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.

Accurate recognition and extraction of rural residential land (RRL) is significant for scientific planning, utilization, and management of rural land. Very-High Resolution (VHR) Unmanned Aerial Vehicle (UAV) images and deep learning techniques can provide data and methodological support for the target. However, RRL, as a complex land use assemblage, exhibits features of different scales under VHR images, as well as the presence of complex impervious layers and backgrounds such as natural surfaces and tree shadows in rural areas. It still needs further research to determine how to deal with multi-scale features and accurate edge features in such scenarios. In response to the above problems, a novel framework named cascaded dense dilated network (CDD-Net), which combines DenseNet, ASPP, and PointRend, is proposed for RRL extraction from VHR images. The advantages of the proposed framework are as follows: Firstly, DenseNet is used as a feature extraction network, allowing feature reuse and better network design with fewer parameters. Secondly, the ASPP module can better handle multi-scale features. Thirdly, PointRend is added to the model to improve the segmentation accuracy of the edges. The research takes a plain village in China as the research area. Experimental results show that the Precision, Recall, F1 score, and Dice coefficients of our approach are 91.41%, 93.86%, 92.62%, and 0.8359, respectively, higher than other advanced models used for comparison. It is feasible in the task of high-precision extraction of RRL using VHR UAV images. This research could provide technical support for rural land planning, analysis, and formulation of land management policies.

Under the background of urban-rural integration, the frequent flow of urban and rural elements has promoted the close connection between rural transformation (RT) and construction land transition (CLT). How is CLT related to RT? By taking the plain area Linqing County in China as the study area, basing the study on the RT and CLT coupling framework of relevance theory, building an RT and CLT evaluation index system, using the multi-factor evaluation method for the evaluation of RT and CLT from 2010 to 2018, and using the grey correlation model to measure the RT and CLT coupling relationship, the results showed that the level of RT was from 0.04 to 97.42, and the level of CLT was from 14.89 to 82.47, showing the trends of gradual increase and fluctuating increase, respectively. Taking 2013 and 2016 as the time point, RT could be divided into the initial stage, growth stage I, and growth stage II, corresponding to the initial stage, high growth stage, and stable development stage of CLT. The coupling degree between the two was in the range of 0.6–0.8 and was in the stage of a medium to high coupling degree. The correlation degree between the subsystem of RT and CLT was over 0.65. In the subsystem of CLT, the correlation degrees between quantitative structure transition and RT and efficiency transition and RT were both lower than 0.65, which were relatively low. Controlling the scale of construction land and taking efficiency transition are the effective methods to guide the deep exploration of potential and are the inevitable way to strengthen the relationship between CLT and RT and promote urban-rural integration development.