Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues

Land cover change (LCC) models are used in many studies of human impacts on the environment, but knowing how well these models predict observed changes in the landscape is a challenge. We used nearly three decades of LCC maps to run several LCC simulations to: (1) determine which parameters associat...

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Main Authors:	Rosa, Isabel M. D., Purves, Drew, Carreiras, João M. B., Ewers, Robert M.
Format:	Online
Language:	English
Published:	Springer Berlin Heidelberg 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4372130/

id	pubmed-4372130
recordtype	oai_dc
spelling	pubmed-43721302015-03-27 Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues Rosa, Isabel M. D. Purves, Drew Carreiras, João M. B. Ewers, Robert M. Original Article Land cover change (LCC) models are used in many studies of human impacts on the environment, but knowing how well these models predict observed changes in the landscape is a challenge. We used nearly three decades of LCC maps to run several LCC simulations to: (1) determine which parameters associated with drivers of LCC (e.g. roads) get selected for which transition (forest to deforested, regeneration to deforested or deforested to regeneration); (2) investigate how the parameter values vary through time with respect to the different activities (e.g. farming); and (3) quantify the influence of choosing a particular time period for model calibration and validation on the performance of LCC models. We found that deforestation of primary forests tends to occur along roads (included in 95 % of models) and outside protected areas (included in all models), reflecting farming establishment. Regeneration tends to occur far from roads (included in 78 % of the models) and inside protected areas (included in 38 % of the models), reflecting the processes of land abandonment. Our temporal analysis of model parameters revealed a degree of variation through time (e.g. effectiveness of protected areas rose by 73 %, p < 0.001), but for the majority of parameters there was no significant trend. The degree to which model predictions agreed with observed change was heavily dependent on the year used for calibration (p < 0.001). The next generation of LCC models may need to embed trends in parameter values to allow the processes determining LCC to change through time and exert their influence on model predictions. Springer Berlin Heidelberg 2014-05-16 2015 /pmc/articles/PMC4372130/ /pubmed/25821401 http://dx.doi.org/10.1007/s10113-014-0614-z Text en © The Author(s) 2014 Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Rosa, Isabel M. D. Purves, Drew Carreiras, João M. B. Ewers, Robert M.
spellingShingle	Rosa, Isabel M. D. Purves, Drew Carreiras, João M. B. Ewers, Robert M. Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues
author_facet	Rosa, Isabel M. D. Purves, Drew Carreiras, João M. B. Ewers, Robert M.
author_sort	Rosa, Isabel M. D.
title	Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues
title_short	Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues
title_full	Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues
title_fullStr	Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues
title_full_unstemmed	Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues
title_sort	modelling land cover change in the brazilian amazon: temporal changes in drivers and calibration issues
description	Land cover change (LCC) models are used in many studies of human impacts on the environment, but knowing how well these models predict observed changes in the landscape is a challenge. We used nearly three decades of LCC maps to run several LCC simulations to: (1) determine which parameters associated with drivers of LCC (e.g. roads) get selected for which transition (forest to deforested, regeneration to deforested or deforested to regeneration); (2) investigate how the parameter values vary through time with respect to the different activities (e.g. farming); and (3) quantify the influence of choosing a particular time period for model calibration and validation on the performance of LCC models. We found that deforestation of primary forests tends to occur along roads (included in 95 % of models) and outside protected areas (included in all models), reflecting farming establishment. Regeneration tends to occur far from roads (included in 78 % of the models) and inside protected areas (included in 38 % of the models), reflecting the processes of land abandonment. Our temporal analysis of model parameters revealed a degree of variation through time (e.g. effectiveness of protected areas rose by 73 %, p < 0.001), but for the majority of parameters there was no significant trend. The degree to which model predictions agreed with observed change was heavily dependent on the year used for calibration (p < 0.001). The next generation of LCC models may need to embed trends in parameter values to allow the processes determining LCC to change through time and exert their influence on model predictions.
publisher	Springer Berlin Heidelberg
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4372130/
_version_	1613202893017448448

Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues

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