Research Center for Urban Forestry of Beijing Forestry University, Key Laboratory for Forest Silviculture and Conservation of Ministry of Education, Key Laboratory for Silviculture and Forest Ecosystem Research in Arid- and Semi-Arid Region of State Forestry Administration, Beijing Forestry University, Beijing, 100083, China.
Research Institude of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, 510520, Guangdong, China.
Sci Rep. 2020 Sep 25;10(1):15803. doi: 10.1038/s41598-020-73006-2.
To quantitatively reflect the relationship between dust and plant spectral reflectance. Dust from different sources in the city were selected to simulate the spectral characteristics of leaf dust. Taking Euonymus japonicus as the research object. Prediction model of leaf dust deposition was established based on spectral parameters. Results showed that among the three different dust pollutants, the reflection spectrum has 6 main reflection peaks and 7 main absorption valleys in 350-2500 nm. A steep reflection platform appears in the 692-763 nm band. In 760-1400 nm, the spectral reflectance gradually decreases with the increase of leaf dust coverage, and the variation range was coal dust > cement dust > pure soil dust. The spectral reflectance in 680-740 nm gradually decreases with the increase of leaf dust coverage. In the near infrared band, the fluctuation amplitude and slope of its first derivative spectrum gradually decrease with the increase of leaf dust. The biggest amplitude of variation was cement dust. With the increase of dust retention, the red edge position generally moves towards short wave direction, and the red edge slope generally decreases. The blue edge position moved to the short wave direction first and then to the long side direction, while the blue edge slope generally shows a decreasing trend. The yellow edge position moved to the long wave direction first and then to the short wave direction (coal dust, cement dust), and generally moved to the long side direction (pure soil dust). The yellow edge slope increases first and then decreases. The R values of the determination coefficients of the dust deposition prediction model have reached significant levels, which indicated that there was a relatively stable correlation between the spectral reflectance and dust deposition. The best prediction model of leaf dust deposition was leaf water content index model (y = 1.5019x - 1.4791, R = 0.7091, RMSE = 0.9725).
为定量反映灰尘与植物光谱反射率的关系。选择城市不同来源的灰尘来模拟叶尘的光谱特征。以冬青卫矛为研究对象。基于光谱参数建立叶尘沉积预测模型。结果表明,在三种不同的灰尘污染物中,反射光谱在 350-2500nm 之间有 6 个主要反射峰和 7 个主要吸收谷。在 692-763nm 波段出现陡峭的反射平台。在 760-1400nm 范围内,随着叶尘覆盖度的增加,光谱反射率逐渐降低,变化范围为煤尘>水泥尘>纯土壤尘。在 680-740nm 波段,光谱反射率随叶尘覆盖度的增加逐渐降低。在近红外波段,其一阶导数光谱的波动幅度和斜率随叶尘的增加逐渐减小。变化幅度最大的是水泥尘。随着灰尘保留量的增加,红边位置一般向短波方向移动,红边斜率一般减小。蓝边位置先向短波方向移动,然后向长波方向移动,而蓝边斜率一般呈下降趋势。黄边位置先向长波方向移动,然后向短波方向移动(煤尘、水泥尘),一般向长波方向移动(纯土壤尘)。黄边斜率先增加后减小。灰尘沉积预测模型的决定系数 R 值达到显著水平,表明光谱反射率与灰尘沉积之间存在相对稳定的相关性。叶尘沉积的最佳预测模型是叶水含量指数模型(y=1.5019x-1.4791,R=0.7091,RMSE=0.9725)。