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Published： 07 July 2021 ，
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孙晓慧，史建康，李新武，吴文瑾，梁雷，宫晨.2021.西沙群岛精细植被分布的遥感制图及动态变化.遥感学报，25（7）： 1473-1488
Sun X H，Shi J K，Li X W，Wu W J，Liang L and Gong C. 2021. Mapping and dynamic changes of refined vegetation distribution in Xisha Islands. National Remote Sensing Bulletin， 25（7）：1473-1488
西沙群岛是南海群岛中岛屿最多、面积最大的群岛，自然环境独特、植物区系特殊、岛上植被一直都是植物学家及地理学家重点关注的问题。本文基于光谱分类，于决策层融合支持向量机与光谱信息散度两类分类器进行西沙群岛典型岛屿植被类型识别，形成典型植被分布图。建立西沙群岛典型植被光谱库，分析西沙群岛典型植被实测光谱与其一阶导数的特性，并基于典型岛屿不同时期的植被分布图进行变化分析。结果表明：（1）采用光谱分类的生产精度及用户精度在西沙群岛主要岛屿的平均值为83.49%、85.54%，Kappa系数为0.8728。（2）2002年—2018年，各典型岛屿主要受到人类活动影响，整体上植被种类及植被面积增加，草海桐形成单优植被群落。（3）经相关性分析，各典型岛屿植被种类数量与本岛面积基本成正相关关系，且面积越大，植被种类随时间增加速度越快；相邻岛屿之间的距离与两岛植被相似性呈正相关，两岛距离越近，植被种类相似性越高。
The Paracel Islands is the largest island with the largest number of islands in the South China Sea Islands. They have a unique natural environment and particular natural flora. The vegetation on the islands has always been a key concern of botanists and geographers. In order to quickly obtain the continuous distribution and long-term dynamic changes of vegetation in a large area of the island
the study integrated multi-source high resolution remote sensing data and measured GPS sampling data
spectral data and other auxiliary information. The vegetation types of typical islands in Paracel Islands were identified based on spectral classification
which fusing Support Vector Machine (SVM) and Spectral Information Divergence (SID) two classifiers on decision-making level and generating the typical vegetation distribution maps. A typical vegetation spectrum library of the Paracel Islands was also established using to analyze the characteristics of the measured spectrum and its first derivative of the typical vegetation in the Paracel Islands
enriching the basic information of vegetation in Paracel Islands. The study compared the classification method of SVM+SID and the general Spectral Angle Mapper (SAM) method，then further obtained the accuracy assessment results of each island. Based on the vegetation distribution maps of typical islands in different periods after the accuracy assessment
the statistical change of the area occupied by each vegetation and the correlation analysis of the vegetation diversity of different islands were conducted. The results demonstrating that: (1) The average production accuracy and user accuracy of the spectral classification method (combining SVM and SID classifiers on decision-making level) were 83.49% and 85.54% and Kappa coefficient was 0.8728 of the most whole islands in Paracel Islands. Therefore
the study achieved good performances in identifying different vegetation types on typical islands. (2) From 2002 to 2018
the vegetation types and its areas increased and tended to be stable.
Scaevola
was prone to form single-superior vegetation community in islands and
Sandbank Grass
on sands account for a large area; In recent years
vegetation on many islands such as Yagong Island
West Sand
Tree Island
etc.
had been artificially interfered
and it had been regularly distributed along buildings
roads in spatial
and the diversity of vegetation species had greatly increased. (3) Through correlation analysis
this study found that the number of vegetation types on each typical island was basically positively correlated with the area of the island
that is
the larger the island area
the richer the plant habitat and the greater the number of species. And the larger the area of the island
the faster the vegetation types increase over time. In addition
the distance between adjacent islands was positively correlated with the similarity of vegetation on the two islands
that is
the closer the distance between the two islands
the more conducive to the mutual penetration of flora and the higher the similarity of vegetation types. But on a long-term scale
the dynamic changes of vegetation in Paracel Islands had been affected by human activities more than other natural factors.
西沙群岛光谱分类植被识别与变化草海桐植被种类数量
Paracel Islandsspectral classificationvegetation identification and changeScaevola taccadanumber of vegetation types
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