Mapping the multi-decadal mangrove dynamics of the Australian coastline

Lymburner, Leo; Bunting, Peter; Lucas, Richard; Scarth, Peter; Alam, Imam; Phillips, Claire; Ticehurst, Catherine; Held, Alex

doi:10.1016/j.rse.2019.05.004

Cited by 76 publications

(83 citation statements)

References 37 publications

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“…The methodology applied to assess the mangrove area changes was supported by multi-date remote-sensed data. Using satellite images, it is possible to identify, calculate, and monitor mangrove areas, as well as the surfaces affected by erosion processes [46][47][48][49].…”

Section: Methodsmentioning

confidence: 99%

Mangrove Forests Evolution and Threats in the Caribbean Sea of Colombia

Daza¹,

Moreno

Portz

et al. 2020

Water

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Colombia has approximately 379,954 hectares of mangrove forests distributed along the Pacific Ocean and the Caribbean Sea coasts. Such forests are experiencing the highest annual rate of loss recorded in South America and, in the last three decades, approximately 40,000 hectares have been greatly affected by natural and, especially, human impacts. This study determined, by the use of Landsat multispectral satellite images, the evolution of three mangrove forests located in the Colombian Caribbean Sea: Malloquín, Totumo, and La Virgen swamps. Mangrove forest at Mallorquín Swamp recorded a loss of 15 ha in the period of 1985–2018, associated with alterations in forest hydrology, illegal logging, urban growth, and coastal erosion. Totumo Swamp lost 301 ha in the period 1985–2018 associated with changes in hydrological conditions, illegal logging, and increased agricultural and livestock uses. La Virgen Swamp presented a loss of 31 ha in the period of 2013–2018 that was linked to the construction of a roadway, alterations of hydrological conditions, illegal logging, and soil urbanization, mainly for tourist purposes. Although Colombian legislation has made efforts to protect mangrove ecosystems, human activities are the main cause of mangrove degradation, and thus it is mandatory for the local population to understand the value of the ecosystem services provided by mangroves.

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Section: Methodsmentioning

confidence: 99%

Mangrove Forests Evolution and Threats in the Caribbean Sea of Colombia

Daza¹,

Moreno

Portz

et al. 2020

Water

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“…The DEA land cover product has been optimized for high-performance computing within the Open Data Cube (ODC) framework and is generating continental maps of land-cover datasets from environmental variables (thematic and continuous), with a focus on those that are generated at a national level within DEA's ODC environment (Lucas et al 2019a) and for multiple points in time. These include the vegetation cover fraction of the Joint Remote Sensing Research Program (Gill et al 2017), Water Observations from Space (WOfS) (Mueller et al 2016), surface reflectance Median Absolute Deviation (MAD) (Roberts et al 2018), and national mangrove distribution (Lymburner et al 2019) (Fig. 13.5).…”

Section: Dea To Map Land Cover and Dynamics Over Timementioning

confidence: 99%

“…Furthermore, likely impacts on policy (e.g., the United Nations Framework Convention on Climate Change or the Convention on Biological Diversity) and land management (e.g., associated with Kakadu National Park) can be indicated and future interventions suggested. In the case of SDG 6.6 ("By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes"), main policy actions to advance this target should address drivers of climate change (Metternicht et al 2018;Asbridge et al 2018), including also environmental monitoring through Digital Earth platforms (Lymburner et al 2019).…”

Section: Dea To Map Land Cover and Dynamics Over Timementioning

confidence: 99%

Digital Earth for Sustainable Development Goals

Metternicht

Mueller

Lucas

2019

Manual of Digital Earth

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Sustainable development is nothing new, but it has proven notoriously difficult to implement in practice. The 2030 Agenda for Sustainable Development, with 17 goals, 169 targets and 232 associated indicators, was approved at the 2015 UN General Assembly and addresses the economic, social and environmental pillars of development, aspiring to attain by 2030 a sustainable future that balances equitable prosperity within planetary boundaries. While the goals are universal (i.e., applicable to both developing and developed countries), it is left to individual countries to establish national Sustainable Development Goal (SDG) targets according to their own priorities and level of ambition in terms of the scale and pace of transformation aspired to. Keywords Sustainable development goals • Digital Earth • Earth Observation • Big Earth data • Indicators • Land cover classification 13.1 Fundamentals of Digital Earth for the Sustainable Development Goals The Digital Earth (DE) exists in parallel to the physical Earth along with some translating elements between them (Sudmanns et al. 2019). Chapter 1 describes the origin, evolution and main elements of Digital Earth, and the links between Digital Earth, Big Data (Chap. 9) and big Earth data. Guo (2017) argues that, from the perspective of big data, big Earth data inherits big data's 'Vs' (volume, velocity and

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“…By organising freely available medium resolution satellite imagery such as Landsat or Sentinel-2 into spectrally and geometrically calibrated stacks of analysis-ready data, these platforms support the development of new automated and cost effective workflows for consistent, rapid and repeatable operational monitoring of environmental change across time and space [4]. Data cube approaches leveraging multi-decadal remote sensing time series have so far been used successfully in a wide range of coastal and inland water applications, including monitoring 33 years of coastal change across global sandy beach shorelines [6], mapping the topography and extent through time of threatened intertidal ecosystems [7][8][9][10], and tracking changes in the distributions of inland waterbodies at both continental and global scale [11,12].…”

Section: Introductionmentioning

confidence: 99%

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

et al. 2019

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Accurately mapping the boundary between land and water (the 'waterline') is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu's method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50-3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.

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Mapping the multi-decadal mangrove dynamics of the Australian coastline

Cited by 76 publications

References 37 publications

Mangrove Forests Evolution and Threats in the Caribbean Sea of Colombia

Mangrove Forests Evolution and Threats in the Caribbean Sea of Colombia

Digital Earth for Sustainable Development Goals

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

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