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Monthly aggregated GLASS FAPAR V6 (250 m): 5th percentile monthly time-series (2006)
<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>
Water properties of Arco Lake, Budd Lake, Deming Lake, and Josephine Lake in Itasca State Park from 2006-2009 and 2019-et seq.
Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from four lakes in the Itasca State Park from 2006-2009 and from 2019-et seq. The data was used to assess the mixing status and major geochemical constituents within the lakes. Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis. Bathymetry data collected in 2022 is supplied as rasters.
Marcell Experimental Forest 30-minute resolution meteorological data, 2006 - ongoing
This data publication contains 30-minute meteorological data collected from 2006 - ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. Air temperature, relative humidity, wind speed and direction, photosynthetic photon flux density, soil temperature, and soil volumetric water content were measured at three meteorological monitoring stations. One station is in an upland clearing, one is under an upland forest canopy, and the third is in a peatland.
Carbon and nitrogen isotopes and concentrations in terrestrial plants from a six-year (2006-2012) fertilization experiment at the Arctic LTER, Toolik Field Station, Alaska.
The data set describes stable carbon and nitrogen isotopes and carbon and nitrogen concentrations from an August 2012 pluck of a fertilization experiment begun in 2006. Fertilization was with nitrogen (N) and phosphorus (P). Fertilization levels included control, F2, F5, and F10, with F2 corresponding to yearly additions of 2 g/m2 N and 1 g/m2 P, F5 corresponding to yearly additions of 5 g/m2 N and 2.5 g/m2 P, and F10 corresponding to yearly additions of 10 g/m2 N and 5 g/m2 P. After harvest, plants were separated by species and then by tissue. Tissues were then dried, ground and analyzed for stable isotopes and concentrations at the University of New Hampshire stable isotope laboratory.
Relative percent cover of plant species in low nutrient LTER moist acidic tundra experimental plots (MAT06) established in 2006 for years 2008, 2010-2025, Arctic LTER Toolik Field Station Alaska.
Relative percent cover of plant species was measured in low nutrient LTER moist acidic tundra experimental plots (MAT06). Treatments include a gradient of nitrogen and phosphorus additions along with ammonium and nitrate alone.
Bonanza Creek LTER: Hourly Snow Depth Measurements from 2006 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
Hourly snow depth measured in meters from the CRREL and CARSNOW climate data stations within CPCRW using a Campbell Scientific data logger and a SR50 sonic snow depth sensor.
Bonanza Creek LTER: Hourly Dew Point Measurements from 2006 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
Calculated dew point values using the air temperature and relative humidity data collected at the corresponding Caribou-Poker Creeks Research Watershed climate station.
Bonanza Creek LTER: Hourly Vapor Pressure (VP) Measurements from 2006 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
Calculated vapor pressure values using the air temperature and relative humidity data collected at the corresponding Caribou-Poker Creeks Research Watershed climate station.
Environmental data from FCE LTER Caribbean Karstic Region (CKR) study in Yucatan, Belize and Jamaica during Years 2006, 2007 and 2008
Several studies have shown that within the Florida Coastal Everglades, periphyton mat properties, (incuding biomass, nutrient and organic content, and community composition) vary predictably in response to water quality.The Florida Coastal Everglades (FCE) wetland system is very similar with respect to climate, geology, hydrology and vegetation, to wetlands found in Jamaica, the Yucatan region of Mexico and parts of Belize. This study was therefore conducted to ascertain (i) the level of similarity between the periphyton diatom communities from karstic wetland sites in Belize, Mexico, Jamaica and comparable sites within the FCE, (ii) the relationship between periphyton biomass, TP levels and diatom community composition at these sites, and (iii) the feasibility of employing diatoms as indicators of water quality at these sites, using models relating diatom community composition to water quality from comparable sites within the FCE. Multiple wetland sites in Jamaica, the Yucatan region of Mexico and parts of Belize were visited between 2006 and 2008, during wet and dry seasons. At each site physico-chemical data were collected along with periphyton samples. The periphyton samples were processed in accordance with standard methods to obtain biomass, organic content and TP measures, and to identify and enumerate diatom and soft algae species. Various aspects of the diatom communities were then compared to previously compiled data on diatom communities from various parts of the FCE. SIMI analysis was used to determine the level of similarity between the systems and Non-Metric Multidimensional Scaling was used to identify relationships between diatom communities and water quality.
Physical Characteristics and Stratigraphy of Deep Soil Sediments from Shark River Slough, Everglades National Park (FCE) from 2005 and 2006
These data represent the results of piston-coring deep (around 1m) soil cores from Shark Slough sites, including FCE LTER site SRS3 and FCE related site NE-SRS1 from November 18, 2005 to February 26, 2006. Soils from 1-cm depth increments were analyzed for bulk density and stratigraphy. These analyses contribute to a paleoecological study to quantify past changes in vegetation and soil accumulation in relation to past climate variation, fire occurrences and water management.
Radiometric Characteristics of Soil Sediments from Shark River Slough, Everglades National Park (FCE) from 2005 and 2006
These data represent the results of radiometric dating of soil cores from Shark Slough sites, including FCE LTER sites SRS3 and SRS4 and FCE related site NE-SRS1 from November 18, 2005 to February 26, 2006. Soils from 1-cm depth increments were analyzed for bulk density and stratigraphy. These analyses contribute to a paleoecological study to quantify past changes in vegetation and soil accumulation in relation to past climate variation, fire occurrences and water management.
Macrofossil Characteristics of Soil from Shark River Slough, Everglades National Park (FCE) from July 2003 to February 2006
These data represent the results of macrofossil analyses on soil cores from Shark Slough sites, including FCE LTER sites SRS2, SRS3 and SRS4 and FCE related site NE-SRS1 from July 24,2003 to February 26, 2006. Soils from 1-cm depth increments were analyzed for macrofossil content (mainly seeds). These analyses contribute to a paleoecological study to quantify past changes in vegetation and soil accumulation in relation to past climate variation, fire occurrences and water management.
Diatom Species Abundance Data from LTER Caribbean Karstic Region (CKR) study (FCE) in Yucatan, Belize and Jamaica during 2006, 2007, 2008
Several studies have shown that within the Florida Coastal Everglades, periphyton mat properties, (incuding biomass, nutrient and organic content, and community composition) vary predictably in response to water quality.The Florida Coastal Everglades (FCE) wetland system is very similar with respect to climate, geology, hydrology and vegetation, to wetlands found in Jamaica, the Yucatan region of Mexico and parts of Belize. This study was therefore conducted to ascertain (i) the level of similarity between the periphyton diatom communities from karstic wetland sites in Belize, Mexico, Jamaica and comparable sites within the FCE, (ii) the relationship between periphyton biomass, TP levels and diatom community composition at these sites, and (iii) the feasibility of employing diatoms as indicators of water quality at these sites, using models relating diatom community composition to water quality from comparable sites within the FCE. Multiple wetland sites in Jamaica, the Yucatan region of Mexico and parts of Belize were visited between 2006 and 2008, during wet and dry seasons. At each site physico-chemical data were collected along with periphyton samples. The periphyton samples were processed in accordance with standard methods to obtain biomass, organic content and TP measures, and to identify and enumerate diatom and soft algae species. Various aspects of the diatom communities were then compared to previously compiled data on diatom communities from various parts of the FCE. SIMI analysis was used to determine the level of similarity between the systems and Non-Metric Multidimensional Scaling was used to identify relationships between diatom communities and water quality.
Periphyton data from LTER Caribbean Karstic Region (CKR) study in Yucatan, Belize and Jamaica (FCE LTER) during 2006, 2007, 2008
Several studies have shown that within the Florida Coastal Everglades, periphyton mat properties, (incuding biomass, nutrient and organic content, and community composition) vary predictably in response to water quality.The Florida Coastal Everglades (FCE) wetland system is very similar with respect to climate, geology, hydrology and vegetation, to wetlands found in Jamaica, the Yucatan region of Mexico and parts of Belize. This study was therefore conducted to ascertain (i) the level of similarity between the periphyton diatom communities from karstic wetland sites in Belize, Mexico, Jamaica and comparable sites within the FCE, (ii) the relationship between periphyton biomass, TP levels and diatom community composition at these sites, and (iii) the feasibility of employing diatoms as indicators of water quality at these sites, using models relating diatom community composition to water quality from comparable sites within the FCE. Multiple wetland sites in Jamaica, the Yucatan region of Mexico and parts of Belize were visited between 2006 and 2008, during wet and dry seasons. At each site physico-chemical data were collected along with periphyton samples. The periphyton samples were processed in accordance with standard methods to obtain biomass, organic content and TP measures, and to identify and enumerate diatom and soft algae species. Various aspects of the diatom communities were then compared to previously compiled data on diatom communities from various parts of the FCE. SIMI analysis was used to determine the level of similarity between the systems and Non-Metric Multidimensional Scaling was used to identify relationships between diatom communities and water quality.
Fish and consumer data collected from Northeast Shark Slough, Everglades National Park (FCE) from September 2006 to September 2008
Three 1m2 throwtrap sites were selected randomly within a few meters of the location. All fish and macroinvertebrates were sampled within each trap using a seine; the trap is determined to be clear of fish and macroinvertebrates once three empty seines are recovered. The count is reset whenever a fish or invertebrate is caught. Once the trap is determined to be clear, five sweeps are done using a couple of nets. The count resets whenever fish is caught, while an extra sweep is added whenever an invertebrate is captured. The trap is finally cleared once the fifth sweep doesn't capture any organisms. All captured organisms are anesthesized in a plastic cup filled with water and MS222; they are then preserved using a 10% formaldehyde solution for identification and archiving in the lab
FCE Redlands 2006 Land Use, Miami-Dade County, South Florida
Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.
FCE Redlands 2006 Roads, Miami-Dade County, South Florida
Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.
Vegetation data collected from Northeast Shark River Slough, Everglades National Park, Florida, USA, September 2006 - April 2025
This project was established in 2006 to document the pattern of abundance of key ecological indicators (e.g., surface water, soil, floc, periphyton and sawgrass) across the NESRS landscape. A total of 30 sites were established and monitored in 2006, 2007 and 2008. After the completion of 1-mile bridge in 2012, additional 10 new sites were established to observe the ecological impact of 1-mile bridge (known as Bridge & Census sites). In 2015, additional 40 sites were established along eight transects (T1-T8, known as near canal sites) in ENP marshes starting at, and roughly perpendicular to the L-29 canal. The purpose of these sites was to monitor the potential effects of Modified Water Deliveries (MWD) operations on changing nutrient concentrations and ratios in key ecological compartments due to increased downstream discharges from the L-29 canal beneath the 1-mile and 2.6-mile bridges and culverts along Tamiami Trail. Data collection is complete.
Aboveground vegetation cover and biomass in plots with experimentally altered precipitation and nutrient inputs at the Jornada Basin LTER site, 2006-ongoing
This dataset contains cover and biomass data collected starting in 2006 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Plant cover measurements are made annually in each plot, from which biomass or net primary production are derived. This is an ongoing study and the dataset will be updated yearly.
North Temperate Lakes LTER: High Frequency Water Temperature Data - Lake Mendota Pier 2006 - 2008
Water temperature was measured on the pier at 1 and 2 m water depth at a frequency of 1 minute.
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