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65 results for “temperature monitoring”
Air temperature at core phenology sites and additional bird monitoring sites in the Andrews Experimental Forest, 2009 to present
The H.J Andrews phenology study air temperature network includes 16 core phenology sites, 40 core bird sites and 128 auxiliary bird sites. This study examines air temperatures at multiple sites within the Andrews Experimental Forest. Air temperatures were recorded 1.5 m above ground at 184 sites distributed on an 800-m incomplete grid throughout much of the Andrews Forest. Data were collected using automated sensors starting in June of 2009 at 56 sites and in June 2011 128 additional sensors were added. These data document the complex spatial and temporal patterns of air temperature variation within the Andrews Forest, which is governed by multiple processes including inversions, regional air mixing, cold air drainage and pooling, and the effects of vegetation on temperature extremes. The data entities provided indicate various methods of data quality checking over time.
Soil temperature at GCE core monitoring sites in the winter of 2019-2020
We deployed one hobo logger in each vegetation zone at each of the ten primary GCE monitoring sites, for a total of 20 loggers. The loggers were deployed at plot number 1 in each zone, to the “outside” (away from plot number 2), parallel in elevation with the middle of plot 1, buried 10 cm deep, lying horizontal, and tied with a string to the upper left hand (looking from the ocean towards the land) corner stake of the plot. Hobos were deployed during fall monitoring in October 2019, and set to start logging on October 15, 1 am, at 15 minute intervals, with the loggers set on Central Time (times were converted to UTC in post-processing). They were retrieved in April 2020 and files were trimmed to end on a standard date. Exact deployment and retrieval dates are on the attached adobe acrobat file.
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2020 in China
<p>In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)
<p>The Enhanced Vegetation Index (EVI), Land Surface Temperature (LST) and Precipitation (P) were used as new data sources based on the spatial distance model to construct an optimized multi-source remote sensing dryness index named Temperature-Vegetation-Precipitation Dryness Index based on the shortcomings of the TVPDIorigin (i.e., TVPDI<sub>o</sub>) data source. The TVPDI<sub>n</sub> of the long time series was also compared and analyzed with the classical drought index - Standardized Precipitation Evapotranspiration Index (SPEI-3) on a 3-month scale, different drought response level products of Solar-Induced Chlorophyll Fluorescence (SIF), soil moisture (SM) from ESA CCI (European Space Agency's Climate Change Initiative), and total crop yield, then the sensitivity and validity of the TVPDI<sub>n</sub> for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification:</p> <p>(1) Compared with the original data source TVPDI<sub>o</sub> using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDI<sub>n</sub>, the overall correlation between the two and SPEI-3 was good, with a maximum of 0.57 and 0.56, respectively (p< 0.1), but the overall TVPDI<sub>n</sub> constructed in this study had a better fit compared to the original data source TVPDI<sub>o</sub> and was more sensitive to the monitoring of dry and wet conditions.</p> <p>(2) According to the comparison of TVPDI<sub>n</sub> with ESA CCI sm, TVPDI<sub>n</sub> showed a high correlation of more than 0.9 with soil water content, which proved that TVPDI<sub>n</sub> was highly consistent with soil moisture; compared with SIF, 54.5% of the regional correlation coefficients were greater than 0.8 (p< 0.01), and spatially, the correlation results were better in the northwest than in the east, indicating that the response of TVPDI<sub>n</sub> to vegetation productivity is more agile in regions with continental climate such as the northwest. The results of correlation with grain yield comparison showed that good positive correlations were presented with TVPDI<sub>n</sub> in Liaodong Peninsula, northern North China Plain, and most of Qilian Mountains, southern edge of Qinling Mountains, middle and lower reaches of Yangtze River, and South China, indicating that TVPDI<sub>n</sub> has a high consistency in the changes of agricultural grain production in the above mentioned regions, and also proving the index in monitoring agricultural aridity and guiding agricultural production The good performance of the index in monitoring agricultural aridity and guiding agricultural production.</p> <p> This dataset is version 2.0, and covers all of China's territory, but the temperature-vegetation- precipitation dryness index of the open water surface are often set to a null value. Note:The data format is "TIF", the spatial resolution is "1 km", the time resolution is "1 month" and dimensionless. The pixel value is the NTVPDI value, and the closer the pixel value is to 0, the drier it is, and the larger the data, the wetter the land surface. The practical utility of this dataset is to compare the degree of dryness and wetness of China's land, to monitor short-term and medium-term droughts, and to substitute model parameters related to soil moisture. This is of great value to the impartial formulation of China's environmental and economic policies, regular monitoring and evaluation of drought and flood conditions. This product will be freely available to all users worldwide and will be continuously improved to suit new goals and needs.</p>
Dataset for 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination'
<p>Associated data for the manuscript 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination' (doi://10.26434/chemrxiv-2023-djhp2)</p> <p> </p> <p> </p>
Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
Dataset for 'Printed ecoresorbable temperature sensors for environmental monitoring'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Printed ecoresorbable temperature sensors for environmental monitoring”.</p> <p>This work aims to study the effect of photonic sintering parameters on the temperature behavior of printed zinc resistors, with the aim to fabricate eco-friendly and ecoresorbable temperature sensors on paper. Biodegradable electronic devices have potential in tackling the increasingly pressing challenge of electronic waste and printing methods allow to reduce toxic byproducts of fabrication and wasted material. The sintering method that is optimized here is based on our previous work combining electrochemical and photonic sintering approaches to enable the fabrication of highly-conductive degradable metal tracks. We optimize the sintering parameters to obtain zinc resistors with a high temperature coefficient of resistance and a linear temperature response curve. The data that was collected in the frame of this work is present in this repository. More information about the contents of the dataset is present in the included README files.</p>
Ground surface temperature data 2007-2021 at different sites of the PERMATHERMAL monitoring network in Livingston and Deception Islands, SouthShetland Archipelago, Antarctica.
<p>Ground Surface Temperature (GST) corrected data adquired between 2007 and 2021 at different stations of the PERMATHERMAL monitoring network at Livingston and Deception Islands, South Shetland Archipelago, Antarctica.</p> <p>(To be completed)</p>
Sonadora elevational plots: long-term monitoring of air temperature
This is a long term monitoring of air temperature at each elevation plot along the Sonadora gradient. At each plot a HOBO sensor is located close to the middle of the plot: at 1m above ground: and placed inside a radiation shield (a plastic cup). Sensors are programmed to sample and store air temperature every hour. A daily average is computed from hourly readings. Sensors are downloaded twice a year: thus blanks represent sensor malfunction: loss of battery: or memory full. Initial blanks were due to lack of enough sensors to cover the gradient. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Continuous soil temperature, specific conductance, and volumetric water content measurements from the F6 Active Layer Monitoring Station (ALMS01), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at F6 (ALMS01), located on the south shore of Lake Fryxell.
Continuous soil temperature, specific conductance, and volumetric water content measurements from the Wormherder Creek Active Layer Monitoring Station (ALMS02), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at Wormherder Creek (ALMS02).
Continuous soil temperature, specific conductance, and volumetric water content measurements from the Von Guerard Stream Active Layer Monitoring Station (ALMS03), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at Von Guerard Stream (ALMS03).
Continuous soil temperature, specific conductance, and volumetric water content measurements from the Green Creek Active Layer Monitoring Station (ALMS04), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at Green Creek (ALMS04).
Continuous soil temperature, specific conductance, and volumetric water content measurements from the Water Track B Active Layer Monitoring Station (ALMS06), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at Water Track B (ALMS06).
Water temperature and discharge at monitoring station along the Rhine
<p>We provide daily average water temperatures and discharge at following Rhine monitoring stations:<br> Basel; Worms; Koblenz; Cologne.<br> We provide the yearly heat input from Nuclear power plants between following stations:<br> upstream Basel; between Basel and Worms; between Worms and Koblenz; between Koblenz and Cologne.<br> We provide the calculated catchment wide air temperature, described in the paper https://doi.org/10.5194/hess-2019-518<br> The data is connected to the publication:<br> Anthropogenic Influence on the Rhine water temperatures<br> https://doi.org/10.5194/hess-24-5027-2020</p>
Temperature and Relative Humidity monitoring
<p>Temperature and relative humidity has been monitored before and after the installation of nature-based solutions during the proGIreg project. Specifically, NBS3 in Dortmund, NBS2, NBS3 and NBS5 in Turin, and NBS5 in Zagreb have been monitored. Continuous measurements have been made inside the NBS and in a control site over three years; for each monitoring site, 6 temperature sensors are used (3 for the NBS site and 3 for the control site).<br>For NBS5 outdoor green walls in Turin and Zagreb, the temperature has been measured inside the building and only for one year, and the temperature measured by a reference weather station in the same district has been used as control.</p> <p>More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.</p>
Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
<p><span>These data on bird species abundance and environmental variables were used in testing and comparing two different species distribution model validation methods that are applied to models which are used to predict the effects of climate change on species' distributions. The aim of the study was to investigate whether different validation methods give different results of the model's predictive performance and to demonstrate that validation methods based on measuring and validating a "static" pattern in distribution can assess model performance over-optimistically compared to methods based on measuring and validating a "change" in the distribution, which can assess the predictive performance more critically. </span></p>
Data from: Estimating a physiologically-based threshold to oxygen and temperature from marine monitoring data reveals challenges and opportunities for forecasting distribution shifts
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