environmental sensor networks for vegetation, animal and soil sciences

International Journal of Applied Earth Observation and Geoinformation 12 (2010) 303–316

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International Journal of Applied Earth Observation andGeoinformation

journa l homepage: www.e lsev ier .com/ locate / jag

Review

Environmental sensor networks for vegetation, animal and soil sciences

A. Zergera,∗, R.A. Viscarra Rosselb, D.L. Swainc, T. Warkd, R.N. Handcocke, V.A.J. Doerra,G.J. Bishop-Hurleye, E.D. Doerra, P.G. Gibbons f, C. Lobseyb

a CSIRO Sustainable Ecosystems, Australiab CSIRO Land and Water, Australiac Centre for Environmental Management, CQUniversity, Australiad CSIRO ICT Centre, Australiae CSIRO Livestock Industries, Australiaf Australian National University, Australia

a r t i c l e i n f o

Article history:Received 22 December 2009Accepted 12 May 2010

Keywords:Environmental sensor networksVegetationAnimalsSoilReview

a b s t r a c t

Environmental sensor networks (ESNs) provide new opportunities for improving our understanding ofthe environment. In contrast to remote sensing technologies where measurements are made from largedistances (e.g. satellite imagery, aerial photography, airborne radiometric surveys), ESNs focus on mea-surements that are made in close proximity to the target environmental phenomenon. Sensors can beused to collect a much larger number of measurements, which are quantitative and repeatable. They canalso be deployed in locations that may otherwise be difficult to visit regularly. Sensors that are commonlyused in the environmental sciences include ground-based multispectral vegetation sensors, soil mois-ture sensors, GPS tracking and bioacoustics for tracking movement in wild and domesticated animals.Sensors may also be coupled with wireless networks to more effectively capture, synthesise and transmitdata to decision-makers. The climate and weather monitoring domains provide useful examples of howESNs can provide real-time monitoring of environmental change (e.g. temperature, rainfall, sea-surfacetemperature) to many users. The objective of this review is to examine state-of-the-art use of ESNs forthree environmental monitoring domains: (a) terrestrial vegetation, (b) animal movement and diversity,and (c) soil. Climate and aquatic monitoring sensor applications are so extensive that they are beyond
the scope of this review. In each of the three application domains (vegetation, animals and soils) wereview the technologies, the attributes that they sense and briefly examine the technical limitations. We conclude with a discussion of future directions.
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Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3042. Sensing terrestrial vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304

2.1. Monitoring natural terrestrial vegetation using sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3042.1.1. Camera-array data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3042.1.2. Point-based sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3052.1.3. Temporal change detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

2.2. Agriculture (crops, viticulture, managed pastures) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3062.3. Sensing terrestrial vegetation – technical challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306

2.3.1. Sensor calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3062.3.2. Power for sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3062.3.3. Logistical issues in deploying sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306

∗ Corresponding author at: GPO Box 284, Canberra, ACT 2602, Australia. Tel.: +61 26242 1691.E-mail address: [email protected] (A. Zerger).

0303-2434/$ – see front matter. Crown Copyright © 2010 Published by Elsevier B.V. All rights reserved.doi:10.1016/j.jag.2010.05.001

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4.3. Wireless sensor networks for soil measurement (WSN). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3114.4. Multi-sensor platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312

5. Future directions and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313. . . . . .

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. Introduction

Recent advances in information and communication technolo-ies (ICT) and the development of Environmental sensor networksESNs) provide new opportunities for using sensors to monitornvironmental change. ESNs can be used to help improve ournderstanding of environmental phenomena and guide naturalesource management (NRM) (e.g. evaluating the effect of cli-ate change and grazing impacts on tree regeneration through a

umber of seasons; tracking weed incursions; monitoring grazingmpacts on native groundcover; and tracking cattle). Environmen-al sensor networks typically refer to sensing that occurs in closeroximity to the target, as opposed to remote sensors that sense at

arge distances from the target. Sensors in the environmental sci-nces include, for example, digital cameras, visible to near-infraredpectrometers, soil water sensors, GPS-enabled movement track-ng devices and bioacoustic sensors. Sensors can be advantageousn environmental applications because they can be: (i) used tocquire time-series data (Hart and Martinez, 2006), (ii) deployedn locations that are difficult to access and therefore they can over-ome practical limitations of traditional monitoring, and (iii) areore time- and cost-effective than traditional field methods. For

nstance, soil moisture probes provide levels of precision, accu-acy and repeatability not traditionally achievable using manualechniques.

Research into ESNs has traditionally focused on the softwarend hardware engineering components of sensor networks. Thisas included hardware design (Klingbeil and Wark, 2008; Rahimit al., 2005), algorithms for distributed computation in sensor net-orks (Ferentinos and Tsiligiridis, 2007; Worboys and Duckham,

006), methods for monitoring and positioning nodes (Kohno etl., 1999; Shih et al., 2008), energy efficiency in sensor and networkesign (Ci et al., 2007) and more recently integrating and hostingensor data via the internet (Liang et al., 2005). Where there is intento discuss environmental applications, there remains a focus onngineering challenges (Hart and Martinez, 2006; Pon et al., 2005;zewczyk et al., 2004). The focus on engineering is not surprisingiven that sensors and sensor networks are enabling technolo-ies that must be developed before applied NRM-focused sensortudies can commence. Recent technical advancements includ-ng hardware miniaturisation, improvements in the efficiency ofolar technology, the widespread availability of wireless commu-ication networks such as 3G networks, and improvements and
ecreases in the cost of sensors, means that novel deploymentsf ESNs are now possible. Thus, it is imperative that the focushifts towards understanding the specific environmental domainshere research and management will most benefit from using
SNs.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313

This review moves away from the traditional focus on ICTelements and focuses on applications for proximal monitoringof terrestrial environmental change. We specifically limit ourtreatment of sensor engineering, wireless network design, algo-rithms for sensor management and supporting technologies suchas database tools for the real-time delivery of sensor data throughthe Internet. Owing to the breadth of possible applications, wehave restricted this review to the following terrestrial applicationsin which we believe new technologies will be most useful: (a)vegetation monitoring (including crops and pastures), (b) animalmovement and diversity assessment, and (c) soil sensing. Climate,aquatic and hydrologic applications are so extensive that theyare considered beyond the scope of this review. As this reviewfocuses on proximal sensors, remote sensing technologies such asmultispectral or hyperspectral images from satellite- and airborne-sensors are not included. However, where proximal sensors areused to calibrate and validate remote sensors, such as through theuse of ground-based hand-held spectrometers, these are discussed,within the context of terrestrial vegetation monitoring applica-tions.

Within each of these application domains, we review the tech-nologies that have been or could be used, and comment on how andwhy environmental sensing in particular could help address par-ticular research questions or management issues. We also brieflyexamine technological limitations. We conclude with a synthesiswhich explores future directions and research needs for applyingsensors in environmental monitoring.

2. Sensing terrestrial vegetation

2.1. Monitoring natural terrestrial vegetation using sensors

The use of ESNs in vegetation studies is dominated by: (i) array-based measurements from digital photography covering smallareas of <5 m, and typically focused on assessing characteristicsof vegetation in rangelands and grasslands at medium scales (e.g.trees, groundcover using digital photography) (Bennett et al., 2000;Booth et al., 2004; Booth and Cox, 2008), and (ii) point-based mea-surements using spectral or other sensors for assessing vegetationcharacteristics such as yield or biomass.

2.1.1. Camera-array dataIn the context of groundcover change and rangelands assess-

304 A. Zerger et al. / International Journal of Applied Earth Observation and Geoinformation 12 (2010) 303–316

3. Animal movement and diversity assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3063.1. Animal movement for environmental monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306

3.1.1. Operator-dependent monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3073.1.2. Autonomous monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3073.1.3. Off-animal control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3083.1.4. On-animal control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308

3.2. Animal movement – technical challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3093.3. Animal diversity assessment for environmental monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3093.4. Diversity assessment – technical challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310

4. Soil sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3104.1. Proximal soil sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3104.2. Proximal soil sensor technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311

ment, studies have focused on applying digital photography (Ewingand Horton, 1999) as a replacement for traditional field methods,and initially to only detect a small number of vegetation attributessuch as the proportion of live vegetation, projected foliage cover,bare ground and litter (Booth et al., 2005). Laliberte et al. (2007)

A. Zerger et al. / International Journal of Applied Earth Observation and Geoinformation 12 (2010) 303–316 305

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o monitor groundcover vegetation change as a response to herbivore grazing.

xtended such analysis to also identify senescent and green vegeta-ion using object-based image classifiers and found relatively highoncordance with manual methods for some attributes includingotal vegetation cover and senescent vegetation. Booth et al. (2006)ote that automated methods of vegetation monitoring should bereated with caution as they do not always replicate the accuraciesnherent in traditional methods such as field-based point-sampling.his can be due to variations in lighting and viewing-geometryonditions which affect the relative brightness of images and theroportion of shadow in the scene, which is particularly prob-

ematic for time-series analysis. However, automated methods foregetation monitoring do provide a permanent record of the field-lot and allow for more rapid data collection.

Groundcover assessment is a key application of camera-arraysBooth et al., 2006) because groundcover response to disturbancend abiotic change is relatively rapid compared to changes in otheregetation strata (e.g. canopy vegetation). Fig. 1 shows a sensorode designed to detect groundcover change at high-temporalesolutions (e.g. hourly) by coupling multispectral, IR and visibleavelength sensors. Changes in canopy structure, regeneration, orefoliation due to dieback or senescence do not occur at rates thatemand the frequent temporal monitoring available in ESNs. Forxample, some of these changes only manifest themselves at sea-onal and annual time scales. Similarly, the annual growth cyclef agricultural crops and intensively managed pastures (e.g. dairyystems), their commercial value, and the fact that inputs such asertilizer and irrigation can engender relatively rapid change makehem well suited to utilise the high-temporal frequency of ESNs.n emerging body of research is also evaluating the practicality ofobile camera systems to capture vegetation attributes along tran-

ects (Graham et al., 2009). These systems are known as networkednfo-mechanical systems (NIMS) and allow researchers to simulateraditional transect-based ecological surveys.

.1.2. Point-based sensorsExamples of point-based sensors include those used to measure

egetation microclimate (e.g. temperature, humidity), or spectralensors that measure either active- or passive-source reflected

rd facing) and digital cameras with intervalometers (visible and IR). Sensors areesources/Smart-Sensor-Network-Technology.html). The system has been designed

light. Spectral sensors may be multispectral (with low spectral res-olution) or hyperspectral (with hundreds of bands), such as froma field spectroradiometer. Laboratory-based spectroradiometers,particularly those focusing on the NIR region, are also used exten-sively in vegetation research, but are beyond the scope of this paper,which focuses on environmental sensing, rather than laboratorystudies.

Spectral sensing of vegetation makes use of the fact thatvegetation absorbs light in the visible part of the spectrum(0.4–0.7 �m), but reflects strongly in the near infrared (NIR) region(∼0.7–1.3 �m). Vegetation indices utilising spectral measurementsin the red (∼0.6–0.7 �m) and NIR, such as the Normalized Differ-ence Vegetation Index (NDVI) (Tucker, 1979) are widely used formonitoring vegetation condition as they are related to the ‘vigour’and ‘greenness’ of live vegetation. For example, a vegetation indexderived from a multispectral sensor (Elvidge and Chen, 1995) couldbe calibrated with pasture biomass (kg DM ha−1) (Davidson andCsillag, 2001; Pearson et al., 1976), or crop characteristics such asyield determined from hyperspectral sensors (Schut et al., 2006;Thenkabail et al., 2000).

There is a rich body of literature in the area of developingapplication-specific spectral measurements of vegetation (e.g. Tianet al., 2001). For example, determining specific spectral character-istics of the ‘red-edge’ portion of the electromagnetic spectrum(Horler et al., 1983; Mutanga and Skidmore, 2007), betweenthe red region and the NIR region, which is an area of rapidchange for chlorophyll (Curran et al., 1991). Other areas of theelectromagnetic spectrum, such as the short-wave infrared, aresensitive to dead vegetation and soil, and spectral sensors can betuned so their wavelengths best match the biophysical features ofinterest.

2.1.3. Temporal change detection
Where ESNs have been applied to temporal change detection,
it is primarily through the sensing of abiotic change related tovegetation attributes. For example, Collins et al. (2006) deployedmicroclimate sensors and sensor network technologies to deter-mine how microclimate varied under different vegetation species.

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n their review of ESNs Hart and Martinez (2006) found that stud-es focused on abiotic variables such as microclimate (temperature,umidity), light levels and soil characteristics including moisturend temperature, rather than on the direct detection of change inegetation attributes.

Advances in ICT such as wireless data protocols that supportreater data volumes at faster speeds and in more remote loca-ions (e.g. 3G technologies) provide new opportunities for thenvironmental sensing of vegetation through time. These technolo-ies can provide time-series information not previously attainablesing manual ecological survey methods as data can be transferredirectly to remote database servers. One of the earliest studieshich coupled digital imaging with wireless networks is MossCam

Graham et al., 2006) which uses a low resolution video cameraRGB) to capture frames every 30 s to monitor net CO2 uptake in apecies of moss (Tortula princeps). Hamilton et al. (2007) also dis-uss an interesting application of a sub-surface installed RGB digitalamera for automated detection of changes in root growth and soiltructure (automated minirhizotron).

.2. Agriculture (crops, viticulture, managed pastures)

ESNs have also been applied in agriculture, viticulture and horti-ulture and these are characterised by a greater focus on the use ofultispectral sensors to detect crop or pasture characteristics (e.g.

iomass, potential yield, light-use efficiency, emergence, disease)Cox, 2002; Richardson et al., 2001; Whelan and McBratley, 2000),nd to a lesser extent change detection (Kuckenberg et al., 2009;ascher and Pieruschka, 2008). Studies can generally be separatedn the basis of scale, making a distinction between crop or pasturessessment at paddock scales (Gang et al., 2007; Handcock et al.,008), and finer scale plant physiological studies (Hafsi et al., 2000;urcell, 2000). Central to agricultural applications of ESNs are these of technologies such as vehicle-mounted multispectral sensorso detect crop characteristics in order to apply variable rates of fer-ilizer (Heege et al., 2008; Jain et al., 2007). Alternatively, visible andIR hand-held and vehicle-mounted sensors have also been use to

dentify within-crop weeds to better direct the application of her-icides (Berge et al., 2008; Hemming and Rath, 2001; Jones et al.,009). In the context of assessing dairy pastures at the paddock-cale there are a number of all terrain vehicle-mounted sensorools. These include the Ellinbank Automatic Pasture Reader (APR)Department of Primary Industries, Victoria) which uses soundaves to determine pasture density, and the Rapid Pasture Metre

C-Dax Systems Ltd.) which uses optical beams to measure pas-ure height, both of which are calibrated to pasture biomass (Futureairy, 2009). Table 1 provides a summary of terrestrial vegetation

ensing studies.

.3. Sensing terrestrial vegetation – technical challenges

.3.1. Sensor calibrationSensor calibration is a crucial requirement for any deployment

iven the importance of reliable measurements from sensors inerrestrial vegetation sensing applications. While sensors can benitially calibrated to take into account unique offset and scaleharacteristics, separating ongoing sensor drift and bias from trueariation in the phenomena being measured is a challenging task.he human labour required to manually recalibrate every sensorn large sensor networks is also prohibitive on an ongoing basis.iven this, automated calibration of sensors in sensor networks is
growing research challenge within the computer science commu-ity (Balzano and Nowak, 2007; Bychkovskiy et al., 2003). One ofhe key ways this can be achieved is through exploiting the corre-ations between measurements from multiple nodes (Balzano andowak, 2007; Bychkovskiy et al., 2003).
servation and Geoinformation 12 (2010) 303–316

Calibration of the sensors, not just for reliability in response, butto derive a physical quantity is essential if the raw observationsfrom the device are to be converted to a quantitative biophysicalvalue of interest. For example, a light-sensing node might mea-sure raw reflected energy in the wavelengths of red and NIR. Thismeasurement can be processed to create a vegetation index suchas NDVI, which can then be empirically calibrated to biomass bycutting and weighing the vegetation being measured. These mea-surements of the vegetation characteristics from the ESN may be ofuse in themselves, such as for determining nitrogen applicationson a crop, or can be used as a data collection tool for calibrat-ing air- or space-borne remote sensing measurements to groundmeasurements.

2.3.2. Power for sensorsDeployments of low-power, long-duration sensor networks

have typically utilised solar energy harvesting, combined withrechargeable batteries, as a means of achieving continuous oper-ation without the need to replace energy storage (Corke et al.,2007; Jeong et al., 2008). The ability to harvest solar energy isgreatly reduced in environments such as forests where there is fre-quently dense foliage over large areas. Recent deployments haveshown that often 1% or less of the available solar energy of openareas is available in dense forest regions (Taneja et al., 2008). Solu-tions to this challenge are being addressed in two main ways.The first is reducing the load of solar-powered nodes by adaptingtheir sampling and communication behaviour based on the amountof available energy (Jeong et al., 2008; Kansal et al., 2006). Theother areas are alternative methods for harvesting energy fromthe environment including wind (Weimer et al., 2006), vibrationand thermal gradients (Gilbert and Balouchi, 2008). An additionalchallenge in forest environments also comes from the impact onradio performance (Foong Lee and Li, 2006). When foliage is wet,in particular, radio signals with carrier signals in the range of900–2400 MHz can be greatly attenuated meaning there can belong periods of time when nodes have very poor link qualities orare completely disconnected. Improved protocols for dealing withradio links in these types of environments are topics of ongoingresearch (Selavo et al., 2007).

2.3.3. Logistical issues in deploying sensorsWhile the use of ESNs for vegetation monitoring has many

advantages over manual measurements, there are also some logis-tical constraints to implementation. These include the choice anddeployment of sensors to match the spatial and temporal charac-teristics of what is being studied. Also, the physical deployment ofESNs has issues such as dust degrading sensor response over time,or animals destroying the sensors or sensor setup. Weather- andanimal-proof casings should be selected for the particular environ-ment. As mentioned previously, sensors will not typically directlymeasure the environmental characteristics of interest, but are anindirect measurement, so calibrations may have to be refined fornew environments or seasons. Many of the logistical issues indeploying sensors, although in the context of a wireless sensorsnetwork, are discussed by Handcock et al. (2009).

3. Animal movement and diversity assessment

3.1. Animal movement for environmental monitoring

Natural biotic resources are mostly managed within multi-use
landscapes (i.e. natural areas interspersed with more intensivehuman uses, including agricultural and urban areas). Both wildand domesticated animals inhabit these landscapes, and landscapestructure and variability influence how animals behave and use theresources. Problems can arise when native animals cannot maintain


Table 1Terrestrial vegetation sensing – summary.

Application Variable sensed Sensor References

Vegetation and cropdetection

Groundcover in rangelands RGB Booth et al. (2005), Booth and Cox (2008), Bennettet al. (2000), Laliberte et al. (2007)

Senescence RGB Hafsi et al. (2000)Flowering (count and extent) RGB Adamsen et al. (2000)Canopy light interception RGB Purcell (2000)Leaf area, budburst, photosyntheticrates and respiration

RGB Gang et al. (2007), Graham et al. (2006, 2009)

Reflectance, light-use efficiency,reflectance

Spectroradiometers Kuckenberg et al. (2009), Rascher and Pieruschka(2008), Gitelson et al. (2003)

Root growth and soil structure RGB Hamilton et al. (2007)Weeds (morphology and colour) Multiple CCD, RGB Hemming and Rath (2001), Berge et al. (2008)Plant litter Spectroradiometers Nagler et al. (2000)Plant composition R, NIR Davidson and Csillag (2001), Hill et al. (1989)

Biophysicalmeasurements

Plant canopy water-status Spectroradiometer, NIR, SWIR Gao (1996), Tian et al. (2001)

Foliar nitrogen Spectroradiometer Mutanga and Skidmore (2007)Chlorophyll Spectroradiometer Mutanga and Skidmore (2007)Crop yield, biomass, vigour, emergence NIR, RGB webcam,

SpectroradiometersEwing and Horton (1999), Wanjura et al. (2003),Richardson et al. (2001, 2007)

Pasture biomass R, NIR Davidson and Csillag (2001), Pearson et al. (1976)Pasture quality CCD, spectroradiometers Schut et al. (2006)

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Application Variable-rate nitrogen application R, NIRTargeted herbicide application RGBFeed allocation (pasture) Sound-

heir natural foraging and dispersal movements, leading to popula-ion declines, or when native or domesticated animals over-utilisend degrade a particular resource. Thus, developing sustainableanagement options for landscapes inhabited by wild and domes-

icated animals requires information on their movement, landscapese and interactions between animals (Bailey et al., 1996; Doerrnd Doerr, 2005; Launchbaugh and Howery, 2005; Schauber et al.,007).

Animal movement has been difficult to study without these of sensing technologies. Individually marked animals may bebserved in different locations, but movement between obser-ations is unknown, making it almost impossible to construct aealistic time-series. The resulting data tend to reflect where searchfforts have been concentrated rather than the actual movementehaviour of the animals particularly when animals can move over

arge distances or enter environments not sampled by researchers.ortunately, over the last 40 years there has been increased use ofensor networks for studying and managing wild and domesticatednimals, permitting the collection of more complete and unbiasedime-series data. The technologies that have been used to moni-or and in some cases manage animals have evolved through four

ain stages: (a) operator-dependent monitoring, (b) autonomousonitoring, (c) off-animal control of movement, and most recently

d) autonomous on-animal control of movement. While all animal-ased sensor systems require animals to be fitted with some

nstrumentation, the off- or on-animal description refers to theoint at which the control decisions are actuated.

.1.1. Operator-dependent monitoringVHF radio was first used successfully to track animals in the early

960s (Cochran and Lord, 1963; Lord and Cochran, 1962). Initialork was motivated by a desire to better understand how changes

n the management of national parks might affect the behaviouralatterns of the resident wildlife (Kolenosky and Johnston, 1967). AHF radio tracking system relies on animals wearing a device that

ransmits a radio signal, the signal is then monitored to determinehe geographical location of the animal being studied (Anderson-precher, 1994). Radio tracking of wild animals has been, andontinues to be extensively used around the world to track animalctivity (Aarts et al., 2008). One advantage of VHF radio tracking is

Heege et al. (2008), Jain et al. (2007)Hemming and Rath (2001), Berge et al. (2008)

, optical-light Future Dairy (2009)

that the animal only needs to be captured once to fit a transmit-ter, subsequent monitoring is then carried out at a remote distancefrom the animal, followed by transmitter removal. The radio trans-mitters that are fitted to the animals can be made small (currentlyas lightweight as 0.35 g) to enable tracking of small mammals, birdsand fish (Dietz and HeideJorgensen, 1995; Fitzgerald et al., 2005;Kelly et al., 2008). Finally the radio transmitters have relatively low-power requirements, which means that either the devices can bemade small or, for larger animals, the unit can last for prolongedperiods allowing long-term monitoring.

Although VHF radio tracking has been widely used to trackanimal location and movement it is constrained by the need foran operator to be on the ground, either directly locating the ani-mal or triangulating the position of the animal being tracked. Therequired presence of an operator constrains the temporal frequencywith which locations can be sampled, and thus the accuracy ofthe time-series data. Animals may also be ‘lost’ relatively easilyif they move outside the perceptual capabilities of the operator’sreceiving equipment. There are also constraints on total samplesize, as a given operator can only monitor the locations of a limitednumber of animals at any one time, particularly if a high-temporalfrequency of locations is desired. The triangulation process mayalso limit spatial precision of location data.

3.1.2. Autonomous monitoringA number of different approaches have been used to develop

autonomous monitoring, in which an operator does not have to bepresent to control the collection of each animal location. In somestudies, fixed location receivers have been used within wildlifeauto-location systems (Larkin et al., 1996). By using automatedscanning of transmitter frequencies and remote data collectiononto data loggers, these systems can reduce sample size con-straints normally associated with VHF tracking, may allow morefrequent data collection per individual and thus a more accuratetime-series, and certainly reduce labour costs. However, they can
generally only be employed over a relatively small area and thatarea must be predetermined, so animals may still be lost from thesystem.
By the early 1990s, satellite-based global positioning systems(GPS) were being used to track animal movement. Satellite-based

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onitoring of animal behaviour has a number of advantages overHF radio including that animals cannot be lost unless the devices

ail, the data usually have greater spatial precision compared to tri-ngulation, animals can be located with a relatively high-temporalrequency to improve the quality of time-series data (subject toatellite availability), and once the devices are fitted they requireo further ongoing human input until the devices are retrieved athe end of the deployment. The GPS device worn by the animal logso-ordinate data at a predetermined frequency and these data aretored in onboard memory. Whilst more recent designs have theapability to wirelessly transmit the data, it is more common forhe devices to be retrieved and the data downloaded via a cablenterface. The GPS devices can be fitted with additional sensors,

hich simultaneously record other aspects of behaviour such asead and neck movement using a tilt switch, physiological param-ters such as skin temperature (which also provides a mortalityensor), or environmental parameters such as ambient tempera-ure.

While GPS devices are being used more widely and have advan-ages over other animal tracking techniques, they also have aumber of limitations. Under certain conditions (e.g. high tree orloud cover) the GPS antennae may be unable to receive enoughatellite transmission data to enable a GPS position to be recorded,r if it does record a position there is a high degree of uncertaintyegarding positional accuracy (Agouridis et al., 2004; Bowman et al.,000; Clark et al., 2006; Dussault et al., 1999; Hulbert and French,001; Janeau et al., 2004; Lewis et al., 2007). The GPS antennalso has a high power requirement. For long-term deploymentshe interval between GPS positions has to be prolonged, and thushe quality of time-series data sacrificed, to enable the GPS unito function throughout the deployment. While there are acknowl-dged issues with positional errors associated with satellite signalrocessing, these are well known and can be partially removed byifferential correction, and in some instances inertial navigationechnology. However, more recent studies have addressed accuracyssues related to prediction errors associated with the time inter-als between positional points and the interactions with animalovement. The errors associated with GPS fix intervals are more

omplex and are confounded by the negative interaction betweenattery power available and the amount of time that a GPS unit isequired to be deployed in the field (Swain et al., 2008a). A corol-ary to the high power requirements of the GPS antennae is thempact that this has on the size and weight of the devices, whichrecludes their use in tracking small animals, for which traditionalHF radiotelemetry with on-ground operators is still often the onlyolution.

Argos satellite-based tracking systems, developed around theame time as GPS tracking devices, are slightly different in thathe data are transmitted directly to the researcher, usually in addi-ion to being stored on the tracking device (CLS Argos, 2008). Theseave the same advantages as automated GPS tracking devices, withhe additional advantage that the device itself does not need toe retrieved for data acquisition. However, they also have somedditional disadvantages. Spatial precision of locations is highlyariable, and may commonly be no better than ±1 km, which maye far too coarse for many NRM applications (Vincent et al., 2002).hese devices are also relatively heavy, which precludes their usen tracking medium or small animals.

In parallel with the development of satellite-based tracking,here have been some developments in the use of ESNs such as pas-ive transponders or passive radio frequency identification (RFID)
evices, which can be as lightweight as 1 g. A panel reader ornother RFID device simply notes whenever other devices comeithin a specified distance (Charmley et al., 2006). Thus, thisroximity technology has tended to be used mostly for collect-
ng data on the presence of animals at particular locations, or for


quantifying interactions between animals, rather than for assess-ing movement paths per se. For example, proximity sensors oncattle have been used to study patterns of association betweenmothers and calves (Swain and Bishop-Hurley, 2007, Fig. 2). Thesedevices still hold some promise for tracking approximate move-ment paths when placed at higher densities in a landscape andorganised into a network. However, individuals could still be lostfrom the system, as the location data depend on proximity to ‘read-ers’ whose locations in the landscape are predetermined by theresearchers.

3.1.3. Off-animal controlWithin animal production systems there has been increased

interest in using RFID devices to monitor individual use of resourceslike watering points (Charmley et al., 2006). As the identificationof the animal is increasingly being linked to other data such asanimal live-weight, management decisions can be made based oncombined data on resource use and animal condition (Bowen etal., 2009). Management is often achievable through autonomouscontrol. The use of electronic gates to automatically change thepaddock an animal can access to ensure growth rate targets aremet through optimal feed regimes is an example of data integrationand subsequent control. In addition it is possible to integrate envi-ronmental information within the decision-making process. Anexample is using remote sensing images to monitor groundcoverand then linking this with automated off-animal control to relocateanimals within the landscape (Handcock et al., 2009). Recent workhas utilised automated image analysis techniques to identify dif-ferent species of wildlife as they move through a ‘race’ to accesswater (Finch et al., 2006). As animals are identified populationnumbers are assessed and control measures can be implementedto reduce the risk of environmental damage. Feral animals suchas wild pigs whose population may risk environmental damagecan be drafted to a separate pen and removed from the ecosys-tem.

3.1.4. On-animal controlRecent integration of GPS monitoring with actuation capability

has the potential to deliver what has been referred to as ‘virtualfencing’ applications (Anderson, 2007). Automated animal controlrelies on the co-ordinates of pre-defined areas within the landscapeto be pre-programmed into a GPS tracking collar (Wark et al., 2009)(see for example Fig. 2). If an animal enters into the area it receivesan audible warning and if it continues this is followed up by a tac-tile stimulation (e.g. electrical pulse) (Bishop-Hurley et al., 2007).Through association animals learn to avoid particular parts of thelandscape (Lee et al., 2007). This technology has the potential toprovide a flexible management system to direct animal movement,and prevent domesticated herbivores accessing environmentallysensitive areas such as riparian zones that are located in extensivelandscapes (Wark et al., 2009).

While the technology is some way off providing a practical toolthat can be used by natural resource managers, the research anddevelopment process has led to some interesting developments inanimal-based sensor networks. In particular, the need to developimproved power management has led to new developments usingGPS duty cycling combined with novel data compression tech-niques that evaluate the true value of a positional data point inrelation to previous records and the ability to reconstruct move-ment paths (Wark et al., 2009). The ‘backbone’ of the automated
animal control system is based around a sensor network wherereal-time monitoring is required to ensure animal welfare objec-tives are met. The distributed environmental sensor network is partof a much wider collection of static sensor nodes that not onlycollect data but also act as receiving stations for the animal data.


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Increased understanding of how animals interact with their nat-ral environment has been greatly facilitated by the developmentf sensors and their integration within sensor networks. The recentdvances in automated animal control have led to a greater integra-ion of sensor and communication technologies. While early workaw VHF radio tracking and GPS tracking as separate animal sen-or technologies, they are now merging and becoming part of aarger network combining mobile and static sensors (Wark et al.,009). This merger is leading to enhanced opportunities for the

ntegration of data enabling a greater understanding of systemsevel interactions.

.2. Animal movement – technical challenges

The greatest challenge for using ESNs in animal movementesearch and management is collecting accurate time-series dataver relatively large spatial scales that track the movements ofmall animals. At the moment, technologies that permit accurateutonomous data collection over appropriate scales (such as GPSracking devices) have high power needs. This precludes their useith the majority of native animals. One possibility is to developew approaches to GPS data collection that require less power.lternatively, innovations could be developed to reduce the cost ofroximity sensors or other low-power radios (Klingbeil and Wark,008), to allow hundreds or even thousands of units to be deployednd interact as part of a sensor network over a large area.

Incorporating mobility as part of a sensor network brings aboutnumber of additional challenges (Dutta and Culler, 2009). Whenetworks contain one or more mobile nodes, the network topologyecomes inherently dynamic meaning that routes for data flow-

ng back to base can change frequently which in turn means thatouting protocols designed for static networks can become far lessffective. In mobile networks, nodes can also become completelyisconnected, which means that data must be locally buffered ifelivery reliability is to be maintained. Recent research (Zhang et
l., 2006) has focused on delay-tolerant networks (DTN) that seeko find broad strategies for maintaining data fidelity (given finitetorage space). The other main challenge for mobile networks isnergy efficient discovery of other nodes (Dutta and Culler, 2009).n static networks, nodes can be guaranteed to discover each other
re used to determine parentage (Swain and Bishop-Hurley, 2007).

via radio beacons despite very low radio duty cycles (where radiosspend most of their time ‘asleep’ in order to save energy). In mobilenetworks, no such guarantee exists and for fast moving nodes (rel-ative to the density of other nodes in the network) opportunitiesto upload data to other nodes in the network can often be missed,hence new strategies are being developed to deal with these chal-lenges (Dutta and Culler, 2008).

3.3. Animal diversity assessment for environmental monitoring

Biodiversity assessment is one of the most common types of eco-logical monitoring, and measures of species richness and evenness(e.g. Shannon’s diversity index) are thought to be strongly relatedto the overall environmental health of a site. Quantification of fau-nal diversity is often performed through repeated, labour-intensiveon-ground surveys such as point counts for birds. Both the spatialextent and the temporal frequency of samples are usually relativelylimited due to the costs and logistical constraints involved in exten-sive field work. Sensors and sensor networks have the potential toautomate such assessments, facilitating concurrent sampling overmany sites and as much or as little temporal frequency as desiredby researchers and managers.

Early technologies used in assessing faunal presence gener-ally involved automated cameras with infrared switches. However,these have mostly been used to identify particular species or indi-viduals rather than diversity, so they will not be considered inthis review. Recently, there as been interest in using bioacousticmonitoring to assess the diversity of sites where species make reg-ular ‘calls’ (Riede, 1993, 1997; Sueur et al., 2008) (see for exampleFig. 3). Bioacoustic sensors can be placed throughout a landscapeand programmed to collect recordings at particular times of day.While such sensor networks dramatically improve the efficiency ofdata collection, those data still need to be analysed in some way.Aside from having trained ecologists listen to each recording (whichdefeats the purpose of using a sensor network to improve efficien-
cies over larger scales), one alternative is to use species recognitionsoftware to detect individual calls of different species. Thus far, suchsoftware has been primarily developed to identify a few closelyrelated species (e.g. Trifa et al., 2008), but it could be applied toall species in the ecosystem. However, such software has to be

310 A. Zerger et al. / International Journal of Applied Earth Ob

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ig. 3. Bioacoustic sensor inside weather-proof housing, with an antenna foretwork communication and a wind noise reduction device on the top of the micro-hone. This unit is designed to remotely record the sound profile of a site foriodiversity assessment.

eveloped anew for every ecosystem and possibly every region,epending on geographic variation in calls and songs, and oftenequires large numbers of recordings specifically from the sites ofnterest.

Recently, progress has been made towards developing a simpleay of assessing site diversity based on the entire ‘sound profile’

f the site. The assumption is that different species living in a par-icular ecosystem will evolve calls and songs at a wide range ofifferent frequencies to avoid acoustic competition with each other.s a consequence, the more species present, the greater the over-ll complexity of the sound profile. Thus far, there has only beenne published attempt to develop and test a method of assess-ng sound profile complexity as a biodiversity surrogate. Sueur etl. (2008) calculated an equivalent of Shannon’s diversity indexased on acoustic entropy over different frequency bands and time

ntervals of a recording. Software embedded within a sensor net-ork could easily calculate this measure for any recording, site,

r set of sites over time and transmit this pre-processed informa-ion directly to land managers. Sueur et al. (2008) found that their

easure of acoustic entropy correlated with the species richness ofimulated recordings, but their method has yet to be tested usingeld-based recordings and actual biodiversity data.

.4. Diversity assessment – technical challenges

Detection and identification of fauna presents a unique setf challenges for environmental sensor networks. Whereas pro-essing of simple scalar measurements, such as temperature andumidity, can be achieved with standard low-power platforms;


detection and identification of fauna is most effectively achievedvia more intensive auditory and visual sensing. Given the high sam-ple rates required to sample these types of signals, more data mustbe handled than can be achieved with the processors and radios ontypical, low-powered wireless sensor platforms. As such, there isincreasing focus (Akyildiz et al., 2007; Greenstein et al., 2006; Xieet al., 2008) on the use of more powerful co-processors which canhandle large quantities of audio and video data, including the cap-ture, signal processing and feature extraction from the raw data.The downside to the use of this additional processing power isthe significant increase in energy consumption. As a consequence,these types of platforms must use this additional processing powersparingly meaning that triggering strategies (when to sample andprocess audio or video) is an important topic of research. A relatedfield of research is that of distributed data storage. The rapidlydecreasing cost and available size of compact-flash storage technol-ogy means that it is often far more efficient to store data (either rawor processed) in flash memory, where features of interest can besearched and retrieved at a later point (Nath, 2009). Even if powerand data storage issues can be solved, the challenge of exactly howto use auditory information to quantify diversity may still remain.While relatively simple measures of the sound profile at a site mightbe ideal, these techniques are in their infancy and still need tobe tested against more traditional field-based diversity measures.Table 2 provides a summary of animal movement and diversitymonitoring.

4. Soil sensing

Concerns over food security, hydrologic processes and globalclimate change are transforming agriculture and the way in whichwe use and manage our soils. Consequently, there is a greater needfor detailed information about soils including its measurement,modelling and mapping. Conventional soil surveys cannot providethe soil information at appropriate resolutions which are requiredto support these resource management challenges. The primaryreason is well known; when spatial, functional soil informationis needed, conventional soil sampling and laboratory analysesare too slow and expensive (e.g. Viscarra Rossel and McBratney,1998a). Proximal soil sensing (PSS) can overcome these shortcom-ings (Viscarra Rossel and McBratney, 1998b). Novel technologiesfor capturing soil information are required to assess the effectsof climate change on soils; for providing parameters as an inputto models, to improve the sustainability and efficiency of foodproduction systems through the use of precision agriculture tech-nologies, and for rapidly and accurately assessing and remediatingcontaminated or degraded lands. A major science challenge is todevelop methods that can characterise variability in space and timeto improve our management of soils.

4.1. Proximal soil sensing

PSS refers to field-based techniques that can be used to mea-sure soil chemical, physical, biological and mineralogical propertiesfrom a distance of approximately less than 2 m above the soilsurface (cf. remote sensing). Proximal soil sensors may be activeor passive; they may be invasive, where there is direct sensor-to-soil contact, or non-invasive, measuring properties of the soilfrom above the surface. Proximal soil sensors may either mea-sure the soil property directly or indirectly by finding a covariate
that is easier and cheaper to measure after developing a pedo-transfer function. The sensors may also be mounted on vehiclesfor on-the-go measurements. A review of on-the-go soil sensorsfor use in precision agriculture is provided by Adamchuk et al.(2004).


Table 2Animal movement and diversity monitoring – summary.

Application Variable sensed (and control) Sensor References

Operator-dependentmonitoring of movement

Spatial location of animals in theenvironment

Radio transceiver (based onhuman tracking)

Cochran and Lord (1963), Lord and Cochran(1962), Swain et al. (2008a,b), Tioli et al.(2009), Aarts et al. (2008), Dietz andHeideJorgensen (1995), Fitzgerald et al. (2005),Kelly et al. (2008), Kolenosky and Johnston(1967), Larkin et al. (1996)

Autonomous monitoring ofmovement

Spatial location of animals in theenvironment

Radiotransceiver fixed antennasystems, GPS, and Argossatellite systems, video

Blake et al. (2001), Broseth and Pedersen(2000), Cargnelutti et al. (2007), Clark et al.(2006), D’Eon et al. (2002), D’Eon (2003), D’Eonand Delparte (2005), Frair et al. (2004), Girardet al. (2002), Hulbert and French (2001), Jerdeand Visscher (2005), Land et al. (2008), Larkinet al. (1996), MacNulty et al. (2008), Rempeland Rodgers (1997), Swain et al. (2008a,b),Turner et al. (2000), Ungar et al. (2005)

Social encounters Radio transceiver Ji et al. (2005), Prange et al. (2006), Swain andBishop-Hurley (2007), Böhm et al. (2009)

Off-animal control ofmovement

Movement through animalhandling facilities

Machine vision and RFID Bowen et al. (2009), Charmley et al. (2006),Finch et al. (2006)

On-animal control ofmovement

Spatial location and relationshipwith pre-defined co-ordinates

GPS Anderson (2007), Bishop-Hurley et al. (2007),Lee et al. (2007, 2009), Monod et al. (2008),Wark et al. (2009)

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ture through the profile (to 1 m depth). Fig. 4 shows an example ofgypsum block sensors for measuring soil moisture. Ramanathanet al. (2006) describe a series of wireless sensor networks formonitoring soil CO2, temperature and moisture, ISEs selective forammonium, calcium, carbonate, chloride, pH, reduction-oxidation,

Diversity assessment Species richness, abundanceand/or evenness (beta diversity)

Although PSS may produce measurements that are less accuratehan conventional laboratory analyses, the rationale for PSS is thatt facilitates the collection of larger amounts of spatial data usingheaper, simpler and less laborious techniques. Thus PSS improveshe efficiency of soil data collection and provides more informa-ion on the patterns of soil variation than conventional techniqueshere only few very accurate measurements are taken (Viscarraossel and Walter, 2004). Furthermore, PSS measurements areade in situ, providing the information at field conditions and intimely manner. Therefore, much of the research on PSS aims to

ntegrate these sensors to develop techniques that can be used initu, which are quantitative, reasonably accurate, rapid and inex-ensive.

.2. Proximal soil sensor technology

Proximal soil sensors are becoming smaller, faster, moreccurate, more energy efficient, wireless, and more intelligentlyrogrammed. They are being developed for the acquisition ofhysical, chemical, biological and mineralogical soil informationhrough the use of electromagnetic, electrochemical and mechani-al methods. Some of this work includes investigation of the use oflectromagnetic induction (EMI) and electrical resistivity, magneticusceptibility, ground penetrating radar (GPR), �-radiometrics,nelastic neutron scattering (INS), diffuse reflectance spectroscopysing visible-near-infrared (vis-NIR) and mid-infrared (mid-IR)nergies, laser-induced breakdown spectroscopy (LIBS), ion selec-ive field effect transistors (ISFET) and ion selective electrodes (ISE),

echanical draft systems and wireless sensors. Table 3 provides aummary of these techniques.

For most soil properties there are different sensing options thatan be used, ranging from direct methods that are physically based,o those which are indirect and correlative (Table 3). The correlative
echniques rely on empirical calibrations and are thus invariablyess accurate than direct methods. Indirect methods also tend to
ork better under restricted local soil conditions and often failhen under other conditions. This conditional success is controlled

y the type of soil: its mineralogy, particle-size distribution, pres-

ry sensor Riede (1993, 1997), Sueur et al. (2008), Trifa etal. (2008)

ence of segregations (e.g. iron oxides and oxyhydroxides), solublesalts, water content, and the abundance and composition of organicmatter. However, indirect techniques are also generally less expen-sive, technologically and methodologically better developed andmore readily available to users.

4.3. Wireless sensor networks for soil measurement (WSN)

Wireless sensor networks for measuring soil have focused onsoil water content and soil temperature. An example of such a sys-tem is the farm-based wireless sensor network developed by Sikkaet al. (2006), which is part of a wider network containing 12 soilmoisture nodes using up to 5 gypsum blocks to measure soil mois-

Fig. 4. Gypsum block sensors used for measuring soil moisture at varying depths.

312 A. Zerger et al. / International Journal of Applied Earth Observation and Geoinformation 12 (2010) 303–316

Table 3Soil sensing for measuring soil attributes – summary. Attributes in bold are physically based measurements, others are correlative.

Application Variable sensed Sensor References

�-rays (10–12 m) Elements, carbon INS Wielopolski et al. (2008)Water content CRN Zreda et al. (2008)Water content TNM/NP Chanasyk and Naeth (1996)Water content, bulk density,compaction

Active �

Potassium, uranium, thorium, clay,sand, iron-oxides

Passive � Bierwirth (1996), Wong and Harper (1999),Viscarra Rossel et al. (2007)

X-rays (10–10 m) Major nutrients, minor nutrients,heavy metals, clay minerals,iron-oxides

XRF – X-rays Kalnicky and Singhvi (2001)

Clay minerals XRD – X-rays Gianoncelli et al. (2008), Sarrazin et al. (2005)Nitrogen UVFe-oxides, carbon Vis Viscarra Rossel et al. (2008)Water content, carbon, clay minerals,Fe-oxides, clay content, CEC, majornutrients, pH, heavy metals

NIR Shepherd and Walsh (2007), Ben-Dor et al.(2008), Viscarra Rossel et al. (2009)

Water content, nitrogen, carbon, clayminerals, Fe-oxides, sand content, claycontent, CEC, pH, buffering capacityand lime requirement, heavy metals

MIR Janik et al. (1998), Viscarra Rossel et al. (2006)

Nitrogen, potassium, nutrients,heavy metals, carbon, sand content,clay minerals

LIBS Cremers et al. (1966), Cremers et al. (2001),Harmon et al. (2005), Mosier-Boss et al. (2002)

Microwave (10–2 m) Water content Microwave Jackson (1993), Whalley (1991)TDR Water content Noborio (2001)FDR and capacitance Water content Dean et al. (1987), Paltineanu and Starr (1997)GPR Water content, structure Huisman et al. (2003)NMR Water content Lubczynski and Roy (2004), Paetzold et al.

(1985)EMI Salinity and sodicity, water content,

clay contentCorwin and Lesch (2005), Sudduth et al. (2001)

Electrical resistivity ER Salinity and sodicity, water content,clay content, CEC

Corwin and Lesch (2005)

Gypsum Soil water suction Johnston (2000)

Electrochemical ECh Nutrients, salinity and sodicity, pH,buffering capacity and limerequirement

Adamchuk et al. (1999), Adsett and Zoerb(1991), Artigas et al. (2001), Birrell andHummel (1997), Viscarra Rossel andMcBratney (1997)

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nd nitrate were also incorporated but with limited success due toensor response variability.

Most research pertaining to wireless environmental sensoretworks has focused on radio communication links where theominant communication medium is open air. A unique andngoing challenge for sensing of soil environments is enablingadio communication links directly through soil (Akyildiz andtuntebeck, 2006; Li et al., 2007). The key driver for addressinghis is that it enables the deployment of networks in environmentsuch as cropped paddocks where harvesters or other equipmentay need to pass over at frequent intervals. Physical radio and

ntenna designs, along with improvements to the core commu-ications architecture are a particular focus of current research tonable improved communication links through soil over longer dis-ances whilst minimising power consumption. A related area ofesearch is also around energy harvesting for underground nodesiven solar energy is not available. Energy sources such as thermalradients, vibration and background radio signals are current areasf focus for ongoing research (Akyildiz and Stuntebeck, 2006).

.4. Multi-sensor platforms

As every soil sensing technology has strengths and weaknessesnd no single sensor can measure all soil properties (Table 3), theelection of a complementary set of sensors to measure the required

n method, NP = neutron probe, XRF = X-ray fluorescence, XRD = X-ray diffraction,ed breakdown spectroscopy, TDR = time domain reflectometry, FDR = frequencye, EMI = electromagnetic induction, ER = electrical resistivity, ECh = electroch.

suite of soil properties is important. Integrating multiple proximalsoil sensors in a single multi-sensor platform (MSP) can provide anumber of operational benefits over single-sensor systems includ-ing the following:

(i) robust operational performance;(ii) increased confidence as independent measurements are made

on the same soil;iii) extended attribute coverage; and

(iv) increased dimensionality of the measurement space, i.e. differ-ent sensors measuring various portions of the electromagneticspectrum.

There are limited references to multi-sensor systems in the sci-entific literature, and where these receive attention they are inearly stages of development. For example, Christy et al. (2004)reported the use of a mobile sensor platform that simultaneouslymeasures soil pH and electrical conductivity (EC). A NIR sensorhas also recently been added to this MSP (Christy, 2008). Tayloret al. (2006) reported the development of a multi-sensor platform
consisting of two EMI instruments, an electrical resistivity sen-sor, a g-radiometer and a high resolution DGPS. Yurui et al. (2008)reported the development of a multi-sensor technique for measur-ing soil physical properties, namely, soil water, mechanical strengthand EC.

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. Future directions and conclusion

This review highlights that the applied operational use of ESNsemains in its infancy and we can expect a rapid advancement inheir potential as technical advancements continue. For example,he miniaturisation, improvement in quality and reduction in costf charge coupled devices (CCDs) can provide a suite of new oppor-unities for proximal image acquisition for vegetation monitoringpplications. In terms of future directions the following are seen asmportant:

(i) A greater focus on validating results from sensor technologiesto determine how effectively they replicate manual monitoringmethods.

(ii) A shift from single-node deployments to multi-node deploy-ments and wireless sensor networks.

iii) Coupling multiple domain sensors to examine causal relation-ships.

iv) In situ data processing and analysis.

As ESN technologies are enabling technologies, sensor devel-pment must occur before extensive testing, calibration andalidation can occur. The review highlights that many of the exam-les remain research-focused exercises that are still evaluating thebility of sensors to replicate or replace conventional monitoringethods. Consequently the studies examined here lack the exper-

mentation across different landscapes and situations to safelyonclude that the technologies provide a robust replacement forxisting methods. For example, the use of multispectral vegetationensors still pose a number of major image calibration challengesradiometric calibration) in different landscapes and for differentegetation types. Hart and Martinez (2006) have argued that these of sensor networks does not mean there will be less need foreldwork as it will still be required to install, monitor and maintainsystem. We concur with this as sensors will allow us to answerifferent questions (particularly those regarding temporal changeor both long-term and rapidly fluctuating phenomenon), and theyill allow us to apply our resource intensive field work to different

nvironmental problems.The examples in this review are generally single-node deploy-

ents with only limited reliance on network technologies. Whilehe ability to capture fine-scale spatial and temporal variationsn the environment by deploying multiple nodes is one of theromises of ESNs, it is only recently that they have commencedtilising wireless technologies (primarily for animal movement)Swain et al., 2008a; Wark et al., 2009). This is driven by two fac-ors. First, miniaturised low-power sensor hardware have becomevailable, along with the associated ICT development in sensoretworks (Hart and Martinez, 2006). Second, there is still muchesearch-based calibration and validation to be completed beforeoving to multiple-node deployments. This calibration and vali-

ation requirement can be effectively explored with a single wiredode. Bearing in mind point (1) above, there remains a need for

urther calibration/validation research to evaluate which environ-ental attributes can be best sensed with sensor technologies. This

nderpinning research is essential before multi-node deploymentsan be applied operationally, and over extended periods of time.

One of the primary short-term opportunities for ESNs are theoupling of sensors from different application domains to exam-ne causality in environmental change. Few if any of the studiesn this review coupled sensors in this way. For example, as cli-

ate/weather sensors are relatively mature technologies, they canlready be coupled with vegetation and soil sensors to exam-ne responses to rainfall and temperature. Applied examples ofoupling include the use of rainfall data to assess rates of weedncursions to better target mitigation in space and time; or the

servation and Geoinformation 12 (2010) 303–316 313

use of temperature data to improve our understanding of cropresponses to frost damage.

Finally, we see significant opportunities for more innovativein situ solutions to data processing and filtering to reduce theamount of environmental data that requires storage, transfer viaa sensor network, and ultimately processing at a later time. Somesensor applications such as multispectral sensing, animal move-ment and aspects of soil sensing may not have this requirementas data volumes are already relatively low (e.g. an individual inte-ger representing spectral response in a particular wavelength; or alatitude and longitude with a time stamp). However, some sen-sor deployments, and in particular image and video acquisition(Graham et al., 2006), require the storage and transfer of relativelylarge datasets which makes the use of wireless sensor networksvery challenging. Some in situ analyses (e.g. image classifications)could significantly reduce data volumes thus enabling more effec-tive operational deployments. In some remote deployments this isessential if environmental sensors are to be deployed for extendedduration.

ESN technologies provide new opportunities for improving ourunderstanding of environmental change and response to naturaland anthropogenic drivers. Although this review has limited itstreatment of ICT issues, we are aware that successful operationaluse of sensors requires both ICT advances, and detailed testing,validation and reporting of sensor performance in domain appli-cation areas (e.g. vegetation, animal movement and diversity, andsoil science). As many sensor technologies in the vegetation, soilsand animal movement domains are in their infancy, there remainquestions about their thematic accuracy and precision, and the reli-ability of observations through time. These questions may also bedirected at manual field-based recording methods such as inter-operator variability and repeatability of measurements throughtime and space (Marsh and Hanlon, 2007; Mizoue and Dobbertin,2004). Of particular promise is that in some domains, namely cli-mate, terrestrial hydrology and oceanography, the use of ESNstechnologies is ubiquitous. When coupled with synoptic remotesensing technologies such as satellite imaging, these domains pro-vide daily evidence of the future potential of ESNs for terrestrialenvironmental monitoring.

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environmental sensor networks for vegetation, animal and soil sciences

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