Major forest increase on the Loess Plateau, China (2001–2016)

Abstract

The Loess Plateau in China is prone to widespread land degradation (soil erosion, deforestation, and water loss), and therefore, ecological restoration programmes aiming to re-establish the ecosystem by revegetation have been implemented during recent decades. Consequently, a widespread increase in vegetation cover has been reported, but the state and dynamics of forests remain largely unknown. Here, we used field and satellite data to produce annual forest probability scores at 250 × 250 m between 2001 and 2016. We classified the forest dynamics into three types: net gain, net loss, and fluctuation. Assuming that an increasing probability score reflects forest growth, we categorised the annual forest areas into different growing stages indicating the transition between planting and mature forest. In total, the area of forest increase was 48,786 km 2 , and the percentage of forested areas of the region changed from 8.19% to 15.82% within 16 years. In areas of forest net gain, there was a clear transition from low-to high-probability forest areas, providing evidence that a

Forest management in southern China generates short term extensive carbon sequestration

“…We applied this model to the annual MODIS bands to generate annual probability maps of the forest/non-forest classification. The probability ranged from 0 to 1 and indicated the likeliness that an area showed characteristics similar to those of the forest training points, implying that temporally increasing forest probability represented the growth of forests 50 . Likewise, a decrease in forest probability was linked with harvesting.…”

“…Third, we used the 500 × 500 m 2 grid from the MODIS resolution to ensure that a minimum of nine pixels (the point was set in the centre pixel) are covered by the class. Finally, the forest training points should mainly represent dense forests that were stable during 2002-2017 (historical Google Earth images were used when possible), so we used the average of all years as input for the model for each of the seven bands, which further reduced noise and guaranteed a stable model 50 . A three-fold cross-validation of the model (excluding random subsets of the training data that were then predicted) was satisfactory (r = 0.93).…”

“…We used similar settings as suggested by refs. 52,50 because of comparable data sources and time periods. We set the significance threshold for the model fitting to 0.05 and the maximum number of trajectory segments to six.…”

“…The forest classes included (1) dense forests (forest probability ≥0.8), (2) forests (≥0.5) and (3) non-forest (<0.5). The threshold number for dense forests is based on Wang et al 50 , who used field observations to identify old forests (above 0.8). Random Forest commonly uses a probability threshold of 0.5 to distinguish if a pixel belongs to a class or not.…”

Target areas for harmonizing the Grain for Green Programme in China's Loess Plateau

“…The Chinas Loess Plateau is one of the hotspots of global land degradation (Wang et al, 2018). Historically, this region has suffered from severe soil erosion, making the Yellow River having the highest sediment loads in the world (Zhao et al, 2013;Li et al, 2016b;Wang et al, 2016).…”