Deep-learning-based burned area mapping using the synergy of Sentinel-1&amp;2 data

We are in a climate and ecological emergency, where climate change and direct anthropogenic interference with the biosphere are risking abrupt and/or irreversible changes that threaten our life-support systems. Efforts are underway to increase the resilience of some ecosystems that are under threat, yet collective awareness and action are modest at best. Here, we highlight the potential for a biosphere resilience sensing system to make it easier to see where things are going wrong, and to see whether deliberate efforts to make things better are working. We focus on global resilience sensing of the terrestrial biosphere at high spatial and temporal resolution through satellite remote sensing, utilizing the generic mathematical behaviour of complex systems—loss of resilience corresponds to slower recovery from perturbations, gain of resilience equates to faster recovery. We consider what subset of biosphere resilience remote sensing can monitor, critically reviewing existing studies. Then we present illustrative, global results for vegetation resilience and trends in resilience over the last 20 years, from both satellite data and model simulations. We close by discussing how resilience sensing nested across global, biome-ecoregion, and local ecosystem scales could aid management and governance at these different scales, and identify priorities for further work. This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.

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“…Combining independent data products [66,67] may reveal more about ecosystem resilience. For example, combining an optical vegetation index (e.g.…”

Section: Remotely Sensing Resiliencementioning

confidence: 99%

“…LiDAR can characterize forest structure and thus biomass, but has relatively short duration missions, limited spatial coverage, and is insensitive at low biomass levels. Combining independent data products [66,67] may reveal more about ecosystem resilience. For example, combining an optical vegetation index (e.g.…”

Section: Remotely Sensing Resiliencementioning

confidence: 99%

A resilience sensing system for the biosphere

Lenton

Buxton

McKay

et al. 2022

Phil. Trans. R. Soc. B

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“…For example, Zhang et al created Deep-IRTarget, a unique backbone network composed of a frequency feature extractor, a spatial feature extractor, and a dual-domain feature resource allocation model, to cope with challenges in feature extraction [ 40 ]. Moreover, the deep learning algorithm is employed in burnt area mapping with the use of Sentinel-12 data [ 41 ]. Zhang et al present a Siamese self-attention (SSA) classification approach for multisensor burnt area mapping, and a multisource data set is created at the object level for training and testing.…”

Section: Related Workmentioning

confidence: 99%

Prediction Model of Adverse Effects on Liver Functions of COVID-19 ICU Patients

Mashraqi

Halawani

Alelyani

et al. 2022

Journal of Healthcare Engineering

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SARS-CoV-2 is a recently discovered virus that poses an urgent threat to global health. The disease caused by this virus is termed COVID-19. Death tolls in different countries remain to rise, leading to continuous social distancing and lockdowns. Patients of different ages are susceptible to severe disease, in particular those who have been admitted to an ICU. Machine learning (ML) predictive models based on medical data patterns are an emerging topic in areas such as the prediction of liver diseases. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from infection could assist medical staff in the treatment of patients, especially those that develop organ failure such as that of the liver. In this paper, we propose a model called the detecting model for liver damage (DMLD) that predicts the risk of liver damage in COVID-19 ICU patients. The DMLD model applies machine learning algorithms in order to assess the risk of liver failure based on patient data. To assess the DMLD model, collected data were preprocessed and used as input for several classifiers. SVM, decision tree (DT), Naïve Bayes (NB), KNN, and ANN classifiers were tested for performance. SVM and DT performed the best in terms of predicting illness severity based on laboratory testing.

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“…Furthermore, forests are home to numerous plants and animals, and they supply fundamental natural resources from timber and food to medicinal plants [3]. However, wildfires affect around 350 million hectares of land annually [4,5], and these fires result in the loss of huge amounts of forest cover. The fundamental causes of forest fires can be categorized into: (i) human activities such as land use land cover (LULC) changes, campfires, smoking, etc., and (ii) natural conditions such as lightning, climatic conditions, vegetation status, and topography [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest

Abdikan

Bayık

Şekertekin

et al. 2022

Forests

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Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%.

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Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data

Cited by 62 publications

References 42 publications

A resilience sensing system for the biosphere

A resilience sensing system for the biosphere

Prediction Model of Adverse Effects on Liver Functions of COVID-19 ICU Patients

Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest

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