Measurements of leaf area index and biomass of different canopy components

Species composition data, by site and date

The Leaf Angle Data Data Set contains leaf angle distributions (LAD) obtained during the 1987 growing season for ten types of plant canopies, from the Konza Long-Term Ecological Research (LTER) area. These data were collected using a direct measurement technique (i.e., a Spatial Coordinate Apparatus (SCA)). The species selected were major species common on the prairie with the leaves were of sufficient size to allow SCA measurement. The objective of this study was to obtain detailed LAD information on the major canopy species of the tallgrass prairie and selected agricultural crops. The LAD information for specific canopies can be used as input for a canopy radiation model. Canopy leaf orientation is an important parameter for plant growth modeling. Four categories of zenith angle distributions were found among the 14 species. These were planophile, plagiophile, erectophile, and uniform. Some canopies were found to have non-uniform leaf azimuth angle distribution. Also there were deferences between the upper and lower parts of the canopies for some species.

Contains sample measurements of the canopy biochemistry measured by the TE-09 team.

The BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves. This data set contains canopy biochemistry data collected in 1994 in the NSA at the YJP, OJP, OBS, BS and OA sites including biochemistry lignin, nitrogen, cellulose, starch, and fiber concentrations. These data were collected to study the spatial and temporal changes in the canopy biochemistry of boreal forest cover types and how a high-resolution radiative transfer model in the mid-infrared could be applied in an effort to obtain better estimates of canopy biochemical properties using remote sensing.

Leaf traits for 11 populations of Dodonaea viscosa subsp. angustissima (Sapindaceae) opportunistically collected from across an elevational gradient (300 to 800 m above sea level) in the Flinders Ranges of South Australia. We present leaf traits for 266 individuals. Traits measured include leaf area and specific leaf area, and elevation.

The dataset consists of species identity and projective foliage cover (PFC) of ground layer vascular plants from five sites located near Mareeba, in northern Queensland. The sites are located in eucalypt communities with altitudes ranging from 380 to 840 m. Data have been collected annually since 1992, in April and May, i.e. during the annual peak of plant species richness. At each site, data collection is carried out using ten 0.5 m2 quadrats deployed within a permanently marked 50 x 10 m plot. For each quadrat, all plant species visible above ground are identified and sampled. PFC data for each species from the ten quadrats are averaged. Any additional species occurring within the 50 x 10 m plot is also recorded and assigned a PFC of 0.1% (Neldner and Butler, 2021).

A total of 53 native Australian species (52x C3, 1x C4) were sampled from 22 plant families and 7 growth forms along a transect in WA spanning 9.56 degrees latitude and 6.85 degrees longitude. Samples were collected using the nationally-accepted AusPlots Rangelands methodology. Samples were stored to preserve isotopic signatures and analysed using standard techniques for mass spectroscopy, including internationally-calibrated standards. Technical replicates of 13% showed very low drift (0.07).

The dataset provides a near real time estimation of 2020 herbaceous mostly annual fractional cover predicted on July 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P96PVZIF., Boyte et al. 2018 https://doi.org/10.5066/P9RIV03D.), but starting this year we are mapping at a 30m resolution (Pastick et al. 2020 doi:10.3390/rs12040725). This dataset was generated using in situ observations from Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; weekly composites of harmonized Landsat and Sentinel-2 (HLS) data (https://hls.gsfc.nasa.gov/); relevant environmental, vegetation, remotely sensed, and geophysical drivers. These data were integrating into regression tree (RT) models for prediction of weekly cloud free Normalized Difference Vegetation Index (NDVI). A total 11,065 AIM plots from years 2016 - 2019 were used to train an ensemble of five-fold RT models using a cross-validation approach (each observation was used as test data once). Cheatgrass (Bromus tectrorum) is the most common species, however, a number of other species were included in this study: Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and Taeniatherum caput-medusae. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2700-m elevation to target areas of unlikely substantial annual grass cover. To target likely sagebrush ecosystems, the mask also removed pixels classified as something other than shrub or grassland/herbaceous by the 2016 National Land Cover Dataset (NLCD).