Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau
Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetatio...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23001620 |
| _version_ | 1797866184894316544 |
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| author | Peiqing Lou Tonghua Wu Sizhong Yang Xiaodong Wu Jianjun Chen Xiaofan Zhu Jie Chen Xingchen Lin Ren Li Chengpeng Shang Dong Wang Yune La Amin Wen Xin Ma |
| author_facet | Peiqing Lou Tonghua Wu Sizhong Yang Xiaodong Wu Jianjun Chen Xiaofan Zhu Jie Chen Xingchen Lin Ren Li Chengpeng Shang Dong Wang Yune La Amin Wen Xin Ma |
| author_sort | Peiqing Lou |
| collection | DOAJ |
| description | Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988–2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 × 104 km2) and from alpine grassland to alpine meadow (17.43 × 104 km2) during 1988–2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influencing factors affecting vegetation greening on the QTP are precipitation (q-statistic = 23.87 %) and temperature (q-statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human development strategies. |
| first_indexed | 2024-04-09T23:19:58Z |
| format | Article |
| id | doaj.art-b2dfaddd27eb4fcc8a8eff06e46d2d29 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-09T23:19:58Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-b2dfaddd27eb4fcc8a8eff06e46d2d292023-03-22T04:35:57ZengElsevierEcological Indicators1470-160X2023-04-01148110020Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet PlateauPeiqing Lou0Tonghua Wu1Sizhong Yang2Xiaodong Wu3Jianjun Chen4Xiaofan Zhu5Jie Chen6Xingchen Lin7Ren Li8Chengpeng Shang9Dong Wang10Yune La11Amin Wen12Xin Ma13Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; Corresponding author.Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaVegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988–2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 × 104 km2) and from alpine grassland to alpine meadow (17.43 × 104 km2) during 1988–2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influencing factors affecting vegetation greening on the QTP are precipitation (q-statistic = 23.87 %) and temperature (q-statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human development strategies.http://www.sciencedirect.com/science/article/pii/S1470160X23001620GreeningLand use/cover changeDeep learningGoogle Earth EngineLandsatQinghai-Tibet Plateau |
| spellingShingle | Peiqing Lou Tonghua Wu Sizhong Yang Xiaodong Wu Jianjun Chen Xiaofan Zhu Jie Chen Xingchen Lin Ren Li Chengpeng Shang Dong Wang Yune La Amin Wen Xin Ma Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau Ecological Indicators Greening Land use/cover change Deep learning Google Earth Engine Landsat Qinghai-Tibet Plateau |
| title | Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau |
| title_full | Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau |
| title_fullStr | Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau |
| title_full_unstemmed | Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau |
| title_short | Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau |
| title_sort | deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the qinghai tibet plateau |
| topic | Greening Land use/cover change Deep learning Google Earth Engine Landsat Qinghai-Tibet Plateau |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23001620 |
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