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.

1. Land cover refers to the natural or artificial elements that cover the surface of the Earth, including water bodies, vegetation, buildings, sandy areas, and bare soil. Different regions (latitud...

Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were analyzed and evaluated for component similarity, type confusion degree, spatial consistency, and accuracy verification. The research found that the three products provided consistent descriptions of land-cover types in the East European Plain. There was a strong correlation in the type area between the different products, with a correlation coefficient >0.85. Medium-to-high-consistency areas represented 92.31% of the total plains area. The low-consistency areas were mainly concentrated on Yuzhny Island, Kola Peninsula, and Pechora River Basin. The comparison revealed high consistency among the three products in identifying forest, cropland, water, and permanent ice/snow types. However, the consistency was poor for shrubs, wetlands, and bare land. Using the GLCVSS_V1 validation dataset, the highest overall accuracy among the assessed land cover data products was observed in the FROM_GLC (73.96%), followed by GlobeLand30 (69.80%) and GLC_FCS30 (67.29%). The FROM_GLC dataset is suitable for studying forests, tundra, water, and providing an overall representation of the region’s land cover. The GLC_FCS30 dataset is more suitable for agricultural research. The differences between products arise from the differences in classification systems, algorithms, and data correction. In the future, it will be necessary to utilize the advantages of different products for data fusion, focusing on areas with high heterogeneity and easily confused types, and improving the reliability of land-cover data products.

Accurate and timely estimation of grass yield is crucial for understanding the ecological conditions of grasslands in the Mongolian Plateau (MP). In this study, a new artificial neural network (ANN) model was selected for grassland yield inversion after comparison with multiple linear regression, K-nearest neighbor, and random forest models. The ANN performed better than the other machine learning models. Simultaneously, we conducted an analysis to examine the spatial and temporal characteristics and trends of grass yield in the MP from 2000 to 2020. Grassland productivity decreased from north to south. Additionally, 92.64% of the grasslands exhibited an increasing trend, whereas 7.35% exhibited a decreasing trend. Grassland degradation areas were primarily located in Inner Mongolia and the central Gobi region of Mongolia. Grassland productivity was positively correlated with land surface temperature and precipitation, although the latter was less sensitive than the former in certain areas. These findings indicate that ANN model-based grass yield estimation is an effective method for grassland productivity evaluation in the MP and can be used in a larger area, such as the Eurasian Steppe.