An examination of historical and future land use changes in Uganda using change detection methods and agent-based modelling

2016 ◽  
Vol 35 (3) ◽  
pp. 247-271 ◽  
Author(s):  
Jingjing Li ◽  
Tonny J. Oyana ◽  
Paul I. Mukwaya
Keyword(s):  
Land Use ◽  
Change Detection ◽  
Land Use Changes ◽  
Detection Methods ◽  
Agent Based ◽  
Agent Based Modelling

Modeling Farmer’s Decision-Making to integrate climate, land use and ecosystem functions

2020 ◽  
Author(s):  
Veronica Gaube ◽  
Claudine Egger ◽  
Christoph Plutzar ◽  
Andreas Mayer ◽  
Helmut Haberl
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Ecosystem Functions ◽  
Land Use Patterns ◽  
Study Region ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Biogeochemical Models

<p>Land use and climate change are important drivers of environmental change and pose a major threat to ecosystems. Although systemic feedbacks between climate and land use changes are expected to have important impacts, research has rarely focused on the interaction between the two drivers. One reason for this could be that forecasts of land use are hardly available on suitable spatial and thematic scales. Agent-based models (ABMs) represent a potentially powerful tool for creating thematic and spatially fine-grained land use scenarios. In order to derive such scenarios, the complex interaction between land users (e.g. farmers) and the broader socio-economic context in which they operate must be taken into account. On landscape to regional scales, agent-based modelling (ABM) is one way to adequately consider these intricacies. ABMs simulate human decisions, and with individual land owners/users as agents, they can simulate usage paths for individual plots of land in thematically fine resolution. Ideally, these simulations are based on an understanding of how farmers make decisions, including anticipated strategies, adaptive behavior and social interactions. In order to develop such an understanding, participatory approaches are useful because they incorporate stakeholders' perspectives into the model calibration, thereby taking into account culture and traditions that often play an important role in land use decisions. A greater proximity to stakeholder perspectives also increases the political relevance of such land use models. Here we present an example where we developed an ABM (SECLAND) parameterised for 1,329 stakeholders, mostly farmers, in the LTSER region Eisenwurzen (Austria) and simulate the changes in land use patterns resulting from their response to three scenarios of changing socio-economic conditions. Summarized in broad categories, the study region currently consists of 67% deciduous and coniferous forests (including logging), 19% grassland, 9% agricultural land and 6% alpine areas. SECLAND simulated small to moderate changes in these percentages until 2050, with little difference between the scenarios. In general, an increase in forests is predicted at the expense of grasslands. The size of agricultural land remains approximately constant. At the level of the 22 land use classes, the trends between the land use change scenarios differ more strongly. This ABM at the individual or farm level is combined with biodiversity and biogeochemical models that analyse how landowners' decision-making affects various ecosystem parameters. We conclude that agent-based modelling is a powerful tool for integrating land use and climate effects into ecosystem projections, especially at regional level.</p>

An open framework for agent based modelling of agricultural land use change

2014 ◽  
Vol 61 ◽  
pp. 19-38 ◽  
Author(s):  
Dave Murray-Rust ◽  
Derek T. Robinson ◽  
Eleonore Guillem ◽  
Eleni Karali ◽  
Mark Rounsevell
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Agent Based ◽  
Open Framework ◽  
Agent Based Modelling

scholarly journals When to Detect Changes in Object-Based Image Analysis: Before or after Classification?

2017 ◽  
Author(s):  
Kaisheng Luo ◽  
Fu-lu Tao ◽  
Juana P. Moiwo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Change Detection ◽  
Coniferous Forest ◽  
Detection Methods ◽  
Landsat Images ◽  
Two Stage ◽  
Smoothing Effects ◽  
Stage Classification ◽  
Elevation Model

This study compared two object-oriented land use change detection methods—detection after classification (DAC) and classification after detection (CAD) —based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.

scholarly journals FIRM DYNAMIC ANALYSIS FOR URBAN LAND USE AND ECONOMIC GROWTH MODELLING

2013 ◽  
Author(s):  
Noordini Che Man ◽  
Soheil Sabri ◽  
Nafisa Hosni ◽  
Harry Timmerman
Keyword(s):  
Economic Growth ◽  
Land Use ◽  
Temporal Analysis ◽  
Urban Land Use ◽  
Modelling And Simulation ◽  
Behaviour Pattern ◽  
Land Use Patterns ◽  
Agent Based ◽  
Economic Agents ◽  
Agent Based Modelling

In urban growth processes, urbanisation is highly influenced by economic growth which triggers the dynamics of economic agents and land uses. This is consisted of complex subsystems which need sophisticated methods like agent-based modelling and simulation to understand the pattern, behaviour and scale of multiple actors. The objective of this paper is to identify the behaviour, pattern and the scale of impact of firms on market in the region in order to foster an accurate agent-based modelling. The Geographic Information System is utilized to geocode the entrance and exit of firms to the market in Greater Kuala Lumpur region. This study has also carried out a temporal analysis considering 18 years performancesof the firms from 1990 to 2007. The findings in this paper show sector 9 (i.e. Financing) has highest percentage of establishment with 35.1 %. In addition, Sector 3 (i.e. Mining) and Sector 5 (i.e. Electricity) have the lowest percentage of establishment with 0.3 %. The result of this study will be a foundation to facilitate developing an agent-based modelling and simulation which helps town planners and decision makers to understand the relationship and interaction between economic growth and dynamic land use patterns in their region.

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