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.

Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, technical challenges such as insufficient mining of shallow-level features, complex transmission of deep-level features, and difficulties in identifying change information features have led to severe fragmentation and low completeness issues of CD targets. To reduce costs and enhance efficiency in monitoring tasks such as changes in national resources, it is crucial to promote the practical implementation of automatic change detection technology. Therefore, we propose a deep learning approach utilizing heterogeneity enhancement and homogeneity restraint for CD. In addition to comprehensively extracting multilevel features from multitemporal images, we introduce a cosine similarity-based module and a module for progressive fusion enhancement of multilevel features to enhance deep feature extraction and the change information utilization within feature associations. This ensures that the change target completeness and the independence between change targets can be further improved. Comparative experiments with six CD models on two benchmark datasets demonstrate that the proposed approach outperforms conventional CD models in various metrics, including recall (0.6868, 0.6756), precision (0.7050, 0.7570), F1 score (0.6958, 0.7140), and MIoU (0.7013, 0.7000), on the SECOND and the HRSCD datasets, respectively. According to the core principles of change detection, the proposed deep learning network effectively enhances the completeness of target vectors and the separation of individual targets in change detection with VHR remote sensing images, which has significant research and practical value.