Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land Use/Land Cover Change on Water Retention in Beijing
文献类型：期刊论文
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| 作者 | Huang, Binbin ; Yang, Yanzheng; Li, Ruonan; Zheng, Hua; Wang, Xiaoke; Wang, Xuming; Zhang, Yan
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| 刊名 | REMOTE SENSING
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| 出版日期 | 2022-02 |
| 卷号 | 14期号:3页码:743 |
| 关键词 | NET PRIMARY PRODUCTIVITY UNDERSTORY VEGETATION CLIMATE VARIABILITY ECOSYSTEM SERVICES NATURAL VEGETATION LOESS PLATEAU SENSING DATA TIME-SERIES RIVER BASIN FOREST |
| 英文摘要 | Maintaining or increasing water retention in ecosystems (WRE) can reduce floods and increase water resource provision. However, few studies have taken the effect of the spatial information of vegetation structure into consideration when assessing the effects of land use/land cover (LULC) change on WRE. In this study, we integrated the remotely sensed leaf area index (LAI) into the ecosystem process-based Biome-BGC model to analyse the impact of LULC change on the WRE of Beijing between 2000 and 2015. Our results show that the volume of WRE increased by approximately 8.58 million m(3) in 2015 as compared with 2000. The volume of WRE in forests increased by approximately 26.74 million m(3), while urbanization, cropland expansion and deforestation caused the volume of WRE to decline by 11.96 million m(3), 5.86 million m(3) and 3.20 million m(3), respectively. The increased WRE contributed by unchanged forests (14.46 million m(3)) was much greater than that of new-planted forests (12.28 million m(3)), but the increase in WRE capacity per unit area in new-planted forests (124.69 +/- 14.30 m(3)/ha) was almost tenfold greater than that of unchanged forests (15.60 +/- 7.85 m(3)/ha). The greater increase in WRE capacity in increased forests than that of unchanged forests was mostly due to the fact that the higher LAI in unchanged forests induced more evapotranspiration to exhaust more water. Meanwhile, the inverted U-shape relationship that existed between the forest LAI and WRE implied that continued increased LAI in forests probably caused the WRE decline. This study demonstrates that integrating remotely sensed LAI with the Biome-BGC model is feasible for capturing the impact of LULC change with the spatial information of vegetation structure on WRE and reduces uncertainty. |
| 源URL | [https://ir.rcees.ac.cn/handle/311016/47288]  |
| 专题 | 生态环境研究中心_城市与区域生态国家重点实验室 |
| 通讯作者 | Wang, Xiaoke |
| 作者单位 | 1.Fujian Normal Univ, Coll Geog Sci, Fuzhou 350007, Peoples R China 2.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Minist Educ, Key Lab Humid Subtrop Ecogeog Proc, Fuzhou 350007, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Binbin,Yang, Yanzheng,Li, Ruonan,et al. Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land Use/Land Cover Change on Water Retention in Beijing, Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land UseLand Cover Change on Water Retention in Beijing.pdf[J]. REMOTE SENSING,2022,14(3):743. |
| APA | Huang, Binbin.,Yang, Yanzheng.,Li, Ruonan.,Zheng, Hua.,Wang, Xiaoke.,...&Zhang, Yan.(2022).Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land Use/Land Cover Change on Water Retention in Beijing.REMOTE SENSING,14(3),743. |
| MLA | Huang, Binbin,et al."Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land Use/Land Cover Change on Water Retention in Beijing".REMOTE SENSING 14.3(2022):743. |
入库方式： OAI收割
来源：生态环境研究中心
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