基于Sentinel-2影像的南四湖菹草群落遥感提取研究

Remote Sensing Extraction of Potamogeton crispus L.in Nansi Lake based on Sentinel-2

基于Sentinel-2遥感数据,选用最大似然监督分类法、随机森林机器学习分类法和基于时序NDVI的物候特征分类法等3种方法,对2021年5月初南四湖沉水植被(菹草群落)进行提取研究,利用同期实测的南四湖菹草群落面积和分布数据对3种方法的提取精度进行分析,结合菹草植被覆盖度分析3种方法对菹草的提取能力。结果表明:①不同方法提取的南四湖菹草群落总面积存在明显差异。监督分类和随机森林方法提取的2021年南四湖菹草群落面积均在100 km^(2)以下,分别为98.97 km^(2)和75.92 km^(2),基于时序NDVI的方法提取面积为207.44 km^(2),最接近实地调查的菹草面积。②无论是全湖还是核心区,监督分类和随机森林法的提取精度均75%左右,平均相对误差(MRE)在0.5左右,平均误差在20~30 km^(2)左右,而基于时序NDVI的方法精度在90%以上,MRE和ME area也最低。③对比植被覆盖度发现,监督分类和随机森林方法只能提取到近湖岸的植被覆盖度较高的菹草,对湖心区覆盖度较低的菹草提取效果差,而时序NDVI的方法对低植被覆盖度菹草群落更敏感,是菹草遥感提取的有效方法。本研究对于创新沉水植被遥感提取方法和指导湖泊生态环境遥感监测具有一定的参考价值。

Based on Sentinel-2 remote sensing data,we selected three methods,including Supervised Classification(Maximum Likelihood Classification),Machine Learning Classification(Random Forest Classification)and Phenological Feature Classification based on time-series NDVI,to extract Potamogeton crispus L.community in Nansi Lake in early May 2021.By using the measured area and distribution data of the Potamogeton cris⁃pus L.community in Nansi Lake,we analyzed the classification accuracy of the three methods during the same period,and analyzed the extraction effects of the three methods for Potamogeton crispus L.in combination with the Fractional Vegetation Cover(FVC).The results showed that(1)there was a significant difference in the total area of the Potamogeton crispus L.extracted by three methods.The areas of the Potamogeton crispus L.community extracted by both Supervised Classification and Random Forest Classification were less than 100 km^(2),which were 98.97 km^(2) and 75.92 km^(2) respectively.While the area extracted by the time-series NDVI method was 207.44 km^(2),which was closest to the measured area of Potamogeton crispus L.(2)Both the whole lake and the core area,the extraction accuracy of Supervised Classification and Random Forest Classification was just about 75%,the Mean Relative Error(MRE)was about 0.5,and Mean Error(ME area)was about 20~30 km^(2),while the accuracy of the time-series NDVI method was above 90%and the MRE and ME area were also the lowest.(3)Comparing the fractional vegetation cover,we found that Supervised Classification and Random Forest Classification could only extract the Potamogeton crispus L.with high fractional vegetation cover near the lake shore and poorly with low cover in the lake core area,while the time-series NDVI method was more sensitive to the low fractional vegetation cover Potamogeton crispus L.community and could extract it well in different areas of the whole lake,which is a potential method for Potamogeton crispus L.remote sensing extraction.This study has some theoretical value for innovative remote sensing extraction methods of submerged vegetation and guiding remote sensing monitoring of lake ecological environment.