The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation
Monitoring autumn vegetation dynamics in alpine regions is crucial for managing local livestock, understanding regional productivity, and assessing the responses of alpine regions to climate change. However, remote sensing-based vegetation monitoring is significantly affected by snowfall. The impact...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4783 |
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| author | Yao Tang Jin Chen Jingyi Xu Jiahui Xu Jingwen Ni Zhaojun Zheng Bailang Yu Jianping Wu Yan Huang |
| author_facet | Yao Tang Jin Chen Jingyi Xu Jiahui Xu Jingwen Ni Zhaojun Zheng Bailang Yu Jianping Wu Yan Huang |
| author_sort | Yao Tang |
| collection | DOAJ |
| description | Monitoring autumn vegetation dynamics in alpine regions is crucial for managing local livestock, understanding regional productivity, and assessing the responses of alpine regions to climate change. However, remote sensing-based vegetation monitoring is significantly affected by snowfall. The impact of autumn snowfall, particularly when vegetation has not fully entered dormancy, has been largely overlooked. To demonstrate the uncertainties caused by autumn snowfall in remote sensing-based vegetation monitoring, we analyzed 16 short-term snowfall events in the Qinghai–Tibet Plateau. We employed a synthetic difference-in-differences estimation framework and conducted simulated experiments to isolate the impact of snowfall from other factors, revealing its effects on vegetation indices (VIs) and autumn phenology estimation. Our findings indicate that autumn snowfall notably affects commonly used VIs and their associated phenology estimates. Modified VIs (i.e., Normalized Difference Infrared Index (NDII), Phenology Index (PI), Normalized Difference Phenology Index (NDPI), and Normalized Difference Greenness Index (NDGI)) revealed greater resilience to snowfall compared to conventional VIs (i.e., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) in phenology estimation. Areas with remaining green vegetation in autumn showed more pronounced numerical changes in VIs due to snowfall. Furthermore, the impact of autumn snowfall closely correlated with underlying vegetation types. Forested areas experienced less impact from snowfall compared to grass- and shrub-dominated regions. Earlier snowfall onset and increased snowfall frequency further exacerbated deviations in estimated phenology caused by snowfall. This study highlights the significant impact of autumn snowfall on remote sensing-based vegetation monitoring and provides a scientific basis for accurate vegetation studies in high-altitude regions. |
| format | Article |
| id | doaj-art-291f0ec5c9fe47928283c06ba6b7cc1b |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-291f0ec5c9fe47928283c06ba6b7cc1b2024-12-27T14:51:12ZengMDPI AGRemote Sensing2072-42922024-12-011624478310.3390/rs16244783The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology EstimationYao Tang0Jin Chen1Jingyi Xu2Jiahui Xu3Jingwen Ni4Zhaojun Zheng5Bailang Yu6Jianping Wu7Yan Huang8Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, ChinaKey Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaNational Satellite Meteorological Center, Beijing 100081, ChinaKey Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaMonitoring autumn vegetation dynamics in alpine regions is crucial for managing local livestock, understanding regional productivity, and assessing the responses of alpine regions to climate change. However, remote sensing-based vegetation monitoring is significantly affected by snowfall. The impact of autumn snowfall, particularly when vegetation has not fully entered dormancy, has been largely overlooked. To demonstrate the uncertainties caused by autumn snowfall in remote sensing-based vegetation monitoring, we analyzed 16 short-term snowfall events in the Qinghai–Tibet Plateau. We employed a synthetic difference-in-differences estimation framework and conducted simulated experiments to isolate the impact of snowfall from other factors, revealing its effects on vegetation indices (VIs) and autumn phenology estimation. Our findings indicate that autumn snowfall notably affects commonly used VIs and their associated phenology estimates. Modified VIs (i.e., Normalized Difference Infrared Index (NDII), Phenology Index (PI), Normalized Difference Phenology Index (NDPI), and Normalized Difference Greenness Index (NDGI)) revealed greater resilience to snowfall compared to conventional VIs (i.e., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) in phenology estimation. Areas with remaining green vegetation in autumn showed more pronounced numerical changes in VIs due to snowfall. Furthermore, the impact of autumn snowfall closely correlated with underlying vegetation types. Forested areas experienced less impact from snowfall compared to grass- and shrub-dominated regions. Earlier snowfall onset and increased snowfall frequency further exacerbated deviations in estimated phenology caused by snowfall. This study highlights the significant impact of autumn snowfall on remote sensing-based vegetation monitoring and provides a scientific basis for accurate vegetation studies in high-altitude regions.https://www.mdpi.com/2072-4292/16/24/4783autumn snowfallvegetation indexvegetation phenologysynthetic difference-in-differenceslinear mixture mode |
| spellingShingle | Yao Tang Jin Chen Jingyi Xu Jiahui Xu Jingwen Ni Zhaojun Zheng Bailang Yu Jianping Wu Yan Huang The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation Remote Sensing autumn snowfall vegetation index vegetation phenology synthetic difference-in-differences linear mixture mode |
| title | The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation |
| title_full | The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation |
| title_fullStr | The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation |
| title_full_unstemmed | The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation |
| title_short | The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation |
| title_sort | impact of autumn snowfall on vegetation indices and autumn phenology estimation |
| topic | autumn snowfall vegetation index vegetation phenology synthetic difference-in-differences linear mixture mode |
| url | https://www.mdpi.com/2072-4292/16/24/4783 |
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