Find research datasets worth reusing

Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2018-11-05 to 2018-12-31 [RAW]

Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. The phenocam "hartheim2" was put into operation on November 5, 2018. There are no phenocam images before that date at this site. Phenocam "hartheim2" shows the view from the main tower at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.

Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2020-01-01 to 2020-12-31 [RAW]

Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2020.  Phenocam "hartheim2" shows the view from the main tower at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.

Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2021-01-01 to 2021-12-31 [RAW]

<div> Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2021.  Phenocam "hartheim2" shows the view from the main tower at 7m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosystem Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation. </div>

Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2022-01-01 to 2022-12-31 [RAW]

<div> Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022.  Phenocam "hartheim2" shows the view from the main tower at at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosystem Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.   </div>

opencc-by-4.0Dec 2021View details →

Distribution maps of vegetation alliances in Europe

This dataset contains information on the occurrence of phytosociological alliances in European territorial units. The first version, including a description of the methods, was published by Preislerová et al. (2022). Version 2 of the dataset contains data on 1115 alliances (as opposed to 1105 in the first version) in 82 European territorial units. These changes reflect the concepts accepted in version 3 of the EuroVegChecklist (Mucina et al. 2016) published on https://floraveg.eu/download/. This version contains syntaxonomic changes in the vegetation of coastal dunes (classes Ammophiletea arundinaceae, Helichryso-Crucianelletea maritimae and Honckenyo peploidis-Leymetea arenarii), Mediterranean pine forests (order Pinetalia halepensis) and bogs (class Oxycocco-Sphagnetea), which were adopted by the European Vegetation Classification Committee in January 2024 following the proposals published by Marcenò et al. (2018, 2024), Bonari et al. (2021) and Jirou&scaron;ek et al. (2022), respectively. The data include a spreadsheet with the database and a set of 1115 maps as individual image files. Recommended citation of the dataset Preislerová Z., Jiménez-Alfaro B., Mucina L., Berg C., Bonari G., Kuzemko A., Landucci F., Marcenò C., Monteiro-Henriques T., Novák P., Vynokurov D., Bergmeier E., Dengler J., Apostolova I., Bioret F., Biurrun I., Campos J.A., Capelo J., Čarni A., Çoban S., Csiky J., Ćuk M., Ću&scaron;terevska R., Daniëls F.J.A., De Sanctis M., Didukh Ya., Dítě D., Fanelli F., Golovanov Y., Golub V., Guarino R., Hájek M., Iakushenko D., Indreica A., Jansen F., Ja&scaron;ková A., Jirou&scaron;ek M., Kalníková V., Kavgacı A., Kucherov I., Küzmič F., Lebedeva M., Loidi J., Lososová Z., Lysenko T., Milanović Đ., Onyshchenko V., Perrin G., Peterka T., Ra&scaron;omavičius V., Rodríguez-Rojo M.P., Rodwell J.S., Rūsiņa S., Sánchez Mata D., Schaminée J.H.J., Semenishchenkov Y., Shevchenko N., &Scaron;ibík J., &Scaron;kvorc Ž., Smagin V., Ste&scaron;ević D., Stupar V., &Scaron;umberová K., Theurillat J.-P., Tikhonova E., Tzonev R., Valachovič M., Vassilev K., Willner W., Yamalov S., Večeřa M. & Chytrý M. (2022). Distribution maps of vegetation alliances in Europe. Applied Vegetation Science, 25, e12642. https://doi.org/10.1111/avsc.12642

opencc-by-4.0Feb 2018View details →

Biophysical effects of vegetation cover change from satellite and models

Vegetation cover changes associated with land use and land cover change (LULCC) can perturb the local surface energy balance, which in turn can affect the local climate. Land surface models (LSMs) can be used to simulate such land-climate interactions, but their capacity to model these biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. This dataset provides idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) that are harmonized with estimations obtained from satellite observations, enabling the inter-comparison and benchmarking of LSM performances and which can serve to identify model limitations and prioritize efforts in model development. The dataset provides the change in latent heat flux, in combined sensible and ground heat flux and in net radiation caused by 15 specific vegetation cover transitions on a 1° by 1° grid at monthly time scale for a synthetic year based on data from 2008 until 2012. The dataset was generated from a collaborative effort lead by JRC within the FP7 LUC4C project (luc4c.eu).

opencc-by-4.0Feb 2019View details →

Vegetation (Comune di Napoli)

ESM and Urban Atlas based data subset, where every Urban atlas element with CODE 14100,14200,32000,33000 was extracted togheter with band40 ESM elements to gather all as vegetation elements with the next combined information: gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035), albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint This data is an input for local effects calculation.

opencc-by-4.0Aug 2021View details →

Spectral library of vegetation from Mediterranean woodlands

Site description: All reflectance measurements have been collected in Mediterranean oak woodland at Herdade da Machoqueira do Grou, located in Central Portugal (39° 08′ 18.9″ N, 9° 19′ 56.22″ W, 165-m height). The site is characterized by a Mediterranean climate with mild winters and hot dry summers. The average annual precipitation recorded at the climate station of Santarém (39° 12′ N, 8° 44′ W) for the period 1981–2010 was 652 mm, and mean daily temperature was  17°C  (<a href="http://www.ipma.pt/pt/oclima/normais.clima/">www.ipma.pt/pt/oclima/normais.clima/</a>). Detailed meteorological measurements of radiation, temperature, and air humidity are also publicly available (Cerasoli et al., 2020). The soil is a cambisol (FAO) with 81% sand, 5% clay, and 14% silt. The tree layer is represented exclusively by cork oak trees (Quercus suber L.) with a tree density of 177 tree ha-1 and leaf area index (LAI) of 1.5. The mean total tree height and height below the canopy are 7.9 and 3.1m respectively (Cerasoli et al., 2015). Tree canopy represents 36% of the soil cover fraction. The understorey is composed of a mixture of shrubs and herbaceous species. The site was plowed in 2013 (Correia et al., 2016), hence the cover fraction of shrubs changed across years. A field survey in 2017 estimated an 18% coverage of shrubs and 41% of herbaceous species, while the remaining 41% was represented by litter and bare soil (Heuschmidt et al., 2020). The most represented shrub species are Cistus salvifolius (cistus) and the Ulex airensis (ulex). In spite of occupying the same habitat, the two species have different growth habits and stress strategies. While the cistus is a semi-deciduous species with shallow roots, decreasing its canopy area during the summer period, the ulex has a deep root system and spine shaped leaves and shoots conferring high drought resistance (Correia et al., 2014). The herbaceous layer is composed of C3 species mainly grasses (44.5%) and legumes (28.7%) (Cerasoli et al., 2015).   Reflectance measurements: All spectral observations were acquired with an ASD FieldSpec3 spectroradiometer (Malvern Panalytical, Boulder, USA) in the range of 350-2300nm. The visible and near-infrared region (350-1000nm) has a spectral resolution (full-width half maximum) of 3nm and a sampling interval of 1.4nm, while the mid infrared region (1000-2500nm) has a spectral resolution of 10nm and a sampling interval of 2.0nm. Canopy spectral data were collected by a fiber optic cable inserted into a pistol grip. A white reference of known reflectance (Spectralon panel, Labsphere, Inc., North Sutton, USA) was used to normalize for variation in atmospheric conditions and to convert the measurements into absolute reflectance. All targets were fully exposed to solar radiation at the time of the measurements. Measurements were performed on cork oak, cistus, and ulex canopies. Herbaceous plots were delimited by a 50X50 cm quadrat. Oak trees canopy measurements were done using a scaffold on the south side of the canopy. All canopy measurements were performed with a nadir view, a field of view angle of 25º, and a distance of about 90cm from the target, which resulted in a field of view of about 1256 cm2. All spectra were collected for 2 hours around solar noon, to minimize the effects of shadowing and solar zenith changes, with five replicates for each target, representing each the average of 25 spectra. All reflectance values in the range 1350-1400nm and 1800-1950nm were excluded, corresponding to the atmospheric water vapor absorption regions. A leaf clip including a white and a black standard was used for the measurement of the reflectance of cork oak leaf blades avoiding main veins.   File description:  The file "specveg_data_spectra" concerns all spectral data, the "specveg_metadata" covers the additional data of every single measured vegetation including photos (URL), and the "specveg_meta" describes all the existing variables.

opencc-by-4.0Aug 2021View details →

Maximum height for the native vegetation in Minas Gerais State, Brazil

Maximum height for native vegetation of Minas Gerais State (Brazil) based on GEDI measurements and environmental factors. The environmental layers included annual average temperature, annual average precipitation, terrain elevation, slope, number of cloud free days, number of months with precipitation below 100 mm. The GEDI height records were overlapped to the environmental layers, and filtered, considered the efficiency frontier.

opencc-by-4.0Dec 2022View details →

Data from: Will Current Protected Areas Harbour Refugia for Threatened Arctic Vegetation Types until 2050? A First Assessment

We present predictions of Arctic vegetation for 2050 based on a combination of climate models (namely,  EC-Earth3-Veg,  IPSL-CM6A-LR, and MRI-ESM2-0), emission scenarios (names, SSP126 and SSP585) and tree dispersal rate scenarios (unrestricted, 20km and 5km) based on the methods of Pearson et al. (2013) and the new raster version of the Circumpolar Arctic Vegetation Map (CAVM) (Raynolds et al. 2019). We additionally present a dataset summarising total areas for each vegetation type in the CAVM and the forecasted models based on the computation of zonal histograms in ArcGIS (zonal_histogram_results.csv), for the total Arctic as well as only within protected areas, defined by the Map of Arctic Protected Areas (CAFF and PAME 2017). We also present a potential map of refugia for what we deem the realistic model (IPSL, SSP585, 20 km tree dispersal) as a raster file. Refugia were identified as regions where the vegetation remained the same between the CAVM and the predictions. Additionally, we present a map of model agreement, showing the degree to which other models agree with the vegetation classification for our refugia. All predictions named according to the tree dispersal rate, climate model, and emissions scenario, preceded by the term "pred". For example: "pred_unres_mri_585" represents the unrestricted tree dispersal, MRI-ESM-0 climate model, and SSP585 scenario-based prediction. The MRI-ESM-0 x SSP585 combination had gaps in data which results in a lack of predictions in some areas; this affects 3 models. Further details and all code associated with these datasets are found <a href="https://github.com/PlekhanovaElena/Arctic_vegetation_prediction">here</a>.

opencc-by-4.0Mar 2023View details →

Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)

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., TVPDIo) data source. The TVPDIn 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 TVPDIn for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification: (1) Compared with the original data source TVPDIo using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDIn, 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 TVPDIn constructed in this study had a better fit compared to the original data source TVPDIo and was more sensitive to the monitoring of dry and wet conditions. (2) According to the comparison of TVPDIn with ESA CCI sm, TVPDIn showed a high correlation of more than 0.9 with soil water content, which proved that TVPDIn 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 TVPDIn 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 TVPDIn 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 TVPDIn 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.  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.

opencc-by-4.0Dec 2022View details →

A harmonized Landsat Sentinel-2 (HLS) dataset for benchmarking time series reconstruction methods of vegetation indices

Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed to fill the gaps present in the images, and some works also presented performance comparisons between them (Roerink et al., 2000; Moreno-Martínez et al., 2020; Siabi et al., 2022). Because of the lack of a ground truth for the reconstructed images, the performance evaluation requires the creation of datasets where artificial gaps are introduced in a reference image, such that metrics like the root mean square error (RMSE) can be computed comparing the reconstructed images with the reference one. Different approaches have been used to create the reference images and the artificial gaps, but in most cases, the artificial gaps are introduced using arbitrary patterns and/or the reference image is produced artificially and not using real satellite images (e.g. Kandasamy et al., 2013; Liu et al., 2017; Julien & Sobrino, 2018). In addition, to the best of our knowledge, few of them are openly available and directly accessible allowing for fully reproducible research. We provide here a benchmark dataset for time series reconstruction method based on the <a href="https://hls.gsfc.nasa.gov/">harmonized Landsat Sentinel-2 (HLS)</a> collection where the artificial gaps are introduced with a realistic spatio-temporal distribution. In particular, we selected six tiles that we considered representative for most of the main climate classes (e.g. equatorial, arid, warm temperature, boreal and polar), as depicted in the preview. Specifically, following the <a href="https://hls.gsfc.nasa.gov/products-description/tiling-system/">relative tiling system</a> shown above, we downloaded the Red, NIR and F-mask bands from both the HLSL30 and HLSS30 collections for the tiles 19FCV, 22LEH, 32QPK, 31UFS, 45WFV and 49MWM. From the Red and NIR band we derived the NDVI as: \(NDVI = {NIR - Red \over NIR + Red}\) only for clear-sky on lend pixels (F-mask bits 1, 3, 4 and 5 equal zero), setting as not a number the remaining pixels. The images are then aggregated on a 16 days base, averaging the available values for each pixel in each temporal range. The so obtained data, are considered from us as the reference data for the benchmarking, and stored following the file naming convention HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI.tif where TILE_NAME is one between the above specified ones, YYYY is the corresponding year (spanning from 2015 to 2022) and DDD is the day of the year from which the corresponding 16 days range starts. Finally, for each tile, we have a time series composed of 184 images (23 images for 8 years) that can be easily manipulated, for example using the <a href="https://github.com/scikit-map/scikit-map/tree/master">Scikit-Map library</a> in Python. Starting from those data, for each image we considered the mask of currently present gaps, we randomly rotated it by 90, 180 or 270 degrees and we added artificial gaps in the pixels of the rotated mask. Doing so, we believe that the spatio-temporal distribution will be still realistic, providing a solid benchmark for gap-filling methods that work on time series, on spatial pattern or combination of the both. The data including the artificial gaps are stored with the naming structure HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI_art_gaps.tif following the previously mentioned convention. The performance metrics, such as RMSE or normalized RMSE (NRMSE), can be computed by applying a reconstruction method on the images with artificial gaps, and then comparing the reconstructed time series with the reference one only on the artificially created gaps locations.  This dataset was used to compare the performance of some gap-filling methods and we provide a <a href="https://github.com/OpenGeoHub/EO-benchmark/blob/main/gap_filling_methods/gap_filling_comparison.ipynb">Jupyter notebook</a> that shows how to access and use the data. The files are provided in GeoTIFF format and projected in the coordinate reference system WGS 84 / UTM zone 19N (EPSG:32619).  If you succeed to produce higher accuracy or develop a new algorithm for gap filling, please contact authors or post on our GitHub repository. May the force be with you! References: <ol> <li> Julien, Y., & Sobrino, J. A. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección, (51), 19-31. <a href="https://doi.org/10.4995/raet.2018.9749">https://doi.org/10.4995/raet.2018.9749</a>  </li> <li> Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6), 4055-4071. <a href="https://doi.org/10.5194/bg-10-4055-2013">https://doi.org/10.5194/bg-10-4055-2013</a>  </li> <li> Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179. <a href="https://doi.org/10.1016/j.rse.2016.11.023">https://doi.org/10.1016/j.rse.2016.11.023</a>  </li> <li> Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901.<a href="https://doi.org/10.1016/j.rse.2020.111901"> https://doi.org/10.1016/j.rse.2020.111901</a>  </li> <li> Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. <a href="https://doi.org/10.1080/014311600209814">https://doi.org/10.1080/014311600209814</a> </li> <li> Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2022). Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images. Computers and Electronics in Agriculture, 193, 106619.<a href="https://doi.org/10.1016/j.compag.2021.106619"> https://doi.org/10.1016/j.compag.2021.106619</a> </li> </ol>

opencc-by-4.0Mar 2019View details →

Amplitude of seasonal cycles of vegetation at global scale AVHRR

Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the amplitude of the cycles.

opencc-by-4.0Mar 2019View details →

Number of seasonal cycles of vegetation at global scale AVHRR

opencc-by-4.0Mar 2019View details →

Stability of seasonal cycles of vegetation at global scale

openCC (other)Oct 2021View details →

Water chemistry and aquatic vegetation data from Les Cheneaux Islands, Northern Lake Huron, Michigan, USA, 2016-2018

Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance their study and management, especially if they can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present environmental data collected to support a study using an unmanned aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM) in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. Data collected includes water chemistry (nitrogen, phosphorus, carbon, suspended solids, chlorophyll a), light profiles, and submerged aquatic vegetation characteristics including cover, species dominance using aquatic vegetation survey methods (AVAS), and biomass.

openCC (other)Apr 2023View details →

Submersed Aquatic Vegetation community multi-year data from the Sacramento - San Joaquin Delta in California

Since 2007, field data have been collected in the Sacramento - San Joaquin Delta in northern California for the purpose of training and validating invasive species maps derived from remote sensing imagery over the Delta. The field crew collected submersed aquatic vegetation (SAV) species location data. For each point they noted attributes such as species name(s), location, cover estimates, and patch size. In addition, a thatching rake tethered to a rope was thrown off the side of the boat and pulled back out of the water; Secchi depth was measured using a Secchi disk and depth to the SAV mat was estimated by the field crew. Points were collected in patches larger than 9 square meters (3 m x 3 m). Point locations were measured using high precision (sub-meter accuracy) Trimble DGPS units (Trimble Navigation Limited, Sunnyvale, California) with Wide Area Augmentation System (WAAS) differential correction. All data points were exported as ArcGIS shapefiles and projected to UTM Zone 10N, Datum WGS-84 however this dataset includes the Latitude and Longitude of each point in decimal degrees. The spatial and attribute data quality was checked by examining photos of the data points and confirming the identify of the documented species.

openCC0Mar 2023View details →

Submersed aquatic vegetation community composition in the Sacramento-San Joaquin Delta integrated across four surveys

Submersed aquatic vegetation (SAV) has become widespread in the Sacramento-San Joaquin Delta (Delta), and the diverse SAV assemblage is dominated by non-native species. SAV negatively impacts this estuarine ecosystem by impeding flows needed for water delivery and flood control, degrading habitat needed by native species, increasing breeding habitat for disease-vectoring mosquitoes, harboring non-native predatory fish, and hindering water recreation. The goal of this published integrated dataset is to facilitate study of these impacts. This data set includes four surveys conducted in the region during 2008-2021. Two of these are short-term special studies that have been completed, and two are ongoing long-term annual surveys. The nearshore survey of SAV and largemouth bass was conducted by the University of California-Davis (UC-Davis) at sites across the Delta during 2008-2010. The Aquatic Weed Control Action was completed by the Department of Water Resources as part of the Delta Smelt Resiliency Strategy and included monthly surveys of four sites during 2017-2018. The ongoing survey of Franks Tract is conducted annually by the SePRO corporation and the Division of Boating and Waterways, and available data are from 2014-2021. The ongoing annual survey conducted by the UC-Davis Center for Spatial Technologies and Remote Sensing covers many areas of the Delta and spans 2007-2008 and 2014-2021. Additional data from these ongoing surveys and data from other surveys will be added in subsequent versions of this data set.

openCC (other)Jun 2023View details →

Plot-based vegetation data for a large tract of old--growth hemlock-northern hardwood forest, Marquette Co., Michigan: 1988

In 1987-88 members of the Burton V. Barnes lab at University of Michigan conducted a landscape inventory of portions of the Huron Mountain Club lands (primarily, the self-declared 'Reserved Area') in Powell Township, northern Marquette County, MI. The data-set deposited here, collect under direction of Philip E. Stuart (then a graduate student in the lab) focuses on the ca. 1200 ha of old-growth, mesic hemlock-northern hardwood forests within the larger property. 313 plots (450 m^2) were established at nodes of an approximately 10 chain (~192 m) grid that fell within these forest types. The data-set includes canopy tree measurements, ground-layer cover estimates (for a subpplot), and a number of soil and topographic variables (measured directly and derived). A description of the study and results is published in Simpson et al. 1990. Occasional Papers of the Huron Mountain Wildlife Foundation Number 4, with associated maps.