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• The stand reached its optimal state and then began to degrade after 20–25 years. • Undergrowth vegetation was in recession after 6–10 years of afforestation. • The contents of soil nutrients fluctuated. • Fertilization practices need to be conducted at 11–15 years. • Stand rebuilding measures need to be implemented at > 40 years. With the extensive planting of Pinus sylvestris var. mongolica , its maladaptation in both premature senility and natural regeneration barrier is increasingly prominent at the southern edge of the Mu Us Sandy Land. Whether the maladaptation will lead to secondary soil degradation is unclear. Taking naturally occurring grasslands as a control, we studied the characteristics of the vegetation and soil physicochemical properties in the 0–20 cm soil layer of P. sylvestris var. mongolica plantations at 1–5, 6–10, 11–15, 20–25, 30–35 and > 40 years after afforestation. The results showed that the specific manifestation of premature senility was that maturity and recession were advanced to 20–25 years and > 40 years in P. sylvestris var. mongolica afforestation, respectively. No natural regeneration seedlings of P. sylvestris var. mongolica were found. Compared with the grasslands, the soil bulk density and particle composition were effectively improved at 20–25, 30–35 and > 40 years after afforestation. With increasing afforestation years, the contents of organic matter, total nitrogen, total phosphorus, alkali-hydrolysable nitrogen, available phosphorus and available potassium fluctuated, and the minimum values occurred at 11–15 years. The contents of a few soil nutrient indices dramatically decreased at > 40 years. Significant correlations between the stands and the soil physicochemical properties were found, and soil nutrients showed signs of secondary degradation caused by the maladaptation of the plantations at > 40 years. Thus, we suggest that fertilization practices need to be conducted to promote more sustainable growth of trees after 11–15 years, and stand rebuilding measures need to be implemented to prevent soil nutrient degradation at > 40 years. [ABSTRACT FROM AUTHOR]

Gully erosion is a serious environmental problem worldwide, causing soil loss, land degradation, silting up of reservoirs and even catastrophic flooding. Mapping gully features from remote sensing imagery is crucial for assisting in understanding gully erosion mechanisms, predicting its development processes and assessing its environmental and socio-economic effects over large areas, especially under the increasing global climate extremes and intensive human activities. However, the potential of using increasingly available high-resolution remote sensing imagery to detect and delineate gullies has been less evaluated. Hence, 130 gullies occurred along a transect were selected from a typical watershed in the hilly and gully region of the Chinese Loess Plateau, and visually interpreted from a Pleiades-1B satellite image (panchromatic-sharpened image at 0.5 m resolution fused with 2.0 m multi-spectral bands). The interpreted gullies were compared with their measured data obtained in the field using a differential global positioning system (GPS). Results showed that gullies could generally be accurately interpreted from the image, with an average relative error of gully area and gully perimeter being 11.1% and 8.9%, respectively, and 74.2% and 82.3% of the relative errors for gully area and gully perimeter were within 15%. But involving field measurements of gullies in present imagery-based gully studies is still recommended. To judge whether gullies were mapped accurately further, a standard adopting one-pixel tolerance along the mapped gully edges was proposed and proved to be practical. Correlation analysis indicated that larger gullies could be interpreted more accurately but increasing gully shape complexity would decrease interpreting accuracy. Overall lower vegetation coverage in winter due to the withering and falling of vegetation rarely affected gully interpreting. Furthermore, gully detectability on remote sensing imagery in this region was lower than the other places of the world, due to the overall broken topography in the Loess Plateau, thus images with higher resolution than normally perceived are needed when mapping erosion features here. Taking these influencing factors (gully dimension and shape complexity, vegetation coverage, topography) into account will be favorable to select appropriate imagery and gullies (as study objects) in future imagery-based gully studies. Finally, two linear regression models were built to correct gully area (A ip , m²) and gully perimeter ( P ip , m) visually extracted, by connecting them with the measured area ( A , m). The correction models were Ams=1.021Aip+0.139 and Pms=0.949Pip+ 0.722, respectively. These models could be helpful for improving the accuracy of interpreting results, and further accurately estimating gully development and developing more effective automated gully extraction methods on the Loess Plateau of China.ms , m²) and perimeter ( P ms , m). The correction models were Ams=1.021Aip+0.139 and Pms=0.949Pip+ 0.722, respectively. These models could be helpful for improving the accuracy of interpreting results, and further accurately estimating gully development and developing more effective automated gully extraction methods on the Loess Plateau of China., Competing Interests: The authors declare no conflict of interest.