基于无人机多光谱遥感的海岛植被碳储量估算研究——以洞头大竹峙岛为例

doi:10.3969/j.issn.1001-909X.2023.04.008

摘要/Abstract

摘要：

以温州市洞头区大竹峙岛为研究区,采用无人机搭载多光谱传感器获取海岛高分辨率遥感影像,通过比选光谱最佳波段组合,以监督分类方法将植被类型分成乔木、灌丛和草丛,分类精度为99.72%,Kappa系数为0.995 4。通过深度卷积神经网络对乔木和灌丛进行单木分割(精确率为0.79),获得各优势种的空间分布,结合生物量方程反演各乔木、灌丛优势种的生物量空间分布(乔木R²=0.97,灌丛R²=0.99),其中3个灌丛优势种(天仙果、野梧桐、滨柃)的生物量反演方程通过现场采样构建,其余乔木和灌丛优势种生物量反演方程来自文献。根据优势种的生物量和空间分布,计算得到大竹峙岛的乔木碳储量为300.36 t,灌丛碳储量为47.59 t。通过归一化植被指数反演草丛生物量空间分布(R²=0.99),结合根据实测数据构建的草丛优势种(中华结缕草)生物量方程,计算得到大竹峙岛草丛碳储量为21.59 t。

关键词: 海岛, 植被碳储量, 无人机, 多光谱影像

Abstract:

Taking Dazhuzhi Island (Dongtou, Wenzhou) as the research area, UAV equipped with multispectral sensors was used to acquire high-resolution remote sensing images, the optimal spectral band combination was selected to classify the island vegetation, and the vegetation types was divided into arbors, shrubs and herbs by supervised classification. The accuracy of vegetation classification was 99.72%, and the Kappa coefficient was 0.995 4. The spatial distribution of dominant species of arbors and shrubs was obtained by using the deep convolutional neural network (the precision rate was 0.79), and combined with the biomass equations, the spatial distribution of the biomass of dominant species of arbors and shrubs was inversed (arbors’ R²=0.97, shrubs’ R²=0.99). The biomass inversion equations of 3 shrub dominant species (Ficus erecta, Mallotus japonicas, and Eurya emarginata) were constructed by field sampling, and the other dominant species biomass inversion equations were obtained from literature. Based on the biomass and spatial distribution of dominant species, the carbon storage of arbors and shrubbys was 300.36 t and 47.59 t, respectively. Using normalized difference vegetation index (NDVI) to invert the spatial distribution of herb biomass (R²=0.99), combined with the biomass equation of the dominant herb species (Zoysia sinica) constructed from the measured data, the carbon storage of herbs was 21.59 t on Dazhuzhi Island.

Key words: island, vegetation carbon storage, unmanned aerial vehicle (UAV), multispectral remotely sensed image

中图分类号:

Q948

谢家颀, 张钊, 周稳, 王金旺, 陈雅慧. 基于无人机多光谱遥感的海岛植被碳储量估算研究——以洞头大竹峙岛为例[J]. 海洋学研究, 2023, 41(4): 84-93.

XIE Jiaqi, ZHANG Zhao, ZHOU Wen, WANG Jinwang, CHEN Yahui. Research of carbon storage assessment of island vegetation based on UAV multispectral remote sensing:A case study of Dazhuzhi Island in Dongtou[J]. Journal of Marine Sciences, 2023, 41(4): 84-93.

图/表 16

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物种	样本量	胸径或基径/cm		冠幅/m²
物种	样本量	范围	标准差	范围	标准差
台湾相思	15	4.80~18.90	4.06	3.00~48.00	14.89
椿叶花椒	15	6.30~23.00	5.75	4.00~70.68	22.73
黑松	15	6.80~11.70	5.15	4.94~16.38	4.18
天仙果	15	6.80~10.70	0.93	8.78~19.32	2.74
野梧桐	15	1.30~8.77	1.85	47.75~11.40	1.94
滨柃	15	6.90~9.10	0.65	4.32~7.83	1.23

物种	样本量	胸径或基径/cm		冠幅/m²
物种	样本量	范围	标准差	范围	标准差
台湾相思	15	4.80~18.90	4.06	3.00~48.00	14.89
椿叶花椒	15	6.30~23.00	5.75	4.00~70.68	22.73
黑松	15	6.80~11.70	5.15	4.94~16.38	4.18
天仙果	15	6.80~10.70	0.93	8.78~19.32	2.74
野梧桐	15	1.30~8.77	1.85	47.75~11.40	1.94
滨柃	15	6.90~9.10	0.65	4.32~7.83	1.23

类型	物种	样本量	基径/cm		生物量/kg
类型	物种	样本量	范围	标准差	范围	标准差
灌丛	野梧桐	7	2.20~6.80	1.38	0.08~1.33	0.44
	天仙果	8	2.23~5.06	0.89	0.28~1.51	0.65
	滨柃	12	1.08~5.38	1.51	0.05~2.36	0.81

类型	物种	样本量	基径/cm		生物量/kg
类型	物种	样本量	范围	标准差	范围	标准差
灌丛	野梧桐	7	2.20~6.80	1.38	0.08~1.33	0.44
	天仙果	8	2.23~5.06	0.89	0.28~1.51	0.65
	滨柃	12	1.08~5.38	1.51	0.05~2.36	0.81

样地编号	生物量/kg	NDVI	样地编号	生物量/kg	NDVI	样地编号	生物量/kg	NDVI
H13	1.04	0.88	H23	0.32	0.34	H33	0.79	0.73
H14	0.15	0.23	H24	0.83	0.71	H34	0.69	0.66
H15	0.22	0.28	H25	0.94	0.79	H35	0.24	0.34
H16	0.20	0.26	H26	0.53	0.49	H36	0.37	0.34
H17	0.17	0.24	H27	0.31	0.37	H37	0.13	0.26
H18	0.43	0.44	H28	0.37	0.37	H38	0.21	0.22
H19	0.76	0.67	H29	0.15	0.25	H39	0.30	0.40
H20	0.25	0.29	H30	0.57	0.51	H40	0.71	0.70
H21	0.40	0.42	H31	0.59	0.52	H41	0.17	0.31
H22	0.08	0.16	H32	0.30	0.31	H42	0.68	0.53