基于多端元光谱混合分析方法的大兴安岭火后植被盖度恢复研究

火干扰是北方针叶林结构、功能及动态的主要调节因子之一。研究火后植被恢复对理解火干扰和生态系统的交互作用具有重要意义。火烧迹地通常由植被与基质混合组成,在中低分辨率（ > 10 m）遥感影像中表现为混合像元,因此研究亚像元尺度上植被的恢复是精确量化植被恢复的关键。本研究以2000年大兴安岭呼中自然保护区中8700 hm²火烧迹地为研究区,以两期（2014年6月1日和2010年6月22日）中分辨率Landsat ETM+影像（30 m）为基础数据,比较多端元光谱混合分析（Multiple Endmember Spectral Mixture Analysis,MESMA）和归一化植被指数（Normalized Difference Vegetation Index,NDVI）获得的植被盖度,以高分辨率（2 m）WorldView-2影像（2014年7月1日）为验证数据,对两种方法计算的植被盖度精度进行比较。结果表明,MESMA方法获得的植被盖度（R²=0.691）与传统的NDVI获得的植被盖度（R²=0.700）精度无统计差异,中烈度下获得的植被覆盖精度高于低、高火烧烈度。为验证同一端元能否运用到不同时相的Landsat影像中,本研究将从2014年影像中获取的最佳端元运用到2010年影像中获得植被盖度图,结果表明2014年与2010年得到的RMSE（均方根误差）均值分别为0.0015和0.0065,说明最佳端元可用于不同时相的影像分解。本研究表明MESMA方法可有效监测北方针叶林中火后植被盖度恢复,并可运用于时间序列遥感影像监测植被恢复动态。

Forest recovery after wildfire disturbance in Great Xing'an Mountains by Multiple Endmember Spectral Mixture Analysis

Fire is one of the main natural disturbances to regulate the forest structure, function, and dynamics in boreal region. Post-fire ecosystem recovery usually consists of a mixture of vegetation and substrate, which is represented as mixed pixels in low-and medium-resolution (> 10 m) remote sensing images. Therefore, analyzing the ecosystem recovery at the sub-pixel scale is the key to monitor the post-fire ecological process by remote sensing image monitoring. In this analysis, a burn patch of 8700 hm² in Huzhong nature reserve of Great Xing''an Mountains in 2000 was chosen as a case study. Two medium resolution Landsat ETM+ images (30 m) on June 1, 2014 and June 22, 2010 were chosen to monitor post-fire recovery. The green vegetation cover was calculated from Multiple Endmember Spectral Mixture Analysis (MESMA) and a traditional vegetation index (e.g. Normalized Vegetation Index, NDVI), and then validated by a high spatial resolution (2 m) WorldView-2 image (July 1, 2014). The results showed that the accuracy of the green vegetation cover was not significantly different between NDVI (R²=0.700) and MESMA method (R²=0.691). The green vegetation cover was higher under medium severity burn than that under low and high severity burn. To assess the transferability of best endmembers, we applied best endmembers selected for image acquired in 2014 to image acquired in 2010. The mean RMSE for image acquired in 2014 and 2010 is 0.0015 and 0.0065, respectively, indicating that the best endmembers can be used as transfer among different Landsat images. This study shows that the MESMA method can effectively monitor the post-fire vegetation in northern boreal forests, and can be used to monitor the dynamics of post-fire ecosystem recovery with time series remote sensing images.