Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?

The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE > 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.

Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development

Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an Observing System Simulation Experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a Western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010-2020. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from -0.036 to -0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain-snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

A Logic for Monitoring Dynamic Networks of Spatially-distributed Cyber-Physical Systems

Cyber-Physical Systems (CPS) consist of inter-wined computational (cyber) and physical components interacting through sensors and/or actuators. Computational elements are networked at every scale and can communicate with each other and with humans. Nodes can join and leave the network at any time or they can move to different spatial locations. In this scenario, monitoring spatial and temporal properties plays a key role in the understanding of how complex behaviors can emerge from local and dynamic interactions. We revisit here the Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration. Both nodes and edges include attributes modeling physical and logical quantities that can evolve over time. STREL combines the Signal Temporal Logic with two spatial modalities reach and escape that operate over the weighted graph. From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere and surround. We propose both qualitative and quantitative semantics based on constraint semiring algebraic structure. We provide an offline monitoring algorithm for STREL and we show the feasibility of our approach with the application to two case studies: monitoring spatio-temporal requirements over a simulated mobile ad-hoc sensor network and a simulated epidemic spreading model for COVID19.

Synchronization in the connectome: Metastable oscillatory modes emerge from interactions in the brain spacetime network

A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain unclear. Here, we hypothesise that the emergence of transient brain rhythms is a signature of weakly stable synchronization between spatially distributed brain areas, occurring at network-specific collective frequencies due to non-negligible conduction times. We test this hypothesis using a phenomenological network model to simulate interactions between neural mass potentials (resonating at 40Hz) in the structural connectome. Crucially, we identify a critical regime where metastable oscillatory modes emerge spontaneously in the delta (0.5-4Hz), theta (4-8Hz), alpha (8-13Hz) and beta (13-30Hz) frequency bands from weak synchronization of subsystems, closely approximating the MEG power spectra from 89 healthy individuals. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate the role of the spatiotemporal connectome in structuring brain activity in the frequency domain.

A Mobile Robot-Based System for Automatic Inspection of Belt Conveyors in Mining Industry

Mechanical systems (as belt conveyors) used in the mining industry, especially in deep underground mines, must be supervised on a regular basis. Unfortunately, they require high power and are spatially distributed over a large area. Till now, some elements of the conveyor (drive units) have been monitored 24 h/day using SCADA systems. The rest of the conveyor is inspected by maintenance staff. To minimize the presence of humans in harsh environments, we propose a mobile inspection platform based on autonomous UGV. It is equipped with various sensors, and in practice it is capable of collecting almost the same information as maintenance inspectors (RGB image, sound, gas sensor, etc.). Till now such experiments have been performed in the lab or in the mine, but the robot was controlled by the operator. In such a scenario the robot is able to record data, process them and detect, for example, an overheated idler. In this paper we will introduce the general concept of an automatic robot-based inspection for underground mining applications. A framework of how to deploy the inspection robot for automatic inspection (3D model of the tunnel, path planing, etc.) are defined and some first results from automatic inspection tested in lab conditions are presented. Differences between the planned and actual path are evaluated. We also point out some challenges for further research.

Water Resources in Chile

This study quantifies the current and future soil water balance in a spatially distributed way for the whole of Chile and establishes what biomes will be the most affected by variations in water resources. The study of water resources reveals that 90% of surface Chile will reduce its soil water resources in the future if greenhouse gas concentration in the atmosphere does not stop. The most disadvantaged biomes are the forests, where soil water availability could decrease an average of 100 mm/year. Desert biomes could not perceive the hydrological imbalances; however, it is expected its surface increases.

Recalibration of the Local Magnitude (ML) Scale for Earthquakes in the Yellowstone Volcanic Region

ABSTRACT The Yellowstone volcanic region is one of the most seismically active areas in the western United States. Assigning magnitudes (M) to Yellowstone earthquakes is a critical component of monitoring this geologically dynamic zone. The University of Utah Seismograph Stations (UUSS) has assigned M to 46,767 earthquakes in Yellowstone that occurred between 1 January 1984 and 31 December 2020. Here, we recalibrate the local magnitude (ML) distance and station corrections for the Yellowstone volcanic region. This revision takes advantage of the large catalog of earthquakes and an increase in broadband stations installed by the UUSS since the last ML update in 2007. Using a nonparametric method, we invert 7728 high-quality, analyst-reviewed amplitude measurements from 1383 spatially distributed earthquakes for 39 distance corrections and 20 station corrections. The inversion is constrained with four moment magnitude (Mw) values determined from time-domain inversion of regional-distance broadband waveforms by the UUSS. Overall, the new distance corrections indicate relatively high attenuation of amplitudes with distance. The distance corrections decrease with hypocentral distance from 3 km to a local minimum at 80 km, rise to a broad peak at 110 km, and then decrease again out to 180 km. The broad peak may result from superposition of direct arrivals with near-critical Moho reflections. Our ML inversion doubles the number of stations with ML corrections in and near the Yellowstone volcanic region. We estimate that the additional station corrections will nearly triple the number of Yellowstone earthquakes that can be assigned an ML. The new ML distance and station corrections will also reduce uncertainties in the mean MLs for Yellowstone earthquakes. The new MLs are ∼0.07 (±0.18) magnitude units smaller than the previous MLs and have better agreement with 12 Mws (3.15–4.49) determined by the UUSS and Saint Louis University.

Epidemic waves in relief against a structured climatic background

<p>For directly transmissible infectious diseases, seasonality in the course of epidemics may manifest in four major ways: susceptibility of the hosts, their individual and collective behavior, transmissibility of the pathogen, and survival of the latter under evolving environmental conditions. Mechanisms and concepts are not finally settled, notably in a pandemic setting. Climatic seasonality by itself is an aggregate, structured phenomenon that provides a spatially distributed background to the epidemic outbreak and its evolution at multiple timescales. Using advanced methods of data and systems analysis, including epidemiological and climate modeling, the RKI data of the COVID-19 epidemic curve for Berlin and a five-parameter climate data set of the nearby station Lindenberg (Mark) are analyzed in daily resolution over the period March 2020 to October 2021. Aimed to identify extrinsic impacts due to organized intraseasonal climate dynamics, as seen in sunshine duration and surface climate (pressure, temperature, humidity, wind), on intrinsic dynamics of the epidemic system, an established (SEIR) model of the latter and modifications thereof have been subjected to in-depth studies with a view on both genesis and timing of epidemic waves and their potential synchronization with climatic background dynamics. Starting with a case study for the spring 2020 period of shutdown, which unveils remarkable synchronies with the seasonal transition, estimates are given and applied to the follow-up period of individual and combined impacts of climate variables on the SEIR model in different oscillatory (non-equilibrium, lately endemic) regimes of operation.</p>