观测夜间灯光显著提高了全球范围内一年内植被生长的可解释性
Han Yang1, Jiahao Chen1, Chaohui Zhong1
1School of Ecology, Hainan University, Haikou 570000, China.
The Science of the total environment
|June 15, 2024
概括
通过揭示人类影响,夜间灯光 (NL) 数据显著提高了对一年内植被生长的理解. 纳入NL数据改善了全球模型的准确性,特别是在植被和人类活动发生重大变化的地区.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 了解植被生长对于环境管理和预测未来变化至关重要.
- 人类对植被年内变化的影响仍然是一个知识差距.
- 夜光 (NL) 有效地表明人类活动,但它们在一年内植被生长中的作用未得到充分研究.
研究的目的:
- 用季节性夜光 (NL) 观察量化解释年内植被生长的改进.
- 评估人类活动对植被生长动态的影响,用NL表示.
- 研究气候因素,人类活动和植被生长之间的关系.
主要方法:
- 使用主要组件回归 (PCR) 开发了一种多重线性回归模型.
- 植物生长的利用规范差异植被指数 (NDVI) 和人类活动的NL观测.
- 纳入的气候因素:温度,水的可用性和太阳辐射.
主要成果:
- NL的观测显著改善了对年内植被生长的全球理解.
- 模型可解释性 (调整后的R2) 平均提高了54%,在82%的像素中平均提高了11%.
- 在NL和NDVI趋势显著的地区,改善显著,与GDP相反相关,在低收入国家显示出更大的收益.
结论:
- 人类活动,用NL量化,对于解释年内植被生长至关重要.
- 整合NL数据为植被动态和环境管理提供了新的见解.
- 该研究强调了在生态建模中需要考虑人为因素的必要性.
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