Analysis of the Effects of Heavy Metals on Vegetation Hyperspectral Reflectance Properties

Authored by: E. Terrence Slonecker

Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation

Print publication date:  December  2018
Online publication date:  December  2018

Print ISBN: 9781138364769
eBook ISBN: 9780429431166
Adobe ISBN:

10.1201/9780429431166-3

 

Abstract

Fugitive metals in the environment are an inevitable consequence of anthropogenic activity. Commercial, industrial, agricultural and other processes result in the release of metals into the soil and water that inevitably affect vegetation. Although there is some variability, excessive metals in the soil and water generally have a negative effect on plant growth and vitality. The effect of metals on plants is often expressed in terms of hyperspectral reflectance characteristics of the plant and there is a long history of monitoring metal stress in plants through spectroscopy and imaging spectroscopy. This chapter presents a review of previous work on the hyperspectral reflectance of the effect of metals on plants.

 Add to shortlist  Cite

Analysis of the Effects of Heavy Metals on Vegetation Hyperspectral Reflectance Properties

3.1  Introduction

Absolute definitions of “heavy metals” are elusive in modern science. Many different definitions have been proposed. Some are based on density, some on atomic number or atomic weight, and some on chemical properties or toxicity [1]. One definition holds that they are elements with a specific weight higher than 6 g/cm3 [2]. See Figure 3.1. But no single definition fits well in modern usage. The term “toxic metals” has become to some extent synonymous with heavy metals, but that term is equally problematic because levels of toxicity are highly variable between different metals and vegetation species. At best, heavy metals can be classified as a loosely defined subset of elements that exhibit metallic properties and are toxic to living organisms at some level of concentration or exposure. The term “heavy metals” itself has been criticized as functionally meaningless [1,3].

The Periodic Table showing the elements generally considered heavy metals. Lanthanides and actinides are not shown.

Figure 3.1   The Periodic Table showing the elements generally considered heavy metals. Lanthanides and actinides are not shown.

(Modified from Shaw, B. et al. In Heavy Metal Stress in Plants: From Biomolecules to Ecosystems, 2004; Vol. 2, pp. 84–126. 71 )

Metals in the environment, however, are a real concern for a variety of reasons, including their commercial and industrial value, medicinal applications, use in agricultural products, and their toxic effects on human and ecological resources as chemical weapons or as fugitive, uncontrolled, anthropogenic releases into the environment. Some metals, such as selenium, copper, and zinc, are micronutrients that are actually required by most plant and animal life forms in very small doses, while others, such as mercury and lead, are toxic and have no known benefit to living organisms.

Although the toxicity of many heavy metals can vary widely, the term has evolved to have pejorative connotations that make it synonymous with anthropogenic pollution. Heavy metals can occur naturally and can arise from many anthropogenic sources, such as mining and processing of other metals, the smelting of copper, the processing of gold, steel, iron, and coal, the preparation of nuclear fuels, and the production of industrial construction materials. In addition, many computer parts and chips contain heavy metals or involve a production process that results in waste products with heavy metals. Electroplating is a primary source of chromium and cadmium pollution. Arsenic has been used extensively in pesticides and in wood treating [4] and, because of its toxicity, has been used for years as a base compound for chemical warfare weapons such as Lewisite gas [5].

Hyperspectral remote sensing (HRS), also known as imaging spectroscopy, and, to a greater extent, traditional field and laboratory spectroscopy have a long history of being used to investigate the identification of metals and their effects on vegetation in the environment. However, fugitive metals in the environment do not usually exist in their pure form but rather in a soil-water-vegetation matrix as waste rock materials or sediments or as a result of soil deposition. Besides detecting the minerals themselves, spectroscopy and imaging spectroscopy can also be used to detect the composition and condition of vegetation, which can then be used to interpret mineral deposits or metal composition of the soil in the area of vegetation growth. It has long been acknowledged by scientists that a relationship exists between vegetation, soils, and underlying mineral deposits [6]. In several studies, airborne spectroscopy was used to detect “hidden” mineral deposits through forest-covered areas by revealing subtle variations in the reflected spectrum of vegetation under stress due to the presence of heavy metals [7–10]. In addition, recent scientific literature reflects a growing interest in the spectroscopic identification of environmental hazards, many of which are metals in the soils and vegetation. Another area that has received increased attention in the area of spectroscopy of metal stress in vegetation is that of vegetation indices (VIs). This chapter reviews the scientific background of spectroscopy and imaging spectroscopy with respect to the effects of heavy metals on vegetation reflectance.

3.2  Physiology of Metal Stress in Plants

Plants are generally more exposed to pollution risks in the environment because they are stationary and cannot avoid interacting with environmental pollutants such as metals. Plants have evolved various complex strategies for adapting to heavy metal pollution in soil or water media. Plants respond to exposure to heavy metals in several different ways. Metals usually interfere with basic plant metabolism, and enzyme activity is often negatively affected. Metals present in plant tissues can cause plants to form chelate structures, molecules that enclose and isolate metal ions and cause them to lose their functional properties in metabolic cycles such as the citric acid cycle.

Plants generally fall into two categories with respect to strategies for dealing with exposure to heavy metals: accumulators uptake metal ions and process them in some manner, storing them in internal tissues or reducing or processing them in biochemical reactions, whereas excluders generally restrict the uptake of metals by preventing their uptake into plant tissues. This is often accomplished by trapping metal ions in the cell walls of the root tissue.

Whether plants are accumulators or excluders, excess metals in the soil or in plant tissues tend to have negative effects on plant health, growth, and biomass accumulation and can cause visual symptoms at toxic levels. Table 3.1 shows examples of the visual injuries to various flowering plants from metal exposure. These visual symptoms also affect the reflectance characteristics of the typical vegetation spectra. Figure 3.2a and b show increasing visual damage to plant health and the corresponding changes in the blue and red energy absorption troughs at 480 and 680nm, respectively, seen as increasing reflectance and a blue shift.

(a) Visual effects of arsenic stress on

Figure 3.2   (a) Visual effects of arsenic stress on Nephrolepis exaltata (Boston fern). Ferns are planted in clean sand amended with, from left to right, 0, 20, 50, 100, and 200 ppm sodium arsenate. (From Slonecker, E., Remote Sensing Investigations of Fugitive Soil Arsenic and Its Effects on Vegetation Reflectance. George Mason University: Fairfax, Virginia, 2007. 36 ) (b) Laboratory reflectance spectra of arsenic-affected ferns in Figure 3.2a above. Spectra were collected with an ASD full-range spectrometer from 15 cm above the canopy of each plant. Note the loss of photosynthetic absorption at 680 nm, causing higher reflectance, the blue shift, and the general increase in reflectance in shortwave infrared (due to loss of water) with increasing soil arsenic.

(From Barcelo, J., Poschenrieder, C., Journal of Plant Nutrition 1990, 13, 1–37. 12 )

Table 3.1   Examples of Visual Symptoms of Metals Stress in Plants

Metal

Characteristics

References

Arsenic

Red/brown necrotic spots on old leaves, yellow/brown roots, reduced growth

[36,37]

Aluminum

Stunted growth, inhibition of root elongation, purple Coloration, curling and yellowing of leaf tips

[72,73]

Cadmium

Brown edges to leaves, chlorosis, necrosis, curled leaves, stunted roots

[74,75]

Copper

Chlorosis, yellow and purple coloration, decreased root growth and leaf biomass

[76–78]

Lead

Dark green leaves, stunted growth, chlorosis, and blackening of root system

[79]

Mercury

Severe stunting of seedlings and roots, chlorosis, reduced biomass

[80]

Nickel

Chlorosis, necrosis, stunting, reduced root and leaf growth

[81]

Selenium

Interveined chlorosis, black spots, bleaching and yellowing of young leaves, pink spots on roots

[17]

Zinc

Chlorosis, stunting, reduced root elongation

[82]

Source: Modified from Shaw, B. et al. In Heavy Metal Stress in Plants-from Biomolecules to Ecosystems, 2004; Vol. 2, pp. 84–126. 71

Excess metal exposure negatively affects photosynthetic processes and typically induces a general “stress” reaction in plants. In some cases, the absorbed metal ion will replace the central magnesium atom in the chlorophyll molecule, which generally causes oxidative stress in the plant. This substitution reduces or prevents photosynthetic light harvesting and results in a breakdown of photosynthesis [11].

Heavy metal exposure can also interfere with plant-water relations. Metals may alter plasma membrane properties, affect enzyme activities, inhibit root growth and elongation, affect osmotic potential, and generally inhibit the ability of the plant to acquire water [12]. This may be manifested as a general drought-stress response but is actually caused primarily by the interference of heavy metals and not simply the lack of water availability.

In general, many different photosynthetic reactions and physiological processes are negatively affected by plant exposure to heavy metals. These vary widely among different species and metals, but in many cases both light and dark photosynthetic reactions are generally inhibited [13].

3.3  Basic Spectroscopy of Vegetation

Spectroscopy is the study of the interaction between energy and matter as a function of either wavelength (λ) or frequency (v). Historically, spectroscopy referred to the use of visible light dispersed by a prism according to its wavelength and is the parent science to all visible and near-infrared (VNIR) HRS. Dating from the nineteenth century [14], spectroscopic techniques have been used widely in analytical chemistry and astronomy to identify many elemental substances, minerals, and organic compounds.

The use of spectral reflectance methods to gain an understanding of photosynthesis and related vegetative processes is a field of scientific study that has been ongoing for decades [15,16]. Laboratory instruments called spectrometers, spectrophotometers, spectrographs, and spectroradiometers are all different names for instruments that essentially use some type of prism to separate light into its component parts and measure the reflectance and absorption of each of those individual component parts from a target surface. Early instruments separated light into the basic colors of the spectrum. Modern instruments separate light into individual nanometers of reflectance energy.

In this review, “hyperspectral” remote sensing technology is afforded the broadest possible definition. The papers reviewed here represent a variety of spectroscopic remote sensing systems and approaches that include individual leaf-level and plant-level analysis under controlled conditions in the laboratory to spectroscopic measurements of plants in the field to overhead aircraft and satellite systems. The common thread is that multiple bands of energy reflectance are recorded and analyzed with spectroscopic methods.

Different spectroscopic collection perspectives also contain inherent advantages and disadvantages that include complications involving the detection and analysis of the reflected energy signal. Outside of a pure laboratory setting, field collections generally involve variable solar lighting, background effects from soil and other materials, and effects from the bidirectional reflection distribution function. Aircraft and especially satellite sensors contain increasingly significant signal noise from atmospheric moisture and constituent gases.

The majority of papers and research studies reviewed here involve spectrometers used in either a laboratory or field setting. Some utilize aircraft and satellite systems, and a few represent a multiscale data collection from the laboratory to field to aircraft or satellite sensor. While the availability and applications of aircraft and satellite systems is growing significantly, and this will be a prime focus area of future research, hyperspectral research in the laboratory and field represents a critical first step in developing and understanding, in repeatable spectral measurement, the effects of heavy metals on plant reflectance.

3.4  Spectroscopy and Imaging Spectroscopy of Metal Interactions with Plants

Early spectroscopic analysis of vegetation-metal interactions from both laboratory and aircraft sensors can be traced to the late 1970s and early 1980s, when researchers such as Collins, Milton, and Horler demonstrated repeated shifts in the so-called red edge of typical vegetation reflectance-based stress or enhanced growth caused by excessive exposure to metals in the soil [7,17,18]. This has evolved into a fundamental spectroscopic-plant principle that is still widely used today. The red edge of vegetation reflectance is an area usually centered around 720 nm and represented by the typical sharp rise in reflectance in the 680–760 nm range of the classic vegetation spectral signature. Figure 3.3 shows the classic red edge area of vegetation spectra.

Red edge. An important region of vegetation spectra is known as the red edge. Much research has focused on measuring shifts in this region corresponding to stress or enhancement of chlorophyll.

Figure 3.3   Red edge. An important region of vegetation spectra is known as the red edge. Much research has focused on measuring shifts in this region corresponding to stress or enhancement of chlorophyll.

(From Slonecker, T. et al., Remote Sensing 2009, 1, 644. 37 )

Although the general concept of the red edge is easily understood as the area of a sharp rise in reflectance, a variety of definitions and quantitative methods for computing the red edge are found in the literature. Ray [19] defined the red edge as the sharp transition between absorption by chlorophyll in the visible wavelengths and the strong scattering in the NIR from the cellular structure of leaves. The red edge is defined by Horler et al. [18] as the wavelength of maximum ΔR/Δλ, where R is reflectance and λ is the specific wavelength. Guyot [20] defines the red edge as an inflection in the sharp rise in reflectance between 670 and 760 nm. Although variable in the literature, most modern definitions of the red edge involve the peak of the first derivative [21]. Additional red-edge-related measurements include a ratio of R740/R720 and a ratio of first derivative values D715/D705 [22].

The general movement of the spectral features in the red edge area is one of the keys to its analytical strength. When plants are healthy and producing more chlorophyll, the red edge tends to shift toward the right to longer wavelengths. This is also usually accompanied by an increase in the absorption trough at 680 nm as the plant absorbs more energy in the photosynthetic process. When a plant is stressed, such as in the case of excessive heavy metals in the soil, the spectra tend to shift toward the left and shorter wavelengths. Stress also tends to produce an increase in reflectance at the 680 absorption through as less light is being utilized for photosynthesis and chlorophyll production. Figure 3.4 shows an example of this stress based on a laboratory experiment with varying levels of copper sulfate in the soil.

The “blue” shift in the red edge in laboratory-grown sorghum exposed to different levels of copper sulfate in soil.

Figure 3.4   The “blue” shift in the red edge in laboratory-grown sorghum exposed to different levels of copper sulfate in soil.

(From Chang, S., Collins, W., Economic Geology 1983, 78, 723. 10 )

Horler [18] studied the feasibility of utilizing a red edge measurement as an indication of plant chlorophyll status. Using derivative reflectance spectroscopy in the laboratory, plant chlorophyll status, and red edge measurements were acquired from single leaves of several different species under heavy metal stress. By using spectroscopic and laboratory methods to measure the chlorophyll content of the same leaf samples, direct evidence of the red edge–chlorophyll correlation was obtained. Measuring in situ vegetation using a field spectrometer, Ray [19] discovered significant differences in the size and shape of the red edge in different types of arid vegetation and found for a common yellow grass species that there was no chlorophyll “bump” at the green peak and no detectable red edge.

A critical component of spectral analysis of vegetation is the shift in absorption and reflectance features that occur as a result of chemical and nutrient exposures. A general relation between increases in chlorophyll concentration and a “red shift” toward longer wavelengths has been established by several researchers utilizing both laboratory and field spectrographic methods. Gates [23] showed the basic relationship between increased chlorophyll and plant health and the shift of the red edge toward longer wavelengths. Guyot [20] similarly showed that the red edge inflection point shifts to longer red wavelengths as chlorophyll concentrations increase. This general correlation between chlorophyll content and red shift was confirmed by Horler [24] and Baret [25] for different crop species.

More important for this specific research topic, however, is the “blue shift” (i.e., shift toward shorter wavelengths) of the red edge that occurs when vegetation has undergone stress from some mineral or chemical agent. The blue or red shift toward shorter or longer wavelengths, respectively, is one of the keys to detecting stress and growth in all green vegetation. The blue shift is usually accompanied by a general increase in overall reflectance and an increase in the 680 nm absorption feature showing that less light energy is being utilized for photosynthesis.

In some of the first applications comparing field and airborne spectroscopic measurements of metal stress, Collins [8] and Chang and Collins [10] showed a blue shift in the 700–780 nm region of reflectance spectra from conifers affected by metal sulfide. See Figure 3.4. Similar blue shift results have been reported by Schwaller and Tkach from field applications and aerial photographs [26] and Milton in the laboratory [17,27]. In a seminal remote sensing research application using both in situ and airborne measurements, Rock [28] demonstrated a 5 nm blue shift in spruce and fir species in Vermont and Germany as a result of stress caused by airborne pollutant deposition.

Although the underlying physiology is not completely understood, the uptake of heavy metals has the effect of reducing photosynthetic activity and the concentration of chlorophyll. One mechanism of heavy-metal-induced damage in plants that leads to a reduction in photosynthesis involves the in vivo replacement of the central Mg2+ ion in the chlorophyll molecule by a heavy metal ion. This replacement is generally toxic to the plant depending on the metal and, at the very least, inhibits the overall ability of the plant to conduct photosynthesis. In general, the magnesium-chlorophyll molecule has a much higher capacity to release electrons than other metals, and replacement by other metals quenches or reduces the ability of the plant to regulate excess light energy and protect the plant from damage [11,29,30].

In another classic paper utilizing both lab and field spectral measurements, Horler [18] studied the effects of heavy metals on the reflectance spectra of plants. Utilizing both natural vegetation growing in known areas of metal concentrations and specific greenhouse experiments, relationships were established between metal stress, total chlorophyll, chlorophyll a/b ratios, and reduced reflectance at specific wavelengths. Controlled experiments with pea plants and other species showed that the general effect of exposure to cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) was growth inhibition. Also, the pea plants showed changes in the leaf chlorophyll a/b ratios for exposure to Cd and Cu but showed no changes for Pb and Zn. Metal-treated plants in both controlled and natural environments showed a decrease in reflectance at 850, 1650, and 2,200 nm and an increase at 660 nm. Metal concentration in soil has strong negative correlations to reflectance at 1650 and 2200 nm and strong positive correlations at 660 nm. In general, the ability to measure stress effects from heavy metals is dependent on species, the phase of the growth cycle, and the environment.

Kooistra [31] conducted a study to examine the possibilities for in situ evaluation of soil properties in river floodplains using field reflectance spectroscopy of cover vegetation. Results determined that a combination of field spectroscopy and multivariate calibration leads to a qualitative relation between organic matter and clay content, which are intercorrelated with levels of Cd and Zn. The study indicated the potential for these multivariate methods for mapping soil properties using HRS techniques. Kooistra [32,33] conducted two additional studies to investigate the relation between vegetation reflectance and soil characteristics, including elevated concentrations of the metals Ni, Cd, Cu, Zn, and Pb found in floodplain soils along the Rhine and Meuse Rivers in the Netherlands. These studies obtained high-resolution vegetation reflectance spectra in the visible to NIR using a field radiometer [32]. The relationships were evaluated using simple linear regression in combination with two spectral VIs: the difference vegetation index (DVI) and the red edge position (REP). The R 2 values between metal concentrations and vegetation reflectance ranged from 0.50 to 0.73. The results of the study demonstrated the potential of remote sensing data to contribute to the survey of spatially distributed soil contaminants in floodplains under natural grasslands, using the spectral response of the vegetation as an indicator. Modeling the relationship between soil contamination and vegetation reflectance resulted in similar results for DVI, REP, and the multivariate approach using partial least-squares (PLS) regression [32,33].

Similar studies were conducted by Clevers et al. [34,35] in contaminated floodplains in the Netherlands. Analysis of field spectrometer measurements of reflectance found that REP and the first derivative peaks around 705 and 725 nm were the best predictors of heavy metal contamination. Similarly, Slonecker [36,37] showed the spectral relationship between arsenic uptake and spectral reflectance in arsenic-hyperaccumulating Pteris ferns using a PLS regression. Rosso et al. successfully detected plant stress due to metal pollution at the leaf level and reiterated that more investigations need to be undertaken that link their results to canopy-level reflectance [38].

Slonecker [36] used both laboratory spectra and HyMAP imagery spectra of arsenic stress in common lawn grasses to map the distribution of fugitive arsenic and other metals in household lawns in an urban setting. The hyperspectral imagery was processed with a linear spectral unmixing algorithm and mapped with a maximum-likelihood classifier. Classes included grass, arsenic-affected grass, trees, buildings, soil, asphalt, and concrete and showed an overall accuracy of 82.9%. Critical spectral parameters for identifying arsenic stress were located in the green, red, NIR plateau, and water-absorption bands in both laboratory and imagery spectra. Validated against comprehensive ground sampling efforts, final maps of the arsenic-affected grass showed an overall producer's accuracy of 55.8% and an overall user's accuracy of 82.7%. See Figure 3.5.

Healthy and stressed grass signatures from both laboratory and hyperspectral imagery. The same critical areas in the green, red, near-infrared, and shortwave infrared show the patterns of spectral separation between healthy and stressed grass that enable the image processing algorithm to separate, identify, and map arsenic-stressed grasses.

Figure 3.5   Healthy and stressed grass signatures from both laboratory and hyperspectral imagery. The same critical areas in the green, red, near-infrared, and shortwave infrared show the patterns of spectral separation between healthy and stressed grass that enable the image processing algorithm to separate, identify, and map arsenic-stressed grasses.

(From Slonecker, E., Remote Sensing Investigations of Fugitive Soil Arsenic and Its Effects on Vegetation Reflectance. George Mason University: Fairfax, Virginia, 2007. 36 )

Gallagher [39] utilized field spectrometry and Ikonos multispectral satellite measurements to assess basal area, plant productivity, and chlorophyll content of gray birch growing in soils containing elevated metals in a New Jersey Brownfields site. Biomass production, measured by a red/green ratio index, showed an inverse relationship (R2 = 0.46 – 0.81) to soil zinc concentration. The relationship was stronger when the total metal levels (TMLs) were higher. Threshold TMLs were established for several species beyond which the normalized difference vegetation index (NDVI) decreased at both the assemblage and individual tree level.

Mars and Crowley [40] utilized AVIRIS and digital elevation model (DEM) data to evaluate hazardous waste contamination in southeastern Idaho, including mine waste dumps, wetland vegetation, and other relevant vegetation types. With the mapped information and the DEM, the delineation of mine dump morphologies, catchment watershed areas above each mine dump, flow directions from the dumps, stream gradients, and the extent of downstream wetlands available for selenium absorption were determined. Compared to ground-truth maps, the AVIRIS imagery correctly identified 76% of all mine waste pixels. Additionally, Mars and Crowley were able to characterize the physical settings of mine dumps and test hypotheses concerning the causes of selenium contamination in the area [40].

Ren et al. [41] found that rice exposed to lead in the soil weakened the photosynthetic process of rice as measured by field spectral measurements. Lead concentrations in rice could be reliably predicted by changes in the normalized band absorption depth, blue shifts in the red edge region, and the distance of the shift.

3.5  Vegetation Indices

One area that has received recent attention in the area of spectroscopy of metal stress in vegetation is that of vegetation indices (VIs), which are mathematical manipulations of digital number values of two or more bands of data; they have been a fundamental part of the remote sensing analysis of vegetation for decades. VIs typically stretch or enhance a particular part of the reflected electromagnetic spectrum (EMS) known to relate to specific vegetation qualities such as chlorophyll content, leaf moisture, pigment ratios, and stress level. The search for stressed or unusual growth patterns in cover vegetation, such as potential metal stress patterns, has been enhanced by the use of one or more VIs reported in the scientific literature.

The most widely known and used VI is the NDVI, which is calculated by the following general band formula:

NDV = NIR Red NIR + Red
where NIR is the reflectance from the near-infrared band, and R is the reflectance from the red visible band. The NDVI was first proposed by Pearson and Miller in 1972 [42] and has been widely utilized as a general measure of vegetation condition and has both broadband and narrowband formulas for its computation. Although the NDVI has been the most widely used VI, it has clear limitations. The NDVI becomes saturated in areas of multilayered canopy and shows nonlinear relationships with critical vegetation parameters such as the leaf area index (LAI). As a result, substantial efforts have been devoted to developing new indices that improve on the shortcomings of the NDVI [43].

VIs have often been developed for specific purposes and optimized to assess a specific condition or process. Also, the emergence and increasing availability of hyperspectral data and imagery have resulted in a new class of VIs, known as narrowband indices, that capitalize on the increased spectral resolution of hyperspectral data.

For example, Penuelas [44] proposed a structurally insensitive pigment index (SIPI) that incorporates a NIR band (800 nm) to minimize internal leaf structure effects such as increased scattering due to refractive index discontinuities between air and cell walls inside leaves. Gamon [45] developed the Photochemical Reflectance Index (PRI) to estimate the physiological parameters of sunflowers undergoing nitrogen stress. Huete [46] developed a VI that accounts for, and minimizes, the effect of soil background conditions. The soil-adjusted vegetation index (SAVI) equation introduces a soil-brightness-dependent correction factor, L, that compensates for the difference in soil background conditions. NIR is the reflectance from the near-infrared band, and R is the reflectance from the red visible band. Applying a correction for the soil provides more accurate information on the condition of the vegetation itself. The Triangular Vegetation Index (TVI) was developed as a very precise measure of chlorophyll concentration and absorption and depends on very specific narrow wavelengths [47].

Agricultural vegetation applications of both field and airborne hyperspectral data analysis have been conducted by several researchers, showing the promise of this technology in monitoring plant production for food supplies. Strachan [48] and Daughtry [49] both showed that very narrow, crop-specific VIs could be developed and utilized from hyperspectral data and applied to the assessment of agricultural productivity. In general, the use of VIs has seen a significant increase with the development and availability of hyperspectral data. Elvidge and Chen [50], Blackburn [51,52], and Thenkabail et al. [53,54] have demonstrated the effectiveness of narrowband VIs, which continues as one of the most important analytical approaches in the area of spectroscopic analysis of vegetation. Table 3.2 shows the several VIs that are mentioned in this paper along with the spectral calculation and literature source.

Table 3.2   Vegetation Indices Specifically Referenced in This Paper

Name

Acronym

Formula

Reference

Anthocyanin Reflectance Index

ARI

(1/R550) − (1/R700)

Gitleson et al. [84]

Difference Vegetation Index

DVI

2.4 * MSS7 – MSS5

Richardson and Wiegand [85]

Modified Triangular Vegetation Index 2

MTVI2

1.5[1.2(R800 − R550) − 1.3 (R670 − R550)]/SQRT[(2 * (R800 + 1)2) –(6 * R800 – 5 * SQRT(R670)) – 0.5]

Haboudane et al. [86]

Moisture Stress Index

MSI

(R1599 − R819)

Hunt and Rock [87]

Normalized Difference Vegetation Index (Broadband)

NDVI

(NIR − RED)/(NIR + RED)

Rouse [88]

Normalized Difference Vegetation Index

NDVI

(R800 − R670)/(R800 + R670)

Sims and Gamon [89] (Narrowband)

Normalized Pigment Chlorophyll Index

NPCI

(R680 − R430)/(R680 + R430)

Peñuelas et al. [44]

Photochemical Reflectance Index

PRI

(R531 − R570)/(R531 + R570)

Gamon et al. [45]

Red Edge Position

REP

R1Dmax: (R1D690 − R1D740)

Curran et al. [90]

Red Edge Vegetation Stress Index

RVSI

((R714 – R752)/2) – R733

Merton [91]

Soil-Adjusted Vegetation Index

SAVI

(1 + 0.5) (R800 − R670)/(R800 + R670 + 0.5)

Huete [46]

Structure-Insensitive Pigment Index

SIPI

(R800 − R445)/(R800 − R680)

Penuelas et al. [44]

Triangular Vegetation Index

TVI

0.5 * (((120 * (R750 − R550)) −(200 * (R670 − R550)))

Broge and Leblanc 2000 [47]

VIs have also played an important role in the detection and analysis of stress due to heavy metals (Table 3.3). Reusen et al. [55] successfully mapped heavy metal contamination in Belgium through the expressions of vegetation stress in conifers near abandoned zinc smelting facilities. Utilizing imaging data from an airborne hyperspectral sensor (CASI), they utilized a Spectral Angle Mapper (SAM) classification to build a mask for pine trees and then computed 18 separate VIs of stress. The Edge-Green First derivative Normalized difference (EGFN) VI proved to be the best indicator of zinc stress in the pine trees in the surrounding area [55].

Table 3.3   Some Key Spectral Features and Vegetation Indices Related to Metal Stress in the Literature

Spectral Feature

Metal(s)

Vegetation Type

Sensor

Reference(s)

DVI, REP

Ni, Cd, Cu, Pb, Zn

Floodplain, ryegrass

ASD

[32]

EGFN

Zn

Conifer

CASI

[55]

NDVI

Cr, Pb, Zn, V

Gray birch

ASD

[39]

RGI

Ikonos

NDVI

Ni, Cd, Cu, Pb, Zn

Rice

Landsat TM

[83]

PRI

General HM

Floodplain

ASD

[56]

PRI

As

Ferns

ASD

[36,37]

REP

Pb

Rice

ASD

[41]

REP

Cu

Peas, maize

PE 554

[61]

Zn

Sunflower

REP

General HM

Floodplain Bluegrass, ryegrass

ASD

[34,35]

RVI

Hg

Mustard spinach

ASD

[59]

NDVI, REP

NPCI, PRI,

General HM

Stinging nettles

ASD

[56]

REP

Reed canary grass

Meadow foxtail

R850

Cd, Cu, Pb, Zn, As

Peas

PE 554

[18]

R1650

Cd, Cu, Pb, Zn, As

Peas

PE 554

[18]

CR1730

General HM

Floodplain

ASD

[56]

R2200

Cd, Cu, Pb, Zn, As

Peas

PE 554

[18]

Götze et al. [56] used reflectance spectroscopic methods in both the laboratory and field to quantify and separate heavy metal stress in floodplain vegetation. Testing a series of VIs, they showed that metal stress could be uniquely separated from other forms of stress such as water or nutrient stress. The indices that proved to be most sensitive to the stress from heavy metals in the soil were the normalized pigment chlorophyll index (NPCI), the PRI, the REP, and the continuum removed band depth at 1730 nm (CR1730) [56].

Using both field and laboratory measurements, Slonecker [36] showed that the PRI was sensitive to metal stress in the form of inorganic arsenic, Thorhaug [57] showed that the PRI was sensitive to the effects of low salinity in seagrass health, and Gallagher [39] showed that a red/green ratio index had an inverse relationship with zinc concentrations in gray birch trees.

Several VIs seem to dominate the literature with respect to metal stress in vegetation. The REP described earlier is the most dominant spectral feature used to assess plant stress. It has been used by many researchers to evaluate decreases in plant chlorophyll, biomass, or physiological health with respect to metal stress [7–10,17,24,34,35,39,41,56,58–63].

The PRI was developed by Gamon et al. [45] as a narrowband hyperspectral indicator of changes in the pigment balance of plants due to photosynthetic stress. Originally designed to track diurnal changes in photosynthetic efficiency, the PRI is sensitive to changes in carotenoid pigments and the epoxidation state of the xanthrophyll cycle. This is a measure of photosynthetic light use efficiency and the rate of carbon dioxide uptake. The PRI measures the relative reflectance on either side of the green maxima around 550 nm and compares reflectance parameters in both the red and green regions simultaneously. Because the change in pigment concentrations due to metal stress in most vascular plants is similar, the PRI has been shown to be a successful indicator of a variety of stress conditions, including stress from soil metals. Slonecker [36] computed a suite of 67 broadband and hyperspectral VIs and used a PLS and stepwise linear regression (SLR) analysis to isolate the best VIs for explaining arsenic stress in Boston ferns and arsenic hyperaccumulating Pteris ferns. The results showed for the control Boston ferns that the PRI, along with the Moisture Stress Index, the red edge vegetation stress index and the modified TVI2 provided the best model for explaining the level of arsenic uptake. These indices measure plant stress in one form or another, which generally increases with higher concentrations of soil arsenic. The best indices for the hyperaccumulating Pteris ferns were the broadband green index (GI), the sum green index (SGI), and the carotenoid reflectance index (CRI1), all relating to the green part of the spectrum. Although not fully understood, the different indices for stressed and hyperaccumulating species reflect key differences in internal plant physiology [36].

Götze et al. [56] found that four indices were highly correlated between heavy metal content and chlorophyll content. The R 2 values for the NCPI (0.91), PRI (0.75), REP (0.80), and the continuum-removed spectra at 1730 nm (0.74) were all sensitive to metal stress in plants. Although the underlying physiology is not fully understood, the authors speculate that the correlation could be related to lignin or protein production in the plant synthesis. Further, this study shows promising results for using these values to separate heavy metal stress from water and nutrient stress [56].

3.6  Emerging Statistical Methods

A wide variety of analytical methods can be noted in a review of the hyperspectral analysis of vegetation and vegetation stress. One of the fundamental issues relates to the fact that the analysis of hyperspectral data presents unique analytical problems for standard multivariate techniques because of the highly correlative and overlapping nature of data. The large numbers of independent variables (>1,500 spectral bands) and the highly correlated nature of those variables stem from the fact that each individual spectral band is only a few nanometers away from the spectral bands above and below it, and the result is that each spectral band records an energy pattern that is similar to its neighboring bands. Highly correlated independent variables create a condition known as collinearity, which violates the assumptions of linear regression. To develop a predictive and effective linear model, variables must be independent. The overall result of a collinearity condition is that correlated independent variables have unstable coefficients, and although the model developed may have a high r2 value and low residuals, it will perform poorly outside of the immediate data set that was used to develop it.

In recent years, a special statistical technique has emerged that addresses the problems of numerous, highly correlated variables. The technique, known as partial least squares (PLS), was first introduced in 1966 by Swedish mathematician Herman Wold as an exploratory analysis technique in the field of econometrics [64]. It was specifically designed to help researchers in situations of small, nonnormally distributed data sets with numerous but highly correlated explanatory variables. General PLS and all of its variants consist of a set of regression and classification tasks as well as dimension reduction techniques and modeling tools. Sometimes called a “soft” modeling technique, the strength of PLS resides in its relaxation, or “softening,” of the distribution, normality, and collinearity restrictions that are inherent in standard multiple linear regression techniques [65,66].

The underlying assumption of all PLS methods is that the observed data are generated by a system or process that is driven by a small number of latent (not directly observed or intuitive) variables. Projection of the observed data to their latent structure by means of PLS is a variation of principal component analysis (PCA). PLS generalizes and combines features from PCA and multiple regression and is similar to canonical correlation analysis in that it can also relate a set of independent variables to a set of multiple dependent response variables and extract latent vectors with maximum correlation [67,68].

The overall goal of PLS processing of laboratory spectral data is the reduction of 2,151 variables (bands 350–2,500 spectrometer data) down to a manageable number of variables (approximately 100) that have a high probability of significance in a predictive model. The PLS regression produces a number of significant factors using a “leave-one-out” cross-validation method [60]. At several stages in the PLS process, diagnostic checks are performed, sometimes graphically, to help isolate variables for deletion in the model that do not have any significant predictive value or are outliers. The end result of a PLS run is a variable importance in projection (VIP) table. The VIP represents the value of each variable in fitting a PLS model for both predictors and responses. The VIP for each factor is defined as the square root of the weighted average times the number of predictors. If a predictor has a relatively small coefficient (in absolute value) and a small value of VIP, then it is a prime candidate for deletion. Variables with VIP values less than 0.8 and outliers are dropped from the variable list. The VIP table results are then typically divided into four to nine groups. The PLS analysis process is then repeated on the individual groups of variables. Typically the process is iterated two to five times until a manageable subset of variables can be identified based on the top VIP scores in each group and some a priori knowledge of the process being modeled. PLS itself can be used to construct a predictive model, but it has some drawbacks. One of the strengths of PLS is its relaxation of collinearity and distribution assumptions, but this can also result in a set of collinear or redundant independent variables. Also, the best combinations of variables are not necessarily reflected in the VIP table values.

In spectral applications, a common practice is to take the final subset of variables and then place them in a SLR model. The stepwise method is a modification of the forward variable selection technique and differs in that variables already in the model do not necessarily stay there. The SLR model computes the F-statistic for each variable and contains parameters for significance levels for variables to enter and stay in the model. The SLR process computes all possible combinations of linear variables and ends when none of the variables outside the model has significance (p-value) at or below the entry level and every variable in the model is significant at the stay level. Using these sigma-restricted parameterization and general linear model methods, the SLR process simply regresses all possible combinations of input variables and returns the model with the best regression coefficient and the lowest residuals [36,65].

PLS is also used as an exploratory/data mining and analysis tool in remote sensing. As a relatively new technique, the full utilization of PLS is still evolving, but it is clear that it has a major role to play in several types of spectral, remote sensing analyses due to the large numbers of potential predictive variables and the highly correlated nature of hyperspectral reflectance and hyperspectral imaging data.

3.7  Summary and Conclusions

This paper has reviewed the hyperspectral applications of detecting the effects on vegetation of heavy metals in soil. Most spectral applications have been in the form of laboratory or field studies with portable spectrometers, as opposed to hyperspectral imagery applications. But because field spectrometers and HRS instruments essentially measure the same phenomenon at high spatial and spectral resolutions, these studies serve as a form of benchmark for airborne or spaceborne remote sensing development and several studies with airborne or spaceborne HRS instruments, such as AVIRIS [40], CASI [55], and HyMAP [36], have successfully demonstrated, metal-specific vegetation applications of hyperspectral imagery.

The metals involved included a wide range of elements, including general heavy metal contamination, as might be expected in industrial or urban floodplains [31–35,56], and metal-specific applications, such as arsenic [17,36,37,69], lead [41], zinc [55], and selenium [17]. Vegetation targets included general forest canopy, general floodplain, common grasses, and species-specific applications. Hyperspectral methods included standard applications of the NDVI and red edge and newer methods that included VIs such as the PRI, NCPI, and EGFN and a very interesting application of continuum removal and 1730 nm [56,70].

Research on hyperspectral detection of heavy metals and their effects on vegetation is in its infancy. Although much research has been carried out on other forms of vegetation condition such as stress or agricultural productivity, specific attention to metals is currently a primary scientific gap that demands research attention.

One of the direct needs for hyperspectral research is developing the ability to differentiate metal-induced stress from other types of stress such as a drought or nutrient stress. Greenhouse experiments, where stress levels are controlled and then measured with a field spectrometer, could be extremely valuable in determining where metal stress can be reliably and uniquely identified in spectra and for establishing underlying mechanisms causing spectral variation. Götze et al. [56] made a breakthrough in the identification of specific stress agents, and additional work in this area is encouraged.

Further, controlled experiments could be conducted to determine whether stress from specific metals can be uniquely identified using hyperspectral methods. As various metals interact differently with plant biochemistry and photosynthetic processes, it is feasible that stress patterns due to specific metals could be identified and utilized effectively. There could also be specific indicator species that identify the presence of metals in soil, and development of this line of research would have commercial as well as ecological value.

Additional studies that utilize both field and overhead instruments and scale up the spectral responses as a function of spatial scale are needed and represent a critical gap in the current state of the science. Lastly, data-mining efforts, such as those using PLS, that systematically consider thousands or even millions of possible band combinations and compute their statistical relevance against a known data set, would be a valuable approach to teasing out very narrow and specific spectral parameters that are not fully understood.

3.8  Future Applications

A better understanding of the spectral response to metals in soil has three primary and valuable applications. First, economic prospecting for metal deposits was one of the early applications and remains just as viable today. Second, metals often hinder agricultural productivity, and a method of monitoring their presence remotely would have immediate application to food production throughout much of the world. Third, the problem of fugitive hazardous wastes in the environment is not one that is likely to diminish in the future. As the global population grows, the need for natural resource exploitation will increase dramatically, along with the negative side effects of mining, industrial byproducts, and both controlled and fugitive wastes. As this review has indicated, there have been numerous successful hyperspectral applications of remote sensing for the location and monitoring of hazardous metals in the environment. Unlike earlier systems, HRS has the potential to identify specific materials based on molecular structure, and although considerable laboratory research continues, overhead aircraft and satellite remote sensing applications are still in their infancy due to complex atmospheric interferences, cost, and data availability. But all of these factors are steadily improving, and there is opportunity for considerable research in the area of hyperspectral monitoring of metal effects on vegetation.

References

1
Duffus, J. , Heavy metals–a meaningless term. Pure and Appied. Chemistry 2002, 74 , 793–807.
2
Alloway, B. , Heavy Metals in Soils. Springer: 1995.
3
Nieboer, E. , Richardson, D. , The replacement of the nondescript term [] heavy metals’ by a biologically and chemically significant classification of metal ions. Environmental Pollution Series B, Chemical and Physical 1980, 1 , 3–26.
4
Nriagu, J. O. , Arsenic in the Environment: Cycling and Characterization. John Wiley & Sons, Inc.: New York, NY, 1994; Vol. 1 .
5
Albright, R. , Cleanup of Chemical and Explosive Munitions: Locating, Identifying Contaminants, and Planning for Environmental Remediation of Land and Sea Military Ranges and Ordnance Dumpsites. William Andrew Publishing: 2008; p. 267.
6
Sabins, F. , Remote sensing for mineral exploration. Ore Geology Reviews 1999, 14 , 157–183.
7
Collins, W. , Spectroradiometric detection and mapping of areas enriched in ferric iron minerals using airborne and orbiting instruments. Unpublished PhD dissertation, Columbia University, 120 p. 1978, Remote sensing of crop type and maturity: Photo-grammetric Engineering and Remote Sensing, 1976.
8
Collins, W. , Chang, S. , Kuo, J. , Detection of hidden mineral deposits by airborne spectral analysis of forest canopies. NASA Contract NSG-5222, Final Report 1981, p. 61.
9
Collins, W. , Chang, S. , Raines, G. , Canney, F. , Ashley, R. , Airborne biogeophysical mapping of hidden mineral deposits. Economic Geology 1983, 78 , 737.
10
Chang, S. , Collins, W. , Confirmation of the airborne biogeophysical mineral exploration technique using laboratory methods. Economic Geology 1983, 78 , 723.
11
Küpper, H. , Küpper, F. , Spiller, M. , Environmental relevance of heavy metal-substituted chlorophylls using the example of water plants. Journal of Experimental Botany 1996, 47 , 259.
12
Barcelo, J. , Poschenrieder, C. , Plant water relations as affected by heavy metal stress: A review. Journal of Plant Nutrition 1990, 13 , 1–37.
13
Mysliwa-Kurdziel, B. , Prasad, M. , Strzalka, K. , Photosynthesis in heavy metal stressed plants. Heavy Metal Stress in Plants: From Biomolecules to Ecosystems 2004, 198.
14
Rood, J. J. , Modern Chromatics with Application to Art and Industry. D. Appleton and Company: New York, 1879.
15
Shull, C. , A spectrophotometric study of reflection of light from leaf surfaces. Botanical Gazette 1929, 87 , 583–607.
16
Willstatter, R. , Stoll, A. , Investigations on Chlorophyll, 1913. Trans. by Schertz and Merz.). Science Press: Lancaster, Pa, 1928; pp. 290–291.
17
Milton, N. , Ager, C. , Eiswerth, B. , Power, M. , Arsenic-and selenium-induced changes in spectral reflectance and morphology of soybean plants. Remote Sensing of Environment 1989, 30 , 263–269.
18
Horler, D. , Barber, J. , Barringer, A. , Effects of heavy metals on the absorbance and reflectance spectra of plants. International Journal of Remote Sensing 1980, 1 , 121–136.
19
Ray, T. , Murray, B. , Chehbouni, A. , Njoku, E. The red edge in arid region vegetation: 340–1060 nm spectra, In Summaries of the 4th Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop, R.O. Green , Editor, 1993, 149–152.
20
Guyot, G. , Baret, F. , Jacquemoud, S. , Imaging spectroscopy for vegetation studies. Imaging Spectroscopy: Fundamentals and Prospective Application 1992, 145–165.
21
Curran, P. , Dungan, J. , Gholz, H. , Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology 1990, 7 , 33.
22
Vogelmann, J. , Rock, B. , Moss, D. , Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 1993, 14 , 1563–1575.
23
Gates, D. , Keegan, H. , Schleter, J. , Weidner, V. , Spectral properties of plants. Applied Optics 1965, 4 , 11–20.
24
Horler, D. , Dockray, M. , Barber, J. , The red edge of plant leaf reflectance. International Journal of Remote Sensing 1983, 4 , 273–288.
25
Baret, F. , Champion, I. , Guyot, G. , Podaire, A. , Monitoring wheat canopies with a high spectral resolution radiometer. Remote Sensing of Environment 1987, 22 , 367–378.
26
Schwaller, M. , Tkach, S. , Premature leaf senescence; remote-sensing detection and utility for geobotanical prospecting. Economic Geology 1985, 80 , 250.
27
Milton, N. , Eiswerth, B. , Ager, C. , Effect of phosphorus deficiency on spectral reflectance and morphology of soybean plants. Remote Sensing of Environment 1991, 36 , 121–127.
28
Rock, B. , Hoshizaki, T. , Miller, J. , Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sensing of Environment 1988, 24 , 109–127.
29
Küpper, H. , Küpper, F. , Spiller, M. , In situ detection of heavy metal substituted chlorophylls in water plants. Photosynthesis Research 1998, 58 , 123–133.
30
Küpper, H. , Šetlík, I. , Spiller, M. , Küpper, F. , Prášil, O. , Heavy metal-induced inhibition of photosynthesis: targets of in vivo heavy metal chlorophyll formation1. Journal of Phycology 2002, 38 , 429–441.
31
Kooistra, L. , Wehrens, R. , Leuven, R. , Buydens, L. , Possibilities of visible-near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Analytica Chimica Acta 2001, 446 , 97–105.
32
Kooistra, L. , Salas, E. , Clevers, J. , Wehrens, R. , Leuven, R. , Nienhuis, P. , Buydens, L. , Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. Environmental Pollution 2004, 127 , 281–290.
33
Kooistra, L. , Wanders, J. , Epema, G. , Leuven, R. , Wehrens, R. , Buydens, L. , The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Analytica Chimica Acta 2003, 484 , 189–200.
34
Clevers, J. , Kooistra, L. , Assessment of heavy metal contamination in river floodplains by using the red-edge index. Chemical Analysis 2001.
35
Clevers, J. , Kooistra, L. , Salas, E. , Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data. International Journal of Remote Sensing 2004, 25 , 3883–3895.
36
Slonecker, E. , Remote Sensing Investigations of Fugitive Soil Arsenic and Its Effects on Vegetation Reflectance. George Mason University: Fairfax, Virginia, 2007.
37
Slonecker, T. , Haack, B. , Price, S. , Spectroscopic analysis of arsenic uptake in Pteris ferns. Remote Sensing 2009, 1 , 644.
38
Rosso, P. , Pushnik, J. , Lay, M. , Ustin, S. , Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination. Environmental Pollution 2005, 137 , 241–252.
39
Gallagher, F. , Pechmann, I. , Bogden, J. , Grabosky, J. , Weis, P. , Soil metal concentrations and productivity of Betula populifolia (gray birch) as measured by field spectrometry and incremental annual growth in an abandoned urban Brownfield in New Jersey. Environmental Pollution 2008, 156 , 699–706.
40
Mars, J. , Crowley, J. , Mapping mine wastes and analyzing areas affected by selenium-rich water runoff in southeast Idaho using AVIRIS imagery and digital elevation data. Remote Sensing of Environment 2003, 84 , 422–436.
41
Ren, H. , Zhuang, D. , Pan, J. , Shi, X. , Wang, H. , Hyper-spectral remote sensing to monitor vegetation stress. Journal of Soils and Sediments 2008, 8 , 323–326.
42
Pearson, R. , Miller, L. , Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In 1972; p. 1355.
43
Carlson, T. , Ripley, D. , On the relation between NDVI, fractional vegetation cover, and leaf area index* 1. Remote Sensing of Environment 1997, 62 , 241–252.
44
Penuelas, J. , Gamon, J. , Fredeen, A. , Merino, J. , Field, C. , Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sensing of Environment 1994, 48 , 135–146.
45
Gamon, J. , Pe uelas, J. , Field, C. , A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency* 1. Remote Sensing of Environment 1992, 41 , 35–44.
46
Huete, A. , A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988, 25 , 295–309.
47
Broge, N. , Leblanc, E. , Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 2001, 76 , 156–172.
48
Strachan, I. , Pattey, E. , Boisvert, J. , Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment 2002, 80 , 213–224.
49
Daughtry, C. , Walthall, C. , Kim, M. , De Colstoun, E. , McMurtrey III, J. , Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 2000, 74 , 229–239.
50
Elvidge, C. , Chen, Z. , Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment 1995, 54 , 38–48.
51
Blackburn, G. , Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment 1998, 66 , 273–285.
52
Blackburn, G. , Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany 2007, 58 , 855.
53
Thenkabail, P. , Smith, R. , De Pauw, E. , Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 2000, 71 , 158–182.
54
Thenkabail, P. , Smith, R. , De Pauw, E. , Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric Engineering and Remote Sensing 2002, 68 , 607–622.
55
Reusen, I. , Bertels, L. , Debruyn, W. , Deronde, B. , Fransaer, D. , Sterckx, S. , Species Identification and Stress Detection of Heavy-Metal Contaminated Trees. 2003.
56
Götze, C. , Jung, A. , Merbach, I. , Wennrich, R. , Gläßer, C. , Spectrometric analyses in comparison to the physiological condition of heavy metal stressed floodplain vegetation in a standardised experiment. Central European Journal of Geosciences 2010, 2 , 132–137.
57
Thorhaug, A. , Richardson, A. , Berlyn, G. , Spectral reflectance of Thalassia testudinum (Hydrocharitaceae) seagrass: low salinity effects. American Journal of Botany 2006, 93 , 110.
58
Choe, E. , van der Meer, F. , van Ruitenbeek, F. , van der Werff, H. , de Smeth, B. , Kim, K. , Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain. Remote Sensing of Environment 2008, 112 , 3222–3233.
59
Dunagan, S. , Gilmore, M. , Varekamp, J. , Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). Environmental Pollution 2007, 148 , 301–311.
60
Guang-yu, C. , Xin-hui, L. , Su-hong, L. , Zhi-feng, Y. , Spectral Characteristics of Vegetation in Environment Pollution Monitoring [J]. Environmental Science and Technology 2005, 1 .
61
Horler, D. , Barber, J. , Darch, J. , Ferns, D. , Barringer, A. , Approaches to detection of geochemical stress in vegetation. Advances in Space Research 1983, 3 , 175–179.
62
Wickham, J. , Chesley, M. , Lancaster, J. , Mouat, D. , Remote Sensing for the Geobotanical and Biogeochemical Assessment of Environmental Contamination; DOE/NV/10845–27, Nevada Univ., Reno, NV (United States). Desert Research Inst.: 1993.
63
Xia, L. , Shoo-Feng, L. , Zheng, L. , High spectral resolution data applied to identify plant stress response to heavy metal in mine site [J]. Science of Surveying and Mapping 2007, 2 .
64
Wold, H. , Estimation of principal components and related models by iterative least squares. Multivariate Analysis 1966, 1 , 391–420.
65
Tobias, R. , An introduction to partial least squares regression. In Citeseer: 1995; pp. 1250–1257.
66
Abdi, H. , Partial least squares (PLS) regression. In Encyclopedia of Social Sciences Research Methods (eds. M. Lewis–Beck , A. Bryman and T. Futing ) 2003; pp. 1–7.
67
Höskuldsson, A. , PLS regression methods. Journal of Chemometrics 1988, 2 , 211–228.
68
Rosipal, R. , Krämer, N. , Overview and recent advances in partial least squares. Subspace, Latent Structure and Feature Selection 2006, 34–51.
69
Sridhar, B. , Han, F. , Diehl, S. , Monts, D. , Su, Y. , Spectral reflectance and leaf internal structure changes of barley plants due to phytoextraction of zinc and cadmium. International Journal of Remote Sensing 2007, 28 , 1041–1054.
70
Clark, R. , Roush, T. , Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research 1984, 89 , 6329–6340.
71
Shaw, B. , Sahu, S. , Mishra, R. , Heavy metal induced oxidative damage in terrestrial plants. In Heavy Metal Stress in Plants-from Biomolecules to Ecosystems, 2004; Vol. 2 , pp. 84–126.
72
Delhaize, E. , Ryan, P. , Aluminum toxicity and tolerance in plants. Plant Physiology 1995, 107 , 315.
73
Roy, A. , Sharma, A. , Talukder, G. , Some aspects of aluminum toxicity in plants. The Botanical Review 1988, 54 , 145–178.
74
Jastrow, J. , Koeppe, D. , Uptake and Effects of Cadmium in Higher Plants. John Wiley & Sons: 605 Third Ave., New York, NY 10016 USA, 1980; pp. 607–638.
75
Das, P. , Samantaray, S. , Rout, G. , Studies on cadmium toxicity in plants: A review. Environmental Pollution 1997, 98 , 29–36.
76
Kukkola, E. , Rautio, P. , Huttunen, S. , Stress indications in copper-and nickel-exposed Scots pine seedlings. Environmental and Experimental Botany 2000, 43 , 197–210.
77
Mocquot, B. , Vangronsveld, J. , Clijsters, H. , Mench, M. , Copper toxicity in young maize (Zea mays L.) plants: Effects on growth, mineral and chlorophyll contents, and enzyme activities. Plant and Soil 1996, 182 , 287–300.
78
Masarovicová, E. , Cicák, A. , Štefančík, I. , Plant responses to air pollution and heavy metal stress. In Handbook of Plant and Crop Stress. Marcel Dekker, New York (ed. M. Pessaraki ) Marcel Dekker, Inc.: New York, 1999; pp. 569–598.
79
Sharma, P. , Dubey, R. , Lead toxicity in plants. Brazilian Journal of Plant Physiology 2005, 17 , 35–52.
80
Patra, M. , Sharma, A. , Mercury toxicity in plants. The Botanical Review 2000, 66 , 379–422.
81
Khalid, B. , Tinsley, J. , Some effects of nickel toxicity on rye grass. Plant and Soil 1980, 55 , 139–144.
82
Bonnet, M. , Camares, O. , Veisseire, P. , Effects of zinc and influence of Acremonium lolii on growth parameters, chlorophyll a fluorescence and antioxidant enzyme activities of ryegrass (Lolium perenne L. cv Apollo). Journal of Experimental Botany 2000, 51 , 945.
83
Boluda, R. , Andreu, V. , Gilabert, M. , Sobrino, P. , Relation between reflectance of rice crop and indices of pollution by heavy metals in soils of Albufera Natural Park (Valencia, Spain). Soil Technology 1993, 6 , 351–363.
84
Gitelson, A. , Merzlyak, M. , Chivkunova, O. , Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves. Photochemistry and Photobiology 2001, 74 , 38–45.
85
Richardson, A. , Wiegand, C. , Distinguishing vegetation from soil background information (by gray mapping of Landsat MSS data). Photogrammetric Engineering and Remote Sensing 1977, 43 , 1541–1552.
86
Haboudane, D. , Miller, J. , Pattey, E. , Zarco-Tejada, P. , Strachan, I. , Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 2004, 90 , 337–352.
87
Hunt Jr., E. , Rock, B. , Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sensing of Environment 1989, 30 , 43–54.
88
Rouse, J. , Monitoring vegetation systems in the Great Plains with ERTS, In 3rd ERTS-1 Symposium, NASA Goddard Space Flight Center 1974.
89
Sims, D. , Gamon, J. , Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 2002, 81 , 337–354.
90
Curran, P. , Dungan, J. , Macler, B. , Plummer, S. , The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment 1991, 35 , 69–76.
91
Merton, R. Monitoring community hysteresis using spectral shift analysis and the red-edge vegetation stress index, In Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop. NASA, Jet Propulsion Laboratory, Pasadena, California, USA 1998.
Search for more...
Back to top

Use of cookies on this website

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more in our Privacy Policy. By continuing to use the site you are agreeing to our use of cookies.