基于Sentinel-2A数据的东北森林植物多样性监测方法研究

植物多样性监测是开展生物多样性评估, 制定生物多样性保护政策的基础。传统的森林植物多样性监测以实地调查为主, 难以快速获取森林植物多样性的空间分布及其动态变化信息。遥感技术的发展为评估区域尺度森林植物多样性提供了重要工具。该研究选取凉水、丰林和珲春3个国家级自然保护区, 利用Sentinel-2A卫星影像和野外实测数据, 探讨了基于像元和聚类的光谱多样性直接估算方法, 以及基于随机森林回归的森林植物多样性反演方法。研究结果表明: (1)在像元尺度, 基于凸包面积计算的光谱多样性指数对Shannon-Wiener多样性指数的估算精度(R² = 0.74)优于基于变异系数的方法(R² = 0.60); (2)基于像元的光谱多样性估算方法对Shannon-Wiener多样性指数的估算精度优于聚类分析方法(R² = 0.59); (3)基于6个特征变量, 利用随机森林回归算法对Shannon-Wiener多样性指数的估算精度最高(R² = 0.79); (4)上述方法均不能精确估算Simpson多样性指数和物种丰富度。研究发现基于Sentinel-2A卫星影像能较好地反演Shannon-Wiener多样性指数, 为下一步能在大尺度上进行森林植物多样性估算提供了参考和依据。

Study on forest plant diversity monitoring based on Sentinel-2A satellite data in northeast China

Aims Plant diversity monitoring is the basis of biodiversity assessment and developing conservation policy. Traditional forest plant diversity monitoring is mainly based on field surveys, which is difficult to quickly obtain the spatial distribution and dynamic change of forest plant diversity. The development of remote sensing technology provides an important tool for assessing forest plant diversity at the regional scale. In this study, we explored two methods of forest plant diversity estimation based on Sentinel-2A satellite images and field data in three selected national nature reserves (Liangshui, Fenglin, and Hunchun).