Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country.
Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country.
复制标题
DOI：
10.1016/j.scitotenv.2021.152126
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
D. Tran;D. Pearson;A. Palmer;J. Lowry;David Gray;E. Dominati
中科院分区：
文献类型：
--
作者：
D. Tran;D. Pearson;A. Palmer;J. Lowry;David Gray;E. Dominati
了解景观结构如何影响生态系统服务（ES）的提供是迈向更好的景观规划的重要的第一步。由于景观结构往往是异质性的空间，景观结构和ES之间的关系建模必须考虑空间非平稳性。本文探讨了景观结构和ES提供使用丘陵国家和陡峭的土地在新西兰的情况下农场之间的关系。来自土地覆盖和地形数据，如最大斑块指数（LPI），对比度类边缘（CCE），边缘密度（艾德），地形坡度（SLOPE）的指标被用来检查景观的结构和格局。利用InVEST工具和GIS中的空间分析，得出了草地生产力、土壤侵蚀控制和供水措施。多尺度地理加权回归（MGWR）被用来评估景观结构和ES的供应指标之间的关系。其他回归模型，包括普通最小二乘法（OLS）和地理加权回归（GWR），进行评估的性能MGWR。结果表明，景观格局显着影响所有映射ES的供应，这在整个景观，依赖于地形特征和土地覆盖格局和结构的格局。MWGR优于其他OLS和GWR的ES决定因素的解释能力，并有更好的能力来处理空间自相关的存在。景观结构和ES的提供之间的关系的空间和定量的详细变化提供了科学依据，告知可持续的多功能景观的设计。从这一分析得出的信息可以用于空间规划的耕地景观，以促进多ES，满足多个可持续发展目标。
Knowing how landscape structure affects the provision of ecosystem services (ES) is an important first step toward better landscape planning. Because landscape structure is often heterogenous across space, modelling the relationship between landscape structure and the provision of ES must account for spatial non-stationarity. This paper examines the relationship between landscape structure and the provision of ES using a hill country and steep-land case farm in New Zealand. Indicators derived from land cover and topographical data such as Largest Patch Index (LPI), Contrast Class Edge (CCE), Edge Density (ED), and Terrain slope (SLOPE) were used to examine the landscape's structure and pattern. Measures of pasture productivity, soil erosion control, and water supply were derived with InVEST tools and spatial analysis in a GIS. Multiscale Geographically Weighted Regression (MGWR) was used to evaluate the relationship between indicators of landscape structure and the provisioning of ES. Other regression models, including Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR), were carried out to evaluate the performance of MGWR. Results showed that landscape patterns significantly affect the supply of all mapped ES, and this varies across the landscape, dependent on the pattern of topographical features and land cover pattern and structure. MWGR outperformed other OLS and GWR in terms of explanatory power of the ES determinants and had a better ability to deal with the presence of spatial autocorrelation. Spatially and quantitatively detailed variations of the relationship between landscape structure and the provision of ES provide a scientific basis to inform the design of sustainable multifunctional landscapes. Information derived from this analysis can be used for spatial planning of farmed landscapes to promote multiple ES which meet multiple sustainable development objectives.