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PerformanceofSupportVectorMachinesandArtificialNeuralNetworkforMappingEndangeredTreeSpeciesUsingWorldView-2DatainDukudukuForest,SouthAfrica

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 1

Performance of Support Vector Machinesand Artificial Neural Network for Mapping

Endangered Tree Species Using WorldView-2Data in Dukuduku Forest, South Africa

Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, and Elhadi Adam

Abstract—Endangered tree species (ETS) play a significant rolein ecosystem functioning and services, land use dynamics, andother socio-economic aspects. Such aspects include ecological, eco-nomic, livelihood, and security-based and well-being benefits. Thedevelopment of techniques for mapping and monitoring ETS isthus critical for understanding functioning of ecosystems. Theadvent of advanced imaging systems and supervised learningalgorithms has provided an opportunity to map ETS over frag-menting areas. Recently, vegetation maps have been producedusing advanced imaging systems such as WorldView-2 (WV-2) androbust classification algorithms such as support vector machines(SVM) and artificial neural network (ANN). However, delineationof ETS in a fragmenting ecosystem using high-resolution imageryhas largely remained elusive due to the complexity of the speciesstructure and their distribution. Therefore, the aim of the presentstudy was to examine the utility of the advanced WV-2 data formapping ETS in the fragmenting Dukuduku indigenous forestof South Africa using SVM and ANN classification algorithms.Specifically, the study looked at testing the advent of the additionalWV-2 bands in mapping six ETS. WV-2 image was spectrallyresized to separate four standard bands (SB) and four additionalbands (AB). WV-2 image (8 bands: 8B) together with the SB andAB subsets was classified using SVM and ANN methods. Theresults showed the robustness of the two machine learning algo-rithms with an overall accuracy (OA) of 77.00% for SVM and75.00% for ANN using 8B. The SB produced OA of 65.00% forSVM and 64.00% for ANN. The AB produced almost the sameOA of 70.00% for both SVM and ANN. There were significantdifferences between the performances of the two algorithms asdemonstrated by the results of McNemar’s test (Z score ≥ 1.96).This study concludes that SVM and ANN classification algorithmswith WV-2 8B have the potential to map ETS in the Dukudukuindigenous forest. This study offers relatively accurate informationthat is important for forest managers to make informed decisionsregarding management and conservation protocols of ETS.

Manuscript received April 02, 2015; revised July 21, 2015; acceptedJuly 21, 2015.

G. Omer and E. M. Abdel-Rahman are with the School of Agricultural,Earth, and Environmental Sciences, Pietermaritzburg Campus, Universityof KwaZulu-Natal, Pietermaritzburg 3209, South Africa, and also withthe University of Khartoum, Khartoum North 13314, Sudan (e-mail:gekhaled189@gmail.com).

O. Mutanga is with the School of Agricultural, Earth, and EnvironmentalSciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa.

E. Adam is with the School of Geography, Archaeology, and EnvironmentalStudies, University of the Witwatersrand-Johannesburg, Johannesburg 2050,South Africa.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2015.2461136

Index Terms—Artificial neural network (ANN), Dukuduku,endangered tree species (ETS), indigenous forest, support vectormachines (SVM).

I. INTRODUCTION

I NDIGENOUS forests span across different parts of Africawith relatively more existence in the southern and east-

ern parts of the continent [1]. In South Africa, indigenousforests consist of many small, fragmenting, and largely scat-tered patches and cover approximately 0.1% of the country’sland surface [2]–[4]. Apart from their ecological, economic,livelihood security, and well-being, indigenous forests in thecountry provide some medicinal products to the communitiesin the rural areas and contribute to the concept of “ecosystemservices” [5]–[7]. One such indigenous forest in South Africa isthe Dukuduku forest that is located in KwaZulu-Natal Province.Dukuduku forest provides varied products and usable materi-als for human needs that include construction and fence poles,raw material for craft work, livestock browse, and medicine tothe poor rural communities [3], [6]–[9]. Different tree speciesin the forest play a vital role in providing such useful needs.It is interesting to note that local communities in Zululand,South Africa use some of these tree species to treat humandiseases such as fever, stomachache, dysentery, snake andscorpion bites, malaria, inflammations, backache, and facili-tating childbirth [6], [9]–[12]. Therefore, some tree species inthe Dukuduku indigenous forest have become endangered andthreatened because of the rapid harvesting rate and removal[2], [13]. These activities have resulted in over-exploitation ofnatural resources and caused severe forest fragmentation andserious threats to the conservation of tree species diversity inthe indigenous Dukuduku forest ecosystem [3], [14]–[16].

Endangered tree species (ETS) need specific managementand conservation protocols in order to play significant rolesin ecosystem functioning, land use dynamics, and other socio-economic aspects in the value chain [8], [17], [18]. Thatrequires intensive fieldwork to geolocate and identify ETS andcharacterizes as well as estimate their coverage and distribu-tion [18], [19]. In this context, more precise information fromforest survey is needed for mapping and monitoring ETS todevelop sustainable forest management practices. However, tra-ditional field survey protocols are costly, time-consuming, andoften lack the necessary geospatial accuracy. Remotely sensed

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2 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

data were regarded as a precious source of information over thepast decades in classifying and monitoring forest species andvegetation communities [20]–[23]. However, mapping of treespecies (e.g., ETS) still faces complex challenges in relation toambiguous classes. Multiple objects within a pixel can lead tospectral confusion and poor distinction among different covertypes [24]–[26]. In particular, these challenges hinder the clas-sification of tree species when multispectral data are captured infragmenting forests [3], [27]. This is due to broader and fewerspectral measurements collected by some multispectral sensorslike Landsat and SPOT5 which might lead to spectral overlapbetween tree species.

Recently, the advent of new generation satellites with highspectral and spatial resolution such as Sentinel-2, WorldView-3 (WV-3), WorldView-2 (WV-2), RapidEye, and Pleiades hasbrought unique opportunities for mapping trees at species level.Among these satellites, WV-2 and WV-3 offer key spectralbands like yellow, coastal blue, and red edge that help in depict-ing tree characteristics. The utility of WV-2 image, for instance,has been demonstrated in various studies that include amongothers, predicting and mapping forest structural parameters[28], mapping of tree species [29], monitoring plantation forest[30], mapping increaser and decreaser grass species in degradedrangelands [31], and the detection of invasive alien plants [32].These studies discussed the utility of the eight available spectralbands of WV-2 imagery and concluded that the WV-2 data haveconsiderably improved the classification and prediction accura-cies of features of interest compared to other multispectral data.These studies, however, have some limitations related to lackof knowledge on the performance of WV-2 spectral subsets inmapping tree species using advanced and robust classificationalgorithms.

Various advanced classification algorithms such as classi-fication trees, random forest (RF), artificial neural networks(ANN), and support vector machines (SVM) have been usedto extract tree species information from multisensor and mul-tispectral remote sensing images [33]–[36]. Among these clas-sification methods, attention has been accorded to the use ofRF, SVM, and ANN due to their superior image handling capa-bility [33], [37]. RF, SVM, and ANN offer a precise way tomap vegetation cover and tree species from remote sensingimages without having to depend on any assumptions [35],[37]–[40]. RF is widely used for mining and classifying hyper-spectral data for plant secrecies identification and classification[41]–[43]. While, the application of SVM and ANN classifi-cation algorithms has been mainly explored in forest speciesclassification using multispectral imagery [38], [39], [44]. Inaddition, SVM are well-known machine learning algorithmsthat perform accurately in reduction of the complexity of ill-posed problem associated with image classification [37], [44],[45]. It is frequently used to locate multiple linear or potentiallynonlinear class samples by a variety of kernel approaches [46].The kernel approach takes on several methods such as polyno-mial and a radial basis function (RBF) that have revealed accu-rate results for vegetation classification [47]. RBF has manyadvantages which include its effectiveness as it works in an infi-nite dimensional feature space and having a single parameterconversely to the other well-working kernels [48]–[50].

On the other hand, ANN basically is machine learning sys-tems comprising interconnected linkages of modest processingelements. They are characterized by robust pattern recogni-tion power, allowing them to represent complex multivariatedata forms [34]. ANN have many advantages over the statis-tical methods which include easy adaptation to different kindsof data and input structures, and the ability to generalizationfor technique with multiple images [51]–[54], as well as theability to categorize data with limited training data comparedwith traditional classifiers [54]. Previous studies demonstratedthat SVM and ANN perform better than traditional classifi-cation methods like maximum likelihood, minimum distanceto the mean and decision tree [37], [39], [44], [55], [56].These conventional classification methods depend on assump-tions that may limit their utilities for many datasets and tomapping areas with limited training samples [37], [55], [57].Moreover, there are some additional challenges with the uti-lization of the ANN. These include the likelihood of involvinglocal extremum; a lack of strict design packages with theoreti-cal foundation; and the difficulty faced in attempting to controlthe training process [58]. Furthermore, ANN are often referredto as a black-box technique that could encounter an over-fittingproblem on the test dataset [59], [60]. Perfectly describingprocesses that interpret input data into output classes could,however, be challenging due to the combined use of multiplenonlinear activation functions at different layers [57].

To the best of our knowledge, there is rarity and lack ofinformation on how SVM and ANN classification algorithmscan perform on delineating ETS in a fragmenting ecosystemusing very high-resolution WV-2 imagery. Therefore, the objec-tive of the present study was to examine the utility of theadvanced WV-2 satellite data for mapping ETS in the frag-menting Dukuduku indigenous forest of South Africa usingSVM and ANN classification algorithms. Specifically, the studylooked at testing the advent of the additional WV-2 bands inmapping six ETS.

II. METHODOLOGY

A. Study Area

The study area encompasses the inland coastal forest ofSouth Africa known as the Dukuduku indigenous forest. Thearea falls between Mtubatuba and St. Lucia towns in the north-ern KwaZulu-Natal Province (32◦17′23′′E and 28◦52′25′′S)and form part of Mtubatuba Local Municipality (Fig. 1) [3],[61]. The Dukuduku indigenous forest is, therefore, consideredto be among the only remaining indigenous lowland forestsalong South African coastline [3], [62]. The forest coversabout 6500 ha of indigenous tree species. The majority of theland cover classes surrounding the forest consist of sugarcaneand private commercial forest plantations [3], [62]. As earlierpointed out by Ndlovu [61], 29% of this great indigenous foresthas been converted into other land uses which include subsis-tence agriculture and human settlements. In addition, increasinghuman activities in the area has led to an increase in land-scape fragmentation. Therefore, the Dukuduku area is facing

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OMER et al.: PERFORMANCE OF SVM AND ANN FOR MAPPING ETS 3

Fig. 1. Location of the Dukuduku indigenous forest in KwaZulu-NatalProvince of South Africa.

huge threats presented by the destruction of indigenous vegeta-tion, commercial forest plantations, and agricultural farmlands.Some tree species are also cut by the local community fordifferent purposes such as crafts and medicines. The area isdominated by various natural indigenous vegetation speciescomprising different age groups and other forms of land coverclasses. Among these species, the area is dominated by manyrare tree species, e.g., Syzygium cordatum (SyC), cussoniazuluensis (CZ), Ficus natalensis (FN), Canthium inerme (CI),Strychnos madagascariensis (SM), Strychnos spinosa (SS),Apodytes dimidiate (AD), Ozoroa engleri (OE), Barringtoniaracemosa (BR), Albizia adianthifolia (AA), Ekebergia capen-sis (EC), Harpephyllum caffrum (HC), Hymenocardia ulmoides(HU), Sclercarya birrea (ScB), and Trichilia dregeana (TD).These tree species were used for a number of human purposeslike rapid harvesting for woodcarving and traditional medicine[8], [9]. Forest managers in the Dukuduku indigenous forestobserved that six of these species; namely AA, EC, HC, HU,ScB, and TD are under severe threats and endangerment [63].Therefore, the current study focuses on mapping six tree speciesamong other forest species (OFS) and land use/cover (LULC)in the study area.

B. Field Data Collection

Intensive field work was conducted to identify ETS associ-ated with indigenous forest in the study area within one weekof the WV-2 imagery acquisition. We employed purposive sam-pling protocol to geolocate six ETS in the area using a handheldLeica GS20 geographical positioning system (GPS) with a sub-meter accuracy. The networks of road and open paths were usedto assist in selecting the ETS by walking in various directionsin the study area. Fig. 2 shows the morphological and spectralcharacteristics of the six tree species. The target species were

identified with the aid of expert knowledge. In total, 827 sam-ple points were collected from the six ETS and LULC classes inthe study area. The sample points for each class were 101 (AA),71 (EC), 62 (HC), 70 (HU), 80 (ScB), 68 (TD), 75 (sugarcane),75 (grassland), 75 (plantation forest), and 150 (OFS that includecoastal and dune forest species). These points were then used asground-truth data to classify the different WV-2 spectral subsetsbased on the pixel spectral signatures of the classes. For the treeclasses, one pixel was used for individual tree crown.

C. Image Acquisition and Preprocessing

In this study, a cloud-free WV-2 satellite imagery wasacquired from the study area on December 1, 2013. WV-2image is the first very high-resolution satellite imagery thathas the ability to acquire data of eight spectral bands inthe 0.4−1.40µm spectral range with spatial resolution of2 m and swath width of 16.4 km. The eight spectral bandsof WV-2 consist of four standard bands (SB) situated inthe blue (0.450−0.510µm), green (0.510−0.580µm), red(0.630−0.690µm), and NIR-1(0.770−0.895µm), and fouradditional bands (AB) which are coastal blue (0.4−0.45µm),yellow (0.585−0.625µm), red edge (0.705−0.745µm), andNIR-2 (0.860−1.40µm). The WV-2 image was atmospheri-cally corrected using the quick atmospheric correction (QUAC)procedure in interactive data language (IDL) environment forvisualizing images (ENVI) 4.7 software [64]. QUAC deter-mines atmospheric compensation parameters directly from theinformation contained within the image (pixel spectra), thusallowing for the retrieval of accurate reflectance spectra [65].No geometric correction was made on the WV-2 image as itwas provided already corrected by DigitalGlobe. The imagewas referenced to the universal transverse mercator (UTM zone36 South) projection and WGS-84 geodetic datum. The WV-2image was spectrally resized to separate the four SB and fournew bands (AB) subsets. The separated WV-2 subsets togetherwith the eight bands (8B) were then compared for classifyingETS using SVM and ANN supervised learning classificationalgorithms.

III. STATISTICAL ANALYSIS

It is noted that a sufficient number of training samples area prerequisite for a successful classification [53]. It is alsoreported that the sample points should be distributed consis-tently across the spatial extent of the classes to provide arepresentative description of the overall population [66]. In thecurrent study, the SVM and ANN classification algorithms weretrained on 70% (583) of a randomly selected holdout sam-ples and final accuracy assessments were evaluated using theremaining 30% (244) of the dataset. The ground-truth trainingsamples were overlaid over WV-2 spectral subsets and classifi-cation signatures were then created for the six selected ETS andLULC classes in the area. After assessing and adjusting signa-tures, SVM and ANN supervised classification methods wereemployed.

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Fig. 2. Field photographs and the corresponding average spectral reflectance curves of the six tree species extracted from WV-2 image pixels (n = 44 for eachspectrum) located at the center of tree crowns.

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OMER et al.: PERFORMANCE OF SVM AND ANN FOR MAPPING ETS 5

A. SVM Classification Algorithm

SVM classification algorithm is a binary learning techniquethat analyzes data and recognizes patterns [49]. However,numerous pattern recognition applications require multipleclasses. Hence, multiclass SVM problems are solved by gener-ating multiple binary classifiers [67]. Amendments are effectiveto the simple SVM binary classifiers to run as a multiclass clas-sifier using procedures like one against one (OAO) and oneagainst all (OAA). In each class, OAA adopts one binary SVMto alienate a class member from others. Conversely, OAO usesa binary SVM from each pair of classes to separate members ofone class from others. The SVM algorithm is then assigned thecorrect class by using a voting mechanism [50], [68]. The algo-rithm requires no assumption about the data distribution anduses very efficient principles to not over fit the test or new datasample [49], [69]–[71]. In addition, SVM attempts to maximizethe margin, i.e., the distance between the data points of eachclass, to the optimal separating linear hyperplane axes createdfrom each variable [72]. In a two class experiment, the algo-rithm sets two supporting hyperplanes in the boundaries andsearches to maximize the margin between them. Sample pointslying on the supporting hyperplanes are named support vectorsand in the middle of the margin is the optimal hyperplane. Thealgorithm aims to determine a linear discriminant function withmaximum margin to discriminate each class.

However, many classes are not linearly separable, henceSVM projects vectors into a high-dimensional feature spaceby means of a kernel trick and fits the optimum hyperplanethat discriminates classes using an optimization function [73],[74]. Polynomial and RBF kernels are the most commonly usedfunctions for classifying remotely sensed data [37], [75]. Incomparison, a number of authors have found that an RBF out-performs polynomial kernel in classification of remotely senseddata [73]–[75]. Furthermore, RBF is computationally fast andeasy to implement and requires optimizing only two param-eters. These are the cost function (C), which is a value forstandardizing the error of misclassified data points in the train-ing dataset samples, and gamma (γ) which is the kernel widthparameter of the RBF [73].

In the current study, three WV-2 spectral subsets and RBFwere used to find an optimal hyperplane that can differenti-ate among the six ETS in the Dukuduku forest. The C andγ parameters of the RBF were optimized to avoid over-fittingproblems [73]. The regularization of the two parameters wasperformed using a 10-fold cross validation method [48], [73],[74]. The training dataset was divided into 10 subsets of equalsizes. SVM models were then trained on nine subset samples,and tested using the removed one and the process was repeated10 times until all subset samples had served as test samples.The pair parameter that minimizes the classification error wasthen considered as the best value for the final classification pro-cess. The OAO procedure was used to implement a multiclassSVM model as suggested by Hsu and Lin [76] who stated thatthis scheme is more symmetric than OAA with regard to classsizes. The e1071 library version 2.15.2 in R statistical packages[77] was employed for optimizing SVM parameters. The opti-mal SVM parameters were then input into the ENVI software

TABLE IPARAMETERS FOR THE BEST TRAINED AND ANN USED

FOR MAPPING ETS

MLP, multilayer perceptron.

to implement SVM classification algorithm to map the ETS andother classes on WV-2 image.

B. ANN Classification Algorithm: Multilayer Perceptron

In machine learning and related fields, an ANN is a non-parametric classification technique that does not depend on anassumption of data normality [34], [37], [78]. An ANN is amathematical model that attempts to simulate the structure andfunctional aspects of biological neural linkages. It comprises aninterconnected group of artificial neurons and processes infor-mation using a connectionist approach for computation [79].An ANN is originally designed as pattern recognition and dataanalysis tools that mimic the neural storage and analytical oper-ations of the brain. Like SVM, an ANN approach has a distinctadvantage over other classification methods. The algorithm isadvantageous in that it is nonparametric and, therefore, requireslittle or no prior knowledge on the distribution of input data[80]. Moreover, an ANN fits arbitrary decision boundary toseparate among the data points and, therefore, produces highclassification accuracy [52], [81].

Various models of ANN have been used in remote sensingstudies such as RBF, back propagation (BP), and multilayerperceptron (MLP) [34], [37], [58], [82]–[84]. The MLP is acommonly used ANN structure that comprises an input layerand an output layer and one or more hidden layers of nonlin-early activating nodes [34], [78], [85]. The nodes in each layerconnect by a certain synaptic weight to all the nodes in the nextlayer [85]. Perceptron learning occurs through changes in thelinkages weights after items of data are processed. An MLP isa feed forward ANN model that maps input data onto a set ofappropriate output. It is an adjustment of the standard linear per-ceptron that uses three or more layers of neurons (nodes) withnonlinear activation functions [39], [57], [78]. ANN has beenwidely used in classifying land cover and tree species that arenot linearly separable in the original spectral space [37], [58],[79], [86]. Moreover, MLP model using the standard BP algo-rithm is one of the well-known ANN structures of algorithm.This algorithm used standard BP for supervised learning fromTanagra software 1.4 [87].

In the present study, ANN classification algorithm was usedas a supervised nonlinear classification algorithm to map ETSand other classes using WV-2 data. Numerous variations ofinternal network structure, input data, and learning algorithmshave been tested to define optimal classifier features. Table Ishows a list of the parameters used to train all ANN models.The input layers consisted of eight for 8B and four for bothSB and AB, while two hidden layers were found optimum for

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Fig. 3. Classification maps obtained using all eight WV-2 bands (8B). (a) SVMalgorithm. (b) ANN algorithm.

all models (Table I). The structure of the hidden layers was alsotested to assess the necessary number of hidden layers and num-ber of required nodes per layer. This was tested by manuallychanging the number of nodes. The train-test parameters wereexcluded to obtain overall test error rate and then viewed to getconfusion matrix for classifying ETS and other classes usingTanagra software [87], [88]. The ANN was trained with a BP-training algorithm and one hidden layer [89]. The BP algorithmis a supervised method that uses the gradient descent technique,which adjusts weights to minimize the classification error andoptimizes ANN parameters [84], [90]. In the present study, thenumber of hidden layers was set to 2 and the number of trainingiterations was set to a default value of 1000 [87]. The optimum

Fig. 4. Classification maps obtained using four standard WV-2 bands (SB).(a) SVM algorithm. (b) ANN algorithm.

number of nodes was established after manually changing thenumber of nodes (Table I). The optimal parameters were theninput into the ENVI software to map the ETS and LULC onWV-2 image [64].

C. Accuracy Assessment

The accuracy of SVM and ANN classification algorithmswas evaluated using the 30% (n = 244) holdout sample of thedataset. A confusion matrix was constructed to compare the trueclass with the predicted class assigned by SVM and ANN and tocalculate the overall accuracy (OA), producer’s accuracy (PA),

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OMER et al.: PERFORMANCE OF SVM AND ANN FOR MAPPING ETS 7

Fig. 5. Classification maps obtained using four additional WV-2 bands (AB).(a) SVM algorithm. (b) ANN algorithm.

and user’s accuracy (UA) [39], [91]. OA is overall probabilitythat test sample points on the image have been classified prop-erly. PA, which is expressed as a percentage (%), denotes thelikelihood of a certain class being correctly classified, whereasUA refers to the probability that a sample is labeled as a spe-cific class and the classifier accurately assigns it such a class.It has become customary in remote sensing studies to reportthe kappa index of agreement for accuracy assessment pur-poses since kappa also compares two maps that show a setof categories. However, recent studies have shown some lim-itations of kappa since it gives information that is redundantor misleading for practical decision making [92]. The authors

recommend using a more suitable and simpler approach thatfocuses on two parameters of disagreement between maps interms of the quantity [quantity disagreement (QD)] and spa-tial allocation [allocation disagreement (AD)] of the categoriesinstead of kappa variants. The two useful parameters were cal-culated from the cross-tabulation matrices to assess reliabilityof each classification algorithm. The QD is the amount of differ-ence between the number of test data points and the predictedones, while the AD describes the number of expected classesthat have less than optimal spatial location in comparison to thetest data.

According to accuracy metrics achieved for each algorithm ineach accuracy assessment method, a statistical analysis can beperformed to test if there was any significant difference betweenthe classification results of SVM and ANN classification algo-rithms. Therefore, we performed McNemar’s test to examinewhether there were any significant differences among the con-fusion matrices of the two classification algorithms (SVM andANN). McNemar’s test is a nonparametric test based on stan-dardized normal test statistic calculated from error matrices oftwo algorithms given as follows (1) [93], [94]:

Z =f12− f21√f12 + f21

(1)

where f12 denotes the number of samples that are misclassi-fied on the first confusion matrix but correctly classified on thesecond confusion matrix. f21 denotes the number of samplesthat are misclassified on the second confusion matrix but cor-rectly classified on the first confusion matrix. A difference inaccuracy between the confusion matrices of two algorithms thatused different WV-2 spectral subsets is statistically significant(p ≤ 0.05) if a Z-value is more than 1.96 [93], [94].

IV. RESULTS

A. Classification Results

Results of the grid search and 10-fold cross validationmethod indicated optimal values of γ and C for SVM, respec-tively, of 1 and 10 for 8B, 1 and 1000 for SB, and 1 and100 for AB. When these optimal values were input into SVMalgorithm, minimum overall classification errors of 38.40%,39.40%, and 36.90% for 8B, SB, and AB, respectively, wereobtained. Figs. 3–5 show the spatial distribution of the six ETSand other classes in the study area when WV-2 8B, SB, andAB were classified using SVM and ANN. The main visualdifference between the maps in Figs. 3–5 is that a relativelyhomogeneous map was produced when the 8B was used ascompared with other WV-2 spectral subsets. The maps alsoshow that the study area was mainly surrounded by grassland,sugarcane, and plantation forest, while the forest and grass-land on the north eastern part of the study area were relativelypatchy.

The results in Table II show areas under each ETS and otherclasses obtained from different WV-2 spectral subsets usingSVM and ANN classification algorithms. This table demon-strates that when WV-2 8B subset and SVM classifier wereused, the plantation class obtained the largest area (4063.5 ha),

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TABLE IIAREA UNDER EACH ETS AND LULC CLASSES IN THE STUDY AREA OBTAINED USING WV-2 DATA, SVM, AND ANN CLASSIFICATION ALGORITHMS

8B, WV-2 eight bands subset; SB, WV-2 SB subset; and AB, WV-2 AB.

while grassland occupied most of the study area (2472.1 ha)when SB subset and SVM classifier were deployed. With regardto ETS, results show that the AA achieved the largest areawhen WV-2 AB subset and SVM (1316.3 ha) as well as ANN(1315.1ha) classifiers were used, while the HC achieved thesmallest area (478.1 ha) when WV-2 SB subset and SVM wereemployed (Table II).

B. Accuracy Assessment

The ETS (AA, EC, HC, HU, ScB, and TD) and LULC weresuccessfully mapped with a higher OA for 8B followed by ABand SB when SVM was used (Table III). For ANN, a higher OAwas also achieved using 8B followed by AB and SB (Table IV).The SVM classifier obtained high QD values for AB followedby 8B and SB which obtained the same values of QD (Table III).While the ANN classifier obtained high-QD values for AB fol-lowed by 8B and SB that have achieved the same values ofQD, but these values are less than those obtained using SVM(Table IV). Additionally, the tables also show relatively high-AD values for SB followed by AB and 8B that obtained thelowest values when both SVM and ANN algorithms were used(Tables III and IV). Figs. 3–5 show the classification maps(LULC and ETS) of the study area. The maps show nearlysimilar spatial distribution of ETS in the study area.

The individual UA and PA for each ETS achieved by thetwo classification algorithms are shown in Figs. 6 and 7. WhenWV-2 8B subset was classified using SVM, the UA for someETS (AA, HU, HC, and ScB) was relatively higher and rangedbetween 73.17% and 85.71%. While for TD and EC, the accu-racy was less than 55% [Fig. 6(a)]. Additionally, the use ofWV-2 8B subset and ANN resulted in relatively higher UAfor HC (73.68%) and ScB (81.81%). Whereas, all other fourETS (AA, HU, EC, and TD) achieved UA of less than 65%[Fig. 6(b)].

Regarding the PA, the two classification algorithms (SVMand ANN) mapped AA with an accuracy ranging between80.00% and 100.00% (Fig. 7). Overall, all ETS were clas-sified with fairly higher PA, except for HU (Figs. 6 and 7).Additionally, all LULC obtained individual accuracies of morethan 50.00% when SVM, ANN, and all WV-2 subsets wereemployed (Figs. 6 and 7), except grassland class (45.45%)when SVM and WV-2 SB were used [Fig. 7(a)].

According to McNemar’s test, there were significant differ-ences (Z ≥ 1.96) at 95% confidence level among the confusionmatrices of SVM and ANN classification algorithms usingWV-2 8B, SB, and AB spectral subsets (Table V). Regardless ofthe WV-2 subset used, SVM significantly outperformed ANNin mapping the ETS and LULC classes in the study area.

V. DISCUSSION AND CONCLUSION

This study examined the performance of SVM and ANNalgorithms and the potential of new generation multispectralWV-2 data to map the spatial extent of ETS in the Dukudukuindigenous forest of South Africa. LULC in the study area wasalso classified to map target species within different LULC pat-terns and to produce a wall-to-wall thematic map. Tree speciesmaps obtained from commonly used medium-spatial-resolutionmultispectral satellite (30–100 m) have often been less accu-rate for operational application [27], [53]. Conversely, the useof imagery from very high spatial resolution sensors (<5m)has its own limitations in terms of cost, availability, process-ing, and high dimensionality. Limitations that characterize fineand medium scale remotely sensed data prevent the applica-tion and combination of field data with remotely sensed data[53]. Against this background, this study highlights the util-ity of multispectral WV-2 satellite imagery in mapping ETSin a fragmenting Dukuduku forest. The study demonstratesthat WV-2 data are effective in classifying ETS using SVMand ANN classification algorithms. The spatial location of

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OMER et al.: PERFORMANCE OF SVM AND ANN FOR MAPPING ETS 9

TABLE IIICONFUSION MATRIX OF SVM CLASSIFICATION ALGORITHM USING (A) WV-2 EIGHT BANDS,

(B) WV-2 SB, AND (C) WV-2 AB FOR THE 30% TEST DATASETS

The confusion matrix includes OA, QD, and AD for six ETS and LULC classes.AA, Albizia adianthifolia; EC, Ekebergia capensis; HC, Harpephyllum caffrum; HU, Hymenocardia ulmoides; ScB, Sclercarya birrea; TD, Trichiliadregeana; GL, grassland; SC, sugarcane; PF, plantation forests; and OFS, other forest species.

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10 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

TABLE IVCONFUSION MATRIX OF ANN CLASSIFICATION ALGORITHM: USING (A) WV-2 EIGHT BANDS, (B) WV-2 SB, AND

(C) WV-2 AB FOR THE 30% TEST DATASETS

The confusion matrix includes OA, QD, and AD for six ETS and LULC classes.AA, Albizia adianthifolia; EC, Ekebergia capensis; HC, Harpephyllum caffrum; HU, Hymenocardia ulmoides; ScB, Sclercarya birrea; TD, Trichiliadregeana; GL, grassland; SC, sugarcane; PF, plantation forests; and OFS, other forest species.

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OMER et al.: PERFORMANCE OF SVM AND ANN FOR MAPPING ETS 11

Fig. 6. UA (%) of the studied six ETS achieved by SVM (a) and ANN (b) classification algorithms when all WV-2 eight bands (8B), SB, and AB were used.

six selected ETS has been mapped at 2-m spatial resolution.Furthermore, our study indicates that the eight bands of WV-2are found to be very suitable for ETS identification and map-ping. The results indicate the significant improvement in theOA of the classification results that can be largely attributedto the eight bands of WV-2 (Tables III and IV). This resultis in conformity with Cho [2] and Omer et al. [95] whoconcluded that using WV-2 data with advanced classificationmethods for mapping LULC in a fragmenting ecosystem couldlead to improved classification accuracy. There are two rea-sons that may have led to the delineation of ETS in Dukudukuarea with relatively high classification accuracy. First, withregard to the machine learning classification algorithms, SVMis robust and versatile method that produces accurate classi-fication results when it is employed for mapping vegetationcover and tree species using remotely sensed data withouthaving to rely on any statistical assumptions [37], [39], [96],[97]. Second, ANN is also a robust and reliable algorithm thatyields accurate classification results (Table IV). The accuracyincreases when the hidden layers were increased from 1 to 2[53], [98]–[100]. Moreover, tree morphological characteristics

like canopy geometry and structural features as well as leafcolor of the six ETS are seemingly different (Fig. 2), whichcould have resulted in very different spectral features cap-tured by WV-2 data and enabled accurate species classificationmaps.

The comparison between SVM and ANN was employed toinvestigate their ability in tree species mapping. Both SVMand ANN algorithms achieved comparable overall accuracies.SVM produced higher classification accuracy than ANN byabout 2.00% when WV-2 8B was used (Tables III and IV).However, the relatively low overall classification accuracies(ranged between 64.00% and 65.00%) obtained using WV-2 SBquestioned the classification protocol employed and the spec-tral variability captured by a fewer number of WV-2 bands.Combining OFS in one class could have confused the delin-eation of six select ETS when only four WV-2 bands (SB orAB) were analyzed. Nonetheless, that the level of accuracy isquite common in the remote sensing image classification stud-ies that looked at mapping vegetation and tree species level[30], [96], [101]. More ground truth samples could be requiredto improve the accuracy in such a case [30], [95].

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12 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

Fig. 7. PA (%) of the studied six ETS achieved by SVM (a) and ANN (b) classification algorithms when all WV-2 eight bands subset (8B), SB,and AB were used.

It is important to note, however, that SVM and ANN wereunable to fully deal with the high spectral variation inherent insome tree species (e.g., HU). This is a common problem whenmapping heterogeneous landscapes using high spatial resolu-tion images based on per-pixel classification techniques [53].In this regard, an object-based classification approach wouldproduce higher classification accuracy (e.g., [96]). Although,the six ETS could be separated accurately using only the fourSB, the use of the WV-2 AB led to a considerable improve-ment in the classification accuracy. That is expected when theadvanced imaging systems such as WV-2 data with AB areused. For instance, the OA increased from 65.00% and 64.00%to 77.00% and 75.00% for SVM and ANN, respectively, whennew bands were added. The new WV-2 bands are useful fordifferentiating vegetated surfaces and are valuable in vegeta-tion identification [30], [32], [96], [97], [102]. Zhou et al. [103]mentioned that the WV-2 additional wavebands are expectedto provide an increase of up to 30% in classification accu-racy. The new bands also are strongly related to vegetation andtree species characteristics. For example, the yellow band isintended for the detection of “yellowness” in vegetation [104]

like senescent tree crowns (see Hymenocardia ulmoides crownin Fig. 2). In remote sensing, the red edge is the channel ofabrupt change in the leaf reflectance between 680 and 780 nm,due to the combined effects of strong chlorophyll absorption inred bands and high reflectance in the NIR bands [105], [106].The reflectance increases beginning at about 685 nm and anasymptotic reflectance reached at wavelengths beyond 760 nmfor each ETS (Fig. 2). Many spectral features of vegetation arefound within the red edge position that is related with changesin chlorophyll content [107]–[109]. The NIR-2 band that partlyoverlaps the standard NIR-1 band but is less affected by atmo-spheric influence is expected to enable a relatively accurate ETSclassification.

The low UA for the AA and PA for HU indicate that thereis a probability that pixels classified as AA and HU may notactually exist on the ground. That could also be due to spec-tral overlaps between AA and HU, and OFS. The relativelyhigh AD shown in Tables III and IV of the confusion matrixwas expected since pixels covered by multiclasses could prob-ably be mismatched in terms of spatial pattern between testground truth instances and predicated test samples. However,

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OMER et al.: PERFORMANCE OF SVM AND ANN FOR MAPPING ETS 13

TABLE VMCNEMAR’S TEST RESULT FOR COMPARING CLASSIFICATION CONFUSION MATRICES OBTAINED FROM SVM AND

ANN ALGORITHMS USING WV-2 8B, SB, AND AB

SVM, support vector machines classification algorithm; ANN, artificial neural network classification algorithm.

we employed an independent holdout test sample (30%) forassessing the performance of the classification models (SVMand ANN) based on the recommendation by Adelabu et al.[110]. While, Atzberger et al. [111] noted that bootstrap-ping approach produced relatively representative and repeatableaccuracy measures when the performance of predictive modelsis evaluated.

Our study showed that SVM outperformed ANN in distin-guishing among ETS in a fragmenting landscape. SVM offersmore benefits as compared to other classification models suchas ANN. SVM is paired with the kernel trick, exploring, andfinding tuning kernels that create appropriate feature spaceswhere the linear classification is able to classify data cre-ated by nonlinear phenomena [90], [112], [113]. On the otherhand, ANN could encounter an over-fitting problem and resultin low classification accuracy on the test dataset as shownin Tables III and IV [59]. However, researchers have shownthat ANN compares favorably with the established supervisedmachine learning classification algorithms like SVM for treespecies mapping [39], [89], [97], [114], [115]. Since we purpo-sively subset WV-2 bands to test the utility of the SB and ABin mapping ETS, we did not use any feature selection methodlike RF to select a fewer number of bands that might classifyETS with a comparable accuracy to the one produced by the

8B. Although, studies have demonstrated the robustness of RFas variable selection and classification approach in tree speciesmapping [30], [33], [96]. A combination of RF as a featureselection method and SVM as a classification approach wouldhave yielded optimum ETS mapping results.

In summary, our findings are promising for accurate classifi-cation of ETS in a fragmenting area using WV-2 multispectralbands, SVM and ANN classification algorithms. Moreover, therelatively accurate classification results achieved with SVM andWV-2 8B subset in this study provide reliable information ontree species in the Dukuduku area that could be used in thedesign of management plans and policies as a basis for assess-ing and monitoring natural resources, ecological fragmentation,and ecosystem functions and services.

ACKNOWLEDGMENT

The authors would like to thank the University of KwaZulu-Natal, South Africa and the University of Khartoum, Sudanfor funding this study. They also thank the InkanyambaDevelopment Trust and Manukelana Arts and IndigenousNursery in KwaZulu-Natal for facilitating the field data collec-tion. Our appreciation extends to the R development core team

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14 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

and Tanagra team for their open source packages for the sta-tistical analysis. Special thanks to Dr. R. Ismail for his helpfulgreat assistance during data analysis.

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Galal Omer received the M.Sc. degree in forest sci-ence from the University of Khartoum, Khartoum,Sudan, in 2009, and is currently pursuing the Ph.D.degree at the School of Agricultural, Earth, andEnvironmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa.

He is a Lecturer with the Faculty of Forestry,University of Khartoum, Sudan. His research inter-ests include application of remote sensing (RS) inapplied environmental science, with special focus onmapping and monitoring indigenous and fragmented

forests, land use/cover change, and modeling forest biophysiological variables.

Onisimo Mutanga received the Ph.D. degree inhyperspectral remote sensing of tropical grass qual-ity and quantity from Wageningen University-ITC,Wageningen, The Netherlands, in 2004.

He is a Professor with the School of Agriculture,Earth, and Environmental Science, University ofKwaZulu-Natal, Pietermaritzburg, South Africa. Hehas graduated 7 Ph.D. and 14 Masters Students. Hehas authored more than 67 articles and has sev-eral conference proceedings and book chapters. Hisresearch interests include ecological remote sensing.

Elfatih M. Abdel-Rahman received the Ph.D.degree in hyperspectral and multispectral remotesensing of sugarcane crop from the University ofKwaZulu-Natal, Durban, South Africa, in 2010.

He is an Associate Professor with the Facultyof Agriculture, University of Khartoum, Khartoum,Sudan, and a Postdoctoral Research Fellow with theSchool of Agricultural, Earth, and EnvironmentalSciences of the University of KwaZulu-Natal. He iscurrently supervising many M.Sc. and Ph.D. candi-dates. He has authored about 26 peer-reviewed jour-

nal articles, book chapters, and conference proceedings. His research interestsinclude crops and cropping systems mapping for agricultural productivity andfood security, assessing vegetation health, crops yield prediction, land use/landcover change, assessing ecosystems fragmentation and land degradation, andbiomass analysis.

Elhadi Adam received the Ph.D. degree in hyper-spectral remote sensing of wetland vegetation qualityand quantity from the University of KwaZulu-Natalin 2010.

He is Senior Lecturer with the School ofGeography, Archeology and Environmental Studiesat University of Witwatersrand-Johannesburg,Johannesburg, South Africa. He is currently super-vising many M.Sc. and Ph.D. students. He hasauthored more than 25 articles in ISI internationaljournals and conference proceedings. His research

interests include applications of remote sensing (RS) and geographic infor-mation system (GIS) in applied environmental science, with special focus onmapping and monitoring vegetation species, agricultural, and plantation forestsland use.