Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example

Egypt, a country with a harsh natural environment and rapid population growth, is facing difficulty in ensuring its national food security. A novel model developed for assessing food security in Egypt, which applies remote sensing techniques, is presented. By extracting the gray-level co-occurrence matrix (GLCM) mean texture features from Sentinel-1 and Landsat-7 images, the arable land used to grow grain crops was first classified and extracted using a support vector machine. In terms of the classified results, meteorological data, and normalized difference vegetation index (NDVI) data, the Carnegie–Ames–Stanford approach (CASA) model was adopted to compute the annual net primary production (NPP). Then, the NPP yield conversion formula was used to forecast the annual grain yield. Finally, a method for evaluating food security, which involves four dimensions, i.e., quantity security, economic security, quality security, and resource security, was established to evaluate food security in Egypt in 2010, 2015, and 2020. Based on the proposed model, a classification accuracy of the crop distribution map, which is above 82%, can be achieved. Moreover, the reliability of yield estimation is verified compared to the result estimated using statistics data provided by Food and Agriculture Organization (FAO). Our evaluation results show that food security in Egypt is declining, the quantity and quality security show large fluctuations, and economic and resource security are relatively stable. This model can satisfy the requirements for estimating grain yield at a wide scale and evaluating food security on a national level. It can be used to provide useful suggestions for governments regarding improving food security

Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example

Egypt, a country with a harsh natural environment and rapid population growth, is facing difficulty in ensuring its national food security. A novel model developed for assessing food security in Egypt, which applies remote sensing techniques, is presented. By extracting the gray-level co-occurrence matrix (GLCM) mean texture features from Sentinel-1 and Landsat-7 images, the arable land used to grow grain crops was first classified and extracted using a support vector machine. In terms of the classified results, meteorological data, and normalized difference vegetation index (NDVI) data, the Carnegie–Ames–Stanford approach (CASA) model was adopted to compute the annual net primary production (NPP). Then, the NPP yield conversion formula was used to forecast the annual grain yield. Finally, a method for evaluating food security, which involves four dimensions, i.e., quantity security, economic security, quality security, and resource security, was established to evaluate food security in Egypt in 2010, 2015, and 2020. Based on the proposed model, a classification accuracy of the crop distribution map, which is above 82%, can be achieved. Moreover, the reliability of yield estimation is verified compared to the result estimated using statistics data provided by Food and Agriculture Organization (FAO). Our evaluation results show that food security in Egypt is declining, the quantity and quality security show large fluctuations, and economic and resource security are relatively stable. This model can satisfy the requirements for estimating grain yield at a wide scale and evaluating food security on a national level. It can be used to provide useful suggestions for governments regarding improving food security

A Model Design for Risk Assessment of Line Tripping Caused by Wildfires

A power line is particularly vulnerable to wildfires in its vicinity, and various damage including line tripping can be caused by wildfires. Using remote sensing techniques, a novel model developed to assess the risk of line tripping caused by the wildfire occurrence in high-voltage power line corridors is presented. This model mainly contains the wildfire risk assessment for power line corridors and the estimation of the probability of line tripping when a wildfire occurs in power line corridors. For the wildfire risk assessment, high-resolution satellite data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological data, and digital elevation model (DEM) data were employed to infer the natural factors. Human factors were also included to achieve good reliability. In the estimation of the probability of line tripping, vegetation characteristics, meteorological status, topographic conditions, and transmission line parameters were chosen as influencing factors. According to the above input variables and observed historical datasets, the risk levels for wildfire occurrence and line tripping were obtained with a logic regression approach. The experimental results demonstrate that the developed model can provide good results in predicting wildfire occurrence and line tripping for high-voltage power line corridors

Wuhan Ionospheric Oblique Backscattering Sounding System and Its Applications—A Review

For decades, high-frequency (HF) radar has played an important role in sensing the Earth’s environment. Advances in radar technology are providing opportunities to significantly improve the performance of HF radar, and to introduce more applications. This paper presents a low-power, small-size, and multifunctional HF radar developed by the Ionospheric Laboratory of Wuhan University, referred to as the Wuhan Ionospheric Oblique Backscattering Sounding System (WIOBSS). Progress in the development of this radar is described in detail, including the basic principles of operation, the system configuration, the sounding waveforms, and the signal and data processing methods. Furthermore, its various remote sensing applications are briefly reviewed to show the good performance of this radar. Finally, some suggested solutions are given for further improvement of its performance

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index

Application of Biphase Complete Complementary Code for Ionospheric Sounding

This paper illustrates the processes carried out for the application of biphase complete complementary code (CCC) for ionospheric sounding to address the coherent interference problem in multi-station ionospheric sounding. An algorithm to generate the biphase CCC is described, and the detailed process of waveform construction and signal processing is presented. Characteristics of the autocorrelation and cross-correlation are analyzed through simulations, and the technical feasibility of the application of CCC is explored. Experiments of ionospheric sounding with the CCC are also implemented to verify performance. Results demonstrate that the CCC performs well in multi-station ionospheric sounding, and is capable of eliminating the coherent interference in the network of ionosondes, compared to the conventional complementary code