Detecting Vegetation Stress As a Soil Contamination Proxy: a Review of Optical Proximal and Remote Sensing Techniques

Home , Soil contamination

International Journal of Environmental Science and Technology (2019) 16:2511–2524 https://doi.org/10.1007/s13762-019-02310-w

REVIEW

Detecting vegetation stress as a soil contamination proxy: a review of optical proximal and remote sensing techniques

A. Gholizadeh1,2 · V. Kopačková1

Received: 26 June 2018 / Revised: 24 January 2019 / Accepted: 2 March 2019 / Published online: 18 March 2019 © Islamic Azad University (IAU) 2019

Abstract Soil contamination is a worldwide crisis, which diminishes food and agricultural production. Alterations in the soil environment due to soil contamination cause biophysical and biochemical changes in vegetation. Due to dynamic nature of these changes, early monitoring can permit for preventive interferences before intense and sometimes inevitable vegetation and soil problems occur. As plants are rooted in soil substrate, vegetation changes can be used as bio-indicators of soil conditions. Traditionally, vegetation changes have been usually determined by visual analysis or detected after major destructive sampling during the growth period. As the characteristics of vegetation infuence its spectral properties, efective remote and non-contact detection methods ofer an alternative and near real-time way for detecting plant changes, even prior to visual symptoms and negative efects appearance. The aim of the current study is to review the potential of optical proximal and remote sensing techniques at diferent platforms for indirect assessment of plant–soil interactions via monitoring vegetation anomalies related to soil contamination. It is strongly felt that this rapidly progressing technological direction will permit extending the use of the techniques to geology, soil science and precision agriculture and an overall broad range of applications.

Keywords Bio-indicator · Proximal sensing · Remote sensing · Soil contamination · Vegetation stress

Introduction structure, compaction status and soil depth. Both chemical and physical processes can bring water loss and soil toxicity Soil is the main natural resource for food and energy produc- as well as other efects including erosion, deposition and soil tion. It controls the movement of water in the landscape and swelling that all together direct to a reduction in soil pro- operates as a natural flter for probable leaching of contami- ductivity and fertility in space and time (Sahoo et al. 2012; nants into the environmental spheres (Stenberg et al. 2010); Chuncai et al. 2014). however, it can be degraded chemically and physically. Soil contamination, which is a reason for soil degrada- Chemical processes connect to parameters of the soil that tion, is a common and well-known environmental concern. tie to soil chemical components and their reactions, includ- It refers to the inflteration process of non-pedogenic con- ing salinity, fertility decline and contamination, whereas stituents that are not related to the natural generation or physical processes describe alterations in particle size, soil formation of the soil. Soil can be contaminated from dif- ferent sources such as petroleum hydrocarbons (PHCs) and natural gas (from fuel pipelines and tanks leakage) as well Editorial responsibility: Gaurav Sharma. as potentially toxic elements (PTEs) (from opencast mines and anthropogenic activities). Oil and gas accumulation as * A. Gholizadeh well as several chemical alterations in soils can be occured [email protected] due to PHC leakage (Noomen et al. 2006). Anthropogenic 1 Czech Geological Survey, Klarov 3, 11800 Prague 1, activities are also responsible for some signifcant damages Czech Republic to the Earth and mainly cause conversions to the geological, 2 Department of Soil Science and Soil Protection, Faculty hydrological and vegetation condition (Gotze et al. 2016b). of Agrobiology, Food and Natural Resources, Czech Accordingly, such alterations in the soil environment are University of Life Sciences Prague, Kamycka 129, the reasons for some visually perceptible changes including 16500 Prague, Czech Republic

Vol.:(0123456789)1 3 2512 International Journal of Environmental Science and Technology (2019) 16:2511–2524 leaf chlorosis, thin vegetative cover and atrophic growth the vegetation’s attributes infuence its spectral properties, of the vegetation (Schumacher 1996; Smith et al. 2004a; efective remote and non-contacted spectroscopic monitor- Rosso et al. 2005). Garnier et al. (2007) stated that there is ing methods ofer an alternative and near real-time method a relationship between vegetation parameters as reactions of plant changes, even before the appearance of visual and changes in soil condition. For example, diferent kinds symptoms (Bayat et al. 2016). Using various spectroscopic of contaminants are picked up from the soil and transferred techniques at diferent ranges and platforms afects the total to the leaves via transpiration (Brentner et al. 2010), which precision of results; however, one of the gaps in monitor- are then transformed and conjugated with other compounds ing soil contamination using the spectral signatures of veg- in the cell wall (Komossa et al. 1995; Verkleij et al. 2009). etation is the lack of an efciency assessment for diferent Furthermore, some of the contaminants accumulate in leaf platforms, which needs further work. If vegetation status is tissues, bringing stress and interrupting photosynthetic pro- to be used as indicators of soil contamination contents, then cesses (Zinnert et al. 2013). Due to the dynamic nature of the intention of this review is to prepare a source of up-to- these efects, early monitoring of contaminants can allow date information on the past and current role of proximal suppressive interventions before severe and irreversible veg- and remote sensing techniques employing optical sensor etation and soil problems arise. data, when assessing vegetation anomalies due to soil con- The conventional methods of soil contamination assess- tamination. To illustrate this, soil contaminants and their ment in large areas involve feld data collection, chemical relationship with vegetation condition are discussed, as they analyses in a laboratory as well as geostatistical interpola- afect vegetation structure, functioning, growth and yield. tion, which are expensive and time-consuming. For instance There then follows a discussion on capability and accuracy according to Shi et al. (2016), little attention has been given of proximal and remote sensing techniques (in various plat- to soils contaminated with heavy metals due to limited forms) for indirect prediction of soil contamination using funds. In addition, conventional methods are inefective vegetation symptoms, and fnally, there is a short conclu- for detecting small soil changes; therefore, controlling the sion of the issues surrounding the use of remote detection efects of contaminants before causing irreparable impacts, methods for soil analysis based on vegetation parameters for will be neglected (Sanches et al. 2013a). future research. Vegetation changes around areas with contaminated and unhealthy soil have been reported. Due to plant–soil interaction, change in plants morphological and physiological properties can be used as indicators of the ecological, geo- Soil contamination and its relationship chemical and hydrological situation (Zinnert et al. 2013). to vegetation health Furthermore, Roelofsen et al. (2015) stated that in the con- text of nature management, acquiring the soil conditions In order to quantify the vegetation and soil health relation- using their efects on vegetation is an efective alterna- ship, one approach is to understand vegetation stressors, tive due to rather narrow tolerance of plants communities which directly relate to soil condition. Vegetation plays an towards soil factors, which makes them representatives of important role in the movement of contaminants through the site’s conditions. Changes in plants traditionally have the soil; however, vegetation health faces diferent types been investigated by visual inspection or detecting via broad of stresses and changes negatively due to biotic and abi- analysis of destructive sampling during the growth period otic environmental factors (Fig. 1) (Schulze et al. 2005; (Chaerle and Van der Straeten 2001; Fedotov et al. 2016). As Tuominen et al. 2009).

Fig. 1 Biotic and abiotic environmental stressors

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The above-mentioned environmental stressors are global several reasons, for instance, Van der Werf (2006) analysed concerns and afect vegetation structure, health, function- the soil parameters in and around a seep. No signifcant dif- ing and growth in various ways (Bayat et al. 2016). Thus, ferences in mineral composition and pH between the soils research on them and their relation to vegetation and soil were found. However, the level of soil moisture was 13% condition should receive more attention. Due to the insist- lower in the soil close to the seep than the soil at 20 m dis- ing character and long biological half-lives of soil contami- tance from the centre of the seepage. Since long-term seep- nants and their irreparable infuence on soil quality as well age of gas and oil bring groundwater to the surface (Jones as vegetation and human health (Wu et al. 2005; Gholizadeh and Drozd 1983), it is possible that groundwater collected et al. 2015), some of the most common soil contaminants at the edges of the halo causing changes in vegetation pat- and their efects on vegetation and soil are discussed below. terns. According to Noomen et al. (2012), another reason The most common contaminants that cause vegetation for species composition alteration may be that the halo is a stress are PHCs, natural gas, PTEs, acidifed mine substrates shift zone between the areas near the seep with low O2 con- and heavy metals. It is known that natural gas in the soil tents and further areas with optimal O2 concentrations. Since infuences vegetation health (Noomen et al. 2006). As gas species have diferent sensitivity to root zone O2 shortage, itself is invisible, alterations in vegetation health could con- species tolerant to low O 2 concentrations may grow in the sider as a gas leakage indicator. The gas is transferred into halo (Noomen et al. 2012). It can be said that various reasons the plant through the root system, where it is metabolized or may infuence plants contaminated with hydrocarbons. Some passed via the plant in the transpiration stream (Smith et al. examples are the lack of O 2 (Pirone 1960) caused by the 2004b). The stress signs in reaction to gas leaks are assumed hydrocarbons’ degradation, methanotrophic CH4 oxidation common responses to the soil oxygen (O2) displacement (Etiope and Klusman 2002), bacterial O 2 reduction, which (Arthur et al. 1985; Noomen et al. 2012), as natural gas is gone along with an increase in CO2 (Hoeks 1972; Macek causes an extensive range of alterations in the soil depend- et al. 2005; Bergfeld et al. 2006) and fnally a decrease in soil ing on soil type and the length of the gas seepage. Soil O2 porosity, which afects plants (Flower et al. 1981). Although, is displaced by gas (Schumacher 1996). It is also depleted PHCs in the soil have negative infuences on plant growth, by methanotrophic bacteria that use the available methane the reaction of the plants is species-dependent (Van der Meer (CH4) in natural gas as a carbon-based energy source (Smith et al. 2002; Bizecki et al. 2004; Noomen et al. 2006). et al. 2004a; Steven et al. 2006). High carbon dioxide (CO2) Large-scale opencast mines are also a source of contami- concentrations during gas leakage are an additional factor nation and often produce diferent spoil dumps that contain afecting plant growth (Boru et al. 2003). Elevated CO2 acidifed or phytotoxic substrates, which results in changes in the soil destroys trees and other vegetation by acidif- in the hydrology, geology and vegetation status (Schaaf cation of the ground water and by disturbing root respira- et al. 1999; Gotze et al. 2016b). With increasing soil acidi- tion (Macek et al. 2005; Bergfeld et al. 2006; Noomen et al. fcation and pH, nutrient cations are replaced by acidifying 2012). In addition, low O2 and high CO2 concentrations can cations from the exchange sites, so that the base saturation yield variations in soil pH and redox potential (Eh) (Schu- is reduced. However, the direct infuence of soil acidifca- macher 1996), probably causing phytotoxic nutrients mobili- tion on potassium (K) and iron (Fe) seems to be less (Lucas zation (Van der Meer et al. 2002; Noomen et al. 2006). Some and Davis 1961). Soil acidifcation induces nutrients def- researchers stated that the efects of soil natural gas on plants ciency as well as elemental toxicities for plants by zinc (Zn), include a change in the green colour of the leaves (Arthur manganese (Mn), aluminium (Al) and Fe due to the higher et al. 1985; Pysek and Pysek 1989) or decreased numbers of solubility of these elements at low pH values, which limit individuals and restricted growth and reproduction (Pysek plant growth and production, especially that of crop species and Pysek 1989). (Kochian et al. 2004; Fujii 2014). Kopačková (2014) stated Vegetation may also be infuenced by PHCs. Several that acidifcation and acid mine drainage in opencast mines efects including falling of leaves, plants’ lower density, afect the heavy metals and nutrients movement as well as leaf structure changes due to chlorophyll degradation, colour the results of remediation plans for vegetation in mining alteration, weak growth and in excessive cases vegetation areas. Plants growing in regions polluted with heavy metals death may happen for plants grown in soils rich in PHCs confrm stress symptoms including water content reduction, (Smith et al. 2004a; Sanches et al. 2013a, b). Noomen et al. chlorophyll hydrolysis escalation and cellular structure dam- (2006) showed that hydrocarbons caused minor dehydration age (Manios et al. 2003; Clevers et al. 2004; Kooistra et al. of the soil engendering reduced plant water uptake and leaf 2004). Mascher et al. (2002) reported that arsenic (As) is water. Pysek and Pysek (1989) and Bizecki et al. (2004) the cause of oxidative stress in plant tissues that leads to a demonstrated that artifcial hydrocarbon long-term gas leak- serious drop in photosynthetic pigments, thus resulting in age may cause a shift in species diversity. Changes in veg- chlorosis and growth reduction. According to Yang et al. etation patterns due to soil hydrocarbon pollution may have (2004) and Pruvot et al. (2006), lead (Pb) in leaves displaces

1 3 2514 International Journal of Environmental Science and Technology (2019) 16:2511–2524 magnesium (Mg) as the chloroplast central atom and afects cellulose, starch and protein). There is a strong interaction of chlorophyll enzymatic activity, which causes weakened plant light with pigment concentrations in the VIS, strong refec- photosynthesis. However, Lindroos et al. (2007) showed that tance and transmittance by the leaf structure in the NIR and high metal contents in the mineral soil layer do not cause strong absorption features of water in the SWIR (Buitrago a signifcant threat to a Norway spruce (Picea abies) for- et al. 2016), which all cause lower refectances (Van der est ecosystem, due to the superfcial rooting system of the Werf et al. 2008). species. In contrast, Clevers et al. (2004) and Gotze et al. There are two main absorption features in blue (0.45 µm) (2016b) reported that in natural grasslands and foodplains, and in red (0.67 µm) bands that are associated with the two respectively, with a higher level of heavy metal contamina- main leaf pigments (chlorophyll a and b). These strong tion, the availability of nutrients, organic matter and water absorption bands induce a reflectance peak in the yel- supply increased. Generally can be mentioned that it is unde- low-green (0.55 µm) band (Hofer 1978). Sanches et al. niable that vegetation parameters are connected to the soil (2013a) mentioned that during leaf senescence, chloro- and the environment in which the plant is grown (Saberioon phyll decreases, which leads to a refectance rise within the et al. 2014a; Gotze et al. 2016b). red wavelengths. Hence, the spectral response in the VIS range is infuenced by carotenoids that absorb blue light and refect green and red light, which causes yellowing of Vegetation spectral signatures green leaves. Progressive senescence also degrades carotenoids that induces an escalation of leaf refectance within The spectral refectance of vegetation is mainly a function the blue wavelengths (Kumar et al. 2001). According to of tissue optical properties, canopy biophysical properties, Van der Werf et al. (2008), a decline in chlorophyll content soil refectance, viewing geometry and illumination circum- shows the following spectral responses: (1) a reduction in stances (Jacquemoud et al. 1992; Asner 1998). Refected the infrared shoulder height, (2) a reduction in the maxi- electromagnetic radiation can deliver informative data about mum absorption and (3) a shift in the red-edge (RE) posi- the plants’ condition and enable researchers to remotely ana- tion towards shorter wavelengths. Hofer (1978) and Horler lyse the plants’ physiological and chemical characteristics et al. (1983) mentioned that cellulose and leaf pigments are (Cho et al. 2012; Masaitis et al. 2013). transparent to NIR wavelengths and thus in this region, leaf According to Buitrago et al. (2017) regardless of plants structure explains the optical properties. Smith et al. (2004a) species, the spectral assignments of leaves are similar in and Zhang et al. (2012) also stated NIR refectance is highly the optical spectral ranges of the electromagnetic spectrum, afected by cell size, cell layers and mesophyll thickness. In visible–near infrared–shortwave infrared (VIS–NIR–SWIR). fact, in this range, there is a typical refectance plateau in Figure 2 shows the typical spectral response features of the leaf spectrum, and its level depends on the internal leaf green vegetation in detail. According to Gitelson et al. structure. The other optical domain in the SWIR is charac- (2002), dominant factors controlling vegetation refectance terized by the vegetation’s water light absorption, for which can be summarized to photosynthetic pigments (e.g. chlo- there is a negative correlation between refectance and leaf rophyll, carotenoids and xanthophylls), cell structure, leaf water content in this region. It can be concluded that for all water content and biochemical concentrations (e.g. lignin, three of the aforementioned spectral domains, factors that

Fig. 2 Typical spectral response specifcation of green vegetation in detail (Hofer 1978)

1 3 International Journal of Environmental Science and Technology (2019) 16:2511–2524 2515 infuence leaf optical features can be internal from the leaf study of Clevers et al. (2004), the maximum FD of grassland itself or external from the environmental conditions, which spectral features in the RE resulted in a signifcant, negative impact on vegetation health (Guyot 1989). correlation with the heavy metal contents in the soil. They stated metals bring lower chlorophyll concentrations; thus, a higher red refectance is expected. Li et al. (2015) used the Vegetation spectral response to soil refectance spectra of rice plants for successful estimation contamination of soil As contents via their relationship with chlorophyll contents of canopy. Plants growing in heavy metal-polluted The spectral signatures of vegetation grown in contaminated areas show a water content drop, chlorophyll hydrolysis soils have been investigated in order to understand the role of rise and cellular structure damage. These variations afect refected radiation of the VIS–NIR–SWIR range in detecting the refectance spectra of vegetation leaves and canopies, plant stress due to soil contamination. Bammel and Birnie thereby ofering a basis for indirectly predicting soil heavy (1994) studied the possibility of using sagebrush spectral metal concentrations (Shi et al. 2016). response to explore hydrocarbons. They concluded that the blue shift of the RE is the most efcient representative of hydrocarbon-induced stress in sagebrush. Llewellyn and Curran (1999) studied grassland canopies at an oil-polluted Vegetation health monitoring techniques site and found multiple frst derivative (FD) features with and their application peaks at 0.7 and 0.73 µm. They stated that areas with higher contents of oil had a shorter wavelength feature. Van der The above-mentioned materials proved that monitoring Meer et al. (2002) proved that the presence of gas seeps and of vegetation health can be used as an indirect sentinel hydrocarbons also has efects on surface mineralogy and to indicate the availability of contaminants is soil. Tradi- causes increasing spectral responses in the VIS–NIR–SWIR tionally, vegetation health monitoring is performed using range in anomalous vegetation. The hydrocarbons infuence a visual inspection of stress signs or extensive analysis of the vegetation root structure and capabilities and hence the destructive sampling during the growth period (Bayat et al. spectral features. Smith et al. (2004b) showed that vegetation 2016); however, considerable change of anomalies needs grown in soil with high natural gas had increased refectance to be attentively assessed to detect early detection of plant in the VIS and decreased refectance in the NIR–SWIR due stress symptoms due to soil contamination. Evolution of to O 2 shortage. Noomen et al. (2006) proved that ethane vegetation health monitoring tools during the past several (contains up to 10% of natural gas) afects the refectance in years allows the efective detection of stress impacts, even the chlorophyll and water absorption ranges as well as the before the appearance of visible symptoms. Eforts to ef- RE shape and position. Displacement of soil O 2 by nitro- ciently monitor stressed and anomalous vegetation using gen, natural gas, argon and waterlogging was introduced as diferent proximal and remote sensing methods have been a reason for these alterations (Noomen et al. 2006). Souza conducted to improve the consistency, accuracy and reli- Filho et al. (2008) investigated eucalyptus grown in non- ability of assessment; however, the employed techniques contaminated and contaminated soils with hydrocarbons. varied in their spatial, spectral, radiometric, temporal and They showed that more polluted soils caused higher vegeta- directional resolution and, consequently, in their capability tion refectance in the VIS and SWIR and lower refectance vegetation stressors monitoring. According to Lausch et al. in the NIR wavelengths, compared to plants grown outside (2016), the sensor platform defnes the spatial resolution. the contaminated area. Sanches et al. (2013a) also showed a It may be in centimetres (drone-based cameras), 0.5–2 m signifcant change in refectance, particularly in the VIS and (airborne hyperspectral sensors such as AISA, HySPEX infrared (leaf and canopy spectra, respectively), in brachiaria and APEX), 2–10 m (satellite sensors such as WorldView-2, plants contaminated with diesel and gasoline. It is clear that RapidEye and Sentinel-2), 10–30 m (Spot and Landsat) and the spectral detection of hydrocarbons depends on the level up to 250–1000 m or greater (MODIS and NOAA AVHRR). of contaminant and the stage of exposure to a contaminant Spectral resolution is also important, and narrowband hyper- (Pell and Dann 1991; Sanches et al. 2013a). spectral unmanned aerial vehicles (UAVs), the airborne The results of studies on the efects of heavy metals on hyperspectral system or satellite-based hyperspectral and plants’ spectral responses were dissimilar in some cases, as superspectral sensors are able to assess diferent vegeta- the efects depend on the region and type of plant (Duna- tion properties with diferent accuracies (Aasen et al. 2015; gan et al. 2007; Gotze et al. 2016a). Kooistra et al. (2004) Asner and Martin 2016; Wang et al. 2016). Lausch et al. observed that Pb, Zn, cadmium (Cd), nickel (Ni) and copper (2016) emphasized that the sensor’s temporal resolution (Cu) concentrations in river foodplain soils hold reasonable is an additional reason for improving the process of stress relationships with the refectance spectra of grass. In the detection. The following sections present a brief summary

1 3 2516 International Journal of Environmental Science and Technology (2019) 16:2511–2524 of various proximal and remote sensing techniques and their dimension of refectance with wavelet transform (FDWT). application for vegetation health monitoring. Shi et al. (2016) used the same technique for predicting of As in Chinese paddy soils. Moreover, Sanches et al. Proximal sensing (2013a) conducted a feld experiment in which plants were grown in soils contaminated with hydrocarbons. The Proximal sensing is explained as the application of various leaf and canopy refectance spectra were acquired within sensors to attain signals from an object, when the sensor’s 0.35–2.5 µm and mathematically transformed using the FD receiver is in contact with or close to (within 2 m) the object and continuum removal (CR) methods. The results indi- (Viscarra Rossel et al. 2011). Some studies have used difuse cated the capability of the plants’ hyperspectral refectance refectance spectroscopy (DRS), imaging spectroscopy (IS) to be used as an indirect indicator of hydrocarbons. and thermography tools including multispectral and hyper- In addition to retrieved spectral data from non-imaging spectral spectrometers and cameras in various electromag- spectroradiometers, proximal imaging techniques allow netic ranges in the laboratory to focus on detecting early the recognition of spectral and spatial information from symptoms of stressors and tracking changes in vegetation objects of interest at local or detailed scales (Mahlein et al. (Oerke and Steiner 2010; Mahlein et al. 2012). 2012). During early stages of vegetation stress, imaging The potential of non-destructive fluorescence and sensors allow a pixel-wise attribution of spectral features refectance spectroscopy to detect vegetation stress was appropriate for the evaluation of modifcations on a small recognized in several early studies (Chappelle et al. 1984; scale (Steiner et al. 2008). For instance, Masaitis et al. Lichtenthaler and Rinderle 1988). Advances in refectance (2013) investigated the properties of hyperspectral images spectroscopic techniques also provided the opportunity of healthy and stressed (due to air and soil pollution) conif- of using hyperspectral spectrometers. In addition, new erous trees (Scot pine, Norway spruce, and Siberian pine) technologies expansion, especially hyperspectral sensors, under laboratory conditions using a Themis VIS–NIR encouraged the development of a new generation of more 400H (Themis Vision Systems, Richmond, Virginia, precise vegetation indices (VIs), which have ability to USA) hyperspectral imaging camera. The most instructive detect a wide range of stressors (Govender et al. 2009; wavelengths for spectral separation between the healthy Buitrago et al. 2016; Gerhards et al. 2016). In order to and stressed trees were found in 0.7–0.72 µm for Scots study plants stressed by heavy metals in river foodplain pine and Norway spruce (Picea abies) and 0.86–0.89 µm soils, Kooistra et al. (2004) used a Fieldspec FR spec- for Siberian pine (Pinus sibirica). This laboratory study troradiometer (ASD Inc., Denver, Boulder, USA) in the revealed the strong potential of narrowband-based hyper- VIS–NIR–SWIR region and two spectral VIs, the red-edge spectral imaging for accurately monitoring vegetation position (REP) and the diference vegetation index (DVI). health. In order to upscale fndings from experimental The results demonstrated the potential of VIS–NIR–SWIR laboratory settings, close-range proximal sensing can be spectroscopy data to investigate the spatial distribution installed by means of mobile towers (Lausch et al. 2017) of soil contaminants in foodplains under natural grass- or permanently mounted towers furnished with sensors land via the spectral feature of the vegetation as a proxy. (Schimel et al. 2015) to analyse the spectral features of Smith et al. (2004b) employed the LI-1800 spectrometer the canopy level. Brown et al. (2016) introduced pheno- and REP as an index of plant stress to soil O 2 depletion cams, fully automated digital time-lapse cameras, which and provided the foundation for a warning system to dis- are mounted on towers, for detecting and studying the tinguish natural gas leakage by the spectral responses of phenological attributes and the relations of the spectral wheat (Triticum aestivum) and bean growing in a polluted properties to stress in vegetation. However, to the best of soil. Zinnert et al. (2013) used hyperspectral refectance our knowledge there is no study on vegetation stress due data from a Fieldspec FR radiometer (ASD Inc., Denver, to soil contamination using imaging sensors mounted on Boulder, USA) and several refectance indices to assess towers. Table 1 shows the summary of proximal sensing the possible non-contact detection of PTEs and anthropo- application in soil contamination monitoring using vegeta- genic stressors based on plant chlorophyll, water content tion indicators. and variation in electron transport rate (ETR). The results For the potential application of non-imaging and imag- showed the possibility of using hyperspectral refectance ing vegetation health monitoring techniques in geology, soil to detect stress in plants grown in contaminated soils in science and precision agriculture, the results obtained from a laboratory. Their results were in correspondence with diferent studies need to be confrmed at feld scales. As the Gotze et al. (2016b), who examined the spectral signature aforementioned methods are tedious, costly and not relevant of plants contaminated with hydrocarbons and heavy met- to observing vast regions, remote sensing techniques have als, using high-resolution laboratory spectrometry in the received more attention for large-scale monitoring at a high spectral range of 0.35–2.5 µm and REP and the fractal temporal and spatial interval.

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Table 1 Summary of proximal refectance and imaging spectroscopy in vegetation stress (due to soil contamination) monitoring Properties Technique Sensor Vegetation type References

Heavy metals Refectance spectroscopy, DVI, REP Fieldspec spectrometer Grass Kooistra et al. (2004) Natural gas Refectance spectroscopy, REP LI-1800 spectrometer Wheat Smith et al. (2004a) PTE and anthropogenic stressors Refectance spectroscopy, Refectance Fieldspec spectrometer Woody species Zinnert et al. (2013) indices Hydrocabons Refectance spectroscopy, FDWT, REP Fieldspec spectrometer Forage grass Sanches et al. (2013a) Air and Soil pollution Imaging spectroscopy Themis camera Coniferous trees Masaitis et al. (2013) Hydrocabons and heavy metals Refectance spectroscopy Fieldspec spectrometer Grass Gotze et al. (2016a, b) As Refectance spectroscopy Fieldspec spectrometer Rice Shi et al. (2016)

REP red-edge position, PTE potentially toxic element, As arsenic, FDWT fractal dimension of refectance with wavelet transform, DVI diference vegetation index

Remote sensing revisiting-times without the destructive sampling requirement (Faye et al. 2016). UAV-based imaging has also ben- Remote sensing is the data acquirement from a phenomenon efts of resilience in the camera height and spectral resolu- or an object without physical interaction using electromag- tion selection; moreover, the equipment is small and easy netic radiation (Elachi and Van Zyl 2006). According to Tat- to carry, which permits for arrangement at specifc crop life taris et al. (2016), remote sensing permits high-throughput stages (Severtson et al. 2016). monitoring of objects and despite the proximal sensing, UAVs equipped with sensors in diferent spectral ranges it is not ground-based. Nowadays, remote sensing mostly have received increased attention in various felds (Table 2), employs UAVs, airborne and spaceborne sensors. The tech- including vegetation stress detection. However, these stud- nology includes sensors that passively or actively obtain ies were on monitoring of vegetation health due to water electromagnetic refectance through an extensive series of stress (Berni et al. 2009a), temperature stress (Berni et al. frequencies; however, this review focused on passive (opti- 2009b) and nutrient deficiency stress (Saberioon et al. cal) sensors. Remote sensing data have been a substitute 2014b; Severtson et al. 2016; Van der Meij et al. 2017), to conventional ground-based methods as well as proximal and showed that UAV-based remote sensing of vegetation sensing techniques for plant stress (due to soil contamina- and abiotic stressors permitted a non-destructive quantif- tion) detection and play a precious role in presenting time- cation of plant traits that refect the soil condition. They specifc information for geology, soil science and precision concluded that plant–soil feedback interactions in the feld agriculture regarding the capacity to assess biophysical take place and change numerous main plant parameters that indicators and detect spatial variability (Van der Meer et al. can be detected remotely using UAV-based optical sensors. 2002; Gholizadeh et al. 2018). In the future, the spectral, spatial and temporal resolution

UAVs Table 2 Details of UAV cameras Real-time vegetation health management needs the prompt Sensor range Applications acquirement of high spatial and spectral resolution remote Visible Arial mapping and imaging sensing data. Furthermore, small-scale felds with a large Photogrammetry and 3D reconstruction number of separate plots require a short revisit-time and Surveying and land use applications high spatial resolution data for management applications. Plant counting Providing remote sensing data at a suitable spatial (cm level) Multispectral Plant health monitoring and/or spectral resolution frequently and afordably cannot Vegetation index calculation be generally obtained by conventional ground-based or high- Water quality assessment altitude platforms (Berni et al. 2009a; Zhang and Kovacs Plant counting 2012; Colomina and Molina 2014). This gap between data Hyperspectral Plant height monitoring necessities and data accessibility encourages investigation Vegetation index calculation on application of UAVs to take the anticipated high spec- Water quality assessment tral and spatial information, furthermore preparing tempo- Spectral index development ral fexibility. UAVs allow operational resilience; likewise, Mineral mapping they have satisfactory scaling of spatial detail and temporal

1 3 2518 International Journal of Environmental Science and Technology (2019) 16:2511–2524 of the aforementioned sensors will be improved that enable over a spectral range of 0.38–2.5 µm and an additional seg- to expand the number of soil and vegetation parameters and ment to monitor chlorophyll concentration changes related vegetation stressors including soil contaminants that can be to the vegetation stressors. recorded to ofer better stress predictions. Airborne remote sensing has also been used to study the efects of soil contamination stress on vegetation condi- Airborne sensors tion. Van der Meer et al. (2002) employed the hyperspectral airborne Probe-1 data in the VIS–NIR–SWIR region For IS purposes, airborne sensors are adaptable in terms for assessing of hydrocarbons infuences on vegetation. of revisit-time. High spatial and spectral resolutions of air- The availability of hydrocarbons in the Ventura Basin, borne remote sensing, which are between 0.5 and 2 m pixel southern California, had an impact on surface mineral- sizes with 2–20 nm bandwidths in the 0.4–2.5 µm spec- ogy and the spectral responses of anomalous vegetation. tral range, make it capable to be employed in vegetation Using hyperspectral imagery, REP and normalized difer- condition monitoring (Berni et al. 2009b). Moreover, to ft ence vegetation index (NDVI), Van der Meer et al. (2002) the applications requirements, ground sampling distance also demonstrated that it is promising to detect anomalous and swath width can simply be reconciled. Over the past spectral responses yielding from natural gas seeps. Later, years, several airborne systems have been developed and Noomen et al. (2012) used the same image from the same more advantages and enhancements have been provided in airborne sensor to study the vegetation responses caused radiometric and spectral calibration accuracy, swath width by long-term hydrocarbon seepage. They concluded that and the number of spectral channels, sensor size and sig- in spite of high sensitivity of REP to chlorophyll variation nal-to-noise ratio (SNR) (Birk and McCord 1994; Giani- (Van der Meer et al. 2002; Gholizadeh et al. 2016), the netto and Lechi 2004). Some vegetation properties can be Lichtenthaler (LIC) index can be a more reliable indica- retrieved both directly and indirectly by means of a wide tor of vegetation stress due to hydrocarbons than REP. range of airborne hyperspectral sensors. The estimation of Vegetation detection using HyMap airborne imagery in leaf water content with the AVIRIS (Serrano et al. 2000; agricultural felds close to pipeline leakage in the Nether- Cheng et al. 2006), chlorophyll content with the CASI and lands was also efectively studied by Van der Werf et al. HyMap (Yamaguchi et al. 1998; Zarco-Tejada et al. 2004; (2008). Reusen et al. (2003) mapped heavy metals using Kopačková et al. 2014), carotenoid with CASI, ROSIS and vegetation stress in Scots pine (Pinus sylvestris). They DAIS-7915 (Zarco-Tejada et al. 2005) and dry matter con- used the airborne CASI-2 with a spatial resolution of 1 m tent (Riano et al. 2005) is the example of the use of airborne by 1 m in 18 vegetation bands, mostly focused in the RE sensors in vegetation parameter assessment. curve. The edge green frst derivative normalized difer- Some studies have been conducted for vegetation man- ence (EGFN) index, based on the FD of the refectance in agement and diferent stress detection applications using the RE curve and the green peak, clearly indicated veg- airborne platforms. Tuominen et al. (2009), Kuenzer et al. etation stress and was correlated with Zn and Cd concen- (2014) and Pause et al. (2016) reported the feasibility of trations in soil. Using airborne sensors for assessment of LiDAR data and the data fusion of LiDAR and high spec- heavy metals efects on short vegetation (grass, herbs and tral resolution AISA dual airborne hyperspectral data, short shrubs) also showed reliable results in the study of respectively, for direct measuring healthy and stressed Roelofsen et al. (2015). They successfully estimated pH trees in a forest. They stated that the employment of multi- that was infuenced by Al and Mn, employing an APEX sensor remote sensing approaches improved the accuracy airborne sensor. Summary of airborne sensors application of stress detection over time. Rascher et al. (2015) also in soil contamination monitoring using vegetation indica- successfully used the new airborne instrument HyPlant tors can be seen in Table 3.

Table 3 Summary of airborne Properties Technique Sensor References sensors application in vegetation stress (due to soil Hydrocabons Hyperspectral Imaging, REP, NDVI Probe-1 Van der Meer et al. (2002) contamination) monitoring and natural gas Heavy metals Hyperspectral Imaging, EGFN CASI-2 Reusen et al. (2003) Hydrocabons Hyperspectral Imaging, REP, NDVI, YI, CSI HyMap Van der Werf et al. (2008) Hydrocabons Hyperspectral Imaging, REP, LIC Probe-1 Noomen et al. (2012) Al, Mn Hyperspectral Imaging, IV APEX Roelofsen et al. (2015)

REP red-edge position, NDVI normalized diference vegetation index, EGFN edge green frst derivative normalized diference, YI yellowness index, CSI Carter stress index, LIC Lichtenthaler, Al aluminium, Mn manganese, IV vegetation indicator value

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Despite the considerable advancement made in airborne derived from Landsat time-series together with VIs derived remote sensing of stressed vegetation monitoring, there from airborne hyperspectral imageries (ASAS obtained are factors limiting the application of airborne sensors for in 1998 and APEX obtained in 2013) and concluded that research purposes, including the high operating expenses, though the alterations between initially moderately-to-heav- long turnaround times due to processing the high volume ily harmed and originally damaged stands largely levelled of information obtained in large airborne campaigns and out by 2013, it was yet feasible to identify symptoms of the the shortage of private corporations granting economical earlier damage in some cases. Arp (1992) studied indirect products (Berni et al. 2009b). detection of hydrocarbon seepage using anomalous vegetation. They used Landsat imagery, which showed an anomaly Spaceborne sensors in sagebrush (Artemisia tridentata) felds located in a region of hydrocarbon microseepage. The results proved that the As already mentioned, obtaining important information sagebrush anomaly resulted from the rising of gases and for vegetation stress detection is possible using DRS and waters used to retain reservoir pressures in the feld, which IS proximal sensing, UAV and airborne imaging. A pixel produced anoxic, high-pH, high salinity and low-Eh soils spectrum is generated for the potential diferentiation of (Arp 1992). target vegetation attributes using hundreds of connected Recently, the potential of hyperspectral spaceborne sen- spectral bands with a narrow bandwidth (Vane and Goetz sors for detecting hydrocarbon pollution impacts on veg- 1993). Nevertheless, cost-efective and repeated mapping etation has also been studied. Arellano et al. (2015) dem- of the vegetation surfaces, efcient and spatially widespread onstrated the suitability of Hyperion satellite imagery for mapping and large-scale monitoring of stressed vegeta- assessing contamination by oil in the Amazon forest. They tion can be obtained using spaceborne sensors. The main highlighted that levels of chlorophyll content, foliar water advantages of the spaceborne approach remote sensing are content and leaf structural changes were decreased in hydro- summarized in the accessibility of high-temporal images, carbon-polluted tropical forests. To map this efect over frequent revisit-time, the comprehensive monitoring of broader geographical areas, NDVI was applied to hyper- large-scale sites, data reduction and efcient classifcation spectral Hyperion satellite imagery and was introduced as a of results (Thenkabail et al. 2012; Yokoya et al. 2016). In suitable index for petroleum pollution impacts monitoring recent years, the expansion of spaceborne technologies and in forest. The forthcoming sensors from space will generate applications has attained advantages due to the opening of large data streams for land monitoring, which will shortly large satellite information archives to the public (e.g. Land- become accessible to diferent user communities (Berger sat) (Wulder and Coops 2014), including integrated space et al. 2012; Arellano et al. 2015; Gholizadeh et al. 2018). missions evolved for the public domain (e.g. the Sentinel It is expected that in near future, the Italian PRISMA sen- missions of the European space agency (ESA)) (Majasalmi sor will be available, followed by the Japanese HISUI HSR and Rautiainen 2016) and the advancement of open source sensor (with thermal capability), the German EnMap HSR tools for remote sensing data processing (Wegmann et al. (with free data to the scientifc community); thereafter, the 2016). It is expected that such developments will lead to Italian-Israeli SHALOM sensor would be in orbit. These a massive use of satellite data-based techniques for under- sensors, plus the new initiatives such as FLEX sensor (for standing vegetation health, which is based on soil condition. monitoring fuorescence in vegetation) and Sentinel-2 (with However, the inclusion of satellite remote sensing informa- three RE bands) promise that high-quality data will be more tion with lower spectral and spatial resolution domains into frequently available for monitoring vegetation and their vegetation stress detection are still lacking, essentially due stressors from orbit. Table 4 summarizes the key technical to shortage of appropriate sensors. parameters of some of spaceborne sensors. The capability of satellite platforms for monitoring and All the above-mentioned materials showed that the dis- determining the vegetation status due to environmental ciplines of optical proximal and remote sensing are expe- stressors including contamination has been proved theoreti- riencing an inimitable increasing in sensors quantity and cally and practically. For instance, Zurita-Milla et al. (2008) quality. Moreover, the high potential of the techniques in stated that data fusion of the unmixing-based Landsat and assessment of vegetation status as indicators of soil con- MERIS FR data will be able to successfully assess the tamination has been proved; however, each technique has stressed vegetation status by evaluating NDVI, the modi- some advantages and disadvantages, which determine its fed transformed chlorophyll index (MTCI) and the modi- capability. Therefore, selection of the data source highly fed green vegetation index (MGVI). Misurec et al. (2016) depends on the measured attribute, resolution requirement, detected spatio-temporal changes of forest stands in Ore turnaround and revisit-time, cost and value of the informa- Mountains, Czech Republic, which sufers from sever envi- tion and data processing requirement (Dalsted et al. 2003). ronmental pollution. They used the disturbance index (DI)

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Table 4 Technical specifcations of some spaceborne remote sensing sensors Imager Country Organization Launching year GSD (m) Spectral bands (no.) Spectral range (nm) Swath Temporal at nadir res. (days) (km)

Landsat-5 USA NASA/NOAA 1984 30, 57, 120 7 450–2350 185 16 Landsat-7 USA NASA/USGS 1999 30 8 450–2350 185 16 10,400–12,500 ASTER USA NASA 1999 15, 90 15 520–2430 60 16 8125–11,650 IKONOS USA GeoEye 1999 0.82, 3.28 4 445–853 11 3–5 Hyperion USA NASA 2000 30 220 360–2600 7.65 16 CHRIS EU ESA 2001 17, 34 18, 37, 6 400–1050 13 Varies* MODIS USA NASA 2002 250, 500, 1000 19 620–965 2330 16 MERIS EU ESA 2002 300, 1200 15 390–1040 1150 35 SPOT-5 France CNES 2002 10 4 480–1750 80 2–3 Landsat-8 USA NASA/USGS 2013 30 11 430–2300 185 16 10,600–12,510 WorldView-3 USA DigitalGlobe 2014 0.3, 1.24, 3.70 16 400–2365 13.1 1–4 Sentinel-2 EU ESA 2015 10, 20, 60 13 440–2195 290 5 PRISMA Italy ASI 2018 30 247 400–2500 30 NA HISUI Japan METI 2018 30 185 400–2500 30 NA CCRSS China CNSA 2018 30 328 400–2500 40 NA EnMap Germany DLR 2020 30 242 400–2450 30 27 SHALOM Italy/Israel ASI/ISA 2022 10 241 400–2500 10 2

NA not available, NASA National Aeronautics and Space Administration, NOAA National Oceanic and Atmospheric Administration, USGS United States Geological Survey, ESA European Space Agency, CNES Centre National d’Etudes Spatiales, ASI Agenzia Spaziale Italiana, METI Ministry of Economy, Trade and Industry, DLR Deutschen zentrums für Luft- und Raumfahrt, CNSA China National Space Administration, JPL Jet Propulsion Laboratory, ISA Israel Space Agency * Varies: It has a nominal Sun Synchronous polar Orbit (SSO) but no orbit maintenance capability

Table 5 Advantages and Sensor Advantages Disadvantages disadvantages of using optical proximal and remote Proximal sensors Economical Labour intensive sensing techniques at diferent High accurate Time-demanding platforms for monitoring High spectral resolution Low temporal resolution vegetation anomalies due to soil Not suitable for large areas contaminations UAVs Easy to carry High operating costs High spectral resolution Long turnaround times Short revisit-time Surveying small areas Flexibility of camera selection Security issues Airborne sensors High spatial resolution High operating costs High spectral resolution Long turnaround times Flexibility of sensor operation Low temporal resolution Spaceborne sensors Economical Low spectral resolution Frequent revisit-time Sensitivity to weather conditions Large data archives Atmospheric attenuation Applicable for large-scale monitoring

Table 5 illustrates the main advantages and disadvantages Conclusion of using optical proximal and remote sensing techniques at diferent platforms for monitoring vegetation anomalies The current review clearly expanded on the potential of due to soil contamination. existing optical proximal and remote sensing measurement

1 3 International Journal of Environmental Science and Technology (2019) 16:2511–2524 2521 techniques such as proximal sensors, UAVs, airborne Compliance with ethical standards and spaceborne sensors in vegetation stress monitoring, which can be used as a proxy of soil contamination, and Conflict of interest The authors declare no confict of interest. illustrated their advantages and disadvantages. It highlighted that vegetation attributes are expressions of the growing condition (soil) including stressors (contamination). In addition, vegetation spectral properties are func- References tions of plant bioparameters and plants reveal sensitivity to these properties in diferent spectral ranges (VIS, Aasen H, Burkart A, Bolten A, Bareth G (2015) Generating 3D hyperspectral information with lightweight UAV snapshot cameras for NIR and SWIR). 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Front Ecol Environ 14:84–93 (Project No. 18-28126Y) is also appreciated.

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