Ecological priority and green development have become the main theme of the times in China. The policy performance evaluation of ecological protection needs to quantitatively identify the changes of ecosystem quality and services firstly. This paper constructed the analytical framework driven by ecological protection policy of “Goal-oriented—Policy driven—Ecological quality–Service improvement”, and used multi-source data to establish the evaluation approach of “ecosystem quality-ecosystem services-ecosystem services value”. This study took Lishui as a case study to confirm the framework proposed. The results show that the ecosystem quality of Lishui has been steadily improved in recent 10 years. The overall quality of ecosystem services such as vegetation oxygen release, carbon sequestration, pollution removal, cooling service, humidification regulation and water conservation service has been improved by a range of 2%–6%. The value of ecological products has increased from 143.28 billion CNY in 2009 to 150.23 billion CNY in 2019. Lishui has implemented the development concept of “ecological civilization”, and the policies of ecological restoration or land remediation have changed land use and ecosystem quality, which was the main driving force for the improvement of ecological quality and the main promotion of ecological products value. The methods and results can provide insight into the impact of land policies on ecosystem services and decisions that support for further optimizing land ecological protection policies.
This work takes this Asian Hornet’s invasion of Washington in 2020 as an example to build an auxiliary identification system for Destructive Pests to help the government’s rescue work. To help the government to prioritize the resource to the most possible reports and improve the investigation efficiency, we built a binary classification model to classify the report that contains images and text. For image classification, we design a VGG16-based convolutional neural network pretraining with ImageNet, which achieved superior performance with a high F1 score (95.08%) on our dataset. For text classification, we use the Latent Dirichlet Allocation (LDA) model to cluster words into different themes and adopt cosine similarity to calculate the similarity between the new report and the benchmark positive/negative sets. The results demonstrated that our approach can identify target pests efficiently and help the government to prioritize the resource to the most possible reports, improving the investigation efficiency.
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