Accurate maps of past land cover and human land use are necessary for studying the impact of anthropogenic land-cover changes, such as deforestation, on the climate. The maps of past land cover should ideally be separated into naturally occurring vegetation and human-induced changes, thereby enabling the quantification of the effect of human land use on the past climate. We developed a Bayesian hierarchical model that combines fossil pollen-based reconstructions of actual land cover with estimates of past human land use. The model interpolates the fractions of unforested land as well as coniferous and broadleaved forest from the pollen data, and uses the human land-use estimates to decompose the unforested land into natural vegetation and human deforestation. This results in maps of both natural and human-induced vegetation, which can be used by climate modelers to quantify the influence of deforestation on the past climate. The model was applied to five time periods from 1900 CE to 4000 BCE over Europe. The model uses a latent Gaussian Markov random field (GMRF) for the interpolation and Markov chain Monte Carlo for the estimation. The sparse precision matrix of the GMRF, together with an adaptive Metropolis-adjusted Langevin step, allows for rapid inference.

中文翻译：

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从花粉数据和人为土地覆盖变化重建过去人类土地利用

过去土地覆盖和人类土地利用的准确地图对于研究人为土地覆盖变化（如森林砍伐）对气候的影响是必要的。理想情况下，过去的土地覆盖图应分为自然发生的植被和人类引起的变化，从而能够量化人类土地利用对过去气候的影响。我们开发了一个贝叶斯层次模型，该模型将基于化石花粉的实际土地覆盖重建与过去人类土地利用的估计相结合。该模型从花粉数据中插入未林地以及针叶林和阔叶林的比例，并使用人类土地利用估计将未林地分解为自然植被和人类砍伐森林。这导致了自然和人为植被的地图，气候建模者可以使用它来量化森林砍伐对过去气候的影响。该模型应用于欧洲从公元 1900 年到公元前 4000 年的五个时间段。该模型使用潜在高斯马尔可夫随机场 (GMRF) 进行插值，使用马尔可夫链蒙特卡罗进行估计。GMRF 的稀疏精度矩阵与自适应 Metropolis 调整的 Langevin 步长一起允许快速推理。