Shrub clearing and extensive livestock as a strategy for enhancing ecosystem services in degraded Mediterranean mid-mountain areas.

Land abandonment in the Mediterranean mountains since the 20th century has led to a reduction of ecosystem services, due to revegetation and homogenization of the landscape. To counteract these effects, the regional administration of La Rioja in Spain initiated a Plan for Shrub Clearing (PSC) combined with extensive livestock grazing in 1986, which is still in action today. This study analyses the effects of pairing clearing with grazing in an experimental area of the Leza valley (Iberian System) on: (i) the landscape structure and structural diversity; (ii) the production of pasture; (iii) fire control; (iv) soil organic carbon sequestration (also considering soil environmental types); (v) surface water resources.

SpaSSA: Superpixelwise Adaptive SSA for Unsupervised Spatial-Spectral Feature Extraction in Hyperspectral Image.

Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as sensitivity to the window size, high computational complexity under a large window, and failing to extract joint spectral-spatial features. To tackle these issues, in this article, we propose superpixelwise adaptive SSA (SpaSSA), that is superpixelwise adaptive SSA for exploiting local spatial information of HSI.

Embedded product authentication codes in additive manufactured parts: Imaging and image processing for improved scan ability.

The layer-by-layer printing process of additive manufacturing methods provides new opportunities to embed identification codes inside parts during manufacture. These embedded codes can be used for product authentication and identification of counterfeits. The availability of reverse engineering tools has increased the risk of counterfeit part production and new authentication technologies such as the one proposed in this paper are required for many applications including aerospace components and medical implants and devices.

A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19.

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs.

Adaptive Distance-Based Band Hierarchy (ADBH) for Effective Hyperspectral Band Selection.

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking. The lack of proper understanding of the HSI data structure has resulted in the inconsistency in the outcome of UBS.

Effects of active and passive land use management after cropland abandonment on water and vegetation dynamics in the Central Spanish Pyrenees.

The Mediterranean mountains have been subject to significant land abandonment process during the second half of the 20th century. The subsequent natural revegetation following abandonment in rural areas has been widely documented to have substantial implications on the hydrological cycle and the vegetation. The Spanish Pyrenees are one of the most affected areas by these land transformations which could threaten their importance for water supply and agricultural activities in the downstream lowland areas.

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used.

Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments-A Case Study.

Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing capabilities such as vision and adaptive reasoning for real-time collision avoidance and online path planning in dynamically-changing environments. A machine vision module based on low-cost cameras and color detection in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing environment.

Singular Spectrum Analysis: A Note on Data Processing for Fourier Transform Hyperspectral Imagers.

Hyperspectral remote sensing is experiencing a dazzling proliferation of new sensors, platforms, systems, and applications with the introduction of novel, low-cost, low-weight sensors. Curiously, relatively little development is now occurring in the use of Fourier transform (FT) systems, which have the potential to operate at extremely high throughput without use of a slit or reductions in both spatial and spectral resolution that thin film based mosaic sensors introduce. This study introduces a new physics-based analytical framework called singular spectrum analysis (SSA) to process raw hyperspectral imagery collected with FT imagers that addresses some of the data processing issues associated with the use of the inverse FT.

Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging.

Presented in a three-dimensional structure called a hypercube, hyperspectral imaging suffers from a large volume of data and high computational cost for data analysis. To overcome such drawbacks, principal component analysis (PCA) has been widely applied for feature extraction and dimensionality reduction. However, a severe bottleneck is how to compute the PCA covariance matrix efficiently and avoid computational difficulties, especially when the spatial dimension of the hypercube is large.

Impact of climate and land use change on water availability and reservoir management: scenarios in the Upper Aragón River, Spanish Pyrenees.

Streamflows in a Mediterranean mountain basin in the central Spanish Pyrenees were projected under various climate and land use change scenarios. Streamflow series projected for 2021-2050 were used to simulate the management of the Yesa reservoir, which is critical to the downstream supply of irrigation and domestic water. Streamflows were simulated using the Regional Hydro-Ecologic Simulation System (RHESSys).

Study of urban atmospheric pollution in Navarre (Northern Spain).

An ambient air quality study was undertaken in two cities (Pamplona and Alsasua) of the Province of Navarre in northern Spain from July 2001 to June 2004. The data were obtained from two urban monitoring sites. At both monitoring sites, ambient levels of ozone, NOx, and SO2 were measured.