地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (12): 2292-2304.doi: 10.12082/dqxxkx.2021.210494

• 遥感科学与应用技术 • 上一篇    

基于高分影像光谱特征的广西海岸带红树林精细分类与制图

马云梅(), 吴培强*(), 任广波   

  1. 自然资源部第一海洋研究所,青岛 266061
  • 收稿日期:2021-08-23 修回日期:2021-09-18 出版日期:2021-12-25 发布日期:2022-02-25
  • 通讯作者: *吴培强(1984— ),男,山东潍坊人,研究实习员,硕士,主要从事海岛海岸带遥感应用方面研究。 E-mail: wu1416@163.com
  • 作者简介:马云梅(1995— ),女,内蒙古乌兰察布人,硕士,主要从事湿地监测研究。E-mail: maymself@163.com

Fine Classification and Mapping of Mangroves in Guangxi Coastal Zone based on Spectral Characteristics of GF Images

MA Yunmei(), WU Peiqiang*(), REN Guangbo   

  1. First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
  • Received:2021-08-23 Revised:2021-09-18 Online:2021-12-25 Published:2022-02-25

摘要:

准确了解我国海岸带红树林种类组成有助于红树林资源调查、保护和利用。本文基于广西海岸带2018—2020年共 14景GF-2多光谱影像,通过植被指数法和一阶微分法进行光谱特征数据重构,使用支持向量机分类方法,对广西海岸带红树林开展种间精细分类研究。结合现场数据以茅尾海为例,通过与原始数据和一阶微分的分类结果进行对比分析,来验证光谱特征数据重构对红树林种类识别的有效性。结果表明,基于光谱特征重构数据的分类精度最高,为91.55%,Kappa系数为0.8695,分别比原始数据和一阶微分提高了6.92%和11.17%。以此开展了广西整个海岸带红树林类型识别,结果表明,广西主要分布有7种真红树分别为桐花树、白骨壤、无瓣海桑、秋茄、红海榄、木榄、老鼠簕和一种盐沼草本植物茳芏,湿地植被总面积为7402.98 hm2,防城港市、钦州市和北海市红树林面积分别为1826.16、2496.18和3080.47 hm2,其中桐花树和白骨壤为广西红树林优势物种,分布面积最大,分别为3372.09 hm2和3445.17 hm2,二者占总面积的92.09%,其次为茳芏287.50 hm2占总面积3.88%,无瓣海桑与红海榄次之,面积分别为135.97 hm2和126.52 hm2,共占红树林总面积的3.55%,老鼠簕、木榄和秋茄面积极少,均不足20 hm2,三者相加不足红树林总面积的1%。北仑河口、山口和茅尾海3个红树林自然保护区的红树林总面积分别为1009.21、715.56和1546.62 hm2。本文基于高分数据的光谱特征数据重构方法开展红树林精细分类研究,可以为广西红树林管理、保护和重建提供技术和数据支撑。

关键词: 红树林, 种间分类, 广西, 高分数据, 数据重构

Abstract:

Accurate understanding of mangrove species composition in coastal zone of China is helpful for mangrove resource investigation, protection, and utilization. In this paper, based on GF-2 multi-spectral images of Guangxi Coastal zone from 2018 to 2020, the vegetation index method and first-order differential method were used to reconstruct spectral characteristic data. Based on the reconstructed data, the Support Vector Machine (SVM) classification method was used to study the interspecific classification of mangroves in Guangxi coastal zone. Taking Maoweihai as an example, the validity of the reconstructed data for the identification of mangrove species was verified by comparing with the classification results using original data and the first-order differential method. The results show that the classification accuracy of the reconstructed data based on spectral features was the highest (91.55%) and the Kappa coefficient was 0.8695, which was 6.92% higher than the classification accuracy using original data and 11.17% higher than the classification accuracy using first-order differential method. Based on this, mangrove species identification in Guangxi coastal zone was further carried out using the spectral feature reconstruction data. Mangroves in Guangxi can be divided into eight types, namely, Aegiceras corniculatum, Avicennia marina, Rhizophora stylosa, Sonneratia apetala, Kandelia candel, Bruguiera gymnorrhiza, Acanthus ilicifolius, and a salt marsh herbaceous plant Cyperus malaccensis. The total area of typical vegetation for all types of wetlands was 7402.98 hm2. The area of mangrove in Fangchenggang city, Qinzhou City, and Beihai City was 1826.16 hm2, 2496.18 hm2, and 3080.47 hm2, respectively. The dominant species of mangrove in Guangxi were Aegiceras corniculatum and Avicennia marina, with the largest distribution area of 3372.09 hm2 and 3445.17 hm2, respectively, accounting for 92.09% of the total area. Next came the Cyperus malaccensis with an area of 287.50 hm2, accounting for 3.88% of the total area of the mangroves, followed by Rhizophora stylosa and Sonneratia apetala, with an area of 135.97 hm2 and 126.52 hm2, respectively, accounting for 3.55% of the total area of mangroves. The area of Kandelia candel, Bruguiera gymnorrhiza, and Acanthus ilicifolius were all less than 20 hm2, which accounted for less than 1% of the total mangrove area. The total area of mangrove in Beilun Estuary, Shankou, and Maweihai sea mangrove nature reserves was 1009.21 hm2, 715.56 hm2 and 1546.62 hm2, respectively. In this paper, based on the spectral characteristic data reconstruction method using GF images, the fine classification of mangroves was investigated, providing technical and data support for the management, protection, and reconstruction of mangroves in Guangxi.

Key words: mangrove, interspecific classification, Guangxi, high resolution data, data reconstruction