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Modern mine rehabilitation aims at thorough restoration of an ecosystem with emphasis on not only land area covered by vegetation but also the structure and function of that vegetation. However, assessment of current restoration success reveals a lack of attention to the spatial structure and its relationships with vegetation coverage and biomass. A forest's spatial structure is an important attribute of structural diversity. Complex spatial structures mean diverse species composition and spatial dissimilarity, which can provide a base for self-sustaining and regeneration. This study uses WorldView-2 images and field data to train the mind evolutionary algorithm-back propagation neural network (MEA-BP) model for the purpose of mapping three parameters (coverage, biomass, and spatial structure) across mine dumps. The results show that the spectral and textural features could effectively assess the coverage, biomass, and spatial structure, with an R2 of 0.91, 0.86, and 0.62, respectively. The coverage is positively correlated with biomass, while the spatial structure is negatively correlated with coverage and biomass. Pure Hippophae rhamnoides L. had high coverage but very low spatial structure, while the mixed community dominated by Populus L. and Pinus tabulaeformis Carr had high coverage, high biomass, and medium structure at around 10 years. The results suggested that the artificial reforestation improves vegetation coverage and biomass but did not synchronously increase spatial structure. The initially planted species composition, substrates, and succession process have a significant influence on the forest parameter relationships. The future reforestation and optimization of community assemblages should take the relationships and their influence factors and effects on ecosystem services into account. The remote sensing-based data and model has potential and advantages in dynamically guiding the rehabilitation and monitoring of the restored ecosystem. • WorldView-2 image and artificial neural network model were used for mapping forest parameters. • Trade-offs exist among vegetation coverage, biomass, and spatial structure during mined land restoration. • Artificial reforestation improves vegetation coverage and biomass but not synchronously increase spatial structure. • Optimization of community assemblages should consider the forest parameter relationships and effects. [ABSTRACT FROM AUTHOR]

As an essential ecological parameter, soil moisture is important for understanding the water exchange between the land surface and the atmosphere, especially in the Loess Plateau (China). Although Synthetic Aperture Radar (SAR) images can be used for soil moisture retrieval, it is still a challenge to mitigate the impacts of complex terrain over hilly areas. Therefore, the objective of this paper is to propose an improved approach for soil moisture estimation in gully fields based on the joint use of the Advanced Integral Equation Model (AIEM) and the Incidence Angle Correction Model (IACM) from Sentinel-1A observations. AIEM is utilized to build a simulation database of microwave backscattering coefficients from various radar parameters and surface parameters, which is the data basis for the retrieval modeling. IACM is proposed to correct the deviation between the local incidence angle at the scatterer and the radar viewing angle. The study area is located in the Loess Plateau of China, where the main land cover is mostly bare land and the terrain is complex. The Sentinel-1A SAR data in C-band with dual polarization acquired on October 19th, 2017 was adopted to extract the VV&VH polarimetric backscattering coefficients. The in situ measurements of soil moisture were collected on the same day of the SAR acquisition, for evaluating the accuracy of the SAR-derived soil moisture. The results showed that, firstly, the estimated soil moisture with volumetric content between 0% and 20% was in the majority. Subsequently, both the RMSE of estimation values (0.963%) and the standard deviation of absolute errors (0.957%) demonstrated a good accuracy of the improved approach. Moreover, the evaluation of IACM confirmed that the improved approach coupling IACM and AIEM was more efficient than employing AIEM solely. In conclusion, the proposed approach has a strong ability to estimate the soil moisture in the gully fields of the Loess Plateau from Sentinel-1A data. [ABSTRACT FROM AUTHOR]