The threat of invasive alien plant species is progressively becoming a serious global concern. Alien plant invasions adversely affect both ecological services and socio-economic systems. Hence, accurate detection and mapping of invasive alien species is valuable in mitigating adverse ecological and socio-economic effects. Recent advances in active and passive remote sensing technology have created new and cost-effective opportunities for the application of remote sensing to invasive species mapping. In this study, new generation Sentinel-2 (S2) optical imagery was compared to S2 derived Vegetation Indices (VIs) and S2 VIs fused with Sentinel-1 (S1) Synthetic Aperture Radar (SAR) imagery for detecting and mapping the American Bramble (Rubus cuneifolius). Fusion of S2 VIs and S1 SAR imagery was conducted at pixel level and multi-class Support Vector Machine (SVM) image classification was used to determine the dominant land use land cover classes. Results indicated that S2 derived VIs were the most accurate (80%) in detecting and mapping Bramble, while fused S2 VIs and S1 SAR were the least accurate (54%). Findings from this study suggest that the application of S2 VIs is more suitable for Bramble detection and mapping than the fused S2 VIs and S1 SAR. The superior performance of S2 VIs highlights the value of the new generation S2 VIs for invasive alien species detection and mapping. Furthermore, this study recommends the use of freely available new generation satellite imagery for cost effective and timeous mapping of Bramble from surrounding native vegetation and other land use land cover types.

Alien species invasions, Sentinel-1, Synthetic Aperture Radar (SAR), Sentinel-2, Vegetation Indices (VIs), American Bramble, Fusion, Support Vector Machine (SVM)

Global biodiversity is increasingly becoming susceptible to pressure from invasive species (Butchart et al. 2010). Specifically, the rapid spread of invasive alien plants in several regions of the world has adversely impacted ecosystem health, native species diversity and local and national economies (Pysek et al. 2012; Schirmel et al. 2016; Convention on Biological Diversity 2009). Brooks et al (2006) highlight the imperative need for the protection of native biodiversity, a need further emphasised by the United Nations (UN) that declared the period between 2010 and 2020 as the decade of biodiversity (UNEP 2010). Moreover, increased costs associated with invasive alien species eradication and management programmes puts further pressure on biodiversity (Marbuah et al. 2014). The severity of the problem has increased the impetus on development of efficient and cost-effective approaches for the control and management of invasive alien plant species.

In South Africa, approximately two million hectares of land have been invaded by invasive alien plant species (van Wilgen et al. 2012). The south western, southern and eastern coastal and interior regions have been identified as highly vulnerable to invasion (Kotzé et al. 2010; van Wilgen et al. 2012; Clusella-Trullas and Garcia 2017). In KwaZulu-Natal (KZN) province, for instance, Erasmus (1984) notes that the cool and moist conditions favour a range of invasive alien plant species. The American Bramble (Rubus cuneifolius) has particularly thrived in the province’s western mountain ranges (Henderson 2011). Originating from North America, Bramble belongs to the Rosaceae family and has adverse direct and indirect impacts on biodiversity that include changes in nutrient cycling, increase in soil erosion, reduction in rangeland carrying capacity and viability, as well as effects on natural plant succession, fire patterns and behaviour and hydrological processes (Henderson 2001).

To develop optimal mitigation of spread and eradication approaches, determination of spatial cover and extent of Bramble infestation is paramount. Traditionally, surveys have been adopted for mapping and monitoring of invasive alien plant species (Tan et al. 2012; Shah and Reshi 2014). However, reliance on field-based surveys is often restrictive, as they are commonly time consuming, labour and resource intensive and unsuitable in inaccessible sites. Hence, the adoption of remotely sensed imagery for invasive alien species detection and mapping has recently gained popularity. Huang and Asner (2009) attribute this increase to improved sensor technology, facilitating detailed and large scale landscape mapping and monitoring. In the recent past, the majority of invasive alien plant species detection and mapping applications have relied on remotely sensed image spatial and spectral characteristics (Mirik et al. 2013; Müllerová et al. 2013). Other studies have proposed object-based textural and contextual characteristics (Zhou et al. 2008) and landscape thermal characteristics (Eisavi et al. 2015). However, the advent of new sensors with radar scanning capabilities provides new opportunities for invasive plant species detection and mapping (Bradley 2014). For instance, radar’s ability to determine surface structure and roughness, dielectric constant (moisture content) and slope angle and orientation offer great opportunities for invasive species mapping. The European Space Agency’s (ESA) sentinel constellation is a recent satellite that consists of the Sentinel-1 (S1) and Sentinel-2 (S2) earth observation instruments. Both sensors disseminate freely available multispectral optical (S2) and multi-polarised SAR (S1) data. The unique S1 and S2 sensor characteristics, such as large swath widths, medium to fine scale spatial resolutions, short re-visit times and additional bands (Frampton et al. 2013; Sentinel-1 User Handbook 2012) provide numerous opportunities to evaluate the potential of the sensors to improve the reliability of remote sensing approaches for invasive alien plant species mapping.

Conventional remote sensing of invasive alien species utilises spectral wavelengths of absorbed and reflected light by distinguishing certain pigments in leaves and inflorescence (Huang and Asner 2009; Mirik et al. 2013; Weisberg et al. 2017; Müllerová et al. 2013; Bradley 2014). Hence, the potential of S2 to detect and map invasive alien species exists (Rajah et al. 2018). Specifically, the senor’s improved spectral resolution can be used to derive numerous band ratios and indices, useful for vegetation mapping. For example, spectral vegetation indices (VIs), derived from remotely sensed data, have become valuable in mapping and monitoring vegetation species (Jamali et al. 2014; Zhang et al. 2015; Orhan et al. 2014). VIs have several advantages over stand-alone spectral bands that include reduced effect of atmospheric conditions, canopy geometry and shading, decreased effect of soil background on canopy reflectance and enhanced variability of spectral reflectance of target vegetation (Liu et al. 2004; Viña et al. 2011). On the other hand, the unique characteristics of S1 SAR imagery could provide additional variables that could improve invasive alien species detection and mapping. SAR data can operate at wavelengths irrespective of cloud conditions or lack of illumination and is capable of acquiring data during day and night (Sentinel-1 User Handbook 2012). SAR offers detailed information on the often difficult to detect characteristics of vegetation such as shape, moisture and roughness (Chen et al. 2010). However, despite this potential, previous adoption of SAR imagery for invasive alien plant species mapping has been limited by high acquisition cost, limited area coverage and complex data pre-processing (McNairn et al. 2009). Hence, the provision of freely available SAR imagery from the S1 satellite provides new prospects for advancing the mapping and detection of invasive alien plant species.

Asner et al. (2008) and Zhang (2010) note that the fusion of imagery from various sensors, while applying appropriate methodologies, may be valuable for invasive alien species detection and mapping. Furthermore, conventional optical imagery and SAR are commonly believed to be complimentary (Zhu et al. 2012). Considering the above-mentioned advantages, as well as S2s improved spatial, spectral and temporal characteristics valuable for generating VIs, the fusion of these datasets provides a unique opportunity to investigate the value of new generation sensors such as S1 and S2 in mapping alien species. Accordingly, this study sought to determine the performance of conventional stand-alone S2 optical imagery, stand-alone S2 derived VIs and fused S2 VIs with S1 Synthetic Aperture Radar (SAR) imagery in detecting and mapping the American Bramble.

This study sought to determine the potential of derived Vegetation Indices (VIs) and fused VIs and Synthetic Aperture Radar (SAR) imagery to improve invasive alien species detection and mapping. The overall classification accuracy of optical imagery was used as the benchmark for comparison of the results achieved using S2 VIs and fused VIs and SAR. Opposing the expected outcome, fused VIs and SAR imagery produced the lowest classification accuracy (55%) compared to conventional S2 optical imagery (78%). Moreover, S2 derived VIs produced the highest classification accuracy (84%) when compared to conventional S2 optical imagery and fused VIs and SAR.

Poor performance of fused VIs and SAR imagery was unanticipated and opposes research done by Sano et al. (2005), who noted that the combination of VIs and SAR for discrimination within a savannah environment was complementary and improved overall discriminant analysis. Sano et al. (2005) also noted that VIs and SAR were able to easily separate Grassland from woodlands. However, Sano et al. (2005) also reported increased confusion between Grassland and shrub species when utilising fused VIs and SAR. This provides some indication that previous studies have also encountered unanticipated results when combining VIs and SAR for discrimination purposes. Poor overall classification accuracies of fused VIs and SAR imagery can further be attributed to vegetation structure and roughness, as this plays a major role in measured SAR backscatter values. Similar difficulties were documented by Millard and Richardson (2018) who note that, even though it is well established that vegetation roughness influences SAR backscatter, characterising these variables spatially and temporally within natural environments remains a challenge. Although results from fused VIs and SAR were unexpected, similar poor performance using the same combination of variables is not unprecedented. For example, Torma et al. (2004) also experienced poor performance when fusing VIs and SAR.

Patel et al. (2006) and Srivastava et al. (2009) note that the magnitude of SAR backscatter is dependent on SAR band frequency, for instance, SAR backscatter signatures at high frequency (e.g. X-band SAR) are known to be sensitive to subtle variations in vegetation phenology attributed to deep canopy penetration. Sentinel-1 C-band SAR is considered low frequency (decreased canopy penetration) SAR imagery and could have experienced difficulty in discerning between Bramble characteristics and surrounding native vegetation when using fused VIs and S1 SAR imagery (Khosravi et al. 2017; Duguay et al. 2015; Naidoo et al. 2015; Hajj et al. 2014; van Beijma et al. 2014; Turkar et al. 2012). The influence of sensor incident angle on SAR backscatter is known to be interpreted using the same mechanism, particularly for lower frequencies of SAR. Inoue et al. (2002) notes that correlations to plant physiological characteristics, such as Leaf Area Index (LAI), canopy height and stem density, decrease with an increasing incident angle. This is mainly attributed to the penetration of SAR microwaves responsible for backscatter measurements, as smaller incident angles are able to penetrate deeper into canopy cover, hence extract more physiological information (McNairn et al. 2009). The relatively large incident angle of S1 (46°) (Sentinel-1 User Handbook 2012) could have hindered its ability to distinguish vegetation physiological information, which could serve to justify decreased classification accuracies achieved using fused VIs and SAR imagery (de Almeida Furtado et al. 2016; Naidoo et al. 2015; Frampton et al. 2013; Vyjayanthia and Nizalapur 2010). The influence of soil moisture and roughness on leaf and stalk SAR backscatter measurements is considered a weakness of SAR imagery across specific classification applications (Moran et al. 2002). SAR imagery could have served to increase confusion between Bramble and surrounding native vegetation when fused with S2 VIs.

The use of S2 VIs outperformed the benchmark accuracy achieved by conventional S2 optical imagery. Similar results were achieved by Kandwal et al. (2009), where selected VIs performed well in discriminating Lantana camara (Verbenaceae), an invasive alien plant with similar growth pattern and phenology to Bramble. The majority of Vis, selected as VIP indices, were dominated by VIs incorporating the Near Infrared (NIR), Shortwave Infrared (SWIR) and red edge S2 bands. A study by Zhao et al. (2007) produced similar results, where VIs, derived from SWIR, red-edge and NIR bands, were reported to be closely correlated to canopy LAI and canopy chlorophyll density. Chuai et al. (2013), for instance, used NDVI to determine seasonal vegetation correlations with lag-time climatic effects in Inner Mongolia between 1998 and 2007. They established varied seasonal changes and concluded that NDVI provides a reliable measure of vegetation changes attributed to climatic variability. According to Domaç et al. (2004), VIs can extract valuable information by generating a new variable set without inter-band correlation and reduced data dimensionality. Whereas the NDVI has commonly been preferred in vegetation mapping, El-Mezouar et al. (2010) suggests that Soil Adjusted Vegetation Index (SAVI) is more suitable for mapping patchy vegetation characterised by lower percentage cover. A recent study by Tarantino et al. (2019) in Apulia region, southern Italy, concluded that indices like MSAVI on WorldView2 imagery are effective in discriminating the invasive Aillanthus aitissina species. This superior performance is attributed to the indices’ maximal reduction of background soil effect on vegetation reflectance (Qi et al. 1994). Other studies like Groβe-Stoltenbeg et al. (2018) used 15 vegetation indices to determine an Acacia lonifolia cove in a dune ecosystem and concluded that the fusion of vegetation indices with LIDAR could effectively determine the effects of species invasion on the dune landscape.

Eight of the ten VIP VIs selected for Bramble discrimination and mapping were derived from at least one of these three spectral bands. The strong relationship between NIR, SWIR and red edge bands to variable vegetation parameters could have resulted in the increased accuracy of Bramble discrimination and mapping. Moreover, reflectance within the visible region of the spectrum is largely determined by vegetation pigments and is commonly used to quantify vegetation physiological properties (Li et al. 2013; Zhao et al. 2007). The collective capability of combined VIs to discriminate various vegetation parameters could further explain the increased overall classification accuracy achieved using stand-alone vegetation indices.

Several studies (e.g. Royimani et al (in press) – Perthenium, Matongera et al. (2017) - Pteridium aquilinum (L.) Kuhn, Robinson et al. (2016) - Mesquite (prosopts spp.), Peters et al. (1992) - Gutierrezia sarothrae and Oumar (2016) - Lantana camara have discriminated invasive species from native vegetation using spectral variability. Matongera et al. (2017) and Zhao et al. (2009) attribute this to invasive species’ dissimilar biophysical (e.g. texture, canopy, leaf structure and orientation) and biochemical (e.g. chlorophyll and water content) characteristics from the surrounding vegetation species. According to Blossey and Notzold (1995), invasive species are commonly characterised by superior physical development due to disproportionate availability or exploitation of resources. Such differences, particularly volume and height, facilitate their discrimination. Goodwin et al. (1999), for instance, noted that differences in stem heights and flowering periods could be used to discriminate invasive species from native vegetation, while Peerbhay et al. (2016) found that dense infestation, particularly in new habitats, facilitate discrimination. Commonly, the phenology of invasive species differs from native plants. Holland and Aplin (2013) and Page (2010) note that the discreet reflectance of invasive species at different seasons offers great potential for their discrimination. This is consistent with Santos and Ustin (2018) who noted that fennel (Foeniculum vulgare) could be effectively discriminated by considering seasonal foliar variability from surrounding grasses. In this study, Bramble’s broad leafs could have facilitated its discrimination, a finding in agreement with Hong et al. (2014) who successfully discriminated alfalfa in the Prairie Provinces of Canada.

According to Motohka et al. (2010), the potential of green-red VIs for phenological vegetation discrimination exists. VIs derived from ratios of red and green optical bands are known to be sensitive to variations in canopy colour, where changes in visible characteristics of vegetation canopy are often timeously detected (Motohka et al. 2010). The SR672/550 VI, an index derived solely from S2 red and green optical bands, suggests an agreement with Motohka et al. (2010). The SR672/550 VI could have assisted in the discrimination of Bramble as it produces noticeable white inflorescence during summer, a significant phenological trait that could have been exploited. This potential is further enhanced by S2’s higher temporal resolution (five days, with possible higher resolution due to overlap in swaths of adjacent orbits) that facilitates single scene’s image turnover. Although the combined potential of VIs and SAR imagery produced the lowest overall classification accuracy, the potential of the latest and advanced spectrally derived VIs was evident when compared to the benchmark set by conventional S2 optical imagery. While the fusion of S2 VIs and SAR showed limited utility with regard to accurately mapping Bramble, the complementarity of these datasets has previously been documented. Our findings are consistent with existing literature. For instance, in lower Magdalena region, Colombia, Clerici (2017) established that Sentinel 1 and 2 fused dataset, classified using SVM, generated the highest classification accuracy of the existing land use land covers. Hence, the study recommended the use of radar sensor due to its all-weather capability and the spectral wealth of the optical sensor. Niculescu et al. (2018) mapped major vegetation types by fusing S1, S2 and SPOT – 6 in Pays de Brest, France by stacking time series data using Random Forest supervised classification. The study achieved a 93% classification accuracy when the major vegetation indices (Normalised Difference Vegetation Index - NDVI, Normalised Difference Wetness Index NDWI, Inverted Red-Edge Chlorophyll Index - IRECI and Sentinel-2 Red-Edge Position - S2REP) were used in the classification process. The study recommends the use of S1 and S2 due to free availability and improved sensor capabilities.

This study utilised freely available advanced Sentinel-1 radar and Sentinel-2 optical imagery, with the aim of evaluating spectrally derived VIs and fusing Synthetic Aperture Radar (SAR) imagery for improving American Bramble (Rubus cuneifolius) detection and mapping. This study contributes to the evaluation of economically viable, efficient and large scale invasive alien species detection and mapping. Conventional S2 optical imagery was used as a benchmark for comparison with results achieved using S2 VIs and fused VIs and S1 SAR imagery. The use of S2 VIs increased overall classification accuracies when compared to traditional optical imagery results, while the fusion of S2 VIs and S1 SAR decreased the overall accuracies. Hence this study demonstrated that new generation S2 VIs have the potential to increase the detection and mapping of Bramble from surrounding native vegetation. Results further indicate that the fusion of VIs and SAR imagery for Bramble detection and mapping failed to increase overall classification accuracies, hence have limited utility when applied to Bramble detection and mapping. The new generation satellites, such as S1 and S2, possess unprecedented sensor characteristics like higher temporal and spatial resolution, as well as tandem acquisition of SAR data, hence valuable for improved landscape mapping. This study concludes that the recently launched Sentinel satellite, with optical and radar capabilities, holds great promise in landscape delineation and vegetation mapping.