Forest monitoring is critical to the management and successful evaluation of ecological restoration in mined areas. However, in the past, available monitoring has mainly focused on traditional parameters and lacked estimation of the spatial structural parameters (SSPs) of forests. The SSPs are important indicators of forest health and resilience. The purpose of this study was to assess the feasibility of estimating the SSPs of restored forest in semi-arid mine dumps using Worldview-2 imagery. We used the random forest to extract the dominant feature factor subset; then, a regression model and mind evolutionary algorithm-back propagation (MEA-BP) neural network model were established to estimate the forest SSP. The results show that the textural features found using 3 × 3 window have a relatively high importance score in the random forest model. This indicates that the 3 × 3 texture factors have a relatively strong ability to explain the restored forest SSPs when compared with spectral factors. The optimal regression model has an R² of 0.6174 and an MSRE of 0.1001. The optimal MEA-BP neural network model has an R² of 0.6975 and an MSRE of 0.0906, which shows that the MEA-BP neural network has greater accuracy than the regression model. The estimation shows that the tree–shrub–grass mode with an average of 0.7351 has the highest SSP, irrespective of the restoration age. In addition, the SSP of each forest configuration type increases with the increase in restoration age except for the single grass configuration. The increase range of SSP across all modes was 0.0047–0.1471 after more than ten years of restoration. In conclusion, the spatial structure of a mixed forest mode is relatively complex. Application cases show that Worldview-2 imagery and the MEA-BP neural network method can support the effective evaluation of the spatial structure of restored forest in semi-arid mine dumps.

中文翻译：

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使用Worldview-2影像估算半干旱矿山场恢复的森林空间结构

森林监测对于管理和成功评估雷区生态恢复至关重要。然而，在过去，可用的监测主要集中在传统参数上，而缺乏对森林空间结构参数（SSP）的估计。SSP是森林健康和复原力的重要指标。这项研究的目的是评估使用Worldview-2影像估算半干旱矿场中恢复森林的SSP的可行性。我们使用随机森林提取主导特征因子子集。然后，建立了回归模型和思维进化算法-反向传播（MEA-BP）神经网络模型来估计森林的SSP。结果表明，在随机森林模型中，使用3×3窗口发现的纹理特征具有较高的重要性得分。这表明与光谱因子相比，3×3的纹理因子具有相对较强的解释恢复的森林SSP的能力。最佳回归模型具有R ²为0.6174，MSRE为0.1001。最佳MEA-BP神经网络模型的R ²0.6975和MSRE为0.0906，这表明MEA-BP神经网络比回归模型具有更高的准确性。估算表明，与恢复年龄无关，平均为0.7351的树－灌木－草模式具有最高的SSP。另外，除单草配置外，每种森林配置类型的SSP随恢复年龄的增加而增加。经过十多年的恢复，所有模式下SSP的增加范围为0.0047-0.1471。总之，混合森林模式的空间结构相对复杂。应用案例表明，Worldview-2影像和MEA-BP神经网络方法可以有效评估半干旱矿山中恢复森林的空间结构。