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Cai F, Cao C, Qi H, Su X, Lei G, Liu J, Zhao S, Liu G, Zhu K. Rapid migration of mainland China's coastal erosion vulnerability due to anthropogenic changes. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 319:115632. [PMID: 35868186 DOI: 10.1016/j.jenvman.2022.115632] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 06/22/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
With the global rise in sea levels caused by climate change and frequent extreme weather processes, high-density population aggregation and human development activities to enhance coastal areas vulnerability, populations, resources, and the ecological environment are facing huge pressure. Natural coastlines are being destroyed, and increasingly serious problems, such as coastal erosion and ecological fragility, have become disasters in coastal zones. The coastal vulnerability changed by climatic variables has created a major concern at regional, national and global scales. By comparing the data of two periods in the past 40 years, coastline vulnerability of coastal erosion in mainland China were evaluated by use of reverse cloud model and AHP with 10 indicators, including natural, anthropogenic, social and economic factors, etc. The main factors controlling coastal erosion included the proportion of Quaternary strata, the gradual reclamation of marine areas as land areas (in kilometres) and the percentage decrease in coastal sediment entering the sea. The secondary impact factors included the high proportion of artificial coastlines and the impacts of waves and storm surges under the influence of relative sea level changes. Human activities could further influence coastal vulnerability, making the erosion risk a considerable concern. Legislation, coordinated management system and technology are proposed to improve the quality of the marine ecological environment.
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Affiliation(s)
- Feng Cai
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, Fujian, China; Fujian Provincial Key Laboratory of Marine Ecological Conservation and Restoration, Xiamen, 361005, Fujian, China; Fujian Provincial Station for Field Observation and Research of Island and Costal Zone in Zhangzhou, Zhangzhou, 363200, Fujian, China.
| | - Chao Cao
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, Fujian, China; Fujian Provincial Key Laboratory of Marine Ecological Conservation and Restoration, Xiamen, 361005, Fujian, China; Fujian Provincial Station for Field Observation and Research of Island and Costal Zone in Zhangzhou, Zhangzhou, 363200, Fujian, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 591000, Guangdong, China.
| | - Hongshuai Qi
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, Fujian, China; Fujian Provincial Key Laboratory of Marine Ecological Conservation and Restoration, Xiamen, 361005, Fujian, China; Fujian Provincial Station for Field Observation and Research of Island and Costal Zone in Zhangzhou, Zhangzhou, 363200, Fujian, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 591000, Guangdong, China
| | - Xianze Su
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, Fujian, China
| | - Gang Lei
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, Fujian, China; Fujian Provincial Key Laboratory of Marine Ecological Conservation and Restoration, Xiamen, 361005, Fujian, China; Fujian Provincial Station for Field Observation and Research of Island and Costal Zone in Zhangzhou, Zhangzhou, 363200, Fujian, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 591000, Guangdong, China
| | - Jianhui Liu
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, Fujian, China; Fujian Provincial Key Laboratory of Marine Ecological Conservation and Restoration, Xiamen, 361005, Fujian, China; Fujian Provincial Station for Field Observation and Research of Island and Costal Zone in Zhangzhou, Zhangzhou, 363200, Fujian, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 591000, Guangdong, China
| | - Shaohua Zhao
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, Fujian, China
| | - Gen Liu
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, Fujian, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 591000, Guangdong, China
| | - Kai Zhu
- School of Civil Engineering, Fuzhou University, Fuzhou, 350108, Fujian, China
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Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery. REMOTE SENSING 2022. [DOI: 10.3390/rs14133143] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.
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