Future land use changes in a peri-urban context: Local stakeholder views

土地利用 利益相关者 环境规划 环境资源管理 背景(考古学) 耕地 土地利用规划 土地信息系统 土地覆盖 农业 业务 土地管理 地理 环境科学 政治学 土木工程 工程类 公共关系 考古
作者
Eduardo Gomes,Arnaud Banos,Patrícia Abrantes,Jorge Rocha,Markus Schläpfer
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:718: 137381-137381 被引量:24
标识
DOI:10.1016/j.scitotenv.2020.137381
摘要

Future land use/cover change (LUCC) analysis has been increasingly applied to spatial planning instruments in the last few years. Nevertheless, stakeholder participation in the land use modelling process and analysis is still low. This paper describes a methodology engaging stakeholders (from the land use planning, agriculture, and forest sectors) in the building and assessment of future LUCC scenarios. We selected as case study the Torres Vedras Municipality (Portugal), a peri-urban region near Lisbon. Our analysis encompasses a participatory workshop to analyse LUCC model outcomes, based on farmer LUCC intentions, for the following scenarios: A0 - current social and economic trend (Business as Usual); A1 - regional food security; A2 - climate change; and B0 - farming under urban pressure. This analysis allowed local stakeholders to develop and discuss their own views on the most plausible future LUCC for the following land use classes: artificial surfaces, non-irrigated arable land, permanently irrigated land, permanent crops and heterogeneous agricultural land, pastures, forest and semi-natural areas, and water bodies and wetlands. Subsequently, we spatialized these LUCC views into a hybrid model (Cellular Automata - Geographic Information Systems), identifying the most suitable land conversion areas. We refer to this model, implemented in NetLogo, as the stakeholder-LUCC model. The results presented in this paper model where, when, why, and what conversions may occur in the future in regard to stakeholders' points of view. These outcomes can better enable decision-makers to perform land use planning more efficiently and develop measures to prevent undesirable futures, particularly in extreme events such as scenarios of food security, climate change, and/or farming under pressure.
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