收稿：2022-03-13，
纸质出版：2023-06-07
移动端阅览
植被是湿地的核心，易受人类活动和气候变化的影响，湿地植物群落分类与制图可以为湿地生态监测与评估提供科学数据支撑。本研究以鄱阳湖国家级湿地自然保护区为研究区，基于2019年月度Sentinel-1和Sentinel-2时序数据，通过提取影像的水体指数和植被指数、红边指数、纹理特征、光谱特征、雷达极化数据5类，共计240个特征指标，使用随机森林、支持向量机和深度神经网络算法进行分类，探寻一套湿地植被分类最优的特征组合和分类方案。（1）光学数据在湿地分类与制图提取中明显优于雷达数据，雷达数据可以在光学数据不足时，作为光学数据的补充。（2）对时序Sentinel-2的各特征变量进行重要性筛选，有助于提高分类精度，优选时间段主要分布在1月、5月、8月、9月、10月和12月份；（2）当对5组特征变量单独分类时，分类精度排序为红边指数组>水体—植被指数组>光谱特征组>雷达极化数据组>纹理特征组；（3）对比组合变量和单独特征变量，组合变量不一定有助于提高分类效果，分类精度排序为：红边指数分类组>水体—植被指数分类组>组合分类组，其中，红边指数组随机森林分类总体精度达0.81，Kappa系数为0.76；（4）对比3种分类方法，分类精度排序为：深度神经网络>随机森林>支持向量机，其中，深度学习方法并没有太大幅度的提高分类精度，相对随机森林算法仅仅提高了2%。故深度神经网络和随机森林算法都可以作为优选算法。本研究给出的分类方案是，使用Sentinel-2和Sentinel-1多时序数据对湿地植被进行精细化分类，时段选择建议1月、5月、8月、9月、10月和12月份的卫星数据更优，特征变量可选红边指数组或者水体—植被指数组产品，分类方法可根据需求选择深度神经网络或随机森林对湿地植物群落进行分类，可得出较优的分类结果。这个分类方案可以有效的提升鄱阳湖湿地植被制图精度，并为决策部门提供科学的技术方案。
Plant communities play an important role in wetland elements and are vulnerable to human activities and climate change. Wetland plant community classification and mapping provide scientific important data support for wetland ecological monitoring and evaluation. This study aims to develop a classification scheme suitable for the wetland plant communities in the Poyang Lake wetland.
Taking Poyang Lake National Nature Reserve as the research area and on the basis of the monthly Sentinel-1 and Sentinel-2 time-series data in 2019
this study extracts five types of image feature parameters
including water and vegetation index group
red edge index group
texture feature group
spectral feature group
and polarization radar backscatter group
with a total of 240 feature indexes
and uses Random Forest (RF)
Support Vector Machine (SVM)
and Deep Neural Network (DNN) algorithms for classification to explore a set of optimal feature combinations and a suitable classification scheme for wetland vegetation mapping in Poyang Lake.
In conclusion
A classification scheme for wetland plant communities in the Poyang Lake wetland was proposed in this study using multi time-series Sentinel-2 and Sentinel-1 data. The optimal acquisition time periods of satellite data are in January
April
August
September
October
and December. The optimal image feature group can be red edge index group or water and vegetation index group for feature selection. The classification algorithm can select deep learning or RF algorithm to classify wetland plant communities according to the requirements. This classification scheme can effectively improve the accuracy of wetland vegetation mapping in the Poyang Lake and provide scientific and technical solutions for decision-making departments.
Results show the following
2
(1) Compared with radar data
the extraction accuracy of optical data is remarkably better than that of radar data in wetland plant community classification and mapping. Radar data can be used as a supplement to optical data when optical data are insufficient. (2) Screening the importance of each image feature of Sentinel-2 helps improve the classification accuracy. The preferred time periods are mainly distributed in January
May
August
September
October
and December. (3) Five groups of unitary image features are selected to classify separately
and the classification accuracy is as follows: red edge index group > water and vegetation index group > spectral feature group > radar polarization data group > texture feature group. (4) Comparing the combined image feature groups with the unitary image feature groups reveals that the combined image feature group is not necessarily helpful to improve the classification accuracy. The classification accuracy is as follows: red edge index group > water vegetation index group > combined image feature group. Among them
the overall accuracy of the classification scheme using the red edge index group and random forest method is 0.81
and the Kappa coefficient is 0.76. (4) By comparing the three classification algorithms
the classification accuracy is ranked as follows: DNN > RF > SVM. The overall accuracy of the deep learning method does not greatly improve
that is
only 2% higher than the RF algorithm. Thus
the DNN and machine learning method (RF) can be used as optimization algorithms.
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