal and spatial heterogeneity of ES; and, the interactions between biotic and abiotic components in ecosystems influencing processes and services. Bayesian networks have recently gained importance in ecological modelling. The integration of empirical data with expert knowledge and the explicit treatment of uncertainties, demonstrate their usefulness. Publications describing network-based Biodiversity-ES models, demonstrate their application is still limited. A watershed's environmental risk management network modelled from a Biodiversity-ES perspective is discussed. It demonstrates an improvement on conventional approaches, expressing risk in terms of the underlying causal relations between environmental risk events, triggers, controls and consequences. The model is developed in AgenaRisk and two other tools, Netica and Hugin. A comparison between them highlights the dependence on the tool of choice.     

A scalable algorithm for dispersing population

Models of forest ecosystems are needed to understand how climate and land-use change can impact biodiversity. In this paper we describe an ecological dispersal model developed for the specific case of predicting seed dispersal by trees on a landscape for use in a forest simulation model. We present efficient approximation algorithms for computing seed dispersal. These algorithms allow us to simulate large landscapes for long periods of time. We also present experimental results that (1) quantify the inherent uncertainty in the dispersal model and (2) describe the variation of the approximation error as a function of the approximation parameters. Based on these experiments, we provide guidelines for choosing the right approximation parameters, for a given model simulation.     

Coupling agent-based models of subsistence farming with individual-based forest models and dynamic models of water distribution

Subsistence farming communities are dependent on the landscape to provide the resource base upon which their societies can be built. A key component of this is the role of climate and the feedback between rainfall, crop growth, land clearance and their coupling to the hydrological cycle. Temporal fluctuations in rainfall alter the spatial distribution of water availability, which in turn is mediated by soil-type, slope and landcover. This pattern ultimately determines the locations within the landscape that can support agriculture and controls sustainability of farming practices. The representation of such a system requires us to couple together the dynamics of human and ecological systems and landscape change, each of which constitutes a significant modelling challenge on its own. Here we present a proto-type coupled modelling system to simulate land-use change by bringing together three simple process models: (a) an agent-based model of subsistence farming; (b) an individual-based model of forest dynamics; and (c) a spatially explicit hydrological model which predicts distributed soil moisture and basin scale water fluxes. Using this modelling system we investigate how demographic changes influence deforestation and assess its impact on forest ecology, stream hydrology and changes in water availability.     

Patterns and trends in land-use land-cover change research explored using self-organizing map

Research on land change has a long history, has generated numerous publications and continues to receive international research attention. To facilitate the understanding of the patterns and trends of land-change research, this article uses a content-based text-retrieval approach and self-organizing map to analyse more than 700 peer-reviewed remote-sensing and natural-science papers on land-use/cover change (LUCC) from the past two decades. We present the results in map-like displays and discuss papers within the identified clusters to examine the research activities. A new cluster of research, which has emerged in the last 5 years of analysis, has focused on mixed-pixel issues for land-use/cover mapping, particularly in the context of forest catchments. Studies of LUCC consequences after 2000 have been concerned with the effects of forest conversion on soil-nutrient pools and nitrate cycling. Incorporating information on resolutions and extents into the representations reveals a dominant scale of analysis for some research activities. Analysing time frames of examination in the papers suggests that research on long-term LUCC consequences started to use presettlement land survey records. Few attempts, however, have been made to investigate the uncertainties in the historical sources of information for LUCC research, thereby presenting a future research topic.     

Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Rapid global economic development has resulted in a corresponding intensification of urbanization, which has in turn impacted the ecology of vast regions of the world. A series of problems have thus been introduced, such as changes in land-use/land-cover (LULC) and changes in local climate. The process of urbanization predominantly represents changes in land-use, and is deemed by researchers to be the chief cause of climate change and ecological change. One of the principal purposes of the research in this field is to find ways to mitigate the influence of land-use change on local or global environments. In the study presented in this article, satellite images were utilized to extract information regarding land-use in Beijing City, and to develop maps of land surface temperature (LST) during two different periods of time: 2 August 1999 and 8 August 2010. A supervised classification scheme, a support vector machine, was used to derive the land-use change map for the above periods. Maps of surface temperature are derived from the thermal band of Landsat images using the mono-window algorithm. Results from post-classification comparison indicated that an increase in impervious surface areas was found to be dramatic, while the area of farmland decreased rapidly. The changes in LULC were found to have led to a variation in surface temperature, as well as a spatial distribution pattern of the urban heat island phenomenon. This research revealed that the hotspots were mainly located in areas dominated by three kinds of material: bare soil, rooftops, and marble surfaces. Results from the local Moran's I index indicated that the use of lower surface temperature materials will help to mitigate the influence of the urban heat island phenomenon. The results of this research study provide a reference for government departments involved in the process of designing residential regions. Such a reference should enable the development of areas sympathetic to environmental changes and hence mitigate the effects of the growing intensity of urbanization.     

Modeling and simulating residential mobility in a shrinking city using an agent-based approach

Shrinking cities are characterized by a huge oversupply of dwellings and resulting residential vacancies. Discussions among urban planners and policymakers in Europe have focused on the consequences of urban shrinkage following demographic transition, fertility decline and individualization. In this study, the shrinking city of Leipzig in Eastern Germany is singled out as a case basis for the study of residential mobility and land use change using agent-based modeling techniques, in which social scientists developed a concept of household types based on empirical data that form a unique base; these techniques were used to construct a data-driven, agent-based model. The spatially explicit simulation model RESMOBcity presented here ‘translates’ these empirical data via behavioral rules of households. It computes spatially explicit household patterns, housing demands and residential vacancies. Based on three scenarios, population growth, stagnation and shrinkage, we show that population might stabilize within the coming years. The number of households is expected to further increase. We demonstrate that a selective demolition of vacant housing stock can counteract the enormous oversupply of dwellings and better balance housing demand and the number of available flats. Scenario simulation shows that the model can reproduce observed patterns of population, inner-urban migration and residential vacancy in a spatially explicit manner and thus can be applied to the analysis of scenarios of demographic change in urban regions. The presented model acts as a tool supporting the testing of hypotheses in social science research and allowing the quantification of land-use scenarios in urban regions based on household choices.     

Dynamics of land-use and land-cover change in Freetown,Sierra Leone and its effects on urban and peri-urban agriculture – a remote sensing approach

This paper presents findings of a land-use and land-cover (LULC) change mapping exercise conducted in Freetown, Sierra Leone. Nine LULC classes were mapped from multi-temporal Landsat data of 1974, 1986 and 2000. Special attention was given to the growth or otherwise of agricultural land in relation to other LULC classes. Conversion of one land-use/-cover type to the other was identified, and its effects discussed. Major conversions occurred between agricultural lands, grasslands, evergreen forest, built-up areas and barren land. Built-up areas increased by at least 140% between 1974 and 2000, suggesting a high urbanization rate. About 882 ha (27%) of agricultural lands in 1986 were converted to residential purposes in 2000, especially at the urban fringes, in response to an increase in population. Some 14% of evergreen forest was found to have been converted to agricultural land. These major conversions suggest a strong linkage between urbanization, agriculture and deforestation.     

A multi-agent model system for land-use change simulation

This paper presents a multi-agent model system to characterize land-use change dynamics. The replicable parameterization process should be useful to the development of simulation frameworks, important to environmental policy makers to analyze different scenarios during decision making process. The methodological two-fold approach intends to form a solid backbone based on: (i) the systematic and structured empirical characterization of the model; and (ii) the conceptual structure definition according to the agent-based model documentation protocol – Overview, Design concepts and Details. A multi-agent system for land-use change simulation was developed to validate the model, which is illustrated with a case study of the Brazilian Cerrado using LANDSAT ETM images. The simulation results prove the model importance with a figure of merit greater than 50%, what means the amount of correctly predicted change is larger than the sum of any type of error. The results are very good compared with nine popular peer-reviewed land change models.     

A spatial panel approach to the statistical assessment of seismic impacts and building damages: Case study of Taichung,Taiwan

A set of seismic-related statistical models is developed, using pseudo-data generated by an earthquake-engineering simulation model. The Peak Ground Acceleration (PGA) is the principal measure of seismic hazard, the Peak Ground Displacement (PGD) represents secondary impacts, and land-use patterns characterize urban vulnerability. A PGA model based on earthquake magnitude, epicenter-to-site distances, and source depth is formulated as a spatial lag panel (SLP) model to account for PGA spatial interactions (neighborhood effects). A PGD spatial lag panel model is also specified to account for soil liquefaction effects. Finally, a model of seismic damages to buildings is formulated, relating monetary damages (loss in property values) to seismic hazards (PGA and PGD) and urban vulnerabilities (residential, commercial, industrial, and facility building stocks). Pseudo-data are generated under 22 simulated historical seismic events for the city of Taichung, Taiwan. These data are then used for model estimation. By combining the three models, monetary damages can be estimated as a function of land-use patterns, PGA, PGD, their neighborhood effects, and other seismic characteristics. These models can therefore provide seismic information for policy making and help in allocating land to new activities while minimizing potential seismic damages.     

Modelling with stakeholders – Next generation

This paper updates and builds on ‘Modelling with Stakeholders’ Voinov and Bousquet, 2010 which demonstrated the importance of, and demand for, stakeholder participation in resource and environmental modelling. This position paper returns to the concepts of that publication and reviews the progress made since 2010. A new development is the wide introduction and acceptance of social media and web applications, which dramatically changes the context and scale of stakeholder interactions and participation. Technology advances make it easier to incorporate information in interactive formats via visualization and games to augment participatory experiences. Citizens as stakeholders are increasingly demanding to be engaged in planning decisions that affect them and their communities, at scales from local to global. How people interact with and access models and data is rapidly evolving. In turn, this requires changes in how models are built, packaged, and disseminated: citizens are less in awe of experts and external authorities, and they are increasingly aware of their own capabilities to provide inputs to planning processes, including models. The continued acceleration of environmental degradation and natural resource depletion accompanies these societal changes, even as there is a growing acceptance of the need to transition to alternative, possibly very different, life styles. Substantive transitions cannot occur without significant changes in human behaviour and perceptions. The important and diverse roles that models can play in guiding human behaviour, and in disseminating and increasing societal knowledge, are a feature of stakeholder processes today.     

ASTROMOD: a computer program integrating vegetation dynamics modelling,environmental modelling and spatial data visualisation in Microsoft Excel

This paper describes the development of a software framework for predicting vegetation change within the Astroni crater, a state nature reserve near Naples, Italy. ASTROMOD (ASTROni MODel), is designed to allow the analysis of environmental and management scenarios in the crater, thus aiding the reserve’s manager in effective decision making. ASTROMOD comprises a vegetation dynamics model developed for forest ecosystems, combined with models of environmental determinants. It integrates a user-friendly interface for visualising spatial data, a parameter database and a series of programming modules within Microsoft Excel. This approach is a significant departure from other spatial biophysical modelling approaches which require costly and complex software tools.     

Quantifying the spatial differences of landscape change in the Hai River Basin,China, in the 1990s

Quantifying a landscape pattern and its change is essential for monitoring and assessing the ecological consequences of land-use/land-cover (LULC) change and human interference. In this study, a combination of landscape pattern indices and land-use dynamics indices based on remote-sensing images was employed to analyse and compare the spatial and temporal dynamics of the landscape pattern in the Hai River Basin (HRB), China, in the 1990s. During this decade, the change in the landscape pattern was mainly driven by intense human-induced alterations. Overall, the landscape pattern changed considerably, progressively becoming more fragmented and diverse, with widespread encroachment of cropland because of rapid urbanization. The change in the landscape pattern exhibited distinctive spatial differences between the mountains and the plains, as well as between urban and rural areas, with higher fragmentation in the plain region and urban fringes or newly urbanizing areas. The changes in the landscape pattern around Beijing resulted in more fragmented and diverse landscape types. A detailed examination of the Jing–Guang (Beijing–Guangzhou) railway line transect and two other subregions indicated that urbanization contributed mostly to the spatial differences of landscape change through population growth, transportation improvements and rapid economic development. Spatial differences in the change in the landscape pattern were induced by economic growth, population increase and government policy.     

Coupling spectral unmixing and multiseasonal remote sensing for temperate dryland land-use/land-cover mapping in Minqin County,China

Temperate dryland ecosystems in China are undergoing accelerated changes due to natural and anthropogenic disturbances. Using the Minqin oasis as a case study area, this article examined linear spectral mixture analysis (LSMA) with the fixed four-image endmember (EM) model comprising sand, green vegetation, saline land, and dark materials (i.e. the degraded symptoms/EMs) for temperate dryland land-use and land-cover mapping. Dryland covers defined by landscape seasonality of the four EMs at the subpixel level are more easily interpreted to achieve acceptable accuracy, and allow better understanding of the processes of land degradation, such as two distinct salt/water movements were found in the study area. The Minqin oasis faces a significant challenge that requires a long-term monitoring system to understand the relationship between land-use decisions and ecological consequences. The approach developed with the mutiseasonal LSMA, representing dryland land-cover seasonality with the common surface degradation types (i.e. the four EMs) in a tree-structure framework, promises a robust and operative tool for land degradation assessment and monitoring and would be applied in a more different dryland environment in the future.     

Quantifying the main urban area expansion of Guangzhou using Landsat imagery

ABSTRACT

This article takes the main urban area of Guangzhou as the study area. Twelve land-use maps were interpreted from Landsat Thematic Mapper images using feature enhancement technology and Naïve Bayes-based weight vector AdaBoost (WV AdaBoost). The study shows that since 1987, with heavily cultivated land being lost and vegetation cover shrinking, the urban area has doubled in size. The effects of human activities on the landscape have become increasingly apparent and complicated. It also finds that the expansion pattern has been transformed from one in which the city’s main urban area engulfed the surrounding residential area to one in which surrounding towns expanded, driven by the development of the main urban area. By analysing the relationship between the social economy, population, and land-use change, we show that the forces driving expansion of the urban area have differed in different periods. The principal driving forces are national policymaking and adjustment, the aggregation and diffusion of population and economic development. Multitemporal land-use maps allow long-term dynamic monitoring of urban land cover in Guangzhou and provide important details of urban expansion and changes in land use. We employed intervals of 2 and 3 years, and our results provide good supporting data for studies of urban extension trends and reasonable urban planning.     

Innovative approaches to integrated global change modelling

Integrated models are important tools to investigate the interactions between planetary processes and the growing impacts of human populations – in short: global change. Current models still have significant shortcomings, notably in their representation of socio-economic processes and the feedbacks between these and the environmental system. They are also often not designed with sufficient transparency to enable participation of interested parties or effective communication with stakeholders and policy makers. These deficiencies are discussed and possible directions for improvement are identified. This Thematic Issue provides a collection of papers that offer a number of innovative ideas for remedying these shortcomings using novel methods and approaches.     

A general framework for analyzing techno-behavioural dynamics on networks

A general framework for assessing future impacts of technology on society and environment is presented. The dynamics between human activity and technological systems impact upon many processes in society and nature. This involves non-linear dynamics requiring an understanding of how technology and human behaviour influence each other and co-evolve. Conventionally, technological and behavioural systems are analyzed as separate entities. We develop an integrated theoretical and methodological approach termed techno-behavioural dynamics focussing on networked interactions between technology and behaviour across multiple system states. We find that positive feedback between technology learning, evolving preferences and network effects can lead to tipping points in complex sociotechnical systems. We also demonstrate how mean-field and agent-based models are complimentary for capturing a hierarchy of analytical resolutions in a common problem domain. Assessing and predicting co-evolutionary dynamics between technology and human behaviour can help avoid systems lock-in and inform a range of adaptive responses to environmental and societal risk.     

An analysis of deforestation patterns in the extractive reserves of Acre,Amazonia from satellite imagery: A landscape ecological approach

Classified multi-temporal Landsat satellite imagery for one of the extractive reserves in Acre, Amazonia was classified into forest and cleared forest patches. Three indices of landscape structure were applied to the classified satellite imagery to characterize the impact of social and economic processes on the development of the forest landscape. The Lacunarity Index was used to measure landscape homogeneity, the Korcak Patchiness Index to measure the distribution of patches according to their size and the Area-Perimeter Fractal Exponent to measure change in the shape of cleared forest patches. The changing shape, size and spatial frequency of patches in the forest landscape showed that major changes in the forest landscape occurred between 1975-1989. Pre-1975 the region was mainly exploited by family groups who formed an extractive economy based on natural resources. By 1985 the economy had changed, with larger more regular patches indicating a transformed economy based on a settled and well developed farming economy. The techniques are important for measuring the human impact on fragile tropical ecosystems.