Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network

被引:61
作者
Chen, Chen [1 ]
Ma, Yi [2 ,3 ]
Ren, Guangbo [2 ]
Wang, Jianbu [2 ]
机构
[1] Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China
[2] Minist Nat Resources, Inst Oceanog 1, Lab Marine Phys & Remote Sensing, Qingdao 266061, Peoples R China
[3] Minist Nat Resources, Technol Innovat Ctr Ocean Telemetry, Qingdao 266061, Peoples R China
基金
中国国家自然科学基金;
关键词
Coastal wetland; aboveground biomass; Sentinel-2; Generative adversarial network; Sample expansion; LEAF-AREA INDEX; FEATURE-SELECTION; NEURAL-NETWORK; JIAOZHOU BAY; CLASSIFICATION; CHINA; COVER; SATELLITE; QINGDAO; IMAGERY;
D O I
10.1016/j.rse.2021.112885
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Coastal wetlands are main components of the "blue carbon" ecosystems in coastal zones. Salt-marsh biomass is especially important regarding climate-change mitigation. Generating high precision biomass maps for evalu-ating the ecological functions of coastal wetlands is essential; however, conducting accurate biomass inversions with limited in situ observations from coastal wetlands is challenging. We propose a generative adversarial network with a constrained factor model (GAN-CF) for expanding limited in situ salt-marsh biomass observa-tions. We used Sentinel-2 images and a deep belief network based on the conjugate gradient method (CG-DBN) for obtaining land-cover maps and the salt-marsh distribution (species: Phragmites australis, Suaeda glauca, Spartina alterniflora, and mixed species dominated by Tamarix chinensis) in the study area. This study bridges in situ hyperspectral and Sentinel-2 multispectral data by a satellite-band equivalent conversion model. The biomass and multispectral data derived from Sentinel-2 were used as input for the proposed GAN-CF model, which produced and constrained the generated samples based on the features (i.e., spectra, vegetation index, and biomass) of the in situ observations. Aboveground biomass (AGB) maps at 10-m spatial resolution were produced by constructing multiple linear regression models (MLRMs) based on the generated samples of each salt-marsh type using Sentinel-2 images. The quantity and richness of the generated samples improved the AGB estimations in the study area. The inversion accuracy of S. alterniflora was significantly improved (RMSE = 3.71 Mg/ha); the estimated AGB was strongly related to the in situ observations (R = 0.923). The estimated AGB was validated using in situ observations. The total amount of salt-marsh AGB in the study area in 2019 was estimated at 2.36 x 10(5) Mg, with 7.95 Mg/ha average. The salt-marsh biomass in decreasing order was as follows: P. australis (12.7 Mg/ha) > S. alterniflora (11.5 Mg/ha) > mixed species (8.97 Mg/ha) > S. glauca (2.18 Mg/ha). The salt-marsh area in decreasing order was as follows: S. glauca (10,410 ha) > P. australis (7320 ha) > mixed species (6740 ha) > S. alterniflora (5240 ha). By a feasibility analysis we estimated the biomass based on the Sentinel-2 data covering the Yellow River delta wetland in May, July, and September 2019 and the Jiaozhou Bay wetland in September 2019 by using the generated samples. The generated samples based on the 2013-2019 in situ ob-servations constitute a salt-marsh biomass database, which can be useful for quantifying the regional carbon storage and ecological restoration monitoring.
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页数:20
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