Spatial responses of water quality to river density and connectivity alterations on the Taihu Plain.

With the advancement of urbanization, the structure and connectivity of river networks have been changed by the interference of human activities, resulting in a series of water environment problems. Numerous studies have indicated that river networks are associated with water quality; unfortunately, few studies have revealed the contributions of the structure and connectivity of river networks to variations in water quality. Taking one water conservancy region with dense and braided rivers on the Taihu Plain as an example, we depicted the spatial aggregations of water quality using the Getis-Ord Gi* index, quantified the variations in polluted regions using the standard deviational ellipse method, and quantified the influence of river density and connectivity on water quality during the different seasons. The results showed that (1) the water quality during the flood season was better than that during the non-flood season, especially in the western region; (2) the spatial aggregations of most water quality indicators were higher and the polluted regions increased in size during the flood period compared to the non-flood period; and (3) the relative contribution rates of the river density and connectivity exhibited mean values of 62.5% (61.2%) and 37.5% (38.8%) in the flood (non-flood) period. Our results provide theoretical support for enhancing water environment management.

Nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China.

Desert vegetation plays significant roles in securing the ecological integrity of oasis ecosystems in western China. Timely monitoring of photosynthetic/non-photosynthetic desert vegetation cover is necessary to guide management practices on land desertification and research into the mechanisms driving vegetation recession. In this study, nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates are investigated through comparing the performance of linear and nonlinear spectral mixture models with different endmembers applied to field spectral measurements of two types of typical desert vegetation, namely, Nitraria shrubs and Haloxylon. The main results were as follows. (1) The correct selection of endmembers is important for improving the accuracy of vegetation cover estimates, and in particular, shadow endmembers cannot be neglected. (2) For both the Nitraria shrubs and Haloxylon, the Kernel-based Nonlinear Spectral Mixture Model (KNSMM) with nonlinear parameters was the best unmixing model. In consideration of the computational complexity and accuracy requirements, the Linear Spectral Mixture Model (LSMM) could be adopted for Nitraria shrubs plots, but this will result in significant errors for the Haloxylon plots since the nonlinear spectral mixture effects were more obvious for this vegetation type. (3) The vegetation canopy structure (planophile or erectophile) determines the strength of the nonlinear spectral mixture effects. Therefore, no matter for Nitraria shrubs or Haloxylon, the non-linear spectral mixing effects between the photosynthetic / non-photosynthetic vegetation and the bare soil do exist, and its strength is dependent on the three-dimensional structure of the vegetation canopy. The choice of linear or nonlinear spectral mixture models is up to the consideration of computational complexity and the accuracy requirement.