signature of pest-organisms in mato grosso agroecosystems using worldview-2 imagery.pdf

8/13/2019 Signature of Pest-Organisms in Mato Grosso Agroecosystems Using WorldView-2 Imagery.pdf

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PAPER

Signature ofPest-Organisms in

Mato GrossoAgroecosystemsUsing WorldView-2

Imagery

Marcelo de C. Alves

Federal University of Mato GrossoSoil and Rural Engineering DepartmentAv. Fernando Correa da Costa S/NCoxipo District, 78060-900

Cuiab -MT, BrazilP (55-65) 3615-8655F (55-65) [email protected]
mailto:[email protected]:[email protected]:[email protected]


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Signature of Pest-Organisms in Mato Grosso Agroecosystems Using WorldView-2 Imagery

1

Abstract

This paper presents an application in which WorldView-2 images were used to detect the damage causedby nematodes in soybean (Glycine max L.), maize (Zea mays L.), sunnhemp (Crotalaria ochroleuca G.

Don.) and dark sword-grass (Agrotis ipsilon) in maize (Zea mays L.). After the atmospheric correction,signals of pest-organisms were evaluated using different band composition as well as by the soil adjustedvegetation index, leaf area index, fraction of absorbed photosynthetically active radiation, surface albedo,absorbed solar radiation flux and the first principal component analysis derived from WorldView-2multispectral images. The pixel value of the images and derived indexes were submitted to variogramanalysis. It was possible to characterize the spatial variation of nematodes in soybean, maize, sunnhempand dark sword-grass in maize crop using 8-band high resolution satellite image and derived indexes.The spatial and spectral approach adopted in this study enabled to observe the high resolution variabilityof the energy budget of a given landscape and its applicability to detect spatial variation of the infestationof pest-organisms in agroecosystems.

Keywords SAVI, LAI, FPAR, albedo, absorbed solar radiation flux,Agrotis ipsilon, geostatistics.

1. Introduction

Traditional spectral classification of remotely sensed images applied on a pixel-by-pixel basis ignores thepotentially useful spatial information between the values of proximate pixels (Atkinson & Lewis, 2000).Geostatistical analysis can be used in digital image processing to quantify the spatial texture or pattern ofthe distinct spatial properties of Earths surface (Li et al., 2009), based on the value of a pixel and itsneighbors, trying to quantify the spatial autocorrelation relationships in the imagery. The brightnessvalues in imagery constitute a record of spatial properties forming texture or pattern with autocorrelationcharacteristics of a random variable distributed in space, said to be regionalized (Jensen, 2005).

Colombo et al. (2003) used geostatistics to evaluate different spatial variability patterns of vegetationbased on leaf area index derived from IKONOS imagery. According to the authors, the high spatialresolution mapping of leaf area index was useful for management decisions in precision agriculture. Kerryand Oliver (2007) also used geostatistics to analyze observations of soil structure using indicatorstatistics. Aerial photographs with ground pixel size of 3.4 m were geo-corrected, and digital numbers (8bits) of the red, green and blue wavebands were extracted for each pixel and submitted to variogramanalysis. The authors observed correspondence between the range of variograms and kriging maps ofthe waveband images, quantifying the soil structure of the evaluated areas.

Similarly, Pozdnyakova et al. (2002) evaluated spatial and spectral properties of phytophthora root rot ofcranberry using color-infrared aerial photography and geostatistics. According to the authors, the spatialpattern of stressed vegetation was corresponded to the spread of phytophthora root rot infection, causingchronic injury and low yield. The disease developed in surface depressions with low infiltration rates, withhigh soil water content later in the growing season. Kriging results provided relatively accurate surfacemaps which spatially matched the features found on the photographs.

Santoso et al. (2011) used QuickBird imagery to detect basal stem rot disease caused by Ganodermaboninense in palm field. Six vegetation indices derived from visible and near infrared bands were used toidentify palms infected by the disease. The resulting maps enabled to observe older palms with sporadicdisease pattern and younger palms with dendritic pattern with medium to low infection. Basal stem rothad a higher reflectance in the visible bands and a lower reflectance in the near infrared band.

Considering that control strategies require knowledge about bioecology, dynamics and spatial patterns ofthe pest-organisms in the field, in order to determine whether site-specific management is feasible inrelation to the pest potential spread, the objective of the present work is to evaluate if the multispectral


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WorldView-2 images and derived products from the images can be used to detect nematodes and darksword-grass in Mato Grosso agroecosystems, Brazil.

2. Methods

2.1 Study Area

The experiment was conducted at Rotilis farm, in Jaciara, Mato Grosso state, central Brazil. It comprisesthe Brazilian Savannas (Cerrado) zone, with flat elevation, about 700 m above sea level. Thepredominant soil class is the Latossolo Vermelho-Escuro distrofrrico (Typic Acrustox in the SoilTaxonomy). The climate is tropical with a rainy season between October and April and dry seasonbetween March and September.

2.2 Remote-Sensor Data

WorldView-2 images were obtained from the experimental area in the periods of 06/12/2010, 09/16/2010and 01/26/2011. WorldView-2 acquired 11-bit data in nine spectral bands covering panchromatic (671

nm), coastal (band 1, 427 nm), blue (band 2, 478 nm), green (band 3, 546 nm), yellow (band 4, 608 nm),red (band 5, 659 nm), red edge (band 6, 724 nm), NIR1 (band 7, 831 nm), and NIR2 (band 8, 908 nm). Atnadir, the collected nominal ground sample distance was 0.46m (panchromatic) and 1.84 m(multispectral). The images were geometric orthorectified and the final product was resampled to 0.5 m(panchromatic) and 2.0 m (multispectral) (Updike and Comp, 2010).

2.3 Remote-Sensor Data Processing

The radiative transfer algorithm MODTRAN 4 + (Jensen, 2005) was used to correct the eight multispectralbands of each image. The first step of the algorithm compares measured and model of the planetaryalbedos of earth and atmosphere to calculate the surface reflectance.

The solar radiance reflected from a uniform Lambert surface of reflectance received by aspaceborne sensor is defined by (Kaufman, 1985). Model MODTRAN-2 defines a path radiance Lp thatincludes the diffusely reflected ground radiation. Then, the model of the planetary albedo was calculatedwith SENSAT-5 code (Richter, 1996).

Considering that WorldView-2 has variable gain settings, the true gain-values have to be extracted fromthe metadata. The provided calibration file must be updated with value found in the metadata for eachscene.

After atmospheric correction, the images were converted into reflectance in order to generate the spectralcurves of the studied targets. The reflectance images were submitted to 6 image processing proceduresto identify and map the pest-organisms in the field.

Firstly, soil adjusted vegetation index (SAVI) was used to detect plants infected by the nematodes anddark sword-grass. The soil adjusted vegetation index was calculated for each scene (Huete, 1988; Baretand Guyot, 1991).

The SAVI index was chosen to outline the existing spots in crops by minimizing the brightness variationsof the soil in the vegetation index (Huete, 1988). SAVI was divided by 1000 in order to obtain physicalvalues.


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The leaf area index (LAI) was other vegetation index used to evaluate the leaf area affected by the attackof pests. The LAI was determined by an empirical relation between LAI and SAVI, with three parameters(Asrar et al., 1984; Baret and Guyot, 1991).

The parameters ao, a1and a2were set to 0.72, 0.61 and 0.65, referent to soybean crop development(Choudury et al., 1994). The parameters were the same for all evaluated images, in order to compare thealgorithm results among the multitemporal studied images. LAI was divided by 1000 in order to obtainphysical values.

The fraction of absorbed photosynthetically active radiation (FPAR) was also determined empirically(Asrar et al., 1984; Wiegand et al., 1990; Wiegand et al., 1991). FPAR is associated with the greenphytomass and crop productivity, considering that PAR is referent to the spectral 0.4 to 0.7 m region,where plants mainly absorb solar radiation. FPAR was divided by 1000 in order to obtain physical values.

Since LAI and FPAR equations are simple empirical relationships based on the selected vegetation index,the resulted trends are only approximations, therefore it is recommended to use the same parameter setfor all multitemporal scenes.

The ground albedo, related to the wavelength-integrated ground reflectance, was used to substitute thesurface albedo.

Principal components analysis (PCA) was used to explore the linear combination of the 8 multispectralbands of each image, using correlation (PC correl) and covariance (PC cov) matrices approaches. Thelinear combination was the same of the number of the original variables. The main idea was to perform arigid rotation in the coordinate system, resulting new axes positioned towards greater variability in order toobtain the principal components. In this case, the first principal components are responsible for most ofthe variability contained in the original data (Richards, 1999; Jolliffe, 2002). The first principal component,which explained the major amount of the total variance of the 8 bands, was adopted as an indicator toidentify and map the pest-organisms in the field.

True color compositions R5G3B2, false color compositions R8G5B3 and 7R6G4B were also evaluated toidentify signals of the damage caused by the pest-organisms in the evaluated crops.

2.4 Subset of Areas around Spots of Pest-Organisms

Areas around plants with signals of pest-organisms were selected in the images to characterize thestructure and magnitude of the damage caused by these pests in the evaluated crops. The areas 1 and 2were located around spots in soybean (Glycine max L) cultivar TMG132RR, in the image of 01/26/2011.Areas 3 and 4 we were demarcated around spots located in maize (Zeamays L.) cultivar AG1051 (area3) and sunnhemp (Crotalaria ochroleuca G. Don.) (area 4), in the image of 06/12/2010. The area 5 wasdemarcated around the cultivation of maize (Zea mays L.), cultivar AG1051, in the image of 09/16/2010.SAVI, LAI, FPAR, ALBEDO, RSOLAR and the first principal component index were subset from eacharea, in order to perform the detection of spatial signatures (Figure 1).

2.5 Ground Reference Information

Root and soil samples were collected at 0-20 cm depth, in December 10, 2010 and February 10, 2011,around signs of nematode infestation, characterized by injuries in the plants and nodules in the roots. Thesamples were numbered with 1, 2, 4, 5, 6 and 7 codes and georeferenced using Topcon Hiper L1/L2GPS, real time kinematic mode, and submitted to perform soil physicochemical (Embrapa, 1997) andnematode analysis (Jenkins, 1964; Coolen and DHerde, 1972) (Figure 2). In September 2010, it wasdetected high infestation of dark sword-grass (Agrotisipsilon) in the maize crop, cutting off the stem of theplants in the beginning of the tillage.


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2.6 Geostatistical Analysis

The pattern of spatial dependence of infestation by nematode in sunnhemp crop was evaluated by thevariogram analysis. The pixel of each WorldView-2 spectral band and normalized difference vegetationindex were considered to be a random function Z(x), where x denotes the spatial location.

In geostatistics, estimating and modeling the variogram are an important step because the parameters ofthe chosen model describe the spatial correlation structure and are used in kriging (Liebhold et al., 1993;Burrough and McDonnell, 1998). Webster and Oliver (2007) recommended a minimum of 100 data fromwhich to compute a reliable experimental variogram by the usual method, i.e. Matherons (1965) methodof moments estimator.

A theoretical function is then fitted to the experimental values to summarize the spatial relations in thedata. The parameters of the fitted model can be used to estimate or predict at unsampled places at pointsby kriging (Webster and Oliver, 2007).

Several isotropic authorized functions were fitted by weighted ordinary least squares (OLS) (Cressie,1985), but spherical (Olea, 2003) and stable (Wackernagel, 1999) functions provided the best fit. Thebest fitting models were chosen by cross-validation (Cressie, 1993; Chils and Delfiner, 1999).

2.7 Used Software

Digital image processing was done using Erdas IMAGINE 11and Atcor 11softwares. Variograms andcross-validation were performed using the ArcGIS 11software.

3. Results

3.1 Ground reference information

Based on reports of soil samples, it was observed the occurrence of the nematodes in all sampled points,identifying Pratylenchus brachyurus and Helicotylenchus spp. as the majoretiologic agent of the spots inthe areas 1, 2, 3 and 4 (Table 1). Sampled points 1 and 2, presented higher occurrence ofHelicotylenchus spp. in area 2, determining lighter spots on the plants whencompared to the sampledpoints 4, 5, 6 and 7, with higher occurrence of Pratylenchus brachyuruson the roots (Figure 1).

3.2 WorldView2 Data Processing

True color compositions R5G3B2 and false color compositions R8G5B3 and 7R6G4B enabled to identifysignals of the damage caused by the pest-organisms in the evaluated crops, but false color compositionswere better than color composition to detect the damage caused by nematodes in soybean ( Glycine maxL.), maize (Zea mays L.), sunnhemp (Crotalaria ochroleuca G. Don.) and dark sword-grass (Agrotisipsilon) in maize (Zea mays L.) (Figures 1 and 3).

WorldView-2 8 -multispectral bands enabled to derive the soil adjusted vegetation index, leaf area index,fraction of absorbed photosynthetically active radiation, surface albedo, absorbed solar radiation flux andthe first principal component analysis based on covariance and correlation matrices. The principalcomponent based on covariance matrix was chosen because the total variance explained was higherthan the correlation matrix, for all evaluated images of 01/26/2011, 06/12/2010 and 09/16/2010 (Table 2).

SAVI enabled to outline the existing spots in the evaluated crops by minimizing the brightness variationsof the soil in the vegetation index. The damage caused by the pest-organisms was characterized by lowerSAVI values. Higher SAVI values were observed in 06/12/2010, followed by 01/26/2011 and 09/16/2010


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evaluated periods. Similarly to SAVI, LAI images also enabled to detect the damage caused by the pest-organisms, with higher LAI values in 06/12/2010, followed by 01/26/2011 and 09/16/2010 (Figure 4).Maize and sunnhemp LAI values were near to 6 in 06/12/2010, confirming that a quantitative agreementwith field measurements of different croptypes in different seasons cannot be expected. Thus, fieldobservations of LAI would be necessaryto confirm the obtained values. The FPAR, related to beassociated with the green phytomass andcrop productivity, also presented similar pattern as LAI andSAVI, with higher values at regionswithout damage caused by the pest-organisms. Higher values near0.92 occurred in 06/12/2010. Thesurface albedo, related to the wavelength-integrated groundreflectance, presented was similar toSAVI, LAI and FPAR, in the periods of 01/26/2011 and 06/12/2010,but presented distinct patternin 09/16/2010 (Figure 5). The variation of the absorbed solar radiation in09/16/2010, with lowvalues in the plants with damage, determined the albedo variation in relation to theother periods,and caused doubts about the areas with higher damage caused by pest-organisms (Figure6).Otherwise, in the periods of 01/26/2011 and 06/12/2010, the absorbed solar radiation was higher inthe areas with damage caused by the nematodes. The first principal component derived from thePCAanalysis using covariance matrix not only was useful to characterize the greater variability ofall the 8bands of WorldView-2 images, but also enabled to identify and map the pest-organisms inthe field. Areaswith damage in plants were represented by low values of the principal componentindex, in contrast withhigher values in not damaged areas.

3.4 Spatial SignatureThe variogram analysis enabled to detect the structure and magnitude of spatial dependency of the SAVI,LAI, FPAR, surface albedo, absorbed solar radiation flux and the first principal component based oncovariance matrix. The choice on the order of trend removal was chosen observing the variographyanalysis and cross validation results. The second order of trend removal was adopted for the variablesLAI, albedo and solar radiation flux in 06/12/2010 and solar radiation flux in 09/16/2010. Spherical modelsbetter described the spatial pattern of the evaluated variables, except for albedo and solar radiation flux in09/16/2010, wherein the stable model performed better. Considering the subsets of the areas A and B,the range of spatial dependency in 01/26/2011 varied between 370 to 392 m. In relation to the subsets ofthe areas C and D, the range in 06/12/2010 varied between 162 to 169 m. Regarding the area E, in09/16/2010, the range was between 170 and 176 m, except for albedo and solar radiation flux, whichpresented ranges of about 75 m and 36 m, respectivelly (Table 3). The satisfactory implementation of the

geostatistical analysis to characterize the spatial pattern of the damage caused by the pest-organismswas observed in the well-defined structures of the variograms, with binned and average values increasewith the distance until stabilization after a distance that separates the structured and not spatiallydependent data (Figures 7, 8 and 9). In general, geostatistical analysis performed better based on thekriging error coefficients. Better coefficient results were observed for albedo and solar radiation flux in allevaluated periods (Table 4).

3.5 Spectral Signatures

The occurrence of the nematodes and maize plants cut by the dark sword-grass, determined spectralsignature with peaks of lower reflectance in the far red-edge region (724 nm), NIR1 infrared region (831nm), and NIR2 infrared region (908 nm), with higher reflectance peaks in the visible region whencompared to plants located outside of the signals of the pest-organisms (Figure 10). The spatial signature

of health plants, presented peaks of lower reflectance near center wavelengths of the costal (427 nm) andred (659 nm) bands, and higher reflectance peaks near center wavelengths of the green (546 nm) andNIR2 (908 nm) regions.


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The variograms obtained in the present work could be used as prior knowledge about the spatialheterogeneity of high spatial resolution data over particular crop sites and pest-organisms. According toGarrigues et al. (2008), a possible strategy to correct the scaling bias of non-linear estimation processesof land surface variable consists in using variogram model of high spatial resolution data as a proxy forthe spatial heterogeneity within moderate resolution pixel.

4.5 Spectral Signatures

Spectral signatures of the nematodes and maize plants with dark sword-grass were similar to signaturesdetected in other studies. Santoso et al. (2011) observed the same pattern studying basal stem rotdisease in oil palms of North Sumatra with QuickBird imagery. According to the authors, infected palmshad higher reflectance values in the visible bands and lower ones in the NIR band. The pattern of healthplants, presented points of reflectance absorption near 400 wavelength Breunig et al. (2011) studying theMODIS surface reflectance spectra of the soybean cultivars Perdiz and Tabarana for different days afterplanting and opposite view angles, in Tanguro farm, Mato Grosso, Brazil, 2004-2005 growing season,observed points of energy absorption near wavelength of 440 and 650 nm, and peaks of higherreflectance values near 540 and 900 nm.

5. ConclusionsThe spatial and spectral variation of the damage caused by pest-organisms in agroecosystems wascharacterized using WorldView2 multispectral images.

The spatial analysis approach adopted in this study enabled to detect spatial variation in the infestation ofnematode in soybean, maize and sunnhemp field and dark sword-grass in maize, indicating the possibilityof developing strategies to provide more effective control, less environmental impact and sustainability ofthe agroecosystem, according to the philosophy of integrated pest management and precision agriculture.

The use of soil adjusted vegetation index, leaf area index, fraction of absorbed photosynthetically activeradiation, surface albedo, absorbed solar radiation flux and the first principal component analysis based

on covariance matrix, derived from WorldView2 multispectra l images, improved the spatial identification of

the damage in the crops.

6. Acknowledgements

To CNPq for funding our research and 2012 ERDAS IMAGINE-DigitalGlobe Geospatial Challenge, for theopportunity to work with WorldView-2 images.

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Figure 1: Geographic location of the studied area (upper left), false color composition (7R6G4B) of the WorldView-2 imagesof 01/26/2011 (upper right), 06/12/2010 (down left), 09/16/2010 (down right), with the A to E subset areas used forgeostatistical analysis and sampled points 1 to 10, used for determination of the pixel spectral signature (1 to 11)

as well as for laboratorial analysis of nematode (1, 2, 4, 5, 6, 7)


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Figure 2: Images of 12/10/2010 showing the equipment used to collect soil and root samples (top left), base GPS receiver andmeteorological stations (top right), soybean (Glycine max L.), cultivar TMG132RR, with nematode signals

(middle left and middle right), sunnhemp (Crotalaria ochroleuca G. Don.) with nematode signals (down left) andmaize (Zea mays L.), cultivar AG1051 in the harvest period (down right)


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Figure 3: Color (R5G3B2) (left) and false color (R8G5B3) (right) composites of the spatial signaturescaused by nematodes in soybean (01/26/2011) (top), maize and sunnhemp (06/12/2010) (middle) and

dark sword-grass in maize (09/16/2010) (down). The highlighted areas in the downright imageR8G5B3 were cultivated with watermelon


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Figure 4: Soil adjusted vegetation index (left) and leaf area index (right) of the spatial signaturescaused by nematodes in soybean (01/26/2011) (top), maize and sunnhemp (06/12/2010) (middle)

and dark sword-grass in maize (09/16/2010) (down)


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Figure 5: Fraction of absorbed photosynthetically active radiation (left), surface albedo (%) (right),of the spatial signatures caused by nematodes in soybean (01/26/2011) (top), maize and

sunnhemp (06/12/2010) (middle) and dark sword-grass in maize (09/16/2010) (down)


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Figure 6: Absorbed solar radiation flux (W m-2) (left) and the first principal component analysis

based on covariance (right), of the spatial signatures caused by nematodes in soybean (01/26/2011) (top),maize and sunnhemp (06/12/2010) (middle) and dark sword-grass in maize (09/16/2010)(down)


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Figure 7: Variograms of the soil adjusted vegetation index (left) and leaf area index (right) of the spatial signaturescaused by nematodes in soybean (01/26/2011) (top), maize and sunnhemp (06/12/2010) (middle) and

dark sword-grass in maize (09/16/2010) (down)


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Figure 8: Variograms of the fraction of absorbed photosynthetically active radiation (left) and surface albedo (%) (right),of the spatial signatures caused by nematodes in soybean (01/26/2011) (top), maize and sunnhemp (06/12/2010) (middle)

and dark sword-grass in maize (09/16/2010)(down)


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Figure 9: Variograms of the absorbed solar radiation flux (W m-2) (left) and the first principal component analysis

based on covariance (right), of the spatial signatures caused by nematodes in soybean (01/26/2011) (top),maize and sunnhemp (06/12/2010) (middle) and dark sword-grass in maize (09/16/2010) (down)


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Figure 10: Spectral signatures of the points 1 to 11 sampled in the WorldView-2multispectralimages of 01/26/2011, 06/12/2010 and 09/16/2010.

Table 1: Laboratory report of number of nematodes in soil and root samples collected from thegeoreferenced points at December 10, 2010 and February 10, 2011.

Point

Pratylenchus

brachyurus

(soil)

Pratylenchus

brachyurus

(roots)

JunvenilMeloidogyne

(soil)

JunvenilMeloidogyne

(roots)

JuvenilHeterodera

(soil)

Heli1cotylenchus

spp. (soil)Helicotylenchus

spp. (roots)

1 20 0 0 0 70 650 0

2 20 0 0 0 0 730 0

4 610 5181 5570 2010 0 180 90

5 640 9550 80 0 0 680 333

6 30 2483 0 0 20 3740 100

7 0 280 0 0 30 10410 540


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Table 2: Total variance explained (%) of the principal component analysis using covariance and correlation matrices,derived from the 8 band of WorldView-2 multispectral images of 01/26/2011, 06/12/2010 and 09/16/2010.

ComponentNumber

Convariance Matrix Correlation Matrix

01/26/2011 06/12/2010 09/16/2010 01/26/2011 06/12/2010 09/16/2010

1 84.89773 79.60984 88.89321 61.39577 53.51692 70.54838

2 13.31642 18.37303 10.42446 33.65917 35.15794 26.92409

3 0.971357 0.898389 0.456973 2.813785 8.148852 1.506269

4 0.462016 0.480571 0.163137 1.168765 1.471268 0.490006

5 0.189057 0.394561 0.047492 0.667508 0.961262 0.343754

6 0.097246 0.14633 0.006413 0.156252 0.555007 0.115001

7 0.047036 0.070117 0.005267 0.081251 0.127502 0.058751

8 0.019139 0.027155 0.00305 0.057501 0.061251 0.01375


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Table 3: Theoretical variograms used to characterize the structure and magnitude of spatial dependency of the soil adjustedvegetation index (SAVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), surface albedo

(ALBEDO), absorbed solar radiation flux (RSOLAR) and the first principal component analysis based on covariance (PC cov) matrix,derived from WorldView-2 multispectral images of 01/26/2011, 06/12/2010 and 09/16/2010.

Variable {Date}Order of Trend

RemovalModel Type Nugget Major Range Partial Sill

SAVI [01/26/2011] Spherical 0.000093 370.36 0.002288

LAI [01/26/2011] Spherical 0.009595 382.02 0.17902

FPAR [01/26/2011] Spherical 0.0002670 378.94 0.0060337

ALBEDO [01/26/2011] Spherical 0.069075 379.00 3.8415

RSOLAR [01/26/2011] Spherical 0.47003 392.51 141.33

PC cov [01/26/2011] Spherical 3876.8 375.53 384350

SAVI [06/12/2010] Spherical 0.001528 166.89 0.0080352

LAI [06/12/2010] Second Spherical 0.6351 162.16 1.6899

FPAR [06/12/2010] Spherical 0.0057316 169.13 0.024328

ALBEDO [06/12/2010] Second Spherical 2.0951 165.06 8.5919

RSOLAR [06/12/2010] Second Spherical 32.227 163.77 136.14

PC cov [06/12/2010] Spherical 174170 167.00 896760

SAVI [09/16/2010] Spherical 0.00099 175.91 0.009333

LAI [09/16/2010] Spherical 0.02496 176.69 0.15007

FPAR [09/16/2010] Spherical 0.000171 177.37 0.01338

ALBEDO [09/16/2010] Spherical 0.000000 75.141 12.687

RSOLAR [09/16/2010] Second Spherical 0.000000 36.123 36.109

PCcov[09/16/2010] Spherical 72821 170.25 419480


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Table 4: Quality coefficients of the kriging method estimates used to characterize the spatial signature of the injury signalscaused by pest-organisms in Mato Grosso state agroecosystems,using soil adjusted vegetation index (SAVI),leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), surface albedo (ALBEDO),

absorbed solar radiation flux (RSOLAR), principal component analysis based on covariance (PC cov) matrix,derived from WorldView-2 multispectral images of 01/26/2011, 06/12/2010 and 09/16/2010

Variable [Date] SamplesMead

PredictionError

Root Mean

SquarePrediction

Error

MeanStandardized

PredictionError

Root MeanSquare

Standardized

Prediction

Error

AverageStandard

PredictionError

SAVI [01/26/2011] 164940 0.000009 0.007296 0.000810 0.671282 0.010867

LAI [01/26/2011] 164940 0.0001394 0.072596 0.001266 0.672479 0.107933

FPAR [01/26/2011] 164940 0.000018 0.012243 0.000970 0.670807 0.018248

ALBEDO [01/26/2011] 164940 -0.000040 0.268429 -0.000145 0.833151 0.321199

RSOLAR [01/26/2011] 164940 0.000974 1.125825 0.000880 0.944083 1.190445

PC cov[01/26/2011] 164940 -0.056772 60.242251 -0.000737 0.721061 83.492125

SAVI [06/12/2010] 89424 -0.000009 0.028486 -0.000181 0.676659 0.042089

LAI [06/12/2010] 89424 -0.000418 0.574436 -0.000470 0.684242 0.839264

FPAR [06/12/2010] 89424 -0.000021 0.054587 -0.000224 0.675700 0.080775

ALBEDO [06/12/2010] 89424 -0.000615 1.132135 -0.000328 0.732899 1.543760

RSOLAR [06/12/2010] 89424 0.001657 4.493487 0.000201 0.740719 6.063027

PC cov[06/12/2010] 89424 0.308231 313.374960 0.000627 0.697867 448.950330

SAVI [09/16/2010] 43680 0.000002 0.019261 0.000046 0.552120 0.034882

LAI [09/16/2010] 43680 -0.000018 0.092190 -0.000132 0.540011 0.170709

FPAR [09/16/2010] 43680 0.0000007 0.024701 -0.000014 0.544697 0.045345

ALBEDO [09/16/2010] 43680 -0.000090 2.285436 -0.000046 0.962587 2.374562

RSOLAR [09/16/2010] 43680 0.000342 4.205448 0.000959 0.939119 4.478349

PCcov[09/16/2010] 43680 0.052517 156.058404 0.000203 0.535297 291.507148


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