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摘要翻译：
利用以前的地图，地理学家打算研究土地覆盖的演变，以便对未来的景观有一个前瞻性的方法；通过使用旧地图和环境变量预测未来的土地覆盖，通常是通过地理信息系统（地理信息系统）进行的。我们在这里提出用统计方法来对抗这种经典的地理方法：线性参数模型（多指标回归模型）和非参数模型（多层感知器）。这些方法已经在两个已知不同日期土地覆盖情况的实际地区进行了试验；这使我们能够强调这两种统计方法与地理信息系统相比的好处，并讨论如何通过使用统计模型来改进地理信息系统。
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英文标题：
《Various Approaches for Predicting Land Cover in Mountain Areas》
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作者：
Nathalie Villa (GRIMM), Martin Paegelow (GEODE), Maria T. Camacho
Olmedo, Laurence Cornez (GEODE), Fr\'ed\'eric Ferraty (GRIMM), Louis Ferr\'e
(GRIMM), Pascal Sarda (GRIMM)
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
Using former maps, geographers intend to study the evolution of the land cover in order to have a prospective approach on the future landscape; predictions of the future land cover, by the use of older maps and environmental variables, are usually done through the GIS (Geographic Information System). We propose here to confront this classical geographical approach with statistical approaches: a linear parametric model (polychotomous regression modeling) and a nonparametric one (multilayer perceptron). These methodologies have been tested on two real areas on which the land cover is known at various dates; this allows us to emphasize the benefit of these two statistical approaches compared to GIS and to discuss the way GIS could be improved by the use of statistical models.
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PDF链接：
https://arxiv.org/pdf/705.0418
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