Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.

Individuals with abnormalities are key drivers of subtle stress changes in forest ecosystems. Although remote sensing monitoring and deep learning have been developed for forest ecosystems, they are faced with the complexity of forest landscapes, multiple sources of remote sensing data, high monitoring costs, and complex terrain, which pose significant challenges to automatic identification. Therefore, taking pine nematode disease as an example, this paper proposes D-SCNet, an intelligent monitoring network for abnormal individuals applicable to UAV visible images. In this method, the convolutional block attention model and simplified dense block are introduced to enhance the semantic analysis ability of abnormal individual identification, use multi-level information of abnormal individuals well, enhance feature transfer as well as feature weights between network layers, and selectively focus on abnormal features of individuals while reducing feature redundancy and parameter and improving monitoring accuracy and efficiency. This method uses lightweight deep learning models through weak information sources to achieve rapid monitoring of a large range of abnormal individuals in complex environments. With the advantages of low cost, high efficiency, and simple data sources, it is expected to further enhance the practicality and universality of intelligent monitoring of anomalous individuals by UAV remote sensing.

Analyzing wetland landscape pattern evolution is crucial for managing wetland resources. High-resolution remote sensing serves as a primary method for monitoring wetland landscape patterns. However, the complex landscape types and spatial structures of wetlands pose challenges, including interclass similarity and intraclass spatial heterogeneity, leading to the low separability of landscapes and difficulties in identifying fragmented and small objects. To address these issues, this study proposes the multilevel feature cross-fusion wetland landscape classification network (MFCFNet), which combines the global modeling capability of Swin Transformer with the local detail-capturing ability of convolutional neural networks (CNNs), facilitating discerning intraclass consistency and interclass differences. To alleviate the semantic confusion caused by different-level features with semantic gaps during fusion, we introduce a deep–shallow feature cross-fusion (DSFCF) module between the encoder and the decoder. We incorporate global–local attention block (GLAB) to aggregate global contextual information and local detail. The constructed Shengjin Lake Wetland Gaofen Image Dataset (SLWGID) is utilized to evaluate the performance of MFCFNet, achieving evaluation metric results of the OA, mIoU, and F1 score of 93.23%, 78.12%, and 87.05%, respectively. MFCFNet is used to classify the wetland landscape of Shengjin Lake from 2013 to 2023. A landscape pattern evolution analysis is conducted, focusing on landscape transitions, area changes, and pattern characteristic variations. The method demonstrates effectiveness for the dynamic monitoring of wetland landscape patterns, providing valuable insights for wetland conservation.