Name
Abstract | ||
|---|---|---|
Grasslands are crucial ecosystem biomes for breeding livestock and combatting climate change. By 2018, the national nature reserves (NNRs) in the Inner Mongolia Autonomous Region (IMAR) had constituted 8.55% of the land area. However, there is still a knowledge gap about their effectiveness in grasslands. Based on a multiyear time series of the growing season composite from 2000 to 2020, we proposed an effectiveness score to assess the effectiveness of the NNRs, using the 250 m MOD13Q1 NDVI data with Theil-Sen and Mann-Kendall trend analysis methods. We found the following: 22 of 30 NNRs were deemed effective in protecting the Inner Mongolian grasslands. The NNRs increased pixels with a sustainable trend 19.26% and 20.55% higher than the unprotected areas and the IMAR, respectively. The pixels with a CVNDVI < 0.1 (i.e., NDVI coefficient of variation) in the NNRs increased >35.22% more than those in the unprotected areas and the IMAR. The NDVI changes within the NNRs showed that 63.64% of NNRs had a more significant trend of greening than before the change point, which suggests a general greening in NNRs. We also found that the NNRs achieved heterogeneous effectiveness scores across protection types. Forest ecology protection and wildlife animal protection types are the most efficient, whereas wildlife vegetation protection is the least effective type. This study enriches the understanding of grassland conservation and sheds light on the future direction of the sustainable management of NNRs. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/rs14071760 | REMOTE SENSING |
Keywords | DocType | Volume |
NDVI, national nature reserve, protected area, nature reserve, national park, Inner Mongolia, conservation, grassland | Journal | 14 |
Issue | ISSN | Citations |
7 | 2072-4292 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siqing Zhao | 1 | 0 | 0.34 |
| Xiang Zhao | 2 | 27 | 7.80 |
| Jiacheng Zhao | 3 | 0 | 2.37 |
| Naijing Liu | 4 | 0 | 1.01 |
| Mengmeng Sun | 5 | 0 | 0.34 |
| Baohui Mu | 6 | 0 | 0.68 |
| Na Sun | 7 | 0 | 0.68 |
| Yinkun Guo | 8 | 0 | 0.34 |