Multi-Scale Land-Use Disaggregation Modelling: Concept and Application to EU Countries. Lamboni, M., Koeble, R., & Leip, A. 82:183–217. ![Multi-Scale Land-Use Disaggregation Modelling: Concept and Application to EU Countries [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex [Highlights] [::] Development of a scale independent model to disaggregate land use data. [::] Generate high resolution maps for crops cultivated in EU-28. [::] Validation of model results by comparison with detailed survey data. [Abstract] Changes of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 × 105 HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75\,% of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning. [Excerpt: Software] The Land-Use Disaggregation Model (LUDM) aims at predicting the land-use areas within the fine-scale units. LUDM is written in R (R CRAN) and is freely available. The source code is maintained and can be downloaded as a zip file from http://ludm2016.blogspot.com/2016/04/ludm.html. The zip file contains a ReadMe file and accessory files. Use the ReadMe file for suggestions on program instruction and notes on terms of service. LUDM model is free, regulated under the GNU General Public License v3 (http://www.gnu.org/copyleft/gpl.html) and intended for further open-source development.
@article{lamboniMultiscaleLanduseDisaggregation2016,
title = {Multi-Scale Land-Use Disaggregation Modelling: Concept and Application to {{EU}} Countries},
author = {Lamboni, Matieyendou and Koeble, Renate and Leip, Adrian},
date = {2016-08},
journaltitle = {Environmental Modelling \& Software},
volume = {82},
pages = {183--217},
issn = {1364-8152},
doi = {10.1016/j.envsoft.2016.04.028},
url = {http://mfkp.org/INRMM/article/14040113},
abstract = {[Highlights]
[::] Development of a scale independent model to disaggregate land use data. [::] Generate high resolution maps for crops cultivated in EU-28. [::] Validation of model results by comparison with detailed survey data.
[Abstract]
Changes of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 × 105 HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75\,\% of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning.
[Excerpt: Software]
The Land-Use Disaggregation Model (LUDM) aims at predicting the land-use areas within the fine-scale units. LUDM is written in R (R CRAN) and is freely available. The source code is maintained and can be downloaded as a zip file from http://ludm2016.blogspot.com/2016/04/ludm.html. The zip file contains a ReadMe file and accessory files. Use the ReadMe file for suggestions on program instruction and notes on terms of service. LUDM model is free, regulated under the GNU General Public License v3 (http://www.gnu.org/copyleft/gpl.html) and intended for further open-source development.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14040113,~to-add-doi-URL,data-transformation-modelling,environmental-modelling,europe,land-use,license-gnu-gpl,multi-scale,spatial-disaggregation,statistics}
}
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[::] Validation of model results by comparison with detailed survey data. [Abstract] Changes of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 × 105 HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75\\,% of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning. [Excerpt: Software] The Land-Use Disaggregation Model (LUDM) aims at predicting the land-use areas within the fine-scale units. LUDM is written in R (R CRAN) and is freely available. The source code is maintained and can be downloaded as a zip file from http://ludm2016.blogspot.com/2016/04/ludm.html. The zip file contains a ReadMe file and accessory files. 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[::] Generate high resolution maps for crops cultivated in EU-28. [::] Validation of model results by comparison with detailed survey data.\n\n[Abstract]\n\nChanges of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 × 105 HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75\\,\\% of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning.\n\n[Excerpt: Software]\n\nThe Land-Use Disaggregation Model (LUDM) aims at predicting the land-use areas within the fine-scale units. LUDM is written in R (R CRAN) and is freely available. The source code is maintained and can be downloaded as a zip file from http://ludm2016.blogspot.com/2016/04/ludm.html. The zip file contains a ReadMe file and accessory files. 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