Reconstruction of past human land use from pollen data and anthropogenic land cover changes

Abstract

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.

Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects

“…In addressing deforestation detection through statistical methods, our approach is in line with and relates to recent advancements in the field of environmetrics. Notably, the application of Bayesian hierarchical models has enriched understanding of land cover changes (Pirzamanbein & Lindström, 2022), while data science's role in environmetrics, especially in analyzing large datasets including satellite imagery, has become increasingly pivotal (Rodrigues & Carfagna 2023). Furthermore, the employment of nonparametric methods for anomaly detection (Scagliarini et al, 2023) and quantile regression for clustering satellite time series data (Musau et al, 2022) share methodological similarities with our work.…”

Spatial regression modeling via the R2D2 framework

“…Spatially dependent data arise in many applications including ecology (e.g., Plant, 2018), public health (e.g., Reich & Haran, 2018), environmental exposure monitoring (e.g., Berrocal et al, 2020; Self et al, 2021), pollen concentrations (e.g., Pirzamanbein & Lindström, 2022; Zapata‐Marin et al, 2023), and medical imaging (e.g., Masotti et al, 2021). For example, this work is motivated by an analysis of the effect of conservation efforts on aquatic biodiversity (Gill et al, 2017).…”