TERN Landscape Assessment worked together with Airborne Research Australia (ARA) to deliver airborne hyperspectral and lidar data for a number of selected homogenous 5 km x 5 km field sites across several locations in Australia (formally known as the AusCover Supersites). A Riegl Q560 Lidar, a SPECIM AisaEAGLE II hyperspectral scanner (VNIR) and a SPECIM AisaHAWK hyper-spectral scanner were mounted in underwing pods of ARA's ECO-Dimona research aircraft VH-EOS, each one together with its own navigation and attitude system.
The Biomass Plot Library is a collation of stem inventory data across federal, state and local government departments, universities, private companies and other agencies. It was motivated to the need for calibration/validation data to underpin national mapping of above-ground biomass from integration of Landsat time-series, ICESat/GLAS lidar, and ALOS PALSAR backscatter data under the auspices of the JAXA Kyoto & Carbon (K&C) Initiative (Armston et al., 2016). The data has been translated to a common set of tree, plot and site level observations with explicit plot footprints where available.
Ground lidar, also known as Terrestrial Laser Scanning (TLS), is a ranging instrument that provides detailed 3D measurements directly related to the quantity and distribution of plant materials in the canopy. Measurements can be used for applications requiring quantification of vegetation structure parameters, tree and stand reconstruction, and terrain analysis.
Scans have been collected in Australia using two Riegl VZ400 waveform recording TLS instruments. One is co-owned and operated by the Remote Sensing Centre, Queensland Department of Science, Information Technology, Innovation and the Arts (DSITIA) and the TERN AusCover Brisbane Node, University of Queensland. The second is owned and operated by Wageningen University, Netherlands. The field protocols are designed with these instruments in mind, however can be adapted to other TLS instruments.
Hemispherical photography has been used in studies of LAI, measuring canopy architecture of boreal forests, and the light environment in old growth temperate rain forests. Hemispherical photography can provide a number of structural measurements that can be used for calibration and validation of LiDAR derived products.
Field spectroradiometer measurements are collected to relate field based measurements to satellite data products (e.g. Landsat and MODIS NBAR products). For calibration and validation of at-surface reflectance of airborne hyper-spectral image data. Once the at-surface reflectance values of the hyper-spectral image data have been validated, the data can be used for up-scaling to medium spatial resolution Landsat and MODIS data for cal/val of NBAR products.
Leaf Area Index has been collected at the TERN Landscapes Supersites around Australia, using the LAI2200 and CI-110 instruments.
LAI 2200 Plant Canopy Analyzer
The LAI-2200 Plant Canopy Analyzer calculates LAI from radiation measurements collected both above and below the ground below 490 nm with a fisheye optical sensor (148° field-of-view). The solar radiation is measured at five zenith angles. The device which collects the data (LAI-2250 Optical Sensor or wand) then projects the image onto five concentric rings (LI-COR 2009).
LAI estimates are based on four assumptions: (a) the foliage is black (no radiation is transmitted or reflected by the vegetation); (b) the foliage elements are small in comparison to the area of view of each sensor ring and the following guideline is applied: the distance between the sensor and the nearest leaf above it should be at least four times the width of the leaf (LI-COR 2009: 1-7); (c) the foliage is randomly distributed; and (d) the foliage is azimuthally randomly orientated, in other words, leaves face all directions (LI-COR 2009).
High temporal frequency satellite observations of landscapes are necessary to capture highly dynamic spatio-temporal patterns of vegetation growth and productivity and landscape processes of carbon and water fluxes. Satellite observations of landscape seasonality include co-varying phenological changes in vegetation foliage quantity, phenological variations in foliage quality (leaf age, pigment contents, nitrogen, leaf stress...), and external variations in clouds, aerosols, and sun-view angle geometries. Satellite phenology observations of Australian landscapes are additionally complex due to the extensive prevalence of tree-shrub-grass canopies in which each vegetation functional class exhibit unique phonologies. For example, the productivity of the tree layer may increase simultaneous with decreases in grass layer productivity, potentially resulting in a mis-diagnosed satellite phenology. Validating satellite phenology is necessary for proper interpretation of climate variability and consequent shifts in seasonal and interannual biome responses.
Eight-day averages Calperum-Chowilla tower meteorology, Absolute humidity and air temperature (Tair), Precipitation and Net Radiation
MODIS 09 (2.5 km x 2.5 km) Green, Red and NIR reflectances
C-flux, Gross Ecosystem Productivity (GEP) and Ecosystem Respiration (Re)
MODIS 09 (2.5 x 2.5 km) Enhanced Vegetation Index (EVI) and Normalized Vegetation Index (NDVI).
Why are the measurements being made:
Gather information to document, understand, and validate seasonality profiles and patterns of landscape productivity.
To verify satellite observations of dynamic seasonal responses of landscape to climate drivers (rainfall, temperature, radiation, etc.), disturbance, and land use activities; and
To provide the scientific basis for spectral reflectance characteristics of vegetation and help understand reflectance patterns at the micro-scale.
The acquisition of sunphotometer measurements are critical to capture vital data on atmospheric properties during airborne hyperspectral imaging campaigns as well as for measurements coinciding with the overpass of satellite sensors. The atmospheric properties measured are used in atmospheric correction of the remotely sensed image data. This data is primarily for input into atmospheric correction systems. The MicroTops instruments referred to here capture solar radiance data in 5 wavelengths which are used to extract information on aerosol optical thickness and water vapour content. These two key parameters of interest are used as inputs for the atmospheric correction of remotely sensed image data.
The metric of overstorey vegetation cover adopted in many Australian vegetation classification frameworks is Foliage Projective Cover (FPC). FPC is defined as the vertically projected percentage cover of photosynthetic foliage of all strata, or equivalently, the fraction of the vertical view that is occluded by foliage. Overstorey FPC is defined as the vertically projected percentage cover of photosynthetic foliage from tree and shrub life forms greater than 2 m height and was the definition of woody vegetation cover adopted by SLATS. Overstorey FPC is one minus the gap probability at a zenith angle of zero and therefore it has a logarithmic relationship with effective leaf area index . Since Australian plant communities are dominated by trees and shrubs with sparse foliage and irregular crown shapes, overstorey FPC is a more suitable indicator of a plant community’s radiation interception and transpiration than crown cover.
Ground cover is the non-woody vegetation (forbs, grasses and herbs), litter, cryptogamic crusts and rock in contact with the soil surface. Ground cover changes in response to climate variables, vegetation dynamics and land management. Factors such as grazing pressure, tillage and stubble practices, drought and fire all affect ground cover. The quantity of ground cover affects water infiltration, runoff, erosion and carbon sequestration. It is a key indicator of land condition such as soil degradation, pasture production and biodiversity. Estimates of ground cover and changes in the quantity and spatial arrangement of ground cover over time provide land managers, policy-makers and scientists with valuable information for use in planning, monitoring and modelling applications.
FPC and Ground cover can be monitored using remote sensing. From a remote sensing perspective, FPC is the woody green cover in the overstorey while ground cover is the fractional cover of the non-woody vegetation and litter near the soil surface. The field measurement protocol described here is used to derive three categories of cover from satellite imagery— photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil (BS).
Field data on tree structural characteristics can be used for the calibration and validation of LiDAR derived products of tree height, canopy height profiles and allometrically derived Diameter Breast Height (DBH).