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.
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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.
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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.
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.
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.
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.
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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.