Understanding and monitoring water stress is an important consideration in managing native vegetation in dry climates. This is particularly the case in Western Australia where climates are seasonally dry (or becoming increasingly dry in the southwest) and often variable, and where population expansion and resource development is fuelling a growing demand for the use of groundwater and surface water resources. Increasing use of these resources has the potential to impose additional stress on vegetation that relies on these sources of water for growth and survival.
Numerous methods are available for monitoring plant water stress. Broadly, these include:
- leading indicators that give a current snap shot of plant water status/water use
- measures of the physiological response of plants
- lagging indicators that measure the physical response of the plant (refer to Figure 1 below).
Figure 1. Examples of methods for monitoring water stress in vegetation: a) leaf water potential measured using a pressure chamber, b) satellite remote sensing as a method for measuring physiological and physical symptoms of water stress and c) measuring foliage cover using digital photography and image analysis. Methods exist along a continuum that can be considered leading indicators or lagging indicators.
The role of leading and lagging indicators
If available, data for leading indicators provides the opportunity for pre-emptive action to limit impacts (for example, irrigation or slowing abstraction rates where groundwater dependent vegetation is affected). However, as most leading indicators are highly dynamic, changing from day to day or even hour to hour, formulating the correct management response based on this information is far from straightforward. In contrast, data for lagging indicators generally provides more clarity for decision makers: leaf browning, leaf loss or plant death is clear evidence that a problem is present. However, the risk in waiting for these symptoms to appear is that drought damage to plants becomes irreversible. Ideally therefore, a monitoring program should include both leading and lagging indicators. Apart from providing the ability to manage problems early, data for leading indicators (for example, leaf water potential ) can help explain whether lagging indicators of poor health are related to water availability or whether issues such as pests and disease warrant investigation.
Low tech and high tech options
There are several tried and tested methods available for monitoring water stress in plants. Leaf water potential is one leading indicator method that is both practical and direct (Turner 1981). However, the technique works best when measurements can be undertaken at regular intervals (weeks or months). In contrast, instruments that automatically measure and log data can capture the dynamic range of leading indicators across days and weeks and provide a good compliment to other infrequent measures such as leaf water potential; examples of such instruments include sap flow probes, soil moisture sensors or groundwater level recorders. Manual on-ground methods for measuring lagging indicators such as changes in foliage cover (McFarlane et al. 2007) and visual rating systems (Souter et al. 2010) have generally formed the backbone of routine monitoring of water stress in native systems over the years. However, replacement of these methods by remote sensing platforms (satellite, aircraft and UAV) is gathering pace (Smith et al. 2014). Despite this, on ground techniques will continue to play an important role in validating trends detected in remote imagery (groundtruthing).
The choice of platform (satellite, aircraft or UAV) for remotely monitoring water stress depends on scale of coverage and sensor specifications required. Generally, satellite imagery provides an efficient and effective tool for monitoring physical and physiological indicators in most applications. With the capacity for complete coverage of an area (for example, all trees), as opposed to sampling (for example, 1 % of trees), analysis by remote sensing has the potential power to detect small changes in physiological health, thereby performing as a reliable leading indicator. Hyperspectral and thermal imagery taken from aircraft and UAVs is also holds great promise as a tool for monitoring water status (Costa et al. 2013; Cao et al. 2015); however, the methods are not yet developed for routine monitoring in native ecosystems. As with all remote sensing solutions, an array of technical issues must be dealt with before data can inform management decisions (for example, processing, atmospheric correction and groundtruthing).
The selection of suitable complimentary monitoring tools is critical for establishing efficient and informative monitoring program that aims to detect drought stress in vegetation and inform good management decisions. Ideally, such a program should include leading and lagging indicators, one or more logging instruments for complete coverage in time, and a remote sensing component for complete coverage in space. Remote sensing is rapidly developing as the logical centrepiece of such programs.
Astron tailors and implements vegetation monitoring programs across native and commercial systems. These range from minimalist low cost-approaches to advanced instrumented solutions.
For more information contact Dr Robert Archibald, Principal Scientist on (08) 9421 9600.
Cao, Z., Wang, Q., and Zheng, C. , 2015, ‘Best hyperspectral indices for tracing leaf water status as determined from leaf hydration experiments’, Ecological Indicators, vol. 54, pp. 96-107.
MacFarlane, C, Arndt, SK, Livesley, S, Edgar, C, White, D, Adams, M & Eamus, D 2007, 'Estimation of leaf area index in eucalypt forest with vertical foliage using cover and fullframe fisheye photography', Forest Ecology and Management, vol. 242, pp. 756-763.
Costa, J.M., Grant, O.M., and Chaves, M.M. 2013, ‘Thermography to explore plant-environment interactions’, Journal of Experimental Botany, vol. 64, pp. 3937-3949
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Turner, NC 1988, 'Measurement of plant water status by the pressure chamber technique', Irrigation Science, vol. 9, no. 4, pp. 289-308.