Accuracy Assessment of the Planar Morphology of Valley Bank Gullies Extracted with High Resolution Remote Sensing Imagery on the Loess Plateau, China

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 (Aip, m2) and gully perimeter (Pip, m) visually extracted, by connecting them with the measured area (Ams, m2) and perimeter (Pms, m). The correction models were A m s = 1.021 A i p + 0.139 and P m s = 0.949 P i p +   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

Long-termnet transformation and quantitative molecular mechanisms of soil nitrogen during natural vegetation recovery of abandoned farmland on the Loess Plateau of China

The availability of nitrogen (N) can alter vegetation species composition and diversity in degraded ecosystems. A comprehensive understanding of the dynamic fate of ammonium (NH4+-N) and nitrate (NO3--N) processing and the underlying mechanisms are still lacking, particularly in arid to semi-arid degraded ecosystems. We compared and quantified the changes in the rates of net ammonification (R-a), nitrification (R-n) and total mineralization (R-m) and the abundance of bacteria, archaea, and microbial genes related to N transformation on the northern Loess Plateau of China across a 40-year chronosequence of farmland undergoing spontaneous restoration. We found that R-a, R-n, and R-m decreased in grassland soils (0-30-y sites) of different ages and exhibited significant increases at the 40-y sites. The capabilities of the soil to deliver NH4+-N and NO3--N were not a limiting factor during the growing season after 40 years of vegetation recovery. Soil mineral nitrogen may be not suitable for predicting and assessing the long-term (approximately 40 years) restoration success and progress. The abundance of functional N genes showed differences in sensitivity to natural vegetation recovery of abandoned farmland, which likely reflects the fact that the multi-pathways driven by N functional microbial communities had a large influence on the dynamic fate of NH4+-N and NO3--N. Quantitative response relationships between net N transformation rates and microbial genes related to N transformation were established, and these relationships confirmed that different N transformation processes were strongly linked with certain N functional genes, and collaboratively contributed to N transformation as vegetation recovery progressed. Specifically, R-a was controlled by AOA-amoA, AOB-amoA, and nxrA; R-n was governed by napA, narG, nirK, nirS, and nosZ; and R-m was controlled by nifH, apr, AOA-amoA, AOB-amoA, nirS, and nirK. (C) 2017 Elsevier B.V. All rights reserved

Quantitative response relationships between net nitrogen transformation rates and nitrogen functional genes during artificial vegetation restoration following agricultural abandonment

A comprehensive understanding of how microbial associated with nitrogen (N) cycling respond to artificial vegetation restoration is still lacking, particularly in arid to semi-arid degraded ecosystems. We compared soil net N mineralization rates and the abundance of bacteria, archaea, and eleven N microbial genes on the northern Loess Plateau of China during the process of artificial vegetation restoration. The quantitative relationships between net N mineralization rates and N microbial genes were determined. We observed a significant difference of net transformation rates of NH4 (+)-(N) (R-a), NO3--N (R-d), and total mineralization (R-m), which rapidly decreased in 10-year soils and steadily increased in the 10-30-year soils. Different N functional microbial groups responded to artificial vegetation restoration distinctly and differentially, especially for denitrifying bacteria. Stepwise regression analysis suggested that R-a was collectively controlled by AOA-amoA and Archaea; R-d was jointly governed by narG, napA, nxrA, and bacreria; and R-m was jointly controlled by napA, narG, nirK, nirS, norB, nosZ, and nxrA