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Quantifying the Impacts of the 2020 Flood on Crop Production and Food Security in the Middle Reaches of the Yangtze River, China. REMOTE SENSING 2022. [DOI: 10.3390/rs14133140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
This study uses satellite imagery and geospatial data to examine the impact of floods over the main planting areas for double-cropping rice and grain crops in the middle reaches of the Yangtze River. During summer 2020, a long-lasting 62-day heavy rainfall caused record-breaking floods over large areas of China, especially the Yangtze basin. Through close examination of Sentinel-1/2 satellite imagery and Copernicus Global Land Cover, between July and August 2020, the inundation area reached 21,941 and 23,063 km2, and the crop-affected area reached 11,649 and 11,346 km2, respectively. We estimated that approximately 4.66 million metric tons of grain crops were seriously affected in these two months. While the PRC government denied that food security existed, the number of Grains and Feeds imported from the U.S. between January to July 2021 increased by 316%. This study shows that with modern remote sensing techniques, stakeholders can obtain critical estimates of large-scale disaster events much earlier than other indicators, such as disaster field surveys or crop price statistics. Potential use could include but is not limited to monitoring floods and land use coverage changes.
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Improved Extreme Learning Machine Based UWB Positioning for Mobile Robots with Signal Interference. MACHINES 2022. [DOI: 10.3390/machines10030218] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
For the purpose of tackling ultra-wideband (UWB) indoor positioning with signal interference, a binary classifier for signal interference discrimination and positioning errors compensation model combining genetic algorithm (GA) and extreme learning machine (ELM) are put forward. Based on the distances between four anchors and the target which are calculated with time of flight (TOF) ranging technique, GA-ELM-based binary classifier for judging the existence of signal interference, and GA-ELM-based positioning errors compensation model are built up to compensate for the result of the preliminary evaluated positioning model. Finally, the datasets collected in the actual scenario are used for verification and analysis. The experimental results indicate that the root-mean-square error (RMSE) of positioning without signal interference is 14.5068 cm, which is reduced by 71.32% and 59.72% compared with those results free of compensation and optimization, respectively. Moreover, the RMSE of positioning with signal interference is 28.0861 cm, which is decreased by 64.38% and 70.16%, in comparison to their counterparts without compensation and optimization, respectively. Consequently, these calculated results of numerical examples lead to the conclusion that the proposed method displays its wide application, high precision and rapid convergence in improving the positioning accuracy for mobile robots.
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