Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R²) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R², RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

Under the background of urban–rural integration, balanced development between urban and rural areas has been increasingly advocated. Rural population loss (RPL) is not only due to the laws of social and economic development but also the comprehensive action of natural, social, and economic factors. Taking 774 administrative villages in Laiyang County, which is in a hilly region, as our research area, we comprehensively used spatial analysis and geographic detectors to explore the spatial characteristics and driving factors of RPL, which was significantly correlated with rural planning. The research demonstrated that: (1) The rural population in Laiyang County generally had a low level of RPL (1.9%), but each village varied greatly. The village with the greatest RPL had a rate of 56%. The RPL between urban and rural areas, towns and streets, and villages and villages were unbalanced, and rural population flow mainly occurred between urban and rural areas. (2) RPL in Laiyang County was generally low in the central urban area and high in the northern and southern areas. Population loss presents agglomeration globally and high–low agglomeration locally. (3) The distance from village to county, elevation, cultivated land quantity, collective economic income, village area, and ecological service value were the key factors influencing RPL in Laiyang County. When comparing the dominant factors, the interaction between collective income and elevation was the strongest. Exploring the spatial characteristics and influencing factors of RPL provided us with ideas for the classified promotion of rural revitalization, preparation of rural development planning, and promotion of the integrated development of urban and rural areas.

As a combination of toponym and cultural heritage with rich connotation of toponymic word-culture and entity-culture, toponymic cultural heritage has the significance of protection and inheritance. It is important for transmitting culture and expanding the cultural connotations of tourism activities. Previous analyses of such heritage protection and tourism exploitation mostly concentrated on resource value from an internal perspective, and the researches focused on toponymic words while paying less attention to toponymic cultural heritage entities, neglecting the influence of environmental condition. This study selected an ecologically and culturally sensitive region with environmental complexity and cultural diversity as an example and constructed a toponym database from multi-source heterogeneous toponym data; screened out toponymic cultural heritages based on a decision tree model; constructed an evaluation model of heritage protection value based on internal and external conditions by considering environmental effects, including toponymic words and entities; complemented an environmental condition evaluation by including natural and man-made disturbances; further widened resource value evaluation dimension with expert judgment and public judgment; and formulated a relatively comprehensive protection value assessment for such heritage.

Saline–alkali land is an important land resource with high development potential and an important ecological product with high potential ecosystem service value (ESV). It is of great significance for rational development and protection of saline–alkali land resources to carry out research on the evaluation methods of the ESV of saline–alkali land and to scientifically quantify the ESV of saline–alkali land development. Taking typical saline–alkali land in the Yellow River Delta on the Huanghe Island of China as an example, this research constructed an ESV evaluation index system from the perspective of functional value containing three first-level indicators such as economic value, four second-level indicators such as supply service, and eight lowest-level indicators such as food production. It also considered production and operation costs and evaluated the ESV of the Huanghe Island using the ecological economics method. The results showed that the ESV of the Huanghe Island is CNY 133.586 million, and the unit area value is 50,200 CNY/hm², which is a relatively high value. Among them, the supply service value was the highest, accounting for more than half. In recent years, the development of the Huanghe Island has produced high supply and service value, but due to weak background conditions and low vegetation coverage, regulation and support service values are relatively low, the cultural service value is still to be explored, and there is great potential for ecological and social value. This research can provide references for scientific evaluation of the ESV of saline–alkali land and provide a basis for rational development and utilization of the Huanghe Island.

The establishment of a comprehensive framework to identify village development types is crucial to formulate plans for rural development and promote rural revitalization. This study proposed a natural–socioeconomic framework to identify the types of villages based on field survey, statistical data, and multi-source remote sensing images. The framework was constructed by combining the two-dimensional natural suitability/restriction evaluation and the four-dimensional socioeconomic development level evaluation. Then, the modified multiplication-weighted summation method and the coupling coordination degree algorithm were employed to identify the villages’ development types. A total of 774 villages of the Laiyang County, eastern China were used as the study areas to examine the framework. The results demonstrated the following. (1) There were 243,318 and 151 villages with high, moderate, low natural suitability, and 62 villages with natural restrictions; and 158,366 and 250 villages with high, moderate, and low economic development level, respectively. The distribution characteristic of natural evaluation was “high in the southwest and low in the northeast”, and the socioeconomic development level was generally centered on the urban area, which presented a “high–medium–low” circle-layer distribution structure. (2) There were 247 villages with high-level coupling coordination, 464 villages with intermediate coupling coordination, 1 village with low-level coupling coordination, and 62 villages with disordered coupling. (3) Based on the coupling coordination evaluation results, villages in the study area were grouped into five types: urbanization development (31%), construction development (16%), agglomeration linkage development (27%), decrease and improvement development (18%), and relocation and integration development (8%). The framework of villages’ development types identification established in this study can enrich the theory of rural geography, and the applied research results can provide a basis for rural revitalization and development planning.