7 datasets found
  1. O

    1985 Land Cover

    • data.ct.gov
    application/rdfxml +5
    Updated Jan 29, 2025
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    UConn (2025). 1985 Land Cover [Dataset]. https://data.ct.gov/Environment-and-Natural-Resources/1985-Land-Cover/5tpx-ejyh/data?pane=feed
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    application/rssxml, csv, json, xml, application/rdfxml, tsvAvailable download formats
    Dataset updated
    Jan 29, 2025
    Dataset authored and provided by
    UConn
    Description

    This service is published by UConn CLEAR and is made available on CTECO. The thematic raster has multiple classes that represent different land covers. This dataset is part of the Changing Landscape series which includes 7 dates of land cover spanning 30 years, from 1985 to 2015, including change and statistics.


    1985 Land Cover Dataset
    Extent: Lower Long Island Sound Watershed which includes all of Connecticut and parts of New York and Massachusetts.
    Classes: 12 classes include developed, turf & grass, other grasses, agricultural field, deciduous forest, coniferous forest, water, non-forested wetland, forested wetland, tidal wetland, barren and utility right-of-way. See Land Cover Classes for detailed descriptions.
    Dates: Land cover created from 1985 satellite imagery
    Pixel Resolution: 100 feet
    Projection: CT State Plane NAD 83 feet (EPSG 2234)

    More Information

    Credit and Funding
    CLEAR's Changing Landscape has been comprised of a series of projects funded over its 20 year extent by both state and federal agencies, and by support from the University of Connecticut. It is not practicable to list the specifics of each grant separately, but funding agencies include U.S. Environmental Protection Agency, National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), Long Island Sound Study, New England Interstate Water Pollution Control Commission, and the Connecticut Department of Energy and Environmental Protection.

  2. u

    Forest Health Protection Tree Species Metrics Basal Area

    • agdatacommons.nal.usda.gov
    • catalog.data.gov
    • +2more
    bin
    Updated Jul 23, 2025
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    U.S. Forest Service (2025). Forest Health Protection Tree Species Metrics Basal Area [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Forest_Health_Protection_Tree_Species_Metrics_Basal_Area/29614262
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    binAvailable download formats
    Dataset updated
    Jul 23, 2025
    Dataset authored and provided by
    U.S. Forest Service
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

  3. n

    Data from: Functional diversity and trait filtering of insectivorous bats on...

    • data.niaid.nih.gov
    • datadryad.org
    zip
    Updated Jun 20, 2022
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    Guthieri T. Colombo; Raffaello Di Ponzio; Maíra Benchimol; Carlos A. Peres; Paulo Estefano D. Bobrowiec (2022). Functional diversity and trait filtering of insectivorous bats on forest islands created by an Amazonian mega dam [Dataset]. http://doi.org/10.5061/dryad.sn02v6x6w
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    zipAvailable download formats
    Dataset updated
    Jun 20, 2022
    Dataset provided by
    National Institute of Amazonian Research
    University of East Anglia
    Universidade Estadual de Santa Cruz
    Authors
    Guthieri T. Colombo; Raffaello Di Ponzio; Maíra Benchimol; Carlos A. Peres; Paulo Estefano D. Bobrowiec
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description
    1. Mega dams in lowland tropical forests often create large archipelagos, leading to biodiversity decay and disruption of ecosystem functioning in remnant habitat islands.
    2. We investigated the functional diversity and functional trait filtering of aerial insectivorous bats in both insular forest patches created by a vast ~30-yr-old hydropower reservoir and the adjacent mainland continuous forest in Central Amazonia.
    3. Bats were surveyed using passive bat recorders across 34 forest sites. Based on a set of morphological traits derived for each species recorded, we estimated both the bat functional richness, functional evenness and functional dispersion at each surveyed site. We further assessed the effects of local vegetation, patch and landscape features on patterns of functional diversity. The interaction between functional traits, environmental characteristics, and species distribution was investigated using a combination of RLQ and fourth-corner analyses.
    4. We found that mainland sites retained higher functional richness and lower functional evenness compared to forest islands, indicating a more complete functional assemblage in the mainland. Additionally, species composition was affected by local vegetation structure and forest area, with small isolated islands exhibiting pervasive loss of functional traits. RLQ and fourth-corner analyses showed that larger understorey foraging species with greater dispersal capacity, constant frequency-frequency modulated calls, and higher frequency of maximum energy were associated with more isolated small islands. Conversely, forest subcanopy species, exhibiting quasi-constant frequency calls and presenting low dispersal capacity were associated with continuous forests and islands with greater forest area, and were therefore more sensitive to habitat insularization.
    5. Our study calls attention to the pervasive impacts induced by large dams on the functional diversity of tropical insectivorous bats. We recommend that future assessments of the effects of habitat fragmentation on mammals should include traits linked to ecosystem services. In designing and licensing new dams, we suggest the creation of extensive protected areas surrounding mainland forests to minimize the detrimental impacts of small isolated islands and safeguard the full complement of key ecological functions provided by insectivorous bats. Methods Bat surveys Between July and November 2016, we sampled aerial insectivorous bats using a Song Meter 2 BAT+ ultrasound recorder, connected to an SMX-US ultrasonic omnidirectional microphone (Wildlife Acoustics, Maynard, Massachusetts) positioned 1.5 m above ground at the centre of each plot. Each recorder was programmed to operate from dusk to dawn (17:30h to 06:30h) for four consecutive nights, covering the activity period of all bat species. Thus, each plot was sampled for 52h for a total of 1,768 recorder-hours over 136 sampling nights. Each recorder was configured to passively record bat activity in real time. To improve the range and quality of the ultrasound pulse recordings, we set the sample rate to 384 kHz with 32-bit resolution and only sounds with 18 dB exceeding ambient noise were considered. Ultrasounds were saved in WAC format with 30-min, producing 26 files every 13 hours of recordings. Using the Kaleidoscope 4.0.3 program (Wildlife Acoustics, Maynard, Massachusetts), these files were converted into WAV format and subdivided into 5-sec files. Spectrograms with at least two echolocation pulses of a single species or phonic complexes (species with similar calls that cannot be confidently distinguished) in each 5-sec file were defined as a ‘bat pass’, and this is considered as the unit of measure for bat activity. This study was undertaken under licenses for scientific purposes from Instituto Chico Mendes de Conservação da Biodiversidade - ICMBio (#53340-1). As this study did not involve the bat capture, it did not require approval from an ethics committee. All recordings were inspected manually using the Kaleidoscope 4.0.3 program, and all species/phonic complexes were identified using the Amazonian bat ultrasound key sourced from López-Baucells et al. (2016). We separated Pteronotus parnellii into two sonotypes according to Pavan et al. (2018): Pteronotus 55 kHz = P. rubiginosus and Pteronotus 60 kHz = P. alitonus. Eptesicus brasiliensis and E. chirichinus were classified as a single phonic complex (Eptesicus brasiliensis/chirichinus) because of overlapping ultrasound characteristics (López-Baucells et al., 2016). We excluded all Phyllostomidae and Molossidae species from the analysis because these families cannot be reliably identified. Species traits We carefully selected seven functional traits that are widely used to assess the functional composition of bat assemblages across tropical forest landscapes, including two ultrasound characteristics (frequency of maximum energy and call structure), three body measurements (body mass, relative wing loading and aspect ratio), and vertical stratification of flight activity as a measure of forest habitat use. Environmental variables We used five local predictor variables to represent the floristic structure and vegetation clutter of each sampling site based on vegetation surveys within each of our forest plots. In particular, we assessed the (i) number of adult tree species (i.e., diameter at breast height ≥10 cm), (ii) number of adult tree stems (tree abundance), (iii) forest basal area, (iv) fire severity and (v) vegetation clutter. Variables (i) to (iv) were obtained from a previous study within the same forest plots (see Benchimol & Peres, 2015 for further details). Fire severity was a categorical variable derived from the extent to which each island had been affected by fires during the El Niño drought of late 1997 to early 1998 (Benchimol & Peres, 2015) and the number of charred trees and height of char marks on each tree; burnt islands have been regenerating and therefore we assume that fire severity can be a robust proxy for secondary vegetation. We further quantified vegetation clutter by placing 3 m × 3 m white panel sheets on each of the four sides of the recorder, which were photographed from 8 m (see Marciente et al., 2015). Using the ImageJ 1.8.0 software, we then thresholded these photos into black & white images, with black pixels representing the vegetation, and then estimated the mean proportion of vegetation from the four panels per plot. We also obtained four patch and landscape variables: (i) forest area (in hectares), (ii) the shortest distance from each survey island to the nearest continuous forest (in meters), (iii) shortest distance from the recorder to the edge of the islands and the mainland (in meters), and (iv) the amount of landscape-scale forest cover. We used RapidEye© high-resolution (5-m pixel) imagery of the Balbina archipelago to quantify the patch and landscape variables. Forest area was measured for islands and continuous forest. To assess forest area in continuous forests, we assigned a value ten-fold larger than our largest island of 1,690.04 ha, a widely used procedure in fragmented landscape studies to include continuous forests in the analyses (Benchimol & Peres, 2015). Forest cover around each island (i.e., old-growth and/or secondary vegetation in different stages of regeneration) was measured considering four different spatial scales (within a buffer radius of 500 m, 1000 m, 1500 m and 2000 m from each recorder), in order to cover the home range sizes of different aerial insectivorous bat species. We then obtained the proportion of forest cover within each landscape using the QGIS 2.18.14 software. References Benchimol, M., & Peres, C. A. (2015). Predicting local extinctions of Amazonian vertebrates in forest islands created by a mega dam. Biological Conservation, 187, 61–72. http://dx.doi.org/10.1016/j.biocon.2015.04.005 López-Baucells A., Rocha R., Bobrowiec P.E.D., Bernard E., Palmeirim J. & Meyer C. (2016). Field guide to Amazonian bats (1st ed). Editora INPA. Marciente, R., Bobrowiec, P. E. D., & Magnusson, W. E. (2015). Ground-vegetation clutter affects phyllostomid bat assemblage structure in lowland Amazonian forest. PLoS ONE, 10(6), e0129560. https://doi.org/10.1371/journal.pone.0129560 Pavan, A. C., Bobrowiec, P. E. D., & Percequillo, A. R. (2018). Geographic variation in a South American clade of mormoopid bats, Pteronotus (Phyllodia), with description of a new species. Journal of Mammalogy, 99(3), 624–645. https://DOI:10.1093/jmammal/gyy048
  4. Data from: Satellite-derived trait data slightly improves tropical forest...

    • data.niaid.nih.gov
    • datadryad.org
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    Updated Mar 6, 2024
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    Christopher Doughty (2024). Satellite-derived trait data slightly improves tropical forest biomass, NPP, and GPP predictions [Dataset]. http://doi.org/10.5061/dryad.ttdz08m5n
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    zipAvailable download formats
    Dataset updated
    Mar 6, 2024
    Dataset provided by
    Northern Arizona University
    Authors
    Christopher Doughty
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Improving tropical forest biomass predictions can accurately value tropical forests for their ecosystem services. Recently, the Global Ecosystem Dynamics Investigation (GEDI) lidar was activated on the international space station (ISS) to improve biomass predictions by providing detailed 3D forest structure and height data. However, there is still debate on how best to predict tropical forest biomass using GEDI data. Here we compare GEDI predicted biomass to 2,102 tropical forest biomass plots and find that adding a remotely sensed (RS) trait map of LMA (Leaf Mass per Area) significantly (P<0.001) improves field biomass predictions, but by only a small amount (r2=0.01). However, it may also help reduce the bias of the residuals because, for instance, there was a negative relationship between both LMA (r2 of 0.34) and %P (r2=0.31) and residuals. This improvement in predictability corresponds with measurements from 523 individual trees where LMA predicts Diameter at Breast height (DBH) (the critical measurement underlying plot biomass) with an r2=0.04, and spectroscopy (400-1075 nm) predicts DBH with an r2=0.01. Adding environmental datasets may offer further improvements and max temperature (Tmax) predicts Amazonian biomass residuals with an r2 of 0.76 (N=66). Finally, for a network of net primary production (NPP) and gross primary production (GPP) plots (N=21), RS traits are better at predicting fluxes than structure variables like tree height or Height Of Median Energy (HOME). Overall, trait maps, especially future improved ones produced by surface biology geology (SBG), may improve biomass and carbon flux predictions by a small but significant amount. Methods Field leaf trait and spectroscopy data – We used leaf trait and spectral data from an extensive field campaign along an elevation gradient (from 3500 m to 220 m elevation) in the Peruvian Amazon where leaf traits for 60-80% of basal area of trees >10cm DBH were measured within a well-studied 1 ha plot network from April – November 2013 (Enquist et al., 2017). In each one ha plot (N=10 plots), we sampled the most abundant species as determined through basal area weighting (enough species generally to cover ~80% of the plot’s basal area). For each species, we sampled the five (three in the lowlands) largest trees (based on diameter at breast height (DBH)) and sampled one sun and one shade branch. On each of these branches, leaf chemistry and leaf mass area (LMA) were measured with the methodology detailed in Asner et al. (2014). On five randomly selected leaves for each branch, we measured hemispherical reflectance with an ASD Fieldspec Handheld 2 with fiber optic cable, a contact probe that has its own calibrated light source, and a leaf clip (Analytical Spectral Devices High-Intensity Contact Probe and Leaf Clip, Boulder, Colorado, USA) following (Doughty et al., 2017). We measured leaf spectroscopy (400-1075 nm) on the same branches where the leaf traits were collected. Both LMA and Chlorophyll A had previously been shown with this dataset to have a correlation with leaf spectroscopy (Doughty et al., 2017). However, we had not previously tried to compare leaf spectral data with DBH directly. Plot data – Aboveground biomass - We used 2,102 of 19,160 total AGB field plots between +30° and -30° latitude classified as broadleaf evergreen trees by MODIS PFT using public data from Duncanson et al 2022 that was organized and publicly available through ORNL DAAC as an RDS (R data serialization) file. Distribution plots are shown in Fig S1 (AGB) and S2 (residuals). NPP and GPP - We also used 21, 1 ha plots where NPP and sometimes GPP were measured following the GEM protocol (Malhi et al., 2021). We focused on two regions: a Peruvian elevation transect with both NPP + GPP (n= 10, RAINFOR plot codes are ALP11, ALP30, SPD02, SPD01, TRU03, TRU08, TRU07, ESP01, WAY01, ACJ01(Malhi et al., 2017)) and a Bornean logging transect with only NPP (n= 11 RAINFOR plot codes are DAN-04, DAN-05, LAM-01, LAM-02, MLA-01, MLA-02, SAF-01, SAF-02, SAF-03, SAF-04, SAF-05 (Riutta et al., 2018). These plots were chosen because there are large changes in NPP/GPP across the elevation or logging gradient. GEDI data – We used the vertical forest structure (L2A and L2B, Version 2) and biomass (L4a) products from the GEDI instrument (R. Dubayah et al., 2020) between April 2019 to December 2022 for tropical forest regions (R. O. Dubayah et al., 2023). We used a quality filtering recipe developed in collaboration with GEDI Science Team members from the University of Maryland and NASA Goddard to identify the highest quality GEDI vegetation shots (R. Dubayah et al., 2022). A data layer that this iterative local outlier detection algorithm uses to exclude data is publicly available at R. O. Dubayah et al., (2023). For instance, some of the key data filters we applied were: included degrade flags of 0,3,8,10,13,18,20,23,28,30,33,38,40,43,48,60,63,68, L2A and L2B quality flags = 1 (only use highest quality data), sensitivity >= 0.98. With the GEDI data, we used canopy height, the height of median energy (HOME), and the number of canopy layers following Doughty et al 2023 (Doughty et al., 2023). Across all tropical forests, we created 300 by 300 m pixels containing all averaged (mean) GEDI data between 2019 and 2022. Using the centroid coordinates from each of the 2,102 plots, we found the 300 by 300 m averaged GEDI pixel that encompassed the plot. If the plot was not encompassed by the GEDI data, we searched a wider area by incrementally averaging a gradually increasing area of 1, 3, 5, and 10 pixels. In other words, if no 300 by 300 m pixel encompassed the plot, then we averaged all GEDI data an area one pixel out (4 by 4 = 1200 by 1200 m, 6 by 6 = 1800 by 1800m, 11 by 11 = 3300m by 3300m), gradually increasing the square until it encompassed an area with GEDI data. To compare with the NPP/GPP plots we compared RS trait and GEDI data for individual footprints within a 0.03 km radius of the plot coordinates. Remotely sensed leaf trait data – Based on a broader set of field campaigns, Aguirre-Gutiérrez et al., (2021) used Sentinel-2, climatic, topography, and soil data to create remotely sensed canopy trait maps for P=phosphorus % leaf concentration, WD = wood density g.cm-3, and LMA=Leaf mass area g m-2. Other data layers – We compared % one peak to several other climates, soils, leaf traits, and ecoregion maps listed below for the Amazon basin. Each dataset had its own resolution, which we standardized to 0.1 by 0.1 degrees. We used total cation exchange capacity (CEC) from soil grids (Batjes et al., 2020) from 0-5cm in units of mmol(c)/kg. We averaged TerraClimate (Abatzoglou et al., 2018) data between 2000 and 2018 for Vapor Pressure Deficit (VPD in kPa), Mean Monthly Precipitation (MMP) (mm/month), potential evapotranspiration (PET) and maximum and minimum temperature (°C). Statistical analysis – We used the Matlab (Matlab, MathWorks Inc., Natick, MA, USA) function “fitlm” to fit linear models to compare variables such as soil data, environmental data, leaf trait data (at 0.1° resolution) and GEDI structure data (300m and bigger resolution) to field biomass and NPP/GPP estimates. The P values listed are for the t-statistic of the two-sided hypothesis test. We used R to create a linear model to predict the best model ranked by AIC and parsimony using the dredge function from the MuMIn library (Bartoń, 2009). We also used the CAR package (Fox J & S, 2019) and the VIF command to test for multi-collinearity between variables. To account for spatial autocorrelation, we used Simultaneous Auto-Regressive (SARerr) models (F. Dormann et al., 2007) using the R library ‘spdep’ (Bivand, Hauke, & Kossowski, 2013). We tested different neighborhood distances from 10 km to 300 km and found that AIC was minimized at 80 km (Fig S3) and the corresponding correlogram showed reduced spatial autocorrelation (Fig S4). To predict leaf traits with the spectral information, we used the Partial Least Squares Regression (PLSR) (Geladi & Kowalski, 1986) using the PLSregress command in Matlab (Matlab, MathWorks Inc., Natick, MA, USA). To avoid over-fitting the number of latent factors, we minimized the mean square error with K-fold cross-validation. We use 70% of our data to calibrate our model and then the remaining 30% to test the accuracy of our model using r2. We use adjusted r2 which penalizes for small sample sizes throughout the manuscript.

  5. DOI: 10.3334/ORNLDAAC/1854

    • daac.ornl.gov
    • earthdata.nasa.gov
    geotiff
    Updated Jun 16, 2021
    + more versions
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    TANG, H.; MA, L.; LISTER, A.J.; O'NEIL-DUNNE, J.; LU, J.; LAMB, R.; DUBAYAH, R.O.; HURTT, G.C. (2021). DOI: 10.3334/ORNLDAAC/1854 [Dataset]. http://doi.org/10.3334/ORNLDAAC/1854
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    geotiff(1.2 GB), geotiffAvailable download formats
    Dataset updated
    Jun 16, 2021
    Dataset provided by
    Oak Ridge National Laboratory Distributed Active Archive Center
    Authors
    TANG, H.; MA, L.; LISTER, A.J.; O'NEIL-DUNNE, J.; LU, J.; LAMB, R.; DUBAYAH, R.O.; HURTT, G.C.
    Time period covered
    Jan 1, 2010 - Dec 31, 2015
    Area covered
    Description

    This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements. Canopy height and tree cover were derived directly from LiDAR data while AGBD was estimated by statistical models that link remote sensing data and FIA plots at the pixel level. Error in AGBD was calculated at the 90% confidence interval. This approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne LiDAR missions.

  6. n

    ECHIDNA LIDAR Campaigns: Forest Canopy Imagery and Field Data, U.S.A.,...

    • earthdata.nasa.gov
    • s.cnmilf.com
    • +2more
    Updated Nov 29, 2022
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    ORNL_CLOUD (2022). ECHIDNA LIDAR Campaigns: Forest Canopy Imagery and Field Data, U.S.A., 2007-2009 [Dataset]. http://doi.org/10.3334/ORNLDAAC/1045
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    Dataset updated
    Nov 29, 2022
    Dataset authored and provided by
    ORNL_CLOUD
    Area covered
    United States
    Description

    This data set contains forest canopy scan data from the Echidna® Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007 New England Campaign; 2008 Sierra National Forest Campaign; and 2009 New England Campaign. The New England field sites were located in Harvard Forest (Massachusetts), Howland Research Forest (Maine), and the Bartlett Experimental Forest (New Hampshire).

    The objective of the research was to evaluate the ability of the EVI ground-based, scanning near-infrared lidar to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, and other useful forest structural parameters rapidly and accurately.

    The EVI scan data are Andrieu Transpose (AT) Projection images in ENVI *.img and *.hdr file pairs. There are 28 images from the 2007 New England Campaign, 30 images from the 2008 Sierra National Forest Campaign, and 54 images from the 2009 New England Campaign. There are range-weighted mean preview image files (.jpg format) for each AT Projection image.

    Manual measurements of tree structural properties were made during each campaign at EVI scan locations. The field measurements are provided in one file for each campaign (.csv format). Parameters include species identification, DBH, tree height, crown base, etc. organized by field plot.

    There is also a data file (.csv format) which compares EVI derived measurements to the field measured data (DBH, stem density, basal area, biomass, and LAI) from the 2007 New England Campaign.

  7. O

    1985 Other Land Cover

    • data.ct.gov
    application/rdfxml +5
    Updated Jan 29, 2025
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    UConn (2025). 1985 Other Land Cover [Dataset]. https://data.ct.gov/dataset/1985-Other-Land-Cover/6uer-nttq
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    tsv, xml, csv, json, application/rdfxml, application/rssxmlAvailable download formats
    Dataset updated
    Jan 29, 2025
    Dataset authored and provided by
    UConn
    Description

    This service is published by UConn CLEAR and is made available on CTECO. The thematic raster has multiple classes that represent different land covers. This dataset is part of the Changing Landscape series which includes 7 dates of land cover spanning 30 years, from 1985 to 2015, including change and statistics.


    1985 Land Cover Dataset
    Extent: Lower Long Island Sound Watershed which includes all of Connecticut and parts of New York and Massachusetts.
    Classes: 12 classes include developed, turf & grass, other grasses, agricultural field, deciduous forest, coniferous forest, water, non-forested wetland, forested wetland, tidal wetland, barren and utility right-of-way. See Land Cover Classes for detailed descriptions.
    Dates: Land cover created from 1985 satellite imagery
    Pixel Resolution: 100 feet
    Projection: CT State Plane NAD 83 feet (EPSG 2234)

    More Information

    Credit and Funding
    CLEAR's Changing Landscape has been comprised of a series of projects funded over its 20 year extent by both state and federal agencies, and by support from the University of Connecticut. It is not practicable to list the specifics of each grant separately, but funding agencies include U.S. Environmental Protection Agency, National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), Long Island Sound Study, New England Interstate Water Pollution Control Commission, and the Connecticut Department of Energy and Environmental Protection.

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UConn (2025). 1985 Land Cover [Dataset]. https://data.ct.gov/Environment-and-Natural-Resources/1985-Land-Cover/5tpx-ejyh/data?pane=feed

1985 Land Cover

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119 scholarly articles cite this dataset (View in Google Scholar)
application/rssxml, csv, json, xml, application/rdfxml, tsvAvailable download formats
Dataset updated
Jan 29, 2025
Dataset authored and provided by
UConn
Description

This service is published by UConn CLEAR and is made available on CTECO. The thematic raster has multiple classes that represent different land covers. This dataset is part of the Changing Landscape series which includes 7 dates of land cover spanning 30 years, from 1985 to 2015, including change and statistics.


1985 Land Cover Dataset
Extent: Lower Long Island Sound Watershed which includes all of Connecticut and parts of New York and Massachusetts.
Classes: 12 classes include developed, turf & grass, other grasses, agricultural field, deciduous forest, coniferous forest, water, non-forested wetland, forested wetland, tidal wetland, barren and utility right-of-way. See Land Cover Classes for detailed descriptions.
Dates: Land cover created from 1985 satellite imagery
Pixel Resolution: 100 feet
Projection: CT State Plane NAD 83 feet (EPSG 2234)

More Information

Credit and Funding
CLEAR's Changing Landscape has been comprised of a series of projects funded over its 20 year extent by both state and federal agencies, and by support from the University of Connecticut. It is not practicable to list the specifics of each grant separately, but funding agencies include U.S. Environmental Protection Agency, National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), Long Island Sound Study, New England Interstate Water Pollution Control Commission, and the Connecticut Department of Energy and Environmental Protection.

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