A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5
Land desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of great theoretical...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/22/9173 |
| _version_ | 1797457744778756096 |
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| author | Jingyi Yang Qinjun Wang Dingkun Chang Wentao Xu Boqi Yuan |
| author_facet | Jingyi Yang Qinjun Wang Dingkun Chang Wentao Xu Boqi Yuan |
| author_sort | Jingyi Yang |
| collection | DOAJ |
| description | Land desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of great theoretical value and practical significance to carry out accurate identification and monitoring of land desertification and its influencing factors in ecologically fragile areas of China. This is conducive to curbing land desertification and ensuring regional ecological security. Minqin County, Gansu Province, located in northwestern China, is one of the most serious areas of land desertification, which is also one of the four sandstorm sources in China. Based on ENVINet5, this paper constructs a high-precision land desertification identification method with an accuracy of 93.71%, which analyzes the trend and reasons of land desertification in this area, provides suggestions for disaster prevention in Minqin County. and provides a reference for other similar areas to make corresponding desertification control policies. |
| first_indexed | 2024-03-09T16:28:20Z |
| format | Article |
| id | doaj.art-4efd802793e74d9791f6a7c1c099b330 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T16:28:20Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4efd802793e74d9791f6a7c1c099b3302023-11-24T15:05:37ZengMDPI AGSensors1424-82202023-11-012322917310.3390/s23229173A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5Jingyi Yang0Qinjun Wang1Dingkun Chang2Wentao Xu3Boqi Yuan4Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaLand desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of great theoretical value and practical significance to carry out accurate identification and monitoring of land desertification and its influencing factors in ecologically fragile areas of China. This is conducive to curbing land desertification and ensuring regional ecological security. Minqin County, Gansu Province, located in northwestern China, is one of the most serious areas of land desertification, which is also one of the four sandstorm sources in China. Based on ENVINet5, this paper constructs a high-precision land desertification identification method with an accuracy of 93.71%, which analyzes the trend and reasons of land desertification in this area, provides suggestions for disaster prevention in Minqin County. and provides a reference for other similar areas to make corresponding desertification control policies.https://www.mdpi.com/1424-8220/23/22/9173desertificationland classificationfine classificationdeep learninginfluencing factors |
| spellingShingle | Jingyi Yang Qinjun Wang Dingkun Chang Wentao Xu Boqi Yuan A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5 Sensors desertification land classification fine classification deep learning influencing factors |
| title | A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5 |
| title_full | A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5 |
| title_fullStr | A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5 |
| title_full_unstemmed | A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5 |
| title_short | A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5 |
| title_sort | high precision remote sensing identification method for land desertification based on envinet5 |
| topic | desertification land classification fine classification deep learning influencing factors |
| url | https://www.mdpi.com/1424-8220/23/22/9173 |
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