Scheduled maintenance: 8-10 am ACT/NSW each Wednesday. Expect shutdowns and restarts.

Contact us: data at auscover.org.au  |  Disclaimer: Please read

Variables Derived From BioPhysical Products

Last modified by Mariela Soto-Berelov on 2013/02/11 11:44

Key Variables derived from Biophysical products

Phenology Validation (N. Restrepo-Coupe, A. Huete, M. Broich, K. Davies)

Phenology is the study of the timing of recurring climate or weather-driven biological events, the causes of their periodicity, their relationship with biotic (e.g. fruit availability) and abiotic (e.g. rain) drivers and the interrelations between the seasonal cycle of the same or different species. A better knowledge of the relationships of phenological responses to climate drivers (temperature, precipitation, length of the dry season, etc.) will advance our understanding of ecological responses to climate change.

Regional and continental scale phenology are often characterised with the use of different Remote Sensing (RS) products (e.g. vegetation indices) obtained from coarse resolution, but higher temporal frequency, satellites, such as the Advanced Very High Resolution Radiometer (AVHRR) t and the Moderate Resolution Imaging Spectroradiometers (MODIS) (Zhang et al., 2003). The length of the time series (from 10 to 30 years), its high temporal frequency (from twice daily to 2 days), consistency, and quantitative nature of the satellite measurements are highly desirable qualities when extrapolating future ecosystem responses to climate. In this section, we focus on the validation of vegetation phenology (grasses, trees, shrubs, biogenic soil crusts) acquired by satellite sensors, with particular emphasis on MODIS derived phenologic metrics.

Leaf and canopy spectroscopy (L. Su├írez, N. Restrepo-Coupe, L. Chisholm, A. Hueni)

Foliar Chemistry of individual tree crowns with imaging spectroscopy (K. Youngentob, A. Cabello-Leblic)

Until recently, assessing plant chemistry on a landscape-scale has been impractical because it required sampling thousands of leaves in the field for lengthy laboratory analyses. Recent technological advances in infrared spectroscopy and hyperspectral remotes sensing are opening the door to the rapid assessment of leaf chemical composition in the lab and across whole forest canopies (for reviews see Majeke et al. 2008 and Kokaly et al. 2009). Imaging spectroscopy builds upon the extensive laboratory spectroscopy research that has identified strong relationships between the absorption of electromagnetic radiation and various chemical constituents (Curran 1989, Kokaly and Clark 1999, and Ebbers et al. 2002). Molecular vibrations resulting from the rotation, bending and stretching of chemical bonds absorb electromagnetic radiation at frequencies that correspond to their energy state and create harmonics and overtones in the near-infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum. Variations in reflectance at wavelengths that correspond to specific molecular interactions can be used to identify and quantify the chemical composition of materials based on high resolution spectral data (Table 1).

Laboratory spectroscopy methods for estimating foliar chemicals based on visible and infrared portions of the electromagnetic spectrum typically use a combination of spectral feature enhancement and noise reduction techniques along with regression or principle component based modelling. These methods rely on training and testing datasets for model calibration and accuracy assessment. The techniques described in this chapter are based on those laboratory techniques, which are then applied to the airborne spectra collected with an imaging spectrometer, rather than spectra collected by a laboratory or field spectrometer. Several imagery processing steps are required to select relatively pure canopy leaf spectra from an airborne remote sensing image for training and testing data. Similarly, applying the resulting prediction algorithm to an entire image requires careful masking of non-canopy pixels and crown delineation to isolate individual tree crowns within the scene.

This chapter is divided into subsections that will cover the various components of the methodology for estimating foliar chemistry at an individual tree crown level with airborne remote sensing data. These are the methods that will be used to create open-source foliar chemistry maps (chlorophyll a and b, total carotenoids, anthocyanin, carbon, nitrogen, available nitrogen, tannins, total polyphenols, potassium, phosphorous, base cations and metals) for selected TERN sites. An important caveat is that imaging spectroscopy is a relatively new and rapidly developing tool for measuring and monitoring landscape characteristics. The methods presented here for estimating and mapping variations in foliar chemicals across tree canopies with airborne hyperspectral remote sensing data are intended for research purposes. Further refinement and improvements of these methods are expected as the technology and our capabilities continue to develop and evolve.

Persistent Green Vegetation Index (K. Johansen)

 

Tags:
Created by Mariela Soto-Berelov on 2012/11/03 12:00

This wiki is licensed under a Creative Commons 2.0 license
XWiki Enterprise 4.5.3 - Documentation