Estimating Spartina alterniflora fractional vegetation cover and aboveground biomass in a coastal wetland using SPOT6 satellite and UAV data

Zaiming, Zhou; Yang, Yanming; Benqing, Chen

doi:10.1016/j.aquabot.2017.10.004

Cited by 53 publications

(40 citation statements)

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“…For example, spectral signatures can vary among herbaceous or woody vegetation types due to the biochemical and biophysical properties of the vegetation [66,67] and therefore, may be more easily differentiated at finer resolutions. Increased spatial resolution has a proven advantage in delineating vegetation classes and estimating percent cover in salt marshes [42,68] and mangroves [39] using multispectral imagery. Additionally, increased spectral resolution can also aid in the differentiation of cover types like live vegetation and NPV.…”

Section: Discussionmentioning

confidence: 99%

Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

2019

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Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales.

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Section: Discussionmentioning

confidence: 99%

Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

2019

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“…In particular, applications along the coastline might help to identify different ecological zones (Papakonstantinou et al, 2016), determine coastal vegetation features (Duffy et al, 2018), and improve 2‐D/3‐D coastal environment characterization (Mancini et al, 2013; Taddia et al, 2020). UAV images enable the rapid estimation of vegetation cover (Zhou et al, 2018) and aboveground biomass (Doughty & Cavanaugh, 2019) in salt marshes.…”

Section: Novel Techniques and Challenges In Remote Sensing Of Salt Mamentioning

confidence: 99%

Salt Marsh Dynamics in a Period of Accelerated Sea Level Rise

et al. 2020

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Salt marshes are dynamic systems able to laterally expand, contract, and vertically accrete in response to sea level rise. Here, we present the grand challenges that need to be addressed to fully characterize marsh morphodynamics. The review focuses on physical processes and quantitative models. Without predictive models, it is impossible to determine the future marsh evolution under accelerated sea level rise. In these models, one of the challenges is to resolve both horizontal and vertical dynamics within the same framework. Vertically, the marsh has to accumulate enough material to contrast rising water levels. Horizontally, marsh erosion at the ocean side must be compensated by landward expansion in forests, lawns, and agricultural fields. The dynamics of the marsh‐upland boundary are still not fully understood and will require more research in the upcoming years. The complexity of marsh vegetation is seldom captured in predictive models of marsh evolution. More research is needed to understand the effects of each species or species assemblages on hydrodynamics and sediment transport. Here, we further advocate that a sediment budget resolving all sediment fluxes in a marsh complex is the most important metric of marsh resilience. Characterization of these fluxes will enable to connect salt marshes to other landforms and to unravel feedbacks controlling the evolution of the entire coastal system. Current models of marsh evolution rely on sparse data sets collected at few locations. Novel remote sensing techniques will provide high‐resolution spatial data that will inform a new generation of computer models.

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“…It showed that the DVI was highly significantly correlated with biomass and present more stable than other index in the laboratory measurement on different dehydration level of Pyropia. In the study on biomass estimation of Spartina alterniflora, Zhou et al [61] also proved that the quadratic regression model of the DVI was more suitable than other vegetation indices. Moreover, in the present study, the regression model of Biomass = − 5.550DVI 2 + 105.410DVI + 7.530 shows high accuracy on biomass estimation of Pyropia.…”

Section: Discussionmentioning

confidence: 99%

Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Che

Wang³

et al. 2021

Plant Methods

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Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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Estimating Spartina alterniflora fractional vegetation cover and aboveground biomass in a coastal wetland using SPOT6 satellite and UAV data

Cited by 53 publications

References 50 publications

Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

Salt Marsh Dynamics in a Period of Accelerated Sea Level Rise

Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

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