Xuening Fang

Estimation of the cover and management factor based on stratified coverage and remote sensing indices: a case study in the Loess Plateau of China

PurposeWe attempt to describe the cover and management (C) factor more comprehensively through the use of a simple and efficient method.Materials and methodsWe measure the coverage of each vegetation layer and C factor for 152 sampled plots in the Ansai watershed. We propose four stratified coverage indices (green coverage (VG), total coverage (VT), probability coverage (VP), weight coverage (VW)), derive green and yellow vegetation indices from Landsat 8 OLI images to reflect green and residue cover, and construct and validate C factor estimation models from stratified coverage and remote sensing indices, respectively.Results and discussion(1) VT and VP present C factor estimation advantages for grassland and shrub land. VW can better illustrate the C factor due to the relatively complete spatial structuring of woodland and orchard land. For cropland, four stratified coverage indices present the same estimation capacities for the C factor. Except for cropland and grassland, the estimation capabilities of VG are relatively low because the residue layer is ignored. (2) The C factor is more sensitive to yellow vegetation indices, which indicates that senescent fractional cover and litter are important and cannot be ignored. The linear and non-linear models can explain 56.6 and 61.8% of C factor variation, respectively, and the linear model is more accurate than the non-linear model. (3) Compared to traditional indices (projective coverage and single remote sensing indices), stratified coverage indices and a combination of several remote sensing indices can estimate the C factor more effectively.ConclusionsAt the field scale, the C value estimation model can be selected according to the land-use type. At the watershed and regional scales, a linear model is recommended for C factor estimation.

Deviation of land-use data from quickbird and TM remote sensing imagery: A case study of the Tanjiaying small watershed in the loess hilly-gully area of China

Land-use maps provide important data and basic information for accomplishing the optimal allocation of resources and for ensuring sustainable development. However, the land-use data interpreted from various sources of remotely sensed, low-resolution imagery are highly variable. Therefore, an analysis of these deviations in land-use data is imperative for correcting the accuracy of the land-use maps derived from such low-resolution, remote sensing imagery. With ArcGIS 9.3 software, we derived the land-use maps with different accuracies for the Tanjiaying small watershed in the loess hilly-gully area of China by interpreting Landsat 5 Thematic Mapper (TM) and QuickBird images acquired in 2010. We compared the maps derived from these two sources of imagery data and analyzed the resulting deviation distribution with respect to the slope, aspect, and elevation of the land.

Distribution of Shrubland and Grassland Soil Erodibility on the Loess Plateau

Soil erosion is one of the most severe problems facing environments and has increased throughout the 20th century. Soil erodibility (K-factor) is one of the important indicators of land degradation, and many models have been used to estimate K values. Although soil erodibility has been estimated, the comparison of different models and their usage at a regional scale and, in particular, for different land use types, need more research. Four of the most widely distributed land use types were selected to analyze, including introduced and natural grassland, as well as introduced and natural shrubland. Soil particle size, soil organic matter and other relevant soil properties were measured to estimate soil erodibility in the Loess Plateau. The results show that: (1) the erosion productivity impact calculator (EPIC) model and SHIRAZI model are both suitable for the Loess Plateau, while the SHIRAZI model has the advantage of fewer parameters; (2) introduced grassland has better ability to protect both the 0–5 cm soils and 5–20 cm soils, while the differences between introduced and natural shrubland are not obvious at a catchment scale; (3) the K values of introduced grassland, natural grassland, introduced shrubland and natural shrubland in the 0–5 cm layer vary from 0.008 to 0.037, 0.031 to 0.046, 0.012 to 0.041 and 0.008 to 0.045 (t·hm2·h/(MJ·mm·hm2)), while the values vary from 0.009 to 0.039, 0.032 to 0.046, 0.012 to 0.042 and 0.008 to 0.048 (t·hm2·h/(MJ·mm·hm2)) in the 5–20 cm layer. The areas with a mean multiyear precipitation of 370–440 mm are the most important places for vegetation restoration construction management at a regional scale. A comprehensive balance between water conservation and soil conservation is needed and important when selecting the species used to vegetation restoration. This study provides suggestions for ecological restoration and provides a case study for the estimate of soil erodibility in arid and semiarid areas.