@article{Sánchez-Espinosa2019b,

title = {Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus Landsat 8},

author = {A. Sánchez-Espinosa and C. Schröder},

url = {https://www.sciencedirect.com/science/article/pii/S0301479719308850},

doi = {10.1016/j.jenvman.2019.06.084},

year  = {2019},

date = {2019-10-01},

journal = {Journal of Environmental Management},

volume = {247},

pages = {484-498},

abstract = {Environmental studies with Landsat images have revealed many of the problems faced by wetland ecosystem, which are crucial for the conservation of biodiversity and the natural values of our planet. The study of LULC changes in wetlands through remote sensing constantly helps to identify and combat their main environmental threats improving the conservation of these natural habitats. Starting in mid-2015, the Sentinel-2 satellite opens new possibilities in the field of earth observation thanks to its higher spatial, spectral and temporal resolution becoming a powerful source of information for LULC monitoring in wetland areas. However, researchers may ask them selves to what extent Sentinel-2 is an improvement over Landsat 8 for general purposes. This research test if there is a real difference in the quality of the results delivered by both Sentinel-2 and Landsat 8 imagery when basic classification methods are applied.



The study uses Sentinel-2 and Landsat 8 imagery to produce LULC maps in a Mediterranean wetland area applying an object based classification method in order to compare the accuracy and reliability in the surface detected by both satellites. The results show that an object based classification using only the Sentinel-2 and Landsat 8 image information, without band indexes or ancillary data, offers very similar results for most LULC classes, being the overall accuracy around 87–88% with slightly better results when using Sentinel-2. Although using Sentinel-2 leads to an increase in file size and processing times, the analysis of certain LULC classes presents an improvement compared to Landsat 8, detecting more linear and small size elements with a better delineation of image features in the classified map. However, these improvements should not underestimate the value of Landsat imagery in the future since both satellites provide high precision information, so they can and should coexist and be used together to increase data availability in order to have the best possible results in remote sensing research.},

keywords = {Geotechnology, Land and soil, SWOS, Wetlands},

pubstate = {published},

tppubtype = {article}

}

@article{Weise2019,

title = {Wetland extent tools for SDG 6.6.1 reporting from the Satellite-based Wetland Observation Service (SWOS)},

author = {K. Weise and R. Höfer and J. Franke and A. Guelmami and W. Simonson and J. Muro and B. O'Connor and A. Strauch and S. Flink and J. Eberle and E. Mino and S. Thulin and P. Philipson and E. van Valkengoed and J. Truckenbrodt and F. Zander and A. Sánchez-Espinosa and C. Schröder and F. Thinfeld and E. Fitoka and E. Scott and M. Ling and M. Schwarz and I. Kunz and G. Thürmer and A. Plasmeijer and L. Hilarides},

url = {https://doi.org/10.1016/j.rse.2020.111892},

doi = {https://doi.org/10.1016/j.rse.2020.111892},

year  = {2019},

date = {2019-09-15},

journal = {Remote Sensing of Environment},

volume = {247},

abstract = {Wetlands are the most fragile and threatened ecosystems worldwide, and also one of the most rapidly declining. At the same time wetlands are typically biodiversity hotspots and provide a range of valuable ecosystem services, such as water supply and purification, disaster risk reduction, climate change adaptation, and carbon sequestration.



Pressures on wetlands are likely to further intensify in the coming decades due to increased global demand for land and water, and due to climate change. Stakeholders at all levels of governance have to be involved to slow, stop and reverse these processes. However, the information they need on wetland extent, their ecological character, and their ecosystem services is often scattered, sparse and difficult to find and access.



The freely available Sentinel satellite data of the Copernicus Programme, as well as the Landsat archive, provide a comprehensive basis to map and inventory wetland areas (extent), to derive information on the ecological status, as well as long- and short-term trends in wetland characteristics. However, making use of these Earth Observation (EO) resources for robust and standardized wetland monitoring requires expert knowledge on often complex data processing techniques, which impedes practical implementation. In this respect, the Satellite-based Wetland Observation Service (SWOS), a Horizon 2020 funded project (www.swos-service.eu) has developed and made disseminated monitoring approaches based on EO data, specifically designed for less experienced satellite data users.



The SWOS monitoring tools aim at assisting countries in conducting national wetland inventories for their Sustainable Development Goals (SDG) reporting and monitoring obligations, and additionally facilitates other monitoring obligations such as those required by the Ramsar Convention and supports decision-making in local conservation activities. The four main components of the SWOS approach are: map and indicator production; software development; capacity building; and initializing the GEO Wetlands Community Portal. Wetland managers and data analysists from more than fifty wetland sites and river basins across Europe, the Middle East, and Africa investigated the benefits and limitations of this EO-based wetland mapping and monitoring approach.



We describe research that applies the SWOS tools to test their potential for the mapping of wetlands in a case study based in Albania, and show its effectiveness to derive metrics relevant to the monitoring of SDG indicator 6.6.1.},

keywords = {Climate Change, Conservation and management, Environmental conservation, SWOS, Wetlands},

pubstate = {published},

tppubtype = {article}

}

@article{Sánchez-Espinosa2015,

title = {Development of an Indicator to Monitor Mediterranean Wetlands},

author = {A. Sánchez-Espinosa and D. Abdul-Malak and A. Guelmami and C. Perennou},

url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122694},

doi = {https://doi.org/10.1371/journal.pone.0122694},

year  = {2015},

date = {2015-03-31},

journal = {Plos One},

volume = {10},

number = {3},

abstract = {Wetlands are sensitive ecosystems that are increasingly subjected to threats from anthropogenic factors. In the last decades, coastal Mediterranean wetlands have been suffering considerable pressures from land use change, intensification of urban growth, increasing tourism infrastructure and intensification of agricultural practices. Remote sensing (RS) and Geographic Information Systems (GIS) techniques are efficient tools that can support monitoring Mediterranean coastal wetlands on large scales and over long periods of time. The study aims at developing a wetland indicator to support monitoring Mediterranean coastal wetlands using these techniques. The indicator makes use of multi-temporal Landsat images, land use reference layers, a 50m numerical model of the territory (NMT) and Corine Land Cover (CLC) for the identification and mapping of wetlands. The approach combines supervised image classification techniques making use of vegetation indices and decision tree analysis to identify the surface covered by wetlands at a given date. A validation process is put in place to compare outcomes with existing local wetland inventories to check the results reliability. The indicator´s results demonstrate an improvement in the level of precision of change detection methods achieved by traditional tools providing reliability up to 95% in main wetland areas. The results confirm that the use of RS techniques improves the precision of wetland detection compared to the use of CLC for wetland monitoring and stress the strong relation between the level of wetland detection and the nature of the wetland areas and the monitoring scale considered.},

keywords = {Geotechnology, Wetlands},

pubstate = {published},

tppubtype = {article}

}