Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data
The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in frag...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/7/1222 |
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| author | Meng Li Guangjun Wang Aohan Sun Youkun Wang Fang Li Sihai Liang |
| author_facet | Meng Li Guangjun Wang Aohan Sun Youkun Wang Fang Li Sihai Liang |
| author_sort | Meng Li |
| collection | DOAJ |
| description | The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by <i>Alpine kobresia</i> spp., <i>Forb Meadow</i>, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin. |
| first_indexed | 2024-04-24T10:35:45Z |
| format | Article |
| id | doaj.art-91c40a22e94643abb99dbb3056422433 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T10:35:45Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-91c40a22e94643abb99dbb30564224332024-04-12T13:25:40ZengMDPI AGRemote Sensing2072-42922024-03-01167122210.3390/rs16071222Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI DataMeng Li0Guangjun Wang1Aohan Sun2Youkun Wang3Fang Li4Sihai Liang5School of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaHaina Cloud IOT Technology Co., Ltd., Qingdao 266075, ChinaSchool of Water Resources and Environment, China University of Geosciences, Beijing 100083, ChinaThe normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by <i>Alpine kobresia</i> spp., <i>Forb Meadow</i>, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin.https://www.mdpi.com/2072-4292/16/7/1222vegetation trend analysisalpine grasslandannual maximum NDVILandsat and MODISQinghai Lake Basin |
| spellingShingle | Meng Li Guangjun Wang Aohan Sun Youkun Wang Fang Li Sihai Liang Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data Remote Sensing vegetation trend analysis alpine grassland annual maximum NDVI Landsat and MODIS Qinghai Lake Basin |
| title | Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data |
| title_full | Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data |
| title_fullStr | Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data |
| title_full_unstemmed | Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data |
| title_short | Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data |
| title_sort | monitoring grassland variation in a typical area of the qinghai lake basin using 30 m annual maximum ndvi data |
| topic | vegetation trend analysis alpine grassland annual maximum NDVI Landsat and MODIS Qinghai Lake Basin |
| url | https://www.mdpi.com/2072-4292/16/7/1222 |
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