A remote sensing evidence on the marginality, stagementation and spatiotemporal heterogeneity of vegetation evolution characteristics in the Yinshan Mountains, China: Based on PKU GIMMS NDVI (1984–2022) Article Swipe
Mi Yan
,
Jingyang Lu
,
Yingying Ma
,
Chao Ma
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecolind.2025.113193
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecolind.2025.113193
This study focuses on a typical Ecological Vulnerable Area (EVA)-Yinshan mountains (YSMs), using PKU GIMMS NDVI as a vegetation growth indicator, to explore the spatiotemporal heterogeneity and driving mechanisms of vegetation in in desertified steppe ecosystems (DSEs) of arid and semi-arid regions. The Theil-Sen (T-S) Median trend analysis & Mann-Kendall (M-K) trend test, Hurst index, correlation analysis, partial correlation analysis, residual analysis and bivariate spatial autocorrelation (Bi-SA) analysis were used for quantitative analysis. Findings revealed: (1) Vegetation distribution had spatial heterogeneity, with a stepwise distribution from southeast to northwest; (2) Vegetation growth had temporal stagementation, with “stable-plunge-rise” pattern; (3) Climate trend displayed “warmer and wetter”, with growth rates of 0.045 °C/a and 0.558 mm/a, respectively; (4) Vegetation response to precipitation was sensitive with strong spatial correlation (Moran’s I = 0.88, P < 0.01); (5) Influence of human activities on the growth of vegetation was weak. The study emphasizes that the overall ecological quality of the YSMs has long been low. In recent decades, despite some improvements, the ecosystem remains extremely sensitive, fragile and volatile. Consequently, there is an urgent need to strengthen eco-environmental protection and management.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ecolind.2025.113193
- OA Status
- gold
- Cited By
- 4
- References
- 79
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All OpenAlex metadata
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https://openalex.org/W4407359468Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ecolind.2025.113193Digital Object Identifier
- Title
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A remote sensing evidence on the marginality, stagementation and spatiotemporal heterogeneity of vegetation evolution characteristics in the Yinshan Mountains, China: Based on PKU GIMMS NDVI (1984–2022)Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-01Full publication date if available
- Authors
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Mi Yan, Jingyang Lu, Yingying Ma, Chao MaList of authors in order
- Landing page
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https://doi.org/10.1016/j.ecolind.2025.113193Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ecolind.2025.113193Direct OA link when available
- Concepts
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Normalized Difference Vegetation Index, China, Vegetation (pathology), Geography, Remote sensing, Spatial heterogeneity, Physical geography, Environmental science, Ecology, Climate change, Biology, Medicine, Pathology, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(5) | 133 |
| abstract_inverted_index.PKU | 13 |
| abstract_inverted_index.The | 42, 145 |
| abstract_inverted_index.and | 26, 39, 62, 103, 111, 172, 184 |
| abstract_inverted_index.for | 70 |
| abstract_inverted_index.had | 78, 92 |
| abstract_inverted_index.has | 156 |
| abstract_inverted_index.the | 23, 139, 149, 154, 166 |
| abstract_inverted_index.was | 120, 143 |
| abstract_inverted_index.Area | 8 |
| abstract_inverted_index.NDVI | 15 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.YSMs | 155 |
| abstract_inverted_index.arid | 38 |
| abstract_inverted_index.been | 158 |
| abstract_inverted_index.from | 85 |
| abstract_inverted_index.long | 157 |
| abstract_inverted_index.low. | 159 |
| abstract_inverted_index.need | 179 |
| abstract_inverted_index.some | 164 |
| abstract_inverted_index.that | 148 |
| abstract_inverted_index.used | 69 |
| abstract_inverted_index.were | 68 |
| abstract_inverted_index.with | 81, 95, 105, 122 |
| abstract_inverted_index.& | 48 |
| abstract_inverted_index.(M-K) | 50 |
| abstract_inverted_index.(T-S) | 44 |
| abstract_inverted_index.0.045 | 109 |
| abstract_inverted_index.0.558 | 112 |
| abstract_inverted_index.0.88, | 129 |
| abstract_inverted_index.GIMMS | 14 |
| abstract_inverted_index.Hurst | 53 |
| abstract_inverted_index.human | 136 |
| abstract_inverted_index.mm/a, | 113 |
| abstract_inverted_index.rates | 107 |
| abstract_inverted_index.study | 1, 146 |
| abstract_inverted_index.test, | 52 |
| abstract_inverted_index.there | 175 |
| abstract_inverted_index.trend | 46, 51, 100 |
| abstract_inverted_index.using | 12 |
| abstract_inverted_index.weak. | 144 |
| abstract_inverted_index.°C/a | 110 |
| abstract_inverted_index.(DSEs) | 36 |
| abstract_inverted_index.0.01); | 132 |
| abstract_inverted_index.Median | 45 |
| abstract_inverted_index.growth | 19, 91, 106, 140 |
| abstract_inverted_index.index, | 54 |
| abstract_inverted_index.recent | 161 |
| abstract_inverted_index.steppe | 34 |
| abstract_inverted_index.strong | 123 |
| abstract_inverted_index.urgent | 178 |
| abstract_inverted_index.(Bi-SA) | 66 |
| abstract_inverted_index.(YSMs), | 11 |
| abstract_inverted_index.Climate | 99 |
| abstract_inverted_index.despite | 163 |
| abstract_inverted_index.driving | 27 |
| abstract_inverted_index.explore | 22 |
| abstract_inverted_index.focuses | 2 |
| abstract_inverted_index.fragile | 171 |
| abstract_inverted_index.overall | 150 |
| abstract_inverted_index.partial | 57 |
| abstract_inverted_index.quality | 152 |
| abstract_inverted_index.remains | 168 |
| abstract_inverted_index.spatial | 64, 79, 124 |
| abstract_inverted_index.typical | 5 |
| abstract_inverted_index.Findings | 73 |
| abstract_inverted_index.analysis | 47, 61, 67 |
| abstract_inverted_index.decades, | 162 |
| abstract_inverted_index.pattern; | 97 |
| abstract_inverted_index.regions. | 41 |
| abstract_inverted_index.residual | 60 |
| abstract_inverted_index.response | 117 |
| abstract_inverted_index.stepwise | 83 |
| abstract_inverted_index.temporal | 93 |
| abstract_inverted_index.Influence | 134 |
| abstract_inverted_index.Theil-Sen | 43 |
| abstract_inverted_index.analysis, | 56, 59 |
| abstract_inverted_index.analysis. | 72 |
| abstract_inverted_index.bivariate | 63 |
| abstract_inverted_index.displayed | 101 |
| abstract_inverted_index.ecosystem | 167 |
| abstract_inverted_index.extremely | 169 |
| abstract_inverted_index.mountains | 10 |
| abstract_inverted_index.revealed: | 74 |
| abstract_inverted_index.semi-arid | 40 |
| abstract_inverted_index.sensitive | 121 |
| abstract_inverted_index.southeast | 86 |
| abstract_inverted_index.volatile. | 173 |
| abstract_inverted_index.“warmer | 102 |
| abstract_inverted_index.(Moran’s | 126 |
| abstract_inverted_index.Ecological | 6 |
| abstract_inverted_index.Vegetation | 76, 90, 116 |
| abstract_inverted_index.Vulnerable | 7 |
| abstract_inverted_index.activities | 137 |
| abstract_inverted_index.ecological | 151 |
| abstract_inverted_index.ecosystems | 35 |
| abstract_inverted_index.emphasizes | 147 |
| abstract_inverted_index.indicator, | 20 |
| abstract_inverted_index.mechanisms | 28 |
| abstract_inverted_index.northwest; | 88 |
| abstract_inverted_index.protection | 183 |
| abstract_inverted_index.sensitive, | 170 |
| abstract_inverted_index.strengthen | 181 |
| abstract_inverted_index.vegetation | 18, 30, 142 |
| abstract_inverted_index.wetter”, | 104 |
| abstract_inverted_index.correlation | 55, 58, 125 |
| abstract_inverted_index.desertified | 33 |
| abstract_inverted_index.management. | 185 |
| abstract_inverted_index.Mann-Kendall | 49 |
| abstract_inverted_index.distribution | 77, 84 |
| abstract_inverted_index.quantitative | 71 |
| abstract_inverted_index.(EVA)-Yinshan | 9 |
| abstract_inverted_index.Consequently, | 174 |
| abstract_inverted_index.heterogeneity | 25 |
| abstract_inverted_index.improvements, | 165 |
| abstract_inverted_index.precipitation | 119 |
| abstract_inverted_index.respectively; | 114 |
| abstract_inverted_index.heterogeneity, | 80 |
| abstract_inverted_index.spatiotemporal | 24 |
| abstract_inverted_index.autocorrelation | 65 |
| abstract_inverted_index.stagementation, | 94 |
| abstract_inverted_index.eco-environmental | 182 |
| abstract_inverted_index.“stable-plunge-rise” | 96 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.98020568 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |