The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China
The aboveground biomass (AGB) of withered grass is an important early-warning indicator for grassland fire risk. Most grassland fires occur during the dry-grass season. In order to improve the fire-warning efficiency of withered AGB, it is essential to rapidly acquire the amount of withered-grass bi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.1031098/full |
| _version_ | 1828061721571360768 |
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| author | Peng Zang Yanhong Zhang Ziqi Chen Ziqi Chen Guanglei Hou Zhaoli Liu Xingchang Lu |
| author_facet | Peng Zang Yanhong Zhang Ziqi Chen Ziqi Chen Guanglei Hou Zhaoli Liu Xingchang Lu |
| author_sort | Peng Zang |
| collection | DOAJ |
| description | The aboveground biomass (AGB) of withered grass is an important early-warning indicator for grassland fire risk. Most grassland fires occur during the dry-grass season. In order to improve the fire-warning efficiency of withered AGB, it is essential to rapidly acquire the amount of withered-grass biomass. Remote-sensing data has been widely used in monitoring and estimating grassland yields during the growing season. However, applying remote sensing to the estimation of withered grass is still in need of exploration. The aim of this work was to try to establish a remote-sensing estimation model for withered AGB in the dry-grass season. The estimation of aboveground biomass can effectively prevent the occurrence of fire, protect the environment, facilitate local management and reduce economic losses. Our approach was to, first, calculate a dry-grass index based on Sentinel-2 image data and using ENVI, SNAP, and ArcGIS software. Second, a model to estimate the fuel quantity during the dry-grass season was established by regression analysis combined with field-measured data. Finally, the estimation model was used to predict the amount of fuel in different months of the dry-grass season, followed by the fire-defense elements, which were quantified and mapped in the Longzhao Marsh wetlands. It was found that: 1) the two indices were significantly correlated (0.678) with the amount of fuel; 2) the established model could accurately estimate the amount of fuel in the study area during the dry season, and accurate test results demonstrated that the correlation between the estimated results of the best model and the measured values was 0.863, indicating high accuracy; 3) the spatiotemporal variation of withered grass in the study area was obviously different, and the quantities of fuel predicted for the other months were more accurate, which may reflect monthly dynamic changes in actual fuel quantities; and 4) the establishment of a remote-sensing estimation model for fuel quantity in the Longzhao Marsh during the dry-grass season could provide important parameters for fire-risk warning in the western grassland of Jilin Province and Northeast China. |
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| id | doaj.art-06d093692d6b4c6dbbbf08415a21d9ba |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T22:18:12Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Earth Science |
| spelling | doaj.art-06d093692d6b4c6dbbbf08415a21d9ba2023-01-18T04:43:30ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011010.3389/feart.2022.10310981031098The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast ChinaPeng Zang0Yanhong Zhang1Ziqi Chen2Ziqi Chen3Guanglei Hou4Zhaoli Liu5Xingchang Lu6College of Geo-exploration Science and Technology, Jilin University, Changchun, ChinaCollege of Geo-exploration Science and Technology, Jilin University, Changchun, ChinaCollege of Geo-exploration Science and Technology, Jilin University, Changchun, ChinaNortheast Institute of Geography and Agriculture, Chinese Academy of Science, Changchun, ChinaNortheast Institute of Geography and Agriculture, Chinese Academy of Science, Changchun, ChinaNortheast Institute of Geography and Agriculture, Chinese Academy of Science, Changchun, ChinaCollege of Geo-exploration Science and Technology, Jilin University, Changchun, ChinaThe aboveground biomass (AGB) of withered grass is an important early-warning indicator for grassland fire risk. Most grassland fires occur during the dry-grass season. In order to improve the fire-warning efficiency of withered AGB, it is essential to rapidly acquire the amount of withered-grass biomass. Remote-sensing data has been widely used in monitoring and estimating grassland yields during the growing season. However, applying remote sensing to the estimation of withered grass is still in need of exploration. The aim of this work was to try to establish a remote-sensing estimation model for withered AGB in the dry-grass season. The estimation of aboveground biomass can effectively prevent the occurrence of fire, protect the environment, facilitate local management and reduce economic losses. Our approach was to, first, calculate a dry-grass index based on Sentinel-2 image data and using ENVI, SNAP, and ArcGIS software. Second, a model to estimate the fuel quantity during the dry-grass season was established by regression analysis combined with field-measured data. Finally, the estimation model was used to predict the amount of fuel in different months of the dry-grass season, followed by the fire-defense elements, which were quantified and mapped in the Longzhao Marsh wetlands. It was found that: 1) the two indices were significantly correlated (0.678) with the amount of fuel; 2) the established model could accurately estimate the amount of fuel in the study area during the dry season, and accurate test results demonstrated that the correlation between the estimated results of the best model and the measured values was 0.863, indicating high accuracy; 3) the spatiotemporal variation of withered grass in the study area was obviously different, and the quantities of fuel predicted for the other months were more accurate, which may reflect monthly dynamic changes in actual fuel quantities; and 4) the establishment of a remote-sensing estimation model for fuel quantity in the Longzhao Marsh during the dry-grass season could provide important parameters for fire-risk warning in the western grassland of Jilin Province and Northeast China.https://www.frontiersin.org/articles/10.3389/feart.2022.1031098/fullwithered grass biomassdry grass indexspatial-temporal variationSentinel-2 dataNortheast China |
| spellingShingle | Peng Zang Yanhong Zhang Ziqi Chen Ziqi Chen Guanglei Hou Zhaoli Liu Xingchang Lu The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China Frontiers in Earth Science withered grass biomass dry grass index spatial-temporal variation Sentinel-2 data Northeast China |
| title | The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China |
| title_full | The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China |
| title_fullStr | The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China |
| title_full_unstemmed | The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China |
| title_short | The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China |
| title_sort | inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of northeast china |
| topic | withered grass biomass dry grass index spatial-temporal variation Sentinel-2 data Northeast China |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.1031098/full |
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