Vegetation patterns in a South American coastal wetland using high-resolution imagery

Gonzalez, Eliana Belén; Trilla, Gabriela González; Martín, Laura San; Grimson, Rafael; Kandus, Patricia

doi:10.1080/17445647.2019.1644545

Cited by 12 publications

(3 citation statements)

References 48 publications

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“…Optical remote sensing data have been used widely since the 1970s to study vegetation and the environment [12]. In order to improve the accuracy of research, several scholars have recently used fine spatial resolution images in vegetation wetland studies [13][14][15][16][17]. Fine spatial resolution images offer many advantages for the identification of wetland boundaries and plant formations.…”

Section: Introductionmentioning

confidence: 99%

Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

Niculescu

Boissonnat

Lardeux³

et al. 2020

Remote Sensing

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In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work is to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophytes communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.

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Section: Introductionmentioning

confidence: 99%

Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

Niculescu

Boissonnat

Lardeux³

et al. 2020

Remote Sensing

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“…To explore saltmarsh growth patterns, ecologically-relevant patch metrics (as identified by previous studies: Gonzalez et al, 2019;Kelly et al, 2011) were calculated for each classified image using the package landscapemetrics version 1.5.4 (Hesselbarth et al, 2019). Selected classlevel patch metrics were patch area, class area, core area, number of patches, cohesion, Euclidean nearest-neighbour distance, clumpiness, contiguity and shape, while landscape-level metrics were contagion, Shannon's diversity and Shannon's evenness (Table 3).…”

Section: Discussionmentioning

confidence: 99%

Blue carbon ecosystem monitoring using remote sensing reveals wetland restoration pathways

Lanceman

Sadat‐Noori²,

Gaston³

et al. 2022

Front. Environ. Sci.

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In an era of climate and biodiversity crises, ecosystem rehabilitation is critical to the ongoing wellbeing of humans and the environment. Coastal ecosystem rehabilitation is particularly important, as these ecosystems sequester large quantities of carbon (known in marine ecosystems as “blue carbon”) thereby mitigating climate change effects while also providing ecosystem services and biodiversity benefits. The recent formal accreditation of blue carbon services is producing a proliferation of rehabilitation projects, which must be monitored and quantified over time and space to assess on-ground outcomes. Consequently, remote sensing techniques such as drone surveys, and machine learning techniques such as image classification, are increasingly being employed to monitor wetlands. However, few projects, if any, have tracked blue carbon restoration across temporal and spatial scales at an accuracy that could be used to adequately map species establishment with low-cost methods. This study presents an open-source, user-friendly workflow, using object-based image classification and a random forest classifier in Google Earth Engine, to accurately classify 4 years of multispectral and photogrammetrically derived digital elevation model drone data at a saltmarsh rehabilitation site on the east coast of Australia (Hunter River estuary, NSW). High classification accuracies were achieved, with >90% accuracy at 0.1 m resolution. At the study site, saltmarsh colonised most suitable areas, increasing by 142% and resulting in 56 tonnes of carbon sequestered, within a 4-year period, providing insight into blue carbon regeneration trajectories. Saltmarsh growth patterns were species-specific, influenced by species’ reproductive and dispersal strategies. Our findings suggested that biotic factors and interactions were important in influencing species’ distributions and succession trajectories. This work can help improve the efficiency and effectiveness of restoration planning and monitoring at coastal wetlands and similar ecosystems worldwide, with the potential to apply this approach to other types of remote sensing imagery and to calculate other rehabilitation co-benefits. Importantly, the method can be used to calculate blue carbon habitat creation following tidal restoration of coastal wetlands.

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“…The NDVI shows the greenness of an area (Glanville et al, 2016). The NDVI has been used in the identification of GDEs in different studies (Table 2) based on the principle that ecosystems are able to maintain consistent greenness and remain physiologically active even during prolonged dry periods, and also exhibit low inter-annual leaf area changes between dry and wet years are defined as potentially groundwater dependent (Gonzalez et al, 2019;Barron et al, 2014;Gou et al, 2015;Pérez Hoyos et al, 2016).…”

Section: Available Remotely Sensed Algorithms In Groundwater-dependen...mentioning

confidence: 99%

Progress in the remote sensing of groundwater-dependent ecosystems in semi-arid environments

Rampheri

Dube

Dondofema

et al. 2023

Physics and Chemistry of the Earth, Parts A/B/C

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Vegetation patterns in a South American coastal wetland using high-resolution imagery

Cited by 12 publications

References 48 publications

Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

Blue carbon ecosystem monitoring using remote sensing reveals wetland restoration pathways

Progress in the remote sensing of groundwater-dependent ecosystems in semi-arid environments

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