Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment
Xilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes par...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5745 |
| _version_ | 1797464106274390016 |
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| author | Lingxin Bu Quan Lai Song Qing Yuhai Bao Xinyi Liu Qin Na Yuan Li |
| author_facet | Lingxin Bu Quan Lai Song Qing Yuhai Bao Xinyi Liu Qin Na Yuan Li |
| author_sort | Lingxin Bu |
| collection | DOAJ |
| description | Xilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes particularly important. Based on multi-source data, a random forest regression algorithm was used to construct a grassland biomass estimation model, which was then used to analyze the spatiotemporal variation characteristics of grassland biomass. A quantitative assessment of drought risk (DR) in different grassland types was applied based on the theory of risk formation, and a structural equation model (SEM) was used to analyze the drivers of drought risk in different grassland types. The results show that among the eight selected variables that affect grassland biomass, the model had the highest accuracy (R = 0.90) when the normalized difference vegetation index (NDVI), precipitation (Prcp), soil moisture (SM) and longitude (Lon) were combined as input variables. The grassland biomass showed a spatial distribution that was high in the east and low in the west, gradually decreasing from northeast to southwest. Among the grasslands, desert grassland (DRS) had the highest drought risk (DR = 0.30), while meadow grassland (MEG) had the lowest risk (DR = 0.02). The analysis of the drivers of drought risk in grassland biomass shows that meteorological elements mainly drive typical grasslands (TYG) and other grasslands (OTH). SM greatly impacted MEG, and ET had a relatively high contribution to DRS. This study provides a basis for managing different grassland types in large areas and developing corresponding drought adaptation programs. |
| first_indexed | 2024-03-09T18:02:15Z |
| format | Article |
| id | doaj.art-a28d3c3af783496ebdac6a715c9773e2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:02:15Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a28d3c3af783496ebdac6a715c9773e22023-11-24T09:49:46ZengMDPI AGRemote Sensing2072-42922022-11-011422574510.3390/rs14225745Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk AssessmentLingxin Bu0Quan Lai1Song Qing2Yuhai Bao3Xinyi Liu4Qin Na5Yuan Li6College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, ChinaXilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes particularly important. Based on multi-source data, a random forest regression algorithm was used to construct a grassland biomass estimation model, which was then used to analyze the spatiotemporal variation characteristics of grassland biomass. A quantitative assessment of drought risk (DR) in different grassland types was applied based on the theory of risk formation, and a structural equation model (SEM) was used to analyze the drivers of drought risk in different grassland types. The results show that among the eight selected variables that affect grassland biomass, the model had the highest accuracy (R = 0.90) when the normalized difference vegetation index (NDVI), precipitation (Prcp), soil moisture (SM) and longitude (Lon) were combined as input variables. The grassland biomass showed a spatial distribution that was high in the east and low in the west, gradually decreasing from northeast to southwest. Among the grasslands, desert grassland (DRS) had the highest drought risk (DR = 0.30), while meadow grassland (MEG) had the lowest risk (DR = 0.02). The analysis of the drivers of drought risk in grassland biomass shows that meteorological elements mainly drive typical grasslands (TYG) and other grasslands (OTH). SM greatly impacted MEG, and ET had a relatively high contribution to DRS. This study provides a basis for managing different grassland types in large areas and developing corresponding drought adaptation programs.https://www.mdpi.com/2072-4292/14/22/5745biomass inversionprairie drought riskclimate variabilityhuman activities |
| spellingShingle | Lingxin Bu Quan Lai Song Qing Yuhai Bao Xinyi Liu Qin Na Yuan Li Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment Remote Sensing biomass inversion prairie drought risk climate variability human activities |
| title | Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment |
| title_full | Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment |
| title_fullStr | Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment |
| title_full_unstemmed | Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment |
| title_short | Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment |
| title_sort | grassland biomass inversion based on a random forest algorithm and drought risk assessment |
| topic | biomass inversion prairie drought risk climate variability human activities |
| url | https://www.mdpi.com/2072-4292/14/22/5745 |
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