基于BP神经网络的流域生态恢复度计算——以福建长汀朱溪小流域为例

以福建省长汀县朱溪小流域为研究对象,通过野外调查、室内分析以及遥感影像提取相结合的方法获取数据。利用Matlab7.0软件建立BP神经网络生态恢复模型,定量评价退化生态系统的恢复程度。选择土壤理化性质(有机质、全N、全P、全K、容重和p H)、植被结构(植被盖度)、物种多样性指数(Shannon-Wiener指数)和热环境(地表温度)等4个方面的9个指标建立退化生态系统评价体系,并作为生态恢复模型的输入层数据,生态恢复度作为输出层数据。使用Matlab7.0进行数据预处理、样本训练、样本检验并建立生态恢复模型。利用建立的生态恢复模型对整个朱溪小流域生态恢复度进行定量评价。结果表明,生态恢复模型预测结果与流域生态恢复的实际情况基本吻合,利用BP神经网络模型定量评价退化生态系统的恢复程度具有可行性。朱溪小流域内生态恢复程度极低的区域面积仅占0.94%,95.48%区域为中等恢复程度,说明生态保护措施已初见成效;生态恢复程度高的区域面积仅占3.62%,意味着未来仍需加强治理和保护工作。

Calculation of ecological recovery based on bp neural network: a case study of Zhuxi Small Watershed in Changting County, Fujian Province

Environmental degeneration has seriously restricted the economic and social development of countries around the world. To tackle the problem, the projects of ecological restoration and reconstruction have been or are being carried out in many places. Under this background, many scholars try to assess the effects of ecological restoration through statistical method, comprehensive evaluation method, fuzzy evaluation method and grey evaluation method. However, it is difficult to discern the non-liner correlation between each assessment indicator and the degree of ecosystem restoration, as well as to decide the contribution ratio of each indicator. The methods mentioned above were complicated in assessing the contribution ratio of indicators; whereas, the back propagation neural network can solve the problems about non-linear model and contribution ratio of indicators effectively through adjusting the weight of each indicator automatically in the training process of this model. The research focuses on the small watershed of Zhuxi in Changting County, Fujian Province. The data was acquired from field investigation, lab analysis and remote sensing images which the features are extracted from. The ecosystem restoration model which can quantitatively evaluate the degree of the ecosystem restoration is built using back propagation neural network (BP-NN) by Matlab7.0 software. Firstly, four aspects covering nine indicators are chosen to assess the restored ecosystem, including soil physicochemical properties (soil organic matter, soil total N, soil total P, soil total K, soil bulk density, pH), indices of species diversity(Shannon-Wiener), thermal environment (surface temperature) and vegetation structure (vegetation coverage). The nine indicators are the input variables and the values of ecological restoration are output of the BP-NN. Secondly, the ecosystem restoration model is built by data preprocessing, sample training and sample test using Matlab7.0 software. Lastly, the ecological restoration of Zhuxi small watershed is quantitatively evaluated by the model. The results show that the predicted values from ecosystem restoration model are in accordance with the real situation, which indicates BP-NN model is feasible in quantitative evaluation of restored ecosystem. The area of extremely low ecosystem restoration in Zhuxi small watershed occupies only 0.94% and the area of medium ecosystem restoration accounts for 95.48%, which indicates that the measures of ecological protection have achieved initial results. However, the area of high ecosystem restoration accounts for only 3.62%, suggesting more work should be done in managing and protecting environment in future. The selection of assessment indicators is another core for building the model. Based on former researches, we add thermal environment data (surface temperature) to the model in this study, which can make the assessment system more comprehensive, and achieve more ideal simulation result. In further research, more indicators including biomass, composition of litter, arbor density, and height and so on will be admitted to the assessment system for more accurate result. For direct perception of the output of the model, the dot data of model output was transformed into the surface data to create the map for the degree of ecosystem restoration.