On the difference in the net ecosystem exchange of CO2 between deciduous and evergreen forests in the southeastern United States.

The southeastern United States is experiencing a rapid regional increase in the ratio of pine to deciduous forest ecosystems at the same time it is experiencing changes in climate. This study is focused on exploring how these shifts will affect the carbon sink capacity of southeastern US forests, which we show here are among the strongest carbon sinks in the continental United States. Using eight-year-long eddy covariance records collected above a hardwood deciduous forest (HW) and a pine plantation (PP) co-located in North Carolina, USA, we show that the net ecosystem exchange of CO2 (NEE) was more variable in PP, contributing to variability in the difference in NEE between the two sites (ΔNEE) at a range of timescales, including the interannual timescale. Because the variability in evapotranspiration (ET) was nearly identical across the two sites over a range of timescales, the factors that determined the variability in ΔNEE were dominated by those that tend to decouple NEE from ET. One such factor was water use efficiency, which changed dramatically in response to drought and also tended to increase monotonically in nondrought years (P < 0.001 in PP). Factors that vary over seasonal timescales were strong determinants of the NEE in the HW site; however, seasonality was less important in the PP site, where significant amounts of carbon were assimilated outside of the active season, representing an important advantage of evergreen trees in warm, temperate climates. Additional variability in the fluxes at long-time scales may be attributable to slowly evolving factors, including canopy structure and increases in dormant season air temperature. Taken together, study results suggest that the carbon sink in the southeastern United States may become more variable in the future, owing to a predicted increase in drought frequency and an increase in the fractional cover of southern pines.

A QoS-guaranteed coverage precedence routing algorithm for wireless sensor networks.

For mission-critical applications of wireless sensor networks (WSNs) involving extensive battlefield surveillance, medical healthcare, etc., it is crucial to have low-power, new protocols, methodologies and structures for transferring data and information in a network with full sensing coverage capability for an extended working period. The upmost mission is to ensure that the network is fully functional providing reliable transmission of the sensed data without the risk of data loss. WSNs have been applied to various types of mission-critical applications. Coverage preservation is one of the most essential functions to guarantee quality of service (QoS) in WSNs. However, a tradeoff exists between sensing coverage and network lifetime due to the limited energy supplies of sensor nodes. In this study, we propose a routing protocol to accommodate both energy-balance and coverage-preservation for sensor nodes in WSNs. The energy consumption for radio transmissions and the residual energy over the network are taken into account when the proposed protocol determines an energy-efficient route for a packet. The simulation results demonstrate that the proposed protocol is able to increase the duration of the on-duty network and provide up to 98.3% and 85.7% of extra service time with 100% sensing coverage ratio comparing with LEACH and the LEACH-Coverage-U protocols, respectively.

An Alternative Body Temperature Measurement Solution: Combination of a Highly Accurate Monitoring System and a Visualized Public Health Cloud Platform.

To quickly isolate suspected cases to control the epidemics, this study proposes a body temperature monitoring system with a thermography based on the Internet of Things (IoT) architecture. The collected data are transmitted to a back-end platform via wireless communication. Using the analyzed data, the platform provides services, such as instant alerts for any anomalies, infectious disease outbreak prediction, and risk level assessment for a given area, and it will be a great help to epidemic prevention. The mean absolute percentage error and root mean square error of the proposed monitoring system under an extensive series of experiments are 0.04% and 0.0204°C, respectively. It shows that the body temperature measured by the thermal imaging sensor in the system can accurately represent the actual body temperature after specific calibrations that take the environmental temperature into account. It can also be expanded to a decision supporting system to help schools or government agencies to make proper decisions to stop the spread of infectious diseases.

Reconstructing Taiwan's land cover changes between 1904 and 2015 from historical maps and satellite images.

A new reconstruction of changes in Taiwan's land cover and estimated uncertainty between 1904 and 2015 is presented. The reconstruction is made by integrating geographical information from historical maps and SPOT satellite images, to obtain spatially explicit land cover maps with a resolution of 500 × 500 m and distinguishes six land cover classes: forests, grasslands, agricultural land, inland water, built-up land, and bare soil. The temporal resolution is unbalanced being derived from four historical maps describing the land cover between 1904 and 1994 and five mosaic satellite images describing the land cover between 1995 and 2015. The uncertainty of the historical maps is quantified to show the aggregation error whereas the uncertainty of the satellite images is quantified as classification error. Since 1904, Taiwan, as a developing country, has gone through a not unusual sequence of population growth and subsequent urbanization, a decoupling of the demand for agricultural land from population growth, and a transition from shrinking in forest area to forest expansion. This new land cover reconstruction is expected to contribute to future revisions of global land cover reconstructions as well as to studies of (gross) land cover changes, the carbon budget, regional climate, urban heat islands, and air and water pollution at the national and sub-national level.

The structure of turbulence near a tall forest edge: the backward-facing step flow analogy revisited.

Flow disturbances near tall forest edges are receiving significant attention in diverse disciplines including ecology, forest management, meteorology, and fluid mechanics. Current theories suggest that near a forest edge, when the flow originates from a forest into a large clearing, the flow retains its forest canopy turbulence structure at the exit point. Here, we propose that this framework is not sufficiently general for dense forested edges and suggest that the flow shares several attributes with backward-facing step (BFS) flow. Similar analogies, such as rotor-like circulations, have been proposed by a number of investigators, though the consequences of such circulations on the primary terms in the mean momentum balance at the forest clearing edge have rarely been studied in the field. Using an array of three triaxial sonic anemometers positioned to measure horizontal and vertical gradients of the velocity statistics near a forest edge, we show that the flow structure is more consistent with an intermittent recirculation pattern, rather than a continuous rotor, whose genesis resembles the BFS flow. We also show that the lateral velocity variance, v'2, is the moment that adjusts most slowly with downwind distance as the flow exits from the forest into the clearing. Surprisingly, the longitudinal and vertical velocity variances (u'2 and w'2) at the forest edge were comparable in magnitude to their respective values at the center of a large grass-covered forest clearing, suggesting rapid adjustment at the edge. Discussions on how the forest edge modifies the spectra and co-spectra of momentum fluxes, effective mixing length, and static pressure are also presented.

Variability in net ecosystem exchange from hourly to inter-annual time scales at adjacent pine and hardwood forests: a wavelet analysis.

Orthonormal wavelet transformation (OWT) is a computationally efficient technique for quantifying underlying frequencies in nonstationary and gap-infested time series, such as eddy-covariance-measured net ecosystem exchange of CO2 (NEE). We employed OWT to analyze the frequency characteristics of synchronously measured and modeled NEE at adjacent pine (PP) and hardwood (HW) ecosystems. Wavelet cospectral analysis showed that NEE at PP was more correlated to light and vapor pressure deficit at the daily time scale, and NEE at HW was more correlated to leaf area index (LAI) and temperature, especially soil temperature, at seasonal time scales. Models were required to disentangle the impacts of environmental drivers on the components of NEE, ecosystem carbon assimilation (Ac) and ecosystem respiration (RE). Sensitivity analyses revealed that using air temperature rather than soil temperature in RE models improved the modeled wavelet spectral frequency response on time scales longer than 1 day at both ecosystems. Including LAI improved RE model fit on seasonal time scales at HW, and incorporating parameter variability improved the RE model response at annual time scales at both ecosystems. Resolving variability in canopy conductance, rather than leaf-internal CO2, was more important for modeling Ac at both ecosystems. The PP ecosystem was more sensitive to hydrologic variables that regulate canopy conductance: vapor pressure deficit on weekly time scales and soil moisture on seasonal to interannual time scales. The HW ecosystem was sensitive to water limitation on weekly time scales. A combination of intrinsic drought sensitivity and non-conservative water use at PP was the basis for this response. At both ecosystems, incorporating variability in LAI was required for an accurate spectral representation of modeled NEE. However, nonlinearities imposed by canopy light attenuation were of little importance to spectral fit. The OWT revealed similarities and differences in the scale-wise control of NEE by vegetation with implications for model simplification and improvement.

Monitoring street-level spatial-temporal variations of carbon monoxide in urban settings using a wireless sensor network (WSN) framework.

Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management.

Are ecosystem carbon inputs and outputs coupled at short time scales? A case study from adjacent pine and hardwood forests using impulse-response analysis.

A number of recent studies have attributed a large proportion of soil respiration (R(soil)) to recently photoassimilated carbon (C). Time lags (tau(PR)) associated with these pulses of photosynthesis and responses of R(soil) have been found on time scales of hours to weeks for different ecosystems, but most studies find evidence for tau(PR) on the order of 1-5 d. We showed that such time scales are commensurate with CO(2) diffusion time scales from the roots to the soil surface, and may thus be independent from photosynthetic pulses. To further quantify the role of physical (i.e. edaphic) and biological (i.e. vegetative) controls on such lags, we investigated tau(PR) at adjacent planted pine (PP) and hardwood (HW) forest ecosystems over six and four measurement years, respectively, using both autocorrelation analysis on automated soil surface flux measurements and their lagged cross-correlations with drivers for and surrogates of photosynthesis. Evidence for tau(PR) on the order of 1-3 d was identified in both ecosystems and using both analyses, but this lag could not be attributed to recently photoassimilated C because the same analysis yielded comparable lags at HW during leaf-off periods. Future efforts to model ecosystem C inputs and outputs in a pulse-response framework must combine measurements of transport in the physical and biological components of terrestrial ecosystems.