Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an...

Long term data fusion for a dense timeseries analysis with MODIS andLandsat imagery in an AustralianSavanna

Michael SchmidtThomas UdelhovenTony GillAchim Röder

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Long term data fusion for a dense time seriesanalysis with MODIS and Landsatimagery in an Australian Savanna

Michael Schmidt,a Thomas Udelhoven,b Tony Gill,c and Achim RöderbaRemote Sensing Centre, Department of Science, Information Technology, Innovation

and the Arts, Environment and Resource Sciences, GPO Box 2454, Brisbane 4001, Australiamichael.schmidt@derm.qld.gov.au

bUniversity of Trier, Remote Sensing Department, Behringstraße 21, 54286 Trier, GermanycOffice of Environment and Heritage, Department of Premier and Cabinet, 209 Cobra Street,

Dubbo NSW 2830, Australia

Abstract. The spatial resolution of Landsat imagery has proven to be well suited for the analysis ofvegetation patterns and dynamics at regional scale; however, the low temporal frequency is often alimitation for the quantification of vegetation dynamics. The spatial and temporal adaptive reflec-tance fusion model (STARFM) combines moderate resolution imaging spectrometer (MODIS) andLandsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) imagery to a high spatio-temporal resolution dataset. A time series of 333 STARFM images was generated between February2000 and September 2007 (8-day interval) at Landsat spatial and spectral resolution for a12 × 10 km heterogeneous test area within the North Queensland Savannas. Time series ofobserved Landsat and predicted STARFM images correlated high for each spectral band (0.89to 0.99). The STARFM algorithm was tested in a regionalization study where sudden change eventswere analyzed for a pallustrine wetland. A MODIS subpixel analysis showed a very close relation-ship between STARFM normalized difference vegetation index (NDVI) data and MODIS NDVIdata (root mean square error of 0.027). A phenological description of the major vegetation classeswithin the region revealed distinct differences and lag times within the ecosystem. The 2004 dryseason NDVI minimum-map correlated highly with the validated 2004 foliage projective coverproduct (r2 ¼ 0.92) from the Queensland Department of Environment and Resource Management.© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.JRS.6.063512]

Keywords: STARFM; Landsat; MODIS; data fusion; Queensland; savanna; phenology; foliageprojective cover; time series.

Paper 11172 received Sep. 12, 2011; revised manuscript received Feb. 20, 2012; accepted forpublication Feb. 29, 2012; published online May 18, 2012.

1 Introduction

Mapping andmonitoring subtle changes invegetation cover requires datawith an adequate temporaland spatial resolution. Landsat imagery have proven to be useful in many vegetation monitoringapplications at the regional scale.1 A dense time series of Landsat imagery can, on a regionallevel, contribute to a better ecosystem understanding and an improved estimation of carbon fluxesfor vegetation communities.2,3 The access to Landsat thematicmapper (TM) and enhanced thematicmapper plus (ETM+) imagery free of charge4 allows the use of all available and suitable imagery fortime series analysis; however, Landsat imagery has a repeat interval of 16 days at best. Clouds, cloudshadows, or smoke from fires can cause significant gaps in the temporal data coverage. These gapscan be filledwith data fusionor data blending algorithms that combine high temporal and low spatialresolution imagery, such asmoderate resolution imaging spectrometer (MODIS),with low temporaland finer spatial resolution imagery (e.g., LandsatTM∕ETMþ). The result is a combined time serieswith both a high temporal and high spatial resolution, as shownbyRefs. 5 and 6which canbeused tocharacterize vegetation dynamics and plant phenology at much higher spatial and temporal detail

Journal of Applied Remote Sensing 063512-1 Vol. 6, 2012

than either image source alone. This approach avoids many of the mixed pixels problems thattypically challenge low spatial resolution imagery in a heterogeneous landscape.7 Reference 8,for example, demonstrates theusefulness of integratinghigh temporal advancedvery high resolutionradiometer (AVHRR) data with high spatial resolution Landsat TM∕ETMþ time series.

The spatial and temporal adaptive reflectance fusion model (STARFM) algorithm5 and itsextensions have been tested mainly within coniferous dominated environments5,9,10 or agricul-tural areas.11 There appears to be a lack of studies that used STARFM on long time series of datain Australia, especially the complex Savanna regions.

The focus of this application is to test the STARFM algorithm in a regionalization study toinvestigate if relevant ecological processes can be captured, which might not be possible witheither Landsat or MODIS imagery alone. So far in Australia, very few studies have analyzed thephenological behaviour of vegetation in relation to species compositions.12 The potential ofphenological parameters to identify different vegetation characteristics is demonstrated incombination with regional ecosystem data and foliage projective cover (FPC) data. FPC isthe best available description for woody vegetation extent and density in Queensland andsuccessfully derived across Queensland with annual Landsat TM and ETMþ imagery.13

This study demonstrates the long term application (7.5 years) of the data blending approach(Feb. 2000 to Sept. 2007) of Landsat TM∕ETMþ and MODIS imagery time-series. Theobjectives are to: (a) investigate the applicability of the STARFM algorithm for long termtime series generation in an Australian savanna environment, (b) to investigate if the time seriesis appropriate for describing ecological processes and vegetation dynamics in a dry-land savannaecosystem and phenological patterns of species compositions, and (c) to discuss if the synthe-sized time series has advantages over a Landsat time series.

2 Data and Regional Description

2.1 Regional Settings

A 12 × 10 km sample region in a typical Australian northern savanna region was chosen. Thearea includes homogeneous woody forests vegetation, grasslands and heterogeneous areas with amixture of surface covers, such as a palustrine wetland and riparian vegetation. Regional eco-system (RE) data of Queensland are generally mapped at 1:100,000 scale (http://www.derm.qld.gov.au/REDATA). The major forested communities in the test region are shown in Fig. 1 and aremapped by RE data as (a) low open-woodland to occasionally low open-forest of Eucalyptusshirleyi (silver-leaved ironbark), and (b) semi-evergreen vine thicket with many codominant spe-cies on young igneous rock, Woodland to open-woodland of Eucalyptus platyphylla (poplar gum),Corymbia clarksoniana (Clarkson’s bloodwood), Corymbia tessellaris (Moreton Bay ash), andEucalyptus tereticornis (bluegum). The class non remnant was cleared in 1999 and transformedinto pastural land use. The northern part of the subset is part of the Great Basalt Wall national parkand has undergone very little change in the recent history (e.g., no fire history).

2.2 Satellite Imagery

A time series of all available Landsat 5 TM and Landsat 7ETMþ imagery (without the SLC offproblem, path/row 95/74) have been used as supplied by Geosciences Australia. A visual cloudscreening was performed on all 104 available Landsat imagery where all 24 cloud affectedimages were discarded, leaving a total of 90 (53 TM and 37 ETMþ) Landsat images.

MODIS bidirectional reflectance distribution function (BRDF)-model parameters(MCD43A1) and the MODIS BRDF/Albedo quality product (MCD43A2) data were used.The data product is a quasi-roll on version of the 16-day MODIS composites and is producedevery eight days;6 333 MODIS images starting from February 18, 2000 until September 30, 2007were used in this study.

2.3 Foliage Projective Cover

FPC is defined as the horizontal percentage cover of photosynthetic foliage of all strata and providesa more biophysically meaningful description of vegetation cover than spectral vegetation indices,

Schmidt et al.: Long term data fusion for a dense time series analysis : : :

particularly for Australian vegetation.14 Overstory FPC is one minus the gap probability at a zenithangle of zero, and, therefore, it has a logarithmic relationwith effective Leaf Area Index.15 Based onannual Landsat data, the woody vegetation extent of Queensland has been mapped for the periodof 1988 to 2010,13 and a time series approach using annual overstory FPC imagery was used todelineate a robust estimate of woody vegetation extent and cover.16 Late dry season imagerywas used to gain the best possible separability with green nondeciduous overstory and the dryunderstory. Reference 13 shows that the FPC product has a prediction accuracy of root meansquare error (RMSE) of <10% using field and airborne LiDAR data validation. Pixels are classifiedas non woody (0% FPC) or woody (FPC 1% to 100%).

3 Methods

3.1 Data Preparation

To ensure radiometric consistency of the Landsat imagery, an algorithm to derive standardizedsurface reflectance for Landsat TM and ETMþ imagery was applied.17 Nadir BRDF-adjustedreflectances, with a solar-zenith angle of 45 deg were calculated for the Landsat data. The Ross-Thick Li-Sparse reciprocal BRDF model and the corresponding parameters from the MCD43A1productwere used to deriveMODISBRDFadjusted reflectancewith a solar-zenith angle of 45 deg.

The MODIS quality product (MCD43A2) was rigorously applied allowing only pixels with a‘good and very good’ BRDF inversion as input in the STARFM algorithm. Geolocation errorsfor MODIS are reported with 50 m at nadir and the Landsat geolocation was reported to have lessthan 12.5 m.18 The MODIS imagery were resampled (nearest neighbour) to match the Landsatgrid, which were stored in a (oversampled) 25-m resolution.18

3.2 The STARFM Algorithm

The STARFM algorithm predicts pixel values based on spatial weights determined by regionalstatistics between spectrally similar fine resolution Landsat and coarse resolution MODIS imagepairs. Changes in reflectance in the coarse resolution MODIS images are applied to the fineresolution Landsat image; the algorithm is explained in detail in Ref. 5.

STARFM can be operated in two modes: 1. with one image pair of Landsat and MODIS and aMODIS image for the prediction date, or 2. with two Landsat and MODIS image pairs and aMODIS prediction between the dates of the image pairs. The algorithm was operated in mode 2here. Input data are a Landsat and MODIS image pair at date 1 (Fig. 2) in combination with the

Fig. 1 Location of the study region in the Queensland savanna region within the Burdekin rivercatchment (outlined in grey are the major catchments in Queensland, Australia). The inset (right)shows a Landsat true color composite with the major vegetation types outlined.

MODIS BRDF quality flag file and the respective cloud mask for the Landsat image, the samedata combination for date 2 of MODIS and Landsat data. For all cloud free Landsat images theconsecutive Landsat image and the closest MODIS image date were identified and used as twoinput pairs (in combination with the associated quality file). All MODIS image dates betweeneach of the consecutive Landsat images were predicted using the STARFM algorithm at the fineresolution Landsat scale;5 Fig. 2 illustrates the process graphically.

3.3 Validation of the STARFM Time Series

Following this procedure 333 STARFM image predictions were generated automatically. A timeseries of STARFM reflectances for the six Landsat spectral bands was predicted for each MODISobservation. Spectral features for observed and predicted imagery were analyzed with scatter-plots and linear regressions for STARFM predictions between two consecutive Landsat imagesin a dry season (June/July 2002) with little expected change. The Landsat input data and theSTARFM predicted time series were analyzed per spectral band for outliers, biases and simila-rities relative to the MODIS imagery for a homogeneous 1.5 × 1.5 km area.

3.4 Vegetation Index Time Series Analysis

Time series of the normalized difference vegetation index (NDVI) were generated based on allthe single date image predictions from STARFM, MODIS, and Landsat imagery. NDVI waschosen as a robust vegetation index for being closely related to vegetation greenness:19

NDVI ¼ ðband 3 − band 4Þ∕ðband 3þ band 4Þ;With band 4 ¼ visible red; and band 3 ¼ near infrared:

NDVI was used here for simplicity, but any vegetation index could have been used to high-light the behaviour of the approach.

Spatial surface features were analyzed with two tests to verify if they can be identified inconjunction with their temporal trajectories to warrant that the data blending indicates a gain ininformation. At the sub-MODIS scale, we tested if the simulated STARFM time series inheritsmore information than either Landsat or MODIS time series alone. This was performed on 1. theexample of a palustrine wetland with infrequent inundation processes and the locally relatedgreening up process; and 2. by an analysis of a 500 × 500 m area. The area was classifiedinto different surface features (forest and pasture). The STARFM time series was spatialweighted per surface type and compared to the corresponding MODIS time series. The aimwas to investigate if the STARFM time series information at subpixel MODIS resolution issimilar to the time series within the same area covered by MODIS.

3.5 Phenological Analysis on Vegetation Communities

STARFM time series were processed with TimeSat, applying a Savitzky-Golay filter20 to elim-inate potential outliers and to derive phenological parameters. Phenological parameters werecompared over time for different land cover and species compositions and can help to describedifferences in vegetation characteristics. The phenological parameters of the start season, stopseason, length of the season, minimum value, maximum value, amplitude, small integral, largeintegral, left derivative and right derivative were generated as described in Ref. 20. The smallintegral describes the area between the fitted function and the average level of the left and right

Landsat 30m

MODIS 500mtime

Date 1 Date 2STARFM prediction

Fig. 2 Schematic of the automated data selection for the STARFM data fusion procedure whereMODIS images are used to produce STARFM data in between Landsat image dates (see text fordetails).

minima of a season and thus the seasonally active vegetation.20 The large integral describes thearea ‘underneath’ the small integral to the zero level and represents the total vegetation produc-tion.20 The seasonal integral of vegetation curves have been related to a measure of carbon andnet primary production.21 The derivatives (left and right) give indications of the gradients in thegreening up and drying off phases.20

3.6 Comparison with an Established and Validated Dataset

The Queensland State Department of Environment and Resource Management monitors changein vegetation density and extent annually with dry season FPC imagery. A seasonal minimumNDVI map for 2004 was created STARFM data and correlated with the independently validatedFPC13 dataset. Both datasets were re-sampled (neared neighbor) to a common 100 m spatialresolution to avoid any mis-registration errors. Lakes and other water bodies are masked outin the FPC product and were buffered by 2 pixels.

4 Results

4.1 The STARFM Time Series

Figure 3 depicts an example of a Landsat and MODIS input image and a simulated STARFMoutput for a 16 day period with two consecutive Landsat input images within a dry season withvery little expected change between image observations. The two STARFM predictions inbetween the Landsat observations and the respective MODIS input imagery are shown.

(c) STARFM 20020626 (d) STARFM 20020704

(a) Landsat 20020619 (b) Landsat 20020705

(e) MODIS 20020626 (f) MODIS 20020704

Fig. 3 (a) and (b) Near infrared bands (same stretch for all images) for the STARFM input Landsatimages for Date 1 and 2. (c) and (d) The respective STARFM predictions. (e) The MODIS imagefor the prediction image and (f) the MODIS images for Date 2. The dotted box in (c) indicates arelatively homogeneous areas used in Fig. 5 (date format: yyyymmdd).

The STARFM simulated image shows a similar detail and sharpness as the input Landsatimage. The spectral properties of the simulated imagery and the Landsat input images are shownin Fig. 4. Figure 4(a) depicts a plot of Landsat date 1 and Landsat date 2 observations (channel 4only) in order to estimate the amount of change between observations. A regression slope of1.02, a standard residual error of 0.01, and r2 of 0.987 indicates that there was very little change.Figure 4(b) shows a scatterplot of the observed Landsat image date 1 and the first predictedimage. The r2 value of 0.987 is very high, and the slope of 1.00 also suggests little or no change.

Fig. 4 Scatterplots of all pixels within the study area of (a) the two Landsat images (20020619 and20020705) with very little change in between. (b) to (e) Scatterplots of all Landsat and STRFMimage combinations. The grey shades represent the point density.

The scatter within the second predicted image [Fig. 4(c)] with an r2 of 0.983 is slightly higher,and the slope of 1.012 indicates a similar change. Scatterplots of the STARFM predictions and thesecond Landsat image (date 2) are also shown. Figure 4(e) shows that the images with only oneday between the observation and prediction are very similar (r2 of 0.997 and a slope of 0.992).Figure 4(d) represents the difference of nine days between observation and prediction and showssimilar features as Fig. 4(c). Figure 4(d) and 4(e) display a different set of points in the scatterplottowards the low reflectances, which are close to the edges of one particular water body.

Time series of MODIS, Landsat and STARFM imagery are shown in Fig. 5 for all spectralbands. The time series represents average values for each date of an image observation for a 1.5 ×1.5 km area, as indicated in Fig. 3. The area was chosen in a relatively homogeneous forestedarea of the national park without any reported changes, large enough to compare MODIS,Landsat and STARFM time series directly.

Figure 5 shows that the general behavior of theMODIS and STARFM time series is similar butLandsat observations have a strong influence on the STARFM predictions. The figures include allof the cloud free Landsat images. Some Landsat input images have unexpected data values inbands 1 to 3. These were checked, and it was concluded that the values were related tosmoke plumes of nearby bushfires (April 11, 2000, December 25, 2001, and December 12,2002) which were not detected by the initial cloud screening. Outliers of the STARFMpredictionswere checked and are due to erroneousMODIS flag files and data gaps in theMODIS 8-day image

Fig. 5 Timeseriesof reflectancesof the inputMODIS (blue)andLandsat (LS in red) imagery (band1):0.45 to 0.52 μm, blue-green; band 2: 0.52 to 0.60 μm, green; band 3: 0.63 to 0.69 μm, red; band 4:0.76 to 0.90 μm, near infrared; band 5: 1.55 to 1.75 μm, mid-infrared; band 6: 2.08 to 2.35 μm mid-infrared for the area indicated in Fig. 3(c). The output STARFM (SFM) images are shown in green.

product. Figure 6 shows a difference plot between the observed Landsat and the predictedSTARFM data over time.

The scatter in bands 1 to 3 is largely dominated by the aforementioned outliers with themajority of the data points being aligned around the median difference line. Table 1 showsthe median difference between the Landsat input time series, the standard deviation and as ameasure of correlation the coefficient r of a Pearson and Spearman correlation.

2000 2002 2004 2006 2008

Band 1

2000 2002 2004 2006 2008

Band 2

2000 2002 2004 2006 2008

Band 3

2000 2002 2004 2006 2008

Band 4

2000 2002 2004 2006 2008

Band 5

2000 2002 2004 2006 2008

Band 6

Fig. 6 Data plot of 90 Landsat image dates minus the closest STARFM prediction; the solid linerepresents the median difference and the dotted line the standard deviation (note that the scalevaries between different plots).

Table 1 Comparison of the temporal difference between the Landsat input and the STARFMpredicted imagery with the median difference standard deviation and the correlation coefficientsof a Pearson and Spearman correlation.

Band Median difference* Standard deviation r Pearson r Spearman

1 2016 59.35 0.714 0.891

2 −0.90 34.20 0.880 0.956

3 1.25 33.41 0.917 0.926

4 −4.43 15.91 0.990 0.985

5 −11.05 17.96 0.981 0.962

6 −1.62 21.78 0.982 0.979

*In reflectance * 10,000.

The median difference indicates a bias between observations and predictions, and is highest inband 5 and lower in the other bands. The standard deviation of the difference is highly influencedby the outliers in bands 1 to 3 as is the correlation coefficient r from the Pearson correlation. The rvalues in the Spearman correlation are higher in bands 1 to 3 than the Pearson coefficient, as thiscoefficient is less affected by outliers. Figures 5, 6, and Table 1 show that on average the spectralcharacteristics of Landsat imagery could be captured well by the generated STARFM time series.

4.2 Vegetation Index Time Series Analysis

STARFM predictions for bands 3 and 4 were found to behave well over time and suitable forfurther analysis, despite some outliers in band 3. The data were used to calculate NDVI time seriesinformation to characterise the vegetation behaviour. The slightly noisy (raw) NDVI time seriesimagery was visually found to be well represented in the study region by temporal filtering with aSavitzky-Golay filter. During the filtering local polynomials (in this case of second order) arefitted to each point of the time series with a certain window width. The width of the movingwindow determines the degree of smoothing, but it also affects the ability to follow rapidchange.20 A width of 2 was chosen here as a compromise to keep the vegetation variabilityand smooth the data by also removing outliers. Figure 7 shows a raw and filtered time trajectoryfor a 3 × 3 Landsat-pixel averagewithin the mixed vegetation community within the study region.

An image subset of a palustrine wetland with infrequent inundation processes was studied tomonitor surface change on the basis of the Landsat, STARFM and MODIS data (Fig. 8).

Figure 8(g) compares Landsat, MODIS, and STARFM time series for the spatial locationindicated in Fig. 8(a) in a 3 × 3 pixel area on Landsat and STARFM, and a 1 × 1 pixel areain MODIS resolution. The protected wetland of high ecological value can be identified at Land-sat and STARFM spatial resolution as well as the episodic inundations [low NDVI values inFig. 8(g)]. These inundations appear to be missed or are hardly visible in the MODIS data.The inundation event in November 2002 is visible in the MODIS NDVI data [Fig. 8(b)]while the two inundations prior, in 2002, are not captured at all. The rapid greening up inthe dried wetland is spatially variable and distinctly different compared to the surroundingsin October 2001 [Fig. 8(c)–8(e)], and is clearly visible in the high resolution images, whileon the MODIS scale the general greening of the vegetation is evident.

These events can be clearly identified with the spatial resolution of the Landsat and STARFMimagery. The Landsat time series with about 10 to 12 images per year is already temporally highin resolution and covers the inundation processes, but the greening up in January/February 2003and 2004 is missed, with a lengthy gap in the Landsat time series. This peak is evident in theMODIS and the STARFM imagery. The STARFM time series is the only of the three time seriesto capture all the processes.

In order to analyze the temporal behavior of the simulated data and the spectral consistency adata subset in a heterogeneous surface area was selected at the border of two surface types withforest and grassland with no or little overstory. Figure 9 shows a 500 × 500 m area, which repre-sents one MODIS pixel [Fig. 9(a)]. The geolocation of both datasets matches as the MODISimagery were used to generate the STARFM data. The area was separated into two surfaceareas using a woody vegetation FPC mask.

filtered

Fig. 7 Original and filtered STARFM NDVI time series with a Savitzky-Golay filter for a spatialmean in a forested area 1.5 × 1.5 km2 (indicated in Fig. 3).

A time series plot for the area inside the box in Fig. 9 is shown in Fig. 10 for MODIS(in blue), and for the STARFM data for the woodland (green) and grassland (brown) areas,including the pixels which cover a dirt road (Fig. 9).

The time series of the woodland and grassland were spatially weighted by their area and theweighted timeseries is shown inFig. 10 (in red).The (blue)MODIS trajectory and the (red) summedSTARFM trajectory are very similar, with a RMSE of 0.027 and a mean difference of −0.011.

4.3 Phenological Analysis on Vegetation Communities

A time-series of STARFM generated NDVI data were extracted for four different surface vegeta-tion communities (Fig. 1) to study the vegetation behaviour over time in this regionalized study

2005200420032002

LandsatMODISStarFM

Fig. 8 Spatial example of a wetland area (red dot in a) for two time steps in (a) and (d) Landsat, (b)and (e) MODIS, and (c) and (f) STARFM data. (g) A subset of the time series shows the temporalresolution and the events as captured in the different spatiotemporal domains; the two eventsdepicted are indicated with dotted grey lines.

Fig. 9 Spatial subset in a heterogeneous surface area represented by (a) one MODIS NDVI pixeland (b) STARFM NDVI data. The green mask in (c) represents woody vegetation and the box isthe extent of the image subset analysed.

area. Three x three Landsat-pixel areas in homogeneous (low coefficient of variation) locationswithin the vegetation communities were chosen. The respective time series are shown in combina-tion with local rainfall data in Fig. 11; the season onset and offset dates were calculated with Time-Sat and plotted as dots for each season. Themean FPCvalues for the year 2004 are given inTable 2.

The temporal characteristics of the different vegetation communities are summarized using anumber of phenological parameters (Table 2). The start of the season is in all years shown as theearliest for the non remnant pastural vegetation communities. The greenness in grass dominatedareas reacts quickest to rainfall events, and show the largest inter-annual amplitude and thehighest gradients in the time series curves. The gradient of the drying off period (right derivative)at the end of the wet season is for all vegetation compositions, and years greater than the leftderivative in the greening up period. The length of the growing season is with 187 to 285 days theshortest for the grassland and 223 to 315 days the longest for the mixed species site, withthe highest FPC value (46.1).

The variations in the start-, stop-, and mid-season dates generally reflect the variable rainfallregime in the North Queensland savanna. It is also noticeable that these parameters differ

2000 2002 2004 2006

ModisMean forest STARFMMean pasture STARFMWeighted average STARFM

Fig. 10 Time series of different surface fractions within one MODIS pixel with RMSE ¼ 0.027 anda mean difference of −0.011 between MODIS and the weighted average STARFM areas of agrassland and forested area.

2000 2001 2002 2003 2004 2005 2006 2007

mixed speciesnon remnantPoplar GumSilverleaved IronbarkRainfall

Fig. 11 Time series of four different vegetation communities within the study region, the “start” and“stop” season points are indicated as dots. In blue is the monthly total rainfall shown; the dottedline represents the 100 year average, source: http://www.longpaddock.qld.gov.au/silo/.22

Table 2 Phenological parameters per vegetation community over the six seasons, indicated arethe different foliage projective cover (FPC) values.

FPC ¼ 46.1 Mixed species

Season Start Mid Stop Length Min Max AmplitudeSmallInt.

LargeInt.

Rightderi.

Leftderi.

1 2000-10-15 2001-03-12 2001-08-06 317 0.488 0.704 0.217 6.217 26.619 12.3 25.0

2 2001-11-27 2002-03-03 2002-10-06 285 0.416 0.696 0.279 6.585 22.323 10.3 30.5

3 2003-02-02 2003-04-14 2003-09-05 223 0.399 0.627 0.228 4.315 16.189 17.3 29.0

4 2003-12-03 2004-03-13 2004-09-08 265 0.395 0.623 0.227 4.964 18.825 13.9 24.1

5 2004-11-03 2005-01-15 2005-09-17 269 0.417 0.592 0.176 4.022 18.947 5.0 28.3

6 2006-01-09 2006-05-01 2006-09-30 261 0.484 0.641 0.157 3.512 20.349 7.1 19.2

FPC ¼ 0 non remnant Pasture

LargeInt.

Rightderi.

Leftderi.

1 2000-10-13 2001-02-08 2001-06-14 244 0.229 0.645 0.417 9.738 17.128 39.3 49.4

2 2001-09-24 2002-03-15 2002-06-28 278 0.228 0.577 0.349 6.986 15.382 21.6 11.8

3 2003-01-24 2003-04-12 2003-07-21 178 0.218 0.572 0.354 5.910 11.180 37.7 61.7

4 2003-11-30 2004-02-10 2004-07-14 227 0.219 0.570 0.351 6.432 13.160 13.4 44.3

5 2004-10-30 2005-01-26 2005-08-10 285 0.259 0.632 0.373 8.464 18.132 11.6 45.0

6 2005-12-29 2006-05-03 2006-08-10 224 0.300 0.649 0.349 6.521 15.461 23.8 18.6

FPC ¼ 42.8 Poplar Gum

LargeInt.

Rightderi.

Leftderi.

1 2000-10-23 2001-01-29 2001-08-09 290 0.405 0.667 0.262 5.680 21.132 11.7 28.8

2 2001-12-02 2002-04-07 2002-09-27 299 0.371 0.631 0.260 6.588 21.187 10.4 19.4

3 2003-01-16 2003-04-23 2003-08-22 218 0.400 0.566 0.167 3.481 15.081 12.9 28.4

4 2003-12-03 2004-02-12 2004-07-26 236 0.386 0.602 0.216 3.681 15.819 8.8 21.2

5 2004-11-20 2005-01-22 2005-09-15 298 0.377 0.652 0.275 5.887 20.667 5.8 37.8

6 2006-01-02 2006-05-04 2006-10-06 277 0.414 0.676 0.262 5.632 20.793 9.9 15.3

FPC ¼ 17.7 Silverleaved Ironbark

LargeInt.

Rightderi.

Leftderi.

1 2000-10-25 2001-02-06 2001-07-17 265 0.336 0.621 0.285 6.303 18.069 17.8 31.0

2 2002-01-15 2002-03-23 2002-08-16 213 0.305 0.663 0.358 5.778 14.519 15.5 50.7

3 2003-02-02 2003-03-19 2003-08-09 188 0.303 0.490 0.187 2.715 10.430 7.7 36.1

4 2003-12-07 2004-02-08 2004-06-02 179 0.271 0.570 0.299 3.642 10.193 16.9 32.7

5 2004-11-23 2005-01-30 2005-10-04 315 0.272 0.604 0.332 7.118 18.248 6.9 43.4

6 2006-01-13 2006-05-05 2006-11-13 304 0.320 0.572 0.252 5.667 18.503 7.1 16.5

over time and between the four displayed species compositions (see Fig. 11). After the peak ofthe drought in 2003/2004 the seasonal minimum NDVI value and mostly the maximum NDVIvalue for all species increased steadily. Figure 12 provides graphical summaries for the threeparameters given in Table 2: Amplitude, small integral, and large integral.

The variation of the amplitude is greatest between seasons for the classes of Poplar Gum andSilverleaved Ironbark (FPC: 42 and 17.2, respectively), which are both influenced by a grassunderstory [Fig. 12(a)]. Season 3 (2003) marks one of the driest years in Queensland which isreflected in all three plots of Fig. 12. The pure pastoral area, however, has the highest amplitude,but also the lowest variation or highest regularity between seasons, as pastures tend to reactquickly with a greening up after rainfall events. This is supported by the high values for theright derivative in Table 2. The highest seasonal variation in the NDVI signal is at the pasturesite [Fig. 11(b)], while the curve shapes are similar for the Silverleaved-Ironbark and PoplarGum. The seasonally active part of the vegetation appears largest for the pastural area and mod-erately high for the mixes species site with the highest FTP value (46.1), and generally lowest forthe two remaining vegetation communities. The parameter related to vegetation production20

appears to have a FPC gradient for the species compositions. The low FPC site, SilverleavedIronbark, shows similar or even lower values than the pasture site (except for season 6).

4.4 Seasonal NDVI Minimum Comparison with Validated FPC Data

The phenological parameters can be displayed spatially, Fig. 13(b) shows the 2004 minimumNDVI value next to the 2004 annual FPC image [Fig. 13(a)]. The seasonal minimum NDVIvalue from 2004 depicts a similar spatial pattern and information content as the FPC image.The correlation between the two spatial vegetation representations is remarkably high withan r2 of 92.4% [Fig. 13(c)].

This suggests that a NDVI time series for this study area has potential to describe a complexbiophysical variable such as the FPC.

5 Discussion

The major motivation for this study was to test if the STARFM approach delivers a time series ofsimulated data that is useful for vegetation analysis at a regional level in Australia. The outliers inthe Landsat input images could be identified and were checked in the original images. Theseimages were influenced by smoke plumes caused by bush fires and aerosols. Consequently theSTARFM predictions were affected, e.g., band 1 in December 2001 (Fig. 5). Masking theaffected areas would have resulted in a better time series correlation in bands 1 to 3 betweenthe observed and predicted images, similar to the relatively unaffected bands 4 to 6. The highest

(c)(b)(a)

1 2 3 4 5 6

Season

1 2 3 4 5 6

Season

1 2 3 4 5 6

Season

mixed speciesnon remnantPoplar GumSilverleaved Ironbark

Fig. 12 Graphical display of the seasonal variations for the parameters amplitude (a), smallintegral (b) and large integral (c). One value is displayed per year representing the respectiveseason (see Table 2).

standard deviations in band 1 are thus most likely caused by variations in aerosol loadings andwater vapor variations. When the MODIS quality data were incorrectly classified as good qualitythe STARFM predictions had unrealistically high reflectances (>1) and were set to no-data. Abias between Landsat and MODIS data in band 5 is apparent, which is not prominent in the otherspectral bands. The STARFM predictions appear to have gained the temporal information fromthe MODIS imagery, but are spectrally still aligned with the Landsat data and show a slight bias(see Table 1).

The small study region (12 × 10 km) may have contributed to some of the STARFM outliersshown in Fig. 4, as there may not have been sufficient pixels for the spatial correlations, espe-cially in heterogeneous areas, as indicated in Ref. 5. However, the results presented here showthat meaningful outcomes could be archived in this regionalization study. The enhancedSTARFM23 algorithm claims to have improved on some of the algorithms’ limitations, especiallyfor heterogeneous areas. At the time of this work, the enhanced version was not available to theauthors. Therefore the STARFM algorithm with the two image pair mode was applied here.Despite the potential for further improvements of the data blending algorithm appear the resultspresented here sensible and very promising. This case study has proven useful for understandingthe phonological processes in Australian savannas and warrants further research in this and otherdata blending methods.

The analysis indicates that the STARFM fusion approach of ‘downscaling’ MODIS datausing Landsat imagery provides additional of information compared to either a MODIS or Land-sat time series alone. This is for an area where a fairly high density of 10 to 12 Landsat imagesper annum is already available. Many regions are likely to have lower density of suitable Landsatimagery. It would be very useful to quantify how many input images are needed to capture therelevant processes for particular vegetation communities. However, with irregularly occurringevents, such as inundations, floods or fires, this question is difficult to answer and was beyondthe scope of this project. Reference 5 suggests that change events should ideally be covered usingfiner resolution image observations directly before and after the event.

NDVI0.7

(a) (b)

0.0 0.2 0.4 0.6 0.8 1.0

r2 = 0.924Residual std. err = 4.33

Median residual = -0.036p = 0.00000n = 9216

Fig. 13 The validated FPC map of 2004 (black areas are masked water bodies) for the region (a)in comparison with the seasonal minimum NDVI image of 2004 (b). The correlation between thetwo images (resampled to a common 100 m grid with nearest neighbour) is r 2 ¼ 92.4.

Time series information based on high temporal and high spatial resolution input across anumber of years have the potential to provide detailed phenological descriptions of regionalvegetation communities which may not be achieved otherwise. No region-specific assumptionswere made here so that the approach is generally applicable, as long as overlapping (spatially andtemporally) Landsat and MODIS exist.

NDVI is a commonly applied vegetation index with known limitations. For example, it canbe influenced by the soil background. However, by studying pixel based time series it can beassumed that the influence is similar over time and the behavior of the time series can be inter-preted with little bias. The derivation of a more elaborate vegetation description, e.g., with spec-tral unmixing outputs for green- and nongreen vegetation fractions (e.g., Refs. 24 and 25) couldpotentially give further insights to ecological processes and changes. Useful information can begained from the NDVI time series application, at sub-MODIS scale. Figures 11 and 12 andTable 2 provide detailed information of the vegetation time series for different vegetation com-munities. The scope of this project only allowed for brief interpretation of these time series. Theyappeared to be logical in an ecological context and can serve as a basis for further interpretationand studies to gain an elaborate ecological description. In addition, this approach should beanalyzed further in combination with ground data (e.g., from permanent monitoring sites) todescribe the phenological variation of surface vegetation.

A high temporal time series makes the analysis of sudden change events (Fig. 8) possiblewhich is of particular interest for the mapping of forest management activities such as, landclearing or selective logging, or fire management. The analysis of these time series in high spatialand temporal resolution offers considerable potential for further studies. This could includeresearching different vegetation structural parameterizations or the estimation of net primaryproductivity, carbon stock and fluxes, and the systems lag times. Estimates of pasture biomassand ground cover are valuable information for natural resource management for assessing landcondition, supporting fodder or fuel load management decisions, or as inputs for soil erosionmodeling. Analyzing the temporal patterns of the phenological indices and their trends couldlead to a better ecosystem understanding. Figure 13 demonstrates the potential for estimatingFPC using the vegetation seasonal minimum approach, as the two datasets appear to exhibit asimilar amount of information. The FPC mapping assumes to be combination of overstory FPCand herbaceous components plus an error term. A major limitation of the FPC product is that theoverstory and understory are not decoupled. The dense time series approach can help to identifythe point in time with the best separation of overstory and understory. Consequently, this couldbe used to simplify the FPC image predictions, and to reduce the amount work that is needed tocreate the FPC imagery. Separating over and understory has been challenging in the character-ization of Australian vegetation.26 This separation is important to provide a better understandingof trends in vegetation, and allow for improved monitoring of vegetation thinning, clearing, andregrowth.

6 Conclusions

A multi year time series of simulated imagery in high spatial (Landsat) resolution with a hightemporal frequency (8-days) using MODIS data were generated. Surface features were analyzedwith a NDVI time series of Landsat, MODIS, and STARFM data for the inundation and greeningup of a pallustrine wetland. The STARFM time series inherited more information about theecological processes than either Landsat or MODIS have alone. A subpixel analysis was under-taken in a heterogeneous surface area. This showed that the MODIS time series data and thespatially weighted STARFM time series of two land cover types have a very similar behaviorover time (RMSE ¼ 0.027).

Phenological parameters of the STARFM time series were interpreted for four different vege-tation communities in a dry land, savanna region. The results show that a time series analysis (ofreflectance data as well as derived phenological parameters) based on STARFM products can beperformed and that a biophysical parameter, namely FPC, is highly correlated to the seasonalNDVI seasonal minimum estimates (r2 ¼ 0.92). The high temporal time series informationcould thus lead to more process oriented approaches for capturing small and large scale changeson the Earth surface.

Acknowledgments

The author would like to thank the Queensland International Fellowship office who made thiscollaborative effort possible; and DERM for supporting it. Many thanks also to the anonymousreviewers. We’d like to thank F. Gao for providing the STARFM code.

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Michael Schmidt is a senior scientist at the Department of Environmentand Resource Management in Brisbane, Australia. He received his receivedhis PhD degree from the faculty of mathematics and natural sciences in2003 at University of Bonn, Germany. His current research interestsinclude rangeland monitoring, ground cover mapping, and time seriesanalysis.

Thomas Udelhoven is a professor at the University of Trier, Germany.Some of the research in his group focuses on hyperspectral remote sensing,LiDAR, and time series applications.

Tony Gill is a remote sensing scientist at the New South Wales Departmentof Premier and Cabinet in Dubbo, Australia. He received his PhD from theUniversity of Queensland, Australia in 2009. His current research interestsinclude radiative transfer modeling and woody vegetation mapping.

Achim Röder is a research fellow at the University of Trier, Germany. Hiscurrent research interests include land use interpretation and rangelandmapping and monitoring.

Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an...

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