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The Greater Mekong Subregion (GMS) economic cooperation program is an effective and fruitful regional cooperation initiative for socioeconomic development in Asia; however, the vegetation change trends and directions in the GMS caused by rapid development remain unknown. In particular, there is a current lack of comparative studies on vegetation changes in various countries in the GMS. Based on the MODIS normalized difference vegetation index (NDVI) time series data, this study analyzed the spatiotemporal patterns of vegetation coverage and their trends in the GMS from 2000 to 2022 using the Theil–Sen slope estimation, the Mann–Kendall mutation test, and the gravity center migration model. The key findings were as follows: (1) the NDVI in the GMS showed an overall upward fluctuating trend over the past 23 years, with an annual growth rate of 0.11%. The NDVI changes varied slightly between seasons, with the greatest increases recorded in summer and winter. (2) The spatial distribution of NDVI in the GMS varied greatly, with higher NDVI values in the north–central region and lower NDVI values in the south. (3) A total of 66.03% of the GMS area showed increments in vegetation during the studied period, mainly in south–central Myanmar, northeastern Thailand, Vietnam, and China. (4) From 2000 to 2022, the gravity center of vegetation greenness shifted northward in the GMS, especially from 2000 to 2005, indicating that the growth rates of vegetation in the north–central part of the GMS were higher than those in the south. Furthermore, the vegetation coverage in all countries, except Cambodia, increased, with the most pronounced growth recorded in China. Overall, these findings can provide scientific evidence for the GMS to enhance ecological protection and sustainable development.

Microbial and enzymatic stoichiometry have been widely used to reflect microbial nutrient limitations. However, the dominant drivers of microbial and enzymatic C:N:P ratios are not well known, which hinders our understanding of whether microbial and enzymatic stoichiometry can accurately reflect microbial resource limitation. Here, we studied vertical patterns (0–100 cm) of factors influencing microbial and enzymatic C:N:P ratios in five forest ecosystems (one tropical, two subtropical, and two temperate) in southwestern China. Our results showed that microbial and enzymatic C:P were not regulated by soil C:P, while N: P followed the same pattern. This suggests microbial resource status is weakly related to microbial and enzymatic C:N:P ratios and that understanding stoichiometry may be inadequate to disentangle nutritional limitations. Soil C:N affected microbial and enzymatic C:N:P ratios likely by altering microbial community structure in all soil horizons. Overall, our study highlighted the important role of soil C:N in regulating C, N, and P cycles, and challenged current methods for modeling microbial nutrient limitations.

The classification of land cover types is an important task for monitoring land use. Moreover, with the continuous application of high resolution remote sensing images, the time and space span is becoming larger, which greatly increases the difficulty of classification of target types. And there are few results that can effectively deal with deep land category information. Furthermore, the extraction and fusion of deep features still need to be improved. In this article, a multiangle attention fusion network is proposed for the classification of land cover types. The network uses a 50-layer residual network as a feature extraction network, and an adaptive special-shaped window attention module is added to the deep layer of the network to extract deep semantic information, building the connection between global information. In addition, the multiangle interactive attention fusion module is used to fuse feature maps at different levels, and an interactive attention mechanism is established at different angles. Finally, a new decoder module is proposed for the decoder to adjust the fused feature map again. Through experiments on three datasets, the results show that the method proposed in this article is more accurate than the previous network for the segmentation results of different categories. It can realize the accurate division of land types in remote sensing images, and has good generalization ability.

The present paper simplifies the naturally formed dunes (riverbeds) as large-scale three-dimensional staggered wavy walls to investigate the features of the accompanying secondary flows and streamwise vortices via large-eddy simulation. A comparison between the swirling strength and the mean velocities suggests where a secondary flow induces upwash or downwash motions. Moreover, we propose a pseudo-convex wall mechanism to interpret the directionality of the secondary flow. The centrifugal instability criterion is then used to reveal the generation of the streamwise vortices. Based on these analytical results, we found that the streamwise vortices are generated in the separation and reattachment points on both characteristic longitudinal–vertical and horizontal cross-sections, which is related to the curvature effect of the turbulent shear layer. Furthermore, the maximum Görtler number characterized by the ratio of centrifugal to viscous effects suggests that, for fixed ratio of spanwise- to streamwise-wavelength cases, the strongest centrifugal instability occurring on the longitudinal–vertical cross-section gradually dominates with the increases in amplitude. A similar trend for the cases with varied spanwise wavelength can also be found. It is also found that the streamwise vortices are generated more readily via transverse flow around the crest near the separation and reattachment points when the ratio of spanwise- to streamwise-wavelength equals 1.

The analysis of land cover types is helpful for detecting changes in land use categories and evaluating land resources. It is of great significance in environmental monitoring, land management, land planning, and mapping. At present, remote sensing imagery obtained by remote sensing is widely employed in the classification of land types. However, most of the existing methods have problems such as low classification accuracy, vulnerability to noise interference, and poor generalization ability. Here, a multi-scale contextual semantic guidance network is proposed for the classification of land cover types by deep learning. The whole model combines an attention mechanism with convolution to make up for the limitation that the convolution structure can only focus on local features. In the process of feature extraction, an interactive structure combining attention and convolution is introduced in the deep layer of the network to fully extract the abstract information. In this paper, the semantic information guidance module is introduced in the cross-layer connection part, ensuring that the semantic information between different levels can be used for mutual guidance, which is conducive to the classification process. A multi-scale fusion module is proposed at the decoder to fuse the features between different layers and avoid loss of information during the recovery process. Experiments on two public datasets demonstrate that the suggested approach has higher accuracy than existing models as well as strong generalization ability.

Because clouds and snow block the underlying surface and interfere with the information extracted from an image, the accurate segmentation of cloud/snow regions is essential for imagery preprocessing for remote sensing. Nearly all remote sensing images have a high resolution and contain complex and diverse content, which makes the task of cloud/snow segmentation more difficult. A multi-branch convolutional attention network (MCANet) is suggested in this study. A double-branch structure is adopted, and the spatial information and semantic information in the image are extracted. In this way, the model’s feature extraction ability is improved. Then, a fusion module is suggested to correctly fuse the feature information gathered from several branches. Finally, to address the issue of information loss in the upsampling process, a new decoder module is constructed by combining convolution with a transformer to enhance the recovery ability of image information; meanwhile, the segmentation boundary is repaired to refine the edge information. This paper conducts experiments on the high-resolution remote sensing image cloud/snow detection dataset (CSWV), and conducts generalization experiments on two publicly available datasets (HRC_WHU and L8 SPARCS), and the self-built cloud and cloud shadow dataset. The MIOU scores on the four datasets are 92.736%, 91.649%, 80.253%, and 94.894%, respectively. The experimental findings demonstrate that whether it is for cloud/snow detection or more complex multi-category detection tasks, the network proposed in this paper can completely restore the target details, and it provides a stronger degree of robustness and superior segmentation capabilities.