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International Journal of Applied Earth Observation and Geoinformation 19 (2012) 276–285

Contents lists available at SciVerse ScienceDirect

International Journal of Applied Earth Observation andGeoinformation

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dentification of cropping activity in central and southern Queensland,ustralia, with the aid of MODIS MOD13Q1 imagery

.J. Pringle ∗, R.J. Denham, R. Devadasandscape Sciences (ESP), DSITIA, GPO Box 2454, Brisbane, QLD 4001, Australia

r t i c l e i n f o

rticle history:eceived 13 June 2011ccepted 30 May 2012

eywords:ODISDVIime seriesropandom forest

a b s t r a c t

Cropping activity has an importance that extends beyond farming communities, to governments, privateindustries, and to scientific research. We have developed a remote sensing-based method to detect arablecropping activity in central and southern Queensland, Australia, based on time series analysis of the NDVIlayer of MODIS-Terra MOD13Q1 (250-m pixel) imagery. Local auto-regression was used to characterisephenological cycles in the NDVI time series. A random forest was then used to model three broad classesof agricultural vegetation (Grazing, Summer Cropping and Winter Cropping), as a function of phenologicalmetrics and the local variance of the NDVI time series. The latter was found to be the most importantdistinguishing factor between the three classes. Pixel-by-pixel predictions of the random forest wereobtained bi-annually for the study area over a 10-year period. Moderate agreement was seen betweenthe predictions of the random forest and (independent) visual interpretation of Landsat imagery (Cohen’sindex of agreement, �c, of 0.59). We then demonstrated how the random forest’s predictions can be used

to define the consistency of cropping activity at the spatial scale of an individual farm property; whencompared with (independent) visual interpretation of Landsat imagery the agreement was also moderate(�c = 0.68). In comparison with other crop-mapping approaches in the literature, our results have beenachieved: (i) without restricting the method to annual NDVI time series; (ii) without assuming that thetime series is regularly spaced and periodic; (iii) by considering only the ‘greening-up’ phase of thephenological cycles.

. Introduction

Farmers’ cropping activities – by which we mean ‘what growshere, when, how’ – have an importance that extends beyond

ocal farming communities, to governments, private industries, ando scientists. Globally, some of the major environmental issueselated to cropping activity are carbon sequestration (Swift, 2001;al, 2004; Jha et al., 2010), soil erosion by water and wind (Lal,998; Nordstrom and Hotta, 2004; Liu et al., 2008), and environ-ental pollution by nitrogenous fertiliser (Strebel et al., 1989;alton and Brand-Hardy, 2003; Mathers et al., 2007). Such environ-ental issues are intertwined with a host of social and economic

onsiderations, such as food security, rural migration, and develop-ent pressures from mining companies and/or urban expansion;

omprehensive accounts can be found in Rosegrant and Cline

2003), Meyerson et al. (2007), and Hamblin (2009). Governmentsorldwide are increasingly challenged to address these prob-

ems. Effective governance must stay abreast of trends in cropping

∗ Corresponding author. Tel.: +61 7 3170 5680.E-mail address: matthew.pringle@qld.gov.au (M.J. Pringle).

303-2434/$ – see front matter. Crown Copyright © 2012 Published by Elsevier B.V. All rittp://dx.doi.org/10.1016/j.jag.2012.05.015

Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.

activity, in an attempt to balance social, economic, and environ-mental concerns.

In December 2011 the State Government of Queensland,Australia, passed the Strategic Cropping Land (SCL) Act, to alleviatethe development pressures felt by Queensland’s most impor-tant cropping areas. See www.derm.qld.gov.au/land/planning/strategic-cropping/index.html for a comprehensive overview.Developers and landholders must now ask the government todetermine whether an area of interest meets the legislated SCL cri-teria; if it does, a proposed development can only proceed underexceptional circumstances, or at least with appropriate mitigationmeasures. One criterion to be satisfied is the so-called ‘History-of-Cropping Test’, where SCL is defined by the growth of at least threecrops between 1st January 1999 and 31st December 2010. This testhas created a demand for an objective method of determining crop-ping activity in Queensland. Addressing this demand forms the aimof our study. We contend that the demand can be met by statisticalmodelling of an archive of remotely sensed imagery.

1.1. Rationale for detecting cropping activity

Since February 2000 the MODIS (MODerate-resolution ImagingSpectroradiometer) sensor, on board the National Aeronautics and

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pace Administration (NASA) Terra satellite, has been viewing dailyhe vegetation of Earth. From the data acquired by MODIS-Terra twoegetation indices are produced: the Normalised Difference Vege-ation Index (NDVI), and the Enhanced Vegetation Index (EVI). Its well-established that these indices correlate with the biomassf vegetation (e.g. Huete et al., 1997, 2002). EVI was developed as

robust form of vegetation index (VI), more sensitive to canopyiomass and less susceptible to atmospheric and soil interferencehan NDVI. Both indices are available at (nominal) spatial resolu-ions of 250 m, 500 m, and 1000 m (van Leeuwen et al., 1999), as6-day or 8-day composite images, depending on the spatial resolu-ion. At the 250-m spatial resolution – what we might consider theest resolution for detecting crops – Wardlow and Egbert (2010)emonstrated that NDVI and EVI were able to detect crops withlmost identical accuracy.

A logical approach to detecting temporal change in vegetations to examine the phenological cycles of a VI time series. We define

phenological cycle as a localised portion of a time series, spanningrom trough to trough as the VI fluctuates. (We must acknowledgehat ‘phenology’ is a term applied strictly to individual species, andhat remote sensing-based observations of phenology are neces-arily aggregated measures over a landscape.) Phenological cyclesay be modelled by: (i) using a regression model to summarise the

ehaviour of a MODIS VI through time, and recording various phe-ological metrics associated with each cycle of the modelled timeeries (e.g. the cycle’s amplitude, and the Julian day when the max-mum VI is reached); and then, (ii) using a classification algorithmo allocate the metrics of each cycle to a vegetation class. Popular

ethods for (i) are harmonic analysis (Morton et al., 2006; Potgietert al., 2007; Zhang et al., 2008) and wavelet analysis (Sakamotot al., 2005, 2006; Galford et al., 2008). Popular methods for (ii) areaximum likelihood (Potgieter et al., 2007; Mingwei et al., 2008;

ritz et al., 2008) and decision trees (Morton et al., 2006; Changt al., 2007; Wardlow and Egbert, 2008).

Point (i) above (i.e. summarising the behaviour of a MODIS VIhrough time) creates problems that are worthy of elaboration.onsider harmonic analysis. The key assumption of harmonic anal-sis is that the underlying sine and cosine functions are ‘global’in the terminology of Jönsson and Eklundh, 2004), i.e. they muste present for the duration of the modelled time series. Conse-uently, harmonic analysis can lack sensitivity, particularly in areashere cropping land may undergo prolonged fallow in the event of

drought. The use of short, annual time series negates the issue (e.g.orton et al., 2006; Potgieter et al., 2007; Zhang et al., 2008), but

aises the possibility of edge effects when the algorithm is appliedo the data of consecutive years. Specifically, in systems whererops are grown all year round, there is a risk that the model’s end-f-year cropping prediction will not match its prediction for thetart of the next year, even though the same crop is still growing.n alternative approach, able to deal with sporadic phenologicalycles while avoiding annual edge effects, is to smooth the entireime series (i.e. all MODIS data available from February 2000 toresent). Jönsson and Eklundh (2004) devised the TIMESAT soft-are, based on Savitsky–Golay filtering, for smoothing entire time

eries of remotely sensed data. The method, though able to copeith even weakly periodic time series, requires regularly timed

bservations. This poses a further problem because in strict termsODIS VI composites do not form a regularly spaced time series:

or any pixel, the values seen in consecutive composites might becanned anywhere between 1 and 16 days apart.

An alternative method to summarise a MODIS VI time series –ree from the problems inherent with harmonic analysis and filter-

ng – is to smooth the entire time series with a local autoregressiveunction that implicitly handles unevenly spaced data, such as apline. The derivatives of the autoregressive function will iden-ify the individual cycles, and the smoothed values of the function

bservation and Geoinformation 19 (2012) 276–285 277

can be used to find the phenological metrics associated with eachcycle. This last notion corresponds closely to the method used byTIMESAT (Jönsson and Eklundh, 2004; Boschetti et al., 2009). Oncedefined, the phenological metrics, or indeed any other readily avail-able environmental variables that may be related to the cycle, canbe used in conjunction with a classification algorithm to allocatethe cycle to a vegetation class.

2. Methods

2.1. Study area

The SCL Management Area covers 42-million ha, almost one-quarter of Queensland, Australia, and is divided into five broadagro-climatic zones: Wet Tropics, Coastal Queensland, WesternCropping, Eastern Darling Downs, and Granite Belt (Fig. 1). A pre-liminary delineation of potential SCL (i.e. the green and grey areasof Fig. 1) has been derived by government-based experts, based onknown soil attributes and landuse. It is intended that, with time,ground-based assessment will gradually adjust the mix of green,grey and white areas, at the spatial resolution of an individual farmproperty. Also delineated are two so-called ‘Protection Areas’, con-sidered to be critical for Queensland’s food and fibre production,but under intense and imminent development pressure. Propertiesinside a Protection Area are exempt from the History-of-CroppingTest described in Section 1.

We restricted the study area to the 24-million ha part of theWestern Cropping zone not inside a Protection Area. Within thisrestricted area, fields – i.e. the smallest unit of homogeneous cropmanagement – are generally large enough to be resolved by 250-m MODIS imagery. This is not the case for the other zones, wherehigher-resolution satellite imagery alone will be needed to estab-lish cropping history; an issue currently beyond our scope. Themain crops of the study area can be regarded as either winter-growing (e.g. barley, chickpea, oat, wheat) or summer-growing (e.g.cotton, maize, mungbean, sorghum). In general, the largest area isplanted to wheat during winter, and to sorghum during summer.

2.2. MODIS-Terra MOD13Q1 time series

The MODIS-Terra MOD13Q1 product is a 16-day compositeimage of VIs, in a sinusoidal projection, with a (nominal) spatial res-olution of 250 m. We obtained 268 MOD13Q1 (Collection 5) imagesfor the period February 2000 to September 2011 (inclusive), forMODIS tiles h31v11, h30v11, and h31v10. The SCL ManagementArea is included within the spatial extent of these tiles. Collection5 images have thirteen layers. The layers of interest for our studywere: (i) NDVI; (ii) surface reflectance in the mid-infrared (MIR)band (2105–2155 nm); (iii) the information that related, for eachpixel, the Julian day during the 16-day compositing period when(i) and (ii) were recorded; and (iv) the MODLAND QA bitfield of thequality assurance information for the VIs. The latter we convertedto an integer rank between 0 (representing highest quality) and 3(lowest quality). The four MOD13Q1 layers were used as provided,with no additional geometric or radiometric corrections, and noreprojection (except for presentation purposes). It is well estab-lished that MIR surface reflectance has a biophysical link with thegrowth of vegetation (e.g. Tucker, 1980; Dusek et al., 1985), hencethe reason for its use here. The MIR band is actually sensed at anative spatial resolution of 500 m, but then re-sampled to 250-mduring compositing.

2.3. Sampling

To train a model of cropping activity we obtained three kindsof information: (i) a land-manager sample; (ii) a grazing sample;

278 M.J. Pringle et al. / International Journal of Applied Earth Observation and Geoinformation 19 (2012) 276–285

Fig. 1. Queensland’s Strategic Cropping Land (SCL) Management Area is divided into 5 agro-climatic zones (blue polygons). Within these, potential SCL is delineated (greyand green shading). The inset shows the location of the SCL Management Area relative to Queensland and Australia. (For interpretation of the references to colour in thisfi

apppr2cpeoattousmt

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gure legend, the reader is referred to the web version of the article.)

nd, (iii) a drive-by sample. The land-manager sample aimed torovide detailed historical cropping information for relatively fewroperties. Between 2007 and 2010, 20 land managers from variousroperties across the study area were asked to disclose from theirecords the crops they had grown on their fields since February000, and the sowing and harvest dates. The land managers werehosen on the advice of local extension officers. The informationrovided by the land managers was ascribed to the MOD13Q1 pix-ls that corresponded to their fields. We used SPOT-5-HRG imagery,btained sporadically between 2005 and 2009, as a guide to removeny MOD13Q1 pixels of the land-manager sample that appearedo be spectrally contaminated by, for example, field boundaries,rees, dams, or intra-property trails. As a safeguard against vaguer erroneous record keeping we liaised with the land managersntil we were satisfied that the cycles of the MOD13Q1 NDVI timeeries matched the cropping information they had provided. Infor-ation was discarded if there was a discrepancy but the quality of

he information could be improved no further.The grazing sample comprised selected groups of 3 × 3

OD13Q1 pixels, taken from a known grazing location within0 km of each property used in the land-manager sample. Eachixel of the 3 × 3 group was classed as Natural pastures fromebruary 2000 until October 2008. SPOT-5-HRG imagery was again

used to ensure that each pixel was as free as possible from spectrallycontaminating features.

The drive-by sample, in contrast to the other two, provided spa-tially diffuse ‘snapshots’ of cropping activity. This information wascollected by various parties between 2003 and 2011, who each useda global positioning system and a laptop computer to record thedominant vegetation species at particular roadside locations. Somelocations, expected to be cropping, were actually being used forgrazing at the time of observation. In this case, if no single pasturespecies was dominant the location was classed as Natural pastures.The observations were ascribed to the corresponding MOD13Q1pixels. SPOT-5-HRG imagery was again used to ensure that eachpixel was as free as possible from spectrally contaminatingfeatures.

2.4. Statistical analyses

As the SCL legislation makes no distinction between individual

species, and given that the vast majority of agricultural land inQueensland is used for grazing (86% in 1999 according to Witteet al., 2006), it sufficed to describe only three classes in a model ofcropping activity: Winter Cropping, Summer Cropping, and Grazing.

M.J. Pringle et al. / International Journal of Applied Earth Observation and Geoinformation 19 (2012) 276–285 279

Summercropping

Vegetation

Grazing

Barley (Hordeum spp .)Chickpea (Cicer spp .) Oat (Avena spp .)Wheat (Triti cum spp .)

Cott on (Goss ypium spp .)Maize (Zea spp .)Millet (Pan icum, Setaria,

and E chinochloa spp.) Mungbean (Vign a spp .) Sorghu m (So rghu m spp .) Sunflower (Helian thu s spp .)

Butt erfly pea (Clit oria spp .) Lablab (Lab lab spp .) Leucaena (Leucaena spp .) Natural pastures (variou s spp .)

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that ensemble methods perform at least as well as conventionalclassification methods, and that the (bagged) predictions of the for-mer are relatively stable compared with the predictions of the latter(Prasad et al., 2006; Moriondo et al., 2008; Waske and Braun, 2009).

Table 1The explanatory variables recorded for an individual phenological cycle.

Xi Description

1 Julian day when the cycle starts.2 Days after X1 that the cycle reaches maximum NDVI.3/4 NDVI/MIR at X1.5/6 NDVI/MIR at (X1 + X2).7/8 NDVI/MIR gradient between X1 and (X1 + X2).9/10 Area under the NDVI/MIR curve, accumulated between X1 and

(X1 + X2), base value = NDVI/MIR at X1.

ig. 2. Classification of the cropping and grazing species observed through sam-ling. The area of each species observed is also shown: ‘L’, land-manager sample;

G’, grazing sample; ‘D’, drive-by sample.

ll observed vegetation species were re-classified accordinglyFig. 2).

.4.1. Definition of phenological cycles, and gathering ofxplanatory variables

For each sampled location we drilled through the MOD13Q1magery to form time series of NDVI and MIR surface reflectance.alues of these variables were divided by 10,000 to ensure a max-

mum of 1.0. Where the MODLAND QA was greater than zero (i.e.ot of highest quality) the corresponding NDVI and MIR values werexcluded. Local autoregression (Loader, 2007), implemented in the

statistical software (R Development Core Team, 2011), was thensed to smooth the time series of the 229 NDVI or MIR values closesto when the location was sampled. If all MODLAND QA values wereero, the length-229 time series would correspond to a 10-yeareriod. The abscissa of the time series was the Julian day of obser-ation, converted to decimal year. Various values of the smoothingarameter for the local regression were tested; visual examinationuggested that 0.05 was adequate.

The autoregressive model was used to predict NDVI at the valuesf decimal year corresponding to the midpoint of MOD13Q1 com-ositing periods. The first derivative of each NDVI prediction waslso recorded. Derivatives were examined for change-points wherealues went from negative to positive. The negative derivative thatmmediately preceded a positive derivative defined a single local

inimum in the NDVI time series. An individual phenological cyclen an NDVI time series was defined as the subset of smoothed valuesetween a consecutive pair of local minima. To characterise eachycle only its ‘greening-up’ phase was of interest, i.e. the periodetween the start of the cycle and when the maximum NDVI waseached. Various metrics related to the greening-up phase of themoothed cycle were recorded (Fig. 3). The same phenological met-ics were also recorded for the smoothed MIR time series during thereening-up phase.

In addition to the 12 phenological metrics, we computed twourther quantities. First, the correlation of smoothed NDVI with

moothed MIR during the greening-up phase was recorded. Second,e used a linear mixed-effects model, fitted by residual maximum

ikelihood (Pinheiro and Bates, 2004), to compute the variance ofDVI within the length-229 window of observations. The linear

Fig. 3. A phenological cycle in an NDVI time series. Smoothed data are used todetermine the different attributes of the ‘greening-up’ part of the cycle.

mixed-effects model returned an unbiased estimated of variancewhile simultaneously accounting for mean NDVI at six bi-monthlyperiods (January–February, March–April, May–June, July–August,September–October, and November–December). Constant covari-ance was assumed between each bi-monthly period.

A key for the 14 explanatory variables related to each phenolog-ical cycle is presented in Table 1.

2.4.2. Random-forest classification and extrapolation across thestudy area

The 14 explanatory variables described above were used in con-junction with a classification algorithm to allocate each cycle of thetime series to one of three classes of vegetation (Fig. 2). In recentyears, ensemble decision trees have become popular for problemsrelated to landuse classification (e.g. Gislason et al., 2006; Sesnieet al., 2008; Chan and Paelinckx, 2008).

Let us say that for a particular period of time and a regionof interest we can associate q phenological cycles with one ofr observed classes of vegetation. Each cycle is associated with sexplanatory variables input to the ensemble decision tree. Theresponse variable is the observed classes of vegetation. An ensem-ble decision tree such as the random forest (Breiman, 2001; Liawand Wiener, 2002) is based on the idea that, rather than grow justone tree for classification, we can grow many trees, with each basedon a bootstrap sample of q and s. Each tree predicts a class, and thepredictions over all trees are used to calculate a probability distri-bution for a single aggregated prediction. In general the class withthe greatest probability is used as the final value. The single predic-tion is said to be bagged (‘bootstrap aggregated’). It has been found

11/12 Area under the NDVI/MIR curve, between X1 and (X1 + X2), basevalue = 0.

13 Correlation of NDVI with MIR between X1 and (X1 + X2).14 Variance of the (seasonally detrended) NDVI time series.

280 M.J. Pringle et al. / International Journal of Applied Earth Observation and Geoinformation 19 (2012) 276–285

Fig. 4. NDVI time series for three MOD13Q1 pixels: (a) irrigated cotton cropping; (b) rain-fed cropping; and (c) grazed natural pastures. Only NDVI data of the highest qualitya f eacha

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re shown. The grey line is the fit of a local regression function. Ticks at the bottom ossociated with a phenological cycle is indicated.

n regard to the random forest, non-linearity between the responseariable and the explanatory variables is dealt with implicitly, asre interactions between the explanatory variables. Additionally,he relative importance of each explanatory variable to the classi-cation is quantified. This is done by shuffling the values of eachxplanatory variable in turn, which has the effect of inducing noisen the dataset. Those explanatory variables with the greatest impor-ance to the model are those that, upon shuffling, decrease most theccuracy of the model (Breiman, 2001). Accuracy is assessed by theut-of-bag error rate (Breiman, 2001), εb, a quantity ideally as smalls possible. All these features make the random forest a particularlyttractive classification algorithm for our study.

Using the R statistical software (R Development Core Team,011), we wished to generate a random forest with as little com-lexity as possible but not unduly compromise εb. We thereforexamined how εb changed according to perturbation of ntree, theumber of trees in the forest. The two remaining user-definedarameters that affect the complexity of a random forest are: (i)he number of observations required to form a terminal node in aree; and, (ii) the number of randomly selected explanatory vari-bles used by each tree. The default for (i) is 1, which we increasedrbitrarily to 10 to decrease memory load. The default value ofii) (i.e. the integer part of

√s) was used throughout. A parsimo-

ious random forest will use the smallest value of ntree that doesot adversely affect εb. The εb was recorded for 20 replicates of

andom forests generated with ntree = {1, 2, . . ., 500}. We plottedb as a function of ntree then determined the optimum number ofrees subjectively. The random forest was then re-made using thiseduced value of ntree, to form a parsimonious model.

panel show where phenological cycles start. Where known, the vegetation species

The parsimonious random forest was applied to the explanatoryvariables calculated for MOD13Q1 pixels inside the study area on1st August and 1st February each year between August 2000 andAugust 2010. The bi-annual dates maximised the chance of cor-rectly detecting Winter Cropping and Summer Cropping. As in Section2.4.1, the data input to the models was based on the nearest 229MOD13Q1 images to the date of interest. The model returned theprobabilities of Grazing, Summer Cropping and Winter Cropping ateach pixel on each date. Pixels that intersected known areas of:(i) roads; (ii) non-agricultural land use; or, (iii) heavy tree cover(defined as >11% foliage projective cover (FPC)—Scarth et al., 2008)were excluded from prediction a priori. Each of these spatial lay-ers was available in-house. Land use was determined on a 1999baseline, according to specifications of the Australian Collabora-tive Land Use and Management Program (adl.brs.gov.au/landuse/).Long-term FPC (1988–2009) was estimated from Landsat imagery,using a procedure based on the work of Armston et al. (2009).

2.4.3. Validation of the random forest predictionsTo validate the predictions of the random forest we used time

series of Landsat TM and ETM+ imagery acquired between 2000and 2010, for those scenes that covered the study area. Imagery,which was obtained by download from the United States Geo-logical Survey (glovis.usgs.gov/), was processed according to themethod of Danaher (2002), where an empirical (Queensland-wide)

calibration was applied to account for the bidirectional reflectancedistribution function, thereby correcting the imagery to top-of-atmosphere reflectance. From the 21 bi-annual maps of croppingactivity, we selected, at random in space and time (and without

rth Observation and Geoinformation 19 (2012) 276–285 281

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M.J. Pringle et al. / International Journal of Applied Ea

eplacement), 200 pixels from each class. The proportions of thelasses predicted by the random forest were noted. The identi-ies of the predicted classes were withheld while we established,hrough visual interpretation of the Landsat time series, the land-over at the time and place in question. If landcover could note confidently established – due to, for example, cloud con-amination or a pixel containing roughly equal proportions ofifferent landcovers – the pixel was excluded. Agreement betweenhe MOD13Q1-predicted and Landsat-observed classes was thenssessed with the methods expounded in Congalton and Green2009), central to which is Cohen’s index of agreement (�c; Cohen,960), which we implemented in a Bayesian context (Denhamt al., 2009). This accounted for the sampling proportions andllowed us to associate confidence intervals with accuracy state-ents.

.4.4. Addressing the History-of-Cropping TestWe used the probability-based output of the random forest

o identify whether a pixel had grown ≥3 crops between 1stugust 2000 and 1st August 2010. For each MOD13Q1 pixel

n the study area we extracted the array of p(Grazing) andp(Cropping) = p(Summer cropping) + p(Winter Cropping)}, for all bi-nnual maps. Within this 2 × 21 array we occasionally found thathe probabilities were identical in consecutive growing seasons,ndicating that the tail of a phenological cycle had carried over tonother growing season. The repeated values were removed, leav-ng for each pixel a 2 × n array. Locations with n < 3 were excludedrom further analysis. Binomial distribution theory (Snedecor andochran, 1989, Chapter 7) was used to compute the probability that,ver the n growing seasons, a MOD13Q1 pixel had grown ≥3 crops:

(≥ 3 crops) = 1 − {p(0 crops) + p(1 crop) + p(2 crops)} (1)

here p(0 crops) =∏n

i=1p(Grazing)i, p(1 crop) = ni=1

(∏nj=1p1,j

)i,

nd p(2 crops)=∑b

i=1

(∏nj=1p2,j

)i. The quantity p(0 crops) is the

robability that p(Cropping) never exceeded p(Grazing) in any of the growing seasons. For the probability that p(Cropping) exceeded(Grazing) in just one out of n, p1 = p(Grazing), but one of its n ele-ents was replaced with a value of p(Cropping), determined by i. For

(2 crops), b = n!/{2! (n − 2)!} (i.e. the number of ways that any twoumbers can be chosen from the set 1, . . ., n), and p2 = p(Grazing),ut two of its n elements were replaced with values of p(Cropping),etermined by i. The resulting surface of p(≥ 3 crops) over all pix-ls is known herein as the ‘MOD13Q1-based probability map’.

To validate the MOD13Q1-based probability map, we selected,t random and without replacement, 393 properties of >10 harom a digital cadastral database of the study area. This corre-ponded to 1% of the properties available. We then allocated eachuthor (MJP, RJD and RD) with a common subsample of 47 and anxclusive subsample of 115 properties (one author received onedditional property to make up the difference). Independent of theOD13Q1-based probability map, the Landsat imagery described

n Section 2.4.3 was used to determine, through visual interpreta-ion, whether or not ≥3 crops had been grown on each propertyince 1999. Note that, in line with the SCL definition, if morehan one field of a property was cropped simultaneously theyere considered only a single cropping event. Each author notedhether they were certain or uncertain of their classification for

ach property. The Landsat-based classes were compared with theOD13Q1-based probability map, where the latter was classified

ccording to the rule that, for a property to have grown ≥3 cropshere had to be at least two contiguous pixels with p(≥ 3 crops) ≥

.9 wholly inside the property boundary.

Inter-rater reliability was assessed on the common subsam-le with Fleiss’ index of agreement (�F; Fleiss, 1971). Agreementetween the Landsat-based reference data and the (classified)

Fig. 5. Random forest out-of-bag error (εb) as a function of the number of trees(ntree). Dots represent values observed over twenty replications; the solid line is themedian.

MOD13Q1-based probability map data was assessed again withBayesian methods (Section 2.4.3), though here the prior samplingproportions were naively assumed equal at p = 0.5.

3. Results

3.1. NDVI time series

Fig. 4 shows exemplar NDVI time series (observed andsmoothed) for two MOD13Q1 pixels associated with the land-manager sample, and another pixel associated with the grazingsample. The vegetation species matched to the phenological cyclesare also indicated. Cycles associated with a cotton-growing field(Fig. 4a) were regular from year to year, and attained similar NDVImaxima. This is because cotton is grown in a tightly managed envi-ronment, generally free from limitations of water, nutrients andpests, so the crop almost always reaches its potential. In contrast,the phenological cycles for a rain-fed cropping field (Fig. 4b) wereirregular, with a prolonged fallow period (April 2001–May 2003)enforced by a severe drought in eastern Australia. The maximumNDVI attained during each cycle varied substantially. The pixelassociated with the grazed area (Fig. 4c) was about 2 km from thepixel used for Fig. 4b; while its phenological cycles appeared moreregular through time than those of the nearby cropping field, themaximum NDVI values attained by the cycles varied substantially.

3.2. Random-forest classification and extrapolation across thestudy area

The random forest was reduced from 500 trees to 200. Forestssmaller than 50 trees severely compromised εb, while forests with>200 trees made no improvement to εb (Fig. 5). The four mostimportant explanatory variables were (in the notation of Table 1):(1) ‘Variance of the (seasonally detrended) NDVI time series’; (2)‘Julian day when the cycle starts’; (3) ‘Area under the NDVI curve,accumulated between X1 and (X1 + X2), base value = NDVI at X1’; (4)‘NDVI at X1’. The primacy of temporal variance reflected the rela-tively large fluctuations in biomass seen in cropped fields. Galford

et al. (2008) reported a similar effect in MODIS EVI time series inBrazil. MIR-related variables were generally the least important.

The error matrix for the Landsat-observed and MOD13Q1-predicted validation data is shown in Table 2. The median

282 M.J. Pringle et al. / International Journal of Applied Earth Observation and Geoinformation 19 (2012) 276–285

Table 2Error matrix for model validation at the scale of 250-m pixels.

Observed Total p UA (%)

G S W

Predicted G 150 4 1 155 0.917 96.5(93.0, 98.7)S 44 45 15 104 0.045 43.2(33.5, 52.6)W 22 10 113 145 0.038 77.8(70.6, 84.0)

Total 216 59 129 404PA (%) 97.3(96.8, 97.8) 42.0(24.7, 65.5) 69.8(47.2, 83.2)

A f the pu rall a

p>st7(ptgsi

3

it≥AdT(eoMsi

bIclicadgdpfit

4

atGTaWo

bbreviations: G, Grazing; S, Summer Cropping; W, Winter Cropping; p, proportion oser’s accuracy. Quantities in brackets are the 95% confidence intervals. Median ove

roducer’s and user’s accuracies for Grazing were excellent (both95%), and highly certain, reflecting how the vast majority of thetudy area is devoted to this class. For Summer Cropping and Win-er Cropping the median user’s accuracies were smaller (43.2% and7.8%, respectively), and smaller again for the producer’s accuracies42% and 69.8%, respectively). The uncertainties associated with theroducer’s accuracies of the two cropping classes were larger thanhe respective values for the user’s accuracies. These results sug-ested that predicted occurrences of cropping, particularly duringummer, were overestimated at the expense of Grazing. The medianndex of agreement, �c, was moderate at 0.59.

.3. Addressing the History-of-Cropping Test

Fig. 6a presents an extract of the MOD13Q1-based probabil-ty map for a property in the study area that was independent ofhe training data. According to the map, the land manager grew3 crops almost certainly on two fields between August 2000 andugust 2010 (denoted ‘F1’ and ‘F2’). This should be sufficient evi-ence for the property as a whole to pass the History-of-Croppingest. When cross-referenced with selected Landsat 5 TM imageryFig. 6b) – each acquired at a time when winter-growing crops arexpected to be near peak biomass – it was apparent that either F1r F2 grew a winter crop in all six years examined. According to theOD13Q1-based method, fields F3 and F4 were cropped less con-

istently than F1 and F2, and even less so for F5 and F6. This patterns supported by visual interpretation of the Landsat imagery.

We investigated further the agreement between the MO13Q1-ased probability map and visual interpretation of Landsat imagery.

n terms of using solely the latter to determine whether or not ≥3rops have been grown on a property, inter-rater reliability wasarge even when uncertain assessments were used in the compar-son (kF = 0.85). Inter-rater reliability was perfect when only theertain assessments were used (kF = 1). Table 3 shows the accuracyssessment for the Landsat-observed and MOD13Q1-predictedata. The median index of agreement was �c = 0.68, which sug-ested moderate agreement between the two contrasting ways ofetecting cropping activity. The greatest source of error was thatroperties mapped as having ≥3 crops with MOD13Q1 were oftenound to have fewer than 3 crops when appraised with Landsatmagery. This overestimation of cropped areas is consistent withhe results of the pixel-scale validation (Table 2).

. Discussion

This study was borne of a demand for information on croppingctivity across a large spatial extent, provided at temporal and spa-ial scales compatible with the policy requirements of the Stateovernment of Queensland. With the aid of a time series of MODIS-

erra MOD13Q1 imagery we trained a statistical model, in form of

random forest, to identify areas of Grazing, Summer Cropping, andinter Cropping. As well as immediately serving the SCL History-

f-Cropping Test, the information generated might conceivably

redictions associated with the class; PA, median producer’s accuracy; UA, medianccuracy = 93.4% (90.1, 95.5); median �c = 0.59 (0.48, 0.69).

find applications in hydrological modelling or crop forecasting. Theadvantages of our method relative to other MODIS-based crop phe-nology studies (Section 1.1) are that: (i) we have not restricted themethod to annual VI time series; (ii) we have not assumed thatthe time series is regularly spaced and periodic; and, (iii) we haveconsidered only the greening-up phase of the phenological cycles.

An important disadvantage of the method is that areas of non-agricultural land use or heavy tree cover must be excluded a priori.In pursuit of an operational method, we assumed that the avail-able information on each of these two variables was error-free.This assumption is unrealistic for those locations where, for exam-ple: (i) mining developments have consumed agricultural land inthe 13 years since the baseline land use map for Queensland wasgenerated (Witte et al., 2006); or, (ii) long-term FPC is close to thethreshold of 11% (Scarth et al., 2008). Such errors are inevitable but,given the large study region, we consider their overall effect to benegligible, and, anyway, spurious locations can always be appraisedwith higher-resolution imagery.

The random forest was shown to have moderate predictiveaccuracy. We have not compared the random forest with otherclassifiers such as maximum likelihood, multinomial regression,and support vector machines: this is a complex topic, beyond thescope of our current study. The single greatest source of error in therandom forest was the prediction of grazing areas as Summer Crop-ping. This is because sorghum, the dominant summer-growing cropof the study area (Fig. 2), is generally rain-fed, and its phenologi-cal cycle coincides with that of the natural pastures that aboundthroughout the study area. There is a much better separabilitybetween Grazing and Winter Cropping because the species of the lat-ter are essentially grown out-of-season, on the soil profile’s store ofsummer rainfall. In conducting the accuracy assessment, MOD13Q1pixels of mixed class were excluded because mixing effects werenot an explicit consideration in the field sample. The implicationsof this issue for the accuracy assessment are not clear.

The most obvious way to improve the predictive accuracy of therandom forest would be to incorporate the existing land use mapas an explanatory variable. We are, however, reluctant to pursuethis option because it risks creating a circular situation if the modeloutput were ever used to inform a future landuse map. Alterna-tive approaches to separate cropping from grazing might involvethese notions: (i) due to machinery requirements, cropping fieldsmight be associated with a set of characteristic shapes (e.g. highlyangular, or circular if under pivot irrigation), rarely seen undergrazing conditions in Queensland; or, (ii) for grazing, the pheno-logical cycles might be heterogeneous spatially, relative to thoseof a monoculture crop. The latter would open the door to land-cover modelling that combines geostatistics with random-forestclassification (Rodriguez-Galiano et al., 2012).

Even with moderate accuracy, however, the output of the

random forest revealed a wealth of information about farmers’propensities for growing crops at certain times of year. In thecontext of SCL assessment for an individual property, we do notconsider the random forest’s overestimation over Summer Cropping

M.J. Pringle et al. / International Journal of Applied Earth Observation and Geoinformation 19 (2012) 276–285 283

F he MOv f the

o

atdsp

TC

Ac

ig. 6. Exemplar property, divided into six fields (denoted F1–F6): (a) extract of tarious dates (red = Band 5, green = Band 4, and blue = Band 2). (For interpretation of the article.)

nd Winter Cropping to be a particularly serious problem, given that

he History-of-Cropping Test is an intermediate step in the proce-ure: if the test is passed, a property must then meet stringentoil-related criteria before it can be considered SCL, and it is at thisoint that many of the misclassified areas will be detected.

able 3omparison of two contrasting methods (MOD13Q1-based and Landsat-based) for determ

Landsat

≥3 crops Othe

MOD13Q1 ≥3 crops 115 41

Otherwise 12 225

Total 127 266

PA (%) 93.5(89.6, 96.4) 78.2

bbreviations: p, proportion of the predictions associated with the class; PA, median proonfidence intervals. Median overall accuracy = 84.2% (80.0, 87.7); median �c = 0.68 (0.61,

D13Q1-based probability map; and, (b) six false-colour Landsat 5 TM images forreferences to colour in this figure legend, the reader is referred to the web version

To address the History-of-Cropping Test, our intention was that

(i) the output of the random forest would be used to derive rel-atively coarse-scale, large-area estimates of the probability of ≥3crops grown between August 2000 and August 2010; (ii) for an indi-vidual property the coarse-scale estimates could then be reinforced

ining whether a sample of properties of >10 ha have grown ≥3 crops.

Total p UA (%)

rwise

156 0.5 73.6(66.3, 80.0)237 0.5 94.9(91.6, 97.2)

393(73.7, 82.6)

ducer’s accuracy; UA, median user’s accuracy. Quantities in brackets are the 95% 0.76).

2 arth O

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5

ibMtfmmftsTeo

A

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R

A

B

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C

84 M.J. Pringle et al. / International Journal of Applied E

sing a suite of finer-resolution imagery (Landsat, SPOT, aerial pho-ography). Unfortunately cloud-free, finer-resolution imagery isnly available sporadically, which precludes it from explicit timeeries analysis. As SCL legislation applies from January 1999 – 18onths prior to the earliest predictions of the random forest – some

rops will inevitably be missing from the MOD13Q1-based prob-bility estimates, so the true probability of ≥3 crops grown wille underestimated in cropping areas. Fine-resolution imagery isherefore the only recourse for establishing cropping activity prioro August 2000. Critically for the SCL assessment, we have shownhat different users can reliably identify cropping activity in Landsatmagery, provided they are sufficiently experienced. We acknowl-dge that, in deriving Table 3, a rule (i.e. that there had to be ateast two contiguous pixels with p(≥ 3 crops) ≥ 0.9 wholly insidehe property boundary) had to be applied to the MOD13Q1-basedrobability map in order to make it compatible with the results ofisual interpretation of Landsat imagery. The rule, while plausible,ay not have been optimal. Missed crops due to gaps in the Land-

at imagery would also have adversely affected the quality of thegreement seen in Table 3.

. Conclusions

We have developed a method to detect arable cropping activ-ty in central and southern Queensland, Australia. The method isased on time series analysis of the NDVI layer of MODIS-TerraOD13Q1 (250-m pixel) imagery: local auto-regression charac-

erised phenological cycles in the NDVI time series, then a randomorest modelled agricultural vegetation (classified as Grazing, Sum-er Cropping and Winter Cropping) as a function of phenologicaletrics and the local variance of the NDVI time series. The latter was

ound to be the most important distinguishing factor between thehree classes. Predictions of the random forest were accurate at thecale of MOD13Q1 pixels (Cohen’s index of agreement, �c, of 0.59).he predictions of the models were also found to assess, with mod-rate accuracy (�c = 0.68), farm-scale estimates of the consistencyf cropping activity between 2000 and 2010.

cknowledgements

This study was funded by the Queensland Government’s QScapenitiative. We are indebted to Mr. Craig Thomson (for the immenseampling effort), Dr. Andries Potgieter (for contributing some ofis own observations to the training data), and Dr. Baisen Zhangfor making the MOD13Q1/Landsat assessment procedure possi-le). Many thanks also to the numerous extension officers and

and-managers who have kindly shared their time and knowledgeince this project began in 2007.

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