Large Spatial Areas Research Articles

A hierarchical method and workflow for the semi‐automated mapping of valley bottom geomorphic units using publicly available remote sensing datasets

AbstractGeomorphic units (GUs) are the landforms that make up the valley bottom and are produced by fluvial processes that determine river structure and function. Mapping of GUs can be used to interpret river type and behaviour and to analyse river condition and recovery processes. The advancement of remote sensing technologies and big‐data acquisition are enabling the development and operationalisation of tools to semi‐automate the mapping of assemblages of GUs across large spatial areas. In this study, we develop a hierarchical method that combines a landscape classification approach (Geomorphons) with supervised classification using light detection and ranging data (LiDAR) and satellite images to semi‐automate the mapping of GUs across valley bottoms. We have also produced a new method for identifying and mapping pools in the absence of bathymetry data. We applied our method on 78 river sections in coastal catchments of NSW, Australia. We were able to identify 20 refined GU types and four further sub‐types of bank‐attached bars. Our method produced GU maps that are consistent with desktop manual delineation from aerial images and digital elevation models. Our hierarchical method offers GU maps with varying accuracy and resolution, accommodating a user's decisions regarding amount of effort invested relative to map quality and accuracy required. Initial runs produce maps with 12 preliminary GUs with 61%–75% consistency when compared to desktop manual mapping. With additional effort and manual corrections, a higher level of GU identification is possible (i.e. refined GU mapping increases the consistency to 70%–81%). The delineation of more intricate sub‐types of GU or sub‐units on compound GUs, which is essential for interpretation of river behaviour, condition, and recovery, still requires on‐site field verification to achieve the best results.

Seasonal monitoring of biochemical variables in natural rangelands using Sentinel-1 and Sentinel-2 data

ABSTRACT Rangelands are natural ecosystems that serve as essential sources of forage for domesticated livestock and wildlife. Therefore, accurately mapping nutrient levels in rangelands is crucial for sustainable development and effective management of grazing animals. Remote sensing tools offer a reliable means to explore nutrient concentrations across large spatial areas. This study aimed to estimate and map seasonal foliar concentrations of nitrogen (N), phosphorus, and neutral detergent fibre (NDF) in mesic tropical rangelands of Limpopo using Sentinel-1, Sentinel-2, and the integration of S1 and S2 data. Fieldwork was conducted to collect samples for seasonal foliar nutrients (N, P, and NDF) during early-summer (November-January 2020), winter (July-August 2021), and late-summer (February-March 2022). Various conventional and red-edge-based vegetation indices were computed. The results demonstrate that integration data from S1 and S2 can effectively estimate and predict foliar concentrations of N, P, and NDF in mesic rangelands throughout the seasons, achieving R2 values of 0.76, 0.78, and 0.71, with corresponding RMSE values of 0.13, 0.04, and 2.52. Notably, red-edge variables emerged as the most significant parameters for predicting seasonal N, P, and NDF concentrations. Additionally, factors such as season and slope significantly influenced the distribution and occurrence of these foliage nutrients, with higher foliage production observed during late-summer and on steeper slopes. The study concludes that the integration of S1 and S2 data can effectively monitor the seasonal dynamics of biochemical parameters. This finding holds significant implications for policymakers and rangeland users, offering a comprehensive understanding of the intricate variations within rangeland ecosystems. Further research could expand on these findings by applying the knowledge to various datasets, exploring different rangelands, and examining additional ecological factors such as slope altitude to detect foliar fibre biochemicals. Finally, the applications of this research extend beyond individual properties, providing practical tools for sustainable rangeland management and informed decision-making in resource utilization and conservation.

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a large scale is required for their control and management. However, the cooccurrence of INWS and native grass species results in highly heterogeneous grass communities and generates mixed pixels detected by remote sensors, which causes uncertainty in classification. The continuous coverage of INWS at the pixel level has not yet been achieved. In this study, objective 1 was to test the capability of Senginel-2 imagery at estimating continuous INWS cover across complex alpine grasslands over a large scale and objective 2 was to assess the performance of the state-of-the-art convolutional neural network-based regression (CNNR) model in estimating continuous INWS cover. Therefore, a novel CNNR model and a random forest regression (RFR) model were evaluated for estimating INWS continuous cover using Sentinel-2 imagery. INWS continuous cover was estimated directly from Sentinel-2 imagery with an R2 ranging from 0.88 to 0.93 using the CNNR model. The RFR model combined with multiple features had a comparable accuracy, which was slightly lower than that of the CNNR model, with an R2 of approximately 0.85. Twelve green band-, red-edge band-, and near-infrared band-related features had important contributions to the RFR model. Our results demonstrate that the CNNR model performs well when estimating INWS continuous cover directly from Sentinel-2 imagery, and the RFR model combined with multiple features derived from the Sentinel-2 imager can also be used for INWS continuous cover mapping. Sentinel-2 imagery is suitable for mapping continuous INWS cover across complex alpine grasslands over a large scale. Our research provides information for the advanced mapping of the continuous cover of invasive species across complex grassland ecosystems or, more widely, terrestrial ecosystems over large spatial areas using remote sensors such as Sentinel-2.

An analysis of winter rain-on-snow climatology in Svalbard

Rain-on-snow (ROS) events are becoming an increasingly common feature of the wintertime climate Svalbard in the High Arctic due to a warming climate. Changes in the frequency, intensity, and spatial distribution of wintertime ROS events in Svalbard are important to understand and quantify due their wide-ranging impacts on the physical environment as well as on human activity. Due to the sparse nature of ground observations across Svalbard, tools for mapping and long-term monitoring of ROS events over large spatial areas are reliant on remote sensing, snow models and atmospheric reanalyses. However, different methods of identifying and measuring ROS events can often present different interpretations of ROS climatology. This study compares a recently published Synthetic Aperture Radar (SAR) based ROS dataset for Svalbard to ROS derived from two snow models and a reanalysis dataset for 2004–2020. Although the number of ROS events differs across the datasets, all datasets exhibit both similarities and differences in the geographical distribution of ROS across the largest island, Spitsbergen. Southern and western coastal areas experience ROS most frequently during the wintertime, with the early winter (November–December) experiencing overall most events compared to the spring (March–April). Moreover, we find that different temperature thresholds are required to obtain the best spatial agreement of ROS events in the model and reanalysis datasets with ground observations. The reanalysis dataset evaluated against ground observations was superior to the other datasets in terms of accuracy due to the assimilation of ground observations into the dataset. The SAR dataset consistently scored lowest in terms of its overall accuracy due to many more false detections, an issue which is most likely explained by the persistence of moisture in the snowpack following the end of a ROS event. Our study not only highlights some spatial differences in ROS frequency and trends but also how comparisons between different datasets can confirm knowledge about the climatic variations across Svalbard where in-situ observations are sparse.

Otolith morphometry and Fourier transform near-infrared (FT-NIR) spectroscopy as tools to discriminate archived otoliths of newly detected cryptic species, Etelis carbunculus and Etelis boweni

Cryptic speciation was recently verified in Etelis carbunculus, an important component of federally managed bottomfish fisheries in the Pacific Territories of the United States. As a result, archived otolith collections used for fishery assessment are now contaminated with newly described E. boweni in areas where these species co-occur. We compared the efficacy of otolith morphometrics and Fourier transform near-infrared (FT-NIR) spectroscopy to discriminate species first using voucher (i.e., known species) otoliths (n = 93) from the SW Pacific, then applied optimal models to archived otoliths (n = 91) collected around Guam. Significant and distinguishable differences in otolith morphometrics as well as FT-NIR spectral absorbance patterns were observed between E. carbunculus and E. boweni voucher samples. Classification models applied using both morphometric measurements (quadratic discriminant analysis) and FT-NIR spectral data (partial least squares discriminant analysis) were able to predict species with a high (93 – 100%) degree of accuracy despite a relatively large spatial area of specimen collection ( ± 10° latitude and longitude) and regardless of whether otoliths were whole (i.e., unbroken). Further, each method identified members of newly described E. boweni in the archived collection of E. carbunculus otoliths captured around Guam, providing strong evidence that the species’ distributions overlap in this region. The purported identification of both E. carbunculus and E. boweni in the archived catch from Guam has important implications for fisheries management; therefore, it is imperative that the corresponding otolith collections are examined to ensure that the otoliths are assigned to the correct species.

A combined method for multi-channel active noise control based on online adaptive estimation and offline convolutional neural network

Multi-channel active noise control (ANC) has been widely used to attenuate low-frequency noise in relatively large spatial areas. Traditional multi-channel systems are achieved by optimizing the controller weights with adaptive algorithms that minimize the power of all error signals. However, nonlinearity in ANC systems can significantly degrade the performance of conventional adaptive algorithms, which is even more severe in multi-channel systems. Researchers suggested using neural networks (NN) to deal with the nonlinear problems. However, applying NN to multi-channelANC systems is challenging in real-time processing because of the high computational burden. Therefore, a multi-channel ANC method that combines NN and adaptive method is proposed. The proposed controller consists of both linear and nonlinear parts, where the linear part is estimated adaptively to track the change of primary noise in real applications while the nonlinear part is modeled offline with a small-scale convolutional NN (CNN). This method has the advantages of solving the nonlinear problem in ANC systems and achieving the capability in tracking primary noise source positions. At last, the performance of the proposed algorithm is verified through simulation experiments.

Ecosystem multifunctionality, maximum height, and biodiversity of shrub communities affected by precipitation fluctuations in Northwest China.

Dryland ecosystems face serious threats from climate change. Establishing the spatial pattern of ecosystem multifunctionality, maximum height and the correlation of biodiversity patterns with climate change is important for understanding changes in complex ecosystem processes. However, the understanding of their relationships across large spatial areas remains limited in drylands. Accordingly, this study examined the spatial patterns of ecosystem multifunctionality, maximum height and considered a set of potential environmental drivers by investigating natural shrub communities in Northwest China. We found that the ecosystem multifunctionality (EMF) and maximum height of shrub communities were both affected by longitude, which was positively correlated with the precipitation gradient. Specifically, the EMF was driven by high precipitation seasonality, and the maximum height was driven by high precipitation stability during the growing season. Among the multiple biodiversity predictors, species beta diversity (SD-beta) is the most common in determining EMF, although this relationship is weak. Unlike tree life form, we did not observe biodiversity-maximum height relationships in shrub communities. Based on these results, we suggest that more attention should be paid to the climatical fluctuations mediated biodiversity mechanisms, which are tightly correlated with ecosystem's service capacity and resistance capacity under a rapid climate change scenario in the future.

Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model.

Globally, natural wetlands have suffered severe ecological degradation (vegetation, soil, and biotic community) due to multiple factors. Understanding the spatiotemporal dynamics and driving forces of natural wetlands is the key to natural wetlands' protection and regional restoration. In this study, we first investigated the spatiotemporal evolutionary trends and shifting characteristics of natural wetlands in the Northeast Plain of China from 1990 to 2020. A dataset of driving-force evaluation indicators was constructed with nine indirect (elevation, temperature, road network, etc.) and four direct influencing factors (dryland, paddy field, woodland, grassland). Finally, we built the driving force analysis model of natural wetlands changes to quantitatively refine the contribution of different driving factors for natural wetlands' dynamic change by introducing the sparrow search algorithm (SSA) and extreme gradient boosting algorithm (XGBoost). The results showed that the total area of natural wetlands in the Northeast Plain of China increased by 32% from 1990 to 2020, mainly showing a first decline and then an increasing trend. Combined with the results of transfer intensity, we found that the substantial turn-out phenomenon of natural wetlands occurred in 2000-2005 and was mainly concentrated in the central and eastern parts of the Northeast Plain, while the substantial turn-in phenomenon of 2005-2010 was mainly located in the northeast of the study area. Compared with a traditional regression model, the SSA-XGBoost model not only weakened the multicollinearity of each driver but also significantly improved the generalization ability and interpretability of the model. The coefficient of determination (R2) of the SSA-XGBoost model exceeded 0.6 in both the natural wetland decline and rise cycles, which could effectively quantify the contribution of each driving factor. From the results of the model calculations, agricultural activities consisting of dryland and paddy fields during the entire cycle of natural wetland change were the main driving factors, with relative contributions of 18.59% and 15.40%, respectively. Both meteorological (temperature, precipitation) and topographic factors (elevation, slope) had a driving role in the spatiotemporal variation of natural wetlands. The gross domestic product (GDP) had the lowest contribution to natural wetlands' variation. This study provides a new method of quantitative analysis based on machine learning theory for determining the causes of natural wetland changes; it can be applied to large spatial scale areas, which is essential for a rapid monitoring of natural wetlands' resources and an accurate decision-making on the ecological environment's security.

Deep Spatial Prediction via Heterogeneous Multi-source Self-supervision

Spatial prediction is to predict the values of the targeted variable, such as PM2.5 values and temperature, at arbitrary locations based on the collected geospatial data. It greatly affects the key research topics in geoscience in terms of obtaining heterogeneous spatial information (e.g., soil conditions, precipitation rates, wheat yields) for geographic modeling and decision-making at local, regional, and global scales. In situ data, collected by ground-level in situ sensors, and remote sensing data, collected by satellite or aircraft, are two important data sources for this task. In situ data are relatively accurate while sparse and unevenly distributed. Remote sensing data cover large spatial areas, but are coarse with low spatiotemporal resolution and prone to interference. How to synergize the complementary strength of these two data types is still a grand challenge. Moreover, it is difficult to model the unknown spatial predictive mapping while handling the tradeoff between spatial autocorrelation and heterogeneity. Third, representing spatial relations without substantial information loss is also a critical issue. To address these challenges, we propose a novel Heterogeneous Self-supervised Spatial Prediction (HSSP) framework that synergizes multi-source data by minimizing the inconsistency between in situ and remote sensing observations. We propose a new deep geometric spatial interpolation model as the prediction backbone that automatically interpolates the values of the targeted variable at unknown locations based on existing observations by taking into account both distance and orientation information. Our proposed interpolator is proven to both be the general form of popular interpolation methods and preserve spatial information. The spatial prediction is enhanced by a novel error-compensation framework to capture the prediction inconsistency due to spatial heterogeneity. Extensive experiments have been conducted on real-world datasets and demonstrated our model’s superiority in performance over state-of-the-art models.

Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data

Accurate and timely monitoring of flooded crop areas is crucial for disaster rescue and loss assessment. However, most flooded crop monitoring methods based on synthetic aperture radar (SAR) imagery were developed for rice, which is probably inappropriate for crops with complex canopy structures that strongly attenuate SAR signals. Additionally, these methods often rely on empirical thresholds and region-specific reference samples, limiting their reliability and applicability on a larger spatial scale. To address these issues, we developed a novel flooded crop mapping approach at a regional scale using Sentinel-1 time-series data and an unsupervised Gaussian Mixture Model (GMM). Our approach leverages a Flood Separability Index (FSI) derived from the fitted probability density function of flooded and non-flooded crop areas in a GMM. This allows us to overcome the limitations of manual input selection in previous studies. The multi-temporal GMM was constructed using the time-series images with optimal polarization to estimate the flooded crop extents on a regional scale. We also investigated the scattering mechanisms of three typical crop disaster structures within an agricultural landscape area. Our results indicate that the proposed multi-temporal GMM is robust in crop planting areas with complex canopy structures. The performance of both single-temporal and multi-temporal GMMs surpasses that of baseline methods such as Otsu and K-means. Compared with VV polarization, VH polarization exhibits greater potential for accurately mapping flooded crops in complex agricultural regions. Our approach does not require labeled samples or many predefined parameters, making it fast and feasible for mapping flooded crops with complex canopy structures in large spatial areas.

Edge computing at sea: high-throughput classification of in-situ plankton imagery for adaptive sampling

The small sizes of most marine plankton necessitate that plankton sampling occur on fine spatial scales, yet our questions often span large spatial areas. Underwater imaging can provide a solution to this sampling conundrum but collects large quantities of data that require an automated approach to image analysis. Machine learning for plankton classification, and high-performance computing (HPC) infrastructure, are critical to rapid image processing; however, these assets, especially HPC infrastructure, are only available post-cruise leading to an ‘after-the-fact’ view of plankton community structure. To be responsive to the often-ephemeral nature of oceanographic features and species assemblages in highly dynamic current systems, real-time data are key for adaptive oceanographic sampling. Here we used the new In-situ Ichthyoplankton Imaging System-3 (ISIIS-3) in the Northern California Current (NCC) in conjunction with an edge server to classify imaged plankton in real-time into 170 classes. This capability together with data visualization in a heavy.ai dashboard makes adaptive real-time decision-making and sampling at sea possible. Dual ISIIS-Deep-focus Particle Imager (DPI) cameras sample 180 L s-1, leading to >10 GB of video per min. Imaged organisms are in the size range of 250 µm to 15 cm and include abundant crustaceans, fragile taxa (e.g., hydromedusae, salps), faster swimmers (e.g., krill), and rarer taxa (e.g., larval fishes). A deep learning pipeline deployed on the edge server used multithreaded CPU-based segmentation and GPU-based classification to process the imagery. AVI videos contain 50 sec of data and can contain between 23,000 - 225,000 particle and plankton segments. Processing one AVI through segmentation and classification takes on average 3.75 mins, depending on biological productivity. A heavyDB database monitors for newly processed data and is linked to a heavy.ai dashboard for interactive data visualization. We describe several examples where imaging, AI, and data visualization enable adaptive sampling that can have a transformative effect on oceanography. We envision AI-enabled adaptive sampling to have a high impact on our ability to resolve biological responses to important oceanographic features in the NCC, such as oxygen minimum zones, or harmful algal bloom thin layers, which affect the health of the ecosystem, fisheries, and local communities.

Simultaneous estimation of fractional cover of photosynthetic and non-photosynthetic vegetation using visible-near infrared satellite imagery

In vegetation-abundant ecosystems, accurate fractional cover estimation of multiple targets, including non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV) and bare soil (BS) is an important indicator of carbon storage and climate change, and a contributing factor to soil and water conservation, wildfire, and drought. Satellite remote sensing began with sensors limited to the visible and near infrared (VNIR, 380 nm to 1000 nm), and advancing to sensors spanning the whole solar domain (350 nm to 2500 nm) over the last five decades. However, NPV is still difficult to distinguish from BS in satellite images covering large spatial areas, since their spectral reflectances are always mixed and have subtly different spectral features. Many spectral indices have been proposed to identify NPV, however, most include a short-wave infrared band which is lacking on many current and historical spaceborne sensors. To fully utilize current and historical airborne/satellite images with only VNIR bands, a novel spectral index – the VNIR spectral shape index (SSI) – is proposed in this paper. Using spectra of PV, NPV, and BS acquired from world-recognized spectral libraries, the SSI was devised using only blue, green, red, and NIR bands. When applied to Sentinel-2 MSI images, the SSI was effective in estimating/mapping the fractional cover of PV, NPV, and BS using the triangular space method. The resulting fractional cover was evaluated in three 3 km × 2 km study areas including one grassland, one woodland, and one cropland. The spectral reflectance collected by spectroradiometer in field work and digital image collect by digital camera mounted on unmanned aerial vehicle (UAV) support that the triangular space method is able to estimate the fractional cover of photosynthetic and non-photosynthetic vegetation simultaneously, and that endmember selection relying solely on satellite images is reasonable. Comparing the abundance of each target in UAV images to corresponding MSI pixels, the SSI index and triangular space method proved effective for fractional cover estimation in varied vegetated ecosystems. For fractional cover evaluation in grassland, woodland, and cropland, the R2 reaching to 0.59, 0.62, and 0.70, respectively. These results demonstrate that the SSI is feasible for multiple sensors, enabling potential large temporal and/or spatial scale applications.

Combining 2D encoding and convolutional neural network to enhance land cover mapping from Satellite Image Time Series

The use of high spatial resolution Satellite Image Time Series (SITS) provides an opportunity for a wide spectrum of Earth surface monitoring applications such as Land Use/Land Cover (LULC) mapping. Whereas the majority of Time Series (TS) classification literature concentrates on the analysis of raw 1D signals, here, we investigate a framework for LULC mapping based on 2D encoded multivariate SITS data to enhance their classification performances. In this novel approach, multivariate SITS data are transformed from 1D signals to 2D images using several encoding techniques namely Gramian Angular Summation field (GASF), Gramian angular difference field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Successively, a new multi-band image is derived and it is used as input to a state-of-the-art convolutional neural network (CNN) classification model. The possibility to effectively encode multivariate TS data into 2D representations paves the way to reuse the huge amount of research findings coming from the general field of computer vision and build on reliable and robust methods that have been demonstrated their quality in a multitude of downstream applications. Experiments carried out on three real-world benchmarks covering large spatial areas with contrasted land cover features, namely: Dordogne department in France, Reunion Island an oversee French territory and Koumbia municipality in Burkina Faso, underline the quality of the proposed framework when compared to standard approaches for land cover mapping from SITS and recent methods for multivariate TS classification. Matter of fact, our new framework outperforms the classification performances of standard land cover classification strategies based on the raw TS information achieving an average F1-score of 89.34%, 90.26% and 78.94% for the Reunion Island, Dordogne and Koumbia study site, respectively with an increasing of at least 2.5 points w.r.t. the best competing approach.

Observation of shoot growth: a simple and operational decision-making tool for monitoring vine water status in the vineyard

In vineyard management, the monitoring of vine water status is of great importance, because this variable influences harvest quality, yield and, in the longer term, vineyard sustainability. Numerous tools and methods have been proposed to monitor vine water status, but they often involve the use of costly and complex equipment and can be logistically demanding. Methods based on the observation of vine shoot growth are interesting potential alternatives, because they are simple to carry out and therefore potentially better adapted for use in production vineyards. However, these methods have never been evaluated or compared to reference measurements made on several cultivars and during several vintages. The objective of this article was to study their characteristics (validity range, specificity and sensitivity) in order to be able to give recommendations for their rigorous implementation in an experimental or operational context. The study was carried out using the iG-Apex method to measure vine shoot growth and predawn leaf water potential as reference measurements in 55 fields located in the Tavel vineyard (Occitanie, France) during the 2008 to 2012 vintages. The results showed that iG-Apex can be used as an operational tool for monitoring vine water status at field scale and for a predawn leaf water potential ranging from -0.2 MPa to -0.8 MPa. Nevertheless, precautions must be taken when interpreting the results, as the method is not specific to water constraint and is also sensitive to other phenomena. Furthermore, it could be relevant to use this method for the collective monitoring of vine shoot growth over large spatial areas, in addition to more precise and more localised monitoring carried out with reference measurements.

Synthesis on the effectiveness of soil translocation for plant community restoration

Abstract Many degraded ecosystems need active restoration to conserve biodiversity and re‐establish ecosystem function, both highlighted targets of the UN Decade on Ecosystem Restoration and the proposed EU Nature restoration law. Soil translocation, where both plant propagules and their associated soil biota are co‐introduced, has increasingly been proposed as a powerful restoration technique for terrestrial ecosystems. However, a synthesis of the effectiveness of this method across ecosystems is lacking. To address how soil translocation affects restoration success, we performed a meta‐analysis synthesizing data from 46 field experiments and their respective reference ecosystems in 17 countries across four continents. In each experiment, vegetation composition was recorded in response to soil translocation treatments and the resultant vegetational changes (diversity and composition) were quantified. We found that soil translocation leads to plant community development further away from the control and more towards the reference plant communities compared with treatments where only plant propagules were introduced. However, the variability of effect sizes among experiments was large, suggesting strong dependence of restoration success on restoration context. We found that restoration success was more likely on loamy soils and when translocation treatments were implemented over larger spatial areas (>180 m2). Furthermore, we found that restoration success either consistently increased or decreased over time depending on the experiment. Not only is this congruent with positive feedbacks between plant and soil communities driving plant community development, but it also suggests that the composition of the translocated plant and soil communities, and initial starting conditions, are critical for long‐term restoration success. Synthesis and applications. Our analysis highlights soil translocation can be a successful restoration method across a broad range of ecosystems. However, its implementation needs to depend on a thorough evaluation of local conditions and the potential added value. Further refinement of soil translocation techniques is needed to increase success rates.

Evaluating community science sampling for microplastics in shore sediments of large river watersheds

A community science project in the Ottawa River Watershed in Canada interacted with an existing volunteer base to collect sediment from 68 locations in the watershed over approximately 750 km. Ninety-one percent of the distributed kits were returned with 42 volunteers taking part in the project. After analysis, particle concentrations were relatively low compared to previous freshwater microplastic sediment research, with contributing factors including (but not limited to) the large size of the watershed, a lower population base compared to other researched freshwater watersheds, the relative size and discharge of the Ottawa River and the large seasonal fluxes experienced in the river basin. Utilising community science for sampling large freshwater watersheds demonstrated its advantages in the research, especially spatially. However, careful consideration to research design and implementation is essential for community science projects examining microplastics in freshwater sediments. Research teams should ensure they are responsible for strict quality assurance and quality control protocols, especially in the laboratory with sample preparation and processing. Nonetheless, community science is potentially an extremely useful approach for researchers to use for microplastic sampling projects over large spatial areas.

Weighted-average time-lapse seismic full-waveform inversion

As seismic data can contain information over a large spatial area and are sensitive to changes in the properties of the subsurface, seismic imaging has become the standard geophysical monitoring method for many applications such as carbon capture and storage and reservoir monitoring. The availability of practical tools such as full-waveform inversion (FWI) makes time-lapse seismic FWI a promising method for monitoring subsurface changes. However, FWI is a highly ill-posed problem that can generate artifacts. Because the changes in the earth’s properties are typically small in terms of magnitude and spatial extent, discriminating the true time-lapse signature from noise can be challenging. Different strategies have been proposed to address these difficulties. In this study, we propose a weighted-average (WA) inversion to better control the effects of artifacts and differentiate them from the true 4D changes. We further compare five related strategies with synthetic tests on clean and noisy data. The effects of seawater velocity variation on different strategies also are studied as one of the main sources of nonrepeatability. We tested different strategies of time-lapse FWI (TL-FWI) using the Marmousi and the SEG Advanced Modeling time-lapse models. The results indicate that the WA method can provide the best compromise between accuracy and computation time. This method also provides a range of possible answers of other TL-FWI strategies.

CriPAV: Street-Level Crime Patterns Analysis and Visualization.

Extracting and analyzing crime patterns in big cities is a challenging spatiotemporal problem. The hardness of the problem is linked to two main factors, the sparse nature of the crime activity and its spread in large spatial areas. Sparseness hampers most time series (crime time series) comparison methods from working properly, while the handling of large urban areas tends to render the computational costs of such methods impractical. Visualizing different patterns hidden in crime time series data is another issue in this context, mainly due to the number of patterns that can show up in the time series analysis. In this article, we present a new methodology to deal with the issues above, enabling the analysis of spatiotemporal crime patterns in a street-level of detail. Our approach is made up of two main components designed to handle the spatial sparsity and spreading of crimes in large areas of the city. The first component relies on a stochastic mechanism from which one can visually analyze probable×intensive crime hotspots. Such analysis reveals important patterns that can not be observed in the typical intensity-based hotspot visualization. The second component builds upon a deep learning mechanism to embed crime time series in Cartesian space. From the embedding, one can identify spatial locations where the crime time series have similar behavior. The two components have been integrated into a web-based analytical tool called CriPAV (Crime Pattern Analysis and Visualization), which enables global as well as a street-level view of crime patterns. Developed in close collaboration with domain experts, CriPAV has been validated through a set of case studies with real crime data in São Paulo - Brazil. The provided experiments and case studies reveal the effectiveness of CriPAV in identifying patterns such as locations where crimes are not intense but highly probable to occur as well as locations that are far apart from each other but bear similar crime patterns.

Simulating oil-driven abundance changes in benthic marine invertebrates using an ecosystem model

Field studies showed that benthic macrofauna and meiofauna abundances increased with sediment oil concentration in areas affected by the Deepwater Horizon (DWH) oil spill. Benthic invertebrate biomass shows a dome-shaped relationship with respect to petrogenic hydrocarbon concentrations suggesting a positive effect on biomass at low-to-medium oil concentrations and a negative effect at high concentrations. If this is due to enrichment of the benthic food web, then this adds to an emerging picture of a food web response over a large spatial area with both abundance increases and decreases as a result of DWH. We would be obliged to consider long term multispecies effects beyond the initial pulse disturbance in modeling impacts and recovery of economically valuable species. An Atlantis ecosystem model of the Gulf of Mexico is used to simulate three mechanisms that could explain observed changes in the invertebrate community. Scenario 1 is that stimulation of surface primary productivity occurred as a result of nutrient loading caused by diversion of Mississippi River water into Barataria Bay (a mitigation action taken during the DWH oil spill). Scenario 2 is that enrichment of the benthos occurred due to detrital loading from marine oil snow sedimentation and flocculent accumulation (MOSSFA). Scenario 3 is that predator declines and/or avoidance of oiled areas caused a release of predation mortality on benthic invertebrates. Scenario 2 (MOSSFA) stimulated the detritus-driven food web and was best able to cause a net increase in invertebrate biomass despite a realistic amount of oil toxicity. Scenario 3 (predator release) plausibly could have contributed to changes in benthic invertebrates. Scenario 1 (nutrient loading) had little impact on the benthos suggesting the benthic food web is decoupled from local pelagic production sources.

Estimating R 0 from early exponential growth: parallels between 1918 influenza and 2020 SARS-CoV-2 pandemics.

The large spatial scale, geographical overlap, and similarities in transmission mode between the 1918 H1N1 influenza and 2020 SARS-CoV-2 pandemics together provide a novel opportunity to investigate relationships between transmission of two different diseases in the same location. To this end, we use initial exponential growth rates in a Bayesian hierarchical framework to estimate the basic reproductive number, R 0, of both disease outbreaks in a common set of 43 cities in the United States. By leveraging multiple epidemic time series across a large spatial area, we are able to better characterize the variation in R 0 across the United States. Additionally, we provide one of the first city-level comparisons of R 0 between these two pandemics and explore how demography and outbreak timing are related to R 0. Despite similarities in transmission modes and a common set of locations, R 0 estimates for COVID-19 were uncorrelated with estimates of pandemic influenza R 0 in the same cities. Also, the relationships between R 0 and key population or epidemic traits differed between diseases. For example, epidemics that started later tended to be less severe for COVID-19, while influenza epidemics exhibited an opposite pattern. Our results suggest that despite similarities between diseases, epidemics starting in the same location may differ markedly in their initial progression.