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

2022 ◽  
Vol 270 ◽  
pp. 112885
Author(s):  
Chen Chen ◽  
Yi Ma ◽  
Guangbo Ren ◽  
Jianbu Wang
Keyword(s):  
Salt Marsh ◽  
Aboveground Biomass ◽  
Coastal Wetlands ◽  
Salt Marsh Vegetation ◽  
Generative Adversarial Network ◽  
Marsh Vegetation ◽  
Adversarial Network ◽  
Sentinel 2

scholarly journals Evaluation of Vegetation Index-Based Curve Fitting Models for Accurate Classification of Salt Marsh Vegetation Using Sentinel-2 Time-Series

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5551
Author(s):  
Chao Sun ◽  
Jialin Li ◽  
Luodan Cao ◽  
Yongchao Liu ◽  
Song Jin ◽  
...  
Keyword(s):  
Time Series ◽  
Salt Marsh ◽  
Curve Fitting ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation ◽  
Sentinel 2

The successful launch of the Sentinel-2 constellation satellite, along with advanced cloud detection algorithms, has enabled the generation of continuous time series at high spatial and temporal resolutions, which is in turn expected to enable the classification of salt marsh vegetation over larger spatiotemporal scales. This study presents a critical comparison of vegetation index (VI) and curve fitting methods—two key factors for time series construction that potentially influence vegetation classification performance. To accomplish this objective, the stability of five different VI time series, namely Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), and Water-Adjusted Vegetation Index (WAVI), was compared empirically; the suitability between three curve fitting methods, namely Asymmetric Gaussian (AG), Double Logistic (DL), and Two-term Fourier (TF), and VI time series was measured using the coefficient of determination, and the salt marsh vegetation separability among different combinations of VI time series and curve fitting methods (i.e., VI time series-based curve fitting model) was quantified using overall the Jeffries–Matusita distance. Six common types of salt marsh vegetation from three typical coastal sites in China were used to validate these findings, which demonstrate: (1) the SAVI performed best in terms of time series stability, while the EVI exhibited relatively poor time series stability with conspicuous outliers induced by the sensitivity to omitted clouds and shadows; (2) the DL method commonly resulted in the most accurate classification of different salt marsh vegetation types, especially when combined with the EVI time series, followed by the TF method; and (3) the SAVI/NDVI-based DL/TF model demonstrated comparable efficiency for classifying salt marsh vegetation. Notably, the SAVI/NDVI-based DL model performed most strongly for high latitude regions with a continental climate, whilst the SAVI/NDVI-based TF model appears to be better suited to mid- to low latitude regions dominated by a monsoonal climate.

scholarly journals Seed coating methods and availability for applying in the restoration of salt marsh vegetation in coastal wetlands

2019 ◽  
Vol 34 (12) ◽  
pp. 2601
Author(s):  
SUI Hao-chen ◽  
MA Xu ◽  
YAN Jia-guo ◽  
ZHANG Shu-yan ◽  
GAI Ling-yun ◽  
...  
Keyword(s):  
Salt Marsh ◽  
Coastal Wetlands ◽  
Seed Coating ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation ◽  
Coating Methods

Salt-marsh vegetation in the Shetland Islands

Vegetatio ◽  
1985 ◽  
Vol 61 (1-3) ◽  
pp. 45-54 ◽  
Author(s):  
D. H. Dalby
Keyword(s):  
Salt Marsh ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation ◽  
Shetland Islands

The salt-marsh vegetation of New Zealand

2003 ◽  
Vol 33 (2-3) ◽  
pp. 267-288 ◽  
Author(s):  
Manfred Haacks ◽  
Dietbert Thannheiser
Keyword(s):  
New Zealand ◽  
Salt Marsh ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation

Is the salt marsh vegetation a determining factor in the sedimentation processes?

Hydrobiologia ◽  
2008 ◽  
Vol 621 (1) ◽  
pp. 33-47 ◽  
Author(s):  
H. Silva ◽  
J. M. Dias ◽  
I. Caçador
Keyword(s):  
Salt Marsh ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation ◽  
Determining Factor ◽  
Sedimentation Processes

Recolonization and Facilitation in Baltic Salt Marsh Vegetation

2015 ◽  
Vol 52 (3-4) ◽  
pp. 181-191 ◽  
Author(s):  
Kristin Ludewig ◽  
Antonia Wanner ◽  
Kai Jensen
Keyword(s):  
Salt Marsh ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation

scholarly journals A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 9-14
Author(s):  
F. Pineda ◽  
V. Ayma ◽  
C. Beltran
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Structural Similarity ◽  
Generative Adversarial Network ◽  
Acceptable Result ◽  
Adversarial Network ◽  
High Resolution Satellite Images ◽  
Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

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