Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model
Aquatic vegetation is an important marker of the change in the lake ecosystem. It plays an important supporting role in the lake ecosystem, and its abundance and cover changes affect the ecosystem balance. Collecting accurate long-term distribution data on aquatic vegetation can help monitor the cha...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Ecology and Evolution |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fevo.2022.1031678/full |
| _version_ | 1811186857642295296 |
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| author | Guanglei Hou Haobin Zhang Haobin Zhang Zhaoli Liu Ziqi Chen Ziqi Chen Yakun Cao |
| author_facet | Guanglei Hou Haobin Zhang Haobin Zhang Zhaoli Liu Ziqi Chen Ziqi Chen Yakun Cao |
| author_sort | Guanglei Hou |
| collection | DOAJ |
| description | Aquatic vegetation is an important marker of the change in the lake ecosystem. It plays an important supporting role in the lake ecosystem, and its abundance and cover changes affect the ecosystem balance. Collecting accurate long-term distribution data on aquatic vegetation can help monitor the change in the lake ecosystem, thereby providing scientific support for efforts to maintain the balance of the ecosystem. This work aimed to establish an improved CA-Markov model to reconstruct historical potential distribution of aquatic vegetation in the two typical lakes (Xingkai Lake and Hulun Lake) in Northeast China during 1950s to 1960s. We firstly analyzed remote sensing data on the spatial distribution of aquatic vegetation data in two lakes in six periods from the 1970 to 2015. Then, we built a transfer probability matrix for changes in hydrothermal conditions (temperature and precipitation) based on similar periods, and we designed suitability images using the spatial frequency and temporal continuity of the constraints. Finally, we established an improved CA-Markov model based on the transfer probability matrix and suitability images to reconstruct the potential distributions of aquatic vegetation in the two northeastern lakes during the 1950s and 1960s. The results showed the areas of aquatic vegetation in the 1950s and 1960s were 102.37 km2 and 100.7 km2 for Xingkai Lake and 90.81 km2 and 88.15 km2 for Hulun Lake, respectively. Compared with the traditional CA-Markov model, the overall accuracy of the improved model increased by more than 50%, which proved the improved CA-Markov model can be used to effectively reconstruct the historical potential distribution of aquatic vegetation. This study provides an accurate methodology for simulating the potential historical distributions of aquatic vegetation to enrich the study of the historical evolution of lake ecosystem. |
| first_indexed | 2024-04-11T13:52:18Z |
| format | Article |
| id | doaj.art-0379c6faf2654cf699864fea4aa712e6 |
| institution | Directory Open Access Journal |
| issn | 2296-701X |
| language | English |
| last_indexed | 2024-04-11T13:52:18Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Ecology and Evolution |
| spelling | doaj.art-0379c6faf2654cf699864fea4aa712e62022-12-22T04:20:29ZengFrontiers Media S.A.Frontiers in Ecology and Evolution2296-701X2022-11-011010.3389/fevo.2022.10316781031678Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov modelGuanglei Hou0Haobin Zhang1Haobin Zhang2Zhaoli Liu3Ziqi Chen4Ziqi Chen5Yakun Cao6Northeast Institute of Geography and Agriculture, Chinese Academy of Sciences (CAS), Changchun, ChinaNortheast Institute of Geography and Agriculture, Chinese Academy of Sciences (CAS), Changchun, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun, ChinaNortheast Institute of Geography and Agriculture, Chinese Academy of Sciences (CAS), Changchun, ChinaNortheast Institute of Geography and Agriculture, Chinese Academy of Sciences (CAS), Changchun, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun, ChinaNortheast Institute of Geography and Agriculture, Chinese Academy of Sciences (CAS), Changchun, ChinaAquatic vegetation is an important marker of the change in the lake ecosystem. It plays an important supporting role in the lake ecosystem, and its abundance and cover changes affect the ecosystem balance. Collecting accurate long-term distribution data on aquatic vegetation can help monitor the change in the lake ecosystem, thereby providing scientific support for efforts to maintain the balance of the ecosystem. This work aimed to establish an improved CA-Markov model to reconstruct historical potential distribution of aquatic vegetation in the two typical lakes (Xingkai Lake and Hulun Lake) in Northeast China during 1950s to 1960s. We firstly analyzed remote sensing data on the spatial distribution of aquatic vegetation data in two lakes in six periods from the 1970 to 2015. Then, we built a transfer probability matrix for changes in hydrothermal conditions (temperature and precipitation) based on similar periods, and we designed suitability images using the spatial frequency and temporal continuity of the constraints. Finally, we established an improved CA-Markov model based on the transfer probability matrix and suitability images to reconstruct the potential distributions of aquatic vegetation in the two northeastern lakes during the 1950s and 1960s. The results showed the areas of aquatic vegetation in the 1950s and 1960s were 102.37 km2 and 100.7 km2 for Xingkai Lake and 90.81 km2 and 88.15 km2 for Hulun Lake, respectively. Compared with the traditional CA-Markov model, the overall accuracy of the improved model increased by more than 50%, which proved the improved CA-Markov model can be used to effectively reconstruct the historical potential distribution of aquatic vegetation. This study provides an accurate methodology for simulating the potential historical distributions of aquatic vegetation to enrich the study of the historical evolution of lake ecosystem.https://www.frontiersin.org/articles/10.3389/fevo.2022.1031678/fullaquatic vegetationimproved CA-Markov modeltransfer probability matrixsuitability imageshistorical reconstruction |
| spellingShingle | Guanglei Hou Haobin Zhang Haobin Zhang Zhaoli Liu Ziqi Chen Ziqi Chen Yakun Cao Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model Frontiers in Ecology and Evolution aquatic vegetation improved CA-Markov model transfer probability matrix suitability images historical reconstruction |
| title | Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model |
| title_full | Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model |
| title_fullStr | Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model |
| title_full_unstemmed | Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model |
| title_short | Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model |
| title_sort | historical reconstruction of aquatic vegetation of typical lakes in northeast china based on an improved ca markov model |
| topic | aquatic vegetation improved CA-Markov model transfer probability matrix suitability images historical reconstruction |
| url | https://www.frontiersin.org/articles/10.3389/fevo.2022.1031678/full |
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