Review
Environmental sensor networks for vegetation, animal and soil sciences

https://doi.org/10.1016/j.jag.2010.05.001Get rights and content

Abstract

Environmental sensor networks (ESNs) provide new opportunities for improving our understanding of the environment. In contrast to remote sensing technologies where measurements are made from large distances (e.g. satellite imagery, aerial photography, airborne radiometric surveys), ESNs focus on measurements that are made in close proximity to the target environmental phenomenon. Sensors can be used to collect a much larger number of measurements, which are quantitative and repeatable. They can also be deployed in locations that may otherwise be difficult to visit regularly. Sensors that are commonly used in the environmental sciences include ground-based multispectral vegetation sensors, soil moisture 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 transmit data to decision-makers. The climate and weather monitoring domains provide useful examples of how ESNs can provide real-time monitoring of environmental change (e.g. temperature, rainfall, sea-surface temperature) to many users. The objective of this review is to examine state-of-the-art use of ESNs for three 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) we review the technologies, the attributes that they sense and briefly examine the technical limitations. We conclude with a discussion of future directions.

Introduction

Recent advances in information and communication technologies (ICT) and the development of Environmental sensor networks (ESNs) provide new opportunities for using sensors to monitor environmental change. ESNs can be used to help improve our understanding of environmental phenomena and guide natural resource management (NRM) (e.g. evaluating the effect of climate change and grazing impacts on tree regeneration through a number of seasons; tracking weed incursions; monitoring grazing impacts on native groundcover; and tracking cattle). Environmental sensor networks typically refer to sensing that occurs in close proximity to the target, as opposed to remote sensors that sense at large distances from the target. Sensors in the environmental sciences include, for example, digital cameras, visible to near-infrared spectrometers, soil water sensors, GPS-enabled movement tracking devices and bioacoustic sensors. Sensors can be advantageous in environmental applications because they can be: (i) used to acquire time-series data (Hart and Martinez, 2006), (ii) deployed in locations that are difficult to access and therefore they can overcome practical limitations of traditional monitoring, and (iii) are more time- and cost-effective than traditional field methods. For instance, soil moisture probes provide levels of precision, accuracy and repeatability not traditionally achievable using manual techniques.

Research into ESNs has traditionally focused on the software and hardware engineering components of sensor networks. This has included hardware design (Klingbeil and Wark, 2008, Rahimi et al., 2005), algorithms for distributed computation in sensor networks (Ferentinos and Tsiligiridis, 2007, Worboys and Duckham, 2006), methods for monitoring and positioning nodes (Kohno et al., 1999, Shih et al., 2008), energy efficiency in sensor and network design (Ci et al., 2007) and more recently integrating and hosting sensor data via the internet (Liang et al., 2005). Where there is intent to discuss environmental applications, there remains a focus on engineering challenges (Hart and Martinez, 2006, Pon et al., 2005, Szewczyk et al., 2004). The focus on engineering is not surprising given that sensors and sensor networks are enabling technologies that must be developed before applied NRM-focused sensor studies can commence. Recent technical advancements including hardware miniaturisation, improvements in the efficiency of solar technology, the widespread availability of wireless communication networks such as 3G networks, and improvements and decreases in the cost of sensors, means that novel deployments of ESNs are now possible. Thus, it is imperative that the focus shifts towards understanding the specific environmental domains where research and management will most benefit from using ESNs.

This review moves away from the traditional focus on ICT elements and focuses on applications for proximal monitoring of terrestrial environmental change. We specifically limit our treatment of sensor engineering, wireless network design, algorithms for sensor management and supporting technologies such as database tools for the real-time delivery of sensor data through the Internet. Owing to the breadth of possible applications, we have restricted this review to the following terrestrial applications in which we believe new technologies will be most useful: (a) vegetation monitoring (including crops and pastures), (b) animal movement and diversity assessment, and (c) soil sensing. Climate, aquatic and hydrologic applications are so extensive that they are considered beyond the scope of this review. As this review focuses on proximal sensors, remote sensing technologies such as multispectral or hyperspectral images from satellite- and airborne-sensors are not included. However, where proximal sensors are used to calibrate and validate remote sensors, such as through the use of ground-based hand-held spectrometers, these are discussed, within the context of terrestrial vegetation monitoring applications.

Within each of these application domains, we review the technologies that have been or could be used, and comment on how and why environmental sensing in particular could help address particular research questions or management issues. We also briefly examine technological limitations. We conclude with a synthesis which explores future directions and research needs for applying sensors in environmental monitoring.

Section snippets

Monitoring natural terrestrial vegetation using sensors

The use of ESNs in vegetation studies is dominated by: (i) array-based measurements from digital photography covering small areas of <5 m, and typically focused on assessing characteristics of 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 measurements using spectral or other sensors for assessing vegetation characteristics such as yield or biomass.

Animal movement for environmental monitoring

Natural biotic resources are mostly managed within multi-use landscapes (i.e. natural areas interspersed with more intensive human uses, including agricultural and urban areas). Both wild and domesticated animals inhabit these landscapes, and landscape structure and variability influence how animals behave and use the resources. Problems can arise when native animals cannot maintain their natural foraging and dispersal movements, leading to population declines, or when native or domesticated

Soil sensing

Concerns over food security, hydrologic processes and global climate change are transforming agriculture and the way in which we use and manage our soils. Consequently, there is a greater need for detailed information about soils including its measurement, modelling and mapping. Conventional soil surveys cannot provide the soil information at appropriate resolutions which are required to support these resource management challenges. The primary reason is well known; when spatial, functional

Future directions and conclusion

This review highlights that the applied operational use of ESNs remains in its infancy and we can expect a rapid advancement in their potential as technical advancements continue. For example, the miniaturisation, improvement in quality and reduction in cost of charge coupled devices (CCDs) can provide a suite of new opportunities for proximal image acquisition for vegetation monitoring applications. In terms of future directions the following are seen as important:

  • (i)

    A greater focus on validating

References (177)

  • S. Cox

    Information technology: the global key to precision agriculture and sustainability

    Computers and Electronics in Agriculture

    (2002)
  • A. Cremers et al.

    Geometry effects of specific electrical conductance in clays and soils

    Clays and Clay Minerals

    (1966)
  • P.J. Curran et al.

    The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration

    Remote Sensing of Environment

    (1991)
  • A. Davidson et al.

    The influence of vegetation index and spatial resolution on a two-date remote sensing-derived relation to C4 species coverage

    Remote Sensing of the Environment

    (2001)
  • T.J. Dean et al.

    Soil moisture measurement by an improved capacitance technique. Part I. Sensor design and performance

    Journal of Hydrology

    (1987)
  • C.D. Elvidge et al.

    Comparison of broad-band and narrow-band and red and near-infrared vegetation indexes

    Remote Sensing of Environment

    (1995)
  • K.P. Ferentinos et al.

    Adaptive design optimization of wireless sensor networks using genetic algorithms

    Computer Networks

    (2007)
  • B. Gao

    NDWI – a normalised difference water index for remote sensing of vegetation liquidwater from outer space

    Remote Sensing of Environment

    (1996)
  • A.A. Gitelson et al.

    Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves

    Journal of Plant Physiology

    (2003)
  • E.A. Graham et al.

    Budburst and leaf area expansion measured with a novel mobile camera system and simple color thresholding

    Environmental and Experimental Botany

    (2009)
  • J.K. Hart et al.

    Environmental sensor networks: a revolution in the earth system science?

    Earth Science Reviews

    (2006)
  • J. Hemming et al.

    A-precision agriculture: computer-vision-based weed identification under field conditions using controlled lighting

    Journal of Agricultural Engineering Research

    (2001)
  • G. Janeau et al.

    Performance of differential GPS collars in temperate mountain forest

    Comptes Rendus Biologies

    (2004)
  • D. Kalnicky et al.

    Field portable XRF analysis of environmental samples

    Journal of Hazardous Materials

    (2001)
  • M. Kohno et al.

    An adaptive sensor network system for complex environments

    Robotics and Autonomous Systems

    (1999)
  • G. Aarts et al.

    Estimating space-use and habitat preference from wildlife telemetry data

    Ecography

    (2008)
  • V.I. Adamchuk et al.

    An automated sampling system for measuring soil pH

    Transactions of the ASAE

    (1999)
  • F.J. Adamsen et al.

    Method for using images from a color digital camera to estimate flower number

    Crop Science

    (2000)
  • J.F. Adsett et al.

    Automated field monitoring of soil nitrate levels

    Automated Agriculture for the 21st Century

    (1991)
  • C.T. Agouridis et al.

    Suitability of a GPS collar for grazing studies

    Transactions of the ASAE

    (2004)
  • R. Anderson-Sprecher

    Robust estimates of wildlife location using telemetry data

    Biometrics

    (1994)
  • D.M. Anderson

    Virtual fencing – past, present and future

    Rangeland Journal

    (2007)
  • D.W. Bailey et al.

    Mechanisms that result in large herbivore grazing distribution patterns

    Journal of Range Management

    (1996)
  • Balzano, L., Nowak, R., 2007. Blind calibration of sensor networks. In: IEEE/ACM International Conference on...
  • E. Ben-Dor et al.

    A novel method of classifying soil profiles in the field using optical means

    Soil Science Society of America Journal

    (2008)
  • L. Bennett et al.

    Close range vertical photography for measuring cover changes in perennial grasslands

    Journal of Range Management

    (2000)
  • T.W. Berge et al.

    Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals

    Precision Agriculture

    (2008)
  • Bierwirth, P., 1996. Investigation of airborne gamma-ray images as a rapid mapping tool for soil and land degradation –...
  • Birrell, S.J., Hummel, J.W., 1997. Multi-sensor ISFET system for soil analysis. Precision agriculture ‘97. Volume II....
  • S. Blake et al.

    GPS telemetry of forest elephants in central Africa: results of a preliminary study

    African Journal of Ecology

    (2001)
  • M. Böhm et al.

    Contact networks in a wildlife-livestock host community: identifying high-risk individuals in the transmission of bovine TB among badgers and cattle

    PLoS ONE

    (2009)
  • D. Booth et al.

    Technical note: lightweight camera stand for close-to-earth remote sensing

    Journal of Range Management

    (2004)
  • D. Booth et al.

    Image analysis compared with other methods for measuring ground cover

    Arid Land Management and Research

    (2005)
  • D.T. Booth et al.

    Image-based monitoring to measure ecological change in rangeland

    Frontiers in Ecology and the Environment

    (2008)
  • M.K. Bowen et al.

    Evaluation of a remote drafting system for regulating sheep access to supplement

    Animal Production Science

    (2009)
  • J.L. Bowman et al.

    Evaluation of a GPS collar for white-tailed deer

    Wildlife Society Bulletin

    (2000)
  • H. Broseth et al.

    Hunting effort and game vulnerability studies on a small scale: a new technique combining radio-telemetry, GPS and GIS

    Journal of Applied Ecology

    (2000)
  • B. Bychkovskiy et al.

    A collaborative approach to in-place sensor calibration

    Lecture Notes in Computer Science

    (2003)
  • B. Cargnelutti et al.

    Testing Global Positioning System performance for wildlife monitoring using mobile collars and known reference points

    Journal of Wildlife Management

    (2007)
  • D.S. Chanasyk et al.

    Field measurement of soil moisture using neutron probes

    Canadian Journal of Soil Science

    (1996)
  • Cited by (60)

    • OCAGraph: An effective observation capability association model for Earth observation sensor planning

      2022, International Journal of Applied Earth Observation and Geoinformation
      Citation Excerpt :

      Earth observation (EO) sensors, such as satellite and hydrological sensors, are used to analyze and collect geospatial information from Earth environments (Nittel et al., 2004; Zerger et al., 2010).

    • Pedometrics timeline

      2019, Geoderma
    • Citizen science can improve conservation science, natural resource management, and environmental protection

      2017, Biological Conservation
      Citation Excerpt :

      However, volunteers usually contribute by collecting data in projects designed by professional scientists. The information technology revolution and the advent of the Internet and location-aware mobile technologies equipped with cameras and other sensors (Hart and Martinez, 2006; Zerger et al., 2010) have greatly increased the capacity of what citizen scientists can do, leading to the rising use of citizen science data in peer-reviewed publications (Ries and Oberhauser, 2015). People can now access, store, manage, analyze, and share vast amounts of data and communicate information quickly and easily (Poelen et al., 2014).

    View all citing articles on Scopus
    View full text