Soil moisture estimation of eucalyptus forests in Portugal with l-band SAR using polarimetric - Decompositions and machine learning

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesSoil moisture is a critical ecological parameter because it is a primary input for all processes that involve the complex interaction between land surface and the atmosphere. Remote sensing, especially using microwaves, has shown great promise in measuring soil moisturewith several operating satellites focused on its continuous estimation and monitoring on a global scale. Portugal is predominantly characterized by Mediterranean and semi-arid climates that feature low and sporadic precipitation. Over 10% of Portugal’s land area has been planted with Eucalyptus globulus- a non-native, fast-growing tree primarily planted for industrial use. Some studies have demonstrated that eucalyptus plantations adversely affect water availability, but overall results have been inconclusive as there are numerous other confounding variables. The goals of this study were to determine, using fully polarimetric L-band SAR and machine learning, if soil moisture could be accurately predicted in eucalyptus forests, and if there is a significant difference in soil moisture inside eucalyptus forests relative to other forests. Vegetated surfaces complicate the estimation of soil moisture because their structure and water content contribute significantly to backscatter of the radar signal. Thus, four polarimetric decompositions were compared to separate vegetative versus surface backscatter. The inputs from those decompositions, as well as several additional radar indices and polarizations from the microwave images, were used as feature inputs into two different machine learning models. After a feature selection process, the soil moisture estimations were retrieved and compared using cross-validation. The best overall soil moisture retrieval for Eucalyptus forests came from Random Forest with a RMSE of 0.021, a MAE of 0.017, and a MBE of 0.001. Through a statistical t-test, predicted soil moisture values in eucalyptus forests did not differ significantly as compared to other forest types in the study area

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Methods for sugarcane harvest detection using polarimetric SAR

Thesis (MA)--Stellenbosch University, 2017.ENGLISH ABSTRACT: Remote sensing has long been used as a method for crop harvest monitoring and harvest classification. Harvest monitoring is necessary for the planning of and prompting of effective agricultural practices. Traditionally sugarcane harvest monitoring and classification within the realm of remote sensing is performed with the use of optical data. However, when monitoring sugarcane, the growth period of the crop requires a complete set of multi-temporal image acquisitions throughout the year. Due to the limitations associated with optical sensors, the use of all weather, daylight independent Synthetic Aperture Radar (SAR) sensors is required. The added polarimetric information associated with fully polarimetric SAR sensors result in complex datasets which are expensive to acquire. It is therefore important to assess the benefits of using a fully polarimetric dataset for sugarcane harvest monitoring as opposed to a dual polarimetric dataset. The dual polarimetric dataset which is less complex in nature and can be acquired at a fee much less than that of the fully polarimetric dataset. This thesis undertakes the task of identifying the value of fully polarimetric data for sugarcane harvest identification and classification. Two main experiments were designed in order to complete the task. The experiments make use of fully polarimetric RADARSAT-2 C-band imagery covering the southern part of Rèunion Island. Experiment 1 made use of a multi temporal single feature differencing technique for sugarcane harvest identification. Polarimetric decompositions were extracted from the fully polarimetric data and used along with the inherent SAR features. The accuracy with which each SAR feature was able to predict the sugarcane harvest date for each field was assessed. The polarimetric decompositions were superior in classification accuracy to the inherent SAR features. The Van Zyl volume decomposition component achieved an accuracy of 88.33% whereas the inherent SAR backscatter feature (HV) achieved an accuracy of 80%. Hereby displaying the value of the added information associated with fully polarimetric SAR data. The SAR backscatter channels did not achieve accuracies as high as the polarimetric features but did display promise for single feature sugarcane harvest identification when using only a dual polarimetric dataset. Experiment 2 assessed six different machine learning classifiers, applied to single-date, dual- and fully polarized imagery, to determine appropriate combinations of machine learning classifier and SAR features. Polarimetric decompositions were extracted from the fully polarimetric data and mean texture measures were then calculated for all SAR features for both the dual- and full polatrimetric data. A multi-tiered feature reduction method was undertaken in order to reduce dataset dimensionality for the dual- and fully polarised datasets. In general, the reduction in features resulted in improved accuracies. The best sugarcane harvest accuracy was achieved using the Maximum likelihood classifier using on the HV and VV backscatter channels (96.18%). The results from Experiments 1 and 2 indicate that SAR C-band data is suitable for sugarcane harvest monitoring and mapping in a tropical region where optical data have limitations associated with cloud cover and large amounts of moisture in the atmosphere. With the availability of dual polarised Sentinel-1 SAR data, future research should be focussed on the use of a dual polarimetric sugarcane harvest monitoring tool and should be extended to focus not only on sugarcane but other crops which contribute largely to the agriculture and economic sectors.AFRIKAANS OPSOMMING: Afstandswaarneming word lankal reeds gebruik as ‘n metode in die monitering van die oes van gewasse asook vir oes-klassifikasie. Oes-monitering is nodig vir die beplanning en stimulering van effektiewe landboupraktyke. Tradisioneel word suikerriet oes-monitering en klassifisering, binne die raamwerk van afstandswaarneming, uitgevoer met die gebruik van optiese data. Tog, met die monitering van suikerriet, vereis die groeiperiode van die gewas ‘n volledige stel multi-temporale beeldverwerwings dwarsdeur die jaar. As gevolg van die beperkings geassosieer met optiese sensors, word die gebruik van daglig onafhanklike sintetiese gaatjie radar sensors, eerder bekend as Sintetiese Apertuur Radar (SAR) sensors, vir gebruik in alle weersomstandighede, vereis. Die bykomende polarimetriese informasie geassosieer met ten volle gepolarimetriese SAR sensors lei tot komplekse datastelle wat duur is om aan te skaf. Dit is daarom belangrik om die voordele van die gebruik van ‘n ten volle gepolarimetriese datastel vir suikerriet oes-monitering in teenstelling met ‘n tweeledige polarimetriese datastel wat minder kompleks van aard is en teen ‘n fooi veel minder as dié van die ten volle gepolarimetriese datastel verkry kan word, te evalueer. Hierdie tesis onderneem die taak van die identifisering van die waarde van ten volle gepolarimetriese data vir suikerriet oes-identifikasie en -klassifikasie. Twee hoof-eksperimente is ontwerp om die taak te voltooi. Die eksperimente gebruik ten volle gepolarimetriese RADARSAT-2 C-band beelde wat die suidelike deel van Reunion-eiland dek. Met eksperiment 1 is gebruik gemaak van 'n multi-temporale enkelkenmerk differensie- tegniek vir suikerriet oes-identifisering. Polarimetriese ontledings is uit die ten volle gepolarimetriese data geneem en saam met die inherente SAR kenmerke gebruik. Die akkuraatheid waarmee elke SAR kenmerk in staat was om die suikerriet oes-datum vir elke veld te voorspel, is geëvalueer. Die polarimetriese ontledings was beter in klassifikasie- akkuraatheid as die inherente SAR kenmerke. Hiermee word die waarde van die bykomende inligting geassosieer met ten volle gepolarimetriese SAR data, geopenbaar. Die SAR teruguitsaaiingskanale het nie akkuraathede so hoog soos die polarimetriese kenmerke bereik nie, maar het belofte getoon vir enkelkenmerk suikerriet oes-identifikasie wanneer slegs van 'n tweeledige polarimetriese datastel gebruik gemaak word. Met eksperiment 2 is ses verskillende masjien-leer klassifiseerders, toegepas op enkeldatum, tweeledige en ten volle gepolariseerde beelde, geëvalueer om toepaslike kombinasies van masjien-leer klassifiseerder en SAR kenmerke te bepaal. Polarimetriese ontledings is geneem uit die ten volle gepolarimetriese data en beteken dat tekstuur afmetings toe bereken is vir alle SAR kenmerke vir beide die tweeledige- en ten volle gepolarimetriese data. 'n Multi-reeks kenmerkreduksie-metode is onderneem om datasteldimensionaliteit te verminder vir die tweeledige- en ten volle gepolariseerde datastelle. Oor die algemeen het die redusering van kenmerke verbeterde akkuraatheid tot gevolg gehad. Die beste suikerriet oes-akkuraatheid is behaal deur die Maksimum waarskynlikheid klassifiseerder met behulp van die HV en VV teruguitsaaiingskanale (96,18%) te gebruik. Die resultate van eksperimente 1 en 2 dui daarop dat SAR C-band data geskik is vir suikerriet oes- monitering en kartering in 'n tropiese streek waar optiese data beperkings toon wat geassosieer word met wolkbedekking en groot hoeveelhede vog in die atmosfeer. Met die beskikbaarheid van tweeledige gepolariseerde Sentinel-1 SAR data, behoort toekomstige navorsing gefokus te wees op die gebruik van 'n tweeledige polarimetriese suikerriet oes- moniteringshulpmiddel en behoort dit uitgebrei te word om te fokus nie net op suikerriet nie, maar ook ander gewasse wat grootliks bydra tot die landbou- en ekonomiese sektore

Crop development monitoring from Synthetic Aperture Radar (SAR) imagery

Satellite remote sensing plays a vital role in providing large-scale and timely data to stakeholders of the agricultural supply chain. This allows for informed decision-making that promotes sustainable and cost-effective crop management practices. In particular, data derived from satellite-based Synthetic Aperture Radar (SAR) systems, provide opportunities for continuous crop monitoring, taking advantage of its ability to acquire images during day or night and under almost all weather conditions. Moreover, an abundance of SAR data can be anticipated in the next 5 years with the launch of several international SAR missions. However, research on crop development monitoring with data from SAR satellites has not been as widely studied as with data derived from passive multi-spectral satellites and contributions can be made to the current state-of-the-art techniques. This thesis aims at improving the current knowledge on the use of satellite-based SAR imagery for crop development monitoring. This is approached by developing novel methodologies and detailed interpretations of multitemporal SAR and Polarimetric SAR (PolSAR) responses to crop growth in three different test sites. Chapter two presents a detailed analysis of the Sentinel-1 SAR satellite response to asparagus crop development in Peru, investigating the capabilities of the sensor to capture seasonality effects as well as providing an interpretation of the temporal backscatter signature. This is complemented with a case study where a multiple-output random forest regression algorithm is used to successfully retrieve crop growth stage from Sentinel-1 data and temperature measurements. Following the limitations identified with this approach, a methodology that builds upon ideas of Bayesian Filtering Frameworks (BFFs) for crop monitoring is proposed in chapter three. It incorporates Gaussian processes to model crop dynamics as well as to model the remote sensing response to the crop state. Using this approach, it is possible to derive daily predictions with the associated uncertainties, to combine in near-real-time data from active and passive satellites as well as to estimate past and future crop key events that are of strategic importance for different stakeholders. The final section of this thesis looks at the new developments of the SAR technology considering that future open access missions will provide Quad Polarimetric SAR data. An algorithm based on multitemporal PolSAR change detection is introduced in chapter four. It defines a Change Matrix to encode an interpretable representation of the crop dynamics as captured by the evolution of the scattering mechanisms over time. We use rice fields in Spain and multiple cereal crops in Canada to test the use of the algorithm for crop monitoring. A supervised learning-based crop type classification methodology is then proposed with the same method by using the encoded scattering mechanisms as input for a neural-network-based classifier, achieving comparable performances to state-of-the-art classifiers. The results obtained in this thesis represent novel additions to the literature that contribute to our understanding and successful use of SAR imagery for agricultural monitoring. For the first time, a detailed analysis of asparagus crops is presented. It is a key crop for agricultural exports of Peru, the largest exporter of asparagus in the world. Secondly, two key contributions to the state of the art BFFs for crop monitoring are presented: a) A better exploitation of the SAR temporal dimension and an application with freely available data and b) given that it is a learning-based approach, it overcomes current limitations of transferability among crop types and regions. Finally, the PolSAR change detection approach presented in the last thesis chapter, provides a novel and easy-to-interpret tool for both crop monitoring and crop type mapping applications

Advanced techniques for classification of polarimetric synthetic aperture radar data

With various remote sensing technologies to aid Earth Observation, radar-based imaging is one of them gaining major interests due to advances in its imaging techniques in form of syn-thetic aperture radar (SAR) and polarimetry. The majority of radar applications focus on mon-itoring, detecting, and classifying local or global areas of interests to support humans within their efforts of decision-making, analysis, and interpretation of Earth’s environment. This thesis focuses on improving the classification performance and process particularly concerning the application of land use and land cover over polarimetric SAR (PolSAR) data. To achieve this, three contributions are studied related to superior feature description and ad-vanced machine-learning techniques including classifiers, principles, and data exploitation. First, this thesis investigates the application of color features within PolSAR image classi-fication to provide additional discrimination on top of the conventional scattering information and texture features. The color features are extracted over the visual presentation of fully and partially polarimetric SAR data by generation of pseudo color images. Within the experiments, the obtained results demonstrated that with the addition of the considered color features, the achieved classification performances outperformed results with common PolSAR features alone as well as achieved higher classification accuracies compared to the traditional combination of PolSAR and texture features. Second, to address the large-scale learning challenge in PolSAR image classification with the utmost efficiency, this thesis introduces the application of an adaptive and data-driven supervised classification topology called Collective Network of Binary Classifiers, CNBC. This topology incorporates active learning to support human users with the analysis and interpretation of PolSAR data focusing on collections of images, where changes or updates to the existing classifier might be required frequently due to surface, terrain, and object changes as well as certain variations in capturing time and position. Evaluations demonstrated the capabilities of CNBC over an extensive set of experimental results regarding the adaptation and data-driven classification of single as well as collections of PolSAR images. The experimental results verified that the evolutionary classification topology, CNBC, did provide an efficient solution for the problems of scalability and dynamic adaptability allowing both feature space dimensions and the number of terrain classes in PolSAR image collections to vary dynamically. Third, most PolSAR classification problems are undertaken by supervised machine learn-ing, which require manually labeled ground truth data available. To reduce the manual labeling efforts, supervised and unsupervised learning approaches are combined into semi-supervised learning to utilize the huge amount of unlabeled data. The application of semi-supervised learning in this thesis is motivated by ill-posed classification tasks related to the small training size problem. Therefore, this thesis investigates how much ground truth is actually necessary for certain classification problems to achieve satisfactory results in a supervised and semi-supervised learning scenario. To address this, two semi-supervised approaches are proposed by unsupervised extension of the training data and ensemble-based self-training. The evaluations showed that significant speed-ups and improvements in classification performance are achieved. In particular, for a remote sensing application such as PolSAR image classification, it is advantageous to exploit the location-based information from the labeled training data. Each of the developed techniques provides its stand-alone contribution from different viewpoints to improve land use and land cover classification. The introduction of a new fea-ture for better discrimination is independent of the underlying classification algorithms used. The application of the CNBC topology is applicable to various classification problems no matter how the underlying data have been acquired, for example in case of remote sensing data. Moreover, the semi-supervised learning approach tackles the challenge of utilizing the unlabeled data. By combining these techniques for superior feature description and advanced machine-learning techniques exploiting classifier topologies and data, further contributions to polarimetric SAR image classification are made. According to the performance evaluations conducted including visual and numerical assessments, the proposed and investigated tech-niques showed valuable improvements and are able to aid the analysis and interpretation of PolSAR image data. Due to the generic nature of the developed techniques, their applications to other remote sensing data will require only minor adjustments

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

Electromagnetic backscatter modelling of icebergs at c-band in an ocean environment

This thesis outlines the development of an electromagnetic (EM) backscatter model of icebergs. It is a necessary first step for the generation of in-house synthetic aperture radar (SAR) data of icebergs to support optimum iceberg/ship classifier design. The EM modelling was developed in three stages. At first, an EM backscatter model was developed to generate simulated SAR data chips of iceberg targets at small incidence angles. The model parameters were set to mimic a dual polarized dataset collected at C-Band with the Sentinel-1A satellite. The simulated SAR data chips were compared with signatures and radiometric properties of the satellite data, including total radar cross section (TRCS). A second EM model was developed to mimic the parameters of a second SAR data collection with RADARSAT-2; this second data collection was at larger incidence angles and was fully polarimetric (four channels and interchannel phase). The full polarimetric SAR data allowed for a comparison of modelled TRCS and polarimetric decompositions. Finally, the EM backscatter models were tested in the context of iceberg/ship classification by comparing the performance of various computer vision classifiers using both simulated and real SAR image data of iceberg and vessel targets. This step is critical to check the compatibility of simulated data with the real data, and the ability to mix real and simulated SAR imagery for the generation of skilled classifiers. An EM backscatter modelling tool called GRECOSAR was used for the modelling work. GRECOSAR includes the ability to generate small scenes of the ocean using Pierson-Moskowitz spectral parameters. It also allows the placement of a 3D target shape into that ocean scene. Therefore, GRECOSAR is very useful for simulating SAR targets, however it can only model single layer scattering from the targets. This was found to be limiting in that EM scattering throughout volume of the iceberg could not be generated. This resulted in EM models that included only surface scattering of the iceberg. In order to generate realistic SAR scenes of icebergs on the ocean, 3D models of icebergs were captured in a series of field programs off the coast of Newfoundland and Labrador, Canada. The 3D models of the icebergs were obtained using a light detection and ranging (LiDAR) and multi-beam sonar data from a specially equipped vessel by a team of C-CORE. While profiling the iceberg targets, SAR images from satellites were captured for comparison with the simulated SAR images. The analysis of the real and simulated SAR imagery included comparisons of TRCS, SAR signature morphology and polarimetric decompositions of the targets. In general, these comparisons showed a good consistency between the simulated and real SAR scene. Simulations were also performed with varying target orientation and sea conditions (i.e., wind speed and direction). A wide variability of the TRCS and SAR signature morphology was observed with varying scene parameters. Icebergs were modelled using a high dielectric constant to mimic melting iceberg surfaces as seen during field work. Given that GRECOSAR could only generate surface backscatter, a mathematical model was developed to quantify the effect of melt water on the amount of surface and volume backscatter that might be expected from the icebergs. It was found that the icebergs in a high state of melt should produce predominantly surface scatter, thus validating the use of GRECOSAR for icebergs in this condition. Once the simulated SAR targets were validated against the real SAR data collections, a large dataset of simulated SAR chips of ships and icebergs were created specifically for the purpose of target classification. SAR chips were generated at varying imaging parameters and target sizes and passed on to an iceberg/ship classifier. Real and simulated SAR chips were combined in varying quantities (or targets) resulting in a series of different classifiers of varying skill. A good agreement between the classifier’s performance was found. This indicates the compatibility of the simulated SAR imagery with this application and provides an indication that the simulated data set captures all the necessary physical properties of icebergs for ship and iceberg classification

The integration of freely available medium resolution optical sensors with Synthetic Aperture Radar (SAR) imagery capabilities for American bramble (Rubus cuneifolius) invasion detection and mapping.

Doctoral Degree. University of KwaZulu- Natal, Pietermaritzburg.The emergence of American bramble (Rubus cuneifolius) across South Africa has caused severe ecological and economic damage. To date, most of the efforts to mitigate its effects have been largely unsuccessful due to its prolific growth and widespread distribution. Accurate and timeous detection and mapping of Bramble is therefore critical to the development of effective eradication management plans. Hence, this study sought to determine the potential of freely available, new generation medium spatial resolution satellite imagery for the detection and mapping of American Bramble infestations within the UNESCO world heritage site of the uKhahlamba Drakensberg Park (UDP). The first part of the thesis determined the potential of conventional freely available remote sensing imagery for the detection and mapping of Bramble. Utilizing the Support Vector Machine (SVM) learning algorithm, it was established that Bramble could be detected with limited users (45%) and reasonable producers (80%) accuracies. Much of the confusion occurred between the grassland land cover class and Bramble. The second part of the study focused on fusing the new age optical imagery and Synthetic Aperture Radar (SAR) imagery for Bramble detection and mapping. The synergistic potential of fused imagery was evaluated using multiclass SVM classification algorithm. Feature level image fusion of optical imagery and SAR resulted in an overall classification accuracy of 76%, with increased users and producers’ accuracies for Bramble. These positive results offered an opportunity to explore the polarization variables associated with SAR imagery for improved classification accuracies. The final section of the study dwelt on the use of Vegetation Indices (VIs) derived from new age satellite imagery, in concert with SAR to improve Bramble classification accuracies. Whereas improvement in classification accuracies were minimal, the potential of stand-alone VIs to detect and map Bramble (80%) was noteworthy. Lastly, dual-polarized SAR was fused with new age optical imagery to determine the synergistic potential of dual-polarized SAR to increase Bramble mapping accuracies. Results indicated a marked increase in overall Bramble classification accuracy (85%), suggesting improved potential of dual-polarized SAR and optical imagery in invasive species detection and mapping. Overall, this study provides sufficient evidence of the complimentary and synergistic potential of active and passive remote sensing imagery for invasive alien species detection and mapping. Results of this study are important for supporting contemporary decision making relating to invasive species management and eradication in order to safeguard ecological biodiversity and pristine status of nationally protected areas