Identification and Prediction of the Invasion Pattern of the Mikania micrantha with WaveEdgeNet Model Using UAV-Based Images in Shenzhen.
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| Title: | Identification and Prediction of the Invasion Pattern of the Mikania micrantha with WaveEdgeNet Model Using UAV-Based Images in Shenzhen. |
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| Authors: | Lin, Hui1,2,3 (AUTHOR), Yin, Yang1,2,3 (AUTHOR), He, Xiaofen3,4 (AUTHOR) changchanglinye@163.com, Long, Jiangping1,2,4,5 (AUTHOR), Zhang, Tingchen1,2,3,5 (AUTHOR), Ye, Zilin1,2,3 (AUTHOR), Deng, Xiaojia1,2,5 (AUTHOR) |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 3, p437. 27p. |
| Subjects: | Invasive plants, Deep learning, Feature selection, Drone photography, Habitats, Geographic spatial analysis, Maximum entropy method, Introduced species |
| Geographic Terms: | China, Shenzhen (Guangdong Sheng, China : East) |
| Abstract: | Highlights: What are the main findings? A high-quality dataset was initially constructed and WaveEdgeNet was proposed. A novel feature selection framework is proposed in this study. What are the implications of the main findings? We introduced the variables representing the distance to different land use types. Investigated the influence of various environmental factors on Mikania micrantha. Mikania micrantha is one of the most detrimental invasive plant species in the southeastern coastal region of China. To accurately predict the invasion pattern of Mikania micrantha and offer guidance for production practices, it is essential to determine its precise location and the driving factors. Therefore, a design of the wavelet convolution and dynamic feature fusion module was studied, and WaveEdgeNet was proposed. This model has the abilities to deeply extract image semantic features, retain features, perform multi-scale segmentation, and conduct fusion. Moreover, to quantify the impact of human and natural factors, we developed a novel proximity factor based on land use data. Additionally, a new feature selection framework was applied to identify driving factors by analyzing the relationships between environmental variables and Mikania micrantha. Finally, the MaxEnt model was utilized to forecast its potential future habitats. The results demonstrate that WaveEdgeNet effectively extracts image features and improves model performance, attaining an MIoU of 85% and an overall accuracy of 98.62%, outperforming existing models. Spatial analysis shows that the invaded area in 2024 was smaller than that in 2023, indicating that human intervention measures have achieved some success. Furthermore, the feature selection framework not only enhances MaxEnt's accuracy but also cuts down computational time by 82.61%. According to MaxEnt modeling, human disturbance, proximity to forests, distance from roads, and elevation are recognized as the primary factors. In the future, we will concentrate on overcoming the seasonal limitations and attaining the objective of predicting the growth and reproduction of kudzu before they happen, which can offer a foundation for manual intervention. This study lays a solid technical foundation and offers comprehensive data support for comprehending the species' dispersal patterns and driving factors and for guiding environmental conservation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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