Land Use Change Model Research Articles

Urban land use change and regional access: A case study in Beijing, China

In the recent past Beijing has experienced rapid development. This growth has been accompanied by many problems including traffic congestion and air pollution. Understanding what stimulates urban growth is important for sustainable development in the coming years. In this paper, we first estimate a binary auto-logistic model of land use change, using physical and socioeconomic characteristics of the location and its access to major centers within the city as predictors. We find that variables determining regional access, like time distance to the city center, the Central Business District (CBD), industrial centers, employment centers, and the transportation system, significantly impact urban land conversion. By using measures of access to predict land use change we believe that we can better understand the planning implications of urban growth not only in Beijing but other rapidly developing cities.

Scenarios and Modeling of Land Use and Cover Changes in Portugal from 1980 to 2040

In this study, land use and cover changes in continental Portugal are analyzed for years 1980, 1995 and 2010 using samples of the Landyn research project. The modeling approach includes testing the hypothesis that land cover changes are generated by a first-order Markov process. Results show that the changes in land use and cover are dependent of the previous moment in time, i.e., they follow a Markov process. Accordingly, multi-decadal land cover projections of Landyn simplified land cover classes are legitimately presented and analyzed for continental Portugal and its regions for years 2020, 2030 and 2040. To make these results spatially explicit, a modelling approach which combines Markov chains with cellular automata is carried out using hypothetical scenarios. The quantitative and spatially explicit information provided by this study enables a better understanding of tendencies in land cover change and may be useful for territorial planning and management.

Simulation vs. Definition: Differing Approaches to Setting Probabilities for Agent Behaviour

While geographers and economists regularly work together on the development of land-use and land-cover change models, research on how differences in their modelling approaches affects the results is rare. Answering calls for more coordination between the two disciplines in order to build models that better represent the real world, we (two economists and a geographer) developed an economically grounded, spatially explicit, agent-based model to explore the effects of environmental policy on rural land use in New Zealand. This inter-disciplinary collaboration raised a number of differences in modelling approach. One key difference, and the focus of this paper, is the way in which processes that shape the behaviour of agents are integrated within the model. Using the model and a nationally representative survey, we compare the land-use effects of two disciplinary-aligned approaches to setting a farmer agent’s likelihood of land-use conversion. While we anticipated that the approaches would significantly affect model outcomes, at a catchment scale they produced similar trends and results. However, further analysis at a sub-catchment scale suggests the approach to setting the likelihood of land-use conversion does matter. While the results outlined here will not fully resolve the disciplinary differences, they do outline the need to account for heterogeneity in the predicted agent behaviours for both disciplines.

Modular ABM development for improved dissemination and training

Agent-based models (ABMs) have become an important tool for advancing scientific understanding in a variety of disciplines and more specifically have contributed gains to natural resource management in recent decades. However, a key challenge to their utility is the lack of convergence upon a common set of assumptions for representing key processes (such as agent decision structure), with the outcome that published ABM tools are rarely (if ever) used beyond their original development team. While a number of ABM frameworks are publicly available for use, the continued development of models from scratch is a signal of the continuing difficulty in capturing sufficient modeling flexibility in a single package. In this study we examine ABM sharing by comparing co-citation networks from several well-known ABM frameworks to those used in the land-use change modeling community. We then outline a different publication paradigm for the ABM community that could improve the sharing of model structure and help move toward convergence on a common set of tools and assumptions.

Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps

Timely and accurate information on the global cropland extent is critical for applications in the fields of food security, agricultural monitoring, water management, land-use change modeling and Earth system modeling. On the one hand, it gives detailed location information on where to analyze satellite image time series to assess crop condition. On the other hand, it isolates the agriculture component to focus food security monitoring on agriculture and to assess the potential impacts of climate change on agricultural lands. The cropland class is often poorly captured in global land cover products due to its dynamic nature and the large variety of agro-systems. The overall objective was to evaluate the current availability of cropland datasets in order to propose a strategic planning and effort distribution for future cropland mapping activities and, therefore, to maximize their impact. Following a very comprehensive identification and collection of national to global land cover maps, a multi-criteria analysis was designed at the country level to identify the priority areas for cropland mapping. As a result, the analysis highlighted priority regions, such as Western Africa, Ethiopia, Madagascar and Southeast Asia, for the remote sensing community to focus its efforts. A Unified Cropland Layer at 250 m for the year 2014 was produced combining the fittest products. It was assessed using global validation datasets and yields an overall accuracy ranging from 82%–94%. Masking cropland areas with a global forest map reduced the commission errors from 46% down to 26%. Compared to the GLC-Share and the International Institute for Applied Systems Analysis-International Food Policy Research Institute (IIASA-IFPRI) cropland maps, significant spatial disagreements were found, which might be attributed to discrepancies in the cropland definition. This advocates for a shared definition of cropland, as well as global validation datasets relevant for the agriculture class in order to systematically assess existing and future cropland maps.

Sensitivity Analysis of a Land-Use Change Model with and without Agents to Assess Land Abandonment and Long-Term Re-Forestation in a Swiss Mountain Region

Land abandonment and the subsequent re-forestation are important drivers behind the loss of ecosystem services in mountain regions. Agent-based models can help to identify global change impacts on farmland abandonment and can test policy and management options to counteract this development. Realigning the representation of human decision making with time scales of ecological processes such as reforestation presents a major challenge in this context. Models either focus on the agent-specific behavior anchored in the current generation of farmers at the expense of representing longer scale environmental processes or they emphasize the simulation of long-term economic and forest developments where representation of human behavior is simplified in time and space. In this context, we compare the representation of individual and aggregated decision-making in the same model structure and by doing so address some implications of choosing short or long term time horizons in land-use modeling. Based on survey data, we integrate dynamic agents into a comparative static economic sector supply model in a Swiss mountain region. The results from an extensive sensitivity analysis show that this agent-based land-use change model can reproduce observed data correctly and that both model versions are sensitive to the same model parameters. In particular, in both models the specification of opportunity costs determines the extent of production activities and land-use changes by restricting the output space. Our results point out that the agent-based model can capture short and medium term developments in land abandonment better than the aggregated version without losing its sensitivity to important socio-economic drivers. For comparative static approaches, extensive sensitivity analysis with respect to opportunity costs, i.e., the measure of benefits forgone due to alternative uses of labor is essential for the assessment of the impact of climate change on land abandonment and re-forestation in mountain regions.

Assessing groundwater pollution hazard changes under different socio-economic and environmental scenarios in an agricultural watershed

This paper proposes a modeling approach for assessing changes in groundwater pollution hazard under two different socio-economic and environmental scenarios: The first one considers an exponential growth of agriculture land-use (Relegated Sustainability), while the other deals with regional economic growth, taking into account, the restrictions put on natural resources use (Sustainability Reforms). The recent (2011) and forecasted (2030) groundwater pollution hazard is evaluated based on hydrogeological parameters and, the impact of land-use changes in the groundwater system, coupling together a land-use change model (Dyna-CLUE) with a groundwater flow model (MODFLOW), as inputs to a decision system support (EMDS). The Dulce Stream Watershed (Pampa Plain, Argentina) was chosen to test the usefulness and utility of this proposed method. It includes a high level of agricultural activities, significant local extraction of groundwater resources for drinking water and irrigation and extensive available data regarding aquifer features. The Relegated Sustainability Scenario showed a negative change in the aquifer system, increasing (+20%; high–very high classes) the contribution to groundwater pollution hazard throughout the watershed. On the other hand, the Sustainability Reforms Scenario displayed more balanced land-use changes with a trend towards sustainability, therefore proposing a more acceptable change in the aquifer system for 2030 with a possible 2% increase (high–very high classes) in groundwater pollution hazard. Results in the recent scenario (2011) showed that 54% of Dulce Stream Watershed still shows a moderate to a very low contribution to groundwater pollution hazard (mainly in the lower area). Therefore, from the point of view of natural resource management, this is a positive aspect, offering possibilities for intervention in order to prevent deterioration and protect this aquifer system. However, since it is quite possible that this aquifer status (i.e. groundwater quality) changes in the near future, the implementation of planning measures and natural resource management is recommended.

ART-P-MAP Neural Networks Modeling of Land-Use Change: Accounting for Spatial Heterogeneity and Uncertainty

Spatial land-use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART-Probability-Map (ART-P-MAP), tailored to address these issues in the context of spatial modeling of land-use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell-level uncertainty measurement. Compared with two baselines, ART-P-MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land-use change problem. Finally, we conclude that, as a general probabilistic regression model, ART-P-MAP is applicable to a broad range of land-use change modeling approaches, which deserves future research.

Landnutzungswandel durch demographischen Wandel? Evidenzen und Schlussfolgerungen


 
 
 Demographic change is often mentioned as one main driver for land use change. Since the 1970s, experts of spatial planning discuss consequences of demographic change. Currently, the topic is of importance in societal debates, too. However, does demographic change really directly influence land use change? As yet, comprehensive models of land use change are missing that describe the consequences of demographic change on land use in par- ticular; mainly partial models exist up to now. Thus, this paper contributes to the questions if there is any empirical evidence for direct relations between demographic change and land use change and what consequences this has for spatial research and spatial development policies. For an- swering these questions, we conducted a comprehensive literature analysis for the period from 2005 to 2013 with regard to the German discourse and discussed the results at two expert workshops. The analysis shows a lack of refer- ences that provide evidence for a direct influence of demo- graphic change on land use. This is in particular significant for the following types of land use: agriculture, forestry, tourism, and nature protection. Also indirect cause effect relations cannot be proved fully due to the lack of empiri- cism. Consequently, serious uncertainties exist regarding the effectiveness of spatial planning and spatial develop- ment approaches within this context.
 
 

Forecasting the urban power load in China based on the risk analysis of land-use change and load density

Abstract Along with the increasing development of the urban society and economy, urban power is facing an increasing number of risk factors. The conventional load forecasting methods cannot guarantee the accuracy of the prediction; however, scientific urban load forecasting has a greatly significant meaning to urban power planning and supply. This paper firstly analyzes the risk factors that affect the fluctuation of the power load. From the perspective of the spatial load, the factors influencing the fluctuation of the urban power load are mainly determined by the change in land use and load density per unit area. Secondly, based on the basic principle of cellular automata, the rules and model of land-use change are established by considering the risk factors. From the aspect of land-use change, the cellular change rules of the land use with the risk factors are proposed and the methods of land classification change are presented by combining them with geographic information system (GIS) technology. Meanwhile, after comprehensively considering the effect of the risk factors on the load density fluctuations, the power load forecasting model is established based on the risk analysis of the land-use change and load density. Finally, taking a specific city as an example, the case study results show that this model is scientific.

Multi-label class assignment in land-use modelling

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.

Impacts of climate change on land-use and wetland productivity in the Prairie Pothole Region of North America

Wetland productivity in the Prairie Pothole Region (PPR) of North America is closely linked to climate. A warmer and drier climate, as predicted, will negatively affect the productivity of PPR wetlands and the services they provide. The effect of climate change on wetland productivity, however, will not only depend on natural processes (e.g., evapotranspiration), but also on human responses. Agricultural land use, the predominant use in the PPR, is unlikely to remain static as climate change affects crop yields and prices. Land use in uplands surrounding wetlands will further affect wetland water budgets and hence wetland productivity. The net impact of climate change on wetland productivity will therefore depend on both the direct effects of climate change on wetlands and the indirect effects on upland land use. We examine the effect of climate change and land-use response on semipermanent wetland productivity by combining an economic model of agricultural land-use change with an ecological model of wetland dynamics. Our results suggest that the climate change scenarios evaluated are likely to have profound effects on land use in the North and South Dakota PPR, with wheat displacing other crops and pasture. The combined pressure of land-use and climate change significantly reduces wetland productivity. In a climate scenario with a +4 °C increase in temperature, our model predicts that almost the entire region may lack the wetland productivity necessary to support wetland-dependent species.

Determination of the optimal land use allocation pattern in Nowshahr County, Northern Iran

Regional models of land use change are evaluated as a way of identifying land use pattern based on recent quantitative and probabilistic approaches. Differentiation between models in this area is the result of the approach to the concept of land use and type of indicators used in the modeling process. Since the determination of the optima land use pattern, due to the different uses of land based on the availability of regional capabilities and spatial difference in land use, depends on an integrated model, there must be a model that can assess the existing conditions of the region and present reasonable models of land use according to regional capacities and potentials. In this research, we developed a model to present the Optimal Land Use Allocation Pattern (OLUAP) for maximizing the advantages of change and determining the methods of influencing land use change priorities. This model uses spatial models and analysis, moderates the cost-benefit function, and calculates the allocation priorities of various areas based on regional conditions. The model results show that a wide scope of Nowshahr area is allocated to natural and agricultural uses. At the same time, a limited area has been assigned to urban and industrial uses. In addition, it shows that northern strip of the region due to higher accessibility of services and infrastructures is more suited than other regions to urban and industrial areas, and these two uses have first and second priority, respectively. Besides, according to the man-made environment and the special environmental conditions, the model allocates the central lowlands and southern parts sporadically to agricultural and natural areas as first and second priorities. Since the model is spatial optimization, the suggestions of the model show the OLUAP. Therefore, one can see the optimal condition for each plot and compare it with the existing land use.

Geographic Modeling: Approaching Reality in Land Use Simulation Pragmatically

Geographic research necessitates different types of models that aim at reducing the complexity of reality, enabling us to analyze its representations. Models allow us to infer exploratory and confirmatory findings about causal links between different geographic conditions, different social, political, and economic actors, their behaviors, and land use and its dynamics. However, the spatial, temporal, and attributive nature of data complicates the appropriate model choice for specific research purposes. This conceptual paper keeps track of model construction in the context of land use and land use change. It reveals different issues where particular caution is required, such as spatial and temporal dependency and heterogeneity, scale dependency, and bidirectional causality. With geographic research being increasingly under the influence of econometric methods, we contend that it is necessary to provide intermediary qualitative layers of evidence between conceptual and, especially, empirical and simulative models of land use and land use change to ensure robust findings about causal relationships between observed variables.

Digital Modeling of Land Use Changes in Some Parts of Eastern Nigeria

Landuse expansion, spatial and temporal variability of the area has been studied between 1986 and 2002 via statistical classification approaches based on the remotely sensed images obtained from Landsat Thematic Mapper (TM) and Extended Thematic Mapper (ETM+) sensors. Multi-temporal images, landuse/land cover changes were detected by means of remote sensing. From the result of supervised classification, the process of landuse/land cover changes and the model of expansion were analyzed by Geographic Information System (GIS) technologies. Seven cover classes were identified namely light vegetation, thick vegetation, swamp vegetation, settlement, sand dune, bare soil/erosional areas and water body. The confusion matrix showed a high overall classification accuracy of 77% and Ksat statistics of 71% for the classified map. Digital Elevation Model (DEM) of the area was created from digitized topographic contour lines at 1:50,000 scale. Additional information was derived from geologic and vegetation maps of the area to delineate spatial extent of landuse/land cover. Maps generated for these years were overlaid to obtain a change detection map showing the dynamic growth in land use changes for the two periods. Result also showed that during the study period from 1986 to 2002, the vegetation reduced from 47.7% to 14.4% in the study, more than double in 16 years, showing a strong trend of expansion of settlement as well as growth in baresoil area, perhaps due to sand mine or erosion. The research work shows that land use/land cover change detection using multi-temporal images by means of remote sensing and GIS modeling are good means of analyzing dynamic changes in time sequence.

Modeling the Sociospatial Constraints on Land-Use Change: The Case of Periurban Sprawl in the Greater Boston Region

Land-use-change drivers related to institutional dynamics, including historical path dependencies and political dynamics associated with urban land transformation, are difficult to relate to specific spatial locations and thus are not easily included in spatial models of urban land-use change. In this paper we describe a land-use model with variables representing such institutional dynamics in the Greater Boston region, a metropolitan area characterized by periurban sprawl, for the period 1985–99. An aggregate land-use model is developed at the municipal level, based on a narrative analysis drawn from in-depth interviews with town planners, state officials, and land developers, to explain land-development patterns documented over that study period using aerial photography. Explanatory variables, including town financial variables, school quality measures, and spatial variables associated with access and location, are linked to land-change outcomes through the selection environment framework, a framework borrowed from economic geography to describe how regional growth patterns are shaped by locally specific institutional, market, and spatial contexts that constrain individual land-use decision makers. Results of the analysis suggest that institutional dynamics associated with housing values and associated tax revenues, educational expenditures, and exclusive zoning practices significantly explain municipal land-use change in the suburban or periurban context.

Implications of Using 2 m versus 30 m Spatial Resolution Data for Suburban Residential Land Change Modeling

This study assesses the advantages and disadvantages of using 2 m spatial resolution data versus 30 m resolution data for a simulation model of land-use and land-cover change (LUCC). The model projects LUCC from 2005 to 2055 in the town of Lynnfield, Massachusetts, USA. This article describes four scenario storylines and then projects land-use and land-cover under each of the four scenarios with 2 m data and again with 30 m data. The disagreement between the 2 m output and its corresponding 30 m output ranges between 5.7% and 11.0% of the town. The disagreement due to allocation over small distances is greater than the disagreement due to the quantity of new residential growth. The projected quantities of new residential land-use in 2055 differ between the two resolutions by 1% of the town, whereas the visual differences in the spatial allocations are distinct and substantial. The results for this case study show that 30 m resolution data provides several practical and theoretical advantages over 2 m resolution data, due mainly to the fact that the 30 m resolution data match more closely the size of the patches of change.

The incorporation of fungal to bacterial ratios and plant ecosystem effect traits into a state-and-transition model of land-use change in semi-arid grasslands

Feedbacks between plants, microbes and their growth traits are important in the maintenance of nutrient cycling functions. Despite this, there is little understanding of the role of these relationships in the transitions between alternate vegetation states in semi-arid and arid lands. We investigated the relationships between vegetation, soil nutrients and soil microbes across grasslands and agricultural fields described within an existing conceptual state-and-transition model of agricultural de-intensification in the semi-arid Riverine Plains grasslands of south-eastern Australia. Sites represented the proposed transition from annual exotic pastures to native perennial grasslands with agricultural de-intensification. Microbial community composition was assessed using phospholipid fatty acid analysis (PLFA).The native grassland state and the native pasture state were characterized by a higher fungal to bacterial ratio (F:B). The native grassland state had a slightly lower bacterial PLFA biomass whilst the native pasture state had a slightly higher fungal PLFA biomass, although these differences were non-significant. Only the recently cultivated, heavily grazed state was characterized by high soil nutrient availability (soil P and K) and leaf traits indicating rapid growth and resource utilization (high SLA, low LDMC). Thus, the association of these ecosystem properties with a lower F:B was not as close as expected. States with a higher F:B were not characterized by higher total soil C or C:N as hypothesized.This study further extends our knowledge of the association between fungal dominance and agricultural de-intensification to a semi-arid system with relatively old, nutrient poor soils. It highlights the need for a better understanding of the mechanistics behind this association and the implications for C cycling and storage in such systems.

Geo-information Based Spatio-temporal Modeling of Urban Land Use and Land Cover Change in Butwal Municipality, Nepal

Abstract. Unscientific utilization of land use and land cover due to rapid growth of urban population deteriorates urban condition. Urban growth, land use change and future urban land demand are key concerns of urban planners. This paper is aimed to model urban land use change essential for sustainable urban development. GI science technology was employed to study the urban change dynamics using Markov Chain and CA-Markov and predicted the magnitude and spatial pattern. It was performed using the probability transition matrix from the Markov chain process, the suitability map of each land use/cover types and the contiguity filter. Suitability maps were generated from the MCE process where weight was derived from the pair wise comparison in the AHP process considering slope, land capability, distance to road, and settlement and water bodies as criterion of factor maps. Thematic land use land cover types of 1999, 2006, and 2013 of Landsat sensors were classified using MLC algorithm. The spatial extent increase from 1999 to 2013 in built up , bush and forest was observed to be 48.30 percent,79.48 percent and 7.79 percent, respectively, while decrease in agriculture and water bodies were 30.26 percent and 28.22 percent. The predicted urban LULC for 2020 and 2027 would provide useful inputs to the decision makers. Built up and bush expansion are explored as the main driving force for loss of agriculture and river areas and has the potential to continue in future also. The abandoned area of river bed has been converted to built- up areas.

Impacts of Land-Use Change on Groundwater Supply: Ecosystem Services Assessment in Kona, Hawaii

AbstractPayments for watershed management link upstream inhabitants whose actions affect water resources with downstream water users. This paper evaluates the effect of plausible shifts in watershed land use on hydrologic services on the Kona coast of Hawai’i Island by measuring vegetation effects on hydrologic fluxes, modeling land-use change impact on the water-supply aquifer, and evaluating the local water department’s associated pumping expenses. Transitions between native and plantation forest will have a 25–40% greater impact on the aquifer than the transition from pasture to either forest type. However, for all transitions, the value to the water department is just 2–5% of the opportunity cost to landowners. To provide context for these findings, the effects of these land transitions on carbon storage, provision of native bird habitat, and land stewardship are assessed. Estimates show that delivery of other services does not always increase when water services increase and suggest that the value of...