| 基于3种遥感指数的2000~2020年尼泊尔地区植被生长动态的对比 |
| 摘要点击 1304 全文点击 269 投稿时间：2023-11-01 修订日期：2024-01-24 |
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| 中文关键词 山地 植被动态 遥感指数 完整海拔梯度 尼泊尔 |
| 英文关键词 mountain vegetation dynamics remote sensing indices complete altitude gradient Nepal |
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| 中文摘要 |
| 遥感指数被广泛用于监测气候变化和人类活动影响下植被生长动态,但基于不同类型遥感指数监测山区植被动态的一致性尚不清楚. 本文以尼泊尔地区为例,对比研究了最常用3类遥感指数[归一化植被指数（NDVI）、叶面积指数（LAI）和净初级生产力（NPP）]量化山区植被生长动态的时空一致性. 结果表明：①不同遥感指数多年平均值的空间分布格局差异较大,尤其是低海拔地区,NDVI、 LAI和NPP的最高值分别出现在低山区、中山区和高山区；②从长期变化趋势来看,3种遥感指数整体均呈现增加趋势,但NDVI呈现增加趋势的面积占比（82%）明显大于NPP和LAI（58%和56%）；③随海拔增加,NDVI和LAI呈增加趋势的面积占比逐渐减小,NPP则呈现出先增加后减小的趋势；④像元尺度上,三者长期变化趋势完全一致（变化方向和显著性水平均相同）的面积占比仅9.6%. 研究结果强调了遥感指数监测山区植被动态的极大不确定性和发展更为稳健遥感数据产品的重要性. |
| 英文摘要 |
| Remote sensing indices have been widely used to monitor the vegetation growth dynamics induced by climate change and human activities, and yet the consistency of the vegetation dynamics revealed by different remote sensing indices in mountains is unclear. Using Nepal as a case study, this study explored the spatial-termporal consistencies of the three widely-used remote sensing indices （i.e., normalized difference vegetation index （NDVI）, leaf area index （LAI）, and net primary production （NPP）） in quantifying the vegetation growth dynamics in mountainous regions. The results indicated that the spatial distributions of the multi-year mean estimates varied greatly by remote sensing index, especially in the low-altitude regions. The maximum NDVI, LAI, and NPP occurred in the low, medium, and high mountain regions, respectively. Although all three indices showed an overall increasing tendency from a long-term perspective, the area percentage of the lands with a significant trend was obviously larger in NDVI （82%） than that in NPP （58%） and LAI （56%）. In addition, the land area percentages with vegetation growth enhancement decreased gradually by the rise of altitude for both the NDVI and LAI indices but decreased after an increase for the NPP index. Only 9.6% of the lands showed consistent long-term trends （with the same change directions and significant levels） in the three indices on a per-pixel basis. Our findings highlight the large uncertainties of remote sensing indices in monitoring vegetation growth dynamics in mountainous areas, and the importance of developing reinforced remote sensing products in future efforts. |
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