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Simulation results from Dyna-Clue application, for the year 2012 compared to initial map of 2006 and observed map of 2012 from CLC. Grid-cell size is 100 m x 100 m .
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The long-standing awareness of the environmental impact of land-use change (LUC) has led scientific community to develop tools able to predict their amount and to evaluate their effect on environment, with the aim supporting policy makers in their planning activities. This paper proposes an implementation of the Dyna-CLUE model applied to the Litor...
Citations
... This research aims at investigating whether Copernicus data and services could contribute to LU/LC monitoring in European countries, which are increasingly experiencing soil sealing. As land is a finite resource, proper and effective management of its use, based on monitoring of LU/LC changes using different techniques, is crucial to protect soil from degradation and loss of ecological functions [7,8,12,17,29,[50][51][52][53][54]. The analysis revealed that the CLMS data is functional for achieving the research aims, but insufficient for a comprehensive examination due to a lack of high-resolution data in many European locations and the restricted temporal coverage of most of the data. ...
Copernicus, the European initiative for monitoring the Earth, provides an extensive range of data types that allow consumers, public authorities, and scientists to get free, open, and comprehensive knowledge of the world. Therefore, it is recognized as one of the largest geodatabases storing a great deal of data provided by satellites and in-situ sensors, which are then processed to generate reliable and up-to-date information on a large number of pressing environmental and security concerns. As a result, it could be a valid option for examining the state of landscape and its evolution over time. More knowledge about land changes might assist in developing an effective strategy to tackle the soil sealing phenomena, which is largely caused by climate change and anthropogenic pressure and is being experienced by all European countries. Thus, this study examines how Copernicus earth observation data and geographical services might help with changes in terrain cover at the European level. The land cover change maps were evaluated after looking at all the data, when it was possible to perform this task, while in other cases, Google Earth Engine, a cloud platform designed by Google to manage large geographic data, was used to produce the maps. The benefits and drawbacks of the Copernicus platform have been examined. It proves to be a functional platform for achieving research goals, but it is insufficient for a global study because of the absence of data in many European cities and the low resolution of many of them.
... The morphology and landscapes of the region are considerably heterogeneous in its different parts, so much so that it can be divided into several sub-regions: broad plains, internal hilly areas and mountain ranges, sparse mountains, promontories, volcanoes, and three main islands. The population and most human activities are concentrated on the plains, especially the Volturno and Sele valleys, determining a vast, complex pattern of continuous and discontinuous urban fabric mixed with industrial areas and plots of intensive agricultural land [25]. The surrounding hilly areas are less populated and mainly shaped by extensive agriculture. ...
... Regression analysis was based on dependent variables derived from the 2012 landcover map. The introduction of a new land-cover type such as energy crops, not yet existing in the current land-use classification, made it necessary to use a proxy variable to represent its hypothetical spatial distribution in compliance with a criterion of suitability [25,33]. Then, a suitability map based on Multi-Criteria Evaluation (MCE) was used to simulate a possible land-use pattern of energy crops in the related scenario. ...
In the last two centuries, land-use change (LUC) has been the most important direct change driver for terrestrial ecosystems. In contrast with the consequent ecosystem degradation, forward-looking spatial policies and target landscape and land-use planning processes are needed from a sustainability perspective. The present paper proposes a framework of action, including different landscape-planning and ecological approaches: from spatial modelling to recognize LUC and build different scenarios, to ecosystem service (ES) assessment to evaluate possible environmental impacts. Three different scenarios were explored: Trend, No Tillage, and Energy crops. The sediment delivery ratio and carbon storage and sequestration ESs were assessed and compared for each scenario. The results show that regional development in line with past trends could lead to further land degradation (with ES value losses, in a decade, greater than 5%). Instead, the two scenarios proposed in compliance with EU policies could bring benefits, if only those related to moderate LUCs and respecting the naturally grass-vegetated land. The aim of the paper is to support decision makers and local communities in the landscape planning landscape planning process. From the local to global scale, guided and shared LUC management allows us to implement sustainable development, based not only on a deep knowledge of the physical environment but also of social and economic issues.
... Morphology and landscapes of the region are considerably heterogeneous in its different parts, so much so that it can be divided into several subregions: broad plains, internal hilly areas and mountain range, sparse mountains, promontories, volcanoes and three main islands. Population and most human activities are gathered on the plains, especially the Volturno and Sele Valleys, determining a vast complex pattern of continuous and discontinuous urban fabric mixed with industrial areas and plots of intensive agriculture land (Pindozzi et al., 2017). The surrounding hilly areas are less populated and mainly shaped by extensive agriculture. ...
... Regression analysis was based on dependent variables derived from the 2012 land-cover map. The introduction of a new land-cover type such as energy crops, not yet existing on the current landuse classification, made it necessary to use a proxy able to represent its hypothetical spatial distribution in compliance with a criterion of suitability (Hellmann and Verburg, 2011;Pindozzi et al., 2017). Then a suitability map based on Multi-Criteria Evaluation (MCE) was used to simulate a possible land-use pattern of energy crops on the related scenario. ...
In the last two centuries, land use change (LUC) has been the most important direct changes driver for terrestrial ecosystems. To contrast the consequent ecosystems degradation, forward-looking spatial policies and target landscape and land-use planning processes, promoting a sustainable land use change, are needed. The present paper proposes a framework of action including different landscape planning and ecological approaches: from the spatial modelling to recognize the LUC and build different scenarios, to the ecosystem services (ESs) assessment to evaluate the possible environmental impacts. Three different scenarios were built: Trend, No-Tillage and Energy crops. The Sediment Delivery Ratio and Carbon Storage and Sequestration ESs were assessed and compared for each scenario. The aim of the paper is to support decision-makers and local communities into the landscape planning process. Results show that a regional development in line with past trend could lead to further land degradation. Instead, the two scenarios proposed in compliance with EU policies, could bring benefits only if related to moderate LUCs and respecting the naturally grass-vegetated land. From the local to global scale, a guided and shared LUC management allows implementing sustainable development, basing on a deep knowledge of physical-environmental but also social and economic issues.
... These results of land-cover change simulations performed with Dyna CLUE indicate a good representation of the model and align with the results of other research carried out in different continents (van Vliet et al., 2011;Manuschevich and Beier, 2016;van Vliet et al., 2016;Pindozzi et al., 2017;Romano et al., 2018). However, there are some points of land-cover modeling to consider that can introduce uncertainties. ...
The effect of different forest conservation policies on water provision has been poorly investigated due to a lack of an integrative methodological framework that enables its quantification. We developed a method for assessing the effects of forest conservation policies on water provision for rural inhabitants, based on a land-use model coupled with an eco-hydrological model. We used as a case study the Lumaco catchment, Chile, a territory dominated by native forests (NF) and non-native tree farms, with an extended dry period where nearly 12,600 people of rural communities get drinking water through water trucks. We analyzed three land-use policy scenarios: i) a baseline scenario based on historical land-cover maps; ii) a NF Recovery and Protection (NFRP) scenario, based on an earlier implementation of the first NF Recovery and Forestry Development bill; and iii) a Pristine (PR) scenario, based on potential vegetation belts; the latter two based on Dyna CLUE, and simulated between 1990 and 2015. Impacts on water provision from each scenario were computed with SWAT. The NFRP scenario resulted in an increase of 6974 ha of NF regarding the baseline situation, and the PR scenario showed an increase of 26,939 ha of NF. Despite large differences in NF areas, slight increases in inflows (Q) were found between the NFRP and the PR scenarios, with relative differences with respect to the baseline of 0.3% and 2.5% for NFRP and PR, respectively. Notwithstanding, these small differences in the NFRP scenario, they become larger if we analyze the cumulative values during the dry season only (December, January, and February), where they reach 1.1% in a normal year and 3.1% in a dry year. Flows increases were transformed into water truck costs resulting in up to 441,876 USD (monthly) of fiscal spending that could be avoided during a dry period.
... From using environment, social and economic variables, they have the ability to predict the location and quantity of past and future change (Veldkamp and Lambin 2001;Lourdes et al., 2011;Zhu et al., 2010;Ellis et al., 2010). (Verburg et al., 2006;Khan et al., 2018;Lourdes et al., 2011;Pindozzi et al., 2017;Trisurat et al., 2019) ...
This paper aims to improve the understanding of environmental and socioeconomic drivers on land use change (LUC) through public participation (PP), and provide recommendations for long-term policy making to support sustainable land use management. Public participation (PP) was necessary to help understand and address the problem and concerns of stakeholders within the study area. Through two collaboration workshops seven individual future land use scenarios were created. Using the FLUS (Future land use simulation) model, land use was projected up till 2060, after which logistic regression analysis took place to find the most significant driver. Results found that LUC within the baseline scenario and the ones chosen by stakeholders were very different, however concluded that Paddy field extent would decrease in the future to be replaced by more drought resilient agriculture; Perennials & Orchards and Field Crops. Outcomes from future scenarios propose that future LUC was driven by environment spatial factors such as elevation and climate, not soil suitability. With, first hand interviews suggesting it is indirect external factors such as, crop price that drive LUC. Overall the study provides steps towards dynamic LUC modelling where future scenarios have been tailored to details specified by the public through their participation.
... In these contexts, the building, analysis and assessment of land use change (LUC) scenarios are important tools to attain sustainable development and resilience (Pindozzi et al., 2016). In line with the modelling approach, Spatial Multicriteria Decision Analysis (SMCDA) allows us to solve spatial decision problems (Rigillo and Cervelli, 2014;Cervelli et al., 2016b;Pindozzi et al., 2017), thanks to the combination of quantitative and qualitative inputs, such as risks, costs, benefits and stakeholders' views (Passuello et al., 2012;Verburg et al., 2011;Giove et al., 2009). This approach involves several potential alternatives associated with geographical locations that generate different LUC scenarios (Verburg et al., 2008;Søndergaard et al., 2017). ...
Starting from the identification of marginal areas, this work presents a possible physical-mathematical approach as a support to landscape planning, based on the pragmatic determination of the predictable environmental effects connected to land use changes (LUC) and related to objective and quantitative ecological indicators for environmental impact assessment. "Fringe areas", which are more suitable to change in a medium-short time frame, were determined through a spatial multicriteria decision analysis (S-MCDA) process. Three land use changes scenarios were identified and analysed, namely: the current situation, energy crop cultivation in marginal lands, and the possible abandon-ment of lands such as these. Energy crop cultivation in marginal lands is widely considered to be a useful opportunity for farmers, against the progressive risk of under-utilization or abandonment; nevertheless, the large areas needed can cause important environmental side-effects. In order to assess the possible variations in environmental components in the ex-ante planning phase, scenarios were assessed in terms of habitat and biodiversity ecosystems services (using both monetary and indexes approach), focusing also on possible environmental fragmentation analysis by means of landscape metrics, which are simple measures used to deepen landscape configuration and structure. The S-MCDA process allowed about 10% of the study area with less favourable environmental conditions to be defined, where land use change is desirable in a medium-short time frame. For the energy crops scenario, the ecosystem services (ESs) approach highlights positive repercussions in terms of habitat quality and biodiversity value. Similar trends are highlighted by different ESs assessment methods adopted (monetary and indexes), confirming themselves. Also, landscape pattern analysis confirmed positive habitat connectivity trends: the delineation of fringe areas has preserved, in energy crops scenario, natural and semi-natural classes, reducing the risk of disturbance with respect to the biodiversity and habitat. This condition assumes that adopted S-MCDA method can contribute positively and significantly to the definition of LUC scenarios and land management. In conclusion, marginal lands can become an opportunity to improve socioeconomic conditions and to enhance land image, while respecting the environment. LUC scenarios building, and their assessment by means of ecological indicators become a dynamic and structured tool in the land use planning /management process to support decision maker choices and to re-calibrate interventions, with the aim of contributing to sustainable policies of land management (ecological corridors, compensation and / or mitigation measures, etc.), emphasizing land sustainable management benefits (such as climate change adaptation or disaster risk reduction).
... An important element of water resource sustainability is the ability to properly plan for the short-to-medium term future (Wilson and Weng 2011;El-Khoury et al. 2014). Such planning should efficiently capture the physical, socioeconomic, political, institutional, and other drivers that influence contemporary LULC (Lambin et al. 2001;National Research Council 2013;Wilson 2015), and further probe into their role in future landscape configuration (Rounsevell et al. 2006;Pindozzi et al. 2017). The product of such exercise is projections of LULC for a watershed that can be invaluable to natural resource scientists, planners, and decision makers in assessing and planning for future water resources. ...
... The MLP-MC assumed that these public lands might be transitioning since some of them were deliberately excluded from model construction in a bid to reduce model complexity. To improve model performance in these locations when possible, publics lands should be fully encoded into the model in the form of spatial constraints to prevent incorrect transitions from taking place in those locations (Verburg, Tabeau, and Hatna 2013;Schaldach et al. 2011;Pindozzi et al. 2017). Additionally, model uncertainty was high close to the major urban centers in the LCRW. ...
Watershed planning is a pivotal exercise for all jurisdictions irrespective of size, landscape complexity, or other nuances. As a result of the intricate relationship between land use/land cover (LULC) and water resources, it becomes prudent to not only develop historical and contemporary LULC data for watershed planning purposes, but more importantly, the production of future LULC datasets has the potential to better inform watershed planners. This study explored an optimal workflow that can be adopted for the production of baseline LULC input images from a moderate spatial resolution sensor such as Landsat, and the identification, translation, and configuration of land change drivers and regional comprehensive plan prescriptions in the creation of future LULC data for a regional watershed. The study conducted in the Lower Chippewa River Watershed, Wisconsin, USA demonstrated that an object-based hybrid classification approach resulted in the generation of improved projected images with a 15% increase in area under the curve (AUC) value compared to a pixel-based hybrid classification method even though both methods displayed comparable overall image classification accuracies (≤ 1.8%). Results further displayed that configuring anthropogenic drivers in a trend format rather than individual year values can result in a more efficient training of a multi-layer perceptron neural network – Markov Chain model. The calibrated and validated model demonstrated that on average, residential, commercial, institutional, green vegetation/shrub, and industrial LULC are expected to grow through 2050, though at a slower rate (12%) compared to contemporary period (39%), while forest and agricultural lands are slated to decline (−2%).
Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).
The European Bioeconomy Strategy aims to facilitate the transition from a take‐make‐dispose fossil economy into one fostering circular bio‐based value chains linking sustainable land use with cutting‐edge products. Optimised designs, implementation and monitoring rely on continuous interactions between policymakers and modellers who run multiple scenarios for environmentally, economically and socially desirable futures. This paper leverages a multi‐layered framework that cross‐references 39 policies and 32 models to assess how they address the five principle objectives of the Bioeconomy Strategy in terms of accompanying sectors, value‐chains, and multi‐dimensional indicators. The framework identifies gaps in bioeconomy knowledge both in policy and modelling. Overall, the analysis found little mention of the wide range of bio‐based products, technologies and processes, bio‐refineries, waste, and land conservation. Bio‐based product policies can be simulated only in a limited number of models, compared, for example, to the wide range of modelling capacities that can model bioenergy. Additionally, in both policy and modelling realms, integration of market and biophysical drivers within the full scope of the value chain is scarce. Multidisciplinary studies combining multiple models perform best in this respect by integrating a more comprehensive range of relevant policies, bioeconomy drivers and indicators. Findings point to a more significant issue in policy‐modelling information exchange, and this paper discusses the challenges and opportunities for future improvements in this collaboration.
