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

doi:10.12082/dqxxkx.2021.210494

摘要/Abstract

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

马云梅, 吴培强, 任广波. 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[J]. 地球信息科学学报, 2021, 23(12): 2292-2304.DOI:10.12082/dqxxkx.2021.210494

MA Yunmei, WU Peiqiang, REN Guangbo. Fine Classification and Mapping of Mangroves in Guangxi Coastal Zone based on Spectral Characteristics of GF Images[J]. Journal of Geo-information Science, 2021, 23(12): 2292-2304.DOI:10.12082/dqxxkx.2021.210494

图/表 13

图1

表1

图2

表2

红树林类型及外貌特征

	桐花树	白骨壤	红海榄	秋茄
整株
根叶花果
外貌特征	桐花树(Aegiceras corniculatum) 叶子无尖、花量大、果实形似“小辣椒”、常绿灌木或小乔木、高1~5 m、耐寒、喜低盐、多分布于有淡水输入的海湾河口	白骨壤(Avicennia marina) 小叶、指状呼吸跟、黄色或橙色小花、果实近扁球形、最耐盐、耐淹、常绿灌木或小乔木、高1~6 m、常分布于淡水注入较少的海湾区域	红海榄(Rhizophora stylosa ) 支柱根、四瓣花边、淡黄色小花、果实长圆柱形、常绿乔木或灌木、高可达8 m、较耐盐、多分布于河口外侧盐度较高的红树林內滩	秋茄(Kandelia candel) 板状根或密集小支柱根、五瓣花边、淡黄色小花、果实瘦长呈棒棍状、常绿灌木或小乔木、高2~6 m、最耐寒、常分布于桐花树和白骨壤内缘
	无瓣海桑	木榄	老鼠簕	茳芏
整株
根叶花果
外貌特征	无瓣海桑(Sonneratia apetala) 小叶、笋状呼吸根、花中柱头呈蘑菇状、果实为球形、常绿大乔木、高可达16 m、喜低盐、耐淹、较耐寒、生长在河口或岸边有淡水调节的滩涂	木榄(Bruguiera gymnorrhiza) 伸出滩面的曲状呼吸根和基部的板状根、多瓣花边的淡红色花、果实较红海榄更粗且略短、常绿乔木或灌木,高可达6~8m、耐淹能力较差、多分布于红树林內滩	老鼠簕(Acanthus ilicifolius) 叶子多为长椭圆形且叶缘带刺、花朵为淡紫色、果实长圆形、灌木或亚灌木、高0.5~2 m、喜淡、多生长在有淡水输入的高潮带和受潮汐影响的水沟两侧	茳芏(Cyperus malaccensis) 盐沼植物、叶片短、叶鞘长、褐色小花、成熟时为黑褐色、常被潮水冲倒、高1~2 m、适宜生长在水源充足的碱性土壤,常与红树林生长在一起

表2

表3

图3

图4

图5

表4

图6

图7

图8

表5

参考文献 27

[1]	Giri C, Ochieng E, Tieszen L L, et al. Status and distribution of mangrove forests of the world using earth observation satellite data[J]. Global Ecology and Biogeography, 2011, 20(1):154-159. doi: 10.1111/geb.2011.20.issue-1
[2]	范航清, 王文卿. 中国红树林保育的若干重要问题[J]. 厦门大学学报:自然科学版, 2017, 56(3):323-330.
	[ Fan H Q, Wang W Q. Some important issues of mangrove conservation in China[J]. Journal of Xiamen University: Natural Science, 2017, 56(3):323-330. ]
[3]	Patrick O D, Kalacska M, ArroyoMora J, et al. Spectral complexity of hyperspectral images: A new approach for mangrove classification[J]. Remote Sensing, 2021, 13(13):2604-2631. doi: 10.3390/rs13132604
[4]	Chen L, Wang W, Zhang Y, et al. Recent progresses in mangrove conservation, restoration and research in China[J]. Journal of Plant Ecology, 2009, 2(2):45-54. doi: 10.1093/jpe/rtp009
[5]	Wang L, Jia M, Yin D, et al. A review of remote sensing for mangrove forests: 1956-2018[J]. Remote Sensing of Environment, 2019, 231:111223. doi: 10.1016/j.rse.2019.111223
[6]	Donato D C, Kauffman J B, Murdiyarso D, et al. Mangroves among the most carbon-rich forests in the tropics[J]. Nature Geoscience, 2011, 4(5):293-297. doi: 10.1038/ngeo1123
[7]	王乐, 时晨, 田金炎, 等. 基于多源遥感的红树林监测[J]. 生物多样性, 2018, 26(8):838-849. doi: 10.17520/biods.2018067
	[ Wang L, Shi C, Tian J Y, et al. Mangrove monitoring based on multi-source remote sensing[J]. Biodiversity, 2018, 26(8):838-849. ]
[8]	Lombard F, Andrieu J. Mapping mangrove zonation changes in Senegal with landsat imagery using an OBIA approach combined with linear spectral unmixing[J]. Remote Sensing, 2021, 13(10):1961-1984. doi: 10.3390/rs13101961
[9]	王武霞, 苏奋振, 冯雪, 等. 中越北部湾红树林差异性研究[J]. 地球信息科学学报, 2017, 19(2):264-272. doi: 10.3724/SP.J.1047.2017.00264
	[ Wang W X, Su F Z, Feng X, et al. Study on the difference of mangrove forest between China and Vietnam in Beibu Gulf[J]. Journal of Geo-information Science, 2017, 19(2):264-272. ]
[10]	潘卫华, 陈家金, 王岩. 近20年福建红树林和互花米草群落时空变化及景观特征[J]. 生态与农村环境学报, 2020, 36(11):1428-1436.
	[ Pan W H, Chen J J, Wang Y. Temporal and spatial changes and landscape characteristics of mangrove and Spartina alterniflora communities in Fujian province in recent 20 years[J]. Journal of Ecology and Rural Environment, 2020, 36(11):1428-1436. ]
[11]	KOUROSH NIYA Ali, 黄金良. 利用遥感和GIS对伊朗最大红树林群体的变化进行检测和模拟[J]. 华东师范大学学报(自然科学版), 2020(S1):94-98.
	[ KOUROSH NIYA Ali, Huang J L. Detection and simulation of changes in Iran's largest mangrove population using remote sensing and GIS[J]. Journal of East China Normal University (Natural Science), 2020(S1):94-98. ]
[12]	毛丽君. 基于遥感的广东湛江红树林湿地动态变化研究[D]. 南京:南京林业大学, 2011.
	[ Mao L J. Study on dynamic changes of mangrove wetland in Zhanjiang, Guangdong based on remote sensing[D]. Nanjing: Nanjing Forestry University, 2011. ]
[13]	Zhu Y H, Liu K, Liu L, et al. Retrieval of mangrove aboveground biomass at the individual species level with WorldView-2 images[J]. Remote Sensing, 2015, 7(9):12192-12214. doi: 10.3390/rs70912192
[14]	王诗文, 何宏昌, 付波霖, 等. 基于Sentinel-2数据的山口红树林生理参数反演及时空特征[J]. 科学技术与工程, 2021, 21(9):3698-3707.
	[ Wang S W, He H C, Fu B L, et al. Retrieval of physiological parameters and temporal and spatial characteristics of mangrove in mountain pass based on Sentinel-2 data[J]. Science Technology and Engineering, 2021, 21(9):3698-3707. ]
[15]	徐芳. 基于Sentinel-2的红树林提取及碳储量估算研究[D]. 兰州:兰州交通大学, 2020.
	[ Xu F. Extraction and carbon storage estimation of mangrove forests based on Sentinel-2[D]. Lanzhou: Lanzhou Jiaotong University, 2020. ]
[16]	Wang T, Zhang H, Lin H, et al. Textural-spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 imagery[J]. Remote Sensing, 2016, 8(1):1-15. doi: 10.3390/rs8010001
[17]	马云梅, 吴培强, 潘良浩, 等. 基于GF-2影像的广西茅尾海红树林物种分类及盖度估算分析[J]. 海洋科学, 2019, 43(6):60-70.
	[ Ma Y M, Wu P Q, Pan L H, et al. Analysis of mangrove species classification and coverage estimation based on GF-2 image in Maoweihai Sea, Guangxi[J]. Marine Science, 2019, 43(6):60-70. ]
[18]	王德智. 结合UAV-LiDAR和卫星遥感数据的红树林多尺度观测方法研究[D]. 武汉:中国地质大学, 2020.
	[ Wang D Z. Mangrove multi-scale observation using UAV-LiDAR and satellite remote sensing data[D]. Wuhan: China University of Geosciences, 2020. ]
[19]	Cao J J, Liu K, Zhuo L, et al. Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102:414-423.
[20]	徐逸, 甄佳宁, 蒋侠朋, 等. 无人机遥感与XGBoost的红树林物种分类[J]. 遥感学报, 2021, 25(3):737-752.
	[ Xu Y, Zhen J N, Jiang X P, et al. Mangrove species classification using unmanned aerial vehicle remote sensing and XGBoost[J]. Journal of Remote Sensing, 2021, 25(3):737-752. ]
[21]	Heumann B W. An object-based classification of mangroves using a hybrid decision tree-Support vector machine approach[J]. Remote Sensing, 2011, 3(11):2440-2460. doi: 10.3390/rs3112440
[22]	Demir B, Erturk S. Clustering-based extraction of border training patterns for accurate SVM classification of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(4):840-844. doi: 10.1109/LGRS.2009.2026656
[23]	陈云浩, 冯通, 史培军, 等. 基于面向对象和规则的遥感影像分类研究[J]. 武汉大学学报·信息科学版, 2006, 31(4):316-320.
	[ Chen Y H, Feng T, Shi P J, et al. Study on Remote sensing Image classification based on Object Oriented and Rules[J]. Geomatics and Information Science of Wuhan University, 2006, 31(4):316-320. ]
[24]	苏岫, 耿杰, 马晓瑞, 等. 基于多种植被指数信息与联合稀疏表示的红树林种类识别[J]. 海洋环境科学, 2017, 36(1):114-120.
	[ Su X, Geng J, Ma X R, et al. Mangrove species identification based on multiple vegetation index information and joint sparse representation[J]. Marine Environmental Science, 2017, 36(1):114-120. ]
[25]	梁超, 刘利, 刘建强, 等. 基于HY-1C CZI影像光谱指数重构数据MNF变换的红树林提取[J]. 海洋学报, 2020, 42(4):104-112.
	[ Liang C, Liu L, Liu J G, et al. Mangrove extraction based on MNF transform of spectral index reconstruction data from HY-1C CZI image[J]. Journal of Ocean, 2020, 42(4):104-112. ]
[26]	马云梅. 基于国产多源高分遥感的广西红树林种间分类研究[D]. 呼和浩特:内蒙古师范大学, 2020.
	[ Ma Y M. Interspecific classification of mangrove forest in Guangxi based on domestic multi-source high-resolution remote sensing[D]. Hohhot: Inner Mongolia Normal University, 2020. ]
[27]	夏鹏, 孟宪伟, 李珍, 等. 广西海岸带近百年来人类活动影响下环境演变的沉积记录[J]. 沉积学报, 2012, 30(2):325-332.
	[ Xia P, Meng X W, Li Z, et al. Sedimentary records of environmental evolution under the influence of human activities in Guangxi Coastal zone in the past hundred years[J]. Journal of Sedimentary, 2012, 30(2):325-332. ]

序号	卫星名称	成像时间/年	分辨率/m	景数/景	覆盖范围
1	GF-2 PMS	2018	4	6	防城港湾北部、茅尾海保护区、大风江南部、铁山港北部
2	GF-2 PMS	2019	4	3	大风江北部、北海银滩至营盘镇、铁山港南部
3	GF-2 PMS	2020	4	5	北仑河口保护区、防城港湾南部、廉州湾、钦州湾、山口保护区

地物类型	地物照片	假彩色影像	地物属性及影像特征
桐花树			影像颜色特征明显,为亮红色,色调平滑细腻,植被盖度较高,成面状分布于整个研究区,多与白骨壤分布于靠海一侧
白骨壤			影像颜色为浅红色,纹理特征相比与桐花树较为粗糙,相比于桐花树植被盖度较低,与桐花树一起连片分布于红树林外滩,分布面积很大
红海榄			影像颜色特征明显,为暗红色,色调较平滑,植被盖度高,明显高于桐花树,分布面积较小但集中
秋茄			颜色为深红色,色调较均一,纹理较粗糙,盖度较小,常分布于桐花树和白骨壤的内缘沿岸一侧,分布范围很小
无瓣海桑			颜色为暗红色,色调杂乱,纹理特征明显粗糙,植被盖度高,多成条带状分布于沿岸附近,少数向海方向延伸与桐花树混生
木榄			颜色特征明显,为亮红色,纹理特征较为平滑,植被盖度较低,分布面积很小,常零星分布于桐花树中,很难见到连片分布
老鼠簕			颜色为暗红色,色调均一,纹理较细腻,植被盖度较低,以连片状小面积分布于堤附近的水沟两侧,分布范围很小
茳芏			颜色为深灰枣红色,色调均一,纹理平滑细腻,在茅尾海分布范围较广,多分布于河口附近的浅潮滩,与桐花树和无瓣海桑混生的面积较大

种类	原始数据		一阶微分		特征数据重构
种类	UA	PA	UA	PA	UA	PA
桐花树	78.00	97.41	74.04	96.65	88.38	96.68
无瓣海桑	96.08	71.51	93.09	72.23	95.28	82.44
秋茄	99.16	52.93	73.02	10.36	98.33	92.79
老鼠簕	93.04	41.55	76.74	19.08	94.09	73.60
茳芏	95.14	98.36	94.46	99.01	94.92	98.68
OA	84.63		80.38		91.55
Kappa系数	0.7520		0.6770		0.8695

种类	北仑河口保护区	防城港湾	茅尾海保护区	钦州湾	大风江	廉州湾	北海银滩	铁山港	山口保护区	总计
桐花树	380.20	89.56	1142.84	279.44	267.28	786.67	57.68	28.30	340.12	3372.09
白骨壤	610.66	483.60	-	266.76	575.32	31.56	259.73	968.38	249.16	3445.17
无瓣海桑	-	-	104.11	-	-	30.19	1.67	-	-	135.97
红海榄	-	-	-	-	-	-	0.24	-	126.28	126.52
老鼠簕	-	-	5.83	-	-	-	-	-	-	5.83
木榄	17.13	-	-	-	-	-	-	-	-	17.13
秋茄	1.22	-	6.34	-	-	-	5.21	-	-	12.77
茳芏	-	-	287.50	-	-	-	-	-	-	287.50
总计	1009.21	573.16	1546.62	546.20	842.60	848.42	324.53	996.68	715.56	7402.98