Dataset Information

Multitemporal optical and radar metrics for wetland mapping at national level in Albania.

ABSTRACT: Wetlands are highly dynamic, with many natural and anthropogenic drivers causing seasonal, periodic or permanent changes in their structure and composition. Thus, it is necessary to use time series of images for accurate classifications and monitoring. We used all available Sentinel-1 and Sentinel-2 images to produce a national wetlands map for Albania. We derived different indices and temporal metrics and investigated their impacts and synergies in terms of mapping accuracy. Best results were achieved when combining Sentinel-1 with Sentinel-2 and its derived indices. We reduced systematic errors and increased the thematic resolution using morphometric characteristics and knowledge-based rules, achieving an overall accuracy of 82%. Results were also validated against field inventories. This methodology can be reproducible to other countries and can be made operational for an integrated planning that considers the food, water, and energy nexus.

SUBMITTER: Muro J

PROVIDER: S-EPMC7452495 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multitemporal optical and radar metrics for wetland mapping at national level in Albania.

Muro Javier J Varea Ana A Strauch Adrian A Guelmami Anis A Fitoka Eleni E Thonfeld Frank F Diekkrüger Bernd B Waske Björn B

Heliyon 20200819 8

Wetlands are highly dynamic, with many natural and anthropogenic drivers causing seasonal, periodic or permanent changes in their structure and composition. Thus, it is necessary to use time series of images for accurate classifications and monitoring. We used all available Sentinel-1 and Sentinel-2 images to produce a national wetlands map for Albania. We derived different indices and temporal metrics and investigated their impacts and synergies in terms of mapping accuracy. Best results were a ...[more]

PMID: 32904253

Similar Datasets

Project description:Surface water plays a vital role in the spread of infectious diseases. Information on the spatial and temporal dynamics of surface water availability is thus critical to understanding, monitoring and forecasting disease outbreaks. Before the launch of Sentinel-1 Synthetic Aperture Radar (SAR) missions, surface water availability has been captured at various spatial scales through approaches based on optical remote sensing data. A critical drawback of the latter is data loss due to cloud cover, however few studies have quantified this. This study evaluated data loss due to clouds in three Western Ghats (India) districts. These forest-agricultural mosaic landscapes, where water-related diseases are prevalent, experience the Indian monsoon. We compared surface water areas mapped by thresholding 10m Sentinel-1A SAR data with the optical 30m Landsat-derived Joint Research Centre (JRC) Global Surface Water product, currently the only globally available long-term monthly surface water data product. Backscatter thresholds were identified manually, and our Bayesian algorithm found these thresholds were very likely (>97%). While the Sentinel-1 SAR-based and JRC's optical-based approach mapped surface water extent with high overall accuracy (> 98%) when the cloud cover was low, the unmapped surface water area was substantial in the JRC product during the monsoon months. Across the districts, the average cloud cover in the July-August period was 92% or 90% for 2017 and 2018 respectively, resulting in 25% or 23% of the surface water area being unmapped. Also, the more detailed 10m resolution of Sentinel-1A SAR helped detect the many small water features missed by 30m JRC. Thus, for predicting water-related disease risks linked to small water features or monsoon rainfall, Sentinel-1A SAR is more effective. Finally, automatic backscatter thresholding for unvegetated surface water mapping can be effective if threshold values are adapted to regional-specific backscatter spatial and temporal variations.

Dataset Information

Multitemporal optical and radar metrics for wetland mapping at national level in Albania.

Publications

Multitemporal optical and radar metrics for wetland mapping at national level in Albania.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets