Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling Method
The upper Luanhe River Basin is a significant ecological barrier guarding the Beijing–Tianjin–Hebei region in China. Quantitative measures of vegetation productivity can be used to assess ecosystem carbon sequestration capacity and monitor regional ecological environmental heal...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/9638381/ |
| _version_ | 1818366633321693184 |
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| author | Qinru Liu Liang Zhao Rui Sun Tao Yu Shun Cheng Mengjia Wang Anran Zhu Qi Li |
| author_facet | Qinru Liu Liang Zhao Rui Sun Tao Yu Shun Cheng Mengjia Wang Anran Zhu Qi Li |
| author_sort | Qinru Liu |
| collection | DOAJ |
| description | The upper Luanhe River Basin is a significant ecological barrier guarding the Beijing–Tianjin–Hebei region in China. Quantitative measures of vegetation productivity can be used to assess ecosystem carbon sequestration capacity and monitor regional ecological environmental health. Although several vegetation productivity products have been generated, poor spatiotemporal resolution limits their application in ecosystem service assessment. In this article, vegetation net primary productivity (NPP) from 2000 to 2017 with a resolution of 30 m in the upper Luanhe River Basin was generated based on a data fusion model and the multisource data synergized quantitative (MuSyQ) NPP model. Then, the variation trend of NPP and its climate controls were analyzed. Compared with forest NPP observation data, we derived an <italic>R</italic><sup>2</sup> of 0.68 and the root-mean-square error of 81.70 gC<sup>.</sup>m<sup>−2.</sup>yr<sup>−1</sup>. Annual NPP had a fluctuating increasing trend from 2001 to 2017, with values ranging between 3.43 and 5.00 TgC<sup>.</sup>yr<sup>−1</sup>, with an annual increase trend of 0.04 TgC<sup>.</sup>yr<sup>−1</sup>. Precipitation was significantly correlated with NPP in the upper part of the Luanhe River basin, which is an important reason for the interannual variation of NPP. Grassland had a stronger correlation to precipitation than forest because it is more sensitive to precipitation. The area where the temperature is significantly correlated with annual NPP only accounts for 2% of the study area, indicating that temperature has a weak influence on NPP. Furthermore, human activities, such as forest management, fertilization, and irrigation, can change the trend of annual NPP. |
| first_indexed | 2024-12-13T22:39:15Z |
| format | Article |
| id | doaj.art-f5f23ded37894c35b57feb10a165c9e7 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-13T22:39:15Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-f5f23ded37894c35b57feb10a165c9e72022-12-21T23:28:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-011535336310.1109/JSTARS.2021.31327239638381Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling MethodQinru Liu0Liang Zhao1https://orcid.org/0000-0002-0904-0768Rui Sun2https://orcid.org/0000-0002-2070-3278Tao Yu3Shun Cheng4Mengjia Wang5Anran Zhu6Qi Li7State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaResearch Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, ChinaSaihanba Machine Forest Center, Chengde, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaThe upper Luanhe River Basin is a significant ecological barrier guarding the Beijing–Tianjin–Hebei region in China. Quantitative measures of vegetation productivity can be used to assess ecosystem carbon sequestration capacity and monitor regional ecological environmental health. Although several vegetation productivity products have been generated, poor spatiotemporal resolution limits their application in ecosystem service assessment. In this article, vegetation net primary productivity (NPP) from 2000 to 2017 with a resolution of 30 m in the upper Luanhe River Basin was generated based on a data fusion model and the multisource data synergized quantitative (MuSyQ) NPP model. Then, the variation trend of NPP and its climate controls were analyzed. Compared with forest NPP observation data, we derived an <italic>R</italic><sup>2</sup> of 0.68 and the root-mean-square error of 81.70 gC<sup>.</sup>m<sup>−2.</sup>yr<sup>−1</sup>. Annual NPP had a fluctuating increasing trend from 2001 to 2017, with values ranging between 3.43 and 5.00 TgC<sup>.</sup>yr<sup>−1</sup>, with an annual increase trend of 0.04 TgC<sup>.</sup>yr<sup>−1</sup>. Precipitation was significantly correlated with NPP in the upper part of the Luanhe River basin, which is an important reason for the interannual variation of NPP. Grassland had a stronger correlation to precipitation than forest because it is more sensitive to precipitation. The area where the temperature is significantly correlated with annual NPP only accounts for 2% of the study area, indicating that temperature has a weak influence on NPP. Furthermore, human activities, such as forest management, fertilization, and irrigation, can change the trend of annual NPP.https://ieeexplore.ieee.org/document/9638381/Climate controlsdownscalingnet primary productivity (NPP)spatiotemporal variationthe upper Luanhe River basin |
| spellingShingle | Qinru Liu Liang Zhao Rui Sun Tao Yu Shun Cheng Mengjia Wang Anran Zhu Qi Li Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling Method IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Climate controls downscaling net primary productivity (NPP) spatiotemporal variation the upper Luanhe River basin |
| title | Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling Method |
| title_full | Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling Method |
| title_fullStr | Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling Method |
| title_full_unstemmed | Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling Method |
| title_short | Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling Method |
| title_sort | estimation and spatiotemporal variation analysis of net primary productivity in the upper luanhe river basin in china from 2001 to 2017 combining with a downscaling method |
| topic | Climate controls downscaling net primary productivity (NPP) spatiotemporal variation the upper Luanhe River basin |
| url | https://ieeexplore.ieee.org/document/9638381/ |
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