Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China
The dynamics of vegetation changes and phenology serve as key indicators of interannual changes in vegetation productivity. Monitoring the changes in the Nanling grassland ecosystem using the remote sensing vegetation index is crucial for the rational development, utilization, and protection of thes...
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MDPI AG
2025-01-01
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| author | Zhenhuan Liu Sujuan Li Yueteng Chi |
| author_facet | Zhenhuan Liu Sujuan Li Yueteng Chi |
| author_sort | Zhenhuan Liu |
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| description | The dynamics of vegetation changes and phenology serve as key indicators of interannual changes in vegetation productivity. Monitoring the changes in the Nanling grassland ecosystem using the remote sensing vegetation index is crucial for the rational development, utilization, and protection of these grassland resources. Grasslands in the hilly areas of southern China’s middle and low mountains have a high restoration efficiency due to the favorable combination of water and temperature conditions. However, the dynamic adaptation process of grassland restoration under the combined effects of climate change and human activities remains unclear. The aim of this study was to conduct continuous phenological monitoring of the Nanling grassland ecosystem, and evaluate its seasonal characteristics, trends, and the thresholds for grassland changes. The Normalized Difference Phenology Index (NDPI) values of Nanling Mountains’ grasslands from 2000 to 2021 was calculated using MOD09A1 images from the Google Earth Engine (GEE) platform. The Savitzky–Golay filter and Mann–Kendall test were applied for time series smoothing and trend analysis, and growing seasons were extracted annually using Seasonal Trend Decomposition and LOESS. A segmented regression method was then employed to detect the thresholds for grassland ecosystem restoration based on phenology and grassland cover percentage. The results showed that (1) the NDPI values increased significantly (<i>p</i> < 0.01) across all grassland patches, particularly in the southeast, with a notable rise from 2010 to 2014, and following an eastern to western to central trend mutation sequence. (2) the annual lower and upper NDPI thresholds of the grasslands were 0.005~0.167 and 0.572~0.727, which mainly occurred in January–March and June–September, respectively. (3) Most of the time series in the same periods showed increasing trends, with the growing season length varying from 188 to 247 days. (4) The overall potential productivity of the Nanling grassland improved. (5) The restoration of the mountain grasslands was significantly associated with the grassland coverage and mean NDPI values, with a key threshold identified at a mean NDPI value of 0.5 for 2.1% grassland coverage. This study indicates that to ensure the sustainable development and conservation of grassland ecosystems, targeted management strategies should be implemented, particularly in regions where human factors significantly influence grassland productivity fluctuations. |
| format | Article |
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| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-b4dae845f3cf4184a7efc883184b8e3e2025-08-20T02:12:29ZengMDPI AGRemote Sensing2072-42922025-01-0117345110.3390/rs17030451Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of ChinaZhenhuan Liu0Sujuan Li1Yueteng Chi2Carbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, ChinaCarbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, ChinaCarbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, ChinaThe dynamics of vegetation changes and phenology serve as key indicators of interannual changes in vegetation productivity. Monitoring the changes in the Nanling grassland ecosystem using the remote sensing vegetation index is crucial for the rational development, utilization, and protection of these grassland resources. Grasslands in the hilly areas of southern China’s middle and low mountains have a high restoration efficiency due to the favorable combination of water and temperature conditions. However, the dynamic adaptation process of grassland restoration under the combined effects of climate change and human activities remains unclear. The aim of this study was to conduct continuous phenological monitoring of the Nanling grassland ecosystem, and evaluate its seasonal characteristics, trends, and the thresholds for grassland changes. The Normalized Difference Phenology Index (NDPI) values of Nanling Mountains’ grasslands from 2000 to 2021 was calculated using MOD09A1 images from the Google Earth Engine (GEE) platform. The Savitzky–Golay filter and Mann–Kendall test were applied for time series smoothing and trend analysis, and growing seasons were extracted annually using Seasonal Trend Decomposition and LOESS. A segmented regression method was then employed to detect the thresholds for grassland ecosystem restoration based on phenology and grassland cover percentage. The results showed that (1) the NDPI values increased significantly (<i>p</i> < 0.01) across all grassland patches, particularly in the southeast, with a notable rise from 2010 to 2014, and following an eastern to western to central trend mutation sequence. (2) the annual lower and upper NDPI thresholds of the grasslands were 0.005~0.167 and 0.572~0.727, which mainly occurred in January–March and June–September, respectively. (3) Most of the time series in the same periods showed increasing trends, with the growing season length varying from 188 to 247 days. (4) The overall potential productivity of the Nanling grassland improved. (5) The restoration of the mountain grasslands was significantly associated with the grassland coverage and mean NDPI values, with a key threshold identified at a mean NDPI value of 0.5 for 2.1% grassland coverage. This study indicates that to ensure the sustainable development and conservation of grassland ecosystems, targeted management strategies should be implemented, particularly in regions where human factors significantly influence grassland productivity fluctuations.https://www.mdpi.com/2072-4292/17/3/451grassland restorationphenological thresholdNormalized Difference Phenology IndexNanling mountain area |
| spellingShingle | Zhenhuan Liu Sujuan Li Yueteng Chi Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China Remote Sensing grassland restoration phenological threshold Normalized Difference Phenology Index Nanling mountain area |
| title | Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China |
| title_full | Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China |
| title_fullStr | Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China |
| title_full_unstemmed | Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China |
| title_short | Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China |
| title_sort | detecting the phenological threshold to assess the grassland restoration in the nanling mountain area of china |
| topic | grassland restoration phenological threshold Normalized Difference Phenology Index Nanling mountain area |
| url | https://www.mdpi.com/2072-4292/17/3/451 |
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