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Canopy cover estimation across semi-Mediterranean woodlands:application of high-resolution earthobservation data

Hamed NaghaviAsghar FallahShaban ShataeeHooman LatifiJavad SoosaniHabib RamezaniChristopher Conrad

Canopy cover estimation across semi-Mediterraneanwoodlands: application of high-resolution earth

observation data

Hamed Naghavi,a Asghar Fallah,a Shaban Shataee,b Hooman Latifi,c,*Javad Soosani,d Habib Ramezani,e and Christopher Conradc

aSari University of Agricultural Sciences and Natural Resources,Department of Forestry, P. O. Box 578, Sari, Iran

bGorgan University of Agricultural Sciences and Natural Resources,Department of Forestry, P. O. Box 386, Gorgan, Iran

cUniversity of Wuerzburg, Department of Remote Sensing, Oswald-Kuelpe-Weg 86,D-97074 Wuerzburg, Germany

dLorestan University, Department of Forestry, P. O. Box 465, Khorram Abad, IraneSwedish University of Agricultural Sciences, Department of Forest Resource Management,

901 83 Umeå, Sweden

Abstract. The semi-Mediterranean Zagros forests in western Iran are a crucial source of envi-ronmental services, but are severely threatened by climatic and anthropological constraints.Thus, an adequate inventory of existing tree cover is essential for conservation purposes. Wecombined ground samples and Quickbird imagery for mapping the canopy cover in a portionof unmanaged Quercus brantii stands. Orthorectified Quickbird imagery was preprocessed toderive a set of features to enhance the vegetation signal by minimizing solar irradiance effects. Arecursive feature elimination was conducted to screen the predictor feature space. The randomforest (RF) and support vector machines (SVMs) were applied for modeling. The input datasetswere composed of four sets of predictors including the full set of predictors, the four originalQuickbird bands, selected vegetation indices, and the soil line-based vegetation indices. Thehighest r2 and lowest relative root mean square error (RMSE) were observed in modelingwith total indices and the full data set in both modeling methods. Regardless of the input datasetused, the RF models outperformed the SVM by returning higher r2 and lower relative RMSEs.It can be concluded that applying these methods and vegetation indices can provide useful infor-mation for the retrieval of canopy cover in mountainous, semiarid stands which is crucial forconservation practices in such areas. © 2014 Society of Photo-Optical Instrumentation Engineers(SPIE) [DOI: 10.1117/1.JRS.8.083524]

Keywords: Zagros forests; Quickbird; nonparametric modeling; recursive feature selection; for-est canopy cover.

Paper 14290 received May 23, 2014; revised manuscript received Sep. 14, 2014; accepted forpublication Oct. 3, 2014; published online Nov. 6, 2014.

1 Introduction

The Food and Agriculture Organisation of the United Nations (FAO) implies a minimum treecanopy cover of 10% for defining a land as forest.1 Based on this definition, the United NationsEnvironment Program and the International Union of Forestry Research Organizations have cat-egorized Iran as one of the so-called “low forest cover countries” due to its 9.4% forest ratio tothe total land area.2 From the total forest area of ca. 12,400,000 ha, the semi-Mediterraneanforests located on the Zagros mountain chain account for ∼6;100;000 ha. They cover a substan-tially diverse environmental gradient stretching along a 1300-km range.3,4 During the last

*Address all correspondence to: Hooman Latifi, E-Mail: [email protected]

0091-3286/2014/$25.00 © 2014 SPIE

Journal of Applied Remote Sensing 083524-1 Vol. 8, 2014

http://dx.doi.org/10.1117/1.JRS.8.083524





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decades, they have been managed, particularly with respect to their multiple socio-economic andecological functions as well as for provision of nontimber-oriented services, such as secondaryforest products, water supply, and runoff reduction, as well as for preventing soil erosion.3,5

However, they have been historically facing a number of serious threats including overgrazing,cultivation in understory, and occasional wildfires.5 Therefore, obtaining accurate and up-to-dateinformation on forest cover is considered as a prerequisite not only for forest management, butalso for long-term socioeconomic purposes.6

Remote sensing-derived products have been shown to be advantageous input data forGeographic Information Systems databases due to their large area coverage which is coupledwith their cost-effectiveness.7 Earth observation data of different spatial and spectral resolutionsare widely employed in collecting information on forest stands.8 One crucial forest parameter isthe amount of canopy coverage which enables an indirect assessment of aboveground biomassand, in turn, the carbon emission and sequestration (see Ref. 9). It is also considered a crucialfactor which prevents soil erosion by avoiding the direct “splash effect” of rain drops hitting theground, especially in arid and semiarid areas. A destructive measurement of canopy cover hasbeen reported to be tedious,10 particularly across hard-to-access mountainous sites,11 whichincludes the majority of the Zagros mountain chain. Thus, a motivation lies in indirect estimationof canopy cover (as a measure of vertical projection of tree crowns).9 As an alternative to the fieldmeasurements, manual photogrammetric analysis has been applied12 which is still relativelylabor-intensive and based on user subjectivity. The satellite-borne earth observation datasetsin various forms have been stated to significantly improve the characterization of the Earth’sland surface (see e.g., the review provided in Ref. 13).

Medium resolution optical imagery has often been applied for estimating canopy cover inclassification14,15 and regression16,17 contexts. However, mixed performances have been reportedwhich mostly seem to be site-dependent. For example, Ref. 17 applied reflectance values fromLandsat Thematic Mapper (TM) for canopy cover estimation which resulted in the bestlinear model including TM original 3, 4, 5, and 7 bands (r2 ¼ 0.74), while the NormalizedDifference Vegetation Index (NDVI), components of the Kauth–Thomas transformation, andthe Atmospherically Resistant Vegetation Index also performed well (r2 ¼ 0.72, 0.70, and0.69, respectively). In the Zagros area of Iran, Ref. 18 reported an overall accuracy (OA)and Cohen’s Kappa of 65.50% and 0.48 for a classification of four classes of crown densityby applying a crisp classifier (maximum likelihood) on Advanced Spaceborne ThermalEmission and Reflection Radiometer (ASTER) data.

Reference 16 reported a moderate coefficient of determination (r2 ¼ of 0.64) for a supportvector machines (SVMs) model of tree canopy cover based on segmented ETM+ data. Yet,higher r2 rates of up to 0.83 have been previously reported by using ETM+ scenes for whicha forest-nonforest delineation has been done.19 With a combination of Quickbird, airborneLiDAR, and field measurement data, Ref. 20 estimated the crown cover as a means to derivethe carbon budget across two Californian test sites in the US. This revealed a significant corre-lation (r ¼ 0.82, P < 0.05) among Quickbird-estimated and field measured crown diameters onthe single-tree level. In stands located in the Zagros mountains, Ref. 21 tested the LISS-III andLISS-IV instruments of the Indian Remote Sensing Satellite (IRS) data which resulted in thehighest OA ¼ 65.77% and Kappa ¼ 0.45 for an LISS-III image classification for four canopycover classes. An LISS-IV image yielded, however, a slightly lower performance (63.27% and0.41, respectively).

The main objective of this study is to investigate the combined use of Quickbird imagery andnonparametric methods in canopy cover assessment across a portion of the sparse woodlands.Similar to Mediterranean and semi-Mediterranean types of woodland, Zagros forests are mainlycomprised of stands with sparse canopies and strong background soil reflectance. Soil reflec-tance is likely to vary from place to place and mainly depends on the soil type. It may also changewith the observation data for a specific location due to the existing variations among its surfacestatus and the amount of left-over dead vegetation. A specific group of vegetation indices (VIs)are based on a relationship between red and near infrared reflectance of bare soil (i.e., the so-called soil line).22 The reader is referred to Refs. 22–25 for further notes on the significance of thesoil line in remote sensing studies. Therefore, the second interest lies in exploration of if and howthe soil line-derived information may contribute to a refinement for modeling canopy closure in

Naghavi et al.: Canopy cover estimation across semi-Mediterranean woodlands. . .


this (and similar) sparse woodlands, where the effect of background soil negatively affects theaccurate characterization of foreground vegetation.

In our study, the image metrics are tested for their predictive ability when estimating forestcanopy cover values using two common nonparametric modeling approaches. The increasedapplication of nonparametric methods (adopted from the machine learning domain) alongwith the growing availability of geodata at high spatial resolution have increased the opportu-nities to estimate forest parameters with greater accuracy.26 References 27–32 provide somerecent examples of applying methods such as random forest (RF) and SVMs for estimationof forest attributes. The practical implementation of such methods is eventually shown by exem-plified wall-to-wall maps of canopy cover across the entire study area.

2 Material and Methods

2.1 Materials

2.1.1 Test site

The test site is located in central Zagros, lying 45-km south of Khorram Abad, the center ofLorestan province in western Iran (48° 27’ 32” to 48° 34’ 07” E and 33° 14’ 39” to 33° 18’07” N) (Fig. 1). The site elevation ranges from 1860 m to ∼2070 m above sea level. Thestudy site is topographically steep, with the southern slopes being generally dominant. The forest

Fig. 1 Illustration of the test site: Quickbird panchromatic-sharpened multispectral image (a), loca-tion of study area in Lorestan province (b) and in Iran (c) (source of Iran map: www.wikipedia.org).



www.wikipedia.org

www.wikipedia.org

www.wikipedia.org

stands are classified as semi-Mediterranean forests, forming mainly coppice-formed stands ofvarious density classes. Similar to the majority of Zagros forests, the vegetation cover status isaffected by a set of factors including altitude and geographical aspects. The Persian Oak(Quercus brantii var. persica) is the most abundant tree species, while other species includeAcer monspessulanum L, Crataegus spp, Pyrus spp, and Lonicera nummularifolia var. persica(Jaub. & Spach).

2.1.2 Remote sensing dataset

High spatial resolution Quickbird imagery was acquired on September 24, 2010. The dataset wascomposed of a single panchromatic image with a ground sampling distance (GSD) of 0.6 m andfour multispectral channels featuring 2.44-m GSD located within blue (450 to 520 nm), green (520to 600 nm), red (630 to 690 nm), and near-infrared (760 to 900 nm) spectral domains. The imagerywas completely cloud-free over the study area and was acquired under a relatively clear atmos-phere with a 16-deg off-nadir look angle. High resolution panchromatic data was fused with thecoarser resolution multispectral data to simulate a multispectral high-resolution dataset. To accom-plish this, the Gram–Schmidt pan sharpening method within the ENVI 5 package was applied.33

2.2 Methodology

Figure 2 illustrates the methodology followed in this study.

Fig. 2 The flowchart of methodology.



2.2.1 Field measurements

An inventory of the study area was accomplished by ground sampling. The approximate numberof sample plots was estimated as suggested in Ref. 34 (as reported in Ref. 21):

nplots ¼ t2cv2�1

E

�2

; (1)

where t is the value of Student’s t distribution, CV is the coefficient of variation, and E is theallowable rate of estimation error. CV was calculated using the results from a preliminary inven-tory of 30 sample plots. A threshold of allowable sampling error rate was set to 10% due to thecommon protocols of the Forest, Rangeland, and Watershed Management Organization of Iran.The number of sample plots calculated by Eq. (1) was 141.3 but, in order to maintain an allow-able sampling error rate, the sampling was conducted by means of a number of 142 samplesfeaturing a 0.1 ha size, which were designed over a systematic sampling grid. The sampling gridincluded 200 × 500-m plot distances. The individual tree species as well as two perpendiculartree crown diameters were recorded in each sampling unit. The crown area of each tree wascalculated as in Ref. 35

A ¼ π

4× d1 × d2 ; (2)

where d1 and d2 are the two measured perpendicular diameters of each tree crown. Canopy coverin each sample plot was calculated by the following equation

C ¼P

ni¼1 Ai

1000× 100; (3)

where Ai is the crown area of each tree, and n is the number of trees in each sample plot.The descriptive statistics of the sample plots are summarized in Table 1.

2.2.2 Preprocessing of image data

Orthorectification. The image scene was orthorectified by means of 33 ground controlpoints, which were collected by a Trimble R3 differential global positioning system. Due tothe existing limitations in data acquisition, a digital elevation model (DEM) from ASTERwas additionally used to account for topographic correction. The applied DEM was acquiredon a 1 arc sec (∼30-m at the equator) grid and was referenced to the 1984 World GeodeticSystem (WGS84). The georectified image yielded a total RMSE ¼ 0.44 pixel, an root meansquare error (RMSE) in X axis ¼ 0.33 pixel, and an RMSE in Y axis ¼ 0.28 pixel,respectively.36

Vegetation indices. VIs have been generally reported to enhance the vegetation signal byminimizing solar irradiance and soil background effects. A general classification of the VIs intoslope-based and distance-based approaches follows a definition in Ref. 37. The slope-based VIse.g., NDVI, band ratio (NIR/RED), and Thiam’s Transformed Vegetation Index (TTVI), werecomputed using the red and near infrared bands. Moreover, a set of distance-based VIs such asModified Soil Adjusted Vegetation Index, Perpendicular Vegetation Index, and DifferenceVegetation Index were derived by regressing the infrared band (as independent variable) onthe red band (as dependent variable) [Fig. 3(a)]. Following Refs. 38 and 39, the red band

Table 1 Statistical analysis of the estimation of canopy cover in sample plots.

NMin

(m2∕1000 m2)Max

(m2∕1000 m2)Mean

(m2∕1000 m2)Std. deviation(m2∕1000 m2)

Estimatederror (%)

Sample plots 142 0 944.34 496.53 261.21 8.83



was used as the independent variable when computing the remainder of distance-based VIs suchas the Transformed Soil Adjusted Vegetation Index [Fig. 3(b) and Table 2]. The soil line wasobtained via a linear regression between the infrared band and red band for bare soil pixels. Thecorresponding equations of soil lines for slope-based and distance-based VIs are summarized inTable 2.

Orthogonal transformation. In addition to the VIs described above, we used principalcomponent analysis to provide a standard measure of dimensionality reduction. Moreover,the Tasselled Cap transformation was applied to increase the optimal visibility of vegetationcover.46 In this transformation, the multiple bands in a multispectral image can be visualizedby defining an N-dimensional space where N is the number of bands. As such, the yieldedthree axes can be interpreted as the degree of brightness, greenness, and wetness as calculatedby the Tasselled Cap coefficients. In addition to the described metrics, the texture analysis basedon a method suggested in Ref. 47 and implemented within Ref. 48 was applied to determine thelevel and type of texture in the image scene.

2.2.3 Feature selection

The high-dimensional feature spaces formed by numerous remote sensing predictors almostalways bear a relatively high degree of redundancy in the information needed for modelingand classification. This redundancy has been reported to highly affect the performance and effi-ciency of the majority of modeling and classification approaches.49,50 Therefore, applyinga proper scheme for pruning the predictor feature space via feature selection is essential.51

Fig. 3 The calculated soil line with infrared (a) and red (b) bands as response.

Table 2 Summary of the implemented spectral VIs.

VI Formula References

(NDVI) NDVI ¼ ðNIR − REDÞ∕ðNIRþ REDÞ 40

(DVI) DVI ¼ α � NIR − RED 41

(MSAVI) MSAVI ¼ ½ðNIR − REDÞ∕ðNIRþ REDþ LÞ� � ð1.0þ LÞ 42L ¼ 1.0 − 2.0 � a � ½ðNIR − REDÞ∕ðNIRþ REDÞ� � NIR − b � RED

(PVI) PVI ¼ ðNIR − b � RED − aÞ∕ffiffiffiffiffiffiffiffiffiffiffiffiffiffi1þ b2

p43

(TSAVI) TSAVI ¼ ½b � ðNIR − b � RED − aÞ�∕½b � NIRþ RED − b � aþ X � ð1.0þ b2Þ� 44

(SAVI) SAVI ¼ ½ðNIR − REDÞ∕ðNIRþ REDþ LÞ� � ð1þ LÞ 25

(TTVI) TTVI ¼ ½ABSðNDVIþ 0.50Þ�.50 45



The main objective of feature selection is to augment the efficiency of classification andregression, which is analogous to reaching a specified performance with the highest possibledegree-of-freedom.30 In addition, care must be taken to avoid retaining a high number of possiblyredundant attributes.52 In this study, we relied on a backward selection as a recursive featureelimination (RFE) algorithm by means of a number of 100-bootstraps, which were evaluatedby a fivefold cross validation. The routine has been implemented in Ref. 53 within the R librarycaret. The processing chain follows

Begin Process

1.1 for each resampling iteration do1.2 Partition data into training and test/hold-back set via resampling1.3 Tune/train the RF model on the training set using all predictors1.4 Predict the held back samples1.5 Calculate variable importance or rankings1.6 for Each subset size Si, i ¼ 1: : : S do1.7 Keep the Si most important variables1.8 Tune/train the model on the training set using Si predictors1.9 Predict the held back samples1.10 [Optional] Recalculate the rankings for each predictor1.11 end1.12 end1.13 Calculate the profile over Si using the held back samples1.14 Determine the appropriate number of predictors1.15 Estimate the final list of predictors to keep in the final model1.16 Fit the final model based on optimal Si, using the original training set

End Process

Here, four runs of RFE were carried out on four input datasets. The datasets composed of foursets of predictors including (1) the full set of predictors, (2) the four original Quickbird bands,(3) the entire VIs, and (4) the soil line-based VIs. The information corresponding to the four datasets is shown in Table 3. The four sets of predictors were compared in terms of bootstrap RMSEvalues, based on which the final sets of predictors were selected from each of the four featurespace sets. In other words, the RFE seeks the optimum value for a so-called tuning parameter(here the number of predictors involved). The tuning has been reported to reduce the compli-cation of the subsequent modeling while enhancing its stability. Here, a resampling techniquewas used to modify the value of the tuning parameter into an optimal one (see Ref. 54).

2.2.4 Modeling

Random forest. The RF is a machine learning algorithm proposed in Ref. 55 for regressionand classification. The method converges based on ensembles of classification and regressiontrees. To initialize RF, a set of parameters including the number of trees to grow as well as thenumber of variables used to split each node should be decided.56 Here, each unpruned tree wasset to grow 100 bootstrap resamples.

For the modeling, sets of response and predictor variables were initially built, into which thetotal number of canopy cover observations for all samples were sorted. Then, five groups ofequal sample size were derived to guarantee a full coverage of all the available range of values.Following a 100-times bootstrap on the five groups, training and test samples from each of thefive sample groups were drawn with replacements. Then, the subsets were aggregated to form asingle-input dataset for the modeling. For further technical details on the modeling set-up as wellas on RF, the reader is referred to Refs. 57 and 58.

SVM. The SVM is a machine learning method for solving classification and regression prob-lems and works based on the statistical learning theory.59–61 The SVM is especially well knownvia its ability to minimize the effect of outliers.62 This method has recently found numerous



applications in remote sensing.63,64 With a basic underlying idea of structural risk minimization,the algorithm finds an optimal separation among the classes by locating two bounding hyperplanes (called the support vectors) and seeking to maximize the margin between them and thedata points that are located on these supporting hyper planes.65 Following the feature selectionmade by an RFE as described above, we processed the outputs by further using an R routineimplemented within the caret library, within which 100-bootstraps were drawn to train the data.The formation of individual samples as model inputs as well as the validation of the modelfollowed those which were already described in the section of Random forest.

In both cases of SVM and RF, the trained data were used to fit the classification onthe remaining ½ hold-out samples for subsequent validation.

The entire modeling, validation, and plotting processes were implemented within the Rsoftware for statistical computing66in an open source domain.

2.2.5 Accuracy assessment

As described above, validations were conducted based on cross validations in the case of each ofthe four basic remote sensing datasets. As previously mentioned, these included the datasets

Table 3 The information on the four input datasets.

Datasets Predictors

Original bands Blue band

Green band

Red band

Near infrared band

Soil line indices DVI

SAVI

WDVI

MSAVI

MSAVI2

PVI

PVI2

All indices Soil line indices

NDVI

TNDVI

TTVI

Red/near infrared

Texture analysis of original bands

PCA (first, second and third components)

Tasselled cap (brightness, greenness, wetness)

Near infrared/red band

Sqrt (Near infrared/red band)

Full dataset Original bands

All indices



based on original bands, soil line indices, and all VIs as well as all predictors. Following the RFand SVM modeling in each of the 100 permutations, fivefold cross-validations were used inwhich the diagnostics of accuracy including the relative RMSE (in %), coefficient of determi-nation (r2), and relative bias (in %) were derived. A higher number of folds in the cross validationwas avoided due to the presumably low number of sample units in hold-out test samples in someof the individual datasets (see Ref. 58). For technical notations regarding the calculation of theaccuracy diagnostics, the reader may refer to Refs. 30 and 57.

The one-way analysis of variance (ANOVA) and Student–Newman–Keuls (SNK) test wereused to determine significant differences between the means of the groups. The ANOVA uses anF statistic to test the between the group-similarity of mean, whereas the SNK test identifies thedifferences among the sample means. The selected models from both approaches were used toproduce wall-to-wall prediction maps of canopy cover classes across the entire study area.

3 Results

3.1 Modeling

3.1.1 Feature selection

The reaction of the bootstrapped RMSE values to the different sets of descriptor variables (i.e.,the defined tuning parameter) is illustrated in Figs. 4(a)–4(c). This figure shows the outcomes ofthe parameter tuning process in detail by rendering the individual values of the RMSE for thethree feature sets (the results of original dataset are excluded). The RFE contributed to tune thenumber of predictors for each of the three envisaged datasets representing variable feature spacesizes. One may see the minimal improvement of the RMSE following the inclusion of a certainnumber of predictors to the tuning model (10 for all predictors, 5 for all indices, and 5 for soil lineindices). The parameter tuning results for the original bands are not shown since its substantiallyfewer number of predictors hindered an effective tuning based on estimation error (Fig. 4).

In addition to the intrafeature space differences, the range of individual values of 100 resam-ples for the four feature selection runs shows a reduced variance while increasing the number ofpredictors, which supports the stability of the richer yielded models. However, the observedimprovements of the individual RMSEs by larger feature space sizes should still be interpretedwith caution to avoid a possible overfitting of the trained models (see e.g., Ref. 30).

3.1.2 Modeling results

The result of RF and SVM modeling is summarized as three common diagnostics of accuracy,including the relative RMSE (in %), the coefficient of determination (r2) and the bias (in %).Figure 5 shows the values of RMSE as obtained by RF and SVM models across the four inves-tigated feature spaces. By using an off-the-shelf (i.e., full) dataset, the median RMSE values of10.26% (RF) and 24.73% (SVM) were returned, respectively. Applying all the indices resulted ina median RMSE of 10.35% (RF) and 24.16% (SVM). The models based on soil-line indicesresulted in median rates of 10.95% (RF) and 26.73% (SVM), whereas the sole use of the fouroriginal bands returned slightly lower median RMSE values of 12.18% (RF) and 27.56%(SVM). Across all of the calculated RMSEs, one may see the notably-lower variance withinthe individual RMSEs of RF compared with those of SVM.

The amount of variance explained by the models (reflected by r2) was also highly variedbased on the type of model used, as well as based on the family of predictors applied withineach model. As shown by Fig. 6, the RF models explained more overall higher variances thanthose of the SVMmodels. When employing the entire predictor dataset, the RF and SVMmodelsexplained a median r2 of 0.69 and 0.50, correspondingly. By using all indices, the r2 rates forboth methods remained relatively constant (r2 ¼ 0.70 and 0.49 for RF and SVM, respectively).Modeling based on soil line indices resulted in a slightly lower amount of variance as explainedby both models (r2 ¼ 0.67 and 0.47 for RF and SVM), whereas the sole use of the original bandsresulted in lower r2 rates of 0.61 (RF) and 0.45 (SVM), as expected. This was also the only case



where the rates showed a notable reduction compared to all other cases, in which the differenceswere only marginal.

The RF models returned overall significantly lower bias % rates than the SVM models(Fig. 7). Regardless of the feature space setup (with all predictors, all indices, soil line indices,or original bands), the RF models returned approximately unbiased predictions of the canopycover. In contrast, the SVM models turned out notably higher bias values, ranging from medianrates of 0.30% (for soil line indices) to 1.79% (for original bands). Although the median biasrates of SVMmodels were shown to be only marginally biased, some individual values (resultingfrom individual resampled datasets) were highly biased, with the lowest one being up to 6.8%when applying the original bands. Due to the importance of the bias issue (as a decisive measurewhen discussing the general validity of spatial models), a profounder look at the yielded bias isprovided in the figures (Fig. 8 for RF and Fig. 9 for SVM), in which the prediction residuals for

Fig. 4 Individual outcomes of parameter tuning process based on recursive feature elimination byapplying (a) full data set, (b) all indices, (c) indices based on soil line. The results of original bandswas excluded as it turned out a similar trend.



all the modeling cases have been plotted versus the canopy cover response values. Here, one mayobserve the tendency of both RF and SVM models to overestimate lower and underestimatehigher canopy cover response values. Within the RF modeling approach, this over- and under-estimation showed more or less similar patterns regardless of the input dataset that was used. Inthe case of SVM, reducing the size of the feature space down to the original bands obviouslyresulted in an increased over- and underestimation in the models.

3.1.3 Comparing RF and SVM models

We observed significant differences between the r2 and RMSE returned by RF models via anANOVA test (Table 4). In addition, an SNK test revealed significant differences among the high-est r2 rates achieved by the use of all indices (yet only slightly more than the rates returned by thefull dataset) and the lowest rates returned by the original spectral bands. Significant differenceswere also observed between the lowest rate of RMSE achieved by the all predictors (which wasmarginally lower than those achieved by all indices) and the highest rate returned by the originalbands (Table 5).

The results of the ANOVA test proved the significant differences among the r2 and RMSE asreturned by SVM models for all four input datasets (Table 6). Furthermore, a comparison ofmeans by SNK showed the highest r2 and the lowest RMSE to be returned by the full datasetand all indices, respectively (Table 7).

In addition to the results reported above, a further Student’s t-test revealed a significantdifference between the r2 and RMSE returned by RF and SVM models, which displayed thesuperiority of RF over SVM (Tables 8 and 9).

Figure 10 summarizes the wall-to-wall RF and SVM prediction maps of the canopy coverbased on the four previously described predictor datasets. A visual inspection of the spatial mapsbased on local knowledge (and the previously conducted inventory) showed that the RF modelswere notably more precise than the SVM models in realistically reflecting the different levels ofcanopy closure on the ground. The SVM-based maps showed obvious shortages in rendering thelinear structure of most open areas (e.g., those located in the middle of the scene), though some

Fig. 5 Relative RMSE values obtained by random forest (RF) (a) and support vector machine(SVM) (b) models by applying full predictors, total indices, soil line indices, and original bands.



Fig. 6 Coefficient of determination (r 2) rates obtained by RF (a) and SVM (b) models by applyingfull predictors, total indices, soil line indices, and original bands.

Fig. 7 The bias % values obtained by RF (a) and SVM (b) models by applying full predictors, totalindices, soil line indices, and original bands.



degree of success was observed while using the soil-line indices. In contrast, the RF outputscovered the entire range of canopy cover distribution within the site very well. Nevertheless,the four RF maps showed only minor differences to each other, which support the previousplot-based results (Figs. 5 and 6). However, a complete area-based validation of the predictionmaps is obviously impossible due to the lack of highly intensive, area-based ground truth.

4 Discussion

Due to the continuous potential for over-stocking across sparse woody vegetation of semiaridwoodlands, a careful assessment of forest cover (and thus the total area of forest expansion) iscrucial (e.g., Ref. 67). Here, we carried out an area-based analysis of canopy cover by combiningdesign-based field data and Quickbird earth observation data across the typical semi-Mediterranean landscape of western Iran. In addition to this primary objective, a secondary effortwas to test the ability of soil line VIs for modeling canopy cover in such areas, where the soilbackground reflectance has a substantial effect on the conventional VIs such as NDVI (Refs. 68and 69) and, in turn, on the general performance of remotely sensed vegetation assessments (e.g.,Ref. 70). This study confirmed that a combination of Quickbird imagery and nonparametricmethods (especially RF) turned out middle to high accuracies for plot-based canopy mapping.

4.1 Feature Selection

In the RFE tuning carried out here, we followed a procedure similar to the one presented inRef. 54 by performing a 100-times permutated tuning, each including random draws of bootstrap

Fig. 8 Selected residual plots for the four input datasets and RF models: one may observe atendency of the RFmodel to overestimate lower and underestimate higher canopy cover responsevalues. This over- and underestimation showed roughly similar patterns regardless of each ofthe four input dataset.



resamples from the original pool of input samples to build RF and SVM models, which werevalidated using a cross validation (although Refs. 54 and 71 both used the method in a clas-sification context by evaluating Cohen’s Kappa rates) and evaluated by their returnedRMSE. The RFE indicated that the accuracy of the tuned models based on the external bootstrapincreases along with the increase in the number of predictors. This was expected, since methodssuch as RF have been shown to be resistant to problems caused by a high number of appliedpredictors (see e.g., Ref. 72). Figure 5 also shows that the variance of the bootstrapped RMSE

Fig. 9 Selected residual plots for the four input datasets and SVM models. Like RF, a tendency ofSVM models to overestimate lower and underestimate higher canopy cover response values wasobserved. Within SVM models, a reduction in the size of feature space (from all bands to the origi-nal bands) generally resulted in an increased over- and underestimation.

Table 4 Comparison of r 2 and RMSE produced by random forest (RF) using analysis of variance(ANOVA) test.

Sum of square df Mean square F Sig

r 2 Between groups 0.405 3 0.135 41.511 0.000

Within groups 1.288 396 0.003 — —

Total 1.69 399 — — —

RMSE Between groups 256.106 3 85.369 58.587 0.000

Within groups 577.020 396 1.457 — —

Total 833.126 399 — — —



Table 5 Comparison of means of r 2 and RMSE produced by RF using Student–Newman–Keuls(SNK) test.

r 2 N

Subset for alpha ¼ 0.05

1 2 3

Original bands 100 0.61 — —

Indices based on soil line 100 — 0.67 —

Full dataset 100 — 0.69 0.69

Total indices 100 — — 0.70

RMSE 1 2 3

Full dataset 100 10.26 — —

Total indices 100 10.35 — —


Original bands 100 — — 12.18

Table 6 Comparison of r 2 and RMSE produced by support vector machine (SVM) using ANOVAtest.

Sum of square df Mean square F Sig

r 2 Between groups 0.157 3 0.052 9.67 0.000

Within groups 2.147 396 0.005 — —

Total 2.304 399 — — —

RMSE Between groups 699.964 3 233.321 28.419 0.000

Within groups 3251.191 396 8.210 — —

Total 3951.155 399 — — —

Table 7 Comparison of means of r 2 and RMSE produced by SVM using SNK test.

r 2 N

Subset for alpha ¼ 0.05

1 2 3

Original bands 100 0.45 — —


Total indices 100 — — 0.49

Full dataset 100 — — 0.50

RMSE 1 2 3

Total indices 100 24.16 — —

Full dataset 100 24.73 — —


Original bands 100 — 27.56 —



values decreases when more predictors are added to the tuning set. One may also note thatalthough the parsimony in predictors has been stated as a crucial factor deciding for the robust-ness of such numerical tree-based models such as RF which work on large ensembles of sol-utions have been shown to be, at least within small-scale sample sets, still robust. (e.g., Ref. 30).

4.2 Modeling Approaches

Apart from the case where the original bands were solely applied, the information summarized inthe calculated slope-based and distance-based VIs proved to be able to return accurately pre-dicted canopy cover (shown by low RMSE) and explain up to ca. 70% of the variance (i.e., r2)within the range of sample data.

The reported RMSE and r2 diagnostics for both modeling procedures support the use of RFmodels to produce sufficient accuracies needed for practical canopy cover mapping in suchsparse woody stands. Though the results might still be site-specific, the returned RMSE valuesprove the feasibility of the applied RF models. This is also in line with a number of previousexperiences which used optical information for canopy cover assessments. For example, Ref. 73reported an enhanced RMSE of up to 15% and r2 of 0.60 for nonlinear least squares models ofcanopy cover using intensity metrics from satellite-borne LiDAR data. In addition, Ref. 74achieved r2 rates of ca. 0.68 to 0.78 for linear models of canopy cover using imaging spectros-copy data.

Nevertheless, there are site-dependent factors which are presumably responsible for thedegree of inaccuracy returned by the models. These include the inherent growth status of cop-pice-formed stands (often consisting of multiple stems and crowns overlapping each other indifferent dimensions) and the general dominance of sparse canopy. Furthermore, one mayalso note the image-related factors including the signal (i.e., reflectance) mixture of pixels, par-ticularly within those fragmented pixels (or patches) being surrounded by a bare soil signal. Inthe case of RF, the yielded individual values of RMSE and bias were shown to be almost invari-ant. This stability was even retained regardless of the feature space size used (i.e., input datasets),with a subtle increased variance only when using the original bands. This was in contrast to theprevious reports of Refs. 18 and 21 across the Zagros geographical domain that reported anincreased instability of a parametrically classified canopy cover. We attribute this mainly tothe methods applied here (RF) as well as to the fact that the applied VIs are more feasiblefor describing continuous responses, as also described in Refs. 75 and 76.

Inter alia, a goal was pursued here to minimize the existing vegetation/soil spectral overlap byderiving a site-specific soil line equation, which enables the incorporation of a set of soil-line

Table 8 Summary of mean statistics for results of RF and SVM modeling.

N Mean Std. deviation Std. error mean

r 2 RF 100 0.70 0.05 0.005

SVM 100 0.50 0.08 0.008

RMSE RF 100 10.26 1.19 0.12

SVM 100 24.16 2.73 0.27

Table 9 Comparison of means between RF and SVM using paired Student’s t-test.

Paired differences

t df SigMean Std. deviation Std. error mean

r 2 0.2 0.06 0.006 30.39 99 0.000

RMSE −13.90 2.52 0.25 −56.29 99 0.000



Fig. 10 The wall-to-wall prediction maps of RF (a) and SVM (b) models of canopy cover.



VIs. Compared to the sole use of the original reflectance bands, the soil line information wasshown here to be significantly more influential when discriminating the sample-based canopycover values. The soil line-based input dataset returned reliable predictions and was only margin-ally worse than the datasets with higher numbers of predictors in explaining the variance withinthe data (see the r2 rates, in particular those of RF models). However, due to the fact that theremote sensing predictors were calculated based on plot extensions overlaid on the image data,an inconsistency between the spatial scale of the measured plots and the applied image data wasinevitable. As previously described in methods in Sec. 3.1, the plot-scale canopy was calculatedas an aggregated value of the single tree-measured rates within each plot, which was larger thanthe GSD of the applied Quickbird scene. This results in tree crowns being covered by multiplepixels which in turn increases the intrapixel variance. The previous experiences during this studyrevealed that this can notably exacerbate the results in the case of classifying the canopy values(the common case in Iranian administrative level) instead of regressing them. This may alsoobviously increase the uncertainty of the classified canopy as a function of the spatial pixelsize (see results of Ref. 21 who compared IRS-, LISS-III, and LISS-IV image data in a clas-sification context). Therefore, this study suggests solving similar problems (sample-based can-opy cover estimates) in a continuous modeling context using robust models such as RF and soilline indices. The visual results of the mapped predictions confirm this as well. A postclassifi-cation of the modeled values may increase their practical implementation for administrativepurposes.

However, the modeling outcomes here may be partially affected by the within-sample degree-of-freedom resulting by the field sampling. Special care was taken to build the individual inputdatasets for modeling by subsampling from the entire range of measured values (see the sectionof Random forest). However, estimation of the canopy might suffer substantially because of therelatively small degree of freedom for the densest plots, which are obviously a function of theirscarcity within such semiarid woodlands (see e.g., Ref. 77 for a similar problem when estimatingother forest inventory attributes). Again, a classification of discrete canopy classes could havebeen strongly prohibitive in this case due to the resulting small within-class sample size. Theresidual plots in Figs. 8 and 9 show that the RF returned yet lower variances of residual valuescompared with the SVM. However, the observed over- and underestimations clearly provideevidence of the risks associated with an unbalanced sampling, which is often inevitable dueto the site properties.

The RF method outperformed SVM, which was in line with studies such as Refs. 56 and 71.The higher accuracy of RF can be attributed to its flexibility in conceptual design, as the methodhas been repeatedly reported to enable dealing with a high number of predictors (e.g., Refs. 55and 78). However, the high level of subsampling in RF (especially in solving small-scale prob-lems like this) may result in producing an incorrect reproduction of the original population.26

5 Conclusion

A comparison of the results obtained by this study and those in similar regions (yet mainly in aclassification context) showed advantages in applying modeling context to assess canopy coveras a response variable. RF obviously outperformed SVM, which supports the previous reports onits feasibility for building robust spatial predictions of environmental entities. However, one ofthe important factors in canopy cover modeling is a homogeneous distribution of sample plots inall canopy cover ranges from sparsest to densest. In the light of the results achieved here, wesuggest further thorough investigations on this issue, since the lack of such homogeneity mightcontribute to a serious disruption in the modeling process as well as in the interpretation of modeldiagnostics. One way of resolving this problem seems to be to the use stratified samplingmethods.

The soil line VIs were shown to be able to produce accuracies comparable with thoseobtained by applying a higher number of VIs and spectral transformations. In this regard, apply-ing data involving an improved spectral resolution would open an area for further research, asdetailed levels of information e.g., NIR domain, are highly sensitive to the leaf water content.79

These are among the motivations for further research, in which segment-based extraction of



canopy density classes by a combination of soil line VIs in a full object oriented paradigm iscurrently being pursued. Due to a high intrapixel variance raised by the spatial resolution asdiscussed above, this is hypothesized to reduce the amount of variance among pixels, whichin turn improves the practical implementation of high resolution imagery for monitoring sparsewoodlands.

Acknowledgments

This survey was accomplished with a financial support from the Sari University of Agriculturaland Natural Resources and the University of Lorestan in Iran. The Department of RemoteSensing of the University of Wuerzburg in Germany is appreciated for providing the first authorwith a research sabbatical as well as the required know-how and technical infrastructure. Weappreciate valuable comments from the two anonymous reviewers, who made a large effortto lead us in improving an earlier version of the manuscript. We thank Jill Dickey for afinal polishing of the English quality.

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Hamed Naghavi received his MSc degree in forestry from Mazandaran University, Iran, in2009. Currently, he is a PhD student in forestry at Sari University of Agricultural Sciencesand Natural Resources. He has a scholarship from Lorestan University, Iran. His research inter-ests include sampling methods, modeling, and image processing.

Asghar Fallah received his MSc degree in 1994 from Tarbiat Modarres University and a PhDdegree in 1999 from the University of Tehran in forest biometry. He is an associate professor offorest inventory in the Department of Forestry at Sari University of Agricultural Sciences andNatural Resources.

Shaban Shataee received his MSc and PhD degrees in forestry from the University of Tehran,Iran. He is an associate professor in the Department of Forestry in Gorgan University ofAgricultural Sciences and Natural Resources, Iran. He is currently the dean of faculty of forestscience. His research interests include modeling, data mining, image processing, and GIS.

Hooman Latifi is an assistant professor at the Department of Remote Sensing of the Universityof Würzburg. He received his PhD degree in forestry/remote sensing from the University ofFreiburg. His main research interests are remote sensing-supported natural resource inventory,environmental health, spatial statistics, airborne remote sensing, and model optimization. Inaddition, he is currently the spokesman of the working group Ecology and Environment ofthe German region of the International Biometric Society.

Javad Soosani received his MSc degree in 1997 from TMU (Tarbiat Modarres University) andhis PhD degree in 2008 from Tehran University in the field of forest biometry and visualization.He is a faculty member of Lorestan University. His research centers on forest biometry, aerialphotography, and image processing as well as dendrochronology and oak decline.



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Habib Ramezani received his PhD degree (in forest science) in 2010 from the SwedishUniversity of Agricultural Sciences (SLU). Currently, he does research in the Department ofForest Resource Management at SLU. His research focuses on sampling methods and monitor-ing landscape pattern.

Christopher Conrad received his PhD degree in geography from the University of Würzburg.He was a research fellow with the German Aerospace Center (2002 to 2006), a researcher inthe Elitenetwork of Bavaria (2007 to 2011), and is currently a junior professor in Würzburg.His research interests include high-resolution agricultural mapping and multitemporal remotesensing. He puts a strong focus on the use of remote sensing for land and water managementin arid Asia.



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