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Diagrammatic representation of a multiscale approach for environmental sensor networks across a landscape. Terrestrial, soil and/or aquatic sensor networks can all be used with multiple sensor modalities and both fixed and mobile sensing platforms. Drawing by Jason Fisher.
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Environmental sensor networks offer a powerful combination of distributed sensing capacity, real‐time data visualization and analysis, and integration with adjacent networks and remote sensing data streams. These advances have become a reality as a combined result of the continuing miniaturization of electronics, the availability of large data stor...
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... fundamental applications of sensor networks for ecological research involve the challenges of environmental monitoring across a wide range of spatial scales from centimeters to kilometers and temporal scales from fractions of a second to hours (Fig. 1). The ability to characterize the spatial and temporal scales of extreme events is of particular significance, as these have a disproportionate role in shaping the ecology, ecophysiology and evolution of plant species (Levine, 1992;Gaines & Denny, 1993;Gutschick & BassiriRad, 2003;Verstraeten et al., ...
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... such as on cables, tracks, robotic vehicles and aircraft (Baldocchi et al., 1984;Clements et al., 2003;Gamon et al., 2006b;Laffea et al., 2006). In our work, we have utilized cable-based robotic systems in long- term and rapidly deployable configurations, called Networked Info-Mechanical Systems (NIMS), to complement fixed sensor deployments ( Fig. 1; Jordan et al., 2007). The use of mobile sensing platforms allows for cyberinfrastructure with intelligent algorithms to utilize adaptive sampling protocols. Several statistical methods to adaptively sample data have been proposed in the literature, including stratified methods, in which initial sparse scans extract regions of high ...
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... of the plant parts. To date, these exchanges have been largely black-boxed, with exchange rates provided by coarse-scale inputs and outputs. A new approach is to place a network of sensors and imagers into the field to measure naturally occurring dynamics and interactions to evaluate the responses of multiple variables simultaneously (Fig. ...
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... (3) Are there emergent trends in the ecosystem responses of tropical forests to global change? (4) What are the key research and management priorities for tropical forest ecosystems in the context of global change? [5][6][7][8][9]. ...
Collaborations between ecosystem ecologists and engineers have led to impressive progress in developing complex models of biogeochemical fluxes in response to global climate change. Ecology and engineering iteratively inform and transform each other in these efforts. Nested data streams from local sources, adjacent networks, and remote sensing sources together magnify the capacity of ecosystem ecologists to observe systems in near real-time and address questions at temporal and spatial scales that were previously unobtainable. We describe our research experiences working in a Costa Rican rainforest ecosystem with the challenges presented by constant high humidity, 4300 mm of annual rainfall, flooding, small invertebrates entering the tiniest openings, stinging insects, and venomous snakes. Over the past two decades, we faced multiple challenges and learned from our mistakes to develop a broad program of ecosystem research at multiple levels of integration. This program involved integrated networks of diverse sensors on a series of canopy towers linked to multiple belowground soil sensor arrays that could transport sensor data streams from the forest directly to an off-site location via a fiber optic cable. In our commentary, we highlight three components of our work: (1) the eddy flux measurements using canopy towers; (2) the soil sensor arrays for measuring the spatial and temporal patterns of CO2 and O2 fluxes at the soil–atmosphere interface; and (3) focused investigations of the ecosystem impact of leaf-cutter ants as “ecosystem engineers” on carbon fluxes.
... This study demonstrated the technology readiness of the model, which was validated in a test environment by forecasting each time step using only data from up to that time, as if a forecasting system were in place during the 2021 calendar year. Performance of this model may continue to improve with further data integration from an expanding array of new broad measurement capabilities such as drones, data sondes, citizen science, and networked nutrient monitoring (El Serafy et al., 2021;Rundel et al., 2009). This study provides a novel contribution toward improving cyanoHAB forecasting by addressing the lack of weekly water surface temperature data with use of a random forest model (Kreakie et al., 2021); testing impacts on model performance relative to satellite uncertainty; demonstrating the ability to iteratively predict for an entire calendar year with performance statistics; adopting the new WHO new Alert Level recreational recommendations; and comparing the INLA model results to two machine learning and four neural network models. ...
... To overcome this issue, some sensors are deployed with telemetry, which uses antennae to wirelessly transmit sensor data to a database [10]. Although the telemetry system is widely used for different applications such as ecology [12,13], agriculture [14] and energy monitoring [15], its application to remote in situ groundwater sensing networks is limited and particularly reliant on proprietary, commercial applications [10]. Thus, a comprehensive regional or national monitoring plan remains costly. ...
Groundwater level monitoring is critical to protect and manage of groundwater resources. A properly designed and executed instrumentation can play important role to increase the quality and reliability of collected data and reducing total monitoring costs. The efficiency of the instrumentation is mainly depending on the accuracy and reliability of the installed sensors. This study presents, the testing and application of a cost-effective pressure sensor (0-689 kPa) range) for water level monitoring based on microelectromechanical system (MEMS) technology and Internet of things (IOT) concept. The sensor performance in term of accuracy, precision, repeatability, temperature was investigated in laboratory columns (with constant water level, increasing and decreasing water levels at various rates) and in-situ conditions in an observation bore (with natural groundwater level fluctuations). The results shows that the MEMS sensor capable of providing reliable and adequate monitoring scheme with an accuracy of 0.31% full scale (FS) (2.13 kPa).
... Environmental data collection with high spatial and temporal resolution is a growing interest to better model and understand ecological changes in the environment induced by climate change [1]. A high-density sensor network enables ecologists to create a digital twin of an eco-system, which plays a critical role in optimizing conservation strategies and policies for the natural environment [2]. Conventional manual sensor deployment methods are labor and cost-intensive tasks. ...
... The landscape of environmental science is rapidly changing due to the development of new technologies. The advances have been spurred by the decreasing cost, size, and weight, and improved reliability of environmental sensing hardware and software (Rundel et al. 2009). The emergence of citizen science and robotic systems have also facilitated the application of sensing technologies to environmental science as fundamental data-gathering tools. ...
The last decade has seen an explosion in data sources available for the monitoring and prediction of environmental phenomena. While several inferential methods have been developed that make predictions on the underlying process by combining these data, an optimal sampling design for when additional data is needed to complement those from other heterogeneous sources has not yet been developed. Here, we provide an adaptive spatial design strategy based on a utility function that combines both prediction uncertainty and risk-factor criteria. Prediction uncertainty is obtained through a spatial data fusion approach based on fixed rank kriging that can tackle data with differing spatial supports and signal-to-noise ratios. We focus on the application of low-cost portable sensors, which tend to be relatively noisy, for air pollution monitoring, where data from regulatory stations as well as numeric modeling systems are also available. Although we find that spatial adaptive sampling designs can help to improve predictions and reduce prediction uncertainty, low-cost portable sensors are only likely to be beneficial if they are sufficient in number and quality. Our conclusions are based on a multi-factorial simulation experiment, and on a realistic simulation of pollutants in the Erie and Niagara counties in Western New York.
... In general, there are several common IoT technologies that are used for water quality estimation. The summarized list includes [22][23][24][25][26][27]: ...
The Internet of Things (IoT) has become widespread. Mainly used in industry, it already penetrates into every sphere of private life. It is often associated with complex sensors and very complicated technology. IoT in life sciences has gained a lot of importance because it allows one to minimize the costs associated with field research, expeditions, and the transport of the many sensors necessary for physical and chemical measurements. In the literature, we can find many sensational ideas regarding the use of remote collection of environmental research. However, can we fully say that IoT is well established in the natural sciences?
... Low-cost sensor networks have raised a strong interest in the past years, notably for air pollution monitoring [1,2,3]. The deployment of large sensor networks at reasonable cost to provide information at high spatial resolution can now be envisioned, although several issues have yet to be tackled [4,5]. Among the multiple challenges for these technologies, the improvement of their data quality is a major one [4,6]. ...
In recent years, low-cost sensors have raised strong interest for environmental monitoring applications. These instruments often suffer from degraded data quality. Notably, they are prone to drift. It can be mitigated with costly periodic calibrations. To reduce this cost, in situ calibration strategies have emerged, enabling the recalibration of instruments while leaving them in the field. However, they rarely identify which instruments actually need a calibration because of drift, so that in situ calibration may instead degrade performances. Therefore, a novel drift detection algorithm is presented in this work, exploiting the concept of rendez-vous between measuring instruments. Its originality lies mainly in the comparisons of values determining the state of the instruments, for which the quality of the measurement results is taken into account. It defines the concept of compatibility between measurement results. A case study is developed, showing an accuracy of 88% for correct detection of drifting instruments. The results of the diagnosis algorithm are then combined with calibration approaches. Results show a significant improvement of the measurement results. Notably, an increase of 15% of the coefficient of determination of the linear regression between their true values and the measured values is observed with the correction and the error on the slope and on the intercept respectively is reduced by 50% and 60% at least. Note to Practitioners —In this paper, we investigate the problem of drift detection in sensor networks. This work was motivated by the fact that faulty nodes are rarely detected in existing in situ calibration algorithm prior to the correction of the instruments. Moreover, existing fault diagnosis algorithms for sensor networks do not specifically target drift and are often applicable to either (dense) static or mobile sensor networks but not both. We propose an algorithm designed for the detection of drift faults regardless of the type of sensor network and of the measurand. Specific attention is paid to the metrological quality of the measurement results used to carry out the diagnosis. The output of the algorithm provides information that can be exploited for the recalibration of faulty instruments. In future work, we will aim at providing tools and recommendations for the adjusment of the parameters of the diagnosis algorithm but also more elaborated approaches based on the results of our diagnosis algorithm to calibrate faulty nodes.
... Technological advances in manufacturing miniaturized machine tools and Internet network connecting systems have prompted the development of cyberinfrastructures to address ecological questions (Rundel et al. 2009;Torresan et al. 2021). The use of satellite imagery and statistical modelling has recently allowed the generation of a spatially continuous map of forest tree density at a global scale (Crowther et al. 2015). ...
... Electronic data collection requires a continuous data flow from the sensor network. Gaps may derive from instrument failures, power interruptions and bad weather, or instrument problems and maintenance stops (Rundel et al. 2009). Therefore, gap-filling techniques are required to produce continuous data time series (Moffat et al. 2007). ...
The goal to limit the increase in global temperature below 2 °C requires reaching a balance between anthropogenic emissions and reductions (sinks) in the second half of this century. As carbon sinks, forests can potentially play an important role in carbon capture. The Paris Agreement (2015) requires signatory countries to reduce deforestation, while conserving and enhancing carbon sinks. Innovative approaches may help foresters take up climate-smart management methods and identify measures for scaling purposes. The EU’s funding instrument COST has supported the Action CLIMO (Climate-Smart Forestry in Mountain Regions – CA15226), with the aim of reorienting forestry in mountain areas to challenge the adverse impacts of climate change.
Funded by the EU’s Horizon 2020, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools. This book addresses different combinations of CLIMO’s driving/primary objectives and discusses smarter ways to develop forestry and monitor forests under current environmental changes, affecting forest ecosystems.
... The exact location of the tipping point depends on species-and stress-specific sensitivity. Although dynamic phenomena are intrinsically difficult to observe, efficient monitoring of spatially and temporally dynamic phenomena is possible through multiscale sampling schemes based on a coarse-to-fine hierarchy system (Rundel et al. 2009). With this approach, the region of interest can be identified and surveyed through low-spatial/time resolution sensors (e.g., airborne surveys and sparse ground-based devices), and selected areas that need high-resolution and real-time observation can be monitored. ...
... Multispectral and/or hyperspectral imaging systems may provide for automated detection of living root dynamics (Bodner et al. 2018), though establishing a sensor network belowground requires considering trade-offs between expensive vs. low-cost multimodal minirhizotrons (Rahman et al. 2020). In this sense, preliminary work with GPR would gather initial imaging analysis of coarse root turnover (Stover et al. 2007), in order to integrate soil texture and soil microclimate (temperature, moisture) and contribute to determine the positions of soil sensor nodes in patchy forest stands (Rundel et al. 2009). ...
Trees are long-lived organisms that contribute to forest development over centuries and beyond. However, trees are vulnerable to increasing natural and anthropic disturbances. Spatially distributed, continuous data are required to predict mortality risk and impact on the fate of forest ecosystems. In order to enable monitoring over sensitive and often remote forest areas that cannot be patrolled regularly, early warning tools/platforms of mortality risk need to be established across regions. Although remote sensing tools are good at detecting change once it has occurred, early warning tools require ecophysiological information that is more easily collected from single trees on the ground.
Here, we discuss the requirements for developing and implementing such a tree-based platform to collect and transmit ecophysiological forest observations and environmental measurements from representative forest sites, where the goals are to identify and to monitor ecological tipping points for rapid forest decline. Long-term monitoring of forest research plots will contribute to better understanding of disturbance and the conditions that precede it. International networks of these sites will provide a regional view of susceptibility and impacts and would play an important role in ground-truthing remotely sensed data.
... Other examples of robots-for-ecology are found among the growing array of biomimicking robots and robot groups (including the sorts of "robot swarms" discussed above) that are designed to carry out specialized research tasks optimally. These include robots developed to mimic bacterial locomotion (Dhariwal et al. 2004; see also Hart andMartinez 2006 andRundel et al. 2009). By mimicking bacterial response to changing chemical concentrations-for example, by mimicking chemotaxic behaviors-such robots are very efficient at helping locate, trace, and monitoring chemical sources and changes in all sorts of environments and for all sorts of research purposes. ...
... Furthermore, there are biomimicking robots that may be crucially useful and completely innocuous. For instance, nanobots that mimic bacterial chemotaxic locomotion can be used to locate chemical contaminant sources, monitor chemical gradients, and aid in removing contaminants, with negligible impacts due to their presence in a system (see Dhariwal et al. 2004; see also Hart and Martinez 2006, Lampton 1993, and Rundel et al. 2009). Yet, we mention such cases to again stress that we believe that certain environmental robots may be crucially useful and valuable for environmental protection and remediation. ...
New robotics technologies are being used for environmental research, and engineers and ecologists are exploring ways of integrating an array of different sorts of robots into ecosystems as a means of responding to the unprecedented environmental changes that mark the onset of the Anthropocene. These efforts introduce new roles that robots may play in our environments, potentially crucial new forms of human dependence on such robots, and new ways that robots can enhance life quality and environmental health. These efforts at once introduce a variety of new and unprecedented ethical concerns. This work uses a previously developed functional taxonomy of kinds of environmental robots to develop a list of key ethical questions to push forward the sub-field and study of Environmental Robot Ethics. By identifying unique concerns raised by the different sorts of existing environmental robotics technologies, this paper aims to provide resources for further critical analysis of the ethical issues and tradeoffs environmental robots present.
