EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.

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...

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Main Authors: Jan Salecker, Claudia Dislich, Kerstin Wiegand, Katrin M Meyer, Guy Pe Er
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0222949
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author Jan Salecker
Claudia Dislich
Kerstin Wiegand
Katrin M Meyer
Guy Pe Er
author_facet Jan Salecker
Claudia Dislich
Kerstin Wiegand
Katrin M Meyer
Guy Pe Er
author_sort Jan Salecker
collection DOAJ
description 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.
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spelling doaj.art-49db5b54317f410b9c76fdcab7a8579a2022-12-21T19:58:57ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01149e022294910.1371/journal.pone.0222949EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.Jan SaleckerClaudia DislichKerstin WiegandKatrin M MeyerGuy Pe ErSpatially-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.https://doi.org/10.1371/journal.pone.0222949
spellingShingle Jan Salecker
Claudia Dislich
Kerstin Wiegand
Katrin M Meyer
Guy Pe Er
EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.
PLoS ONE
title EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.
title_full EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.
title_fullStr EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.
title_full_unstemmed EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.
title_short EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.
title_sort efforts lgraf a landscape generator for creating smallholder driven land use mosaics
url https://doi.org/10.1371/journal.pone.0222949
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