EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics
Autoři:
Jan Salecker aff001; Claudia Dislich aff001; Kerstin Wiegand aff001; Katrin M. Meyer aff001; Guy Pe´er aff003
Působiště autorů:
Ecosystem Modelling, Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Goettingen, Germany
aff001; Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, Germany
aff002; Synthesis Centre (sDiv) of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
aff003; UFZ - Helmholtz Centre for Environmental Research, Dept. Economics and Dept. Ecosystem Services, Leipzig, Germany
aff004; University of Leipzig, Leipzig, Germany
aff005
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222949
Souhrn
Spatially-explicit simulation models are commonly used to study complex ecological and socio-economic research questions. Often these models depend on detailed input data, such as initial land-cover maps to set up model simulations. Here we present the landscape generator EFFortS-LGraf that provides artificially-generated land-use maps of agricultural landscapes shaped by small-scale farms. EFForTS-LGraf is a process-based landscape generator that explicitly incorporates the human dimension of land-use change. The model generates roads and villages that consist of smallholder farming households. These smallholders use different establishment strategies to create fields in their close vicinity. Crop types are distributed to these fields based on crop fractions and specialization levels. EFForTS-LGraf model parameters such as household area or field size frequency distributions can be derived from household surveys or geospatial data. This can be an advantage over the abstract parameters of neutral landscape generators. We tested the model using oil palm and rubber farming in Indonesia as a case study and validated the artificially-generated maps against classified satellite images. Our results show that EFForTS-LGraf is able to generate realistic land-cover maps with properties that lie within the boundaries of landscapes from classified satellite images. An applied simulation experiment on landscape-level effects of increasing household area and crop specialization revealed that larger households with higher specialization levels led to spatially more homogeneous and less scattered crop type distributions and reduced edge area proportion. Thus, EFForTS-LGraf can be applied both to generate maps as inputs for simulation modelling and as a stand-alone tool for specific landscape-scale analyses in the context of ecological-economic studies of smallholder farming systems.
Klíčová slova:
Agent-based modeling – Agriculture – Crops – Gene mapping – Oil palm – Rubber – Simulation and modeling – Indonesia
Zdroje
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PLOS One
2019 Číslo 9
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