Land use profoundly changes the terrestrial ecosystem and landscape patterns, and these changes reveal the extent and scope of the ecological influence of land use on the terrestrial ecosystem. The study area selected for this research was the middle reaches of the Heihe River. Based on land use data (1986, 2000, and 2014), we proposed an ecological risk index of land use by combining a landscape disturbance index with a landscape fragility index. An exponential model was selected to perform kriging interpolation, as well as spatial autocorrelations and semivariance analyses which could reveal the spatial aggregation patterns. The results indicated that the ecological risk of the middle reaches of the Heihe River was generally high, and higher in the northwest. The high values of the ecological risk index (ERI) tended to decrease, and the low ERI values tended to increase. Positive spatial autocorrelations and a prominent scale-dependence were observed among the ERI values. The main hot areas with High-High local autocorrelations were located in the north, and the cold areas with low-low local autocorrelations were primarily located in the middle corridor plain and Qilian Mountains. From 1986 to 2014, low and relatively low ecological risk areas decreased while relatively high risk areas expanded. A middle level of ecological risk was observed in Ganzhou and Minle counties. Shandan County presented a serious polarization, with high ecological risk areas observed in the north and low ecological risk areas observed in the southern Shandan horse farm. In order to lower the eco-risk and achieve the sustainability of land use, these results suggest policies to strictly control the oasis expansion and the occupation of farmland for urbanization. Some inefficient farmland should transform into grassland in appropriate cases.

No abstract

Land cover changes are the main factors driving the evolution of regional ecological quality. These changes must be considered in the strategic formulation of regional or national ecological policies. The forest-steppe ecotone in the Greater Khingan Mountains is an important ecological barrier in northern China. To measure the effect of ecological protection in recent years, Landsat images, object-oriented image segmentation, and convolutional neural networks were used to create land cover datasets of the forest-steppe ecotone. The Carnegie–Ames–Stanford approach (CASA) and the dimidiate pixel model were used to derive net primary productivity (NPP) and fractional vegetation cover (FVC) to assess the ecological quality of this area. The results showed that only grassland and urban land increased, whereas saline–alkali land and desert areas initially increased and then decreased from 2010 to 2018, indicating that the desertification process was substantially curbed. Total NPP increased by 26.3% (2000–2010) and 10.8% (2010–2018). However, NPP decreased slightly in the center of the study area. FVC first decreased and then increased, and the increased areas were concentrated in the forest-steppe ecotone, saline–alkali land, and desert zone in Xin Barag Left Banner. These observations indicate that the ecological quality has gradually improved due to the strict protection of forest and grassland resources and the suppression of desertification. Our results provide potential insights for land use planning and the development of environmental protection measures in the forest-steppe ecotone.

Bidirectional Scattering Distribution Functions (BSDFs) encode how a material reflects or transmits the incoming light. The most commonly used model is the microfacet BSDF. It computes the material response from the microgeometry of the surface assuming a single bounce on specular microfacets. The original model ignores multiple bounces on the microgeometry, resulting in an energy loss, especially for rough materials. In this paper, we present a new method to compute the multiple bounces inside the microgeometry, eliminating this energy loss. Our method relies on a position-free formulation of multiple bounces inside the microgeometry. We use an explicit mathematical definition of the path space that describes single and multiple bounces in a uniform way. We then study the behavior of light on the different vertices and segments in the path space, leading to a reciprocal multiple-bounce description of BSDFs. Furthermore, we present practical, unbiased Monte Carlo estimators to compute multiple scattering. Our method is less noisy than existing algorithms for computing multiple scattering. It is almost noise-free with a very-low sampling rate, from 2 to 4 samples per pixel (spp).