Water-related ecosystems' mapping and assessment based on remote sensing techniques and geospatial analysis: The SWOS national service case of the Greek Ramsar sites and their catchments

Fitoka, Eleni; Tompoulidou, Maria; Hatziiordanou, Lena; Apostolakis, A.; Höfer, René; Weise, Kathrin; Ververis, Charalampos

doi:10.1016/j.rse.2020.111795

Cited by 40 publications

(19 citation statements)

References 27 publications

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“…There are still some limitations to this study: (1) There is an issue of data source processing. Although Landsat data have the advantage of spatial resolution, time series, the revisit period is long, which brings great challenges to the high frequency extraction of water surface area.…”

Section: Limitations and Prospectsmentioning

confidence: 98%

“…In recent years, with the intensification of global warming, water resources have been regarded as an important factor influencing human survival and development, and their changes are receiving great attention. Water-related ecosystems play an important role in the water resource [1], of which, lakes are especially important, as their locations and persistence are both affected by climate and human activity and affect climate biological diversity and human well-being [3]- [5]. Lakes are not only vital resources for industry, agriculture and domestic water use [7], but they are also considered sentinels of global climate change due to their sensitivity to climatic changes [8].…”

Section: Introductionmentioning

confidence: 99%

“…It can be well applied to the realization of SDG 6.6.1 and can complete the extent monitoring. For example, Water-related ecosystem' mapping and assessment can be used with remote sensing technology to assess SDG 6.6.1 [1]. Long-time serial water change analysis and floods monitoring are currently research hotspots of remote sensing in hydrology [6].…”

Section: Introductionmentioning

confidence: 99%

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Long Time Series Water Extent Analysis for SDG 6.6.1 Based on the GEE Platform: A Case Study of Dongting Lake

Wang

Jiang

Deng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Understanding the variation regularity of water extent can provide insights into lake conservation and management. In this study, inter-and inner-annual variations of water extent during the period of 1987-2020 were analyzed to understand the temporal and spatial distribution characteristics of Dongting Lake. We applied the Multiple Index Water Detection Rule (MIWDR) to extract the Dongting Lake water extent quickly and accurately based on Google Earth Engine (GEE) platform, and then assessed the extraction accuracy. The water surface analysis results showed that: (1) Based on SDG 6.6.1, the trend of water extent showed the downward fluctuating trend from 1987 to 2020, with the overall average water extent being 1894.48km² . (2) Among the monthly average water area, the largest extent was 2477.14km² (July), and the smallest was 848.14km² (January). Among the seasonal mean water area, summer was the largest, with an area of 2438.06km² , and winter was the smallest at 967.34km² . (3) For the Water Inundation Frequency, seasonal water body accounted for the largest proportion, with 1577.85 km² ; the non-water area was the smallest, with the area of 573.02 km² ; and the permanent water area was 1086.21 km² . Through the analysis of the historical water body extent of the long time series of Dongting Lake, this study reflected support for SDG, for which the research idea and design can help us understand the importance and feasibility of the SDG 6.6.1 Indicator.

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Section: Limitations and Prospectsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Long Time Series Water Extent Analysis for SDG 6.6.1 Based on the GEE Platform: A Case Study of Dongting Lake

Wang

Jiang

Deng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Remote sensing technology can monitor the spatiotemporal changes in surface water bodies in a scientific, refined, and large-scale manner and can calculate the SDG 6.6.1 indicators more accurately and better serve the goal of achieving SDG 6.6.1. Studies, such as those of Fitoka et al and Weise et al, based on the SWOS plan, are all aiming to develop spatial mapping methods of the wetland areas and to apply their research results to the calculation and analysis of indicators in SDG 6.6.1 [4,5]. Mulligan et al used remote sensing and spatial ecosystem service models to determine priority areas for sustainable management at the national and basin scales in the study area and analysed the contribution of nature to SDG 6 [6].…”

Section: Introductionmentioning

confidence: 99%

Surface Water Extraction and Dynamic Analysis of Baiyangdian Lake Based on the Google Earth Engine Platform Using Sentinel-1 for Reporting SDG 6.6.1 Indicators

Jiang

Ling

et al. 2021

Water

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Surface water is an essential element that supports natural ecosystem health and human life, and its losses or gains are closely related to national or local sustainable development. Monitoring the spatial-temporal changes in surface water can directly support the reporting of progress towards the sustainable development goals (SDGs) outlined by the government, especially for measuring SDG 6.6.1 indicators. In our study, we focused on Baiyangdian Lake, an important lake in North China, and explored its spatiotemporal extent changes from 2014 to 2020. Using long-term Sentinel-1 SAR images and the OTSU algorithm, our study developed an automatic water extraction framework to monitor surface water changes in Baiyangdian Lake at a 10 m resolution from 2014 to 2020 on the Google Earth Engine cloud platform. The results showed that (1) the water extraction accuracy in our study was considered good, showing high consistency with the existing dataset. In addition, it was found that the classification accuracy in spring, summer, and fall was better than that in winter. (2) From 2014 to 2020, the surface water area of Baiyangdian Lake exhibited a slowly rising trend, with an average water area of 97.03 km2. In terms of seasonal variation, the seasonal water area changed significantly. The water areas in spring and winter were larger than those in summer and fall. (3) Spatially, most of the water was distributed in the eastern part of Baiyangdian Lake, which accounted for roughly 57% of the total water area. The permanent water area, temporary water area, and non-water area covered 49.69 km2, 97.77 km2, and 171.55 km2, respectively. Our study monitored changes in the spatial extent of the surface water of Baiyangdian Lake, provides useful information for the sustainable development of the Xiong’an New Area and directly reports the status of SDG 6.6.1 indicators over time.

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“…Interacting endogenous and exogenous factors drive the formation and functioning of catchment areas, and, geographically, any river basin develops within the boundaries shaped by a number of climatic factors (Trifonova, 2005(Trifonova, , 2008. Proceeding from these positions, a river basin is regarded as a natural ecosystem (Fitoka et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Assessment of organic matter temporal dynamics in the klyazma basin using remote sensing and qgis trends.earth

Трифонова

Мищенко

Shutov

2021

Nexo Revista Científica

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The article addresses the dynamics of biological processes in various landscapes within a holistic natural geosystem—a catchment area. The Klyazma river (the fourth order tributary to the Volga) was selected as the object of study. The natural complex of the Klyazma river basin is a combination of different landscapes, each marked by a diverse composition of geomorphological and soil-vegetation structures. The study is based on remote sensing data and the Trends.Earth Land Degradation Monitoring Project (Land Cover Dataset, European Space Agency 2015, 300 m spatial resolution) implemented using the open-source Quantum GIS 2.18. Four landscape provinces and eight site were identified in the studied catchment area according to the geomorphological structure and the soil and vegetation cover. The ecosystem parameters Gross Primary Productivity, Net Primary Productivity, and Ecosystem Respiration were measured in the identified sites. In different landscapes the biological processes, characterizing the organic matter dynamics in the form of plant production and organic matter accumulation, differ in both rate and intensity, and variously respond to the changes in climate parameters and land use. The river basin, as a holistic ecosystem, showed sufficient stability of the dynamic processes. This suggests that holistic natural ecosystems, such as catchment areas, have internal compensatory mechanisms that maintain the development stability over long period of time, while irrational land use remains the main damaging factor.

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Water-related ecosystems' mapping and assessment based on remote sensing techniques and geospatial analysis: The SWOS national service case of the Greek Ramsar sites and their catchments

Cited by 40 publications

References 27 publications

Long Time Series Water Extent Analysis for SDG 6.6.1 Based on the GEE Platform: A Case Study of Dongting Lake

Long Time Series Water Extent Analysis for SDG 6.6.1 Based on the GEE Platform: A Case Study of Dongting Lake

Surface Water Extraction and Dynamic Analysis of Baiyangdian Lake Based on the Google Earth Engine Platform Using Sentinel-1 for Reporting SDG 6.6.1 Indicators

Assessment of organic matter temporal dynamics in the klyazma basin using remote sensing and qgis trends.earth

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