Understanding abandoned mine land (AML) changes during land reclamation is crucial for reusing damaged land resources and formulating sound ecological restoration policies. This study combines the linear programming (LP) model and the CLUE-S model to simulate land-use dynamics in the Mentougou District (Beijing, China) from 2007 to 2020 under three reclamation scenarios, that is, the planning scenario based on the general land-use plan in study area (scenario 1), maximal comprehensive benefits (scenario 2), and maximal ecosystem service value (scenario 3). Nine landscape-scale graph metrics were then selected to describe the landscape characteristics. The results show that the coupled model presented can simulate the dynamics of AML effectively and the spatially explicit transformations of AML were different. New cultivated land dominates in scenario 1, while construction land and forest land account for major percentages in scenarios 2 and 3, respectively. Scenario 3 has an advantage in most of the selected indices as the patches combined most closely. To conclude, reclaiming AML by transformation into more forest can reduce the variability and maintain the stability of the landscape ecological system in study area. These findings contribute to better mapping AML dynamics and providing policy support for the management of AML.

Realizing real-time and rapid monitoring of crop growth is crucial for providing an objective basis for agricultural production. To enhance the accuracy and comprehensiveness of monitoring winter wheat growth, comprehensive growth indicators are constructed using measurements of above-ground biomass, leaf chlorophyll content and water content of winter wheat taken on the ground. This construction is achieved through the utilization of the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) model. Additionally, a correlation analysis is performed with the selected vegetation indexes (VIs). Then, using unmanned aerial vehicle (UAV) multispectral orthophotos to construct VIs and extract texture features (TFs), the aim is to explore the potential of combining the two as input variables to improve the accuracy of estimating the comprehensive growth indicators of winter wheat. Finally, we develop comprehensive growth indicator inversion models based on four machine learning algorithms: random forest (RF); partial least squares (PLS); extreme learning machine (ELM); and particle swarm optimization extreme learning machine (PSO-ELM), and the optimal model is selected by comparing the accuracy evaluation indexes of the model. The results show that: (1) The correlation among the comprehensive growth indicators (CGIs) constructed by EWM (CGI_ewm) and FCE (CGI_fce) and VIs are all improved to different degrees compared with the single indicators, among which the correlation between CGI_fce and most of the VIs is larger. (2) The inclusion of TFs has a positive impact on the performance of the comprehensive growth indicator inversion model. Specifically, the inversion model based on ELM exhibits the most significant improvement in accuracy. The coefficient of determination (R²) values of ELM-CGI_ewm and ELM- CGI_fce increased by 20.83% and 20.37%, respectively. (3) The CGI_fce inversion model constructed by VIs and TFs as input variables and based on the ELM algorithm is the best inversion model (ELM-CGI_fce), with R² reaching 0.65. Particle swarm optimization (PSO) is used to optimize the ELM-CGI_fce (PSO-ELM-CGI_fce), and the precision is significantly improved compared with that before optimization, with R² reaching 0.84. The results of the study can provide a favorable reference for regional winter wheat growth monitoring.

A total of 94 soil samples from different soil depths (0∼25 cm, 25∼50 cm) were collected of farmland soil around the plain of Beijing, and the concentrations of five heavy metal elements (Cd, Cr, Pb, As, and Hg) were measured using standard methods. The safety utilization evaluation method of heavy metals was based on three different evaluation methods. Then, the governance principles and specific management control strategy were determined in detail according to the core pollution source analysis of each safety grade zone. The results show that there are four different comprehensive safe utilization areas: safe, low-risk, medium-risk, and high-risk utilization. Among them, the study area was dominated by low-risk utilization areas, and the risk trend was gradually weakening from the center of the city to the periphery. Based on the characteristics of different security zones, this study puts on the governance principles of priority protection, long-term monitoring and moderate optimization, strengthens early warning, and cooperates with effective repair and priority governance. And then it puts forward practical control strategies according to the core pollution sources of each safety utilization zone. Our findings may provide a clear direction for rational utilization of land resources and renovation in the future.

It is important to clarify the iron-copper interaction pattern to effectively extract the characteristic bands and improve the inversion accuracy of copper content in soil. In this study, based on experimental samples, spectral feature analysis and analysis of variance (ANOVA) were used to deeply uncover the iron-copper interaction pattern. And used natural samples to build a random forest model to analyze the effect of interaction patterns on inversion accuracy. The results of the study showed that the effect of iron content in soil on spectral reflectance varied with copper content in soil, and similarly, the effect of copper content in soil on spectral reflectance varied with iron content in soil. The effect of iron, copper and their interaction on the spectral reflectance of soil varied with the wavelength. In the wavelength from 400 to 2,500 nm, the effect of iron on the spectral features was more than copper, and in the characteristic wavelength of iron (600–700 nm), even more than 5 times that of copper, the effect of iron on the spectral reflectance played a major role, and the iron content in soil must be considered in the inversion of copper content in soil. The Pearson correlation coefficient method was used as the selected characteristic wavelength, the selected wavelength was used as the independent variable, and the copper content in the soil was the dependent variable. Inversion model was built by the random forest algorithm, and the determination coefficient was 0.73. Under the condition of considering the interaction, the coefficient of determination was 0.87. It was shown that the characteristic wavelength was selected by considering the iron-copper interaction, which can better characterize the response signal of copper in soil. This paper provided a new idea for the accurate inversion of copper content in soil, which can provide technical support for the rapid detection of copper content in soil.