Geocarto International ISSN: 1010-6049 (Print) 1752-0762 (Online) Journal homepage: http://www.tandfonline.com/loi/tgei20 Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data Charles Otunga, John Odindi, Onisimo Mutanga & Clement Adjorlolo To cite this article: Charles Otunga, John Odindi, Onisimo Mutanga & Clement Adjorlolo (2018): Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data, Geocarto International, DOI: 10.1080/10106049.2018.1474274 To link to this article: https://doi.org/10.1080/10106049.2018.1474274 Accepted author version posted online: 08 May 2018. Published online: 24 May 2018. Submit your article to this journal Article views: 15 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tgei20 Geocarto InternatIonal, 2018 https://doi.org/10.1080/10106049.2018.1474274 Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data Charles Otungaa , John Odindia, Onisimo Mutangaa and Clement Adjorlolob a School of agricultural, earth & environmental Sciences, Discipline of Geography, University of KwaZulu-natal, Pietermaritzburg, South africa; bSouth african national Space agency (SanSa) – earth observation, Pretoria, South africa ABSTRACT ARTICLE HISTORY Integrating the Red Edge channel in satellite sensors is valuable for plant species discrimination. Sentinel-2 MSI and Rapid Eye are some of the new generation satellite sensors that are characterized by finer spatial and spectral resolution, including the red edge band. The aim of this study was to evaluate the potential of the red edge band of Sentinel-2 and Rapid Eye, for mapping festuca C3 grass using discriminant analysis and maximum likelihood classification algorithms. Spectral bands, vegetation indices and spectral bands plus vegetation indices were analysed. Results show that the integration of the red edge band improved the festuca C3 grass mapping accuracy by 5.95 and 4.76% for Sentinel-2 and Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. The results demonstrate that the use of sensors with strategically positioned red edge bands, could offer information that is critical for the sustainable rangeland management. received 6 December 2017 accepted 27 april 2018 KEYWORDS red edge band; c3 Festuca; grass species mapping; discriminant analysis Introduction Grasslands cover approximately 30% of the Earth’s surface and provide a range of critical socio-economic and ecological services (Eriksson et al. 1995; Cousins and Eriksson 2002; Gardi et al. 2002; Critchley et al. 2004).These include; provision of grazing, wildlife habitat, control of soil erosion, biological regulation of pests, preservation of species and genetic diversity, recreation, water and climate change regulation through carbon fixation (Parton et al. 1996; Daily et al. 1997; Leadley et al. 1999; Conant et al. 2001; Flanagan et al. 2002; Li et al. 2004; t Mannetje 2006; Corcoran 2010; O’Mara 2012). In South Africa, grassland is the second largest biome, covering approximately 28.4% of the country. The country’s grasslands are mainly found in the interior Highveld and higher elevation, and in patches along the Eastern Cape and KwaZulu-Natal coast (Matsika 2008; O’Connor and Kuyler 2009; Bond and Parr 2010; van Wilgen et al. 2012; Everson and Everson 2016). Generally, sustainable delivery of the above named services is highly influenced by the stability of grassland ecosystems. To ensure such sustainability, an understanding of key drivers to their productivity, as well as approaches to determine such productivity is necessary (Black 2006; He 2008). However, most of these grasslands are commonly characterized by complex grass species stand structure and CONTACT charles otunga pesatoango@yahoo.ca, charlesotunga95@gmail.com, 212558378@stu.ukzn.ac.za © 2018 Informa UK limited, trading as taylor & Francis Group 2 C. OTUNGA ET AL. abundance, posing a spatial mapping challenge (Mutanga and Rugege 2006; Everson and Everson 2016). C3 and C4 grasses represent a unique species functional type that influence the functioning and provision of ecosystem services. The geographic distribution of these grass species is found in the Southern and Eastern Africa, Middle East and the former Soviet Union, the Java, Aldabra Atoll and North and Central America (Ellis et al. 1980; Pearcy et al. 1981; Hattersley 1983). They are an important source of forage for livestock and wildlife and are valuable environmental and biodiversity resources (Niu et al. 2008; Mansour et al. 2012; Pau and Still 2014; Shoko et al. 2016). The C3/C4 grass photosynthetic pathways represent a unique functional type of species that share physical and chemical processes, phenological, annual or seasonal and structural properties. Studies have recently highlighted that changes in climate and land use/land cover continue to impose substantial changes in the C3 and C4 spatial distribution (Adjorlolo 2013; Xia et al. 2014). These anticipated changes will most likely result in a shift in their distribution, abundance, forage availability, palatability and composition (Diaz and Cabido 1997; Adjorlolo et al. 2016). Such changes would likewise affect the intake potential of livestock, hence influence livestock production and by extension food security in a country (Knox et al. 2011; Singh et al. 2017). Studies (e.g. Low and Rebelo 1998; Van Wyk and Van Oudtshoorn 1999; Terry et al. 2008; Botha et al. 2012) show that the C3 Festuca costata grass species, with low grazing value, continues to proliferate in the high value C4 rangelands in the eastern higher altitudes of South Africa. In the end, such proliferation would represent a significant threat to rangeland biodiversity, as well as local and regional ecosystem function (D’Antonio and Vitousek 1992; Rossiter et al. 2003; Zhu et al. 2007; Thomas 2013). Such infiltration may also compromise rural livelihoods as a large proportion of the population in the region depend on grasslands as the main forage source for livestock (Meadows and Hoffman 2002). Hence, determination of C3 (Festuca spp.) spatial distribution is valuable for understanding their spatial distribution and optimization of rangeland management (Marshall 2013). Such spatial information is critical hence required for decision-making and resource planning by stakeholders. There has been an increased adoption of remotely sensed data in mapping grass vegetation distribution (Ullah et al. 2012; Nestola et al. 2016). This has however not been optimally utilized as adoption of these data in heterogeneous landscapes are limited by their coarse spatial resolution and the mixed pixel phenomenon. Consequently, low temporal resolution of sensors which do not capture the variability in the reflectance of different vegetation species (Franke et al. 2012; Darvishzadeh 2015), further impedes species mapping. Even though hyper spectral data, with higher mapping accuracy remain popular for C3 Festuca grasses mapping (Liu and Cheng 2011), their high cost per unit area still remains an impediment to wide adoption as repetitive monitoring is necessary for determining C3 Festuca grass spatial distribution (Marshall 2013). Therefore, relatively affordable and/or freely available high spatial multispectral remotely sensed data are necessary for mapping grass species distribution in rangeland ecosystems at both local and regional levels. Recently, a suite of new generation sensors like GeoEye, WorldView-2/3, Landsat 8 Operational Land Imager (OLI), SPOT-7, TerraSAR-X, ALOS-2, Rapid Eye and Sentinel-2 MSI, with mediumto-higher spatial and spectral resolution have emerged. Such sensors, with the additional strategically positioned red edge channels have opened up additional opportunities for improved vegetation mapping (Cheng and Sustera 2009). Specifically, these new sensors, with finer spatial resolutions and improved spectral characteristics offer a great opportunity for discriminating vegetation species in heterogeneous landscapes, hence is seen as a trade-off between benefits offered by multispectral and hyperspectral remotely sensed data (Adam et al. 2010; Ramoelo et al. 2015; Tarantino et al. 2016; Dhau et al. 2017; Dube et al. 2017; Sibanda et al. 2017). Whereas, remotely sensed data and associated techniques have been critical in C3/C4 grassland mapping, there is paucity in literature that seek to evaluate the influence of the red edge channel in high spatial resolution multispectral sensors (e.g. Rapid Eye, WorldView-2/3 and Sentinel-2 MSI) for discrimination and mapping of C3(Festuca spp.) grass species distribution. The value of the additional Rapid Eye and Sentinel-2 MSI bands, for instance, has been demonstrated in a number of diverse studies GEOCARTO INTERNATIONAL 3 Figure 1. location of Fort nottingham commonage rangeland study area in in KwaZulu-natal Province, shown with bands 3, 2 and 1 from the rapid eye satellite imagery as background. that include, mapping alien and indigenous vegetation (Odindi et al. 2016), classifying insect defoliation levels in an African savanna (Adelabu et al. 2014), land use classification (Schuster et al. 2012), estimation of nitrogen in a savanna ecosystem (Ramoelo et al. 2012) and crop assessment (Eitel et al. 2007; Forkuor et al. 2017). Hence, these applications provide scope for testing its value in mapping vegetation species in heterogeneous landscapes. In this study, we compare the performance of a commercial and a freely available sensor in delineating C3/C4 grass species. Specifically, we evaluate the value of both Sentinel-2 MSI’s and the Rapid Eye’s red edge channel and associated vegetation indices. The enhanced spectral (i.e. three red-edge bands) and spatial (10–20-m resolution) capabilities of Sentinel-2 MSI were tested to ascertain their contribution in improving mapping and discrimination accuracy of C3 Festuca and coexisting C4 grass species in Fort Nottingham commonage rangeland using DA algorithm, due to its ability to discriminate and project mapping and classification that is based on performance of continuous variables in separating specified categories in the classification process. Materials and methods Study area The study was conducted in Fort Nottingham Commonage, Kwazulu-Natal province, South Africa (Figure 1). The area is between 1519 and 1750 m above sea level and is characterized by a sub-tropical humid climate with about 950 mm of rain per year, mainly received between the summer months of October and March (Mucina and Rutherford 2006; Stears and Shrader 2015). The area has moderately cool temperatures, with mean annual temperature ranging from 14.2 to 15.1 °C. Heavy frosts occur in 34 days (Kruger and Shongwe 2004; Mucina and Rutherford 2006; Boothway et al. 2012). Vegetation in the study area is homogeneous, characterized by an extensive coverage of the Eastern Mist belt 4 C. OTUNGA ET AL. Table 1. training and validation/test/evaluation data-sets used. Training data-set 2/3 (n = 80) of the data Presence (1) 51 (34) absence (0) 69 (46) total 80 (66.7%) Test/evaluation data-set 1/3 (n = 40) of the data 17 23 40 (33.3%) Forest and Drakensberg Foothill Moist Grassland that supports a number of rare and threatened species (Edwards 1983; Mucina and Rutherford 2006). Specifically, vegetation in the area has been classified as northern KwaZulu-Natal moist Grassland, which is characterized by a forb-rich grassland, dominated by short bunch grasses that include; Themeda triandra, Hyparrhenia hirta, Tristachya leucothrix, Sporobulus africanus and patches of indigenous bushclumps (Killick 1990; Mucina and Rutherford 2006; Shaw and Escott 2011; Stears and Shrader 2015). Additionally, the shallow-rooted cool-season C3 grass species consists of Festuca costata, which coexists with other C3 grass species that include Poa binate, Agrostis barbulegera and Merxmuellera macowanii. Generally, the Festuca costata and Merxmuellera macowanii remain green during winter as they have lower water and nitrogen use efficiency, hence lower radiation use efficiency (Pearcy and Ehleringer 1984). In this regard, Festuca grass species, like most C3 grasses have shown increased encroachment due to elevated CO2 levels resulting from high emission of greenhouse gases (Norby and Luo 2004; Langley and Megonigal 2010). Soils with red and yellow brown apedal B-horizons and lithocutanic B-horizons are common within the study area (Boothway et al. 2012). As noted by King (2002), these soils are primarily formed on fine-grained sandstone, shale, siltstone and mudstone which alternate in horizontal progressions. The commonage rangeland area provides suitable habitat for endemic and priority species such as Oribi, African Crowned crane, Blue Crane, Samango monkey, Long toed tree frog, Tree hyrax, Grey Rhebuck, Cape and Bearded Vultures, Serval, Midlands Dwarf Chameleon and Amur falcon (Botha et al. 2012). A number of grazing paddocks also exist that are utilized for grazing by wild herbivore such Oribi antelope, similarly the commonage rangeland support cattle grazing from individual farmers through a lease arrangement that also determine various rangeland or land use intensities. The area forms part of the Tugela Macro Ecological Corridor that is an important biodiversity corridor linking the protected areas in the uKhahlamba Drakensberg World Heritage Site, hence critical in protecting the integrity of the rich fauna and flora within the area and KwaZulu-Natal as a whole. The study area is also recognized for its local significance to the C3/C4 grass species diversity, hence ideal for the current study. Field data collection A random stratified sampling strategy as explained by Hirzel and Guisan (2002)) was adopted for this study. Well-distributed random points (n = 120) within the grazing paddocks of the entire commonage (Figure 1) were generated on an existing GIS layer using ArcMap 10.4 software. The generated points and their attributes were then randomly divided into 2/3 (66.7%) and 1/3 (33.3%) for training the models and accuracy assessment, respectively (Table 1). Field data collection was conducted from 27 April to 5 May 2015 to record the field locations of C3/C4 grasses and to compliment the remotely sensed data. During data collection, the generated random sample points were input into a sub-metre handheld Trimble GPS device, which was used to navigate to the 120 random point locations within the Commonage. At each random point a 10 m × 10 m plot was demarcated. Within this10 m × 10 m demarcated plots, a 1 m × 1 m quadrant was randomly used to capture the plots grass species variability. A total of 360, 1 m × 1 m subplots were thus sampled for presence or absence of C3 (Festuca spp.) grass species. Based on the previous study by Botha et al. (2012), we recognized three dominant C3/C4 grass species types (Festuca costata, Themeda triandra & Tristachya leucothrix) within the 11 grazing paddocks visited. All the grass species GEOCARTO INTERNATIONAL 5 that were found to be growing on the random sampling plot were recorded on the basis of a simple ordinal abundance scale ranging from 0 to 1, where 1 indicated that the C3 Festuca grass species was present at that plot and 0 that other coexisting C3/C4 grass species were occurring. In each of the visited random plots, Festuca grass species, e.g. Festuca costata identified was considered presence (1), while other co-occurring C3/C4 grass species (which included Themeda triandra & Tristachya leucothrix), observed was considered absence (0). Each 1 m × 1 m sampling site was considered to have absence or presence of Festuca grass when the identified grass species covered approximately four-fifths of the target sample plot. The data collection exercise also involved recording a number of metadata which included; species count, percentage species count, sample plot’s Geographic or X-, Y-Coordinates, topography (topographic position, slope, aspect and elevation) which were generated from the 5-m digital elevation model (DEM). Satellite image data In this study, we utilized Sentinel-2 MSI and results compared to those that were produced when Rapid Eye satellite imagery was used. The Sentinel-2 MSI satellite was chosen as it delivers freely and readily available higher resolution, high-quality deca-metric images that incorporates the red edge band (Immitzer et al. 2016). The Sentinel-2 image was freely downloaded from the Scientific Data Hub website (https://scihub.copernicus.eu/).The data were acquired in 13 spectral bands, spanning from the visible through the near infrared (NIR) and the additional strategically positioned red edge, to the short wave infra-red (SWIR) at 10, 20 and 60 spatial resolutions (Cole et al. 2014). The Sentinel-2 MSI has four 10-m spatial resolution bands that include Band 2 (490 nm), Band 3 (560 nm), Band 4 (665 nm) and Band 8 (842 nm). Bands acquired at 60-m (coastal aerosol band 1, water vapour band 9 and cirrus band 10) spatial resolution are dedicated for detecting atmospheric features and were therefore excluded from the analysis (Drusch et al. 2012). We re-sampled the Sentinel-2 20-m bands to 10-m. The red-edge bands are centred at 705 and 740 nm with a band width of 15 nm and a spatial resolution of 20 m (Clevers et al. 2017). A cloud-free commercial multi-spectral Earth observation mission Rapid Eye Ortho rectified (Level 3A product) satellite imagery was acquired on 2 May 2015 to coincide with ground data collection. Rapid Eye is a multispectral Earth observation mission that includes a constellation of five mini identical satellites providing multispectral images in the blue, green, red, red edge and near-infrared spectral domains (Tyc et al. 2005). The mission provides high-resolution multispectral imagery along with an operational GIS (Geographic Information System) service on a commercial basis. Its ability to combine attributes of high spatial and temporal resolution sensors make it unique amongst other Earth observation platforms (Eitel et al. 2007). The rapid Eye sensor image has five optical bands in the 400–850-nm range, including a unique Red Edge band 4 (690–730 nm) Blue band 1 (440–510 nm), Green band 2 (520–590 nm), Red band 3 (630–685 nm) and NIR band 5 (760–850 nm) (Table 2) and provides 5-m pixel size at nadir (RapidEye 2016) critical for vegetation characterization. Rapid Eye’s red edge band is particularly sensitive to chlorophyll content, hence valuable for vegetation characterization. Detailed characteristics of the images used for analysis are presented in Table 2. Image data pre-processing Sentinel-2 MSI image was atmospherically corrected using the SEN2COR procedure available in the Sentinel-2 SNAP (Sentinel Application Platform) toolbox, that converted top-of-atmosphere (TOA) reflectance into top-of-canopy (TOC) reflectance (Louis et al. 2016). SEN2COR performs a pre-processing of TOA image data and applies a scene classification for atmospheric correction. Atmospheric bands of Sentinel-2 are used to derive maps of aerosols, water vapour and cirrus clouds (Louis et al. 2016). The Sentinel-2 atmospheric correction was based on the ATCOR algorithm (Richter and Schläpfer 2002). 6 C. OTUNGA ET AL. Table 2. rapid eye and Sentinel-2 MSI satellite image spectral characteristics. Satellite Image Sentinel-2 MSI (VIS–nIr) Sentinel-2 MSI (red-edge–SWIr) rapid eye Band Spatial Spectral Wavelength Band Description Blue 2 Green 3 red 4 nIr 8 red-edge 5 red-edge 6 red-edge 7 red-edge 8a Blue 1 Green 2 red 3 red-edge 4 nIr 5 Resolution (m) 10 10 10 10 20 20 20 20 5 5 5 5 5 Range (nm) 458–523 543–578 650–680 855–875 698–713 733–793 773–793 855–875 440–510 520–590 630–690 690–730 760–850 Centre (nm) 490 560 665 842 705 740 783 865 Width (nm) 20 65 35 30 70 70 55 40 90 The Rapid Eye satellite image was provided pre-processed for radiometric, sensor and geometric anomalies by the data providers using the digital terrain elevation data (DTED) level 1 Shuttle Radar Terrain Mission (SRTM). The image was geo-registered to the Universal Transverse Mercator UTM zone 36 South projection and the geo-rectification validated using orthophotos derived from the study area’s aerial photograph, 5-m digital elevation model (DEM) and 14 ground truthing points. The digital numbers of the Rapid Eye satellite image data were converted to radiance and then to top of atmosphere (TOA) reflectance (see Barsi et al. 2003; Chander et al. 2009). To retrieve surface reflectance, atmospheric correction was executed using the Improved Dark Object Subtraction (DOS) technique as described by Chavez (1996) and Chavez (1988). According to Hadjimitsis et al. (2003), the DOS approach has been found to provide a reasonable correction in the visible bands. It is similarly recommended over more sophisticated techniques that require the acquisition of atmospheric or other auxiliary data for a cloudless image scene. The pre-processed image was then utilized to derive the spectral signatures for statistical analysis. Spectral data analysis To determine the value of the red edge bands, supervised classification of both Sentinel-2 MSI and Rapid Eye satellite image was performed using five different sets of spectral feature sets. Apart from the standard Sentinel-2 MSI and Rapid Eye sensor spectral bands, the analysis also comprised the usual vegetation index incorporation; the normalized difference vegetation index (NDVI) and the normalized difference vegetation index – Red Edge (NDVI-RE). For both Sentinel-2 MSI and Rapid Eye, only the reflective bands in the visible, NIR and SWIR sections of the EM and red edge were subsequently used in the analysis. This is because as noted by Seelig et al. (2008), Ceccato et al. (2002), Danson and Bowyer (2004), the reflectance in these bands varies with different species of vegetation. We also extracted some vegetation indices (VIs) that are normally relevant for vegetation studies; normalized difference vegetation index (NDVI), and red edge-dependent NDVI (NDVI-RE) (see Huete et al. 2011; Förster et al. 2012; Schuster et al. 2012; Kim and Yeom 2015). The analysis was executed in four stages (a), (b), (c) and (d) shown in Table 3. Discriminant analysis (DA) and maximum likelihood classification (MLC) Observed C3 (Festuca spp.) grass species distribution We utilized Discriminant analysis (DA) algorithm to evaluate the capability of Rapid Eye and Sentinel-2 MSI satellite image in distinguishing the reflectance of C3 (Festuca spp.) from other coexisting C4 grass species. This algorithm is one of the data mining techniques that is used to discriminate a single Table 3. Satellite image spectral bands and computed vegetation indices. Applied variables Spectral bands chosen bands Vegetation indices Spectral bands and nDVIs Variables utilized rapid eye: Blue band 1 (440–510 nm), Green band 2 (520–590 nm), red band 3 (630–685 nm), red edge band 4 (690–730 nm) and nIr band 5 (760–850 nm) Sentinel-2 MSI: Blue band 2 (458–523 nm); Green band 3 (543–578 nm); red band 4 (650–680 nm); red-edge band 5 (698–713 nm); red-edge band 6 (733–793 nm); red-edge band 7 (733–793 nm); nIr band 8 (855–875 nm) rapid eye: Blue band 1 (440–510 nm), red band 3 (630–685 nm), red edge band 4 (690–730 nm) and nIr band 5 (760–850 nm)Sentinel-2 MSI: Blue band 2 (458–523 nm); Green band 3 (543–578 nm); red band 4 (650–680 nm); red-edge band 5 (698–713 nm); red-edge band 6 (733–793 nm); red-edge band 7 (733–793 nm); nIr band 8 (855–875 nm) normalized vegetation indices (nDVIs): nDVI; nDVI-re rapid eye’s & Sentinel-2 MSI bands + nDVIs Analysis process (a) (b) (c) (d) GEOCARTO INTERNATIONAL 7 8 C. OTUNGA ET AL. classification variable using multiple attributes (Fernandez 2002). It is a powerful tool for analysing and describing group differences and for classifying cases into groups formed on the basis of their similarities and differences on multiple variables. The analysis also assigns observations to one of pre-defined groups based on the knowledge of a number of attributes (Brown and Wicker 2000). The algorithm was chosen due to its ability to discriminate and project classification based on the performance of continuous variables in separating specified categories in the classification process (Sibanda et al. 2015; Matongera et al. 2017). A number of studies have recently successfully used the DA in discriminating different land covers (Beck et al. 1997; Fraley and Raftery 2002; Ju et al. 2003; Sibanda et al. 2015). The analysis aggregates wavebands into components, also known as latent factors (Dong et al. 2013; Sibanda et al. 2016). The impact of these latent factors is measured using Eigen vectors or variable scores. Consequently, the most effective latent factors in discriminating grass reflectance in this study were those that had the highest scores. To determine the potential of integrating the red edge band of Sentinel-2 MSI and Rapid Eye satellite sensors’ spectral configuration in mapping and discrimination of C3 Festuca and coexisting C4 grasses, we utilized the Discriminant Analysis (DA). Before we applied the DA, the data was randomly split into 70 and 30% for training/classification and accuracy assessment, respectively. As noted by Adelabu et al. (2014), Adjorlolo and Mutanga (2013), Sibanda et al. (2015) among others, this partitioning is a standard requirement for all machine learning algorithms. The training sample trains the DA algorithm in mapping the grass species, whereas the test sample validates the performance of the model. In this regard, the DA function was utilized to: (a) determine whether the integration of the red edge channel of the two satellite sensors would increase the discrimination accuracy of C3 Festuca and coexisting C4 grass species. (b) identify the best (most influential) spectral bands of the two sensors’ spectral configuration that are useful for mapping and discriminating the two grass species and (c) identify the most relevant variables among standard bands, vegetation indices and their combination thereof in the mapping and discriminating C3 Festuca and other coexisting C4 grass species. Next, we conducted a Wilks’s Lambda (Rao’s approximation) and chi-Square (chi-square asymptotic approximation) tests using variable importance in the projections (VIPs), to examine the magnitude of variation within the C3 (Festuca spp.) and coexisting C4 grass species class covariance matrices. The measures evaluate the statistical significance of the discriminatory power of the discriminant function. In this regard, the ratio of within-group variability to total variability on the discriminator variables, is an inverse measure of the importance of the functions (Brown and Wicker 2000; Walde 2014). Consequently, values close to 1 indicate that almost all of the variability in the discriminator variables is due to within-group differences (differences between cases in each group); while values close to 0 indicate that almost all of the variability in the discriminator variables is due to group differences (Betz 1987; Brown et al. 1995). The DA was executed within XLSTAT for Microsoft Excel 2013 platform and Statistical Product and Service Solutions – SPSS (PASW-predictive analytic software, Statistics 18 for Windows) (XLSTAT 2013). Discriminant analysis thus aggregated the wavebands into components or latent factors (Fernandez 2002). As noted by Sibanda et al. (2017, the influence of these underlying factors is measured using Eigen vectors or variable scores which points at the discrimination power of the function. Furthermore, the most effective latent factors in discriminating C3 (Festuca spp.) and other coexisting C3/C4 grass species reflectance were taken to be those having the highest scores. Overall, the DA results were presented as classification and cross-validated results. In this regard, the variable screening ensures the identification of a few, most influential input variables that can further enhance the model predictive accuracy. This process is critical as demonstrated in literature that the relative individual variable influence is important because not all model input variables are equally relevant in any modelling process (Ramoelo et al. 2015). The model thus computes the individual variable’s relative influence based on the contribution of each variable in reducing overall model deviance. Maximum likelihood classification (MLC) was also utilized to further show C3 Festuca and other coexisting C4 grass species spatial distribution given that the DA has no capability of map generation hence was only utilized to compare improvement in classification accuracy. The ML algorithm is a GEOCARTO INTERNATIONAL 9 robust and simple parametric approach in remote sensing image classification (Jensen 1996; Foody and Dash 2007; Dixon et al. 2014). The method is based on the assumption that the data may be modelled by a set of multivariate normal distributions. It therefore takes into account both the spectral variability within and between classes (Fahsi et al. 2000; Liu et al. 2002; Weng 2002). According to DiPietro et al. (2002 using training sets from the same image in MLC provides an excellent method for mapping vegetation classes. In this study, we have not apply a post-classification processes like majority filtering for the enhancement of our classification results as the study focused on the result of the individual pixel classification to evaluate the Red Edge effect on classification accuracy. For the description and illustration of results, accuracy assessment was performed for each of the classification algorithms using confusion/error matrix. Accuracy assessment Classification accuracy for the C3 (Festuca spp.) and other coexisting C3/C4 grass species discrimination was assessed using the summary parameters as well as classification overall accuracies – quantity disagreement and allocation disagreement (Pontius and Millones 2011). A confusion/error matrix was used to compare the true class with the class assigned by the classifier, and to calculate the user’s accuracy (UA), the producer’s accuracy (PA) and overall accuracy. According to Congalton (1991), PA is calculated as the total number of correctly classified cases divided by the total number of cases in that class, indicated by the estimation data. Furthermore, UA is calculated as the total number of correctly classified cases of one category, divided by the total number of cases classified in that category (see Story and Congalton 1986; Bobbe et al. 2001). In that regard, the PA gives a measure of commission errors that correspond to those pixels belonging to the class of interest that the classifier has failed to detect, while the UA gives a measure of commission errors that corresponds to those pixels from other classes that the classifier has assigned to a class of interest (Bhaskaran et al. 2010). As noted by Zhou et al. (2014), in the current study, the commission error stems from the incorrect inclusion of the species in the other grass species class or category, whereas omission error occurs when a sample of a particular grass species class is excluded from the class under consideration (Liu et al. 2007). Consequently, to compare the accuracy change of inclusion and exclusion of red edge band and also of Sentinel-2 and Rapid Eye sensor, a McNemar test was run in addition to the confusion/error matrix. McNemar test is a nonparametric test used to compare classification abilities. It has demonstrated robustness as well as a higher precision in comparing accuracy assessments in classification studies (Zhang and Xie 2012; Rodriguez-Galiano and Chica-Rivas 2014). The McNemar’s test is a better statistic for comparing accuracies of classification methods than the kappa index and it is simple to compute (Petropoulos et al. 2012; Adelabu et al. 2013). The kappa chi-squared requires that independent data are used to assess accuracies, but in this study the same points are used in all methods thus the McNemar’s test was more appropriate as it is also more precise and sensitive (Manandhar et al. 2009). The test is based on a chi-square (χ2) statistic, which is calculated from two error matrices stated as ( ) ( )2 ( ) 𝜒 2 = f12 −f21 ∕ f12 + f21 where f12 is the number of classes that are incorrectly classified by the first algorithm and are appropriately classified by the second algorithm, whereas f21 is the number of classes that are properly classified by the first algorithm and incorrectly classified by the second classifier algorithm (Kumar et al. 2002; de Leeuw et al. 2006; Manandhar et al. 2009). It is therefore a strong test that has been successfully utilized to compare classification accuracies (Sibanda et al. 2016; Shoko and Mutanga 2017a). Hence, at a confidence of 95%, a McNemar test result (indicated by z score) above 1.96 indicate that the classification accuracies derived from the different sensors and associated variables are significantly different (Rodriguez-Galiano and ChicaRivas 2014; Sibanda et al. 2016). In the current study, a McNemar test was run to compare the change 10 C. OTUNGA ET AL. Figure 2. overall classification accuracies achieved from the Mlc when: (a) rapid eye and (b) Sentinel-2 variables were used in the analysis process. in classification accuracy of inclusion and exclusion of red edge band and also when Sentinel-2 and Rapid Eye were used in mapping and discrimination of C3 (Festuca spp.) and coexisting C4 grasses. Results The influence of sensors spectral bands on C3 Festuca and coexisting C4 grass species mapping and discrimination Using variables scores, derived from the DA model, Sentinel-2 MSI sensor provided more bands which have great potential (indicated by the high variable scores) in mapping and discriminating C3 Festuca and coexisting C4 grass species. The red edge bands (5, 6,7), notably centred at 705-, 740-, 783- and 865-nm, the VIS–NIR bands (2 and 8) centred at 490- and 842-nm spectral bands of the Sentinel 2 were the most influential in mapping C3 (Festuca costata) and coexisting C4 (Tristachya leucothrix) grasses. Relatively, for the Rapid Eye sensor, the blue (between 450 and 510 nm) and red edge bands between 705 and 745 nm were also the most influential bands. The DA analysis thus brought forth the five most important spectral bands to be used in subsequent analysis. We found Band2 and Band8 (in the VIS–NIR), Band5, band6 and Band7 (in the red-edge channel) of Sentinel-2 MSI sensor, to have great potential (indicated by the high variable scores) in mapping and discriminating C3 Festuca and coexisting C4 grass species. Consequently, compared to the Rapid Eye sensor, a set of best (most influential) spectral bands was also identified. In particular, the significant spectral channels in Rapid Eye image were identified as Band3, Band4 and Band5. Figure 2, thus, shows the influence of the spectral bands of the two sensors on discriminating between C3 Festuca and coexisting C4 grass species, using variables scores, derived from the DA model. We however did not utilize all bands of the Sentinel-2 sensor in the analysis. Similar to Féret et al. (2015) and Immitzer et al. (2016), we considered bands 1 (coastal aerosol), 9 (water vapour) and 11 (SWIR cirrus) for the Sentinel-2 to be irrelevant to vegetation analysis, hence not useful to the study (see Figure 3). Maximum likelihood classification results are shown in Figure 4 below. C3 (Festuca spp.) and other coexisting C4 grass species were distributed heterogeneously across the study site. Results show that the integration of the red edge band improved the C3 (Festuca spp.) mapping and discrimination accuracy from (72.5–77.5%) – representing 5.00% for Sentinel-2 MSI, (75.00–80.00%) – representing 5.00% for Rapid Eye when the red edge bands were included and excluded in the analysis respectively. (Table 4). In this regard, the overall accuracy increased by 5% when the red edge band was incorporated in the MLC analysis. Generally, the inclusion of the red edge band has led to a general improvement of the classification accuracy results. GEOCARTO INTERNATIONAL Figure 3. Most influential variables (bands) in mapping and discrimination of c3 Festuca and coexisting c4 grass using both Sentinel-2 MSI and rapid eye image bands and Da. 11 12 C. OTUNGA ET AL. Figure 4. Grass species distribution maps generated when: Standard bands of rapid eye image were included (a), (b) the red edge band was excluded, (c) Standard bands of Sentinel-2 MSI image were included and (d) the red edge band was excluded in the Mlc analysis. Outstanding accuracy increases in terms of producer’s and user’s accuracies are also observed for C3 (Festuca spp.) and other coexisting C4 grass species, with respect to analysis a, b, c and d when Sentinel-2 MSI, Rapid Eye red edge bands and NDVI-RE are included and excluded, respectively (Figures 4, 5 and Tables 4–6.). Mapping and discrimination of C3 Festuca and other coexisting C4 grass species using Sentinel-2 MSI and the Rapid Eye optimal spectral bands versus analysis excluding and excluding red-edge Results from our analysis demonstrate that the integration of the red edge band improved the C3 (Festuca spp.) mapping and discrimination accuracy from (77.38–83.33%) – representing 5.95% for Sentinel-2 MSI, (79.76–84.52%) – representing 4.76 for Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. Consequently, mapping and discrimination accuracy improvement was realized from (72.62–75.00%) – representing 2.38% for Sentinel-2 MSI, (70.24–76.19%) – representing 5.95% for Rapid Eye, when the red edge indices were incorporated and excluded in the analysis, respectively (see Tables 6, 7(a)–(d) and Figure 5 below). Furthermore, McNemar’s test results from our analysis have also shown that there were statistically significant differences (z > 1.96) in classification accuracy changes when the red edge band of Sentinel-2 and Rapid Eye sensors were included and excluded in the analyses. For example, the z-scores were 2.78 and 2.32 when the red edge band of Sentinel-2 were included and excluded, respectively, while the z-scores were 2.56 and 2.24 when the red edge band of Rapid Eye sensors were included and excluded, respectively. GEOCARTO INTERNATIONAL 13 Table 4. confusion error matrix derived from the Mlc results when: (a) the rapid eye’s standard optimal bands were included and (b) the red edge channel band was excluded in the mapping and discrimination process. Observed (a) classified (b) classified Shrubs/ C3 Thickets Festuca C3/C4 Other Coexisting Shrubs/thickets 2 1 1 c3 Festuca 1 17 2 other coexisting 1 2 13 c3/c4 total 4 20 16 omission error 50 15 18.75 Producer’s accuracy 50 85 81.25 the overall accuracy is (2 + 17 + 13)/40 = 32/40 = 80.00% Shrubs/thickets 3 1 0 c3 Festuca 0 14 2 other coexisting 1 6 13 c3/c4 total 4 21 15 omission error 25 33.33 13.33 Producer’s accuracy 75.00 66.67 86.67 the overall accuracy is (3 + 14 + 13)/40 = 30/40 = 75.00% Commission User’s Overall error accuracy accuracy 50 15 18.75 50 85 81.25 80.00 25 12.5 35 75.00 87.5 65 75.00 Total 4 20 16 40 4 16 20 40 note: the bold values represents the percentage accuracies. Figure 5. overall classification accuracies achieved from the Da when: (a) the standard optimal bands of rapid eye and Sentinel-2 were included (b) the red edge channel band was excluded (c) the nDVI-re was included and (d) when the nDVI-re was excluded in the analysis process. Discussion User’s accuracy forms a guide to the reliability of the map, and as a predictive device, it tells the user of the classified map generated that in our assessment, of the area labelled C3 Festuca on the map for example, in Table 6(a) 76.92% actually corresponds to C3 Festuca on the ground. Consequently, Producer’s accuracy in our case informs us that, of the actual coexisting C3/C4 grass species, for example, 85.71% was correctly classified. Commission error (%) = 1 – User’s accuracy, while Omission error (%) = 1 – Producer’s accuracy (%) (Congalton and Green 2008). Comparing the results of our analysis; a, b, c and d have revealed that the additional red edge channel has influenced the overall classification accuracy. This is supported by the results from the analyses based on inclusion as well as exclusion, of the red edge band in both DA and MLC algorithms we utilized. The overall classification 14 C. OTUNGA ET AL. Table 5. confusion error matrix derived from the Mlc results when: (a) the Sentinel-2 MSI’s standard bands were included and (b) the red edge channel bands were excluded in the mapping and discrimination process. Shrubs/ Observed (a) classified (b) classified Thickets C3 Other Festuca Coexisting C4 Commission User’s Overall Error Accuracy Accuracy Total Shrubs/thickets 2 1 1 c3 Festuca 0 16 4 coexisting c4 0 3 13 total 2 20 18 omission error 0 20 27.78 Producer’s accuracy 100 80 72.22 overall accuracy is (2 + 16 + 13)/40 = 31/40 = 77.50 4 20 16 40 27.78 50 20 18.75 50 80 81.25 77.5 Shrubs/thickets 3 1 1 c3 Festuca 0 12 3 coexisting c4 0 6 14 total 3 19 18 omission error 43 36 37 Producer’s accuracy 66.7 64 63 overall accuracy is (3 + 12 + 14)/40 = 29/40 = 72.50 5 15 20 40 43 40 33 57 60 67 72.5 note: the bold values represents the percentage accuracies. Table 6. confusion error matrix derived from the Da when: (a) the standard most influential bands of Sentinel-2 MSI were included (b) the red edge channel bands were excluded (c) the VIs (nDVI, nDVI-re) were included and (d) when the VIs (nDVI, nDVI-re) were excluded in the analysis process. C3 (a) classified (b) classified (c) classified (d) classified Other Commission User’s Overall Error Accuracy Accuracy Observed Festuca Coexisting C4 Total c3 Festuca coexisting c4 total omission error Producer’s accuracy 30 5 35 14.29 85.71 9 40 49 18.37 81.63 39 45 84 23.08 11.11 76.92 88.89 83.33 c3 Festuca coexisting c4 total omission error Producer’s accuracy 29 9 38 23.68 76.32 10 36 46 21.74 78.26 39 45 84 25.64 20 74.36 80 77.38 c3 Festuca coexisting c4 total omission error Producer’s accuracy 32 14 46 30.43 69.57 7 31 38 18.42 81.58 39 45 84 17.95 31.11 82.05 68.89 75.00 c3 Festuca coexisting c4 total omission error Producer’s accuracy 24 8 32 25.00 75.00 15 37 52 28.85 71.15 39 45 84 38.46 17.78 61.54 82.22 72.62 note: the bold values represents the percentage accuracies. accuracy improvement can be linked to the additional information provided when the red edge band was included in the analysis. This represented an overall mapping accuracy increase of 5.95 and 4.76% for Sentinel-2 MSI and Rapid Eye when DA algorithm was utilized, respectively. Improvement was also realized when the vegetation indices were incorporated in the analysis, i.e. the accuracy increased by GEOCARTO INTERNATIONAL 15 Table 7. confusion error matrix derived from the Da when: (a) the standard influential bands of rapid eye image were included (b) the red edge channel band was excluded (c) the VIs (nDVI, nDVI-re) were included and (d) when the VIs (nDVI, nDVI-re) were excluded in the analysis process. (a) classified (b) classified (c) classified (d) classified C3 Other Observed Festuca Coexisting C4 Total Commission User’s Overall Error Accuracy Accuracy c3 Festuca coexisting c4 total omission error Producer’s accuracy 32 6 38 15.79 84.21 7 39 46 15.22 84.78 39 45 84 17.95 13.33 82.05 86.67 84.52 c3 Festuca coexisting c4 total omission error Producer’s accuracy 29 7 36 19.44 80.56 10 38 48 20.83 79.17 39 45 84 25.64 15.56 74.36 84.44 79.76 c3 Festuca coexisting c4 total 27 8 35 12 37 49 39 45 84 30.77 35.56 69.23 64.44 76.19 omission error Producer’s accuracy 22.86 77.14 24.49 75.51 c3 Festuca coexisting c4 total omission error Producer’s accuracy 25 11 36 30.56 69.44 14 34 48 29.17 70.83 39 45 84 35.90 24.44 64.10 75.56 70.24 note: the bold values represents the percentage accuracies. 2.38% for Sentinel-2 MSI (72.62–75.00%) and 5.95% for Rapid Eye (70.24–76.19%), when the vegetation indices (NDVI-RE) was incorporated and excluded in the analysis, respectively. The classification accuracy produced when using the Sentinel 2 Red Edge bands did not differ significantly (z < 1.96) from those produced when Rapid Eye Red Edge bands were included and excluded in the analyses. Our results hence confirm the earlier reported potential of the strategically positioned and/or additional bands of different sensors in enhancing their ability in discriminating vegetation species (Immitzer et al. 2016; Laurin et al. 2016; Shoko and Mutanga 2017b). Both the C3 Festuca and coexisting C4 grass species seemed to be affected by an accuracy improvement resulting from the incorporation of the red edge band in the analysis. The influence of the red edge band in discriminating different C3/C4 grasses could be attributed to the fact that, it is insensitive to the soil background effect (Vane et al. 1993; Datt and Paterson 2000; Clevers et al. 2001; Schumacher et al. 2016). Furthermore, literature shows that the Red Edge region of the electromagnetic spectrum is highly associated with vegetation characteristics, such as chlorophyll and LAI, which directly affect the reflectance of vegetation (Curran et al. 1990, 1995; Clevers and Gitelson 2013; Delegido et al. 2013). In this study, C3 (Festuca costata) and other coexisting C4 (Tristachya leucothrix) grass species were satisfactorily discriminated at optimal overall accuracies based on the Red Edge waveband. The improvement of classification accuracy obtained when Vis (NDVI-RE, REP and REIP) were used could be explained by the fact that the wavebands are more sensitive to vegetation traits when compared to individual bands only. Furthermore, VIs are highly sensitive to vegetation spectral and temporal characteristics, while reducing the sensitivity from atmospheric noise, view/Sun angle, soil background and topographic effects when compared to individual bands (Thenkabail et al. 2011). In 16 C. OTUNGA ET AL. that regard, the satisfactory performance of VIs in discriminating C3 Festuca grasses could be attributed to their ability to reduce noise while being sensitive to the different C3/C4 grass species spectral signatures. Furthermore, the plausible performance of red edge-associated VIs could be attributed to the fact that these indices are derived from a region in the electromagnetic spectrum that is highly sensitive to variations in grass vegetation chlorophyll. Conclusion The purpose of this study was to evaluate the value of the Rapid Eye’s and Sentinel-2 MSI’s Red Edge channel for mapping and discriminating C3 Festuca grass species. Based on these findings, we conclude that: • The study confirmed the high value of the red-edge and infrared bands from new relatively affordable or freely available multispectral sensors; Rapid Eye and Sentinel-2 MSI for grass vegetation mapping. • The study has demonstrated the potential of Sentinel-2 MSI’s and Rapid Eye’s red edge bands in improving mapping and discrimination accuracy of C3 Festuca grass species • The use of new generation sensors (Rapid Eye and Sentinel-2 MSI) with additional channels containing the red edge band is promising for C3 Festuca grass species mapping. • Sentinel-2 MSI provides a great opportunity for global vegetation monitoring due to its enhanced spatial, spectral and temporal characteristics compared with the commercial Rapid Eye image data. • To ensure that there is a wider applicability of the procedures, the methods we have presented in this study need to be tested on other C3/C4 grass species spatial distribution in different locations. • Studies should now move towards embracing the readily and freely available new generation multispectral sensors with strategically positioned bands more so in sub-Saharan Africa where data availability still remains a challenge Overall, results have indicated the potential of the new generation sensors, with strategically positioned spectral bands such as the red edge in mapping C3 Festuca grass species. 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