Timely spatially explicit warning of areas with high fire occurrence probability is an important component of strategic plans to prevent and monitor fires within South American (SA) Protected Areas (PAs). In this study, we present a five\uffe2\uff80\uff90level alert system, which combines both climatological and anthropogenic factors, the two main drivers of fires in SA. The alert levels are: High Alert, Alert, Attention, Observation and Low Probability. The trend in the number of active fires over the past three years and the accumulated number of active fires over the same period were used as indicators of intensification of human use of fire in that region, possibly associated with ongoing land use/land cover change (LULCC). An ensemble of temperature and precipitation gridded output from the GloSea5 Seasonal Forecast System was used to indicate an enhanced probability of hot and dry weather conditions that combined with LULCC favour fire occurrences. Alerts from this system were first issued in August 2020, for the period ranging from August to October (ASO) 2020. Overall, 50% of all fires observed during the ASO 2017\uffe2\uff80\uff932019 period and 40% of the ASO 2020 fires occurred in only 29 PAs were all categorized in the top two alert levels. In categories mapped as High Alert level, 34% of the PAs experienced an increase in fires compared with the 2017\uffe2\uff80\uff932019 reference period, and 81% of the High Alert false alarm registered fire occurrence above the median. Initial feedback from stakeholders indicates that these alerts were used to inform resource management in some PAs. We expect that these forecasts can provide continuous information aiming at changing societal perceptions of fire use and consequently subsidize strategic planning and mitigatory actions, focusing on timely responses to a disaster risk management strategy. Further research must focus on the model improvement and knowledge translation to stakeholders.
", "keywords": ["0106 biological sciences", "Atmospheric Science", "Land cover", "Flood Risk", "Precipitation", "01 natural sciences", "Environmental science", "Impact of Climate Change on Forest Wildfires", "Global Flood Risk Assessment and Management", "Meteorology", "Engineering", "Machine learning", "False alarm", "Civil engineering", "0105 earth and related environmental sciences", "Climatology", "Global and Planetary Change", "Tropical Cyclone Intensity and Climate Change", "Geography", "Warning system", "Geology", "FOS: Earth and related environmental sciences", "15. Life on land", "Computer science", "Earth and Planetary Sciences", "13. Climate action", "Environmental Science", "Physical Sciences", "Land use", "Telecommunications", "FOS: Civil engineering"]}, "links": [{"href": "https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/cli2.19"}, {"href": "https://doi.org/10.1002/cli2.19"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Climate%20Resilience%20and%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/cli2.19", "name": "item", "description": "10.1002/cli2.19", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/cli2.19"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-20T00:00:00Z"}}, {"id": "10.1038/s41467-024-48252-x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:18:13Z", "type": "Journal Article", "created": "2024-05-08", "title": "A unifying modelling of multiple land degradation pathways in Europe", "description": "AbstractLand degradation is a complex socio-environmental threat, which generally occurs as multiple concurrent pathways that remain largely unexplored in Europe. Here we present an unprecedented analysis of land multi-degradation in 40 continental countries, using twelve dataset-based processes that were modelled as land degradation convergence and combination pathways in Europe\uffe2\uff80\uff99s agricultural (and arable) environments. Using a Land Multi-degradation Index, we find that up to 27%, 35% and 22% of continental agricultural (~2 million km2) and arable (~1.1 million km2) lands are currently threatened by one, two, and three drivers of degradation, while 10\uffe2\uff80\uff9311% of pan-European agricultural/arable landscapes are cumulatively affected by four and at least five concurrent processes. We also explore the complex pattern of spatially interacting processes, emphasizing the major combinations of land degradation pathways across continental and national boundaries. Our results will enable policymakers to develop knowledge-based strategies for land degradation mitigation and other critical European sustainable development goals.Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).
", "keywords": ["550", "Arid", "Soil Moisture", "0211 other engineering and technologies", "FOS: Mechanical engineering", "02 engineering and technology", "Digital Soil Mapping Techniques", "Normalized Difference Vegetation Index", "630", "Agricultural and Biological Sciences", "Engineering", "Pathology", "GE1-350", "2. Zero hunger", "QE1-996.5", "Vegetation Monitoring", "Water content", "Ecology", "Geography", "Statistics", "Life Sciences", "Hydrology (agriculture)", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Soil Erosion and Agricultural Sustainability", "6. Clean water", "Satellite Observations", "Archaeology", "Physical Sciences", "Leaf area index", "Telecommunications", "Medicine", "Vegetation (pathology)", "Environmental Engineering", "Data set", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Aerospace Engineering", "Soil Science", "Environmental science", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "FOS: Mathematics", "Biology", "Radar", "Synthetic Aperture Radar Interferometry", "Canopy", "FOS: Environmental engineering", "Soil Properties", "Paleontology", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Agronomy", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Mathematics"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/3707/2021/essd-13-3707-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-3707-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-3707-2021", "name": "item", "description": "10.5194/essd-13-3707-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-3707-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-07T00:00:00Z"}}, {"id": "10044/1/97038", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:25:49Z", "type": "Journal Article", "created": "2022-04-01", "title": "Terahertz Metastructures for Noninvasive Biomedical Sensing and Characterization in Future Health Care [Bioelectromagnetics]", "description": "According to a recent report [1] from the Cancer Research Agency of the World Health Organization, cancer is a dominant cause of mortality worldwide, leading to 10 million deaths in 2020 alone. Diagnosing a patient from the early stages tremendously raises the chance of survival. Current clinical cancer detection approaches including X-ray, magnetic resonance imaging (MRI), and biomarker analysis not only fail to provide a precise border of the malignant tissue, especially in the early stages of cancer, but also can be invasive and lead to tissue damage. Recent progress in EM biosensor technologies has the potential to deliver a point-of-care diagnosis and surpass conventional methods regarding accuracy, time, and cost.", "keywords": ["Technology", "Organizations", "Science & Technology", "Sensors", "Tissue damage", "610", "Engineering", " Electrical & Electronic", "02 engineering and technology", "Cancer detection", "Costs", "Point of care", "ARRAYS", "3. Good health", "0906 Electrical and Electronic Engineering", "Engineering", "Magnetic resonance imaging", "CELLS", "Telecommunications", "PLASMONS", "1005 Communications Technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "Electrical & Electronic", "Networking & Telecommunications"]}, "links": [{"href": "https://eprints.gla.ac.uk/263337/1/263337.pdf"}, {"href": "http://xplorestaging.ieee.org/ielx7/74/9747968/09748039.pdf?arnumber=9748039"}, {"href": "https://doi.org/10044/1/97038"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Antennas%20and%20Propagation%20Magazine", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10044/1/97038", "name": "item", "description": "10044/1/97038", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10044/1/97038"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "11590/484290", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:25Z", "type": "Journal Article", "created": "2024-05-08", "title": "A unifying modelling of multiple land degradation pathways in Europe", "description": "AbstractLand degradation is a complex socio-environmental threat, which generally occurs as multiple concurrent pathways that remain largely unexplored in Europe. Here we present an unprecedented analysis of land multi-degradation in 40 continental countries, using twelve dataset-based processes that were modelled as land degradation convergence and combination pathways in Europe\uffe2\uff80\uff99s agricultural (and arable) environments. Using a Land Multi-degradation Index, we find that up to 27%, 35% and 22% of continental agricultural (~2 million km2) and arable (~1.1 million km2) lands are currently threatened by one, two, and three drivers of degradation, while 10\uffe2\uff80\uff9311% of pan-European agricultural/arable landscapes are cumulatively affected by four and at least five concurrent processes. We also explore the complex pattern of spatially interacting processes, emphasizing the major combinations of land degradation pathways across continental and national boundaries. Our results will enable policymakers to develop knowledge-based strategies for land degradation mitigation and other critical European sustainable development goals.