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A spatial allocation procedure to model land-use/land-cover changes:Accounting for occurrence and spread processes

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... fire suppression) in a high fire risk, densely populated, forested Mediterranean region. We adopt a landscape dynamic meta-modelling approach, coupling two existing spatially explicit models, a fire-succession model (Brotons et al., 2013) and a LULC change model (Aquilué et al., 2017). The resulting meta-model accounts for spatio-temporal interactions between fire ignitions, fire spread (that depends on landscape composition and species fire sensitivity), fire suppression, LULC transitions, and ecological processes (mainly post-fire regeneration and afforestation). ...
... The MEDLUC is a spatially explicit land-use land-cover change model designed to reproduce any LULC transition (Aquilué et al., 2017). Given a LULC map with a few discrete categories, a land transition (e.g. ...
... Landscape composition influences landscape level processes that are key to our system, such as the probability of fire ignition (González-Olabarria et al., 2012) and the land-cover transitions (Aquilué et al., 2017). For fire ignition risk and land transitions the interplay between human presence on the territory and natural areas is relevant. ...
Thesis
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Highly managed forest landscapes are complex socio-ecological systems exposed to multiple drivers of change. Their dynamics emerge from the multi-scale interplays between ecological processes, natural disturbance regimes, anthropic activities, and exogenous factors such as climate. Global changes are expected to interfere on these processes leading to decreases in the resilience of forest landscapes (i.e. the capacity to cope with and adapt to exogenous pressures and disturbances) to various single or compound disturbances. New forest management strategies adapted to these future environmental conditions need to be developed and investigated. In this thesis, I present two simulation modelling approaches to characterise forest ecosystems to then evaluate landscape-scale forest management strategies to be applied in an uncertain global change context. All the proposed strategies seek at enhancing resilience of forest landscapes to shifting disturbances regimes. I apply each of these two modelling approaches to two distinct highly managed forest regions. Firstly, I explore the performance of large-scale fuel reduction policies in shaping the fire regime of a European Mediterranean fire-prone landscape using a landscape dynamic meta-model. I developed a spatially explicit land-use/land-cover change model which is then coupled to an existing spatially explicit fire-vegetation dynamics model. This meta-modelling framework was useful to study the variability on fire suppression effectiveness due to agricultural conversion. Agricultural land (a low-load fuel that reduces fire intensity and allows fire brigades get closer to fire fronts) was allocated in the landscape at various annual rates, following a scattered versus an aggregate spatial pattern, and according to three storylines depicting potential fire management policies in the region. Secondly, I focused on understanding whether improving functional diversity or connectivity of a fragmented agro-forested landscape in south-eastern Canada fosters ecosystem resilience to natural and anthropogenic disturbances. Here, I introduced a multiscale evaluation of forest resilience based on the response of species functional traits and spatial network properties. I tested the approach to investigate if these alternative management strategies prevent decreases in resilience under future scenarios of drought, pest outbreak, and harvesting. In the fire-prone Mediterranean landscape, I uncovered a non-linear relationship between the amount of new agricultural land allocated within the landscape and the fire suppression effectiveness. Fire suppression effectiveness barely increased at low / moderate annual conversion rates to agricultural land, but it sharply did at high annual conversion rates, meaning that land changes to a low-load fuel land-cover need to progressively accumulate before the system becomes more fire resistant. However, further increases on the agricultural conversion rate did not report clear benefits on fire suppression, meaning that the landscape reached its capacity of influencing the fire regime through fire-fighting actions. Moreover, when agricultural land was allocated in few large patches, effectiveness was higher (at the same rate of conversion to agricultural land) and forest core area was better maintained. In the fragmented agro-forested landscape of south-eastern Canada, enrichment of the less functionally rich forest patches by functionally different tree species, rather than targeting either less or the more connected patches, had a larger impact in improving both, diversity and functional connectivity at the landscape scale. Multi functional enrichment of functionally poor patches was even more cost-effective than a strategy based on multispecies plantations (at random or in riparian zones). Moreover, enriching with pest-resistant species was successful in reducing pest-induced mortality. However, planting drought-tolerant species did not do better at preventing drought-induced mortality than the strategy aimed at increasing overall biodiversity of the landscape. Although there is an increasing number of models to simulate landscape dynamics and approaches to evaluate ecosystem resilience, the methods developed in this thesis to (1) investigate spatially explicit interactions between land-cover changes, fire behaviour, and fire suppression, and (2) evaluate system-level properties related to forest ecosystem resilience to natural and anthropogenic disturbances are innovative in several ways. First, land-use/land-cover changes are modelled as an emergence contagion process, second the landscape dynamic meta-model has a fire suppression module sensitive to fuel loads spatial configuration, and third ecosystem resilience measures are based on species functional response traits and spatial network topology. In addition, in both examples, the landscape management approaches suggested are totally different from what is currently being done, challenging conventional management regimes. In conclusion, this thesis proposes broad and original methodologies to evaluate resilience-based scenarios for forest landscapes facing global changes. Keywords: model coupling; network analysis; disturbances regimes; forest resilience; landscape management.
... Although natural afforestation is the main land-use trend affecting forest cover in Catalonia (Aquilué et al. 2017), deforestation linked to urbanization and agriculture expansion is the main land-use change threat for forests in the region (Catalán et al. 2008;Aquilué et al. 2017). Potential land-use change risk (henceforth BDeforestation risk^) was assessed through a regression model predicting probability of deforestation at the level of Catalonia. ...
... Although natural afforestation is the main land-use trend affecting forest cover in Catalonia (Aquilué et al. 2017), deforestation linked to urbanization and agriculture expansion is the main land-use change threat for forests in the region (Catalán et al. 2008;Aquilué et al. 2017). Potential land-use change risk (henceforth BDeforestation risk^) was assessed through a regression model predicting probability of deforestation at the level of Catalonia. ...
... Topographic factors were not considered in the deforestation modeling due to their underlying influence on land-use change (Ameztegui et al. 2010;Gil-Tena et al. 2016) and, therefore, the strong correlation with the context-dependent land-cover predictors. Since future scenarios of land-use are not readily available for the study area at a fine spatial resolution (Verburg et al. 2010;Aquilué et al. 2017), we assumed that the recent deforestation trends observed in the last two decades provide a reliable estimation of future deforestation risk in the short term in the study area (i.e., we assumed a BAU scenario). ...
Article
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Assessment of potential forests’ threats due to multiple global change components is urgently needed since increasing exposure to them could undermine their future persistence. We aim to assess the risks to the persistence of monospecific forests in Western Mediterranean Europe posed by climate change, fire, and land-use changes (i.e., deforestation) in the short and medium terms (horizon 2040). We specifically evaluate whether the degree of risk related to the likelihood of hazard occurrence varies depending on seral stage, tree species, and climate gradients. We performed the risk assessment on forests of Catalonia (NE Spain) through a combination of correlative and process-based modeling approaches and future global change scenarios. Overall, climate suitability of forests showed a general decrease by 2040, with the exception of xeric Pinus halepensis forests mainly distributed in the driest climate of the study area. Forest stands dominated by low drought-tolerant species were at higher risk of losing climatic suitability than forests dominated by Mediterranean species. The highest fire and deforestation risks were predicted for forest stands in dry climate where human pressures are higher. Nevertheless, high deforestation risk was also attained outside the driest areas. Deforestation risk was lower in old-growth than in younger stands, whereas old-growth forests in the Wet climate or dominated by Pinus sylvestris were projected to be at higher fire risk than younger forests. Our results suggest that conservation actions should target forest stands in dry climate. Moreover, old-growth forest stands should also be prioritized due to their particular sensitivity to disturbances and their high ecological value.
... While there are a wide variety of land use change models available as the result of broad interest and research, which can be categorized along economic/non-economic, spatiallyexplicit/aggregated, and empirical/process-based lines [15,16]; demand-allocation methods [17][18][19][20][21] are particularly well-suited to situations where a projected land use change is available at a coarse scale and the intent is to downscale the projection. Demand-allocation models tend to input exogenously generated transition quantities (or quotas) for a given area (e.g., county), for example via socioeconomic models [10,18]; then allocate those quotas within the study area [22]. ...
... While there are a wide variety of land use change models available as the result of broad interest and research, which can be categorized along economic/non-economic, spatiallyexplicit/aggregated, and empirical/process-based lines [15,16]; demand-allocation methods [17][18][19][20][21] are particularly well-suited to situations where a projected land use change is available at a coarse scale and the intent is to downscale the projection. Demand-allocation models tend to input exogenously generated transition quantities (or quotas) for a given area (e.g., county), for example via socioeconomic models [10,18]; then allocate those quotas within the study area [22]. This allocation can be driven by empirically estimated transition probability models (e.g., [18,19]), or it can be the result of process-based models such as cellular automata or related hybrids [21,[23][24][25]. ...
... Demand-allocation models tend to input exogenously generated transition quantities (or quotas) for a given area (e.g., county), for example via socioeconomic models [10,18]; then allocate those quotas within the study area [22]. This allocation can be driven by empirically estimated transition probability models (e.g., [18,19]), or it can be the result of process-based models such as cellular automata or related hybrids [21,[23][24][25]. Such models can be employed using fuzzy or crisp logic [26]. ...
Article
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Future land use projections are needed to inform long-term planning and policy. However, most projections require downscaling into spatially explicit projection rasters for ecosystem service analyses. Empirical demand-allocation algorithms input coarse-level transition quotas and convert cells across the raster, based on a modeled probability surface. Such algorithms typically employ contagious and/or random allocation approaches. We present a hybrid seeding approach designed to generate a stochastic collection of spatial realizations for distributional analysis, by 1) randomly selecting a seed cell from a sample of n cells, then 2) converting patches of neighboring cells based on transition probability and distance to the seed. We generated a collection of realizations from 2001–2011 for the conterminous USA at 90m resolution based on varying the value of n, then computed forest area by fragmentation class and compared the results with observed 2011 forest area by fragmentation class. We found that realizations based on values of n ≤ 256 generally covered observed forest fragmentation at regional scales, for approximately 70% of assessed cases. We also demonstrate the potential of the seeding algorithm for distributional analysis by generating 20 trajectories of realizations from 2020–2070 from a single example scenario. Generating a library of such trajectories from across multiple scenarios will enable analysis of projected patterns and downstream ecosystem services, as well as their variation.
... Land use/land cover (LULC) changes have been identified as the main driving forces of local, regional, and global environmental changes, which have been stressed increasingly in the evaluation of anthropogenic effects on the environment (Verburg et al., 2015). LULC changes are the results of dynamic human-environment interactions in processes operating at differing spatiotemporal scales (Aquilué et al., 2017;NRC, 2014;Verburg and Overmars, 2009). ...
... However, except for this conceptual framework at the global scale and several integrated models (e.g., integrating CGE models with ABM), ABMs remain fragmented and face a tricky obstacle in representing human decision processes at regional and global scales. This may be because of the barriers on data availability, agent attributes in model parameterization, as well as the scaling and aggregation issues for macroscale applications (Aquilué et al., 2017;Rindfuss et al., 2004;van Delden et al., 2011). ...
... This will provide muchneeded diversity in innovative methodology from which the next generation of LULC change models is more likely to benefit (NRC, 2014;Rounsevell et al., 2014). Aquilué et al. (2017) introduced a novel spatial demand-allocation procedure to simulate LULC dynamics. Their study explicitly addressed two critical phases inherent in land conversions: the occurrence and spread of land change, corresponding to the initiation of new changes ("patch-of-change") and the generation of the final spatial patterns. ...
Article
Land use/land cover (LULC) change models are powerful tools used to understand and explain the causes and effects of LULC dynamics, and scenario-based analyses with these models can support land management and decision-making better. This paper provides a synoptic and selective review of current LULC change models and the novel frameworks that are being used to investigate LULC dynamics. Existing LULC models that explore the interactions between human and the environment can be pattern- or process-based, inductive or deductive, dynamic or static, spatial or non-spatial, and regional or global. This review focuses on the spectrum from pattern- to process-based approaches and compares their strengths, weaknesses, applications, and broad differences. We draw insights from the recent land use change literature and make five suggestions that can support a deeper understanding of land system science by: (1) overcoming the difficulties in comparing and scaling Agent Based Models; (2) capturing interactions of human-environment systems; (3) enhancing the credibility of LULC change modeling; (4) constructing common modeling platforms by coupling data and models, and (5) bridging the associations between LULC change modeling and policy-making. Although considerable progress has been made, theoretical and empirical efforts are still needed to improve our understanding of LULC dynamics and their implications for policy-oriented research. It is crucial to integrate the key elements of research involved in this study (e.g., use of common protocols and online portals, integration of top-down and bottom-up approaches, effective quantification and communication of modeling uncertainties, generalization and simplification of models, increased focus on the theoretical and empirical bases of models, and open comparative research) to bridge the gaps between small-scale process exploration and large-scale representation of LULC patterns, and to use LULC change modeling to inform decision-making.
... The demand is a quantity of change at a time step. Thus, this relationship can select the cells to be transformed to the target land-cover class and to allocate model land transitions from one land-cover class to another at a given spatial pixel (Aquilué et al., 2017). LULCC modelling is comprised of several foundational elements: 1) the drivers of land change, 2) pattern recognition of land cover on a regional scale, 3) the local history of land use, and 4) the geographical context and characteristics of the region (Heisterman et al., 2006;Sohl et al., 2010). ...
... Yeh (2002, 2004) used CA with a data-mining technique to describe the transition of LULCC as a single LULC category. The explicit transition rules of CA can be updated through the induction of data mining parameters and processes, such as demand-allocation relation (Aquilué et al., 2017). ANNs are very effective at modelling complex non-linear mapping relations involving many factors while their transition function is calculated by the CA model. ...
Article
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Land-use and land-cover change (LULCC) are of importance in natural resource management, environmental modelling and assessment, and agricultural production management. However, LULCC detection and modelling is a complex, data-driven process in the remote sensing field due to the processing of massive historical and current data, real-time interaction of scenario data, and spatial environmental data. In this paper, we review principles and methods of LULCC modelling, using machine learning and beyond, such as traditional cellular automata (CA). Then, we examine the characteristics, capabilities, limitations, and perspectives of machine learning. Machine learning has not yet been dramatic in modelling LULCC, such as urbanization prediction and crop yield prediction because competition and transition between land cover types are dynamic at a local scale under varying natural drivers and human activities. Upcoming challenges of machine learning in modelling LULCC remain in the detection and prediction of LULC evolutionary processes if consider their applicability and feasibility, such as the spatio-temporal transition mechanisms to describe occurrence, transition, spreading, and spatial patterns of changes, availability of training data of all the change drivers, particularly sequence data, and identification and inclusion of local ecological, hydrological, and social-economic drivers in addressing the spectral feature change. This review points out the need for multidisciplinary research beyond image processing and pattern recognition of machine learning in accelerating and advancing studies of LULCC modelling. Despite this, we believe that machine learning has strong potentials to incorporate new exploratory variables in modelling LULCC through expanding remote sensing big data and advancing transient algorithms.
... CA models for urban growth simulation (hereafter termed as urban CA model) typically comprise two major components: a quantitative demand module that estimates the demand of urban growth, and a spatial allocation module that determines the spatial arrangement of these demands (White & Engelen, 1993). Urban CA models following this demand-allocation framework (Aquilue, De Caceres, Fortin, Fall, & Brotons, 2017) have various advantages. For example, different approaches can be applied in the estimation of the quantitative demand (Brown et al., 2013), error and uncertainty in model outputs can be divided into separate sources (Pontius Jr & Millones, 2011), and distinct drivers for demand estimation and their spatialization can be considered (Aquilue et al., 2017;Veldkampa & Lambinb, 2001). ...
... Urban CA models following this demand-allocation framework (Aquilue, De Caceres, Fortin, Fall, & Brotons, 2017) have various advantages. For example, different approaches can be applied in the estimation of the quantitative demand (Brown et al., 2013), error and uncertainty in model outputs can be divided into separate sources (Pontius Jr & Millones, 2011), and distinct drivers for demand estimation and their spatialization can be considered (Aquilue et al., 2017;Veldkampa & Lambinb, 2001). However, these demand-allocation urban CA models often ignore the dynamic feedback between the quantitative composition that urban gains from non-urban land types and the spatial configuration of urban growth Sohl et al., 2012;Xiang & Clarke, 2003). ...
... We applied our framework to Mediterranean ecosystems since they are prime examples of where wildfires represent the main natural disturbance (Trabaud, 1994), where post-fire regeneration and growth are crucial processes in describing landscape-level patterns and where some of the main forest species are adapted to fire. Moreover, there are many studies on landscape dynamics in Mediterranean ecosystems, such as land-use changes (Aquilué et al., 2017), fire dynamics (Brotons et al., 2013;Duane et al., 2016), management (Miller and Ager, 2013) and the consequences of fire on vegetation (Rodrigo et al., 2004). These studies offer a convenient framework to discuss the integration of local vegetation processes in landscape-level modelling exercises. ...
Article
Landscape models are comprehensive tools that allow for an understanding of landscape dynamics and a means of deriving future projections in the context of global change. Vegetation and ecological processes such as growth, death or regeneration are essential components of forest landscape dynamics, but their inclusion in landscape-level modelling frameworks is not straightforward as there is a trade-off between model feasibility, desirable complexity and the inclusion of relevant ecological processes. If models are to project future landscape dynamics, climatic influence on vegetation processes needs to be integrated; however, this usually leads to a major increment in model complexity. Here, a post-fire regeneration model (in terms of tree species) and a growth model (in terms of basal areas) is presented for Mediterranean forests including climate influences on such processes. The model captures vegetation dynamics at the stand level and accounts for post-fire regeneration and vegetation growth at the landscape level, with inclusion of the dynamically influencing effect of climate. The model was calibrated with 7709 inventory data plots and validated with 233 burned plots in the Mediterranean region of Catalonia (NE Spain). Results show that our model is able to accurately predict tree species post-fire regeneration and biomass growth. They also show that integration of climatic information represents a significant improvement on the predictive accuracy of the model. Overall, this study presents a generic approach to extend local vegetation dynamics information to the landscape level; furthermore, allowing the projection of vegetation dynamics under changing climatic conditions.
... The latter step focuses on the optimization of spatial land use patterns to achieve multiple land use objectives by regulating the use of each parcel at the micro level. In practical land use planning, the quantity of land resources is usually determined first, followed by the spatially explicit land use allocation (Aquilue, De Caceres, Fortin, Fall, & Brotons, 2017;Verburg et al., 2002). Because the spatially explicit allocation of land use directly affects stakeholders' utilization of land resources, more attention has been given to land use stakeholders, e.g., land use individuals or organizations. ...
Article
Spatial planning is a complex land use allocation process involving multiple land use stakeholders, and resolving potential conflicts among stakeholders presents a challenging issue. This study proposed an innovative combination of an agent-based model and heuristic methods (machine learning and artificial intelligence approaches) to address spatial planning issues. The agent-based model simulates the decision-making processes of stakeholders in land use allocation, the machine learning approach obtains the nonlinear behavioral rules of land use agents, and the artificial intelligence approach provides a flexible optimization framework that can incorporate agents’ preferences into land use allocation. The integration of the agent-based model and heuristic methods enables us to adaptively explore nonlinear relationships between agent behaviors and decision-making environments and efficiently identify solutions to land use allocation in a spatially explicit way. The results show that the optimal allocation solutions obtained by the agent-based model are more applicable based on the support of the factual evidence than those obtained by the non-agent-based model. The proposed model can integrate the simulated local decision of stakeholders and global optimization of the specified objectives in land use planning, and thus provide a flexible theoretical framework to support the reform of China's spatial planning system.
... Since land use results from properties of the physical environment and from socio-economic development, relationships to biophysical and socio-economic spatially distributed variables are used to analyze land use change patterns (Mas et al., 2014;Aquilué et al., 2017). Relevant variables haven been used in a large number of land use change studies (Mitsuda and Ito, 2011). ...
Article
Land use patterns arise from interactive processes between the physical environment and anthropogenic activities. While land use patterns and the associated explanatory variables have often been analyzed on the large scale, this study aims to determine the most important variables for explaining land use patterns in the 50 km² catchment of the Kielstau, Germany, which is dominated by agricultural land use. A set of spatially distributed variables including topography, soil properties, socioeconomic variables, and landscape indices are exploited to set up logistic regression models for the land use map of 2017 with detailed agricultural classes. Spatial validation indicates a reasonable performance as the relative operating characteristic (ROC) ranges between 0.73 and 0.97 for all land use classes except for corn (ROC = 0.68). The robustness of the models in time is confirmed by the temporal validation for which the ROC values are on the same level (maximum deviation 0.1). Non-agricultural land use is generally better explained than agricultural land use. The most important variables are the share of drained area, distance to protected areas, population density, and patch fractal dimension. These variables can either be linked to agriculture or the river course of the Kielstau.
... Land-use and land-cover change (LUCC) is considered one of the most profound terrestrial surface changes induced by human activities [1,2]. LUCC emerges from the dynamic interactions between natural and socioeconomic systems, which is identified as a core research field of the studies related to global environmental change [3,4]. Land-use activities caused by needs of development and construction are changing the function and structure of land systems [5]. ...
Article
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A change in the usage of land is influenced by a variety of driving factors and policies on spatial constraints. On the basis of considering the conventional natural and socio-economic indicators, the landscape pattern indicators were considered as new driving forces in the conversion of land use and its effects at small regional extent (CLUE-S) model to simulate spatial and temporal changes of land-use in Beijing. Compared with traditional spatial restrictions characterized by small and isolated areas, such as forest parks and natural reserves, the ecological redline areas increase the spatial integrity and connectivity of ecological and environmental functions at a regional scale, which were used to analyze the distribution patterns and behaviors of land use conversion in the CLUE-S model. The observed results indicate that each simulation scenario has a Kappa coefficient of more than 0.76 beyond the threshold value of 0.6 and represents high agreements between the actual and simulated land use maps. The simulation scenarios including landscape pattern indicators are more accurate than those without consideration of these new driving forces. The simulation results from using ecological redline areas as space constraints have the highest precision compared with the unrestricted and traditionally restricted scenarios. Therefore, the CLUE-S model based on the restriction of ecological redline and the consideration of landscape pattern factors has shown better effectiveness in simulating the future land use change. The conversion of land use types mainly occurred between construction land and cropland during the period from 2010 to 2020. Meanwhile, a large number of grasslands are being changed to construction lands in the mountain towns of northwest Beijing and large quantities of water bodies have disappeared and been replaced by construction lands due to rapid urbanization in the eastern and southern plains. To improve the sustainable use of land resources, it is necessary to adopt the construction and development mode of satellite towns rather than encouraging a disorderly expansion of downtown areas.
... The research on land use simulations has evolved for a long time. Although some scholars have pointed out the uncertainty in land use change simulations (Pontius et al., 2006;Camacho Olmedo et al., 2015;Aquilué et al., 2017), the uncertainty has been attributed to input data, parameters, model structure, processes and their interactions, as well as the mathematical and algorithmic representation (Prestele et al., 2016;Ren et al., 2019). However, we find that the area percentages of the landscapes with and without land use transition can be important for impacting the accuracy of land use change simulations in both hypothetical and real landscape contexts ( Fig. 3 and Table 1). ...
Article
Land use change (LUC) modelling has been widely used to inform landscape planning and adaptive management practices. The validation of LUC modelling results is critical for justifying the usability of LUC models. Along this line, global-level accuracy assessments with the kappa and error matrix approach are accepted as the common method for the validation of LUC models. However, high global-level accuracy does not always guarantee good model performance and high accuracy in characterizing the local LUC. It is necessary to develop indicators to exactly assess the accuracy of land use modelling in characterizing the detailed land use change. In this study, both hypothetical and real landscapes are used to analyze the differences between global-level and local accuracy assessments, and all possible simulation scenarios are considered in the hypothetical landscape by exhaustive methods. The results derived from the hypothetical landscape show that the local accuracy tends to increase with the increase in the proportion of the regional area showing land use transitions throughout the landscape. A real landscape simulation by the Dyna-CLUE model in the middle reaches of the Heihe River Basin (M-HRB) in China also showed a similar trend, where the land use transition from 2000 to 2015 accounted for approximately 10% of the total area. The simulation results showed high global-level accuracies of 97.17% (2010) and 85.01% (2015). The local accuracies for regions with little land use transition were 98.45% (2010) and 96.56% (2015), but the local accuracies for regions with significant land use transitions were only 0.99% and 6.08% in 2010 and 2015, respectively. According to the global-level accuracy, LUC simulations are reliable, but they do not correctly reflect the local changes in regions with significant land use transitions. Therefore, both global-level accuracy and local accuracy should be used to avoid possible misleading LUC modelling results. An effective simulation and accuracy assessment procedure was proposed in this study to increase the credibility of LUC modelling.
... A large pool of research has been carried out to analyze LULC changes and the associated driving forces with the help of LULC change models (Zhu et al. 2010;Chen et al. 2014;Mousazadeh et al. 2015;Sakieh and Salmanmahiny 2016;Bryan et al. 2016;Aquilué et al. 2017;Ghavami and Taleai 2017;Feng and Tong 2018;Jahanishakib et al. 2018). ...
Article
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In the present paper, land use/land cover (LULC) change was predicted in the Greater Isfahan area (GIA), central Iran. The GIA has been growing rapidly in recent years, and attempts to simulate its spatial expansion would be essential to make appropriate decisions in LULC management plans and achieve sustainable development. Several modeling tools were employed to outline sustainable scenarios for future dynamics of LULCs in the region. Specifically, we explored past LULC changes in the study area from 1996 to 2018 and predicted its future changes for 2030 and 2050. For this purpose, we performed object-oriented and decision tree techniques on Landsat and Sentinel-2 satellite images. The CA-Markov hybrid model was utilized to analyze past trends and predict future LULC changes. LULC changes were quantitatively measured using landscape metrics. According to the results, the majority of changes were related to increasing residential areas and decreasing irrigated lands. The results indicated that residential lands would grow from 27,886.87 ha to 67,093.62 ha over1996–2050 while irrigated lands decrease from 99,799.4 ha to 50,082.16 ha during the same period of time. The confusion matrix of the 2018 LULC map was built using a total of 525 ground truth points and yielded a Kappa coefficient and overall accuracy of 78% and 82%, respectively. Moreover, the confusion matrix constructed base on the Sentinel-2 map, as a reference, to judge the predicted 2018 LULC map with a Kappa coefficient of 88%. The results of this study provide useful insights for sustainable land management. The results of this research also proved the promising capability of remote sensing algorithms, CA-Markov model and landscape metrics future LULC planning in the study area.
... The main cause of LUT lies in the fact that rural land has been intensively occupied by urban construction land. In terms of the regulation of LUT, it is necessary to change the way in which the external system of the rural area affects the internal system, promoting the free flow of urban and rural elements [109][110][111]. Land use planning and land consolidation are important engineering techniques to optimize and control the LUT. ...
Article
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The study of land use transition has generally become an important breakthrough point to deeply understand the human-land interaction and reveal major socio-economic development issues and related environmental effects. Attempting to provide scientific support for sustainable land use and environmental management, this review systematically analyzes the overall picture, development trends, key fields and hot topics of land use transition research in the past two decades from a comprehensive perspective, which incorporates two complementary parts including the systematic quantitative literature review (based on CiteSpace) and the traditional literature review. The results reveal that: a. current research presents three characteristics, i.e., focusing on complex social issues, driven by realistic demand, and research branches becoming clearer and more systematic; b. there are four key fields and hot topics in land use transition research, i.e., i. theories and hypothesis of land use transition; ii. measuring land use transition; iii. the impacts of land use transition on “social-economic-ecological” system; iv. drivers and regulation of land use transition. However, challenges remain, current land use transition research is still to some extent fragmented, and it should be enriched by integrating with land system science. The dominant morphology biased should be redressed by underlining the recessive morphology transition process. Meanwhile, new techniques and methods are necessary to observe, track, monitor and model the recessive attributes. Finally, distant drivers of land use transition should not be ignored in this rapidly globalizing world.
Article
With the rapid socioeconomic development in China, the competition for space in land-use conversion is getting fierce. The Wuhan metropolitan area, as one of the main areas of modern agriculture and manufacturing, has been significantly affected by urbanization, industrialization, and national development policies, resulting in regional man-land contradiction. In this complex region, scientifically measuring the land-use/land-cover (LULC) dynamics and exploring the spatiotemporal evolution characteristics of the LULC changes are important tasks for local officials and decision makers in the management of urban expansion and land-use planning. In this study, an integrated logistic multi-criteria evaluation (MCE) cellular automata (CA) Markov (logistic-MCE-CA-Markov) model and a geographic information system (GIS) were used to evaluate and predict the LULC changes. The analysis was based on six LULC maps at equal temporal intervals derived from land-use data for 1990, 1995, 2000, 2005, 2010, and 2015, along with topographic spatial layers (elevation and slope) derived from an ASTER digital elevation model. In addition, other spatial variables (points of interest, gross domestic product(GDP), population density, proximity to urban center, and proximity to transportation line) were incorporated in the simulation process. The simulated results obtained by the integrated logistic-MCE-CA-Markov model had a kappa coefficient of 88.582% and a figure of merit value of 27.935%. The results indicated that, under the influence of the various factors, the future land-use pattern of the Wuhan metropolitan area will be clearly transformed. From 2015 to 2025, it is predicted that the area of arable land and woodland will decrease by 1506.152 km² and 1743.945 km², respectively, and urban land expansion will mainly come from arable land, woodland, and other construction land. With the enhancement of the human disturbance intensity, the natural landscape patches will become segmented, and the number of individual patches will increase gradually, enhancing the spatial heterogeneity. The simulation results could not only be used to monitor future LULC trends, but could also help local decision makers to provide policy support for land-use planning and management.
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With stores that are more than 50 times greater than in terrestrial ecosystems, riverine and coastal ecosystems are the most important carbon (C) stocks worldwide, with most C stored in wetlands. Wetlands are, however, among the most threatened ecosystems worldwide, and face pressures from hydropower development, flow regime alteration, and land use/cover change (LUCC). Despite great efforts to protect and restore wetlands, anthropogenic drivers (e.g., land reclamation) still result in more C loss than gain in most cases. Of these, LUCC is driven by social and economic processes, and is a self-organizing, path-dependent phenomenon. Therefore, we proposed a land use management framework with which to analyze the spatial patterns in the drivers of LUCC, and then verified the strategy using two study areas in the lower reaches and delta of the Yellow River. The first step of the process was to analyze LUCC from historic land use/cover maps (from 1995 to 2015 for the lower river reaches and from 1970 to 2015 for the delta), and identified a suitable land use (waterbody) for the connections. We calibrated and validated the DINAMICA model using data for changes in C. We then set up different scenarios and created connections between high C patches. The simulation results showed that even slight modifications in the connections with water could trigger noticeable changes in the spatial patterns of C gain and loss, and that original hotspots of C loss could be converted to areas of C gain in some cases. Our findings highlight the need to consider both spatial patterns and drivers of LUCC when protecting wetlands and show that water-sediment regulation in the Yellow River should be coordinated with dynamic changes in the landscape in the lower reaches and delta.
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The environmental and socio-economic impacts of wildfires are foreseen to increase across southern Europe over the next decades regardless of increasing resources allocated for fire suppression. This study aims to identify fire-smart management strategies that promote wildfire hazard reduction, climate regulation ecosystem service and biodiversity conservation. Here we simulate fire-landscape dynamics, carbon sequestration and species distribution (116 vertebrates) in the Transboundary Biosphere Reserve Gerês-Xurés (NW Iberia). We envisage 11 scenarios resulting from different management strategies following four storylines: Business-as-usual (BAU), expansion of High Nature Value farmlands (HNVf), Fire-Smart forest management, and HNVf plus Fire-Smart. Fire-landscape simulations reveal an increase of up to 25% of annual burned area. HNVf areas may counterbalance this increasing fire impact, especially when combined with fire-smart strategies (reductions of up to 50% between 2031 and 2050). The Fire-Smart and BAU scenarios attain the highest estimates for total carbon sequestered. A decrease in habitat suitability (around 18%) since 1990 is predicted for species of conservation concern under the BAU scenario, while HNVf would support the best outcomes in terms of conservation. Our study highlights the benefits of integrating fire hazard control, ecosystem service supply and biodiversity conservation to inform better decision-making in mountain landscapes of Southern Europe.
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Fire regimes are shifting or are expected to do so under global change. Current fire suppression is not able to control all wildfires, and its capability to do so might be compromised under harsher climate conditions. Alternative fire management strategies may allow to counteract predicted fire trends, but we lack quantitative tools to evaluate their potential effectiveness at the landscape scale. Here, we sought to quantify changes in fire regimes induced after the implementation of different fire management strategies. We developed and applied a new version of the model MEDFIRE in Catalonia (Mediterranean region of ~32,000 km ² in NE Spain). We first projected burnt area from 2016 to 2100 resulting from climate change under the Representative Concentration Pathway 8.5 scenario of HadGEM-CC model and under current fire suppression levels. We then evaluated the impacts of four fire management strategies: ‘Let it burn’, fixed effort of prescribed burning with two different spatial allocations, and adaptive prescribed burning dynamically adjusting efforts according to recent past fires. Results predicted the emergence of novel climates associated with similar barometric configurations to current conditions but with higher temperatures (i.e. hot wind events). These novel climates led to an increase in burnt area, which was partially counteracted by negative fire-vegetation feedbacks. All prescribed burning scenarios decreased the amount of high-intensity fires and extreme fire events. The ‘Let it burn’ strategy, although less costly, was not able to reduce the extent of high-intensity fires. The adaptive prescribed burning scenario resulted in the most cost-efficient strategy. Our results provide quantitative evidence of fire management effectiveness, and bring to light key insights that could guide the design of fire policies fit for future novel climate conditions. We propose adaptive landscape management focused on the reduction of fire negative impacts rather than on the elimination of this disturbance from the system.
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In densely populated fire-prone regions, interactions between global change drivers, such as land-cover changes and climate change, may increase the frequency and severity of wildfires impacting forest ecosystems, thus diminishing their capability of provisioning key ecosystem goods and services for these societies. Yet, landscape mosaics play a crucial role in fire dynamics and behaviour. Here, we argue that promoting heterogeneous agro-forest mosaics could reduce the area affected by future fires. Specifically, we evaluated 24 landscape-scale management scenarios based on agricultural conversion, i.e. the creation of new agricultural land, that also explicitly incorporated fire suppression. Scenarios differed in the annual rate of such conversion, the spatial pattern (aggregate vs. scattered), and the location of new agricultural patches. To quantify the interactions between vegetation dynamics, fires, land-cover changes, and fire suppression, we coupled two spatially explicit models: a landscape dynamic fire-succession model and a land-cover change model. When applied to the Mediterranean region of Catalonia (NE Spain), new landscape mosaics favoured firefighting extinction capacity only after 15 years (on average) of cumulative land transformations. Agricultural conversion of at least 100 km² year⁻¹ was required to reduce total area burnt. A conversion rate of 200 km² year⁻¹ substantially improved fire suppression effectiveness, but subsequent conversion increases did not. When aggregated, new agriculture patches contributed more effectively to reduction in total area burnt and decreased the edge effect on remaining forest patches. Agricultural conversion in Mediterranean landscapes opens a new window for long-term spatial planning aimed at minimizing negative impacts of wildfire on forest ecosystems. These alternative strategies could help to develop landscape management practices in other fire-prone regions.
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Theme unsuitability is noted to have inhibited the accuracy of groundwater potential zones (GWPZs) mapping approach, especially in a semi-arid environment where surface water supply is inadequate. This work, therefore presents a geoscience approach for mapping high-precision GWPZs peculiar to the semi-arid area, using Buffalo catchment, Eastern Cape, South Africa, as a case study. Maps of surficial-lithology, lineament-density, drainage-density, rainfall-distribution, normalized-difference-vegetation-index, topographic-wetness-index, land use/land cover, and land-surface-temperature were produced. These were overlaid based on analytical hierarchical process weightage prioritization at a constituency ratio of 0.087. The model categorizes GWPZs into the good (187 km ² ), moderate (338 km ² ), fair (406 km ² ), poor (185 km ² ), and very poor (121 km ² ) zones. The model validation using borehole yield through on the coefficient of determination ( R ² = 0.901) and correlation ( R = 0.949) indicates a significant replication of ground situation ( p value < 0.001). The analysis corroboration shows that the groundwater is mainly hosted by a fractured aquifer where the GWPZs is either good (9.3 l/s) or moderate (5.5 l/s). The overall result indicates that the model approach is reliable and can be adopted for a reliable characterization of GWPZs in any semi-arid/arid environment.
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Indonesia currently has 269 million people or 3.49% of the world’s total population and is ranked as the fourth most populous country in the world. Analysis by the Ministry of Public Works and Public Housing of Indonesia in 2010 shows that Java’s biocapacity is already experiencing a deficit. Therefore, optimization needs to be done to reduce deficits. This study aims to optimize and assess spatial allocation accuracy based on land-use/land cover suitability. In this study, the ecological footprint (EF) is utilized as a spatial allocation assessment based on physiological needs. The concept of land suitability aims for optimal and sustainable land use. Moreover, the land suitability model was conducted using the support vector machine (SVM). SVM is used to find the best hyperplane by maximizing the distance between classes. A hyperplane is a function that can be used to separate land-use/land cover types. The land suitability model’s overall-accuracy model was 86.46%, with a kappa coefficient value of 0.812. The final results show that agricultural land, plantations, and pastureland are still experiencing deficits, but there is some reduction. The deficit reduction for agricultural land reached 510,588.49 ha, 18,986.14 ha for plantations, and 1015.94 ha for pastures. The results indicate that the SVM algorithm is efficient in mapping the land-use suitability and optimizing spatial allocation.
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There is an increasing drive to combine agent-based models with empirical methods. An overview is provided of the various empirical methods that are used for different kinds of questions. Four categories of empirical approaches are identified in which agent-based models have been empirically tested: case studies, stylized facts, role-playing games, and laboratory experiments. We discuss how these different types of empirical studies can be combined. The various ways empirical techniques are used illustrate the main challenges of contemporary social sciences: (1) how to develop models that are generalizable and still applicable in specific cases, and (2) how to scale up the processes of interactions of a few agents to interactions among many agents.
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Over the last few years, the power law distribution has been used as the data generating mechanism in many disparate fields. However, at times the techniques used to fit the power law distribution have been inappropriate. This paper describes the poweRlaw R package, which makes fitting power laws and other heavy-tailed distributions straightforward. This package contains R functions for fitting, comparing and visualising heavy tailed distributions. Overall, it provides a principled approach to power law fitting.
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Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.
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Land use and land cover change (LUCC) is an acknowledged cause of the current biodiversity crisis, but the link between LUCC and biodiversity conservation remains largely unknown at the regional scale, especially due to the traditional lack of consistent biodiversity data. We provide a methodological approach for assessing this link through defining a set of major pressures on biodiversity from LUCC and evaluating their extent, distribution, and association with a set of physical factors. The study was performed in the Metropolitan Region of Barcelona (MRB, NE of Spain) between 1956 and 2000. We generated a LUCC map for the time period, which was reclassified into a set of pressures on biodiversity (forestation, deforestation, crop abandonment, and urbanization). We then explored the association of these pressures with a set of physical factors using redundancy analysis (RDA). Pressures encompassed 38.8 % of the MRB area. Urbanization and forestation were the dominating pressures, followed by crop abandonment and deforestation. RDA showed a significant distribution gradient of these pressures in relation to the studied physical factors: while forestation and deforestation are concentrated in remote mountain areas, urbanization mainly occurs in lowlands and especially on the coast, and close to previous urban centers and roads. Unchanged areas are concentrated in rainy and relatively remote mountain areas. Results also showed a dramatic loss of open habitats and of the traditional land use gradient, both featuring Mediterranean landscapes and extremely important for their biodiversity conservation. Implications of these results for biodiversity management are finally discussed.
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The last decade has seen a remarkable increase in the number of modeling tools available to examine future land-use and land-cover (LULC) change. Integrated modeling frameworks, agent-based models, cellular automata approaches, and other modeling techniques have substantially improved the representation of complex LULC systems, with each method using a different strategy to address complexity. However, despite the development of new and better modeling tools, the use of these tools is limited for actual planning, decision-making, or policy-making purposes. LULC modelers have become very adept at creating tools for modeling LULC change, but complicated models and lack of transparency limit their utility for decision-makers. The complicated nature of many LULC models also makes it impractical or even impossible to perform a rigorous analysis of modeling uncertainty. This paper provides a review of land-cover modeling approaches and the issues causes by the complicated nature of models, and provides suggestions to facilitate the increased use of LULC models by decision-makers and other stakeholders. The utility of LULC models themselves can be improved by 1) providing model code and documentation, 2) through the use of scenario frameworks to frame overall uncertainties, 3) improving methods for generalizing key LULC processes most important to stakeholders, and 4) adopting more rigorous standards for validating models and quantifying uncertainty. Communication with decision-makers and other stakeholders can be improved by increasing stakeholder participation in all stages of the modeling process, increasing the transparency of model structure and uncertainties, and developing user-friendly decision-support systems to bridge the link between LULC science and policy. By considering these options, LULC science will be better positioned to support decision-makers and increase real-world application of LULC modeling results.
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Land-use/cover change (LUCC) is a com-plex process that includes actors and factors at different social and spatial levels. A common approach to analyse and simulate LUCC as the result of individual decisions is agent-based modelling (ABM). However, ABM is often applied to simulate processes at local scales, while its application in regional studies is limited. This paper describes first a conceptual framework for ABM to analyse and explore regional LUCC processes. Second, the con-ceptual framework is represented by combining dif-ferent concepts including agent typologies, farm trajectories and probabilistic decision-making pro-cesses. Finally, the framework is illustrated through a case study in the Netherlands, where processes of farm cessation, farm expansion and farm diversification are shaping the structure of the landscape. The framework is a generic, straightforward approach to analyse and explore regional LUCC with an explicit link to empirical approaches for parameterization of ABM.
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A wide variety of ecological applications require spatially explicit, historic, current, and projected land use and land cover data. The U.S. Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States. The newly developed FORE-SCE model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land cover change through 2020 for multiple plausible scenarios. Projected proportions of future land use were initially developed, and then sited on the lands with the highest potential for supporting that land use and land cover using a statistically based stochastic allocation procedure. Three scenarios of 2020 land cover were mapped for the western Great Plains in the US. The model provided realistic, high-resolution, scenario-based land-cover products suitable for multiple applications, including studies of climate and weather variability, carbon dynamics, and regional hydrology.
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This article reviews the historic evolution of qualitative scenario storylines and the various methods used in their development and application in environmental change assessment. The scenario method largely emerged from military strategy and war planning, with the techniques being adopted and advanced further by the business sector. Scenario storylines became widely applied to environmental problems from the 1970s and since then a number of new studies have been developed at both global and regional scales. Many different methods are used in scenario storyline development although most examples applied to environmental change assessment are exploratory and defined through a matrix logic that reflects different dimensions of environmental change drivers. This article discusses several development techniques for scenario storylines, provides examples of existing scenario storylines, discusses the differences between them, and highlights a number of limitations in the current scenario storyline development methods. The credibility, legitimacy, and saliency of future scenario storylines are discussed with respect to personal beliefs, the equifinality of alternative development pathways, the validation and uncertainty of assumptions, stakeholder engagement in visions development, and participatory methods. Copyright © 2010 John Wiley & Sons, Ltd.For further resources related to this article, please visit the WIREs website
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This article presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decision making, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.
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Natural reforestation of European mountain landscapes raises major environmental and societal issues. With local stakeholders in the Pyrenees National Park area (France), we studied agricultural landscape colonisation by ash (Fraxinus excelsior) to enlighten its impacts on biodiversity and other landscape functions of importance for the valley socio-economics. The study comprised an integrated assessment of land-use and land-cover change (LUCC) since the 1950s, and a scenario analysis of alternative future policy. We combined knowledge and methods from landscape ecology, land change and agricultural sciences, and a set of coordinated field studies to capture interactions and feedback in the local landscape/land-use system. Our results elicited the hierarchically-nested relationships between social and ecological processes. Agricultural change played a preeminent role in the spatial and temporal patterns of LUCC. Landscape colonisation by ash at the parcel level of organisation was merely controlled by grassland management, and in fact depended on the farmer’s land management at the whole-farm level. LUCC patterns at the landscape level depended to a great extent on interactions between farm household behaviours and the spatial arrangement of landholdings within the landscape mosaic. Our results stressed the need to represent the local SES function at a fine scale to adequately capture scenarios of change in landscape functions. These findings orientated our modelling choices in the building an agent-based model for LUCC simulation (SMASH–Spatialized Multi-Agent System of landscape colonization by ASH). We discuss our method and results with reference to topical issues in interdisciplinary research into the sustainability of multifunctional landscapes.
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Land use change is characterized by a high diversity of change trajectories depending on the local conditions, regional context and external influences. Policy intervention aims to counteract the negative consequences of these changes and provide incentives for positive developments. Region typologies are a common tool to cluster regions with similar characteristics and possibly similar policy needs. This paper provides a typology of land use change in Europe at a high spatial resolution based on a series of different scenarios of land use change for the period 2000–2030. A series of simulation models ranging from the global to the landscape level are used to translate scenario conditions in terms of demographic, economic and policy change into changes in European land use pattern. A typology developed based on these simulation results identifies the main trajectories of change across Europe: agricultural abandonment, agricultural expansion and urbanization. The results are combined with common typologies of landscape and rurality. The findings indicate that the typologies based on current landscape and ruralities are poor indicators of the land use dynamics simulated for the regions. It is advocated that typologies based on (simulated) future dynamics of land change are more appropriate to identify regions with potentially similar policy needs.
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A new modified random clusters method for the simulation of landscape the matic spatial patterns is presented. It produces more realistic and general results than landscape models that have been commonly used to date in the field of landscape ecology. Simulated patterns are said to be realistic, apart from their patchy and irregular appearance, because the values of the spatial indices as a function of habitat abundance measured in real landscape patterns (number of patches, edge length and patch cohesion index) can be replicated with the proposed landscape model. It allows a wide range of spatial patterns to be obtained, in which fragmentation and habitat abundance can be systematically and independently varied. Furthermore, a degree of control over the irregularity of the shapes of the simulated landscapes can be achieved, and it is also possible to simulate patterns with anisotropy. The proposed method is easy to implement and requires little computation time, which enhances the practical possibilities of this method in different areas of landscape ecology.
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Landscape ecology is based on the premise that there are strong links between ecological pattern and ecological function and process. Ecological systems are spatially heterogeneous, exhibiting considerable complexity and variability in time and space. This variability is typically represented by categorical maps or by a collection of samples taken at specific spatial locations (point data). Categorical maps quantize variability by identifying patches that are relatively homogeneous and that exhibit a relatively abrupt transition to adjacent areas. Alternatively, point-data analysis (geostatistics) assumes that the system property is spatially continuous, making fewer assumptions about the nature of spatial structure. Each data model provides capabilities that the other does not, and they should be considered complementary. Although the concept of patches is intuitive and consistent with much of ecological theory, point-data analysis can answer two of the most critical questions in spatial pattern analysis: what is the appropriate scale to conduct the analysis, and what is the nature of the spatial structure? I review the techniques to evaluate categorical maps and spatial point data, and make observations about the interpretation of spatial pattern indices and the appropriate application of the techniques. Pattern analysis techniques are most useful when applied and interpreted in the context of the organism(s) and ecological processes of interest, and at appropriate scales, although some may be useful as coarse-filter indicators of ecosystem function. I suggest several important needs for future research, including continued investigation of scaling issues, development of indices that measure specific components of spatial pattern, and efforts to make point-data analysis more compatible with ecological theory.
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Land cover and land use changes can have a wide variety of ecological effects, including significant impacts on soils and water quality. In rural areas, even subtle changes in farming practices can affect landscape features and functions, and consequently the environment. Fine-scale analyses have to be performed to better understand the land cover change processes. At the same time, models of land cover change have to be developed in order to anticipate where changes are more likely to occur next. Such predictive information is essential to propose and implement sustainable and efficient environmental policies. Future landscape studies can provide a framework to forecast how land use and land cover changes is likely to react differently to subtle changes. This paper proposes a four step framework to forecast landscape futures at fine scales by coupling scenarios and landscape modelling approaches. This methodology has been tested on two contrasting agricultural landscapes located in the United States and France, to identify possible landscape changes based on forecasting and backcasting agriculture intensification scenarios. Both examples demonstrate that relatively subtle land cover and land use changes can have a large impact on future landscapes. Results highlight how such subtle changes have to be considered in term of quantity, location, and frequency of land use and land cover to appropriately assess environmental impacts on water pollution (France) and soil erosion (US). The results highlight opportunities for improvements in landscape modelling.
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This study aims to predict the spatial distribution of tropical deforestation. Landsat images dated 1974, 1986 and 1991 were classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. The deforestation maps were overlaid with various spatial variables such as the proximity to roads and to settlements, forest fragmentation, elevation, slope and soil type to determine the relationship between deforestation and these explanatory variables. A multi-layer perceptron was trained in order to estimate the propensity to deforestation as a function of the explanatory variables and was used to develop deforestation risk assessment maps. The comparison of risk assessment map and actual deforestation indicates that the model was able to classify correctly 69% of the grid cells, for two categories: forest persistence versus deforestation. Artificial neural networks approach was found to have a great potential to predict land cover changes because it permits to develop complex, non-linear models.
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This is the first part of a two-paper series on the development and application of a cellular automata model of urban development using geographic information systems (GIS) and fuzzy-set approaches. Under the paradigm of fuzzy-set theory, a cellular automata model of urban development was developed based on an understanding of the logistic trend of urban development processes. The model assigns membership of urban areas to multiple states of urban development using a fuzzy membership function. The transition rules based on linguistic variables are applied to represent the non-deterministic nature of urban development controls. By implementing the model in a raster based GIS format, experimental scenarios of development of a virtual city under realistic conditions are presented. Experimental application of the model to an artificial city produced realistic results and demonstrated the model was theoretically feasible and valid. Further work is needed to calibrate the model when applying it to simulate actual urban development. In the second part of the two-paper series, spatio-temporal simulations of urban development in Sydney, Australia, from 1971 to 1996 will be demonstrated and discussed.
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Common understanding of the causes of land-use and land-cover change is dominated by simplifications which, in turn, underlie many environment-development policies. This article tracks some of the major myths on driving forces of land-cover change and proposes alternative pathways of change that are better supported by case study evidence. Cases reviewed support the conclusion that neither population nor poverty alone constitute the sole and major underlying causes of land-cover change worldwide. Rather, peoples’ responses to economic opportunities, as mediated by institutional factors, drive land-cover changes. Opportunities and
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This study assesses the value of enhanced spatial resolution in the agriculture and land use component of an integrated assessment (IA) model. IA models typically represent land use decisions at finer resolution than the energy and economic components, to account for spatial heterogeneity of land productivity and use. However, increasing spatial resolution incurs costs, from additional input data processing, run time, and complexity of results. This study uses the Global Change Assessment Model (GCAM) to analyze land use in the Midwestern United States in three levels of spatial aggregation, and three climate change mitigation scenarios. For visualization and simplification of higher resolution model output, we use non-metric multidimensional scaling. We find that the level of spatial aggregation influences the magnitude but not the direction of land use change in response to the modeled drivers, and in the examples analyzed, increasing spatial resolution reduces the extent of land use change.
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An approach to simulating land-cover change based on pairs of classified images is presented. The method conditions the simulations on three sources of information: an initial land-cover map, maps of the probabilities of each possible class transition, and a description of the spatial patterns of changes (e.g., semivariograms). The method can produce multiple simulated land-cover maps that honor each of these sources of information. The approach is demonstrated for data on forest-cover change near Traverse City, Michigan. The discussion describes extensions to the method and an approach to generating future land-cover scenarios based on socioeconomic information.
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The comparison of spatial patterns is recognized as an important task in landscape ecology especially when spatially explicit simulation modeling or remote sensing is applied. Yet, there is no agreed procedure for doing that, probably because different problems require different algorithms. We explored a variety of existing algorithms and modified some of them to compare grid-based maps with categorical attributes. A new algorithm based on the “expanding window” approach was developed and compared to other known algorithms. The goal was to offer simple and flexible procedures for comparing spatial patterns in grid based maps that do not take into consideration object shapes and sizes of the maps. The difference between maps was characterized by three values: quantity, location, and distance between corresponding categories in the maps. Combinations of these indices work as good criteria to quantify differences between maps. A web-based survey was set up, in which participants were asked to grade the similarity of ten pairs of maps. These results were then used to compare how well the various algorithms can perform relative to the visual comparisons obtained; they were also used to calibrate existing algorithms.
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Land use change is both a cause and consequence of many biophysical and socioeconomic changes. The CLUMondo model provides an innovative approach for global land-use change modeling to support integrated assessments. Demands for goods and services are, in the model, supplied by a variety of land systems that are characterized by their land cover mosaic, the agricultural management intensity and livestock. Land system changes are simulated by the model, driven by regional demand for goods and influenced by local factors that either constrain or promote land system conversion. A characteristic of the new model is the endogenous simulation of intensification of agricultural management versus expansion of arable land, and urban versus rural settlements expansion based on land availability in the neighborhood of the location. Model results for the OECD Environmental Outlook scenario show that allocation of increased agricultural production by either management intensification or area expansion varies both among and within world regions, providing useful insight into the land sparing versus land sharing debate. The land system approach allows the inclusion of different types of demand for goods and services from the land system as a driving factor of land system change. Simulation results are compared to observed changes over the 1970-2000 period and projections of other global and regional land change models. This article is protected by copyright. All rights reserved.
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Land use/cover change (LUCC) modeling is an important approach to evaluating global biodiversity loss and is the topic of a wide range of research in ecology, geography and environmental social science. This paper reports on development and assessment of maps of change potential produced by two spatially explicit models and applied to a Tropical Deciduous Forest in western Mexico. The first model, DINAMICA EGO, uses the weights of evidence method which generates a map of change potential based on a set of explanatory variables and past trends involving some degree of expert knowledge. The second model, Land Change Modeler (LCM), is based upon neural networks. Both models were assessed through Relative Operating Characteristic and Difference in Potential. At the per transition level, we obtained better results using DINAMICA. However, when the per transition susceptibilities are combined to compose an overall change potential map, the map generated using LCM is more accurate because neural networks outputs are able to express the simultaneous change potential to various land cover types more adequately than individual probabilities obtained through the weights of evidence method. An analysis of the change potential obtained from both models, compared with observed deforestation and selected biodiversity indices (species richness, rarity, and biological value) showed that the prospective LUCC maps tended to identify locations with higher biodiversity levels as the most threatened areas as opposed to areas that had actually undergone deforestation. Overall however, the approximate assessment of biodiversity given by both models was more accurate than a random model.
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This paper focuses on determining ignition probabilities of forest fires in Wildland-Urban Interface (WUI) areas to more effectively develop prevention plans. Multivariate Logistic Regression methodology was used to identify the most important biophysical and human variables to explain the emergence of ignition points, incorporating spatial analysis from Remote Sensing and Geographical Information Systems (GIS) data. To test this model we used two representative Wildland-Urban Interface landscapes in a Mediterranean environment, located in Catalonia (northeast Spain): an example of dispersed housing in a forested area associated to metropolitan processes and an agro-forestry mosaic connected with tourism development. For a better understanding a temporal comparison has been made, analyzing data from 1990s and from 2000s. Results show differences in the explicative models; in the former study area, high ignition probabilities are associated to human activity, mainly distance to urban areas and road networks, whereas in the latter they are related with land-use (scrubland and coniferous forest) and mean maximum temperatures. As a consequence, prevention tasks seem to be less difficult in the more metropolitan study area because the spatial model is further disperse in the agro-forestry mosaic. Finally, temporal analysis indicates that both areas were more prone to forest fires in the most recent decade than in the 1990s.
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This paper examines the potential impact of agricultural and trade policy reform on land-use across the EU focussing particularly on the issue of land abandonment. Using a novel combined application of the well established CAPRI and Dyna-CLUE models it estimates the extent of change across Europe under removal of Pillar 1 support payments and trade liberalisation. Overall, it is estimated that around 8 per cent less land will be farmed under these reforms than under the baseline situation. However, some regions, areas and farm types face more significant reductions. The reforms are particularly felt on livestock grazing farms situated in the more marginal areas of Europe, which also coincide with areas of high nature value. Therefore, farmland biodiversity is likely to be reduced in these areas. However, using a range of environmental indicators, relating to nutrient surpluses, GHG emissions, soil erosion and species abundance, an overall improvement in the environmental footprint of agriculture is likely. In addition, the economic efficiency of the agricultural sector will probably improve. The paper considers several possible options available to deal with any negative aspects of land abandonment. Following the FAO (2006), it is argued that untargeted, rather general agricultural policy measures which maintain land in production are likely to be an ineffective and inefficient way to address the perceived negative consequences of abandonment. A more holistic approach to rural development is required, tailored to the specific context within each area.
Article
Neutral landscape models are often employed to represent real landscapes as the null hypothesis. They usually have statistical characteristics similar to real ones. But the spatial characteristics of the real and generated maps are seldom compared. In this study, the neutral landscape models generated by Rule and SimMap are tested against a real forest landscape in Northeastern China. A set of landscape metrics is used for the comparison. Values of some metrics (total number of patches, total perimeter, and aggregation index) suggest that some level of agreement between the maps generated by neutral landscape models and the real landscape do exist at landscape and class levels. But there are also metrics that do not show any agreement between generated maps and the real landscape. Neutral models tend to over-aggregate small classes at higher aggregation levels. Each neutral model has its own strength in representing the real landscape, though neither is perfect. Some metrics, for example, double-logged fractal dimension, are found to have limited capabilities in differentiating landscape structures.
Article
Nature provides a wide range of benefits to people. There is increasing consensus about the importance of incorporating these “ecosystem services” into resource management decisions, but quantifying the levels and values of these services has proven difficult. We use a spatially explicit modeling tool, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), to predict changes in ecosystem services, biodiversity conservation, and commodity production levels. We apply InVEST to stakeholder-defined scenarios of land-use/land-cover change in the Willamette Basin, Oregon. We found that scenarios that received high scores for a variety of ecosystem services also had high scores for biodiversity, suggesting there is little tradeoff between biodiversity conservation and ecosystem services. Scenarios involving more development had higher commodity production values, but lower levels of biodiversity conservation and ecosystem services. However, including payments for carbon sequestration alleviates this tradeoff. Quantifying ecosystem services in a spatially explicit manner, and analyzing tradeoffs between them, can help to make natural resource decisions more effective, efficient, and defensible.
Article
Here I present an integrated framework for species abundance distributions (SADs) that goes beyond the neutral theory without relying on complex mechanistic models. I give some general mathematical results on the relationship between SADs and their underlying dynamics, and analyse an extensive set of marine phytoplankton data in order to test the neutral theory against this broader framework. The main theoretical and empirical results are: (i) the logseries, which is the SAD produced by simple neutral models without migration, is quite robust in response to additional factors, including some forms of niche segregation; (ii) when there is a small but significant deviation from a logseries, the SAD will generally have the form of a power law, regardless of the specific mechanisms; (iii) when the deviation is moderate, the SAD will generally have the form of a lognormal, regardless of the specific mechanisms; (iv) although in a wide range of situations neutral and non-neutral dynamics cannot be distinguished from the SAD alone, some empirical SADs do have the fingerprint of non-neutrality: this is the case of marine dinoflagellates, in contrast to marine diatoms, which adjust to neutral theory predictions. The results for marine phytoplankton illustrate that both neutral and non-neutral mechanisms coexist in nature, and seem to have different weights in different groups of organisms. In addition to the above findings, I discuss several related contributions and point out some important pitfalls in the literature.
Article
The country of Spain is representative of land change processes in Mediterranean member states of the European Union (EU). These land change processes are often triggered by European, national and sub-national policies and include widespread land abandonment and urbanisation trends, as well as an increase in land use intensities accompanied by strong exploitation of water resources. The Mediterranean is part of the dryland ecoregion, which is particularly vulnerable to ecosystem degradation. While remote sensing data permit the characterisation of the temporal dimension of land surface processes, the syndrome-based approach aims to integrate this with information on local/regional socio-economic and physical frameworks. In this study, we incorporated two major drivers of land change, climatic boundary conditions and population density change, to understand the patterns of the assessed land cover changes.
Article
The wildland–urban interface (WUI) is the area where houses meet or in-termingle with undeveloped wildland vegetation. The WUI is thus a focal area for human– environment conflicts, such as the destruction of homes by wildfires, habitat fragmentation, introduction of exotic species, and biodiversity decline. Our goal was to conduct a spatially detailed assessment of the WUI across the United States to provide a framework for scientific inquiries into housing growth effects on the environment and to inform both national policy-makers and local land managers about the WUI and associated issues. The WUI in the conterminous United States covers 719 156 km 2 (9% of land area) and contains 44.8 million housing units (39% of all houses). WUI areas are particularly widespread in the eastern United States, reaching a maximum of 72% of land area in Connecticut. California has the highest number of WUI housing units (5.1 million). The extent of the WUI highlights the need for ecological principles in land-use planning as well as sprawl-limiting policies to adequately address both wildfire threats and conservation problems.
Article
Land use changes and shifts in disturbance regimes (e.g. wildfires) are recognized worldwide as two of the major drivers of the current global change in terrestrial ecosystems. We expect that, in areas with large-scale land use changes, legacies from previous land uses persist and affect current ecosystem responses to climate-associated disturbances like fire. This study analyses whether post-fire vegetation dynamics may differ according to specific historical land use histories in a Mediterranean forest land-scape of about 60,000 ha that was burnt by extensive fires. For that, we assessed land use history of the whole area through the second half of the XXth century, and evaluated the post-fire regeneration suc-cess in terms of: (i) forest cover and (ii) tree species composition (biotic-dispersed, resprouter species, Quercus spp. vs. wind-dispersed species with or without fire-resistant seed bank, Pinus spp.). Results showed that stable forest areas exhibited a higher post-fire recovery than younger forests. Furthermore, the longer since crop abandonment translates into a faster post-fire recovery. Results highlight that to anticipate the impacts of disturbances on ecosystems, historical land trajectories should be taken into account.
Article
Land use change is the result of interactions between processes operating at different scales. Simulation models at regional to global scales are often incapable of including locally determined processes of land use change. This paper introduces a modeling approach that integrates demand-driven changes in land area with locally determined conversion processes. The model is illustrated with an application for European land use. Interactions between changing demands for agricultural land and vegetation processes leading to the re-growth of (semi-) natural vegetation on abandoned farmland are explicitly addressed. Succession of natural vegetation is simulated based on the spatial variation in biophysical and management related conditions, while the dynamics of the agricultural area are determined by a global multi-sector model. The results allow an exploration of the future dynamics of European land use and landscapes. The model approach is similarly suitable for other regions and processes where large scale processes interact with local dynamics.
Article
Neutral landscape models were originally developed to test the hypothesis that human-induced fragmentation produces patterns distinctly different from those associated with random processes. Other uses for neutral models have become apparent, including the development and testing of landscape metrics to characterize landscape pattern. Although metric development proved to be significant, the focus on metrics obscured the need for iterative hypothesis testing fundamental to the advancement of the discipline. We present here an example of an alternative neutral model and hypothesis designed to relate the process of landscape change to observed landscape patterns. The methods and program, QRULE, are described and options for statistical testing outlined. The results show that human fragmentation of landscapes results in a non-random association of land-cover types that can be describe by simple statistical methods. Options for additional landscape studies are discussed and access to QRULE described in the hope that these methods will be employed to advance our understanding of the processes that affect the structure and function in human dominated landscapes.
Article
This paper revises the results of applying a semiautomatic methodology for fire scars mapping from a time series of Landsat MSS images over the forest and shrubby surface of Catalonia (1975–1993). Perimeters of fires which occurred in 1994 and 1995 were added enlarging the whole series to 21 years from TM imagery. Results are a map series of fire history during 21 years as well as a map of the fire recurrence level. Omission errors are 23% for burned areas greater than 2 km2 while commission errors are 8% for areas greater than 0.5 km2. Detected fire scars were incorporated into a geographic information system in order to characterise the fire regime of the study area. Fire size distribution and the number of spot fires originated from each fire as well as the maximum distance reached from the main fire are analysed. A first approach to monitor post-burn regeneration through normalised difference vegetation index is also shown.
Article
Gaining insight into the dynamic nature of landscapes often involves the use of simulation models to explore potential changes over long time frames and extensive spatial areas. However, bridging the gap between conceptual models of landscape dynamics and their simulation on a computer can lead to many pitfalls. If implemented using a general-purpose programming language, the underlying model becomes hidden in the details of the computer code, making it difficult to compare the conceptual and implemented models, and to modify the model. Alternatively, previously built models may contain hidden assumptions and have limited adaptability. Domain-specific languages have been developed in a number of areas to facilitate the construction of models at a level closer to the conceptual model, thereby making model implementation more accessible to domain experts. Such tools to support modelling in the domain of landscape ecology can achieve a balance between the flexibility of programming and the structure and ease of using pre-built models. One of the goals of SELES (Spatially Explicit Landscape Event Simulator) has been to create a language for modelling landscape dynamics that provides ecologists and planners with an appropriate tool to address some of the problems that arise in model development. Our high-level, structured language separates the specification of model behaviour from the mechanics of its implementation, freeing landscape modellers from programming and allowing them to focus on the underlying model. This language is declarative and thus permits a clear representation of the conceptual model, which the SELES engine converts into a computer simulation of landscape change. Our structured framework guides the development of a broad class of spatio-temporal landscape models by aiding model prototyping, modification, verification, comparison, and re-use.
Article
Recent approaches to modeling urban growth use the notion that urban development can be conceived as a self-organizing system in which natural constraints and institutional controls (land-use policies) temper the way in which local decision-making processes produce macroscopic patterns of urban form. In this paper a cellular automata (CA) model that simulates local decision-making processes associated with fine-scale urban form is developed and used to explore the notion of urban systems as self-organizing phenomenon. The CA model is integrated with a stochastic constraint model that incorporates broad-scale factors that modify or constrain urban growth. Local neighborhood access rules are applied within a broader neighborhood in which friction-of-distance limitations and constraints associated with socio-economic and bio-physical variables are stochastically realized. The model provides a means for simulating the different land-use scenarios that may result from alternative land-use policies. Application results are presented for possible growth scenarios in a rapidly urbanizing region in south east Queensland, Australia.
Article
Debates on the urban form have become strongly polarized between the advocates and opponents of the compact and of the dispersed or “sprawled” city. In this paper we argue that this may be the result of an excessive concentration on the study of the American experience and the neglect of other urban contexts, and examine the recent process of urban growth against the background of urban compactness and extreme densification represented by the Barcelona Metropolitan Region (BMR). The comparison of two detailed land-cover maps of 1993 and 2000 shows a progressive transformation in the traditional urban character of the region. Lower urban densities, high losses of non-urban land covers, depopulation of the metropolitan inner core, an increasing importance of single housing or the expansion of transportation infrastructures confirm the generalization of the dispersed urban model. However, the presence of numerous medium sized towns has also proved to be a deterrent of excessive dispersion. In conclusion, polycentric metropolitan areas such as the BMR may be more adjusted to absorb the negative effects of dispersion than monocentric areas.
Article
Important land use changes are expected in the European Union (EU) the coming decades, having effects on carbon stocks in soil and vegetation. We assessed how future land use change (LUC) can influence future carbon stock change in soil and vegetation in the EU. The emphasis is on the role of LUC in the overall carbon balance of the EU biosphere.Because LUC is the most dynamic driving factor of terrestrial carbon stock change, it is important to account for the dynamics of LUC in carbon stock change modelling. The major challenge in coupling a carbon model and a LUC model is the difference in spatial and temporal resolution generally used in these modelling approaches. We used a high-resolution LUC model and a carbon bookkeeping approach that takes into account effects of soil and forest age on carbon stock changes. These approaches best fit the chosen resolution and extent in a consistent manner. Four SRES scenarios that cover a range of possible future developments were evaluated: Global Economy (A1): lean government, strong globalization; Continental Markets (A2): lean government, regional cultural and economic development; Global Co-operation (B1): much governmental intervention, strong globalization; Regional Communities (B2): much governmental intervention, regional cultural and economic development.If land use remains unchanged, carbon sequestration rates are expected to decrease by 4% in 2030 relative to 2000. LUC causes an additional sequestration rate decrease in the A2 scenario of 2% in 2030. In the other three scenarios, sequestration rate increases by 9–16% in 2030 relative to 2000. In 2030, the terrestrial biosphere in the EU is expected to sequester between 90 and 111 Tg C year−1. This is 6.5–8% of the projected anthropogenic emissions. In the B2 scenario, the highest sequestration rate increase is expected (15 Tg C year−1). Clear differences are found in the spatial distribution of sinks and sources between the scenarios, illustrating that land use is an important factor in future carbon sequestration changes that cannot be ignored.
Article
In this paper, two research approaches to specify the relation between land use types and their explanatory factors are applied to the same modelling framework. The two approaches are used to construct land suitability maps, which are used as inputs in two model applications. The first is an inductive approach that uses regression analysis. The second applies a theoretical, actor decision framework to derive relations deductively using detailed field data. Broadly speaking, this classification coincides with the distinction between empirical and theoretical models and the distinction between deriving process from pattern and pattern from process. The two modelling approaches are illustrated by a scenario analysis for a case study in a municipality in the Philippines. Goodness-of-fit of the deductive approach in predicting current land use is slightly lower compared to the inductive approach. Resulting land use projections from the modelling exercise for the two approaches differ in 15 percent of the cells, which is caused by differences in the specification of the suitability maps. The paper discusses the assumptions underlying the two approaches as well as the implications for the applicability of the models in policy-oriented research. The deductive approach describes processes explicitly and can therefore better handle discontinuities in land use processes. This approach allows the user to evaluate a wide range of scenarios, which can also include new land use types. The inductive approach is easily reproducible by others but cannot guarantee causality. Therefore, the inductive approach is less suitable to handle discontinuities or additional land use types, but is well able to rapidly identify hotspots of land use change. It is concluded that both approaches have their advantages and drawbacks for different purposes. Generally speaking, the inductive approach is applicable in situations with relatively small land use changes, without introduction of new land use types, whereas the deductive approach is more flexible. The choice of modelling approach should therefore be based on the research and policy questions for which it is used.
Article
Urban expansion and spatial patterns of urban land have a large effect on many socioeconomic and environmental processes. A wide variety of modelling approaches has been introduced to predict and simulate future urban development. These models are often based on the interpretation of various determining factors that are used to create a probability map. The main objective of this paper is to evaluate the performance of different modelling approaches for simulating spatial patterns of urban expansion in Flanders and Brussels in the period 1988–2000. Hereto, a set of urban expansion models with increasing complexity was developed based on: (i) logistic regression equations taking various numbers of determining variables into account, (ii) CA transition rules and (iii) hybrid procedures, combining both approaches. The outcome of each model was validated in order to assess the predictive value of the three modelling approaches and of the different determining variables that were used in the logistic regression models. The results show that a hybrid model structure, integrating (static) determining factors (distance to the main roads, distance to the largest cities, employment potential, slope and zoning status of the land) and (dynamic) neighbourhood interactions produces the most accurate probability map. The study, however, points out that it is not useful to make a statement on the validity of a model based on only one goodness-of-fit measure. When the model results are validated at multiple resolutions, the logistic regression model, which incorporates only two explanatory variables, outperforms both the CA-based model and the hybrid model.
Article
dinamica, a spatially explicit simulation model of landscape dynamics has been developed. dinamica is a cellular automata model that presents multi-scale vicinity-based transitional functions, incorporation of spatial feedback approach to a stochastic multi-step simulation engine, and the application of logistic regression to calculate the spatial dynamic transition probabilities. This model was initially conceived for the simulation of Amazonian landscape dynamics, particularly the landscapes evolved in areas occupied by small farms. For testing its performance, the model was used to simulate spatial patterns of land-use and land-cover changes produced by the Amazonian colonists in clearing the forest, cultivating the land, and eventually abandoning it for vegetation succession. The study area is located in an Amazonian colonization frontier in the north of Mato Grosso state, Brazil. The model was run for two sub-areas of colonization projects, using an 8-year time span, from 1986 to 1994. The simulated maps were compared with land-use and land-cover maps, obtained from digital classification of remote sensing images, using the multiple resolution fitting procedure and a set of landscape structure measures, including fractal dimension, contagion index, and the number of patches for each type of land-use and land-cover class. The results from the validation methods for the two areas showed a good performance of the model, indicating that it can be used for replicating the spatial patterns created by landscape dynamics in Amazonian colonization regions occupied by small farms. Possible applications of dinamica include the evaluation of landscape fragmentation produced by different architectures of colonization projects and the prediction of a region's spatial pattern evolution according to various dynamic phases.