Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series

Yang, Xiucheng; Zhu, Zhe; Qiu, Shi; Kroeger, K. D.; Zhu, Zhiliang; Covington, Scott

doi:10.1016/j.rse.2022.113047

Cited by 62 publications

(22 citation statements)

References 86 publications

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“…We view the discrepancy between the two change rates as partly owing to the difference between monitoring tidal marsh condition versus cover. As seen in recent analysis of the Northeastern United States, far more change is happening to the marsh condition than to cover, but these condition changes do result in carbon loss 40 . Declining tidal marsh emissions suggests the success of efforts to protect and restore these ecosystems.…”

Section: Uncertainty and Future Analysismentioning

confidence: 99%

Global hotspots of salt marsh change and carbon emissions

Campbell

Fatoyinbo

Goldberg

et al. 2022

Nature

100

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Salt marshes provide ecosystem services such as carbon sequestration1, coastal protection2, sea-level-rise (SLR) adaptation3 and recreation4. SLR5, storm events6, drainage7 and mangrove encroachment8 are known drivers of salt marsh loss. However, the global magnitude and location of changes in salt marsh extent remains uncertain. Here we conduct a global and systematic change analysis of Landsat satellite imagery from the years 2000–2019 to quantify the loss, gain and recovery of salt marsh ecosystems and then estimate the impact of these changes on blue carbon stocks. We show a net salt marsh loss globally, equivalent to an area double the size of Singapore (719 km2), with a loss rate of 0.28% year−1 from 2000 to 2019. Net global losses resulted in 16.3 (0.4–33.2, 90% confidence interval) Tg CO2e year−1 emissions from 2000 to 2019 and a 0.045 (−0.14–0.115) Tg CO2e year−1 reduction of carbon burial. Russia and the USA accounted for 64% of salt marsh losses, driven by hurricanes and coastal erosion. Our findings highlight the vulnerability of salt marsh systems to climatic changes such as SLR and intensification of storms and cyclones.

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Section: Uncertainty and Future Analysismentioning

confidence: 99%

Global hotspots of salt marsh change and carbon emissions

Campbell

Fatoyinbo

Goldberg

et al. 2022

Nature

100

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“…The Yangtze River forms the Yangtze River Delta, an alluvial plain, before entering the sea [47][48][49]. The delta covers parts of two provinces, Zhejiang and Jiangsu, and the city of Shanghai [50,51]. This densely populated low-lying plain also supports a well-developed area of agriculture; the plain is covered by a dense river network, agricultural areas, and more than 200 lakes.…”

Section: Study Areamentioning

confidence: 99%

Extraction of Water Body Information from Remote Sensing Imagery While Considering Greenness and Wetness Based on Tasseled Cap Transformation

Chen

Liang

et al. 2022

Remote Sensing

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Water, as an important part of ecosystems, is also an important topic in the field of remote sensing. Shadows and dense vegetation negatively affect most traditional methods used to extract water body information from remotely sensed images. As a result, extracting water body information with high precision from a wide range of remote sensing images which contain complex ground-based objects has proved difficult. In the present study, a method used for extracting water body information from remote sensing imagery considers the greenness and wetness of ground-based objects. Ground objects with varied water content and vegetation coverage have different characteristics in their greenness and wetness components obtained by the Tasseled Cap transformation (TCT). Multispectral information can be output as brightness, greenness, and wetness by Tasseled Cap transformation, which is widely used in satellite remote sensing images. Hence, a model used to extract water body information was constructed to weaken the influence of shadows and dense vegetation. Jiangsu and Anhui provinces are located along the Yangtze River, China, and were selected as the research area. The experiment used the wide-field-of-view (WFV) sensor onboard the Gaofen-1 satellite to acquire remotely sensed photos. The results showed that the contours and spatial extent of the water bodies extracted by the proposed method are highly consistent, and the influence of shadow and buildings is minimized; the method has a high Kappa coefficient (0.89), overall accuracy (92.72%), and user accuracy (88.04%). Thus, the method is useful in updating a geographical database of water bodies and in water resource management.

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“…(63,64) By definition, qualitative evaluation is the assessment of an image produced by visual observation with the support of feature knowledge. (56,65,66) Quantitative evaluation is the assessment of an image using statistical parameters and is generally based on two factors: non-positional and positional accuracies. (56,67) We mainly conducted qualitative evaluation on two factors: location and shape.…”

Section: Accuracy Assessmentmentioning

confidence: 99%