Object-based classification of semi- arid vegetation to ...342886/UQ342886_OA.pdf · Object-based classification of semi-arid vegetation to support mine rehabilitation and monitoring

Object-based classification of semi-arid vegetation to support minerehabilitation and monitoring

Nisha BaoAlex M. LechnerKasper JohansenBaoying Ye

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Object-based classification of semi-arid vegetation tosupport mine rehabilitation and monitoring

Nisha Bao,a,b,* Alex M. Lechner,b,c Kasper Johansen,d and Baoying YeeaNortheast University, Institute for Geo-informatics and Digital Mine Research,

Shenyang 110819, ChinabThe University of Queensland, Sustainable Minerals Institute, Centre for Mined Land

Rehabilitation, St. Lucia, Queensland 4072, AustraliacUniversity of Tasmania, Centre for Environment, Hobart TAS 7005, Australia

dThe University of Queensland, School of Geography, Planning and EnvironmentalManagement, Biophysical Remote Sensing Group, St. Lucia,

Queensland 4072, AustraliaeChina University of Geosciences, School of Land Science & Technology,

Beijing 100083, China

Abstract. Mining activities result in significantly modified landscapes that require rehabilitationto mitigate the negative environmental impacts and restore ecological function. The aim of thisstudy was to develop a remote sensing method suitable for monitoring the vegetation cover atmine rehabilitation sites. We used object-based image analysis (OBIA) methods and high-spatialresolution SPOT-5 imagery to identify discrete land-cover patterns that occur at fine spatialscales. These patterns relate to spatial processes that are important drivers of successful restora-tion of mine sites. SPOT-5 imagery of the Kidston Gold mine tailing dam in semi-arid tropicalnorth Queensland was acquired in July 2005, comprising four 10-m spectral bands and a 2.5-mpanchromatic (PAN) band. The classification scheme used in this study was adapted to the spa-tial scale of SPOT-5 imagery from mine closure criteria cover requirements, according to a minerehabilitation plan. Four land-cover classes were identified: tree cover, dense grass, sparse grass,and bare ground. First, textural layers (contrast, dissimilarity, and homogeneity) were derived foreach vegetation class except for bare ground from the PAN and multispectral bands. Of all tex-tural layer combinations, homogeneity and contrast in the PAN band were identified using a Z-test as the most useful for differentiating between multiple land-cover classes. Next, an optimalsegmentation scale parameter of 15 was identified using an analysis of spatial autocorrelation.Finally, the SPOT-5 image bands, derived textural layers, and normalized difference vegetationindex (NDVI) were used in an OBIA fuzzy membership classification approach to map vegeta-tion land-cover classes. The classification results were assessed with the traditional error matrixapproach and the object-based accuracy assessment method. The overall classification accuracyusing the error matrix was 92.5% and 81% using the object-based method. The relatively high-classification accuracy demonstrates the potential of SPOT-5 imagery for monitoring mine reha-bilitation. The complete spatial coverage associated with remote sensing data at fine spatialscales has the potential to complement field-based approaches commonly used in rehabilitationmonitoring. Furthermore, SPOT-5 data along with OBIA can characterize vegetation spatial pat-terns at spatial scales appropriate for monitoring rehabilitated landscapes, providing an importanttool for landscape function analysis. © 2014 Society of Photo-Optical Instrumentation Engineers

(SPIE) [DOI: 10.1117/1.JRS.8.083564]

Keywords: remote sensing; object-based image analysis; rehabilitation; restoration; mining;semi-arid; spatial autocorrelation; classification error assessment.

Paper 14071 received Feb. 5, 2014; revised manuscript received Jul. 10, 2014; accepted forpublication Jul. 28, 2014; published online Aug. 21, 2014.

*Address all correspondence to: N. Bao, E-mail: [email protected]

0091-3286/2014/$25.00 © 2014 SPIE

Journal of Applied Remote Sensing 083564-1 Vol. 8, 2014


http://dx.doi.org/10.1117/1.JRS.8.083564





1 Introduction

Mining and mineral processing, particularly surface mining, result in landscapes impacted by arange of environmental issues. These impacts include erosion, drainage of acidic and/or salinewater, and loss of vegetation.1 The re-establishment of vegetation on mining sites is one of theprinciple rehabilitation strategies used to address environmental issues and to restore ecologicalfunction.2–5 Rehabilitation is a complex and long process that can be unsuccessful due to a rangeof abiotic and/or biotic factors.6,7 Vegetation mapping and classification provide critical infor-mation for understanding and evaluating rehabilitation processes in man-made mine site envi-ronments. The ability of fieldwork on its own to provide the monitoring information sufficientfor confidence in the outcomes of rehabilitation in these kinds of environments can be limited.8,9

Spatial analysis can be used with remote sensing data to analyze the spatial distribution of veg-etation conditions and thus to identify if and where successful rehabilitation is occurring.10 Minesite rehabilitation typically needs to be addressed at fine spatial scales due to the heterogeneousnature of the substrate and topography that influences the rehabilitation processes.Characterizing the spatial distribution of land-cover at high-spatial resolution can providekey information to assist in landscape function analysis,11 a commonly used approach for assess-ing and monitoring mine site rehabilitation which emphasizes the importance of consideringspatial processes, rather than only ecosystem composition or structure. For example, drainagepatterns that result from overland flow can influence plant water availability and may be hetero-geneous at relatively fine scales. The use of high-spatial resolution optical image data can poten-tially provide the detailed information required for accurate remote sensing in theseenvironments.1

Remote sensing methods have previously been applied for mapping and monitoring theimpacts of mining on the environment.2,12,13 However, object-based image analysis (OBIA)methods have rarely been applied to these environments. Object-based approaches are usefulfor classifying the landscapes where landscape features occur at multiple spatial scales.14

This allows the analysis of ecological patterns since landscapes can be considered as patcheswhich are logically suited for extraction with object-based segmentation and classification meth-ods.15 OBIA is considered a new paradigm in the analysis of remote sensing image data,16 and isparticularly suited to classifying high-spatial resolution imagery at spatial scales relevant formonitoring of mine rehabilitation. OBIA methods have been shown to produce higher classi-fication accuracies than traditional pixel-based methods in many cases.17 OBIA methods cat-egorize pixels into homogeneous objects using spectral, textual, spatial, and topologicalcharacteristics.18,19 Additionally, a range of auxiliary information can be used in OBIA toimprove classification accuracies—often derived from spectral and spatial characteristics uniqueto high-spatial resolution imagery. Auxiliary information such as image texture describingheterogeneity within image objects can provide useful information to separate vegetation struc-tures with complex canopies.20

Associated with using OBIA methods and high-spatial resolution remote sensing imagery area number of scale-related issues that need to be addressed. Determining the appropriate scale ofimage objects is one of the most important and critical factors affecting the quality of segmen-tation. The determination of the “optimal” scale(s) to segment and evaluate the characteristics ofimage-objects for land-cover classification can be challenging.21,22 Optimal segmentation scalesare driven by the relationship between the segmentation scale and the scale of land-cover fea-tures, which affects the classification accuracies.23 Scales for deriving textural information usedas auxiliary information for OBIA classification also need to be identified.20 Finally, the assess-ment of classification accuracy can be considered in terms of the object size, shape, and positionrather than based on the pixel.24 An important consideration for remote sensing of rehabilitatedlandscapes is ensuring that the spatial patterns of land-cover are accurately extracted.

Vegetation cover on mine sites and its spatial distribution are often used as an indicator ofrehabilitation success. In this study, we describe an OBIA approach using SPOT-5 high-spatialresolution imagery for assessing the rehabilitation of semi-arid vegetation. The approachdescribed in this study was developed to be compatible with the landscape function analysisapproach,11 commonly used across Australia for assessing and monitoring rehabilitation ofmine sites. The aims of this study were to: (1) develop a sound approach for classifying the

Bao et al.: Object-based classification of semi-arid vegetation to support mine rehabilitation. . .



major vegetation types at a rehabilitated mine site and (2) analyze the classification accuracyusing an adaptation of the object-based accuracy assessment approach. The choice of spatialdata, classification methods, and accuracy assessment methods was specifically based on therequirements for providing highly accurate categorical maps of vegetation spatial distribution.We used classification methods specifically suited for high-spatial resolution imagery andextracted fine land-cover scale features that included statistical methods for identification ofthe optimal segmentation scale and texture-based imagery. Furthermore, as these methods auto-mate the main classification steps, only some parts of the classification require manual inter-vention, resulting in a more transferable approach with potentially less time required forclassifying new imagery. The OBIA methods along with high-spatial resolution imageryhave rarely been applied to mapping and monitoring of rehabilitated vegetation on minesites in Australian environments, and hence represent a research area that can improve pastmap mine rehabilitation success (but, see Ref. 8).

2 Study Area

Kidston Gold Mine (18°52′S and 144°09′E) is located in semi-arid tropical north Queensland,Australia (Fig. 1). The vegetation of this region is savannah woodland, commonly found acrossnorthern Australia. Mining at the Kidston Gold Mine started in 1984 and continued for 16 yearsuntil September 2000.

At Kidston, rehabilitation has been undertaken in various areas across the mine over a periodof several years. This study focuses on the rehabilitation of the largest area, which is in the tailingstorage facility; a constructed hillside tailings dam (TD) covering 310 ha (Fig. 1). The rehabili-tation area was divided into regions with different historical rehabilitation ages and methods,which are: TDNA, TDNB, CTD, and TD40ha. The TD was directly planted with tube stock

Fig. 1 Aerial image showing the location of the study area at Kidston gold mine and rehabilitatedareas. TDNA: Tailing dam North A; TDNB: tailing dam North B; CTD: tailing dam central; andTD40: tailing dam 40 ha.




plantings of native tree species (Eucalyptus, Acacia,Melaleuca, and Casuarina) and seeded witha mixture of pasture grasses and legumes in Kidston in 2005.25

3 Methods

3.1 Classification Scheme

The classification scheme used in this study was adapted to the spatial scale of SPOT-5 imageryin discussion with field investigators based on mine closure criteria cover requirements describedin the mine rehabilitation plan25 and based on the landscape function analysis approach.11 Theland-cover classification scheme had four classes: tree cover; dense grassland; sparse grassland;and bare ground (Fig. 2). Each land-cover class was classified based on the vegetation type orabsence of vegetation and percentage of vegetative cover, defined as the percentage area ofground occupied by the vertical projection of the foliage. Images corresponding to eachland-cover class can be seen in Fig. 3. Of all land-cover classes extracted, the most importantelement was tree cover, as the goal rehabilitation at Kidston mine is to return the site to a self-sustaining savannah with woody vegetation cover.25

3.2 SPOT-5 Image Acquisition

SPOT-5 imagery was used for this study based on trade-offs such as acquisition costs, spatialresolution, and demonstrated potential for mapping and analyzing the tree canopy characteristicsin savannah ecosystems.26 The collected SPOT-5 image data included four multispectral (XS)bands with a 10-m pixel size for the green (500 to 590 nm), red (610 to 680 nm), and near-infrared (780 to 890 nm) bands and a 20-m pixel size for the mid-infrared (1580 to1750 nm) band. A panchromatic (PAN) band (480 to 710 nm) with 2.5-m pixels was alsoacquired.

SPOT-5 imagery was acquired on July 16, 2005, for Kidston. Both the XS and PAN bandswere orthorectified and geocorrected using a sensor-specific model in the ERDAS Imagine soft-ware and a digital elevation model (DEM). The DEM was created from aerial photographicstereopairs (0.5-m pixels) acquired on September 9, 2005. The average geometric rootmean-squared error was below 10 m for the XS bands and 2.5 m for the PAN band, followingorthorectification based on 22 ground control points derived from aerial photos.

Land-cover type Class code Description Feature from aerial photo

Tree cover TC Woody vegetation cover over 20%

Dense grassland DG

Grassland - greater than 50% grass

cover (including green and

senescent grasses) and tree cover

absent or less than 20%

Sparse grassland SG

Grassland - less than 50% grass

cover (including green and

senescent grasses) and tree cover

absent or less than 20%

Bare ground BG

Bare soil, sandy soil, rock and road

with woody vegetation below 20%

and no grass cover.

Fig. 2 Land-cover categories for rehabilitated vegetation in the tailing dam.




3.3 Image Preprocessing

3.3.1 Deriving textural images for auxiliary input data

Texture-based information was derived from the XS and PAN data as an auxiliary input for theOBIA classification using the gray-level co-occurrence matrix within a moving window. This isa widely used technique to identify spatially textural features.27 Three textural measures wereused in this study: homogeneity, dissimilarly, and contrast. These three textural features wereidentified by Johansen et al.20 as providing the most significant differentiation of vegetation forclassification from a selection of texture measures. Texture information was derived to separatethe rehabilitated vegetation cover classes of TC, SG, and DG. However, texture information wasnot derived for bare ground, as this additional information was not required to accurately classifythe bare ground. A total of 12 images were derived for each textural feature type, all vegetationclasses, and band combinations [three texture types × four bands (PAN, green, red, and near-infrared)]. The MIR band of SPOT-5 was not used in this part of the analysis due to its lowerspatial resolution (20-m pixels), which reduces the amount of textural information.28 Of these 12images, two images (PAN-homogeneity and PAN-contrast) were selected for inclusion into theOBIA classification based on which images could best differentiate between vegetation classes.Initially, an exploratory analysis was conducted using semi-variograms to identify the appro-priate texture window size. The semi-variogram was calculated using a subset of the studyarea containing a homogenous cover of approximately 50 ha for each vegetation type.However, this analysis failed to identify an optimal window size and a 7 × 7 window sizewas qualitatively selected based on a visual assessment of the semi-variograms which startedto level off at 7 pixels for tree cover (see Fig. 4).

The 12 texture images were then compared statistically using a Z-test to derive a subset of thedata that best identified the differences between land-cover classes.20 The Z-test compared pixelvalues between vegetation class pairs (tree versus dense grass, tree versus sparse grass, and

(a) (b)

(c) (d)

Fig. 3 Images representing different land-cover classes in the tailing dam. (a) Tree cover atTD40ha. (b) Dense grassland at CTD. (c) Sparse grass at TDNB. (d) Bare ground at CTD.




sparse grass versus dense grass) for band and textural measure combinations to identify whichtextural measure had the largest statistical difference and thus was most useful for classification.The following equation was used to calculate the Z-statistic:29

Z ¼ μ1 − μ2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffis21∕n1 þ s22∕n2

p ;

where μ1 and μ2 are the mean pixel values of four different texture bands; n1 and n2 are thenumbers of samples; and s1 and s2 are the sample standard deviations (SDs) of correspondingmeans.

3.3.2 Identification of optimal scale parameter selection formultiresolution segmentation

The multiresolution segmentation algorithm “Fractal Net Evolution Approach” was utilized inthis study.30 Using this approach, the heterogeneity of the segmented objects is a property ofthree parameters: scale, shape, and compactness.30 The size of an object (scale) has an importantinfluence on the quality of segmentation and accuracy of classification.31 The selection of theoptimal scale parameter for segmentation was derived through the assessment of object localvariance as a measure of image spatial structure using the XS and PAN data. Local varianceis commonly calculated with pixel-based data as the mean value of the SD of a moving3 × 3 window calculated at multiple scales through degrading the image to coarser spatialscales.32 In the case of OBIA, segmentation can be conducted at multiple scales and the averageSD within image objects is calculated for brightness and homogeneity values. Local variancewithin adjacent segments is used to estimate the spatial scale of segmented images.15

3.4 Classification Method

We used eCognition Developer 8.7 OBIA software to classify the rehabilitated mine site. Theclassification method was composed of six steps: (1) derive the texture-based auxiliary data,

Fig. 4 Semi-variograms of vegetation classes for each band of multispectral (XS) and panchro-matic (PAN) data. TC: tree cover; DG: dense grass; and SG: sparse grass.




(2) identify the optimal segmentation scale, (3) multiresolution segmentation, (4) use the fuzzymembership function to define land-cover classes, (5) merge the small neighboring objects withsimilar spectral characteristics, and (6) assess the classification accuracy based on objects andcalculate the landscape metrics (Fig. 5).

The OBIA classification was conducted using fuzzy membership rules with the PAN-H andPAN-C textural images and NDVI bands identified from the Z-test (Fig. 5). First, vegetation wasclassified with NDVI using a fuzzy membership range between 0.08 and 0.10. Bare ground wasclassified with the average brightness value of all bands using a fuzzy membership range of 80 to

Fig. 5 Workflow for object-based image analysis (OBIA) classification. TC: tree cover; DG: densegrass; SG: sparse grass; BG: bare ground; PAN-H: homogeneity of PAN; and PAN-C: contrast ofPAN.




90. Finally, PAN textural layers were used to separate the vegetation land-cover classes. Theaverage homogeneity value from the PAN band was used to discriminate between tree coverand dense grass with a fuzzy membership range between 1 and 1.5. The contrast measurementsfrom the PAN band were then used to define the dense grass with a fuzzy membership rangebetween 0.5 and 0.55.

3.5 Object-Based Accuracy Assessment

In the final step, we assessed the accuracy of the classification using an object-based accuracyassessment. The object-based accuracy assessment approach used the properties of classifiedfeatures to assess the geometric classification accuracy (size and position) between classifiedand reference data.24,33 This method is unlike the more commonly used error matrix approach34

which assesses the accuracy within a sample unit such as a pixel, clusters of pixels, or polygon.Using the object-based classification method, the level of agreement between classified objects(C) and the corresponding reference objects (R) can be measured as a function of the number ofobjects and the relative area of inclusion (C ∩ R), commission (C ∩¬R), and omission(¬C ∩ R) (Fig. 6). In this study, we used a modified version of the accuracy measures proposedby Zhan et al.24 to assess the quality of the OBIA classification result. Using these relationships,the similarity between two objects, including overall accuracy, user’s accuracy, and producer’saccuracy, can be measured. Object-based accuracy assessment calculation methods are asfollows:

Overall accuracy ¼ fðC ∩ RÞfðC ∩ RÞ þ fðC ∩¬RÞ þ fð¬C ∩ RÞ (1)

User’s accuracy ¼ fðC ∩ RÞfðCÞ (2)

Producer’s accuracy ¼ fðC ∩ RÞfðRÞ : (3)

The accuracy assessment was performed with aerial photoreference data at multiple samplesizes. The reference data were derived from RGB aerial photos with 0.5-m pixels acquired on

Relationship Equation Description

R The area of intersection between

C and R intersection C

commission C R The area of C not covered by R

omission C R The area of R not covered by C

Union C R The area of union between C and

R

Fig. 6 Z -scores calculated for vegetation class pairs (tree versus dense grass, tree versus sparsegrass, and sparse grass versus dense grass) for spectral band (PAN, NIR, RED, and GREEN) andtexture measure combinations (H: homogeneity, C: contrast, and D: dissimilarity).




September 9, 2005, and classified using manual interpretation. The aerial interpretation wasbased on the differences in greenness and texture that could be identified in the RGB aerialimagery and calibrated with vegetation cover estimates from field transects conducted inJuly 2005. The ground dataset is likely to be considered accurate given the extensive site-based knowledge of the field workers.

Previous research using the object-based accuracy assessment method assessed the accuracyof small discrete objects such as trees35 or buildings.24

3.6 Landscape Pattern Analysis

The landscape pattern of the land-cover classification was described using the following land-scape metrics—largest patch area, mean patch area, number of patches, mean length/width ratio,and landscape shape index calculated—using the Fragstats package.36

4 Results

4.1 Textural Images

Figure 7 shows that the texture measures derived from the PAN band tended to have high Z-scores. The high Z-scores for homogeneity information derived from the PAN band wererecorded for tree versus dense grass and tree versus sparse grass, indicating that this combinationwas useful for discriminating the tree cover from grass. Sparse grass versus tree and sparse grassversus dense grass had, on average, the highest Z-scores in the PAN band for the contrast texturemeasure. These two images textural classes—homogeneity and contrast in the PAN band—wereused in the classification based on which images could best differentiate between vegetationclasses. As the tree and sparse grass land-cover classes could be discriminated based onNDVI and spectral information, the PAN-D which produced the highest Z-score for tree versussparse grass was not selected.

4.2 Optimal Scale for Segmentation

We identified the optimal segmentation using image-object brightness and homogeneity valuesfor the textural and XS and PAN data separately. Segmentation was assessed across a range ofscale parameter values from 5 to 20 at an interval of 1 (Fig. 8). The SD was calculated using themean homogeneity and brightness values of each object at each scale from 5 to 20 (Fig. 8). Localvariance theory suggests that as object size increases the SD between segments increases con-tinuously until it begins to asymptote at the optimal segmentation scale.37,38 At the optimal scale,adjacent objects are the least similar in brightness values and hence have a high SD.38

The SD of the average brightness value for the XS and PAN data increased from 17.99 at thesmallest scale parameter value of 5 and levelled off at 19.94 at a scale parameter value of 15

Fig. 7 The four spatial relationships between classified objects (C) and reference objects (R).




[Fig. 8(a)]. In contrast, the SD of homogeneity decreased from a scale parameter value of 5 to 20[Fig. 8(b)], which was not consistent with the use of local variance theory for selecting the opti-mal scale parameter. Thus, the texture layer in the multiresolution segmentation was excluded forthe analysis of optimal segmentation scale, as the use of small image-objects will result in a noisyclassification. Based on this analysis, a scale parameter value of 15 was chosen for segmentationusing the PAN and XS data. Additionally, a shape parameter of 0.5 and compactness parameterof 0.1 were chosen based on visual examination of the objects’ shapes in respect to the features.

4.3 Classification of Rehabilitated Vegetation

The land-cover classification derived from the OBIA is shown in Fig. 9. The landscape patterncharacteristics of those classes described using landscape metrics are shown in Table 1. Sparse

Fig. 8 The scale parameters of multiresolution segmentation. Segmentation scale versus thestandard deviation (SD) of: (a) average brightness value for image objects of all bands (XSand PAN) and (b) the average homogeneity value of the PAN band.

Fig. 9 Vegetation map of tailing dam based on OBIA classification method (GDA94 zone 55).




grass had the largest area of 183 ha (60% of the total area), followed by bare ground of 51 ha.Sparse grass was mostly found within a contiguous large patch of 164.85 ha, which consisted of90% of its total area. Similar patterns were seen in the other land-cover classes, with a largepercentage total area of each land-cover class found within a single-large patch. All land-cover classes had similar spatial patterns as described by the landscape shape index andmean length/width ratio.

4.4 Classification Accuracy Assessment

In our study, most of the classified objects in the scene were quite large, over 100 ha, particularlysparse grass. The boundaries of some of these objects were so large that almost all the imagewould be required to be digitized to get a statistically robust sample size. Thus, circular bufferareas (sample areas) were used instead of the complete feature extent (Fig. 10). In order to testthe effect of truncating features on the accuracy assessment, multiple buffer sizes were tested.

Twenty randomly located sample points were generated and at each point, overall classifi-cation accuracy was tested within multiple buffers from 10 to 100 m. The smallest buffer size of10 m produced the highest overall accuracy of 91%. The accuracy decreased from buffer sizes of10 to 40 m and finally plateaued at the buffer size of 50 m with an overall accuracy of 80%(Fig. 11). At this buffer size, we measured all other accuracy metrics such as intersection, inclu-sion, commission, and omission. At the 50-m buffer size, the total area covered by all the refer-ence locations was 15 ha, around 5% of the total area in the TD.

Table 1 The landscape metrics generated from the land-cover map of the tailings dam (TB).

Classes Tree cover Dense grass Sparse grass Bare ground

Largest patch area (ha) 22.98 13.98 164.85 36.26

Mean patch area (ha) 1.16 1.66 8.34 1.50

Number of patches 35 20 22 34

Mean length/width ratio 2.04 2.08 2.19 2.36

Landscape shape index 1.77 1.71 2.38 1.99

Area (ha) 39.73 33.37 183.39 51.20

Area (% total) 12.91 10.85 59.60 16.64

Total area (ha) 307.69

Fig. 10 Object-based accuracy assessment was conducted through comparison of classifiedobject to the reference objects. The figure shows the assessment at the optimal buffer size of 50 m.




Accuracy assessment results using the object-based approach were then compared with theresults using the standard error matrix approach. This was conducted using 50 random samplingpoints for each class, where each of the sampling points was composed of a single pixel.

Accuracy assessment using spatial-object relationships was calculated within the referencelocations at a 50-m buffer size based on the level of agreement between classified objects (C) andthe corresponding reference objects (R) (Fig. 12). Tree cover was classified the most accurate ofall land-cover classes with an intersection (C ∩ R) area of 93.25% in comparison to dense grass,which had the lowest intersection value of 42.17%. In contrast, dense grass had the lowest errorsof omission of all land-cover classes of 11.8% and tree cover had the highest errors of omissionof 44.73%. Sparse grass performed best overall for all-object relationship measures of error. Thenumber of objects and the total area of the objects tended to be larger for dense grass and bareground.

Using the object relationship values, the overall accuracy, user’s accuracy, and producer’saccuracy for each land-cover class were calculated (Table 2). Tree cover had the highest overallaccuracy of 64.50% and the highest producer’s accuracy of 91.50%. Dense grass had the highestuser’s accuracy of 78.99%, but it also had the lowest overall accuracy of 38.08% and the lowestproducer’s accuracy of 42.37%.

Fig. 11 Overall accuracy measured with object-based accuracy assessment at multiple buffersizes.

Intersection C R

Commission C R

Area (ha)

num Percentage area

area (ha)

num Percentage area

Omission C R

Union C R

area num

Percentage area area

num Percentage area (ha)

TC 2.21 7 93.25% 0.16 3 6.75% DG 1.32 7 42.17% 1.8 20 57.51% SG 6.73 21 80.41% 1.64 21 19.59% BG 0.85 4 56.67% 0.66 4 44.00% Total 11.11 39 4.26 48

TC 1.06 13 44.73% 2.56 30 108.02%

DG 0.35 6 11.18% 10.57 40 337.70% SG 2.18 18 26.05% 8.49 81 101.43%

BG 0.66 11 44.00% 2.16 23 144.00% Total 4.25 48 18.78 174

Fig. 12 The summary statistic for the objects’ relationship between classified and reference datameasured as an area, number of objects (num), and percentage area. The percentage area ofeach land-cover class is calculated as a proportion of the reference area.




In comparison to object-based accuracy assessment methods, the traditional error matrixapproach showed a much higher overall accuracy of 92.50% compared with 80%, respectively(Table 3). Some patterns were similar between both methods with tree cover having the highestaccuracy regardless of accuracy assessment method. The differences in error values betweenaccuracy measures (e.g., overall, producer’s, and user’s accuracies and Kappa coefficient)were relatively small compared with the object-based approach.

5 Discussion

5.1 Effectiveness of the Object-Based Classification Procedure

This study demonstrated the utility of high-spatial resolution SPOT-5 data along with OBIAclassification methods for mapping rehabilitated vegetation patterns using spectral and texturalcharacteristics and fuzzy memberships. This study addressed multiple-scale effects associatedwith classifying land-cover with remote sensing data that impact on the characterization of land-scape patterns and the accuracy of remote sensing land-cover classification.32,39–41 Specifically,we examined the scale effects associated with the OBIA segmentation scale and accuracy assess-ment plot size. Using the method outlined in this paper, an overall accuracy based on an errormatrix showed a classification accuracy of 92.50%, suitable for assessments of mined land veg-etation. The object-based classification approach is useful for mine-site rehabilitation monitor-ing, as the accurate extraction of features is just as important as accurately estimating land-coverarea. Of the four land-cover classes, tree cover was consistently identified as having the highestaccuracy regardless of the accuracy assessment method.

The method demonstrated in this study allows for reliable monitoring of mine rehabili-tation through the production of fine spatial scale-detailed rehabilitated land-cover maps. A

Table 2 Accuracy assessment result using object-based method and 50-m buffer size.

Overall accuracy (%) Producer’s accuracy (%) User’s accuracy (%)

TC 64.50 91.50 67.64

DG 38.08 42.37 78.99

SG 63.78 80.43 75.50

BG 39.10 48.49 56.07

Overall accuracy: 80%

Table 3 Accuracy assessment result using error matrix method.

Reference data (aerial photo)

TC DG SG BGOverall

accuracy (%)User’s

accuracy (%)Producer’s

accuracy (%)Kappa

Coefficient

Classifiedland cover

TC 47 3 0 0 88.7 94.00 94.0 0.92

DG 3 45 2 0 84.9 93.75 90.0 0.87

SG 0 0 46 4 83.6 90.20 92.0 0.92

BG 0 0 3 47 87.0 92.16 94.0 0.89

Overall accuracy: 92.50%

Overall Kappa coefficient: 0.90




qualitative assessment by field ecologists suggests that the final mapping product is useful foridentifying the distribution of vegetation and can provide an insight into a range of spatiallyexplicit environmental impacts that affect rehabilitation success such as erosion, water avail-ability, and soil properties.42 Once optimal scales and bands have been identified, the clas-sification method was relatively simple in terms of processing. This means there is a greaterlikelihood that nonexpert users could utilize the classification method at other mine sites,with similar vegetation classes and surface characteristics—without significant manualprocessing and testing.

The method developed for this study is based on classifying discrete objects in order to pro-vide mapping products compatible with the landscape function analysis approach, whichincludes the assessment of the spatial distribution of vegetation such as interpatch distanceand patch width (see Ref. 11). The discrimination of a number of classes such as dense andsparse grasses and bare ground was assisted through the use of fuzzy membership classifica-tion—a technique useful for identifying land cover that may contain two or more classesthat gradually intergrade.43,44 Fuzzy sets in the OBIA assign a membership degree to each objectfeature and are well suited to handle sources of vagueness in remote sensing information extrac-tion.18 While the use of fuzzy sets improved the classification accuracy, dense grass had thelowest overall accuracy based on the object-based accuracy assessment. Classes such asdense and sparse grasses have boundaries that are considered as “soft” but homogeneous attheir core.45 Similarly, sparse grass and bare ground represent classes that intergrade andalso had lower classification accuracies in contrast to tree cover. The classification of sparseand dense grasses was particularly problematic using NDVI due to soil background effectsfrom alternating homogenous areas of sand dunes, loess soil plains, and rock with differentreflectances. The higher classification accuracy of areas with tree cover is due to its discreteboundaries, representing distinct differences in image textural properties as compared to theother three land-cover classes.

The utilization of texture measures was particularly important for separating tree cover anddense grass, which had similar spectral characteristics in all bands and NDVI. Dense grass hadlow-structural variation, resulting in increased homogeneity in the textural measures in contrastto tree cover which was more heterogeneous. These differences were observable within the high-spatial resolution PAN band and less so within the lower-spatial resolution XS bands. The ben-efits of including textural information on classification accuracies have been found within otherenvironments where land cover exhibits differences in within-class spatial heterogeneity such astemperate suburban USA31 and forested Mediterranean vegetation.46

A key feature of the OBIA method used in this study was the identification of the optimalsegmentation scale, which determines the average size and number of image segments pro-duced for a specific dataset. The optimal scale parameter in this study appeared to correspondto the tree canopy sizes. The benefit of using the optimal segmental scale is that it maximizesthe separability among landscape features and this, in turn, enhances the classification results.The high-spatial resolution of SPOT-5 is critical to extracting the segmentation objects usefulfor accurately delineating fine spatial scaled features with discrete classification schemes.47,48

The relationship between real-world and image objects relates directly to the quality of seg-mentation and the results of the final classification.30 Our study derived the optimal segmen-tation scale parameter based on the characteristics of land-cover within the rehabilitation TD.For other studies in similar environments, these values represent a good starting point for seg-mentation, as long as a sensor with comparable spatial and spectral resolutions is used.However, the optimal scale parameter will vary between locations due to the differences inlandscape structure and heterogeneity. High-spatial resolution data have been shown to becritical for mapping fine-scale features, whereas coarse spatial resolution data are more usefulfor assessing other kinds of vegetation characteristics such as composition and some structuralproperties.49 Thus, the spatial patterns of the rehabilitated vegetation from postmine areas willgenerally be difficult to classify using moderate-scale satellite imagery such as Landsat datawhere the classification of fine-scale, ecologically important features are important.47 Theimportance of having high-spatial resolution imagery was reflected by the Z-score analysis,identifying the high-spatial resolution PAN-H and PAN-C textural images as important for theclassification.




5.2 Implications for Monitoring the Vegetation Rehabilitation of TDs

In Australia, substantial land rehabilitation is required if mine lease relinquishment is sought. Inthe case of Kidston, the aim of mine rehabilitation is to return the area to a self-sustaining sav-annah woodland of native trees and to introduce native ground cover species to disturbed areas.To confirm whether the aims of rehabilitation have been met, it is essential to monitor biologicalparameters such as vegetation cover and the spatial distribution of vegetation until the ecosystemis self-supporting and self-sustaining.11,50,51 The complete coverage of a mine site provided byremote sensing imagery is useful as mining impacts can often be heterogeneously distributed atfine spatial scales and, therefore, difficult to observe with field plots in some cases.8,52 Usingdiscrete classification systems at fine spatial resolution allows for the quantification of spatialpatterns that can then be related to rehabilitation history, such as with landscape metrics.

This study showed that the largest patch of tree cover, at 22.98 ha, was found within theTD40 area, indicating rehabilitation in this area performed the best. The contiguous natureof vegetation in this area indicates that recruitment is taking place between some of the originalplanting rows. Rehabilitation success in this location is likely to be the result of the higher inten-sity rehabilitation made in this area using tube stock planting and irrigation.25 The dominantland-cover in TDNA and TDNB was sparse grass, which shows that in these areas, treeshave not established since being planted and seeded in 2001, indicating that the rehabilitationmethods were not optimal and may require further intervention. Areas with sparse grass and bareground coincided with low-water availability, partly due to drainage patterns caused bytopography.

A successful monitoring approach for characterizing mining environments at large spatialextents requires high-spatial resolution imagery in order to differentiate the land cover associatedwith mining activities.53,54 Examples of mine site land-cover mapping using medium spatialresolution data acquired by Landsat include mapping the geological features in open-castcoal mining55 and describing the changes in land-cover area through classifying mined landinto water, rehabilitated area, and bare open cast area.2 However, the spatial resolution necessaryfor assessing rehabilitation at mine sites such as Kidston needs to be much finer in order todescribe the vegetation heterogeneity associated with erosional features such as rills and terrac-ettes, highly heterogeneous soils that result from their mobilization and transport due to erosion,and contour ripping resulting in bank and trough patterns. These mining landscapes are oftenpatchy with well-defined source/sink or interpatch/patch sequences, which are responsible forecosystem processes being linked by hydrological processes.11 The scales at which these proc-esses take place can vary from a fraction of a meter in grasslands to tens of meters in semi-aridwoodlands.11

An important element of the method described in this paper is the use of object-based accu-racy assessment, which is likely to better reflect the classification accuracy of the fine-scalevegetation heterogeneity than the traditional point-based confusion matrix approach. Theobject-based accuracy assessment method showed a lower overall accuracy of 80% comparedwith 92.50% for the traditional point-based approach. The differences between the twoapproaches were more pronounced when comparing accuracies for each land-cover class.For example, dense grass had an overall accuracy of 38.08% using the object-based accuracyassessment method versus 84.9% using the traditional approach. The object-based accuracyassessment uses features as the sampling unit, as opposed to the pixel, and thus small featureswill have the same probability of being sampled as large features, in contrast to the point-basederror matrix approach where large features composed of many pixels will have a greater prob-ability of being sampled. The object-based approach is useful for the assessment of rehabilitationcharacteristics, where the accurate extraction of features is more important than accurately esti-mating land-cover area.

The combination of the SPOT XS high-spatial resolution imagery, the OBIA classificationmethod, and the object-based accuracy assessment method fulfils the requirements of mine siterehabilitation monitoring. The classification method described within this paper was relativelysimple, yet provided high overall accuracies, and allowed for the identification of discrete land-cover patterns at the appropriate spatial scales for landscape function analysis methods. Theobject-based accuracy assessment is also important for ensuring that the landscape patterns




are assessed in a sensible manner. Additionally, in comparison to IKONOS, QuickBird, GeoEye-1, and WorldView-2 image data (<5-m pixels), SPOT-5 is approximately 10% of the acquisitioncosts for other methods.26 For annual mine site monitoring, a simple, semiautomated classifi-cation method such as is described in this study and low-image acquisition costs are necessary.

6 Conclusion

This study demonstrated the utility of SPOT XS and PAN data classified with OBIA to describethe landscape patterns in rehabilitated semi-arid vegetation at the Kidston mine site. This studydemonstrated that: (1) SPOT-5 can be used to classify broad vegetation cover classes at accu-racies suitable for mapping spatial patterns using the OBIA method; (2) dissimilarity and homo-geneity texture measures from the PAN band along with the identification of the optimalsegmentation scale are useful for differentiating between spectrally similar vegetation coverclasses such as dense grass and tree cover; and (3) object-based accuracy assessment measureswere useful and provide information about the local error that could not be derived with thetraditional error matrix. The land-cover mapping product generated by these methods can pro-vide key information for characterizing vegetation for assessing rehabilitation success. Furtherresearch is necessary using very high-spatial resolution image data to derive other importantmetrics used for assessing rehabilitation success, such as individual tree canopy cover, tree den-sity, and tree height, which cannot be extracted from SPOT-5 satellite imagery, but are necessaryfor monitoring rehabilitated vegetation performance.

Acknowledgments

We would like to thank SPOT image and particularly Rob Lees for the provision of the SPOTdata used in this study. Also, we would like to thank Andrew Fletcher and David Mulligan fortheir support. The authors thank the editor-in-chief and two anonymous reviewers for theirinsightful commentary.

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