This article presents an unprecedented concept for resilient bridge columns comprising precast modules designed for disassembly. Resiliency is provided by superelastic shape memory alloys that ...minimize permanent drift and engineered cementitious composite that minimizes damage, while keeping the rest of the column elastic. The precast modules consist of prefabricated plastic hinges and prefabricated concrete-filled fiber–reinforced polymer tubes. The columns are very desirable candidates for accelerated bridge construction. Two ¼-scale column models with engineered cementitious composite plastic hinges incorporating two types of shape memory alloy bars, one made of nickel–titanium and the other of copper–aluminum–manganese, were designed and tested under simulated earthquakes. To assess the influence of reusing column components, each of the models was first tested under a series of ground motions, and then the models were disassembled, inspected, reassembled, and subsequently retested. The reassembled models reached the same capacity as the original models but were more flexible. A simple modeling method was able to match the global measured response of the models with a reasonable accuracy.
Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly ...throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource ...use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
Urban forest management and policies have been promoted as a tool to mitigate carbon dioxide (CO
2) emissions. This study used existing CO
2 reduction measures from subtropical Miami-Dade and ...Gainesville, USA and modeled carbon storage and sequestration by trees to analyze policies that use urban forests to offset carbon emissions. Field data were analyzed, modeled, and spatially analyzed to compare CO
2 sequestered by managing urban forests to equivalent amounts of CO
2 emitted in both urban areas. Urban forests in Gainesville have greater tree density, store more carbon and present lower per-tree sequestration rates than Miami-Dade as a result of environmental conditions and urbanization patterns. Areas characterized by natural pine-oak forests, mangroves, and stands of highly invasive trees were most apt at sequestering CO
2. Results indicate that urban tree sequestration offsets CO
2 emissions and, relative to total city-wide emissions, is moderately effective at 3.4 percent and 1.8 percent in Gainesville and Miami-Dade, respectively. Moreover, converting available non-treed areas into urban forests would not increase overall CO
2 emission reductions substantially. Current CO
2 sequestration by trees was comparable to implemented CO
2 reduction policies. However, long-term objectives, multiple ecosystem services, costs, community needs, and preservation of existing forests should be considered when managing trees for climate change mitigation and other ecosystem services.
Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but ...this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.
Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires ...accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.
Significance Despite often decadeslong control efforts, in many regions of the world ambient concentrations of ground-level ozone threaten human and ecosystem health. Furthermore, in many places the ...effects of continuing land use and climate change are expected to counteract ongoing efforts to reduce ozone concentrations. Combined with the rising cost of more stringent conventional technological ozone controls, this creates a need to explore novel approaches to reducing tropospheric ozone pollution. Reforestation of peri-urban areas, which removes ozone and one of its precursors, may be a cost-effective approach to ozone control and can produce important ancillary benefits. We identify key criteria for maximizing the ozone abatement and cost effectiveness of such reforestation and the substantial potential for its application in the United States.
High ambient ozone (O ₃) concentrations are a widespread and persistent problem globally. Although studies have documented the role of forests in removing O ₃ and one of its precursors, nitrogen dioxide (NO ₂), the cost effectiveness of using peri-urban reforestation for O ₃ abatement purposes has not been examined. We develop a methodology that uses available air quality and meteorological data and simplified forest structure growth-mortality and dry deposition models to assess the performance of reforestation for O ₃ precursor abatement. We apply this methodology to identify the cost-effective design for a hypothetical 405-ha, peri-urban reforestation project in the Houston–Galveston–Brazoria O ₃ nonattainment area in Texas. The project would remove an estimated 310 tons of (t) O ₃ and 58 t NO ₂ total over 30 y. Given its location in a nitrogen oxide (NO ₓ)-limited area, and using the range of Houston area O ₃ production efficiencies to convert forest O ₃ removal to its NO ₓ equivalent, this is equivalent to 127–209 t of the regulated NO ₓ. The cost of reforestation per ton of NO ₓ abated compares favorably to that of additional conventional controls if no land costs are incurred, especially if carbon offsets are generated. Purchasing agricultural lands for reforestation removes this cost advantage, but this problem could be overcome through cost-share opportunities that exist due to the public and conservation benefits of reforestation. Our findings suggest that peri-urban reforestation should be considered in O ₃ control efforts in Houston, other US nonattainment areas, and areas with O ₃ pollution problems in other countries, wherever O ₃ formation is predominantly NO ₓ limited.
Señor editor: Debido a la interacción de cinco placas tectónicas –la de Norteamérica, la de Cocos, la del Pacífico, la de Rivera y la del Caribe–, México se encuentra en una zona de alta ...sismicidad. El Servicio Sismológico Nacional (SSN) reporta, en promedio, la ocurrencia de 40 sismos por día.Durante los últimos 100 años, en el estado de Puebla se han registrado seis sismos considerados como de gran magnitud o macrosismos. Éstos ocurrieron el 10 de febrero de 1928, el 26 de julio de 1937, el 11 de octubre de 1945, el 24 de mayo de 1950, el 28 de agosto de 1973 y el 15 de junio de 1999. El ocurrido en 1973 ha sido catalogado como el más destructivo, ya que ocasionó más de 500 muertes, 1 600 heridos y cuantiosos daños materiales en las poblaciones de Ciudad Serdán, Tehuacán y la capital del estado.
Air quality improvement by a forested, peri-urban national park was quantified by combining the Urban Forest Effects (UFORE) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) ...models. We estimated the ecosystem-level annual pollution removal function of the park’s trees, shrub and grasses using pollution concentration data for carbon monoxide (CO), ozone (O3), and particulate matter less than 10 microns in diameter (PM10), modeled meteorological and pollution variables, and measured forest structure data. Ecosystem-level O3 and CO removal and formation were also analyzed for a representative month. Total annual air quality improvement of the park’s vegetation was approximately 0.02% for CO, 1% for O3, and 2% for PM10, of the annual concentrations for these three pollutants. Results can be used to understand the air quality regulation ecosystem services of peri-urban forests and regional dynamics of air pollution emissions from major urban areas.
► Air quality regulation functions and ecosystem structure of a peri-urban forest in Mexico were quantified. ► Air pollution removal-formation dynamics were estimated using the UFORE and WRF-Chem models. ► Peri-urban forests positively contributed to air qualtiy improvement in Mexico City. ► Results can be used to quantify the ecosystem services of peri-urban forests.
Coupled models estimated air quality improvement and pollution removal-formation by peri-urban forest ecosystems in the Mexico City airshed.
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