Land Use Change Model Research Articles

Global and Local Modeling of Land Use Change in the Border Cities of Laredo, Texas, USA and Nuevo Laredo, Tamaulipas, Mexico: A Comparative Analysis

This paper estimates global logistic regression and logistic geographically weighted regression (GWR) models of urban growth in the adjacent border cities of Laredo, Texas in the United States and Nuevo Laredo, Tamaulipas in Mexico, for two time periods from 1985 to 2014. Historical land use and land cover patterns were monitored through Landsat imagery from the United States Geological Survey to identify instances of urban growth through land type change. Data on socioeconomic variables related to urban growth were collected from various sources and used as independent variables. In both time periods, the logistic GWR was proven to be a complementary model for the global logistic regression to explore the urban growth effect. In addition, GWR outperformed the global logistic regression model with respect to goodness of fit. These results suggest that local models are complementary to global models to empirically analyze the determinants of urban growth in study areas that contain political borders, presumably because the relationships between socioeconomic factors and urban growth are characterized by spatial heterogeneity in such areas. The spatial variable of the relationship between urban growth and the neighborhood interactions and proximity effect present the idea of complexity and interconnections between the land use change and associated factors.

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

Cellular automata for simulating land-use change with a constrained irregular space representation: A case study in Nanjing city, China

Traditional cell-based cellular automata (CA) models use a regular cellular grid to represent geographic space, and new approaches to CA models have explored the use of a vector representation of space instead of a regular grid to characterize urban space more realistically. However, less attention has been paid to modeling the interaction between the geospatial information and the irregular cells. To date, the majority of spatial boundaries have been created by individual agencies in an uncoordinated manner. As a consequence, the potential uses of the data collected for land-use change models are limited. In this paper, we propose a new vector-based CA model based on a new constrained irregular space representation using the theory of hierarchical spatial reasoning. For dividing the geographic space considering different items, first land patches are considered as the minimum division unit; then aggregation rules, including attribute, geometric and boundary barrier constraints, are defined; and finally different levels of spatial units are formed through land patches based on aggregation rules. The proposed model is used to simulate the land-use changes in Nanjing, Jiangsu Province, China. The performance validation and comparison illustrate the feasibility of the proposed space representation in a CA model. By using this model, it is expected that the use of the real spatial boundaries that are employed in urban planning could help provide a flexible paradigm to consider various drivers or constraints for realistically simulating land-use changes.

Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

Abstract Understanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

Abstract. Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

A generic macroscopic cellular automata model for land use change: The case of the Drâa valley

The study of change in land use has been recently included in territorial planning in order to inform spatial planners and policy makers of the possible future developments and therefore optimize their management decisions. Several models have been proposed to simulate land use change. These models are used to understand and analyze the previous change in order to explore the future evolutions. In this paper, we propose a macroscopic cellular automata model for land use change by introducing local competition between land uses. By implementing our model in Drâa-valley Moroccan region, the land use change from 2003 to 2016 was simulated. The study area is characterized by a vulnerable agricultural area and with height ecological constraints. The land use maps in 2016 were used as a reference to validate the proposed model. The qualitative validation achieved a good accuracy of 80% between the observed data and the simulated data. In order to explore the land use dynamics from 2016 to 2021 two scenarios have been assumed in the study area. In the first scenario we simulated an evolution without ecological constraints. Whereas, in second scenario we have prohibited the construction in the bed of valley in order to protect agricultural area.

Janus: A Python Package for Agent-Based Modeling of Land Use and Land Cover Change

Janus is an open source Python package for agent-based modeling (ABM) of land use and land cover change (LULCC). Many ABMs of LULCC have been created across platforms, some of which are not ideal for large scale, high resolution scenarios. This model provides a simple object-oriented framework for creating ABMs specific to LULCC. The organizational philosophy of the modeling framework is to create software objects (agents) that are associated with specific and contextual attributes which are isolated from where those agents exist in the spatial setting of the model, and still provide clear linkages between the agent, their environment, and other agents in the simulation. In this way, the framework allows for assembly of LULCC ABMs with low (programmatic) overhead, making the models extensible and providing clear mechanisms for integrating them with process-oriented biophysical models. Provided with Janus is a suite of geospatial data preprocessing tools that can use arbitrary land cover products as an input. Crop choice decisions are based on potential crop prices, these can be created synthetically, or drawn from integrated human-Earth systems models such as the Global Change Assessment Model. Janus is publicly accessible through GitHub and provides an example dataset for testing.

Pemodelan Perubahan Tataguna Lahan Terhadap Debit Banjir DAS Tanggul, Jember Menggunakan Model SWAT (Soil and Water Assessment Tool)

Population growth is one of the causes of land-use change that can increase runoff. Tanggul watershed is one of the watersheds which often overflows during the rainy season. This study purpose to analyze the effect of land-use changes on runoff in Tanggul watershed using SWAT (Soil and Water Assessment Tool) model. To make sure the performance of SWAT model calibration and classified by the value of NSE and R2. The result of calibration included in a good category and validation included in a very good category. This study was modeling forest land-use change in 2004-2017 to determine the effect of land-use change on runoff. The result in this model of forest land-use change can increase runoff.

Modelling of land-use change in Thailand using binary logistic regression and multinomial logistic regression

Modelling of land-use change over time gives useful information relating to land development. Logistic regression is arguably one of the most used in the land-use study when a binary outcome variable. Multinomial logistic regression used when multinomial outcome variable. This study aims to model of land-use change using logistic regression and multinomial logistic regression. These methods were applied to data from a survey on the land-use of Krabi Province, Thailand, in 2000, 2009 and 2018. The data is based on an analogue-to-digital conversion which replaces polygonal shapes by coded grid points. The land-use observations were categorized into four, namely developed, rubber plantation, agricultural and undeveloped lands. Our findings show that developed and agricultural lands increased by two periods of study 2000–2009 and 2009–2018. Whereas rubber plantation was decreased on two periods observes. The logistic regression models for developed land, agricultural land and rubber plantation were used for modelling land-use change, consequently as the multinomial logistic regression model with the undeveloped land being the reference category. The results showed that the modelling of land-use in 2009 and 2018 with logistic regression and multinomial logistic regression does not differ by the area under the curves (AUC) and prediction accuracy. Therefore, logistic regression can be used for the modelling of the land-use change using land-use in the previous year as predictors instead of the multinomial logistic regression, since it is not a complicated formula for adjusting spatial correlations using the variance inflation factors (VIF) and displaying the modelling result on 95% confidence intervals (CI) based weighted sum contrasts.

An effective accuracy assessment indicator for credible land use change modelling: Insights from hypothetical and real landscape analyses

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.

Integrating regionalized Brazilian land use change datasets into the ecoinvent database: new data, premises and uncertainties have large effects in the results

Land use change (LUC) is a critical process in the life cycle greenhouse gas emissions of agricultural products and Brazil is a major exporter of these. This work had the objective of integrating refined and regionalized datasets of LUC in Brazil into the ecoinvent database, to better represent its dynamics and heterogeneity. We present the adaptations needs for having it suitable for crops, pasture and forestry in state-level and impacts of modelling assumptions and uncertainties. Adaptation and integration were based in ecoinvent version 3 guidelines and the database requirements to LUC modelling. BRLUC, a method for Brazilian LUC accounting, was the main data source. The workflow for the integration process consisted in identifying necessary adaptations in both sources to allow a better representation of Brazilian LUC. Four new reference products and 27 geographies were added in the database. A total of 566 new datasets were integrated into ecoinvent version 3.6, allowing the incorporation of LUC in Brazilian products in state, regional and national level. GHG emissions reduced, being 42.2% and 99.9% lower to soybean and sugarcane than in ecoinvent v3.5. Four improvements were the main causes: (i) state-level LUC modelling with national official data; (ii) regionalizing carbon stocks; (iii) including pasture and forestry land use categories; (iv) and considering sugarcane as a perennial crop. The way to calculate national-level results based on subnational data was an important source of difference in emissions too. Uncertainties specifically associated with land use substitution patterns were not incorporated, and they can potentially have impacts as large as the uncertainties of all the remaining processes combined. Results showed that small changes in data sources and premises have large impacts on emissions associated with LUC in agricultural products. It also showed the large impacts of uncertainties of LUC patterns. Improving current models in better representing regional LUC patterns, regional carbon stocks and uncertainty accounting could reduce these impacts. Nonetheless, efforts in reducing the complexity of LUC accounting methods could enhance transparency and effectiveness.

From global drivers to local land-use change: understanding the northern Laos rubber boom

Crop booms in forest frontiers are a major contributor to land conversion and deforestation. In this study, we investigate the smallholder-driven northern Laos rubber boom in two case study areas (CSAs) with different speed and intensity of rubber expansion. We assess the relative importance of market, contextual, and behavioral factors in fostering or hindering the conversion of forest to rubber plantations. We develop a Bayesian network (BN) model of land-use change based on household surveys, expert interviews, market price data, and land use maps covering the period 2000–2017. We use regression analysis to inform the structure of the BN, compare model results, and analyze time-varying effects. The BN and regression models incorporate perceived price signals as a combination of market price and local price knowledge, and local self-reinforcing imitation dynamics as a combination of aggregate rubber conversion and imitation behavior elicited in the survey. Results show that deforestation was lower in strictly protected areas but not in forests with lesser protection status. Imitation had a large effect on rubber uptake in both areas. In the CSA that experienced the most intensive spread of rubber, price signals transmitted through social networks had a significant impact, especially throughout the stage of rapid expansion. Rubber expansion continued in both areas during periods of descending prices mainly because of increased cash availability and access to inputs. Our research sheds light on the underlying dynamics of crop booms and contributes to the understanding of agricultural expansion processes.

Impacts of changing society and climate on nutrient loading to the Baltic Sea

This paper studies the relative importance of societal drivers and changing climate on anthropogenic nutrient inputs to the Baltic Sea. Shared Socioeconomic Pathways and Representative Concentration Pathways are extended at temporal and spatial scales relevant for the most contributing sectors. Extended socioeconomic and climate scenarios are then used as inputs for spatially and temporally detailed models for population and land use change, and their subsequent impact on nutrient loading is computed. According to the model simulations, several factors of varying influence may either increase or decrease total nutrient loads. In general, societal drivers outweigh the impacts of changing climate. Food demand is the most impactful driver, strongly affecting land use and nutrient loads from agricultural lands in the long run. In order to reach the good environmental status of the Baltic Sea, additional nutrient abatement efforts should focus on phosphorus rather than nitrogen. Agriculture is the most important sector to be addressed under the conditions of gradually increasing precipitation in the region and increasing global demand for food.

Twenty Years of Post-Soviet Union Urban Land Use Change of St. Petersburg

Since the 1990s, Russia (the former Soviet Union, FSU) has undergone radical institutional transformation and drastic economic, political, and social changes. These changes have resulted in complex transformations of the urban land-use patterns. We extracted four major urban land-use classes (residential, industrial, social well-being, and green land) before (1987) and after (2015) the transformation of St. Petersburg. In particular, we focused on residential and industrial land use. In the core and peripheral areas of St. Petersburg, we calculated the percentage of each land-use class and conducted spatial pattern analyses for 1987 and 2015. Specifically, we measured the degree of interspersion/juxtaposition, aggregation and contagion for the major land-use classes in the study area. Results indicated that St. Petersburg experienced significant urban land restructuring from 1987 to 2015. While the city retained some urban land structure from before FSU, it showed trajectories of urban development similar to those of developed counties in Western Europe. The FSU period was the main stage of the outward expansion of urban administrative boundaries. The process of deindustrialization in the urban core during the post-Soviet Union period lagged behind other international cities. The most significant change in the urban land-use pattern was suburbanization in the peripheral area. The suburbanization of St. Petersburg was different from that of other developed countries in that it had its own unique characteristics, both in the residential style and in the resident class. Russia's institutional transformation had strong impacts on urban land-use patterns in the post-Soviet Union period, thus providing a rich practical and theoretical basis for analyzing new models of urban land-use change.

Climate and land-use change refugia for Brazilian Cerrado birds

As climate and land-use changes threatens biodiversity, the identification of refugia areas for species becomes a crucial strategy in conservation planning. Here, we integrate climate change anomaly with a land-use change model both projected for 2050 to identify refugia areas for 103 bird species that occur in the Brazilian Cerrado. We found that 13% of the Cerrado may serve as refugia for the bird species. In contrast, nearly 35% of the biome might become areas of high risk for those species. Most species (74%) will held from 34% to 85% of their current geographic distribution in areas with less native vegetation, but with low climate anomaly. Apart from the protection of the refugia areas, we suggest restoration of native vegetation in regions that are likely to maintain climate conditions more adequate in the future. These areas should be prioritized to protect places with higher species richness and ease the establishment of corridors that would support climate-induced dispersal from high risk areas to suitable ones. Approaches that allow for the identification and future protection of refugia are fundamental to guarantee the conservation of biodiversity faced with climate change and rapid land-use changes that already taking place.

Spatially-explicit modeling and intensity analysis of China's land use change 2000–2050

Land use change affected by wide ranges of human activities is a key driver of global climate change. In the last three decades, China has experienced unprecedented land use change accompanied by increasing environmental problems. There is a pressing need to project and analyze long-term land use scenarios that are critical for land use planning and policymaking. Using GlobeLand30 data, we examined China's land use change from 2000 to 2010, and developed a novel LandCA model for scenario projections from 2020 to 2050. The observed and projected land use change (2000–2050) was analyzed in terms of the interval, category, and transition levels. Our findings show that land Exchange intensity is more than 3 times greater than land Quantity intensity from 2000 to 2050, and the overall rate of land use change will decelerate from 2010 to 2050. During 2000–2010, the loss of built-up land to other categories was 12.7% while the gain was 32.5%, with a growth rate 3.4 times larger than that during 2010–2050. The total amount of cultivated land continuously decreases but will not violate the Chinese “Cultivated Land Red-Line Restriction” by 2050. We speculate that the government's goal of 26% forest cover by 2050 may not be achieved, as a result of strict land use policies preventing the transformation from cultivated land to forests. This study contributes to new evaluations of long-term land use change in China for the government to adjust policies and regulations for sustainable development.

Agent-Based Simulation of Autonomous Vehicles: A Systematic Literature Review

Autonomous vehicles (AVs) may have a transformative influence on transportation systems. AV use is expected to reduce crash, transport costs, and land use. However, the net effects of the autonomous vehicle are unknown because there are no fully-autonomous vehicles in reality. The simulation systems are indispensable to be built up not only for cost but also for obtaining massive data. An agent-based simulation is capable of simulating the entities in a complex system, such as the AV system. Considering the potential of agent-based models to complement existing models and the difficulty to understand, to duplicate and to compare different agent-based models, this paper review existing agent-based modeling of autonomous vehicles with the help of ODD protocol, which has been applied in the review of agent-based land-use change models and agricultural policy evaluation. The significant variables that researchers have taken into consideration in sensitivity analysis and in the different scenarios are explored in the paper. Fleet size is the primary variable researchers considered, and we also discussed the probable causes of different AV replacement rates. We have assessed the quality of reviewed articles to gain more acceptance from the agent-based modeling community. Most of the reviewed papers follow the ODD protocol of agent decision making. However, in order to improve the level of modeling transparency, the executable and related data shall be available, at least the data source and the programming language/simulation platform should be provided. Several recommendations for further research are also presented.

Modeling And Spatial Analysis Of Change Settlement And Fair Market Land Price Using Markov Chain Model In Banyumanik District

Banyumanik District is located on the outskirts of Semarang with very rapid development. Indicated with the many changes in land that occur, due to the construction of settlements and other physical buildings continues to increase. Changes in land use will also be followed by changes in market land prices. These changes will continue in line with the increasing number and activities of the population in carrying out economic, social and cultural life. Most of the studies was conducted to analyze changes in future land use are based on the use of a model. Land use modeling changes is a method or approach that can be used to understand the causes and effects of these dynamic changes. The Multi-Layer Perceptron (MLP) Neural Network and Markov Chain methods are used in this study to determine which locations or areas of land use are vacant land and agriculture has the potential to change into settlements and test the predictive ability that will be produced by the model. The driving factor for land use change as an input model consists of distance to the road, distance to the area experiencing changes in land use, slope, elevation and fair market land prices. This study aims to (1) predict settlement and its changes in Banyumanik District using High Resolution Satellite Image in 2011-2019, (2) build a model of settlement land use change with the Markov Chain methods and (3) projection of Banyumanik District land use in 2028.

Societal breakdown as an emergent property of large-scale behavioural models of land use change

Abstract. Human land use has placed enormous pressure on natural resources and ecosystems worldwide and may even prompt socio-ecological collapses under some circumstances. Efforts to avoid such collapses are hampered by a lack of knowledge about when they may occur and how they may be prevented. Computational models that illuminate potential future developments in the land system are invaluable tools in this context. While such models are widely used to project biophysical changes, they are currently less able to explore the social dynamics that will be key aspects of future global change. As a result, strategies for navigating a hazardous future may suffer from “blind spots” at which individual, social and political behaviours divert the land system away from predicted pathways. We apply CRAFTY-EU, an agent-based model of the European land system, in order to investigate the effects of human behavioural aspects of land management at the continental scale. We explore a range of potential futures using climatic and socio-economic scenarios and present a coherent set of cross-sectoral projections without imposed equilibria or optimisation. These projections include various behavioural responses to scenarios including non-economic motivations, aversion to change and heterogeneity in decision-making. We find that social factors and behavioural responses have dramatic impacts on simulated dynamics and can contribute to a breakdown of the land system's essential functions in which shortfalls in food production of up to 56 % emerge. These impacts are largely distinct from, and at least as large as, those of projected climatic change. We conclude that the socio-economic aspects of future scenarios require far more detailed and varied treatment. In particular, deviation from simple economic rationality at individual and aggregate scales may profoundly alter the nature of land system development and the achievability of policy goals.

Comparison of Logistic Regression and Geomod Approaches to Forest Change Modeling in the Period of 1988 – 2025

Spatial modelling of land use change is a technique for understanding changes in terms of the location and amount. In this study, logistic regression and Geomod approaches were used for modelling forest change in Gorgan area in Northern Iran in the time period of 1988-2007. To do this, at first, remotely sensed imagery data of the years 1988, 1998 and 2007 were used to produce land use maps. Land use maps accuracy assessments were achieved using Error matrix method and then the maps were used to implement change detection process in two time periods of 1988-1998 and 1998-2007. Results indicated a reduction in forest areas during the mentioned time period. Next, the independent variables were extracted in order to land use change modeling. The Results of the models implementation showed the ability of both models for forest change modeling in this region. Also, the models were used to predict the future condition of forest area in the years 2016 and 2025. The results revealed that the forest area would be associated with a reduction in the future. Comparison of the results of the models using kappa indices showed the successful implementation of both models for forest change modelling in this region. The results of this research reveal the need for appropriate applications of the proper plans to control land use change in order to preserve the environment and ecological balance of the area. Therefore, careful planning can reduce the land use change and its impacts in the future in this region.