A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data
The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/19/4981 |
| _version_ | 1827653145104220160 |
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| author | Qian Wang Lin Zhao Mali Wang Jinjia Wu Wei Zhou Qipeng Zhang Meie Deng |
| author_facet | Qian Wang Lin Zhao Mali Wang Jinjia Wu Wei Zhou Qipeng Zhang Meie Deng |
| author_sort | Qian Wang |
| collection | DOAJ |
| description | The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought. |
| first_indexed | 2024-03-09T21:12:55Z |
| format | Article |
| id | doaj.art-5016110a54f84d4699afbef1866ec442 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:12:55Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5016110a54f84d4699afbef1866ec4422023-11-23T21:41:53ZengMDPI AGRemote Sensing2072-42922022-10-011419498110.3390/rs14194981A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing DataQian Wang0Lin Zhao1Mali Wang2Jinjia Wu3Wei Zhou4Qipeng Zhang5Meie Deng6Department of Geographic Information Science, School of Geography and Environment, Liaocheng University, Liaocheng 252059, ChinaDepartment of Geographic Information Science, School of Geography and Environment, Liaocheng University, Liaocheng 252059, ChinaDepartment of Geographic Information Science, School of Geography and Environment, Liaocheng University, Liaocheng 252059, ChinaGrand Canal Research Centre, The Grand Canal Research Institute, Liaocheng University, Liaocheng 252000, ChinaChongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaDepartment of Geographic Information Science, School of Geography and Environment, Liaocheng University, Liaocheng 252059, ChinaSchool of Management, Wuhan Polytechnic University, Wuhan 430048, ChinaThe accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought.https://www.mdpi.com/2072-4292/14/19/4981random forestgrassland drought monitoringSPEIdrought |
| spellingShingle | Qian Wang Lin Zhao Mali Wang Jinjia Wu Wei Zhou Qipeng Zhang Meie Deng A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data Remote Sensing random forest grassland drought monitoring SPEI drought |
| title | A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data |
| title_full | A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data |
| title_fullStr | A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data |
| title_full_unstemmed | A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data |
| title_short | A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data |
| title_sort | random forest model for drought monitoring and validation for grassland drought based on multi source remote sensing data |
| topic | random forest grassland drought monitoring SPEI drought |
| url | https://www.mdpi.com/2072-4292/14/19/4981 |
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