Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

Cropland mapping via remote sensing can provide crucial information for agri-ecological studies. Time series of remote sensing imagery is particularly useful for agricultural land classification. This study investigated the synergistic use of feature selection, Object-Based Image Analysis (OBIA) segmentation and decision tree classification for cropland mapping using a finer temporal-resolution Landsat-MODIS Enhanced time series in 2007. The enhanced time series extracted 26 layers of Normalized Difference Vegetation Index (NDVI) and five NDVI Time Series Indices (TSI) in a subset of agricultural land of Southwest Missouri. A feature selection procedure using the Stepwise Discriminant Analysis (SDA) was performed, and 10 optimal features were selected as input data for OBIA segmentation, with an optimal scale parameter obtained by quantification assessment of topological and geometric object differences. Using the segmented metrics in a decision tree classifier, an overall classification accuracy of 90.87% was achieved. Our study highlights the advantage of OBIA segmentation and classification in reducing noise from in-field heterogeneity and spectral variation. The crop classification map produced at 30 m resolution provides spatial distributions of annual and perennial crops, which are valuable for agricultural monitoring and environmental assessment studies

Study on the Status, Problems and Suggestions of the Development of Aquatic Product Industry in Nine Mainland Cities of the Guangdong-Hong Kong-Macao Greater Bay Area

The Guangdong-Hong Kong-Macao Greater Bay Area is located at coastal area of the opening frontier in China, with the Pan-Pearl River Delta region as the vast development hinterland, which is an important support point for the country to implement the innovation-driven development strategy and promote the construction of "The Belt and Road Initiative". By analyzing the current situation and existing problems of aquatic product industry in nine mainland cities of the Guangdong-Hong Kong-Macao Greater Bay Area, the future development trend are judged and important development paths are put forward to promote the high-quality development of aquatic product industry in Greater Bay Area. Nine mainland cities in the Greater Bay Area are the dominant industrial agglomeration areas of aquaculture, especially the freshwater aquaculture in Guangdong Province, accounting for 49.58% and 42.28% of the province's output value and output, respectively, and accounting for more than 50% of the province's freshwater aquaculture area, with outstanding freshwater aquaculture capacity, which is the main area of pond aquaculture in Guangdong Province. Nine mainland cities in the Greater Bay Area have a complete chain of aquatic product industry, a high overall technical level, an advantage in aquatic seedling industry, and a relatively concentrated aquatic product processing and circulation industry. Relying on huge market consumption demand in the Guangdong-Hong Kong-Macao Greater Bay Area, the processing volume of freshwater products, the circulation of aquatic products and the total output value of service industry in nine mainland cities all exceed 50% of the whole province. From the perspective of industrial spatial layout, the aquatic product industry of nine mainland cities in the Greater Bay Area generally radiates from southwest to northeast. Jiangmen and Foshan have made outstanding contributions to the development of seawater and freshwater aquaculture in the Greater Bay Area respectively, while Guangzhou and Foshan are the polarization centers of aquatic fingerlings. At present, there are still some problems in the aquatic product industry of nine mainland cities in the Greater Bay Area, such as insufficient breeding technology, low level of production modernization, great constraints on resources and environment, less intensive processing, industrial supervision to be strengthened, and low degree of industrial integration. In order to promote the high-quality development of aquaculture in nine mainland cities in the Greater Bay Area, this study summarizes the basic path, necessary conditions and core motivation, that is, with optimizing the allocation of essential resources as a basis, improving industrial policy design and strengthening talent allocation and technological innovation to drive aquaculture modernization and green industrial development with science and technology. And concrete measures and suggestions on six aspects are put forward as follows: consolidating industrial foundation, improving supervision ability, promoting industrial integration and development, enhancing brand value, strengthening industrial digital construction and giving full play to the advantages of seedling industry

A Convolutional Neural Network for Large-Scale Greenhouse Extraction from Satellite Images Considering Spatial Features

Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a series of impacts on the environment. Therefore, the extraction of large-scale greenhouses is crucial for the sustainable development of agriculture and environmental governance. It is difficult for existing methods to acquire precise boundaries. Therefore, we propose a spatial convolutional long short-term memory structure, which can fully consider the spatial continuity of ground objects. We use multitask learning to improve the network’s ability to extract image boundaries and promote convergence through auxiliary loss. We propose a superpixel optimization module to optimize the main-branch results of network semantic segmentation using more precise boundaries obtained by advanced superpixel segmentation techniques. Compared with other mainstream methods, our proposed structure can better consider spatial information and obtain more accurate results. We chose Shandong Province, China, as the study area and used Gaofen-1 satellite remote sensing images to create a new greenhouse dataset. Our method achieved an F1 score of 77%, a significant improvement over mainstream semantic segmentation networks, and it could extract greenhouse results with more precise boundaries. We also completed large-scale greenhouse mapping for Shandong Province, and the results show that our proposed modules have great potential in greenhouse extraction

A Convolutional Neural Network for Large-Scale Greenhouse Extraction from Satellite Images Considering Spatial Features

Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a series of impacts on the environment. Therefore, the extraction of large-scale greenhouses is crucial for the sustainable development of agriculture and environmental governance. It is difficult for existing methods to acquire precise boundaries. Therefore, we propose a spatial convolutional long short-term memory structure, which can fully consider the spatial continuity of ground objects. We use multitask learning to improve the network’s ability to extract image boundaries and promote convergence through auxiliary loss. We propose a superpixel optimization module to optimize the main-branch results of network semantic segmentation using more precise boundaries obtained by advanced superpixel segmentation techniques. Compared with other mainstream methods, our proposed structure can better consider spatial information and obtain more accurate results. We chose Shandong Province, China, as the study area and used Gaofen-1 satellite remote sensing images to create a new greenhouse dataset. Our method achieved an F1 score of 77%, a significant improvement over mainstream semantic segmentation networks, and it could extract greenhouse results with more precise boundaries. We also completed large-scale greenhouse mapping for Shandong Province, and the results show that our proposed modules have great potential in greenhouse extraction

A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping

Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs). The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far beyond that offered by multispectral images. However, HSIs are inevitably corrupted by noise during acquisition and transmission processes. The presence of noise may significantly degrade the quality of the extracted mineral information. In order to improve the accuracy of mineral mapping, denoising is a crucial pre-processing task. By leveraging on low-rank and self-similarity properties of HSIs, this paper proposes a state-of-the-art HSI denoising algorithm that implements two main steps: (1) signal subspace learning via fine-tuned Robust Principle Component Analysis (RPCA); and (2) denoising the images associated with the representation coefficients, with respect to an orthogonal subspace basis, using BM3D, a self-similarity based state-of-the-art denoising algorithm. Accordingly, the proposed algorithm is named Hyperspectral Denoising via Robust principle component analysis and Self-similarity (HyDRoS), which can be considered as a supervised version of FastHyDe. The effectiveness of HyDRoS is evaluated in a series of mineral mapping experiments using noise-reduced AVIRIS and Hyperion HSIs. In these experiments, the proposed denoiser yielded systematically state-of-the-art performance

Corrosion Evaluation of Pure Mg Coated by Fluorination in 0.1 M Fluoride Electrolyte

In the ongoing research on the application of biodegradable materials, surface treatment of is considered to be a relatively effective solution to the excessive degradation rates of Mg alloys. In this study, to further optimize the proven effective surface coatings of fluoride, a low-voltage preparation fluorination method was used to achieve coating effectiveness under safer conditions. Optical observation, scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive spectroscopy (EDS), and potential dynamic polarization (PDP) experiments were used for the analysis and evaluation. The coating characteristics of the MgF2 coatings treated in the 10–90 V voltage range, including the structure, chemical conformation, and electrochemical corrosion assessment, were fully defined. The anodic fluoridation results showed that a pore structure of 1–14 μm thickness was formed on the Mg alloy substrate, and the coating was composed of Mg fluoride. The results of immersion corrosion and electrochemical corrosion experiments showed that compared with pure Mg, anodic fluorinated samples below 40 V exhibited better corrosion resistance, the prepared MgF2 coating was more uniform, and the surface mostly exhibited point corrosion. When the voltage reached or exceeded 60 V, the prepared coating exhibited poor corrosion resistance, fracture, and protrusions. After corrosion, it mostly exhibited surface corrosion. The results indicate that idealized coatings can be obtained at relatively low and safe voltage ranges. This finding may enable more economical, environmentally friendly, and safe preparation of coatings

Additional file 1 of Identification of molecular subtypes based on inflammatory response in lower-grade glioma

Additional file 1. The clinical information of LGG patients obtained from TCGA, CGGA, and GEO database