Abstract：Suaeda salsa is an important pioneer vegetation in the coastal zone， and the spatial and temporal distribution information of Suaeda salsa is the basic data of coastal wetland ecosystem protection and wetland restoration project. In order to accurately obtain the information on the temporal and spatial distribution of the Suaeda salsa community， it is necessary to construct a Suaeda salsa extraction index. Taking the northern coastal wetland of Liaodong Bay as the study area， Suaeda Principal Components Analysis Index (SPCAI) was constructed using PCA (Principal Components Analysis) method based on Landsat8 OLI. SPCAI was applied to multi-source remote sensing data (Landsat5 TM， Landsat7 ETM＋， Landsat8 OLI， and Sentinel-2 MSI)， and finally performed application to obtain the temporal and spatial distribution information of Suaeda salsa in the northern coastal wetland of Liaodong Bay in the past 21 years. The results are shown as follows. 1) the 4th band (PCA4) response of Landsat data processed by PCA was the most obvious， and the principal component analysis results of Landsat8 OLI satellite data was significantly better than TM and ETM＋data. 2) SPCAI's response to Suaeda salsa was significantly better than NDVI， SAVI， MSAVI， and SPCAI has high extraction accuracy in the four types of data， which can effectively extract Suaeda in the intertidal zone， indicating that SPCAI has good applicability to multi-source remote sensing data. 3) The Suaeda salsa community showed a trend of degradation， and the community distribution mainly experienced three transitions from large patches to small patches scattered distribution(2000-2002， 2002-2011， 2011-2020).
李 微,王文硕,刘旭龙,周瑞琴,孙 涛,张明亮. 基于PCA的盐地碱蓬植被信息提取新指数研究[J]. 华中师范大学学报(自然科学版), 2023, 57(1): 96-104.
LI Wei,WANG Wenshuo,LIU Xulong,ZHOU Ruiqin,SUN Tao,ZHANG Mingliang. Study on new index of Suaeda salsa vegetation information extraction based on PCA. journal1, 2023, 57(1): 96-104.
2023, Vol. 57 
