Precipitation is scarce and evaporation is intense in desert areas. Groundwater is used as the main water source to develop agriculture in the oases. However, the effects of using groundwater on the ecological environment elicit widespread public concern. This study investigated the relationship between soil salinity and groundwater characteristics in Yaoba Oasis through in situ experiments. The relationship of the mineral content, pH, and main ion content of groundwater with soil salt was quantitatively evaluated through a gray relational analysis. Four main results were obtained. First, the fresh water area with low total dissolved solid (TDS) was usually HCO₃⁻ or SO₄²⁻ type water, and salt water was mostly Cl⁻ and SO₄²⁻. The spatial distribution of main ions in groundwater during winter irrigation in November was basically consistent with that during spring irrigation in June. However, the spatial distribution of TDS differed in the two seasons. Second, soil salinization in the study area was severe, and the salinization rate reached 72.7%. In this work, the spatial variability of soil salinization had a relatively large value, and the values in spring were greater than those in autumn. Third, the soil in the irrigated area had a high salt content, and the salt ion content of surface soil was higher than that of subsoil. A piper trilinear diagram revealed that Ca²⁺ and K⁺ + Na⁺ were the main cations. SO₄²⁻, Cl⁻, and HCO₃⁻ were the main anions, and salinization soil mainly contained SO₄²⁻. Fourth, the changes in soil salt and ion contents in the 0–10 cm soil layer were approximately similar to those of irrigation water quality, both of which showed an increasing trend. The correlation of surface soil salinity with the salinity of groundwater and its chemical components was high. In summary, this study identified the progress of irrigation water quality in soil salinization and provided a scientific basis for improving the oasis ecosystem, maintaining the healthy development of agriculture, managing oasis water resources, and policy development. Our findings can serve as a reference for other, similar oasis research.

Land cover classification that uses very high resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, the accuracy and usability of classification systems can still be improved. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed to improve the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image, and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. (1) An adaptive region is generated by gradually extending the region around a central pixel based on two predefined parameters (T₁ and T₂) to utilize the spatial feature of ground targets in a VHR image. (2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting. Each initial classified map is refined in this manner pixel by pixel. (3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated with two remote sensing images with high spatial resolutions of 1.0 m and 1.3 m. Compared with the classical majority voting method and a relatively new post-processing method called the general post-classification framework, the proposed D-AMVS can achieve a land cover classification map with less noise and higher classification accuracies.

In order to realize the optimization of urban spatial patterns in the Yellow River Basin, a study on the inefficient use of urban land in the Yellow River Basin was carried out. In this study, Dali County and Hancheng County in Weinan City are selected as the research areas. Firstly, the analytic hierarchy process is used to build a comprehensive evaluation system for the identification of inefficient land in stock; secondly, the standard deviation ellipse method and spatial kernel density estimation method are used to quantitatively analyze the spatial distribution characteristics of inefficient land. Thirdly, the contribution model is used to analyze the influencing factors of inefficient land use. Finally, corresponding redevelopment suggestions are given for each type of inefficient land. The results show that Dali had the smallest area of inefficient land; second is Xincheng Street in Hancheng City; and Longmen Town, Hancheng City has the largest area. The distribution of inefficient land in Dali and Longmen Town in Hancheng City is relatively balanced, while the distribution of all kinds of inefficient land in Xincheng Street in Hancheng City is not concentrated. The density of the road network is the most important contributing factor to inefficient land use in the study area. This paper comprehensively uses the methods of economics and geography to study inefficient land use, quantifies the spatial-temporal characteristics and influencing factors of land use units, explores the spatial patterns of land use and enriches the research into relevant theories.