Remote Sensing of Soils in the Santa Monica Mountains: II ... · Barbara (Latz et al., 1984) and...

Remote Sensing of Soils in the Santa MonicaMountains: II. Hierarchical Foreground andBackground Analysis

Alicia Palacios-Orueta,* Jorge E. Pinzon,* Susan L. Ustin,*and Dar A. Roberts†

Hierarchical foreground and background analysis are continuously adjusting to changing environmental(HFBA) was used to discriminate soil properties from conditions. The extreme spatial and temporal variabilitytwo valleys in the Santa Monica Mountains Recreation of these processes makes soil properties extremely vari-Area, California. The analysis was organized in two lev- able and, therefore, difficult to measure. Because surfaceels. First, spectral data from laboratory measured soil processes occur at different scales, it is necessary to worksamples were used to train a vector in AVIRIS data for at sufficiently large spatial resolution and coverage forclassifying the soils between valleys. The prediction of or- generalizations to be made. Certain soil characteristics canganic matter and iron contents is performed at a second be used as indicators of long- or short-term landscapelevel of resolution. Results showed that, in the laboratory, stability at different time or spatial scales. Although theresoils could be classified at a high level of accuracy. When is a large set of soil properties that could be used toapplied to the image, the spatial predictions of organic study soil changes, the key to finding a practical way tomatter and iron content were consistent for the first level measure them at large scales is to select the propertiesof classification. The ranges of predicted organic matter most related to soil changes for a specific environment.and iron contents developed at the second level of classi- Imaging spectrometry, a new development in satellitefication were also consistent with the magnitude and dis- and airborne remote sensing, offers a potential way totribution of field samples. The presence of vegetation and map certain soil properties that are relevant to surficialthe steep terrain affect adversely the ability to resolve processes at the landscape scale.these soil properties. Elsevier Science Inc., 1999 Organic matter is a soil property closely related to

soil quality, not only as an indicator of soil erosion anddegradation, but also as a regulating factor of processes

INTRODUCTION such as nutrient availability, water holding capacity, andpermeability. Because values of organic content areSoil variability is a critical issue when modeling at land-highly variable and react very quickly to external changesscape or regional scales. Since soils are the buffer be-(Gerrard, 1992), decomposition rates show high spatialtween surface processes and the underlying rock, theyvariability. The spatial distribution of organic matter con-tent can be an indicator of the rate of decomposition andother processes happening on the soil surface. Another* Center for Spatial Technologies and Remote Sensing, Depart-

ment of Land, Air, and Water Resources, University of California, property related to soil surface characteristics is the ironDavis content. Since iron content varies with erosion class† Department of Geography, University of California, Santa

(Latz et al., 1984) and weathering level (Coleman et al.,Barbara1991), this property can be used to monitor changes inAddress correspondence to Palacios Orueta, Departament de Ter-

modinamica, Facultat de Fisica, Universitat de Valencia, C/ Doctor soil quality.Moliner 50, 46100 Burjassot, Valencia, Spain. E-mail: alicia.palacios Remote sensing techniques are a relevant tool [email protected]

Received 24 March 1998; revised 29 October 1998. dealing with these issues. In the last few years these tech-

REMOTE SENS. ENVIRON. 68:138–151 (1999)Elsevier Science Inc., 1999 0034-4257/99/$–see front matter655 Avenue of the Americas, New York, NY 10010 PII S0034-4257(98)00106-0

Remote Sensing of Soil Properties 139

niques have improved in such a way that they offer the Smith et al. (1994) as an improvement of spectral mixturepotential of direct analysis of soil properties. Several analysis. In this technique, spectral measurements are di-multispectral sensors have already been used for discrim- vided into groups of foreground and background spectraination between soils (Lewis et al., 1975; Agbu et al., that comprise a selected subset of spectra that emphasizes1990; Coleman et al., 1993). Specifically, there are sev- the presence of a signature of interest, that is, the desirederal studies where organic matter and iron content have characteristics (Smith et al., 1994). FBA is a modified se-been analyzed in terms of their reflectance properties quential spectral mixture analysis that uses singular value(Al-Abbas et al., 1972; Stoner and Baumgardner, 1981; decomposition (SVD) to derive a series of vectors by ex-Latz et al., 1984; Coleman and Montgomery, 1987; Hen- tracting user defined sources of “foreground” spectralderson et al., 1989; Coleman et al., 1991). variation while simultaneously minimizing “background”

Imaging spectrometer data are both a set of spatially spectral variation. Pinzon et al. (1995) found that thecontiguous spectra and a set of spectrally contiguous im- method presents good predictions and good r 2 statisticsages (Kruse et al., 1996). Hyperspectral data has been but predictions were not robust in this form. In order toshown to be useful for improved discrimination of min- address this concern, FBA was modified to project theerals (Clark et al., 1990; Kruse et al., 1990; Kruse et al., spectra into a property-specific axis of continuous varia-1993; Cloutis, 1996); the possibilities for using these sen- tion (Pinzon et al., 1998). In HFBA, the FBA equationsors are more extensive than applications for broad band is applied at several levels in a hierarchical way, thus, thesensors such as Landsat TM or SPOT. Palacios-Orueta variability is confined at each step, making it possible toand Ustin (1996) found that data from the Advanced Visi- extract subtle absorption features. Further explanation ofble/Infrared Imaging Spectrometer (AVIRIS), a 224-band this method is found in (Pinzon et al., 1998).airborne imaging spectrometer, were suitable for discrimi- To examine the application of this method to im-nating between similar bare soils. They compared the dis- prove detection of soil properties using an imaging spec-tribution of AVIRIS data with laboratory spectrometry trometer, we chose to map the spatial distribution of or-data, concluding that the distributions of both data sets ganic matter content and total iron from soil samples infollowed the same statistical patterns. Other studies have two watersheds in the Santa Monica Mountains Recre-dealt with soil identification and discrimination directly ation Area. Palacios-Orueta and Ustin (1998) tested theor indirectly using AVIRIS data (Smith et al., 1990; Mus- potential of AVIRIS wavelengths for discriminating soilstard, 1993; Roberts et al., 1993). in these watersheds using laboratory spectrometer data.

Another issue when dealing with remote sensing is They found that soils from each location could be discrim-the problem of scale. An important concern is to not lose inated based on organic matter and iron contents. Thesoil process information when spatially averaging at pixel purpose of this work is to test the performance of HFBAsizes. If the variability within pixels is small, or if high (hierarchical foreground and background analysis) appliedwithin-pixel variability is due to random processes, then to AVIRIS data for the discrimination of these soils.decreasing the resolution of the observations can im-prove the chance of detecting significant sources of varia-

STUDY SITEtion. Conversely, if between pixel variability is high anddetectable, increasing spatial resolution will improve The soils analyzed belong to two watersheds: La Jollaanalysis. While tradeoffs can be made between spatial Valley and Serrano Valley, within the Point Mugu Stateand spectral resolution, the basis for selecting the opti-

park in the western (coastal) region of the Santa Monicamal data set characteristics is not clear at present.Mountains National Recreation Area, California (Fig. 1).Because imaging spectrometry entails the processingThis mountain range is located between Ventura and So-and analysis of potentially hundreds of spectral bands,lano Counties along the coast of the Pacific Ocean. Thetraditional image processing methods are not practicalclimate is typically Mediterranean with dry summers andfor analyzing these types of data. New models need towarm winters. Late 1993 a wildfire removed most of thebe developed to optimize the information that can be ex-vegetation in both valleys. Further information about thistracted from these sensors. Ideally, imaging spectrometryarea is in Palacios-Orueta and Ustin (1998).analysis would treat the spatial and spectral patterns in

the data simultaneously (Kruse et al., 1996). A significantSoils and Parent Materialproblem for soil analysis is the presence of vegetation inThe soils were formed from weathered sandstone, shale,pixels. Because the signatures of soils and vegetation areand basic igneous rocks, and from alluvium derived fromso different, the lesser variability contained within thethese mixed sources (Dibblee and Ehrenspeck, 1990).soil spectral fraction is not significant enough for soil dis-These materials are distributed over three geologic units:crimination when vegetation is also present in the pixel.surficial sediments of Pleistocene age, the Lower TopangaNew methods that are able to diminish the vegeta-formation, and the Conejo Volcanics. The Conejo Volca-tion signature and extract the variability of the soil signa-nics formation occurs mainly in the vicinity of Serranotures need to be investigated and developed. Foreground/

background analysis is a new technique developed by Valley while the Topanga formation and the surficial sed-

140 Palacios-Orueta et al.

ratory at the University of California, Davis to determinethe organic matter content, particle size distribution, andtotal iron content.

Statistical analyses showed that the mean iron con-tent in Serrano Valley soils were significantly higher thanin La Jolla Valley. In contrast, organic matter contentwas significantly higher in La Jolla Valley. Variances weresimilar in both valleys. There was no significant differ-ence in particle size fractions between either location.Other relevant information about soil characteristics is inPalacios-Orueta and Ustin (1998).

Spectroscopic AnalysisThe spectral data set includes laboratory reflectance spec-tra measured in a Varian Cary 5E spectrophotometer.

The soil sample preparation followed the standardizedprocedure from (Henderson et al., 1992). Further infor-Figure 1. Location map of the study area.mation about this procedure and the spectroscopic tech-nique can be found in Palacios-Orueta and Ustin (1998).iments are found in La Jolla Valley. Serrano Valley is com-

posed mainly of basic igneous rocks. The soil moisture re-Geographic Information Systems Databasegime is xeric, and the soil temperature regime is consid-The geographic information was organized in a GIS (Arc/ered as thermic. The steep terrain and the distance toInfo) database. The AVIRIS scenes were georeferencedthe ocean create different environments which, as a con-using control points and combined in the database withsequence, cause high variability in the soils. Most soilsancillary information composed of a USGS Digital Eleva-are classified as Mollisols, in the Xerolls suborder. Sometion Model and digitized geologic map (Dibblee and Eh-soils from the valley bottoms are richer in clay and arerenspeck, 1990). The chemistry data was also included inclassified as Vertisols. There is also a large area coveredthe database. The output maps were included as layersby Inceptisols in La Jolla Valley (Edwards et al., 1970).of the geographical database as well.

DATA AND METHODOLOGYImage Data

Data The AVIRIS imagery for this study was acquired on 11Four data sets are used in this analysis: 1) soil physico- April 1994. The AVIRIS sensor acquires 224 contiguouschemical properties, 2) soil laboratory spectral proper- spectral bands with spectral resolution of 10 nm, be-ties, 3) AVIRIS image data, and 4) spatial data organized tween 400 nm and 2500 nm. Its nominal spatial resolu-in a GIS database. tion is 20 m (Vane et al., 1993). Two adjacent image

scenes were used for this analysis. The two scenes cov-Field Data ered an area of 9 km east–west by 3 km north–south.

Apparent surface reflectance retrieval was accom-Field Soil Data Collectionplished using a radiative-transfer based atmospheric modelEighty-three soil samples of approximately 0.5 L volume(MODTRAN 2) that accounts for spatial variation in at-were collected from the surface top 3 cm of soil frommospheric conditions (Green et al., 1993).La Jolla and Serrano Valleys. Only 74 samples were used

in the analyses because only these could be accuratelyMethodologylocated by differentially corrected Global Positioning Sat-

ellite (GPS) measurements. The sample sites were se- HFBA was developed by Pinzon et al. (1995) as an im-lected to represent the range of aspect, slope, elevation, provement of FBA (Smith et al., 1994). HFBA sequen-and parent materials within the area, although this goal tially derives a series of SVD vectors by extracting spec-could not be completely achieved due to the roughness tral information at different levels of chemical variation.of the terrain. Therefore, the procedure highlights subtle absorption

The locations of the soil samples were identified us- features that can be directly related to a particular soiling a Global Positioning System unit (Trimble Navigation property. These vectors were calculated using a trainingPROXL) with 61 m accuracy after differential correction. set derived from laboratory data. The performance of the

analysis was tested with the whole laboratory spectralLaboratory Analyses data set. The vectors for identifying organic matter and

iron content were applied to the AVIRIS image in a sec-Physicochemical Analysesond step. The vectors were derived by clustering samplesThe soil samples were analyzed by the DANR (Division

of Agriculture and Natural Resources), Analytical Labo- with similar strong spectral features at different levels of


Figure 2. Schematic diagram of HFBA organization showing the sequence of steps in the analysis.

the analysis. Singular value decomposition (SVD) is used level two based on their distribution and variance in eachto solve the HFBA equation at each level in the analysis. soil property domain in order to increase reliability andThe general form of the equation is robustness. Again, the complete data set was used to

identify the vector that obtained the most robust results.R*V5POnce the vectors were derived using the laboratory

where R is the matrix of the reflectance spectra, V is data, they were applied to classify each of the pixels intothe classification vector, and P is the chemical property. one of the two valleys at the first level, and then into aEach spectrum is normalized in order to reduce depend- class level of total iron content or organic matter contentencies on the conditions under which the measurements at the second level. The analyses were done in Matlabare made (Pinzon et al., 1998). The approach taken in (1994).this work (Fig. 2) is to narrow the variance by stratifica-tion of the soil population into small but reliable ranges

RESULTS AND DISCUSSIONof variability that can be consistently detected. This pro-cess is done by first discriminating the soils between the Laboratory Spectrometry Datavalleys and, second, by investigating the variability re-

First Level of Soil Classification: Vector Traininglated to iron and organic matter content within eachSpectra of six soil samples from La Jolla Valley and fivegroup. By using a two-step hierarchical process, there isfrom Serrano Valley were chosen to train the HFBA sys-a smaller range of spectral variability in the second level.tem at the first level. The soils from Serrano Valley haveIn the first step, a vector is derived to minimize variabil-significantly higher iron content and soils from La Jollaity inside the groups and maximizing it between groups.have significantly higher organic matter content (Palac-A subset of soils (11) was chosen to train the vector toios-Orueta and Ustin, 1998). Although other sources ofseparate soils of Serrano and La Jolla Valleys. To avoidvariability between the soils at these two locations areconfusion with names of unique soil phases and series orlikely, the spectral variability due to the combination ofthe older definition of soil type, hereafter, we refer tothese two properties is summarized in this step. HFBAsoil samples expressing the physiochemical properties ofuses a supervised classification scheme where each valleySerrano Valley or La Jolla Valley as “Serrano soils” orwas represented by SVD values in which Serrano soils“La Jolla soils.” The complete set of 74 soil samples wasranged from 0 to 7 and La Jolla from 7 to 14. Then, theused to test the results of the classification scheme. Fi-spectra in the training set were projected by the HFBAnally, the vector chosen was the one that best stabilizesvector to the center of each class. Only four spectralthe system, in the sense of summarizing the greatest vari-samples were misclassified (Table 1) and the rest wereability using the smallest number of samples in the train-

ing set. The physicochemical data were quantified in assigned to intermediate values between classes. Because


Table 1. First Level Classification Error Matrix for La Jollaand Serrano Soilsa

Number of Number of Soil Samples that Belong toSoil SamplesClassified as La Jolla Serrano Total

La Jolla 34 1 35Intermediate 5 2 7Serrano 3 29 32Total 42 32 74

a Classification based on analysis of spectral variability in laboratoryanalyzed soil samples.

of this, an intermediate class was defined, and sevensamples were located in this class. Out of the four spec-tra misclassified, three were collected in La Jolla Valleybut were allocated to Serrano class. These samples showlow or intermediate organic matter contents, which ischaracteristically within the range of organic matter con-tents found in Serrano Valley. The only sample from Ser-rano Valley that was assigned to the La Jolla class hadlow iron and high organic matter content, a pattern thatwas characteristic of soils from La Jolla Valley. The soilsamples classified in the intermediate class were foundto show spectral features intermediate between both val-leys, as well as having transitional values of iron and or-ganic matter content.

Figure 3 shows the mean and standard deviation Figure 3. Mean and standard deviation of the reflectance spec-tra for soil samples collected in La Jolla and Serrano Valleys,spectra for each location and the HFBA vector thatand the HFBA first level training vector for site classification.yielded the best discrimination between valleys. It can be

observed that the two spectral areas most important fordiscrimination between the valleys were near 1000 nm

Second Level: Organic Matter and Iron Contentsand 2200 nm. Although the highest weights resultingAt the second HFBA level, the analysis focused on ex-from the HFBA SVD were assigned to the band at 2200tracting information related to smaller, subtler sources ofnm, a wide area between 700 nm and 1400 nm and cen-variability within each valley. In order to do this, twotered at 1000 nm was consistently negatively weighted.analysis tools were used: the quantization of the range of

This means that a wide area around 1000 nm is impor- chemical data and the selection of the soil samples in thetant in the discrimination while only a few bands around training set. Since the distributions of the variables are2200 nm are significant. Palacios-Orueta and Ustin different between valleys, the way that the classes are(1998) found that, in these soils, the area around 1000 grouped can be a significant factor when searching fornm was not only related to iron but also to organic mat- the variability of that specific property. The selection ofter content; thus low reflectance in this band by itself the soil samples for the training set is a determining fac-is not sufficient to determine the iron or organic matter tor as well; hence, the samples chosen should be repre-contents. From the mean spectra, it is observed that the sentative of the distribution of the range of the chemicalreflectance at 1000 nm is significantly different between variables. The training set selected for each group andvalleys. The absorption bands centered at 2200 nm and each property is the one that produces the vector that2300 nm are most likely due to the presence of Al-OH best stabilizes the system. Since the distributions of ironand Mg-OH in dioctahedral and trioctahedral clays, re- and organic matter were different between valleys, thespectively (Hunt and Salisbury, 1970). The differences in assigned training sets were different as well. In each ofparent material between valleys could produce this ef- the valley groups defined at the first level, two new vec-fect. These results combined with the analysis of classi- tors were trained to classify spectral samples for organicfication errors support the idea that although there must matter and iron contents. The results from this analysisbe other sources of variability, organic matter, and iron are: the HFBA vectors, the regression between the mea-contents play a critical role in the spectral discrimination sured values and the predicted soil property values, and

the comparison between the original and the predictedbetween valleys.


continuous line represents the predicted values and thedashed line represents the measured data. The r 2 valueis 0.72 (n574; p,0.001), and only five samples are out-side 1 standard deviation. The distributions of the pre-dicted and the measured data follow similar patterns as-signing more samples to the centrally placed values. BothHFBA organic matter vectors (Figs. 4a, b) show a con-cave shape around the 700 nm region, although in LaJolla the minimum value is slightly shifted towards 800nm. In these soils the SVD weights increase until reach-ing the highest value at 1400 nm. The band at 2200 nmis highly weighted in Serrano, while in La Jolla the bandcentered at 2300 nm gets highest positive weights. In LaJolla the vector is smoother over a wider range of wave-lengths. This may be due to the higher organic matterand lower iron contents of soils in this valley. Organicmatter features are stronger and more clearly observed.

Iron Determination: Vector TrainingTable 3 shows the centers of the quantized levels foriron content in Serrano and in La Jolla Valleys, the mid-class percentile, as well as the distribution of the trainingset, and complete data set over this range. Figure 6shows the distributions of the predicted and the mea-sured data and the regression analysis for iron contentfor the whole data set. It is observed that both distribu-tions follow the same pattern, although the classificationsystem again overpredicts intermediate values (Fig. 6).The r 2 value is 0.46 (p,0.01), and most of the predictedFigure 4. Second level HFBA vectors trained for organicsamples fall within 1 standard deviation (shown as dashedmatter and iron content. Vectors a and c correspond to soils

having the characteristics of those from Serrano Valley and line on figure). The HFBA iron vector in Serrano (Fig.vectors b and d correspond to those from La Jolla Valley. 4c) shows sharp wavelength features, mainly in the short-

wave infrared and in the 2000–2400 nm regions. Thehighest vector weights are given to the bands near 500

distributions. Vectors a, b, c, and d (Fig. 4) were trained nm and 800 nm but with opposite signs. The vectorwith the soils classified as either Serrano (a, c) or La Jolla weightings markedly show the shape of the ferrous ab-(b, d) for both organic matter (a, b) and iron (c, d). sorption band at 1000 nm. Hunt and Salisbury (1970) re-

ported the existence of three absorption bands at 450Organic Matter Determination: Vector Trainingnm, 510 nm, and 550 nm due to electronic transitions inTable 2 shows the four quantization ranges (R1–R4) forthe ferrous ion. They also reported an absorption due toorganic matter content, midclass percentage value, theferric iron in the region between 2000 nm and 2400 nm,number of samples in the training set, and the wholethe highest vector values are for bands at 2200 nm anddata set. Figure 5 shows the distributions of the mea-2300 nm with opposite signs. In dioctahedral clays thesured and the predicted values for organic matter in both

valleys and the results from the regression analysis. The hydroxyl groups are coordinated around aluminum, and

Table 2. Distribution of Percent Organic Matter Measured in Soil SamplesUsed to Develop the Second Level Training Set and the Distribution for the EntireData Set a

Organic Matter (%)

La Jolla Valley Serrano Valley

R1 R2 R3 R4 R1 R2 R3 R4

Range center 0.98 2.33 3.68 5.03 1.1 2.04 2.99 3.93Training set 3 6 3 3 2 8 1 2Complete data set 4 15 13 10 7 13 7 5

a Ranges show the quantization levels used to train the HFBA vectors.


Figure 5. Mean distributions of percent dry weight soil organic Figure 6. Mean distributions of percent dry weight soil ironcontent based on the quantized ranges for combined data (n5matter based on the quantized ranges for combined data (n5

74) from La Serrano and La Jolla Valleys. The upper graph 74) from La Serrano and La Jolla Valleys. The upper graphshows predicted (solid line) and the measured data (dash dotshows predicted (solid line) and the measured data (dash dot

line) and the lower graph shows the regression analysis (solid line) and the lower graph shows the regression analysis (solidline) for organic matter content and the regression equation.line) for organic matter content and the regression equation.

The prediction accuracy is indicated by the 1:1 regression line The prediction accuracy is indicated by the 1:1 regression line(dash dot line) and one standard deviation (dot lines).(dash dot line) and 1 standard deviation (dot lines).

in trioctahedral clay are coordinated around magnesium to the three bands at 500 nm, 1000 nm, and 2300 nmcould be due to the positive correlation between the fer-or iron. When magnesium is present, the most intense

feature appears at 2300 nm while if aluminum is present, rous iron content and magnesium from the presence offerromagnesic materials.another feature appears at 2200 nm (Hunt and Salisbury,

1970). Most of the soils from Serrano Valley were formed The highest weights in the HFBA vector from LaJolla are given to the band at 2200 nm and to bands aton mafic parent material, which is rich in iron and magne-

sium; thus, this is likely to be the reason for the high 1000 nm and at 500 nm. This vector does not show thecharacteristic sigmoidal shape with a minimum at 1000weights given to these bands. The negative weights given

Table 3. Distribution of Percent Iron Content Measured in Soil SamplesUsed to Develop the Second Level Training Set and the Distributionfor the Entire Data Set a

Total Iron (%)

La Jolla Valley Serrano Valley

R1 R2 R3 R1 R2 R3

Range center 2.03 2.91 3.79 2.6 3.53 4.45Training set 1 9 5 1 6 4Complete data set 3 22 17 2 15 15

a Ranges show the quantization levels used to train the HFBA vectors.


nm, like the one found for Serrano. Although the area range of partial vegetation cover. Since the vectors aretrained with pure soils we expect that in pixels with somestill shows negative weights at 500 nm, the region near

1000 nm shows lower weightings spread over more vegetation, soil characteristics will be emphasized andpixels with higher levels of vegetation will be classifiedbands. Since this region of the spectrum is highly af-

fected by organic matter and La Jolla soils are higher in as out of the range of the predicted soil property values.This allows an a posteriori decision about vegetationthis component, the lower weights given to La Jolla

HFBA vector are probably due to the higher level of or- cover that is derived from the soil information ratherthan an a priori vegetation-based decision. The NDVIganic matter content which masks iron absorption fea-

tures. The band at 2200 nm has higher weights and is (Figure 7.1) was calculated as a reference to compare tothe spatial distribution of the vegetation pixels derivedopposite in sign to the band at 2300 nm. This fact is pos-

sibly due to the lower amount of magnesium and iron from the HFBA but was not used directly to identifyvegetation. Our results showed that the negative (out-of-and higher presence of aluminum. Since the effect of or-

ganic matter is not as strong in this area as in the short- range) values projected by the soil classification vectorwere pixels with high NDVI (.0.5). All pixels with val-wave region, these features are possibly better for dis-ues less than 0 were classified as vegetation.crimination of ferrous iron.

Another factor affecting the range of the HFBA atthe image scale is the presence of the Pacific Ocean dueImage Analysisto its low reflectance in all bands. We expected thatThe vectors trained using laboratory data were appliedmany of the extreme classification values in our resultsto the AVIRIS scenes. The vector obtained in the firstwere from ocean pixels due to measurement and calibra-step was applied to the complete scene, while the vectorstion errors associated with the near zero reflectance. Aobtained at the second level were applied only to pixelshistogram of the SVD results (Fig. 8) shows that the dis-classified in the corresponding class (e.g., organic mattertribution forms a long tail with only a few pixels havingof soils classified as Serrano was predicted using the vec-values higher than 21. Nearly all the pixels with valuestor obtained from the Serrano training set). This identi-higher than 14 were located in the ocean; therefore, wefication is based on the spectral classification and is ap-used this criteria to remove them from further consider-plied to pixels independent of their actual spatial locationation in the analysis.in the image.

The remaining “potential soil” pixels in the imageFirst Level: Classification between Valleys were classified at several levels after examining the distri-The first vector was trained to assign each pixel a classi- bution of HFBA values in the training set. Pixels withfication value that will locate it as belonging to the soils values between 0 and 7 were assigned to the Serrano soilof one of the two valleys. Since there are many pixels class, that is, clearly expressing the physicochemicalwithin the valleys that are composed of mixed materials properties of Serrano Valley soils, and pixels with values(i.e., have litter, green vegetation, or other materials between 7 and 11 clearly expressing the physicochemicalpresent), these are classified outside the range of the characteristics of soils from La Jolla Valley. Pixels withoriginal classes (0–14) defined in the vector training pro- values between 11 and 14 were located in the beach ar-cess. Although the area under study was burned in a ma- eas, and, although they express soil properties due to thejor wildfire only a few months before the image data high albedo of the sand, they are projected into the highwere acquired, there was already a considerable amount extreme of the soil range, and can be classified as beachof vegetation in some areas, mainly in the moister valley pixels (see distribution in Fig. 8). In the map (Fig. 7.2)bottoms. Since the image was acquired in the spring fol- the white color corresponds to the ocean and to areaslowing the wildfire, we expected the amount of dry vege- classified as vegetation dominated. Comparing these re-tation to be low, thus, decreasing the possibility of con- sults with the NDVI it is observed that areas withfusing plant litter with soil. NDVI.0.5 (green color, Fig. 7.1) follow the same spatial

There are also some terrestrial areas in the image pattern as the pixels that were not classified (i.e., white)that were not affected by the wildfire and were largely in Figure 7.2. The image shows that the La Jolla soil pix-vegetation covered. One way to deal with vegetation is els are clustered in patches and the pixels classified asto mask pixels where vegetation is present. Adopting this Serrano soils are more continuously distributed oversolution greatly decreases the surface to be analyzed, but the image.pixels containing small amounts of vegetation would still The results from the laboratory analyses showed thatbe present. Masking the vegetation using an NDVI separation of the classes was based primarily on differ-threshold is an arbitrary decision, and pixels with differences in organic matter and iron content among the sam-ent but undetermined amounts of vegetation would re- ples (Palacios-Orueta and Ustin, 1998). Our results sup-main. This residual contamination is a common concern port this distinction, because AVIRIS is not simplywhen analyzing soils from remote sensors. Our interest mapping different soils in separate locations but, instead,

the HFBA system is mapping soil properties as continu-lies in discriminating soil properties in pixels over a


Figure 7. (7.1) NDVI image forthe Santa Monica Mountain re-gion and location for La Jolla andSerrano Valleys. Greens indicatehigh vegetation (NDVI.0.5), or-anges indicate low plant cover,and brown indicates no vegeta-tion cover. (7.2) First level classi-fication of exposed-soil pixels intosoils of La Jolla and SerranoValleyclasses. The white color corre-sponds to ocean and vegetationdominated pixels as determinedby the HFBA analysis.

ous variables. The actual distribution of organic matter us to select pixels classified as soils having the propertiesassociated with Serrano Valley or La Jolla Valley. Only theand iron in these valleys is possibly related to the steeppixels with sufficient soil component expressed to fallterrain and local microclimates. The properties are notwithin the laboratory data range are retained in the anal-unique to their respective valley soils, but instead theysis. Thus the portion of image variability due to soils isvariability within the valleys is high and representative ofhighlighted. Also, by first dividing variability into La Jollathe larger region.and Serrano classes the application of the organic matterAs we have seen, this first level of classification allowsand iron vectors is optimized.

Organic Matter Content DeterminationFigure 8. Histogram of AVIRIS bare soil pixels after applyingthe classification vector at the first level. Organic matter was estimated applying the vectors

trained with laboratory data. Vector a was used to pre-dict organic matter content in pixels classified as Serranosoils in the first level classification (Fig. 2). Vector b wasused for pixels classified as La Jolla soils. Figure 9 showsthe distribution of the AVIRIS results obtained from thisanalysis. Although the predicted values range from 215to 10, nearly all pixels are within 1–6% organic matter(the same range as laboratory data). We observed thatwith pixels in which soil is not the main spectral compo-nent (e.g., ocean or vegetation pixels) the vectors areprojected to organic matter values outside of this range.The pixels corresponding to the ocean were projected tohave extremely high values of organic matter contentwhile high values of NDVI (higher than 0.5) are pro-jected into low or negative values of organic matter con-tent. For a given pixel, as NDVI increases, the probabil-ity of being projected onto negative values of organic


at the second level. Although the soils with high organicmatter content are not uniquely associated with La JollaValley, it is observed that pixels mapped as La Jolla soilsin Figure 7.2 also show high content of organic matterin Figure 10.1 (e.g., in the northeast area), which is co-incident with locations of soil samples having high or-ganic matter. Palacios-Orueta (1997) performed the sameanalysis for pixels having higher vegetation cover. Shefound that most pixels with high vegetation were classi-fied as having low or negative values of organic matter.While it seems inconsistent that areas of higher vegeta-tion cover would have soils of lower of organic mattercontent, our assumption is that, as vegetation increasesand soils are covered by plant canopies, the remote sens-ing estimations of organic matter are not reliable.

The spatial distribution of organic matter is relatedto the aspect (Fig. 11). The aspect is a factor affecting

Figure 9. Histogram of predicted organic matter content insoil properties in such a way that slopes facing north andAVIRIS exposed-soil pixels based on second level HFBA classi-east are generally cooler and more humid, frequently ac-fication.cumulating higher levels of organic matter. Figure 11shows the histogram of the distribution of organic mattercontent for different aspects. It can be seen that on north-matter also increases. Figure 10.1 shows the results offacing aspects high values tend to predominate while onthe analysis only for those pixels that had a high soilsouth-facing slopes lower values of organic matter pre-component (i.e., organic matter between 0 and 6). Thedominate. Since in the spring at the time when the im-blue colors indicate low organic matter and orange/age was acquired many of the soil sample point locationsbrown colors indicate high organic matter; white pixelswere covered by some vegetation, the regression analyseswere out of range in the first classification level or were

pixels that had negative values of organic matter content between the AVIRIS predicted and the organic matter

Figure 10 (10.1) Results of HFBAsecond level vector applied toAVIRIS pixels predicting per-cent organic matter in soils ofthe study area for pixels withlow vegetation cover. Black anddark blue colors correspond tolow levels and dark orange cor-responds to high organic matterlevels. (10.2) Results of HFBAsecond level vector applied toAVIRIS pixels predicting totalpercent iron content in the studyarea for pixels with low vegeta-tion cover. Black and dark bluecolors correspond to low levelsand dark red corresponds tohigh iron content levels.


Figure 11. Distribution of organic matter content for six levelssummarized for eight aspect orientations, based on coregisteredDEM. Level pixels with zero slope have an undefined aspect.

values of the field samples were not significant. However,the distribution of the AVIRIS soil organic matter for thetwo soil groups and their intermediates (Fig. 12) followthe same trend as the laboratory data. Although it is notdefined in the image classification process, we show herethe distribution of organic matter for the intermediate

Figure 12. Distribution of AVIRIS percent organic matter con-class defined in the training process. The purpose is to tent for pixels having the characteristics of Serrano, inter-show the trend of organic matter content. It can be seen mediate, and La Jolla soils.that although the range for the three classes is the same(0–6) the shape of the curves are different, increasingthe higher values of this property from Serrano to La .0.15, soil organic matter and iron are not correlated.Jolla soils. Most pixels with iron.HBFA 5 also have NDVI.0.15.

Although the regression analyses between the estimatedIron Content Determination iron and organic matter contents were not significant forThe same procedures were followed for iron content,with most of the values ranging from 2% to 6% (Fig. 13).

Figure 13. Histogram of predicted iron content in AVIRISIn this case, the ocean pixels were projected onto nega-exposed-soil pixels based on second level HFBA classification.tive iron contents while pixels with high NDVIs are clas-

sified as having high iron content. The analysis was donefor both valleys, using only pixels where soil was themain component or were classed as vegetation covered.The colors are the same as those for organic matter (Fig.10.1). In this case there were only a few pixels with ironcontents less than 2%. Figure 14 shows the distributionof iron content in the three classes defined in the train-ing process. The frequency of pixels with high levels ofiron content decreases from Serrano soils to La Jollasoils; these results are in agreement with the laboratorydata. Another point that we are interested in is the limitof vegetation at which soils can be clearly discriminated.These results showed that there is an r 250.35 (P,0.05)correlation between organic matter and iron content forpixels with NDVI,0.15. These areas correspond to thebeaches and some scattered pixels. When the NDVI is


nation of the biochemistry contents, there were pixelsclassified out side the ranges of the laboratory data. Afterthe first step for classifying the valleys, pixels with valueshigher than 14 or less than 0 were removed from theanalysis (Fig. 7.2) before applying the vectors for organicmatter and iron content. Pixels located between 11 and14 were not masked but were identified as beach soils.The results obtained at the second step (biochemistrydiscrimination) showed that a few pixels classified withinthe expected range in the first step were distributed out-side of the laboratory range values at the second step.These values ranged from 24 to 10 for iron and from215 to 15 for organic matter content. These pixels weremasked at the second level of the classification. Resultsshow that the combination of using extracted features atthe two levels helps to provide information about otherproperties of the study area, in this case, about the levelsof vegetation cover.

CONCLUSIONS

HFBA was found to be a suitable method for soil analy-sis to determine relative changes in physicochemicalproperties because it is structured so that, at each level,soil properties can be grouped into different quantized

Figure 14. Distribution of AVIRIS percent total iron content ranges according to the variance. There is a combinationfor pixels having the characteristics of Serrano soils, inter- of features that makes this model work more efficientlymediate, and La Jolla soils. than other standard classification methods:

1. It is a mixture model; therefore, it can use contin-NDVI.0.15, the image shows that the areas where or- uous data over the whole spectrum.ganic matter is high also tend to be low in iron content. 2. Since it is a statistical model as well, it can focus

The HFBA vectors for iron and organic matter be- on maximizing variability between classes whilehave differently with respect to the presence of different minimizing variability within classes, optimizingamounts of vegetation. In the 700 nm region (in vegeta- the amount of information extracted.tion this corresponds to the red edge) the iron vectors 3. As a supervised classification algorithm, it can beshow positive or near-zero weights while the organic mat- focused on soil variability by training the vectorster vectors have highly negative weights. This is the reason for specific soil properties.why high values of vegetation are projected to low or 4. The singular value decomposition equation effi-negative HFBA values of organic matter while the iron ciently discriminates between foreground soilvectors project pixels with some amount of vegetation properties and background conditions.present to high HFBA values of total iron. This can ex- 5. Since it is organized in a hierarchical way, theplain the opposite patterns that we observed in the esti- variability is reduced at each level, allowing subtlemated iron and organic matter contents for areas with absorption features to be extracted.high vegetation cover.

The results obtained when training the vectors withAn alternate explanation for this pattern could be athe laboratory data showed that the organization of theconfounding effect that iron and organic matter contentsystem and the singular value decomposition transforma-may have on the spectra. Perhaps soils which have onetion work effectively in predicting organic matter fromproperty in high concentration also show characteristics

of the other property, but it is masked or is not ex- spectral data. Results from the image analysis showedpressed in the spectrum; therefore, the masked property that the HFBA vectors when applied to the image dis-is underestimated. criminates effectively between soils and vegetation and

between different soil properties, although the presence ofSummary of the Classification Process vegetation is still a confounding factor. Although the classi-

fied soils were not uniquely associated with either valley,The classification process showed that in the two levels,discrimination of the soils from the valleys and discrimi- the predictions of organic matter and iron oxide contents


Coleman, T. L., and Montgomery, O. L. (1987), Soil moisture,from the image agreed with the soil characteristics fromorganic matter, and iron content effect on he spectral char-the locations where we collected the soil samples.acteristics of selected Vertisols and Alfisols in Alabama. Pho-To understand the soil variability on the landscapetogramm. Eng. Remote Sens. 53:1659–1663.more completely, it would be informative to analyze the

Dibblee, T. W., and Ehrenspeck, H. E. (1990), Geologic Mapdata more completely in a geographic context, for exam- of the Point Mugu and Triunfo Pass Quadrangles. Venturaple, using a relational GIS database, where other types and Los Angeles Counties, California, Dibblee Geologicalof information such as terrain properties are explicitly Foundation, Santa Barbara.included. Edwards, R. D., Rabey, D. F., and Kover, R. W. (1970), Soil

Survey Ventura Area, California, U.S. Department of Agri-This methodology is based on a hierarchical analysis,culture, Soil Conservation Service, Washington, DC.which implies that variability is reduced at several steps,

Gerrard, J. (1992), Soil Geomorphology. An Integration of Pe-each time becoming more specific. This makes difficultdology and Geomorphology, Chapman and Hall, London.to extend the analysis to different areas without some a

Green, R. O., Conel, J. E., and Roberts, D. A. (1993), Estima-priori knowledge of the soils. One way is to build or ex- tion of aerosol optical depth and calculation of apparent sur-pand upon a library of spectral properties of soils, such face reflectance from radiance measured by the Airborneas the one developed at Purdue University (Stoner and Visible-Infrared Imaging Spectrometer (AVIRIS) usingBaumgardner, 1981). As better soil spectral libraries de- MODTRAN 2. In Imaging Spectrometry of the Terrestrialvelop, the training data limitation may be minimized. Environment, SPIE Conf. 1937, pp. 2–5.

Henderson, T. L., Baumgardner, M. F., Franzmeier, D. P., etNevertheless, the use of HFBA would allow a mecha-al. (1992), High dimensional reflectance analysis of soil or-nism to efficiently reduce the number of field measure-ganic matter. Soil Sci. Soc. Am. J. 56:865–872.ments by calculating the vector from a small sample set

Henderson, T. L., Szilagyi, A., Baumgardner, M. F., Chen, C.or by using a vector developed from an area with similarT., and Landgrebe, D. A. (1989), Spectral band selection for

soil variability. HFBA would be useful for analyzing classification of soil organic matter content. Soil Sci. Soc.changes in soil properties in a temporal framework. Am. J. 53:1778–1784.

Hunt, G. R., and Salisbury, J. W. (1970), Visible and near-infra-red spectra of minerals and rocks: I Silicate minerals. Mod.This research was supported by a fellowship to APO from theGeol. 1:283–300.Instituto Nacional deTecnologia Agraria y Alimentaria and by

Kruse, F. A., Kiereinyoung, K. S., and Boardman, J. W. (1990),a NASA EOS Grant NAS-31359 to SLU and NASA TerrestrialMineral mapping at Cuprite, Nevada with a 63-channel imag-Ecosystems and Biogeochemical Dynamics Branch Granting spectrometer. Photogramm. Eng. Remote Sens. 56:83–92.NAGW-4626-I to SLU and DAR. We wish to thank the Digital

Kruse, F. A., Lefkoff, A. B., and Dietz, J. B. (1993), ExpertEquipment Corporation for providing the DEC Alpha comput-ers under the Sequoia 2000 Grant Cooperative Research Agree- system-based mineral mapping in northern Death Valley,ment #1243. California Nevada, using the Airborne Visible Infrared Imaging

Spectrometer (AVIRIS). Remote Sens. Environ. 44:309–336.Kruse, F. A., Boardman, J. W., and Farrand, W. H. (1996),

Advanced hyperspectral remote sensing analysis workshop.REFERENCESIn ERIM Second International Airborne Remote SensingConference and Exhibition, San Francisco, CA.Agbu, P. A., Fehrenbacher, D. J., and Jansen, I. J. (1990), Sta-

Latz, K., Weismiller, R. A., Van Scoyoc, G. E., and Baumgard-tistical comparison of SPOT spectral maps with field Soilner, M. F. (1984), Characteristic variations in spectral re-

maps. Soil Sci. Soc. Am. J. 54:812–818. flectance of selected eroded alfisols. Soil Sci. Soc. Am. J.Al-Abbas, A. H., Swain, P. H., and Baumgardner, M. F. (1972), 48:1130–1134.

Relating organic matter and clay content to the multispec- Lewis, D. T., Seevers, P. M., and Drew, J. V. (1975), Use oftral radiance of soils. Soil Sci. 114:477–485. satellite imagery to delineate soil associations in the sands

Clark, R. N., Gallagher, A. J., and Swayze, G. A. (1990), Mate- hills region of Nebraska. Soil Sci. Soc. Am. Proc. 39:330–335.rial absorption band depth mapping of imaging spectrome- Matlab (1994), The Mathworks, Inc. Natick, MA.ter data using a complete band shape least-squares fit with Mustard, J. F. (1993), Relationships of soil, grass, and bedrocklibrary reference spectra. In Second Airborne Visible/Infra- over the Kaweah serpentinite melange through spectral mix-red Image Spectrometer (AVIRIS) Workshop, Pasadena, CA, ture analysis of AVIRIS data. Remote Sens. Environ. 44pp. 176–186. (2–3):293–308.

Cloutis, E. A. (1996), Hyperspectral geological remote sensing: Palacios-Orueta, A. (1997), Soil Discrimination with Labora-evaluation of analytical techniques. Int. J. Remote Sens. 17: tory Spectra and Airborne Imaging Spectrometer Data2215–2242. (AVIRIS), Land, Air and Water Resources, Davis, Univer-

Coleman, T. L., Agbu, P. A., and Montgomery, O. L. (1993), sity of California, 120 pp.Spectral differentiation of surface soils and soil properties— Palacios-Orueta, A., and Ustin, S. L. (1996), Multivariate classi-is it possible from space platforms? Soil Sci. 155:283–293. fication of soil spectra. Remote Sens. Environ. 57:108–118.

Coleman, T. L., Agbu, P. A., Montgomery, O. L., et al. (1991), Palacios-Orueta, A., and Ustin, S. L. (1998), Remote sensing ofSpectral band selection for quantifying selected properties soil properties in the Santa Monica Mountains: I. Spectral

analysis Remote Sens. Environ. 65:170–183.in highly weathered soils. Soil Sci. 151:355–361.


Pinzon, J. E., Ustin, S. L., Castaneda, C. M., and Smith, M. O. dances from multispectral images. Remote Sens. Environ.(1998), Investigation of leaf biochemistry by hierarchical 29:1–26.foreground/background analysis. IEEE Trans. Geosci. Re- Smith, M. O., Roberts, D. A., Hill, J., et al. (1994), A new ap-mote Sens. 32:1–15. proach to quantifying abundances of materials in multispec-

Pinzon, J. E., Ustin, S. L., Hart, Q. L., Jacquemoud, S., and tral images. In IGARSS’94: Proceedings International Geo-Smith, M. O. (1995), Using foreground/background analysis sciences Remote Sensing Symposium, Pasadena, CA, pp.to determine leaf and canopy chemistry. In 5th Annual JPL 2372–2374.Airborne Earth Science Workshop: AVIRIS Workshop, Pas- Stoner, E. R., and Baumgardner, M. F. (1981), Characteristicadena, CA, pp. 129–132. variations in reflectances of surface soils. Soil Sci. Soc. Am.

Roberts, D. A., Smith, M. O., and Adams, J. B. (1993), Green J. 45:1161–1165.vegetation, nonphotosynthetic vegetation, and soils in Vane, G., Green, R. O., Chrien, T. G., et al. (1993), The Air-AVIRIS data. Remote Sens. Environ. 44:255–269.

borne Visible Infrared Imaging Spectrometer (AVIRIS). Re-Smith, M. O., Ustin, S. L., Adams, J. B., and Gillespie, A. R.

mote Sens. Environ. 44:127–143.(1990), Vegetation in deserts I. A regional measure of abun-

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