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1,914 results for “2008”
Snowpack in Hemlock, Hardwood, and Open Sites at Harvard Forest 2008-2014
Sub-forest canopy meteorological forcing and snowpack properties can profoundly influence the winter ecology. 3/4 inch graduated PVC snow stakes and web cams were installed at three Harvard Forest (HF) sites including: mixed-hardwood at the Little Prospect Hill (LPH) Eddy Flux Tower, near the Hemlock walk-up tower, and at the open area behind Shaler Hall. Web cameras connected to a laptop at the three sites provided daily images of snowpack surface depth. Snow density profiles were recorded during weekly to monthly site visits from December 2008 to April 2014. Gaps in the datasets were primarily caused by laptop malfunctions and lack of site visits necessary to maintain equipment and to obtain density measurements of the snowpack. There were no site visits to record snowpack properties during the 2011-2012 snow season, a year of unusually low snowfall. These datasets would prove useful for research related to snow ecology and hydrology projects at Harvard Forest.
Lake Wingra Exclosure Experiment at North Temperate Lakes LTER: Secchi Disk Depth 2005 - 2008
Starting in late summer 2005, Wisconsin Dept of Natural Resources (WDNR), Dane County, Friends of Lake Wingra (FOLW), and NTL-LTER initiated a 3-year experiment in Lake Wingra to test the response of the native macrophyte community to clearer water produced from a major carp reduction program. This demonstration-scale experiment includes the construction of a 1.0-hectare rectangular carp exclosure with its solid vinyl walls extending from the lake shoreline to a water depth of 2.9 meters. NTL-LTER conducts the routine limnological monitoring of the lake and exclosure and is leading the science evaluation of potential lake restoration activities. The exclosure experiment was terminated in the fall of 2008. The exclosure was removed from Lake Wingra at that time. Sampling is done both within the exclosure and at a control site located nearby in the littoral zone. The sample location within the exclosure is equidistant from the side walls and approximately 75 meters from the shore in a water depth of approximately 2.5 meters. The control site sample location is approximately 75 meters west of the exclosure sample site at the same approximate distance from shore and water depth. Samples are taken at the same time and on the same schedule as the NTL-LTER limnological sampling on Lake Wingra, e.g., biweekly spring through summer, every 4 weeks in the fall, and once during the winter depending on ice conditions. Parameters measured within the exclosure and at the control site include water temperature, dissolved oxygen, secchi depth and chlorophyll-a. Additional parameters measured only within the exclosure include total Kjeldahl nitrogen, nitrate + nitrite nitrogen, ammonia nitrogen, total phosphorus, dissolved reactive phosphorus and dissolved reactive silica. Secchi disk depth is measured within the exclosure and at a nearby control site in the littoral zone . The disk is circular, 20 cm in diameter, and has alternating black and white quadrants. It is lowered using a c
Lake Wingra Exclosure Experiment at North Temperate Lakes LTER: Chlorophyll 2005 - 2008
Starting in late summer 2005, Wisconsin Dept of Natural Resources (WDNR), Dane County, Friends of Lake Wingra (FOLW), and NTL-LTER initiated a 3-year experiment in Lake Wingra to test the response of the native macrophyte community to clearer water produced from a major carp reduction program. This demonstration-scale experiment includes the construction of a 1.0-hectare rectangular carp exclosure with its solid vinyl walls extending from the lake shoreline to a water depth of 2.9 meters. NTL-LTER conducts the routine limnological monitoring of the lake and exclosure and is leading the science evaluation of potential lake restoration activities. The exclosure experiment was terminated in the fall of 2008. The exclosure was removed from Lake Wingra at that time. Sampling is done both within the exclosure and at a control site located nearby in the littoral zone. The sample location within the exclosure is equidistant from the side walls and approximately 75 meters from the shore in a water depth of approximately 2.5 meters. The control site sample location is approximately 75 meters west of the exclosure sample site at the same approximate distance from shore and water depth. Samples are taken at the same time and on the same schedule as the NTL-LTER limnological sampling on Lake Wingra, e.g., biweekly spring through summer, every 4 weeks in the fall, and once during the winter depending on ice conditions. Parameters measured within the exclosure and at the control site include water temperature, dissolved oxygen, secchi depth and chlorophyll-a. Additional parameters measured only within the exclosure include total Kjeldahl nitrogen, nitrate + nitrite nitrogen, ammonia nitrogen, total phosphorus, dissolved reactive phosphorus and dissolved reactive silica. Chlorophyll is measured within the exclosure and at a nearby control site in the littoral zone. Spectrophotometric analysis and fluorometric analysis are done on integrated samples from surface to 1 meter. The firs
Lake Wingra Exclosure Experiment at North Temperate Lakes LTER: Nutrients 2005 - 2008
Starting in late summer 2005, Wisconsin Dept of Natural Resources (WDNR), Dane County, Friends of Lake Wingra (FOLW), and NTL-LTER initiated a 3-year experiment in Lake Wingra to test the response of the native macrophyte community to clearer water produced from a major carp reduction program. This demonstration-scale experiment includes the construction of a 1.0-hectare rectangular carp exclosure with its solid vinyl walls extending from the lake shoreline to a water depth of 2.9 meters. NTL-LTER conducts the routine limnological monitoring of the lake and exclosure and is leading the science evaluation of potential lake restoration activities. The exclosure experiment was terminated in the fall of 2008. The exclosure was removed from Lake Wingra at that time. Sampling is done both within the exclosure and at a control site located nearby in the littoral zone. The sample location within the exclosure is equidistant from the side walls and approximately 75 meters from the shore in a water depth of approximately 2.5 meters. The control site sample location is approximately 75 meters west of the exclosure sample site at the same approximate distance from shore and water depth. Samples are taken at the same time and on the same schedule as the NTL-LTER limnological sampling on Lake Wingra, e.g., biweekly spring through summer, every 4 weeks in the fall, and once during the winter depending on ice conditions. Parameters measured within the exclosure and at the control site include water temperature, dissolved oxygen, secchi depth and chlorophyll-a. Additional parameters measured only within the exclosure include total Kjeldahl nitrogen, nitrate + nitrite nitrogen, ammonia nitrogen, total phosphorus, dissolved reactive phosphorus and dissolved reactive silica. Parameters characterizing the nutrient chemistry are measured at the surface within the exclosure in Lake Wingra. These parameters include total Kjeldahl nitrogen, nitrate + nitrite nitrogen, ammonia nitrogen, tota
Lake Wingra Exclosure Experiment at North Temperate Lakes LTER: Physical Limnology 2005 - 2008
Starting in late summer 2005, Wisconsin Dept of Natural Resources (WDNR), Dane County, Friends of Lake Wingra (FOLW), and NTL-LTER initiated a 3-year experiment in Lake Wingra to test the response of the native macrophyte community to clearer water produced from a major carp reduction program. This demonstration-scale experiment includes the construction of a 1.0-hectare rectangular carp exclosure with its solid vinyl walls extending from the lake shoreline to a water depth of 2.9 meters. NTL-LTER conducts the routine limnological monitoring of the lake and exclosure and is leading the science evaluation of potential lake restoration activities. The exclosure experiment was terminated in the fall of 2008. The exclosure was removed from Lake Wingra at that time. Sampling is done both within the exclosure and at a control site located nearby in the littoral zone. The sample location within the exclosure is equidistant from the side walls and approximately 75 meters from the shore in a water depth of approximately 2.5 meters. The control site sample location is approximately 75 meters west of the exclosure sample site at the same approximate distance from shore and water depth. Samples are taken at the same time and on the same schedule as the NTL-LTER limnological sampling on Lake Wingra, e.g., biweekly spring through summer, every 4 weeks in the fall, and once during the winter depending on ice conditions. Parameters measured within the exclosure and at the control site include water temperature, dissolved oxygen, secchi depth and chlorophyll-a. Additional parameters measured only within the exclosure include total Kjeldahl nitrogen, nitrate + nitrite nitrogen, ammonia nitrogen, total phosphorus, dissolved reactive phosphorus and dissolved reactive silica. Parameters characterizing the physical limnology are measured within the exclosure and at a nearby control site in the littoral zone at 1-m depth intervals. Measured parameters in the data set include water tempera
North Temperate Lakes LTER: High Frequency Meteorological and Dissolved Oxygen Data - Sparkling Bog North Buoy 2008 - 2012
The instrumented buoy on Sparkling Bog North is equipped with a dissolved oxygen sensor, a thermistor chain, and meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, and lake metabolism. Data are usually collected either at 1 minute or 10 minute intervals. The D-Opto dissolved oxygen sensor is 0.5m from the lake surface, thermistors are at the surface, at 0.25 m and at every .5 m from 0.5 m to 4.5 m, and meteorological sensors measure wind speed, wind direction, relative humidity, and air temperature. The buoy is also equipped with a CO2 monitor and a YSI AutoProfiler that measures several parameters including dissolved oxygen, water temperature, conductivity, pH, ORP, turbulence and chlorophyll-a. After correcting for flux to or from the atmosphere and vertical mixing within the water column, high frequency measurements of dissolved gases such as carbon dioxide and oxygen can be used to estimate gross primary productivity, respiration, and net ecosystem productivity, the basic components of whole lake metabolism. Sampling Frequency: varies for instantaneous sample. Generally 1 minute or 10 minutes. Number of sites: 1
North Temperate Lakes LTER: High Frequency Water Temperature Data - Sparkling Bog North Buoy 2008 - 2012
The instrumented buoy on Sparkling Bog North is equipped with a thermistor chain that measures water temperature at the surface, at 0.25 m and at every .5 m from 0.5 m to 4.5 m. The buoy is also equipped with a dissolved oxygen sensor, meteorological sensors, a CO2 sensor and a YSI AutoProfiler that provide fundamental information on lake thermal structure, weather conditions, and lake metabolism. Prior to May 2009, data were collected at 1 minute or 10 minute intervals. Since May 2009, data are being collected each minute. Hourly and daily water temperature averages are computed from high resolution data. Hourly and daily values may not be current with high resolution data. In 2008, the instrumented buoy was deployed in Sparkling Bog North from March 24 to November 10. In 2009, the buoy was deployed on the ice on March 7 and was not removed for the winter of 2009 to 2010. Sampling Frequency: varies for instantaneous sample. Generally 1 minute or 10 minutes. Number of sites: 1
North Temperate Lakes LTER: High Frequency CO2 and YSI AutoProfiler Data - Sparkling Bog North Buoy 2008
The instrumented buoy on Sparkling Bog North is equipped with a CO2 monitor and a YSI AutoProfiler that measures several parameters including dissolved oxygen, water temperature, conductivity, pH, ORP, turbulence and chlorophyll-a. The buoy is also equipped with a thermistor chain and a D-OPTO dissolved oxygen sensor at depth .5 m as well as meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, and lake metabolism. Data are usually collected either at 1 minute or 10 minute intervals. Sampling Frequency: varies for instantaneous sample. Generally 1 minute or 10 minutes. Number of sites: 1
Lake Wingra Exclosure Experiment at North Temperate Lakes LTER: Sampledate Conditions 2005 - 2008
Starting in late summer 2005, Wisconsin Dept of Natural Resources (WDNR), Dane County, Friends of Lake Wingra (FOLW), and NTL-LTER initiated a 3-year experiment in Lake Wingra to test the response of the native macrophyte community to clearer water produced from a major carp reduction program. This demonstration-scale experiment includes the construction of a 1.0-hectare rectangular carp exclosure with its solid vinyl walls extending from the lake shoreline to a water depth of 2.9 meters. NTL-LTER conducts the routine limnological monitoring of the lake and exclosure and is leading the science evaluation of potential lake restoration activities. The exclosure experiment was terminated in the fall of 2008. The exclosure was removed from Lake Wingra at that time. Sampling is done both within the exclosure and at a control site located nearby in the littoral zone. The sample location within the exclosure is equidistant from the side walls and approximately 75 meters from the shore in a water depth of approximately 2.5 meters. The control site sample location is approximately 75 meters west of the exclosure sample site at the same approximate distance from shore and water depth. Samples are taken at the same time and on the same schedule as the NTL-LTER limnological sampling on Lake Wingra, e.g., biweekly spring through summer, every 4 weeks in the fall, and once during the winter depending on ice conditions. Parameters measured within the exclosure and at the control site include water temperature, dissolved oxygen, secchi depth and chlorophyll-a. Additional parameters measured only within the exclosure include total Kjeldahl nitrogen, nitrate + nitrite nitrogen, ammonia nitrogen, total phosphorus, dissolved reactive phosphorus and dissolved reactive silica. Auxiliary data collected while sampling for the exclosure experiment. Data include time of day, air temperature, cloud cover, wave height, bottom depth, wind speed and direction. Sampling Frequency: generally bi-we
Cascade Project at North Temperate Lakes LTER High Frequency Sonde Data from Food Web Resilience Experiment 2008 - 2011
High-frequency sonde data collected from the surface waters of two lakes in Upper Peninsula of Michigan during the summers of 2008-2011. The food web of Peter Lake was slowly transformed by gradual additions of Largemouth bass (Micropterus salmoides) while Paul Lake was an unmanipulated reference. Sonde data were used to calculate resilience indicators to evaluate the stability of the food web and to calculate ecosystem metabolism.
Cascade Project at North Temperate Lakes LTER: Aquatic heatwave effects on chlorophyll 2008-2019
Temperature and chlorophyll data were collated from multiple datasets to identify the effects of aquatic heatwaves on phytoplankton in three north temperate lakes, Peter Lake, Paul Lake, and Tuesday Lake between 2008 and 2019. Heatwaves were identified using a water temperature model constructed from temperature data from Sparkling Lake and Woodruff Airport between 1989 and 2022. Heatwave characteristics, water color, nutrients, grazing, and lake stability data are included to relate chlorophyll response to heatwaves to other conditions associated with a set of whole-lake experiments. The food web of Peter Lake was manipulated with largemouth bass additions between 2008 and 2011. Nutrient additions were made to Peter Lake and Tuesday Lake between 2013 and 2015, and again in Peter Lake in 2019. Paul Lake was always maintained as an unmanipulated reference lake. Full descriptions of the experiments can be found in Szydlowski et al., "Aquatic heatwaves increase surface chlorophyll concentrations in experimental and reference lakes."
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: biomass of kelp forest species, ongoing since 2008
These data represent values of biomass density for more than 200 species of macroalgae, invertebrates and fish measured in fixed plots at five reefs as part of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. Taxon-specific relationships between size and mass were applied to field measurements of species abundance to estimate biomass density of each species. The five reefs (Arroyo Quemada 34°28.048’N, 120°07.031’W; Carpinteria 34°23.474’N, 119°32.510’W; Isla Vista 34°23.275’N, 119°32.792’W; Mohawk 34°23.649’N, 119°43.762’W; and Naples 34° 25.342’N, 119° 57.102’W) ranged in depth from 5.8 m to 8.9 m (MLLW) and were chosen to represent a range of physical and biological characteristics known to influence subtidal macroalgal assemblages in the region.
SBC LTER: Hourly photon irradiance at the surface and seafloor, ongoing since 2008
These data represent mean hourly values of photosynthetically active radiation (PAR) (in units of mol m-2 s-1) at five subtidal reefs and one coastal location off Santa Barbara, California. Sensors record instantaneous irradiance at one-minute or 30 second intervals, and data are averaged hourly. Sensors are mounted on the sea floor at five sites (Arroyo Quemado, Carpinteria, Naples Reef, Isla Vista, and Mohawk Reef). A surface sensor is deployed on an unobstructed coastal rooftop at the UC Santa Barbara campus; some historical observations are available from sensors mounted above the sea surface at a subset of the five sites.
Benthic Chlorophyll of Seagrass Sediment in Virginia Coastal Bays 2008-2021
This data set contains measurements of benthic chlorophyll content in surface sediments in restored Z. marina plots in the Virginia coastal bays. Samples were collected annually during June-July at restored seagrass plots and adjacent bare sediment plots. GPS locations of sampling plots are available in the companion data set VCR11180.
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2008 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
ChinaHighNO₂: Daily Seamless 10 km Ground-Level NO₂ Dataset for China (2008–2018)
<p>ChinaHighNO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level NO<sub>2</sub> dataset for China <strong>from 2008 to 2018</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84, a root-mean-square error (RMSE) of 7.99 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 5.34 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighNO<sub>2</sub> dataset in your scientific research, please cite the following references (Wei et al., ACP, 2023; Wei et al., EST, 2022):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> <li> <p>Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2022.pdf">Ground-level NO<sub>2</sub> surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence</a>. <em>Environmental Science & Technology</em>, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834</p> </li> </ul> <p><strong>Note that the ChinaHighNO<sub>2</sub> dataset was improved to a 1 km resolution after 2019:</strong></p> <p> all (including <strong>daily</strong>) data for the years after <strong>2019</strong><strong> </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4571660">https://doi.org/10.5281/zenodo.4571660</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Monthly aggregated GLASS FAPAR V6 (250 m): 95th percentile monthly time-series (2008)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Monthly aggregated GLASS FAPAR V6 (250 m): 5th percentile monthly time-series (2008)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
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This data repository contains compiled surface water (tributary and estuarine), wet deposition, and wastewater effluent chemistry, along with discharge, precipitation totals, and monthly effluent flows necessary for the completion of solute budgets for Great Bay, a subregion of Great Bay Estuary, NH/ME, USA. These datasets are part of on-going monitoring programs in the Great Bay Estuary and Lamprey River Hydrological Observatory. A subset of the monitoring data for the 2008 to 2023 period was compiled. The tributary and estuarine monitoring data were requested from the NH Department of Environmental Services Environmental Monitoring Database as part of the Tidal Tributary and Estuary Water Quality Monitoring Programs. The wet deposition chemistry record is maintained as part of the Lamprey River Hydrologic Observatory. Wastewater effluent chemistry was downloaded from the EPA's Enforcement and Compliance History Online Database. The annual (1996 - 2023) seagrass coverage dataset for Great Bay Estuary reflects coverage of Zostera marina seagrass only and was compiled from annual monitoring reports. Mean daily instantaneous discharge data for the three tidal tributaries used in the load calculations are available from the USGS National Water Information System. Hourly precipitation volume data for the Durham, NH SSW station are available from the NCDC U.S. Climate Reference Network, with minor hourly gaps filled using the University of New Hampshire Durham weather station (https://www.weather.unh.edu).
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This data set documents the expansion of the distribution of opossums in Michigan. Each row in the data file consists of a specimen record or observation and includes the year and location (latitude/longitude) of the record.
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