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The rapid and precise acquisition of the agricultural plastic greenhouse (PG) spatial distribution is essential in understanding PG usage and degradation, ensuring agricultural production, and protecting the ecological environment and human health. It is of great practical significance to realize the effective utilization of remote sensing images in the agricultural field and improve the extraction accuracy of PG remote sensing data. In this study, Landsat operational land imager (OLI) remote sensing images were used as data sources, and Shandong Province, which has the largest PG distribution in China, was selected as the study area. PGs in the study area were identified by means of contour recognition, feature set construction of the spatial structure, and machine learning. The results were as follows. (1) Through an optimal segmentation parameter approach, it was determined that the optimal segmentation scale for size, shape, and compactness should be set at 20, 0.8, and 0.5, respectively, which significantly improved PG contour recognition. (2) Among the 72 feature variables for PG spatial recognition, the number of features and classification accuracy showed a trend of first gradually increasing and then decreasing. Among them, fifteen feature variables, including the mean of bands 2 and 5; six index features (NDWI, GNDVI, SWIR1_NIR, NDVI, and PMLI); two shape features, the density and shape index; and two texture features, the contrast and standard deviation, played an important role. (3) According to the recall rate, accuracy rate, and F-value of three machine learning methods, random forest (RDF), CART decision tree (CART), and support vector machine (SVM), SVM had the best classification effect. The classification method described in this paper can accurately extract continuous plastic greenhouses through remote sensing images and provide a reference for the application of facility agriculture and non-point-source pollution control. [ABSTRACT FROM AUTHOR]

Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R2 = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models. [ABSTRACT FROM AUTHOR]

Land use is a process that turns a piece of land's natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth's surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth's surface. In China's Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China's northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km2 of construction land is converted to cropland, and 424.11 km2 of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km2 and 148.42 km2, respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively. [ABSTRACT FROM AUTHOR]