Variable importance for the random forest models for the county and prefecture scale (top axis) and the 5×5 km grid cells scale (bottom axis).

<p>The variable importance is calculated by comparing the mean squared error from models with the original dataset with the mean squared error from models with an altered dataset where the predictor variable is randomly permuted. Acronyms: AET = actual evapotranspiration, PC00-10 = Change in population density between 2000 and 2010, GDP/Area = gross domestic product per km², HII = Human Influence Index.</p

Recent tree cover increases in eastern China linked to low, declining human pressure, steep topography, and climatic conditions favoring tree growth

<div><p>Globally, the extent of forest continues to decline, however, some countries have increased their forest extent in recent years. China is one of these countries and has managed to increase their tree cover through huge reforestation and afforestation programs during recent decades as well as land abandonment dynamics. This study investigates tree cover change in the eastern half of China between 2000 and 2010 on three different scales, using random forest modeling of remote sensing data for tree cover in relation to environmental and anthropogenic predictor variables. Our results show that between the years 2000 and 2010 2,667,875 km² experienced an increase in tree cover while 1,854,900 km² experienced a decline in tree cover. The area experiencing ≥10% increase in tree cover is almost twice as large as the area with ≥10% drop in tree cover. There is a clear relation between topography and tree cover change with steeper and mid-elevation areas having a larger response on tree cover increase than other areas. Furthermore, human influence, change in population density, and actual evapotranspiration are also important factors in explaining where tree cover has changed. This study adds to the understanding of tree cover change in China, as it has focus on the entire eastern half of China on three different scales and how tree cover change is linked to topography and anthropogenic pressure. Though, our results show an increase in tree cover in China, this study emphasizes the importance of incorporating anthropogenic factors together with biodiversity protection into the reforestation and afforestation programs in the future.</p></div

Tree cover change in percent between 2000 and 2010 (TCC) for 5×5 km grid cells.

<p>Green colors indicate an increase, gray colors indicate a slight increase or decrease and red colors indicate a decrease in tree cover between 2000 and 2010. See supporting information <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177552#pone.0177552.s006" target="_blank">S5 Fig</a> for tree cover change (TCC) on county and prefecture scale.</p

Recent tree cover increases in eastern China linked to low, declining human pressure, steep topography, and climatic conditions favoring tree growth - Fig 1

<p><b>Study area and a) actual evapotranspiration for China and b) Study area and tree cover 2000 for China</b>. See supporting information <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177552#pone.0177552.s002" target="_blank">S1 Fig</a> for tree cover 2010 for China.</p

Comparison of selected random forest models with increased complexity for the three scales.

<p>Comparison of selected random forest models with increased complexity for the three scales.</p

Variable importance for the random forest models for the county and prefecture scale (top axis) and the 5×5 km grid cells scale (bottom axis).

<p>The variable importance is calculated by comparing the mean squared error from models with the original dataset with the mean squared error from models with an altered dataset where the predictor variable is randomly permuted. Acronyms: AET = actual evapotranspiration, PC00-10 = Change in population density between 2000 and 2010, GDP/Area = gross domestic product per km², HII = Human Influence Index.</p

Partial dependence plots of the variables in the random forest model on county scale.

<p><b>a)</b> slope, <b>b)</b> elevation, <b>c)</b> GDP per square kilometer, <b>d)</b> actual evapotranspiration, and <b>e)</b> change in population density between 2000 and 2010. The ticks inside the graphs indicate the deciles for the data. The x-axis in partial dependence plot <b>c)</b> has been cut off so big outliners above the 9^th decile are not shown. See supporting information <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177552#pone.0177552.s007" target="_blank">S6D Fig</a> for the complete partial dependence plot of GDP per square kilometer. The x-axis in partial dependence plot <b>e)</b> has been cut off at the 1^st and 9^th decile, so the graph only shows the mid 80 percent of the data. See supporting information <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177552#pone.0177552.s007" target="_blank">S6C Fig</a> for the complete partial dependence plot of change in population density between 2000 and 2010.</p