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首页> 外文期刊>Ecosystems >Regression Techniques for Examining Land Use/Cover Change: A Case Study of a Mediterranean Landscape
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Regression Techniques for Examining Land Use/Cover Change: A Case Study of a Mediterranean Landscape

机译：土地利用/覆盖变化调查的回归技术：以地中海景观为例

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In many areas of the northern Mediterranean Basin the abundance of forest and scrubland vegetation is increasing, commensurate with decreases in agricultural land use(s). Much of the land use/cover change (LUCC) in this region is associated with the marginalization of traditional agricultural practices due to ongoing socioeconomic shifts and subsequent ecological change. Regression-based models of LUCC have two purposes: (i) to aid explanation of the processes driving change and/or (ii) spatial projection of the changes themselves. The independent variables contained in the single 'best' regression model (that is, that which minimizes variation in the dependent variable) cannot be inferred as providing the strongest causal relationship with the dependent variable. Here, we examine the utility of hierarchical partitioning and multinomial regression models for, respectively, explanation and prediction of LUCC in EU Special Protection Area 56, 'Encinares del rco Alberche y Cofio' (SPA 56) near Madrid, Spain. Hierarchical partitioning estimates the contribution of regression model variables, both independently and in conjunction with other variables in a model, to the total variance explained by that model and is a tool to isolate important causal variables. By using hierarchical partitioning we find that the combined effects of factors driving land cover transitions varies with land cover classification, with a coarser classification reducing explained variance in LUCC. We use multinomial logistic regression models solely for projecting change, finding that accuracies of maps produced vary by land cover classification and are influenced by differing spatial resolutions of socioeconomic and biophysical data. When examining LUCC in human-dominated landscapes such as those of the Mediterranean Basin, the availability and analysis of spatial data at scales that match causal processes is vital to the performance of the statistical modelling techniques used here.
机译：在地中海北部盆地的许多地区，森林和灌木丛植被的数量在增加，与农业用地的减少相对应。由于持续的社会经济变化和随后的生态变化，该地区的大部分土地利用/覆盖变化（LUCC）与传统农业实践的边缘化有关。 LUCC的基于回归的模型具有两个目的：（i）有助于解释驱动变化的过程和/或（ii）变化本身的空间投影。不能推断单个“最佳”回归模型中包含的自变量（即最小化因变量变化的自变量）是与因变量提供最强的因果关系。在这里，我们研究了分层划分和多项式回归模型分别在西班牙马德里附近的欧盟特殊保护区56“ Encinares del rco Alberche y Cofio”（SPA 56）中对LUCC的解释和预测的作用。分层划分可以独立地或与模型中的其他变量一起估计回归模型变量对该模型所解释的总方差的贡献，并且是隔离重要因果变量的工具。通过使用分层划分，我们发现驱动土地覆盖转变的因素的综合效应随土地覆盖分类而变化，而较粗的分类减少了LUCC中的解释方差。我们仅将多项式Lo​​gistic回归模型用于预测变化，发现生成的地图的准确性因土地覆盖类别而异，并且受到社会经济和生物物理数据的不同空间分辨率的影响。在以人为主导的景观（例如地中海盆地）中检查LUCC时，与因果过程相匹配的尺度下空间数据的可用性和分析对于此处使用的统计建模技术的性能至关重要。

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