Remote Sensing of Environment 80 (2002) 406 – 417 www.elsevier.com/locate/rse Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization R.L. Dehaan, G.R. Taylor* School of Geology, University of New South Wales, Sydney, NSW 2052, Australia Received 30 October 2000; received in revised form 11 September 2001; accepted 3 October 2001 Abstract Salinization is a major cause of soil degradation in the Murray – Darling Basin of Australia. The objective of this research is to evaluate the utility of field-derived spectra of saline soils and related vegetation for characterizing and mapping the spatial distribution of irrigationinduced soil salinization. A FieldSpec FR hand-held spectrometer was used to measure the spectra of a range of salinized soils and associated vegetation. Strategies for mapping field-derived spectra using hyperspectral (HyMap) imagery were assessed, and a continuumremoved Spectral-Feature-Fitting (SFF) approach adopted. Field-derived spectra of the vegetation comprising of samphire, sea blite, and native grass species are also useful indicators of salinization; however, their absence is not necessarily an indicator of healthy soils. Distribution maps created using the SFF method and a restricted wavelength range of field-derived spectra provide an accurate record of the distribution of both vegetation and soil indicators of salinization at the time of image acquisition. Salinized soil and vegetation indicator class maps show a similar spatial distribution to soil salinization as mapped by ground-based geophysical surveys. D 2002 Elsevier Science Inc. All rights reserved. 1. Introduction Salinization and soil degradation occur in areas where saline groundwaters are elevated to where they approach the ground surface and evaporation exceeds precipitation. In irrigated areas, where the water table approaches the ground surface, salt accumulation occurs in areas known as discharge zones. Capillary rise of saline groundwater causes the direct precipitation of evaporite minerals at the surface in these zones. Salinization may also occur when salts are concentrated in soils by the evaporation of freestanding irrigation water. The destruction of primary clay minerals by the interaction of salt-bearing water with which they are in disequilibrium also occurs (Jankowski and Ackworth, 1999, personal communication) The research described in this contribution was undertaken within irrigated land in the Murray– Darling Basin (MDB) of southeast Australia, but salinization associated with irrigation is a worldwide problem. In the MDB, it is estimated to cost Australia at least US$200 million (1987) annually in lost agricultural produc- * Corresponding author. Fax: +61-2-9385-5935. E-mail address: [email protected] (G.R. Taylor). tion (Ghassemi, Jakeman, & Nix, 1995) with the Kerang and Shepperton areas responsible for 75% of the total productivity losses (Chartres, 1987). The major causes are a combination of poor land management, the removal of trees for agriculture, and crude irrigation practices. The effect of these practices has been to raise the water table to where saline groundwater interacts with the root zone of trees and crops. This causes changes in vegetation cover and ultimately loss of vegetation and agricultural productivity. The key to successfully managing the salinization problem is the early recognition of salinized soils, the implementation of methods to combat incipient salinization such as improved irrigation, drainage, and farming practices, and the monitoring of salinized land on a regular basis. Conventional techniques for identifying and monitoring salinized land include the measurement of water table levels in boreholes, ground-based geophysics, measurements of soil electrical properties using soil pastes and water extracts, EC1:5 (Van Der Lelij & Poolman, 1989), and the visual identification of salinization and degradation. Ground-based electromagnetic (EM) measurements of soil electrical conductivity are generally accepted as the most effective method for rapid acquisition and quantification of soil salinization (Norman, Lyle, Heuperman, & Poulton, 1989). 0034-4257/01/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 0 0 3 4 - 4 2 5 7 ( 0 1 ) 0 0 3 2 1 - 2 R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 Fig. 1. Location of Pyramid Hill. The boundary of the MDB is shown. These methods are time-consuming and expensive, and so efforts are being made to develop more cost-effective methods of mapping soil salinization. These include airborne EM techniques (Street & Anderson, 1993), airborne radar (Taylor et al., 1996), and hyperspectral remote sensing techniques (Dehaan & Taylor, in press). In this paper, we describe the spectral features in the 400 to 2500 nm range of field-derived spectra of selected soils and vegetation from within the Tragowel Plains Research and Development Block (R&D Block) (Fig. 1). These are used to develop a strategy for the use of field-derived spectra for the mapping of salinized soils from HyMap airborne scanner imagery. 1.1. Background studies The potential of remote sensing to identify and monitor soil salinization has been studied using satellite imagery, such as Landsat Thematic Mapper (TM). The low spectral resolution of Landsat TM data, and the use of traditional classification techniques, requiring ground studies in numerous training areas, have meant that the use of this type of imagery has been largely unsuccessful (Fraser & Joseph, 1998). Hyperspectral data allow for the quantitative assessment of endmember abundances on a pixel-by-pixel basis (Sabol, Adams, & Smith, 1992), and hence, interpretative techniques differ greatly from those traditionally applied to satellite data. Hick and Russel (1990), using Geoscan — a 24-channel instrument that approached hyperspectral imaging capabilities, concluded that vegetation vigour, as shown by near-infrared reflectance (NIR), is the best indicator of the impacts of increasing soil salinization. They also showed the importance of minor constituents, such as MgCl2, as contributors to the spectral signature of salinized soils. Taylor, Bennett, Mah, and Hewson (1994) showed how principal components analysis of Geoscan imagery allowed the qualitative mapping of soil salinization at Pyramid Hill, Victoria. Chapman, Rothery, Francis, and Pontual (1989), Crowley (1991), and Drake (1995) describe the spectral features 407 of a number of evaporite minerals from natural playas. Crowley showed that many saline minerals exhibit diagnostic near-infrared absorption bands, chiefly attributed to vibrations of hydrogen-bonded structural water molecules, and that visible, NIR spectra can be used to detect minor hydrate phases within mineral mixtures dominated by halite. Drake, op. cit., quoting Hunt, Salisbury, and Lenhoff (1971), describes gypsum’s absorption features, which occur at around 1000, 1200, 1400, 1600, 1740, 1900, and 2200 nm, as being due to combinations of O – H stretches, H – O – H bending fundamentals, and various overtones. The various hydrated chlorides and sulfates of sodium, potassium, calcium, and magnesium also show these absorption features, but variation in the way the water is held within the crystal lattice causes subtle but consistent differences in absorption feature position. USGS library spectra (Clark, Swayze, Gallagher, King, & Calvin, 1993) of some of these hydrated chlorides and sulfates are shown in Fig. 2a. Crowley (1993) showed that hyperspectral images collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) could successfully identify and map hydrated chlorides and sulfates in playas in western US. 1.2. The HyMap spectrometer The HyMap Imaging System first flew in 1997 and, having 128 channels, is Australia’s first true hyperspectral imager. The HyMap instrument can be configured to give a variety of spectral and spatial resolutions, but generally, bandwidths vary between 10 and 20 nm across the spectral range and spatial resolutions vary from 3 to 10 m according to aircraft height. The instrument fits into any light, twinengine aircraft fitted with a standard camera port and is built by Integrated Spectronics. The HyMap instrument has a high signal-to-noise ratio of > 500:1 (Cocks, Jenssen, Stewart, Wilson, & Shields, 1998). 1.3. Interpretation strategies The high spectral resolution of hyperspectral data sets allow spectra to be unmixed with a higher accuracy and enables the separation of components (such as senesced vegetation and soils) that are difficult to separate with broadband sensors (Sabol et al., 1992). A comprehensive analysis of the materials contributing to the spectral signature of each pixel can therefore be determined. Fieldderived or image-derived endmembers can be used to unmix these spectral components from hyperspectral imagery (Adams, Smith, & Gillespie, 1993). The acquisition of field spectra has disadvantages in that additional expensive field spectrometers and visits to the field are required. However, the field of view of the field instrument is often smaller than the Instantaneous Field of View (IFOV) of an airborne instrument. This allows minor ground components to be identified more readily. Soils are mixtures of mineral- and vegetation-derived constituents. These constituents, and the 408 R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 Fig. 2. (a) USGS library reflectance spectra of various salt species; (b) field-derived reflectance soil terrain element spectra; (c) continuum-removed soil terrain element spectra; (d) field-derived reflectance vegetation terrain element spectra (values offset for clarity); (e) continuum-removed vegetation terrain element spectra. degree of crystallinity of the clay mineral components, demonstrated later, will vary in abundance according to the degree of salinization. Soil spectra and chemistry (also described later) suggest that salinization and spectral prop- R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 erties vary continuously between two extreme endmembers, i.e., highly saline salt scalds and nonsaline bare soil. However, the spatial distribution of salinity is such that large areas have commonly reached a uniform level of salinity and hence map as intermediate soil types at all scales of investigation. In this study, we will use the term ‘‘terrain elements’’ to describe both homogenous areas of common soil or vegetation cover that have distinctive spectral properties at the scale measured by the field spectrometer and also are sufficiently abundant that their distribution can be mapped by field or hyperspectral techniques. A terrain element is an intimate mixture of soil minerals or vegetation species and thus is not likely to be a pure endmember in the classic sense. Image-derived endmembers will fail to include minor terrain elements that never reach abundance levels such that they dominate an entire pixel. These may still be significant indicators of salinization. Field-derived spectral endmembers may therefore be more effective at characterizing differences in the degree of soil salinization. In this paper, we measure field-derived spectra of selected saline soil and associated vegetation terrain elements and determine the best way of using these to map salinized land with HyMap hyperspectral imagery. Spectral absorption features are often mapped best if the analysis is limited to a restricted spectral range that encompasses just the absorption features of interest (Taylor et al., 1994). Continuum removal is the normalization of reflectance spectra using common spectral reference points to create a continuum spectrum, then continuum-normalizing the spectrum by dividing the original by the continuum. This is done for each spectrum and, in the case of imagery, it is done for each pixel spectrum. Continuum removal is a decorrelation technique that maximizes the effects of spectral absorption features. We therefore also investigated whether field-derived terrain element spectra are best mapped using the entire range of bands available or using restricted parts of the spectral range encompassing specific absorption features and whether spectra are best mapped using reflectance or continuum-removed data. We also evaluate several mapping methods including Spectral Angle Mapper (SAM) (Kruse et al., 1993), Matched Filtering (MF) (Boardman, Kruse, & Green, 1995; Harsanyi & Chang, 1994), and Spectral-Feature-Fitting (SFF) (Clark & Swayze, 1995). SAM is an automated method for comparing reference spectra to image spectra. This method calculates the ‘‘spectral angle’’ between the reference and image spectra by treating them as vectors in n-dimensional space with dimensionality equal to the number of bands. It ranks the similarity of reference to image spectra, scoring similar spectra highly. MF allows the abundance of endmembers to be determined without a knowledge of the remaining endmembers. SFF is an absorption feature-based method that matches image spectra to reference spectra using a least squares technique and continuum-removed data. Absorption feature depth, relating to material abundance, is 409 output in one image, while in a second image, the root mean square error is calculated using a least squares technique. Linear unmixing (Adams, Smith, & Johnson, 1986; Gillespie et al., 1990) was not assessed. This method requires all endmembers to be present, and without knowledge of all the endmembers, unmixing results are likely to be spurious. 1.4. Test site description The Tragowel Plains agricultural region is located within the MDB, near the town of Pyramid Hill. Salinization, crop testing, and remediation measures for salinized environments have been studied at the Tragowel Plains R&D Block by the Victorian Department of Agriculture since 1987. Irrigation on the R&D Block is mostly used to support crop cultivation and crop trials, with some annual and perennial pasture grown for sheep and cattle grazing. Vegetation, soil conductivity measured with a Geonic EM38 instrument, and depth to the water table are routinely monitored, making the site an excellent location for the testing of salinity mapping techniques. The location of the R&D Block is shown on all distribution maps as a superimposed line drawing that represents a total of 130 ha within the total image area. Terrain element distribution maps shown in this paper are image swaths oriented approximately northwest (top) to southeast (bottom) with a swath width of 2.5 km. Soils on the R&D Block are developed upon fluviolacustrine sands, silts, and clays belonging to the Shepparton Formation (Macumber, 1991). The soils on the R&D Block form a red/brown sandy loam; however, in salinized areas, the soil’s color changes to grey. Rising water tables first intersect the ground surface in areas of atypical low relief (Norman et al., 1989), which on the R&D Block coincide with a series of palaeo channels. In these palaeo channels, efflorescing salts form white scalds and crusts, and hence, the areal expression of salinized areas resembles abandoned stream channels. 1.5. Sample and image acquisition and preprocessing A hyperspectral image was acquired by the HyMap spectrometer over the Pyramid Hill region in May 1999. The HyMap spectrometer was flown so as to give 5 m IFOV for a data swath width of 512 pixels, providing a ground swath width of approximately 2.5 km. Spectral data were acquired in 128, approximately 20 nm, bands covering the spectral range 400– 2500 nm. A dark current correction was applied to the data. There was little systematic variation in reflectance across the imagery, and hence, no corrections for across-track, path –length differences or shadowing were applied. Images were not registered to geographic coordinates, and therefore, all terrain element distribution maps presented here still contain minor geometric distortions. Calibration of the HyMap images was carried out using field spectra of appropriate bright and dark targets (tar seal, galvanized metal roofing, standing water, and healthy green grass) using the methods of Roberts, Yamaguchi, and Lyon 410 R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 (1986), implemented in the Empirical Line method in the Environment for Visualizing Images (ENVI), Better Solutions Consulting (1993 – 1999). Calibrated imagery and spectra viewed in ENVI show that the spectra of various objects closely resemble those expected from field and library spectra. A FieldSpec FR hand-held spectrometer manufactured by Analytical Spectral Devices, and having a spectral resolution of 1 nm, was used to acquire reflectance spectra in the range 350– 2500 nm of in situ soils and vegetation. This occurred 2 weeks after image acquisition. Instrument calibration and optimization were carried out between each spectral measurement using a Labsphere spectralon panel as a white reference. Spectra were collected using the accessory reflectance probe with its internally controlled light source. The field of view was restricted to 25 mm for all measurements. Five spectral measurements were made for each vegetation type by placing sufficient leaf matter on a flat surface so as to cover the entire 25 mm field of view. Spectra of soils were measured in situ. Spectra and associated soil samples were acquired in areas where salinization was most blatant and along a series of measured traverses across the R&D Block. Delineation of these salinized areas was carried out using 1999 EM38 soil salinization maps provided by staff from the R&D Block. Forty-one soil samples collected from the R&D Block were air-dried and powdered. EC1:5 water extracts, made by adding 25 g of deionized water to 5 g of each sample, were used to measure electrical conductivity, using a TPS900C Conductivity Salinity Meter, and as material for mineralogical analysis using conventional X-ray diffraction (XRD) techniques. 2. Results 2.1. Spectral properties of soils The field spectra of four representative soil samples collected from the R&D Block illustrate the effects of varying degrees of soil salinization. Reflectance and continuum-removed spectra of a salt crust (EC1:5 of 51,800 mS/ cm), highly salinized soil (EC1:5 of 19,780 mS/cm), moderately salinized soil (EC1:5 of 9710 mS/cm), and a low salinized soil (EC1:5 of 2430 mS/cm) are shown in Fig. 2b and c, respectively. These spectra show absorption features at 505, 920, 1415, 1915, and 2205 nm. The salt scald and highly salinized soil show additional distinct absorption features at 680, 1180, and 1780 nm. The four spectra are similar to one another with hydroxyl features at approximately 2200 nm becoming less developed as samples become more salinized. The reduction of the 2200 nm absorption feature intensity seen in the more salinized soils may be a result of a loss of crystallinity in the clay minerals (Fraser, Camuti, Huntington, & Cuff, 1990). A reflectance high at 800 nm becomes more prominent, and the overall slope of the curve between 800 and 1300 nm decreases as samples become more salinized. This decrease in slope between 800 and 1300 nm parallels changes in soil color from red to grey and could be due to either changes in oxidation state of the iron present (Hunt & Ashley, 1979) within the soils or due to variations in the evaporite mineral content within the soils. The deeper uncombined water features at 1415 and 1915 nm broaden and become more asymmetrical as salinization increases, suggesting an increase in soil moisture content (Bowers & Hanks, 1965) with increasing salinization. The increase in depth and complexity of the absorption features at around 680, 920, 1180, and 1780 nm resembles features described by Crowley (1991) and Drake (1995) as being due to hydroxyl ions and water within the lattice of various hydrated evaporite minerals. Forty-one soil spectra were examined. Only soils with an EC1:5 value greater than 9710 mS/cm (6214 ppm of total salts) showed the hydrate-related absorption feature at 1180 nm. The majority of soils from salt crusts and highly salinized areas had salt efflorescence at the surface and EC values ranged from 52,000 to 15,000 mS/cm, while the remaining soil samples collected from low salinized areas had EC values of less than 3000 mS/cm. The less salinized soil samples do not show the complex, hydrate-related, absorption features but often possess a well-defined, narrow, and deep hydroxyl feature at 2200 nm suggestive of wellcrystallized clay minerals in the soils. The spectra of the salt crust and highly salinized soil shown in Fig. 2b and c are consistent with the features that would be shown by damp halite (NaCl) with minor amounts of hydrated sulfate minerals such as bassanite (2CaSO 4H2O), polyhalite (K2Ca2Mg(SO4)42H2O), and gypsum (CaSO42H2O). It is likely that the evaporite materials present in salinized crusts and scalds are mostly sodium chloride (halite) with minor amounts of various sodium, potassium, calcium, and magnesium sulfates. The surface efflorescence is ephemeral due to its sensitivity to changes in water table height and recent climate, making exact determintion of composition and hydration state of the salts involved difficult. 2.2. Chemical properties of soils Groundwater analyses provided by Pyramid Hill Salt show that the groundwater at Pyramid Hill is similar in composition to modern seawater and contains as major constituents dissolved sodium, potassium, magnesium, calcium, chloride, and sulfate. Salt crusts and soils containing hydrated chlorides and sulfates are therefore feasible and XRD and X-ray fluorescence (XRF) techniques were utilized to assist in the identification of such minerals. Forty-one soil samples collected from the R&D Block were crushed in a tungsten carbide bowl using a Rocklabs Tema mill and oven-dried. Splits of these homogenized samples were then examined using a PW 1830 XRD Spectrometer. Cluster analysis of chemical analyses deter- R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 mined by XRF shows that salinization involves the addition of chloride, sulfate, sodium, and calcium ions with minor amounts of other elements. XRD determinations of all soil samples show that they contain quartz, illite, and kaolinite. The more salinized samples also contain halite and traces of anhydrite, gypsum, polyhalite, bassanite, and bloedite. Oven drying prior to analysis by XRD has possibly changed some of the hydrate mineralogy, and hence, more elaborate methods of sample collection, preparation, and analysis are required to more confidently confirm the mineralogy of these soils. 2.3. Spectral properties of halophytic vegetation The appearance of salt-tolerant (halophytic) plants, in conjunction with the disappearance of salt-sensitive plants, is one of the earliest and most recognizable signs of salinization. Halophytic plants have usually developed strategies for survival in saline environments that give them distinctive leaf and stem structures (Larcher, 1980). Fig. 2d and e, respectively, show field-derived reflectance and continuum-removed spectra for the most abundant halophytic vegetation at the time of image acquisition. Samphire (Halosarcia pergranulata), a dense succulent shrub with numerous woody stems and short, segmented branchlets, is characterized by having two reflectance peaks at 555 and 624 nm due to the presence of accessory pigments, a reflectance minimum at 680 nm due to chlorophyll absorption, a distinctive slope to the infrared reflectance plateau between 1250 and 1400 nm, and absorption features at 985, 1188, 1440, and 1925 nm due to absorption by liquid water contained in the plant’s tissue. The green peak predominates, but there is also a small peak in the red. This is attributed to the varying colors exhibited by this species’ segmented branchlets, which are predominantly green but under certain seasonal and saline conditions may also appear yellow and red. A second halophyte, sea blite (Sueda australis), a straggly shrub with small, narrow, succulent leaves that are commonly a purple/red and to a lesser extent light green color, is characterized by a strong peak in the red at 640 nm, a small chlorophyll absorption feature at 680 nm, and a red edge that tapers between 740 and 920 nm towards the NIR high due to cell structure differences. Water absorption features similar to those of samphire described above are also present. The unusual spectral properties of both plants are thought to be due to their predominantly, fleshy, waterrich leaves and distinctive pigments. Both plants often occur in small clumps of 1 –2 m width and therefore comprise mappable terrain elements. Sea barley grass (Critesion marinum) measured in the field at the R&D Block has a typical dry grass spectrum with prominent ligno-cellulose bands and water bands at 1450, 1720, 1920, and 2080 nm. The spectrum shows a uniform increase in reflectivity from 400 to 1320 nm, indicating the absence of chlorophyll absorption (Elvidge, 1990). Sea barley grass occurs in 411 patches up to several meters across and therefore also comprises a mappable terrain element. The field spectra of halophytic plants, round-leaf wilsonia (Wilsonia rotundifolia) — a small perennial, with mat-forming stems and small succulent leaves, commonly green and yellow; the native environmental weed water buttons (Cotula coronopifolia) — a semisucculent plant with jagged leaves, stems, and yellow dome-shaped flower; the native plant creeping saltbush (Altriplex semibaccata); and introduced weed bathurst burr are also shown in Fig. 2d and e. The spectral features of these plants are distinctive in the visible and NIR, but these species are not sufficiently abundant to be mappable from imagery. Fig. 2d and e also show field-derived reflectance and continuum-removed spectra of cultivated mixed rye and clover pasture vegetation. These spectra show simple green vegetation spectra with deep chlorophyll absorption features on either side of the green peak at 450 and 680 nm, a nearly flat infrared reflectance high between 1250 and 1450 nm, and water absorption features at 1000, 1450, and 1950 nm. 3. Mapping of salinization indicators Mapping methods evaluated include SFF, MF, and SAM. Terrain element distribution maps were created using the whole wavelength range, selected wavelength ranges, continuum-removed, and reflectance data for all selected spectra. The Farm Manager, Bill Elder, provided us with independent assessments of vegetation species at the time of image acquisition on a paddock-by-paddock basis; however, no maps that showed their within-paddock spatial distribution were available. We confirmed the spatial distribution of halophytic vegetation shortly after imaging and made a subjective comparison of actual halophytic vegetation ground cover with that identified by field spectra using HyMap imagery. SFF terrain element maps created using restricted wavelengths were the most similar to the subjective assessments of actual ground cover. They showed good results for both the high and moderate indicator classes of salinization, as determined from the ground geophysics, in the classification accuracy assessment (see Salinization Indicator Comparisons), and so discussions from here are limited to mapping results achieved by the SFF method. All terrain element distribution maps were enhanced by examining the image histogram, identifying the break in slope, and stretching the image to only show the SFF scores above the background population for each terrain element spectra. 3.1. Soil terrain element distribution maps SFF terrain element distribution maps created from field-derived soil spectra of the salt crust, highly salinized soil, and low salinized soil, and utilizing selected wave- 412 R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 lengths over the VNIR and SWIR parts of the spectrum, are presented in Fig. 3a –c. The salt crust class occurs in areas where salt scalds have developed and occurs coincident to the edges of regional palaeo channels. These palaeo channels are utilized for surface drainage as part of a farm management plan and represent the strongest expressions of salt efflorescence. The highly salinized soil class occurs in areas adjacent to where the salt crust class occurs and represents soils that are equally salinized, but lack surface salt efflorescence. Both spectra show shallow Fig. 3. SFF terrain element distribution maps. All distribution maps presented here are image swaths oriented approximately northwest (top) with a swath width of 2.5 km. The R&D Block is shown as line drawing superimposed on all maps. (a) Salt crust distribution; (b) highly salinized soil distribution; (c) low salinized soil distribution; (d) samphire distribution; (e) sea blite distribution; (f) sea barley grass distribution. R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 and broad clay absorption features at 2200 nm, indicating a poor clay structure (Fraser et al., 1990). The low saline soil class map can be reconciled to areas of only slight salinization based on results derived from ground geophysics, and coincides with areas laid bare by farming operations such as ploughing. Field-derived spectra of bare, unsalinized soils were not collected from the R&D Block and are not described in this paper. However, Dehaan and Taylor (in press) demonstrated that image-derived endmembers of nonsalinized soils, including bare ploughed paddocks, irrigated ploughed soil, and a foreign roadbase of goethitic/kaosmectite clay, can be directly identified from the HyMap imagery and successfully mapped in the R&D Block. 3.2. Halophytic vegetation terrain element distribution maps Samphire and sea blite occur only in areas where the soil is badly salinized and therefore are useful indicators of salinization. They are often closely associated but not universally so. Their terrain element distribution maps are shown in Fig. 3d and e and provide a good representation of the spatial distribution of these halophytes at the time of image acquisition. Distribution maps for sea barley grass are 413 shown in Fig. 3f and can be reconciled to soils that are moderately salinized but that also contain several other species of grasses (spectra not collected) including tall wheat grass, windmill grass, and hill wallaby grass. Native grasses have been mapped in areas associated with salinization; however, some nonsalinized areas also contained these same species. Therefore, grasses should be used cautiously as indicators of salinization because their distribution is controlled by cropping and grazing practices in addition to their salt tolerance. 3.3. Salinization indicator comparisons The Victorian Government acquired EM38 data covering the R&D Block shortly after HyMap image acquisition. These results have allowed us to test the validity of the hyperspectral results. A synthetic true color composite, comprised of HyMap Bands 3, 9, and 15 (455, 545 and 638 nm) displayed as blue, green, and red, is illustrated in Fig. 4a. This can be compared with a three-color image of the distribution of the three most significant indicators of high salinization illustrated in Fig. 4b. The terrain element spectra used to derive the salinized land indicator map are from a highly salinized soil and the two halophytes samphire and sea blite. Fig. 4. (a) Synthetic true colour composite of HyMap imagery using Bands 3, 9, and 15. (b) Distribution of three salinized land terrain element indicators, highly salinized soil, samphire, and sea blite. Colored areas in this diagram represent areas that are salinized. Mixtures of the terrain element indicators are shown by the cyan and yellow colors. 414 R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 Fig. 5. 1999 EM data in dS/m (solid thick line); EM values are multiplied by five for clarity. SFF scores as DN values for the highly salinized soil class (thin solid line) and averaged SFF scores (dashed thick line) for an X – Y profile across the R&D Block. A traverse across the R&D Block showing EM38 results and the derived SFF scores for the high salinized soil class are presented in Fig. 5. The EM38 data were acquired using a coarse sampling grid of 60 m between each measurement. The HyMap-derived data include 250 readings over the same distance and therefore show considerably more detail and variability over the same measured traverse. The SFF scores for the highly salinized soil class are also illustrated after averaging to the same spatial resolution as the ground geophysics (dashed thick line in Fig. 5). Hyperspectral-derived scores for the highly salinized soil terrain element show an almost identical pattern to the conductivities achieved by ground geophysics. Fig. 6. The best classification results for each mapping method: (a) SFF, (b) MF, and (c) SAM. Black areas represent unclassified; white areas represent high salinization class; grey areas represent moderate salinization. (b) 1999 EM data with the location of the X – Y profile used in Fig. 5 illustrated. R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 415 Table 1 Accuracy assessment results for the best classifications achieved using the mapping methods SFF, SAM, and MF Overall accuracy SFF 62.4% 20,305/32,533 SAM 70.1% 22,822/32,533 MF 55.1% 17,930/32,533 Ground truth High indicators Moderate indicators High indicators Moderate indicators High (EM) Moderate (EM) Total High (EM) Moderate (EM) Total High (EM) Moderate (EM) Total 73.5 12,206 26.5 4394 49.2 7834 50.8 8099 61.6 20,040 38.4 12,493 95.5 21,087 4.5 985 83.4 8726 16.6 1735 91.6 29,813 8.4 2720 84.9 14,569 15.1 2592 78.1 12,010 21.9 3362 81.7 26,579 18.3 5954 Commission Omission Commission Omission Commission Omission 39.09 7834/16,600 35.17 (4394/15,933) 26.47 4394/16,600 49.17 7834/15,933 29.27 8726/22,072 36.22 985/1735 4.47 985/22,072 83.41 8726/1735 45.19 12,010/17,161 43.54 2592/15,372 15.10 2592/17,161 78.13 12,010/15,372 Accuracy assessment results as percentages in bold and as the number of pixels in italics. An error or confusion matrix is an effective way of representing mapping accuracy. The accuracy of each category is plainly described along with both errors of inclusion (commission errors) and errors of exclusion (omission errors) present in the classification (Congalton, 1991). Errors of inclusion represent those pixels that belong to another class but are labelled as belonging to the class of interest. Errors of exclusions represent those pixels that belong to the ground truth class that have not been classified into the proper class. Accuracy assessments were made using the confusion (contingency) matrix implemented in ENVI. HyMap-derived classification maps and classified EM38 data covering the R&D Block were registered to geographic coordinates. EM38 data were then merged into two classes — high salinization ( >8.6 dS/m) and moderate salinization (3.8 – 8.6 dS/m). Terrain element classes were also combined into two representative classes. The samphire, seablite, salt crust, highly and moderately salinized soil classes were merged into a high salinization class, and the sea barley and low salinized soil class were merged to form a moderate salinization class. The best classification results for each of the mapping methods SAM, MF, and SFF are shown in Fig. 6 along with the classified 1999 EM38 data. Accuracy assessments for each of these classifications are shown in Table 1. The SAM method achieved the highest overall accuracy; however, most of this accuracy is attributed to the high salinization class, which showed errors of exclusion (omission errors) of only 4.47%. The moderate salinization class showed a high error of exclusion (omission errors) of 83.41%. Classifications achieved using the SFF mapping method and selected wavelength ranges show modest agreement (62%) with the classes mapped by the 1999 EM38 data. The SFF method, while not achieving the highest overall accuracy, performed the best in that both classes were classified moderately well shown by the overall lower errors of exclusion (omission errors). Similar errors of inclusion (commission error) for both methods were observed. Visual comparison of the two data sets suggests that poor accuracies were probably due to our failure to correctly identify all significant terrain element components such as other dry grasses, nonsalinized soils, and crops such as soybeans and lucerne. However, these results demonstrate that hyperspectral data have the ability to show the extent of salinization in both a spatial and semiquantitative sense. 4. Discussion Soil salinization is caused by a number of factors — the most significant of which is the rise of saline groundwater to where it approaches the ground surface. Capillary rise causes the direct precipitation of saline minerals in surface soils. Local topography, rainfall history, seasonal effects, drainage, farming practices, soil composition, and vegetation cover greatly influence the extent to which the effects of salinization are observed at the ground surface. Indicators of increasing salinization at the surface are therefore likely to be varied because of these factors. EM methods measure soil conductivity properties, while optical remote sensing methods measure the spectral properties of surface materials. A high level of agreement between ‘‘salinization’’ maps produced by the two methods was always unlikely since each method measures the effects of salinization in different ways. Components such as cultural features (dams and drains) and paddocks with crops are not separately mapped in the EM data, whereas HyMap imagery is likely to exclude these components because their spectral properties will differ from the indicator terrain element spectra being mapped. The different spatial resolutions of the EM38 (60 m) data and the HyMap imagery (IFOV of 5 m) used in this study may have also contributed 416 R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417 to the modest results in our accuracy assessment, as class boundaries for the EM data would differ if a higher spatial resolution had been used and cultural features had been removed. Although only modest agreement with EM data was achieved, we believe that HyMap hyperspectral imagery compliments EM data in that it characterizes the surface expression of salinized land. Airborne hyperspectral imagery is likely to offer a cost-effective method of mapping salinized land at a regional scale. If such maps could be generated on a multitemporal basis, they would form an excellent tool for the monitoring of salinized land and assessments of rehabilitation techniques. Satellite hyperspectral imaging systems such as Hyperion (manufactured by TRW) provide the ability to routinely map large areas of the Earth’s surface. Factors such as signal-tonoise ratio and IFOV (Hyperion 30 m) of spaceborne instruments may affect their ability to detect the indicators we have described in this paper especially in areas where these indicators are in low abundances. These factors are the subject of ongoing work. 5. Conclusions (1) Salinized soils have distinctive spectral features in the visible and near-infrared parts of the spectrum, related to combined water in hydrated evaporite minerals. These features allow the recognition of minerals such as gypsum, bassanite, and polyhalite. These minerals identified by XRD, and occurring in salinized areas, allow salinized soils to be mapped by the HyMap hyperspectral scanner. (2) All salinized soils sampled contain halite as the dominant evaporite mineral. A reflectance high, or shoulder at 800 nm, is observed in such soils. A slope reduction between 800 and 1300 nm is also evident and is probably due to elevated contents of evaporite minerals. Clay mineral crystallinity, related to the depth of the 2200-nm hydroxyl absorption feature, is observed to reduce as salinization increases in soils. (3) All mapping methods evaluated were moderately successful. Salinized terrain element distribution maps created using the SFF method and a restricted spectral range covering the hydroxyl (2200 nm), reflectance high (800 nm), slope reduction (800 –1300 nm), and hydrate-related absorption show a spatial distribution similar to results achieved by field mapping and ground-based EM measurements. Mapping of saline soils using HyMap hyperspectral imagery and field-derived spectra is an efficient method that produces, fast, wide-coverage, and reliable distribution maps of both soil and vegetation indicators of salinized land. 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