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Rapid and accurate identification of mangroves using remote sensing images is of great significance for assisting ecological conservation efforts in coastal zones. With the rapid development of artificial intelligence, deep learning methods have been successfully applied to a variety of fields. However, few studies have applied deep learning methods to the automatic detection of mangroves and few scholars have used medium-resolution Landsat images for large-scale mangrove identification. In this study, cloud-free Landsat 8 OLI imagery of the Indus Delta was acquired using the GEE platform, and NDVI and land use data were used to produce integrated labels to reduce the complexity and subjectivity of manually labeled samples. We proposed the use of MSNet, a semantic segmentation model fusing multiple-scale features, for mangrove extraction in the Indus Delta, and compared the performance of the MSNet model with three other semantic segmentation models, FCN-8s, SegNet, and U-Net. The overall performance ranking of the deep learning methods was MSNet > U-Net > SegNet > FCN-8s. The parallel-structured MSNet model was easy to train, had the fewest parameters and the highest validation accuracy, and provided the best results for the extraction of mangrove pixels with weak features. The MSNet model not only maintains the high-resolution features of the image and fully learns the pixels with weak features during the training process but also fuses the multiple-scale underlying features at different scales to enhance the semantic information and improve the accuracy of feature recognition and segmentation localization. Finally, the areas covered by mangroves in the Indus Delta in 2014 and 2022 were extracted using the best-performing MSNet. The statistics show an increase in mangrove-covered areas in the Indus Delta between 2014 and 2022, with a reduction of 44.37 km2, an increase of 170.48 km2, and a net increase of 126.11 km2. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power forecasting is crucial to ensure the safety and stability of power systems, given the penetration of solar energy. Extracting spatial-temporal features from ground-based sky images can greatly improve ultra-short-term PV power forecasting. Previous studies have primarily focused on extracting holistic spatial-temporal features from sky images without considering their interaction, leading to a loss of partial critical features that restricts the improvement of forecasting performance. Hence, this study proposes a novel framework considering the interaction of spatial-temporal features for ultra-short-term PV power forecasting. First, a two-stream network is used to extract spatial and temporal features separately from sky images, aiming to eliminate the negative impact of spatial-temporal feature interaction. Then, a gate unit is employed to fuse the extracted features adaptively. Subsequently, a PV-guided attention mechanism is proposed to enhance forecasting performance by identifying dominant regions within the fused feature map. Last, a time series inference model based on progressive architecture is proposed to forecast future PV power. Comparative results demonstrate that the proposed framework outperforms benchmark frameworks and exhibits higher generalization and robustness in ultra-short-term PV power forecasting. • An end-to-end image-based framework is developed for photovoltaic power forecasting. • Spatial-temporal features are extracted based on considering spatial-temporal feature interaction. • The correlation between image features and photovoltaic power is investigated. • A time series inference model based on progressive architecture is introduced. • The proposed framework outperforms benchmark frameworks for all case studies. [ABSTRACT FROM AUTHOR]