Geography Department
An Open and Flexible Land Use Model for Scenario Assessments alucR (Allocation of Land Use/Cover in R) Carbiocial
Carbiocial
Florian Gollnow, Thomas Mönkemeier, Tobia Lakes Humboldt-Universität zu Berlin | Department of Geography | Geoinformation Science Lab| Unter den Linden 6 | D-10099 Berlin | contact:
[email protected] | http://www.geographie.hu-berlin.de/
Introduction • Land use models are important tools to evaluate possible future land uses under different policy scenarios
• alucR follows the approach to evaluate the competition between land uses to allocate an a priory defined amount of land for the scenario years (similar to • Currently, land use models are often difficult CLUE-S or LUCC-ME) to access, due to proprietary code, restricted availability or high complexity • The code is available and will further be improved on GitHub • The wide use and flexibility of the Renvironment for scientific analysis opens the possibility to provide open code for land use change assessments • The aim of this poster: • To present alucR as an open, accessible and flexible land use model for regional land use change • Applied for an case study in Brazil https://github.com/fg-code/alucR_v01
Fig.1: Allocation procedure for land use scenarios in alucR (inside the box)
Case Study Brazil •
Land use suitabilities determinate the competitive strength which results in its spatial allocation
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Traditional land use suitabilities are often assessed by Logistic Regression, Multi Criteria Analysis or Deterministic Models
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Machine learning approaches, i.e. Boosted Regression Trees - often superior to linear approaches in terms of predictive power – have hardly been used as basis for land use change modelling
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How do land use scenarios vary according to the assessment of suitability using LR and BRT? • Modeling period: 2010-2030 • Land uses: Cropland, Pasture, Urban • Land use demand: linear extrapolation 2010 & 2014
Gollnow, F.
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Study Area: Brazilian Amazon, Mato Grosso and Pará State, along the BR-163
Gollnow, F.
Deforestation and cattle in Novo Progresso, Pará, Brazil0
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To prove the flexibility of input we compare a Logistic Regression (LR) and Boosted Regression Trees (BRT) approach as suitability input for the scenario allocation lacjournal.com
Soy production and harvest, Mato Grosso, Brazil
Results & Discussion
Fridman, P.
Fig. 2: Study Area along the BR-163, Pará and Mato Grosso, Brazil, Data: TerraClass 2010 (INPE)
• We implemented BRT and LR to model cropland, pasture and urban expansion in alucR. • The 2014 Scenario changes were compared to true land use change between 2010 and 2014 (TerraClass, INPE)
Fig. 3: Example Suitability layer LR and BRT for Pasture and Cropland
• Differences between true and modelled change trajectories are prevalent (also other land use/cover types changed). The change bar plots indicate a closer match for the LR approach • Spatial geometries of land uses are not well captured by the competitive allocation approach • The implementation in R language making use of the raster and parallel packages allows for direct access of the code and modifications
Fig. 4: Land use scenarios for 2030 LR and BRT
Carbiocial
Humboldt-Universität
• Outlook: • Comparison with other land use models • Detailed accuracy measures • Interactively calculated suitability layer, depending on the landscape configuration (if relevant) • Account for spatial variation of productivity
Fig. 5:Change from 2010 to 2014 based on TerraClass land use classification and scenario allocations zu Berlin | Department of Geography | Geoinformation Science Lab | Unter den Linden 6 | D-10099 Berlin | e-mail:
[email protected]
