Forecast Land Use Research Articles

Land Use Change Detection and Prediction in Upper Siem Reap River, Cambodia

Siem Reap River has played a crucial role in maintaining the Angkor temple complex and livelihood of the people in the basin since the 12th century. Land use in this watershed has changed considerably over the last few decades, which is thought to have had an influence on river. This study was carried out as part of assessing the land use and climate change on hydrology of the upper Siem Reap River. The objective was to reconstruct patterns of annual deforestation from 1988 to 2018 and to explore scenarios of land use 40 and 80 years into the future. A supervised maximum likelihood classification was applied to investigate forest cover change in the last three decades. Multi-layer perceptron neural network-Markov chain (MLPNN-MC) was used to forecast land use and land cover (LULC) change for the years 2058 and 2098. The results show that there has been a significantly decreasing trend in forest cover at the rate 1.22% over the last three decades, and there would be a continuous upward trend of deforestation and downward trend of forest cover in the future. This study emphasizes the impacts of land use change on water supply for the Angkor temple complex (World Heritage Site) and the surrounding population.

Population-Based Simulation of Urban Growth: The Italian Case Study

Land take is one of the most studied phenomena in land use science. The increased attention to the issue of urban growth from both scientists and decision makers is justified by the dramatic negative effects on land use caused by anthropogenic activities. Within this context, researchers have developed and explored several models to forecast land use changes, some of which establish excellent scenario-based predictions of urban growth. However, there is still a lack of operative and user-friendly tools to be integrated into standard urban planning procedures. This paper explores the features of the recently published model FUTure Urban-Regional Environment Simulation integrated into the GRASSGIS environment, which generates urban growth simulation based on a plethora of driving variables. Specifically, the model was applied to the case study of urbanization in the Italian national territory. Hence, the aim of this work is to analyze the importance of population dynamics within the process of urban growth. A simulation of urban growth up to the year 2035 was performed. Results show that, despite the importance given to demographic aspects when defining urban policies over the last several decades, additional factors need to be considered during planning processes to overcome the housing issues currently experienced in Italy.

Climate Change Impacts on Farmland Values in the Southeast United States

This study uses the Ricardian (hedonic) approach to estimate the impact of potential climate change on agricultural farmland values in the Southeast U.S. as a distinct agricultural region. Using the Agricultural Resource Management Survey and seasonal county-level climate and data, we find that regional farmland values increase with spring and fall temperatures and fall precipitation and decrease with winter and summer temperatures. Long-term climate change projections predict aggregate farmland value losses of 2.5–5% with differential state-level impacts, ranging from large losses in Florida to significant gains in Virginia. The results are consistent with recent research and can be helpful in policy design and forecasting land use change.

A Time Monte Carlo method for addressing uncertainty in land-use change models

ABSTRACTOne of the main objectives of land-use change models is to explore future land-use patterns. Therefore, the issue of addressing uncertainty in land-use forecasting has received an increasing attention in recent years. Many current models consider uncertainty by including a randomness component in their structure. In this paper, we present a novel approach for tuning uncertainty over time, which we refer to as the Time Monte Carlo (TMC) method. The TMC uses a specific range of randomness to allocate new land uses. This range is associated with the transition probabilities from one land use to another. The range of randomness is increased over time so that the degree of uncertainty increases over time. We compare the TMC to the randomness components used in previous models, through a coupled logistic regression-cellular automata model applied for Wallonia (Belgium) as a case study. Our analysis reveals that the TMC produces results comparable with existing methods over the short-term validation period (2000–2010). Furthermore, the TMC can tune uncertainty on longer-term time horizons, which is an essential feature of our method to account for greater uncertainty in the distant future.

Spatio-temporal multinomial autologistic modeling of land-use change: A parcel-level approach

Land-use change models that accurately replicate the complex dynamics of land development provide vital information for urban planning and policy. These models require both detailed data and advanced statistical methods. Many factors influence land-use change decisions, such as parcel characteristics, accessibility to activities, and current and historical neighborhood conditions. Therefore, spatial and temporal components must be incorporated in a model at the highest possible disaggregation level in order to achieve robust results. A spatio-temporal multinomial autologistic model, incorporating space and time and their interactions, is introduced to investigate land-use dynamics at the parcel-level, and is applied to Delaware County, Ohio. It is able to capture the impacts of the existing and historical neighborhood conditions of parcels with high accuracy. Advanced computational methods are used to deal with the computational challenges of parameter estimation. The model is validated, estimating 91.4% of all observations correctly for the period 2005–2010, and is applied to land-use forecasting.

Impact of forecasted land use changes on flood risk in the Polish Carpathians

Flooding is a major environmental hazard in Poland with risks that are likely to increase in the future. Land use and land cover (LULC) have a strong influencing on flood risk. In the Polish Carpathians, the two main projected land use change processes are forest expansion and urbanization. These processes have a contradictory impact on flood risk, which makes the future impact of LULC changes on flooding in the Carpathians hard to estimate. In this paper, we investigate the impact of the projected LULC changes on future flood risk in the Polish Carpathians for the test area of Ropa river basin. We used three models of spatially explicit future LULC scenarios for the year 2060. We conduct hydrological simulations for the current state and for the three projected land use scenarios (trend extrapolation, ‘liberalization’ and ‘self-sufficiency’). In addition, we calculated the amount of flood-related monetary losses, based on the current flood plain area and both actual and projected land use maps under each of the three scenarios. The results show that in the Ropa river, depending on scenario, either peak discharge decreases due to the forest expansion or the peak discharge remains constant—the impact of LULC changes on the hydrology of such mountainous basins is relatively low. However, the peak discharges are very diverse across sub-catchments within the modeling area. Despite the overall decrease of peak discharge, there are areas of flow increase and there is a substantial projected increase in flood-related monetary losses within the already flood-prone areas, related to the projected degree of urbanization.

Accessibility analysis for transportation projects and plans

•A method for analyzing the accessibility impact of specific, proposed transportation projects is demonstrated.•Two suites of transportation projects are compared for their 10-year accessibility impacts in San Antonio, Texas.•Land-use forecasts are incorporated to account for how these transportation projects alter future land-use patterns.•Accessibility impacts are compared and contrasted with mobility impacts, with strongly differing outcomes.

Evaluating Biodiversity Metric Response to Forecasted Land Use Change in the Northern Rio Grande Basin.

The effects of future land use change on arid and semi-arid watersheds in the American Southwest have important management implications. Seamless, national-scale land-use-change scenarios for developed land were acquired from the US Environmental Protection Agency Integrated Climate and Land Use Scenarios (lCLUS) project and extracted to fit the Northern Rio Grande River Basin, New Mexico relative to projections of housing density for the period from 2000 through 2100. Habitat models developed from the Southwest Regional Gap Analysis Project were invoked to examine changes in wildlife habitat and biodiversity metrics using five ICLUS scenarios. The scenarios represent a US Census base-case and four modifications that were consistent with the different assumptions underlying the A1, A2, B1, and B2 Intergovernmental Panel on Climate Change global greenhouse gas emission storylines. Habitat models for terrestrial vertebrate species were used to derive metrics reflecting ecosystem services or biodiversity aspects valued by humans that could be quantified and mapped. Example metrics included total terrestrial vertebrate species richness, bird species richness, threatened and endangered species, and harvestable species (e.g., waterfowl, big game). Overall, the defined scenarios indicated that the housing density and extent of developed lands will increase throughout the century with a resultant decrease in area for all species richness categories. The A2 Scenario, in general, showed greatest effect on area by species richness category. The integration of the land use scenarios with biodiversity metrics derived from deductive habitat models may prove to be an important tool for decision makers involved in impact assessments and adaptive planning processes.

How Green Do We Want to Live in 2100? Lessons Learned from the Homes of the Present-Day Rich

This study explores the extent to which rich Dutch households live green, in the form of green surrounding homes directly and nearby public green. The authors interpret this ‘greenness’ as a signal of how green the wider population wishes to live in the long-term as it grows wealthier over time. In our analyses of property transaction data on the 2009–2012 residential market, we focus on 2303 properties that sold for at least 1 million Euros, the ‘properties of the rich’. Results indicate that the rich live relatively green: on average, and depending on local degrees of urbanization, the parcels of million Euro properties are up to 7.0 times larger than parcels of lower priced properties. We find too, that the rich live closer to public green spaces than the more general population does, especially if such green is highly appreciated by a wide public. Furthermore, the rich are found to live in either very highly urban locations or in the least urban locations—if these are nearby cities. We perform basic long-term land-use forecasts of demand for residential space across local property markets, and findings suggest that preference for green living will increase over time. Our results especially show that how well these green preferences are accommodated by existing residential structures may become increasingly problematic as and if we grow wealthier over time. Our findings may foster long ongoing research and policy debate on urban planning.

Simulating the impacts of future land use change on soil erosion in the Kasilian watershed, Iran

Predicting soil erosion potential is important in watershed management. A rapidly growing Iranian population and climate change are expected to influence land use and soil sustainability. In recent years, northern Iran has experienced significant land use changes due to internal migration along the Caspian coast and conversion of forests and rangelands. Considering the effect of these changes in the future, the purpose of this study is to forecast land use patterns and investigate soil erosion scenarios using the Revised Universal Loss Equation and Markov Cellular Automata. Data from 1981 to 2011 were used as a baseline to estimate changes that might occur in 2030. The results reveal that the mean erosion potential will increase 45% from the estimated 104.52 t ha−1 year−1 in the baseline period. Moreover, the results indicate that land use change from forest area to settlements will be the most significant factor in erosion induced by land use change, showing the highest correlation among erosional factors. Projecting land use change and its effect on soil erosion indicate that conversion may be unsustainable if change occurs on land that is not suited to the use. The method predicts soil erosion under different scenarios and provides policymakers a basis for altering programs related to land use optimization and urban growth. Those results indicated the necessity of appropriate policies and regulations particularly for limiting land use changes and urban sprawl in areas of unfavorable soil erosion risk factors.

Forecasting Urban Vacancy Dynamics in a Shrinking City: A Land Transformation Model

In the past two centuries, many American urban areas have experienced significant expansion in both populating and depopulating cities. The pursuit of bigger, faster, and more growth-oriented planning parallels a situation where municipal decline has also been recognized as a global epidemic. In recent decades many older industrial cities have experienced significant depopulation, job loss, economic decline, and massive increases in vacant and abandoned properties due primarily to losses in industry and relocating populations. Despite continuous economic decline and depopulation, many of these so-called ‘shrinking cities’ still chase growth-oriented planning policies, due partially to inabilities to accurately predict future urban growth/decline patterns. This capability is critical to understanding land use alternation patterns and predicting future possible scenarios for the development of more proactive land use policies dealing with urban decline and regeneration. In this research, the city of Chicago, Illinois, USA is used as a case site to test an urban land use change model that predicts urban decline in a shrinking city, using vacant land as a proxy. Our approach employs the Land Transformation Model (LTM), which combines Geographic Information Systems and artificial neural networks to forecast land use change. Results indicate that the LTM is a good resource to simulate urban vacant land changes. Mobility and housing market conditions seem to be the primary variables contributing to decline.

The reorganization of development rights at inter-municipal level: Different scenarios for an alternative land-use forecast in seriana valley in italy

The paper deals with the issue of inter-municipal urban planning in Italy, exploring in particular the technical procedures used by inter-municipal plans to achieve the objectives established by the involved municipalities, including an effective land-use forecast. Among the planning tools, the transfer of development rights (TDR) is particularly relevant, in fiscal terms, for the form and degree of sophistication characterizing the exchange of monetary sources between local authorities, and in territorial terms, because of the effects that new settlements produce on the territory. The case study investigated by the research is the Seriana Valley, in the province of Bergamo, in the north of Italy, a territory characterized by a low degree of cooperation connoting the municipal level. The research has proposed the identification of some criteria aimed at determining the potential problematic nature of the transformation areas and the movement of the annexes development rights. The exchange of development rights among municipalities has been carried on according to different scenarios, taking into account different possibilities of political agreement between municipalities for a shared and coherent inter-municipal land-use forecast. The research has also explored the role of the so-called Compensation Fund, a tool for collecting the monetary resources coming from the actors involved in the process, studying in particular its role in balancing the externalities produced by the new settlements.

Research and Application of Remote Sensing and GIS Technologies in Determining and Forecasting Land Use Changes by Markov Chain in Y Yen District - Nam Dinh Province

Abstract The land's natural resources are invaluable and a requisite for the existence and development of humans and other organisms on Earth. In recent years, under the strong impact of new directions in economic and social development, the demand for land has been increasing. The percentage of land used for residential living, transportation, irrigation and infrastructure tends to increase, while the share of agricultural land is continuously decreasing. Consequently, the allocation and efficient use of land is one of the most important concerns in order to enable sustainable development, environmental protection and ecology. Therefore, research to determine the volatility and changing trends in land use is necessary. This study uses remote sensing and GIS technology, combined with the Markov Chain to determine variation and forecast the changes in land use in the Y Yen district of the Nam Dinh province of Vietnam. This will create a basis for helping land managers grasp the situation in local land use management.

Modelling spatial association in pattern based land use simulation models

Pattern based land use models are widely used to forecast land use change. These models predict land use change using driving variables observed on the studied landscape. Many of these models have a limited capacity to account for interactions between neighbouring land parcels. Some modellers have used common spatial statistical measures to incorporate neighbour effects. However, these approaches were developed for continuous variables, while land use classifications are categorical. Neighbour interactions are also endogenous, changing as the land use patterns change. In this study we describe a single variable measure that captures aspects of neighbour interactions as reflected in the land use pattern. We use a stepwise updating process to demonstrate how dynamic updating of our measure impacts on model forecasts. We illustrate these results using the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) system to forecast land use change for the Deep Creek watershed in the northern Okanagan Valley of British Columbia, Canada. Results establish that our measure improves model calibration and that ignoring changing spatial influences biases land use change forecasts.

FSAUA: A framework for sensitivity analysis and uncertainty assessment in historical and forecasted land use maps

Land change modelers often create future maps using reference land use map. However, future land use maps may mislead decision-makers, who are often unaware of the sensitivity and the uncertainty in land use maps due to error in data. Since most metrics that communicate uncertainty require using reference land use data to calculate accuracy, the assessment of uncertainty becomes challenging when no reference land use map for future is available. This study aims to develop a new conceptual framework for sensitivity analysis and uncertainty assessment (FSAUA) which compares multiple maps under various data error scenarios. FSAUA performs sensitivity analyses in land use maps using a reference map and assess uncertainty in predicted maps. FSAUA was applied using three well-known land change models (ANN, CART and MARS) in Delhi, India. FSAUA was found to be a practical tool for communicating the uncertainty with end-users who develop reliable planning decisions.

Impact of Forecasted Land Use Change on Design Peak Discharge at Watershed and Catchment Scales: Simple Equation to Predict Changes

AbstractCommon engineering methods for the computation of peak discharge are generally based on the assumption of a stationary watershed. This assumption can potentially lead to inaccurate estimates of peak discharge when considering the lifetime of engineering structures. Future land use change is one of the possible causes of non-stationarity in watershed runoff. This study focuses on a method to integrate the readily available integrated climate and land use scenarios (ICLUS) data sets from the environmental protection agency (EPA), with geographic information system (GIS) and hydrologic modeling. This framework is applied to evaluate the impact of the forecasted land use change on the design peak discharge in the rapidly urbanizing region in Northern Virginia (US) at the watershed (Anderson formula) and catchment [calibrated storm water management model (SWMM)] scales. The results show that the impervious area in the rapidly urbanizing Difficult Run watershed is expected to increase by 99.1% (2070) fr...

Optimal selection and placement of green infrastructure to reduce impacts of land use change and climate change on hydrology and water quality: An application to the Trail Creek Watershed, Indiana

The adverse impacts of urbanization and climate change on hydrology and water quality can be mitigated by applying green infrastructure practices. In this study, the impacts of land use change and climate change on hydrology and water quality in the 153.2km2 Trail Creek watershed located in northwest Indiana were estimated using the Long-Term Hydrologic Impact Assessment-Low Impact Development 2.1 (L-THIA-LID 2.1) model for the following environmental concerns: runoff volume, Total Suspended Solids (TSS), Total Phosphorous (TP), Total Kjeldahl Nitrogen (TKN), and Nitrate+Nitrite (NOx). Using a recent 2001 land use map and 2050 land use forecasts, we found that land use change resulted in increased runoff volume and pollutant loads (8.0% to 17.9% increase). Climate change reduced runoff and nonpoint source pollutant loads (5.6% to 10.2% reduction). The 2050 forecasted land use with current rainfall resulted in the largest runoff volume and pollutant loads.The optimal selection and placement of green infrastructure practices using L-THIA-LID 2.1 model were conducted. Costs of applying green infrastructure were estimated using the L-THIA-LID 2.1 model considering construction, maintenance, and opportunity costs. To attain the same runoff volume and pollutant loads as in 2001 land uses for 2050 land uses, the runoff volume, TSS, TP, TKN, and NOx for 2050 needed to be reduced by 10.8%, 14.4%, 13.1%, 15.2%, and 9.0%, respectively. The corresponding annual costs of implementing green infrastructure to achieve the goals were $2.1, $0.8, $1.6, $1.9, and $0.8 million, respectively. Annual costs of reducing 2050 runoff volume/pollutant loads were estimated, and results show green infrastructure annual cost greatly increased for larger reductions in runoff volume and pollutant loads. During optimization, the most cost-efficient green infrastructure practices were selected and implementation levels increased for greater reductions of runoff and nonpoint source pollutants.

Land-Use Change Prediction by CA-Markov Method for Electric Load Density Map

This paper presents a method to develop the spatial load density map (SLD) based on the top-down load allocation concept of spatial load forecasting. The yearly power load of electric customers as the top-level information is distributed to the total floor areas as the down-level information. To attain the total floor areas of each land-use category, the CA-Markov technique and Floor Area Ratio (FAR) are applied. The land-use change forecasting is achieved by using the CA-Markov technique to develop the model to describe a change of land-use influenced by the CA theory. This technique consists of 2 important parts including Markov chains and logistic regression methods. FAR index is applied to calculate the total floor area of each category that shows the intensity of different land uses. The amount of load is allocated to the proportion of total floor areas which is mapped to electric customer classes. Then, SLD is derived from the total load divided by the area of the polygon shape. This approach helped confine the results of spatial load forecasting so it is not to be excessive. As a result, the minimum value of Pseudo R2, ROC, and KIA are 0.3471, 0.9024 and 0.7120, respectively. Therefore, CA-Markov is a suitable method to forecast land-use change. During the forecasted year, the peak load in a service area is 222.42 MW, which causes the maximum SLD of 28.9 MW/km2. The spatial load density map developed by the proposed method is effectively used for power system planning and expansion.

A Theoretical Learning Model Combining Stochastic Cellular Automata and Economic Indicators to Simulate Land Use Change

The study of change in land use has been included in territorial planning to inform spatial planners and policy makers of the possible developments they face in order to optimize future management decisions. In this paper the authors present the core of an original learning model coupling stochastic Cellular Automata and economic indicators to simulate the land use change. This model is an important step in building an “environmental virtual laboratory” to explore, explain and forecast land use change.

Assessing and integrating uncertainty into land-use forecasting

Uncertainty in land use and transportation modeling has received increasing attention in the past few years. However, methods for quantifying uncertainty in such models are usually developed in an academic environment and in most cases do not reach users of official forecasts, such as planners and policymakers. In this paper, we describe the practical application of a methodology called Bayesian melding and its integration into the land-use forecast published by the Puget Sound Regional Council, a metropolitan planning organization. The method allows practitioners to assess uncertainty about forecasted quantities, such as households, population, and jobs, for each geographic unit. Users are provided with probability intervals around forecasts, which add value to model validation, scenario comparison, and external review and comment procedures. Practical issues such as how many runs to use or assessing uncertainty for aggregated regions are also discussed.