Testing the use of the new generation multispectral data in mapping vegetation communities of Ezemvelo Game Reserve

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science (Geographical Information Systems and Remote Sensing) at the School of Geography, Archaeology & Environmental Studies) Johannesburg. 2017Vegetation mapping using remote sensing is a key concern in environmental application using remote sensing. The new high resolution generation has made possible, the mapping of spatial distribution of vegetation communities. The aim of this research is to test the use of new generation multispectral data for vegetation classification in Ezemvelo Game Reserve, Bronkhorspruit. Sentinel-2 and RapidEye images were used covering the study area with nine vegetation classes: eight from grassland (Mixed grassland, Wetland grass, Aristida congesta, Cynadon dactylon, Eragrostis gummiflua, Eragrostis Chloromelas, Hyparrhenia hirta, Serephium plumosum) and one from woodland (Woody vegetation). The images were pre-processed, geo-referenced and classified in order to map detailed vegetation classes of the study area. Random Forest and Support Vector Machines supervised classification methods were applied to both images to identify nine vegetation classes. The softwares used for this study were ENVI, EnMAP, ArcGIS and R statistical packages (R Development Core, 2012) .These were used for Support Vector Machines and Random Forest parameters optimization. Error matrix was created using the same reference points for Sentinel-2 and RapidEye classification. After classification, results were compared to find the best approach to create a current map for vegetation communities. Sentinel-2 achieved higher accuracies using RF with overall accuracy of 86% and Kappa value of 0.84. Sentinel-2 also achieved overall accuracy of 85% with a Kappa value of 0.83 using SVM. RapidEye achieved lower accuracies using RF with an overall accuracy of 82% and Kappa value of 0.79. RapidEye using SVM produced overall accuracy of 81% and a Kappa value of 0.79. The study concludes that Sentinel-2 multispectral data and RF have the potential to map vegetation communities. The higher accuracies achieved in the study can assist management and decision makers on assessing the current vegetation status and for future references on Ezemvelo Game Reserve. Keywords Random forest, Support Vector Machines, Sentinel-2, RapidEye, remote sensing, multispectral, hyperspectral and vegetation communitiesLG201

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery

Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary

Remote Sensing for Monitoring the Mountaintop Mining Landscape: Applications for Land Cover Mapping at the Individual Mine Complex Scale

The aim of this dissertation was to investigate the potential for mapping land cover associated with mountaintop mining in Southern West Virginia using high spatial resolution aerial- and satellite-based multispectral imagery, as well as light detection and ranging (LiDAR) elevation data and terrain derivatives. The following research themes were explored: comparing aerial- and satellite-based imagery, combining data sets of multiple dates and types, incorporating measures of texture, using nonparametric, machine learning classification algorithms, and employing a geographical object-based image analysis (GEOBIA) framework. This research is presented as four interrelated manuscripts.;In a comparison of aerial National Agriculture Imagery Program (NAIP) orthophotography and satellite-based RapidEye data, the aerial imagery was found to provide statistically less accurate classifications of land cover. These lower accuracies are most likely due to inconsistent viewing geometry and radiometric normalization associated with the aerial imagery. Nevertheless, NAIP orthophotography has many characteristics that make it useful for surface mine mapping and monitoring, including its availability for multiple years, a general lack of cloud cover, contiguous coverage of large areas, ease of availability, and low cost. The lower accuracies of the NAIP classifications were somewhat remediated by decreasing the spatial resolution and reducing the number of classes mapped.;Combining LiDAR with multispectral imagery statistically improved the classification of mining and mine reclamation land cover in comparison to only using multispectral data for both pixel-based and GEOBIA classification. This suggests that the reduced spectral resolution of high spatial resolution data can be combated by incorporating data from another sensor.;Generally, the support vector machines (SVM) algorithm provided higher classification accuracies in comparison to random forests (RF) and boosted classification and regression trees (CART) for both pixel-based and GEOBIA classification. It also outperformed k-nearest neighbor, the algorithm commonly used for GEOBIA classification. However, optimizing user-defined parameters for the SVM algorithm tends to be more complex in comparison to the other algorithms. In particular, RF has fewer parameters, and the program seems robust regarding the parameter settings. RF also offers measures to assess model performance, such as estimates of variable importance and overall accuracy.;Textural measures were found to be of marginal value for pixel-based classification. For GEOBIA, neither measures of texture nor object-specific geometry improved the classification accuracy. Notably, the incorporation of additional information from LiDAR provided a greater improvement in classification accuracy then deriving complex textural and geometric measures.;Pre- and post-mining terrain data classified using GEOBIA and machine learning algorithms resulted in significantly more accurate differentiation of mine-reclaimed and non-mining grasslands than was possible with spectral data. The combination of pre- and post-mining terrain data or just pre-mining data generally outperformed post-mining data. Elevation change data were shown to be of particular value, as were terrain shape parameters. GEOBIA was a valuable tool for combining data collected using different sensors and gridded at variable cell sizes, and machine learning algorithms were particularly useful for incorporating the ancillary data derived from the digital elevation models (DEMs), since these most likely would not have met the basic assumptions of multivariate normality required for parametric classifiers.;Collectively, this research suggests that high spatial resolution remotely sensed data are valuable for mapping and monitoring surface mining and mine reclamation, especially when elevation and spectral data are combined. Machine learning algorithms and GEOBIA are useful for integrating such diverse data

An Evaluation of Sentinel-1 and Sentinel-2 for Land Cover Classification

This study evaluates Sentinel-1 and Sentinel-2 remotely sensed images for tropical land cover classification. The dual polarized Sentinel-1 VV and VH backscatter images and four 10-meter multispectral bands of Sentinel-2 were used to create six land cover classification images across two study areas along the border of the Bolivian Pando Department and the Brazilian state of Acre. Results indicate that Sentinel-2 multispectral bands possess a higher overall performance in delineating land cover types than the Sentinel-1 backscatter bands. Sentinel-1 backscatter bands delineated land cover types based on their surficial properties but did not facilitate the separation of similarly textured classes. The combination of Sentinel-1 and -2 resulted in higher accuracy for delineating land cover through increasing the accuracy in delineating the classes of secondary vegetation from exposed soil. While Sentinel-2 demonstrated the capability to consistently capture land cover in both case studies, there is potential for single date Sentinel-1 backscatter image to act as ancillary information in Sentinel-2 scenes affected by clouds or for increasing separability across classes of mixed multispectral qualities but distinct surficial roughness, such as bare ground versus sparsely vegetation areas

The Random Forest Algorithm with Application to Multispectral Image Analysis

The need for computers to make educated decisions is growing. Various methods have been developed for decision making using observation vectors. Among these are supervised and unsupervised classifiers. Recently, there has been increased attention to ensemble learning--methods that generate many classifiers and aggregate their results. Breiman (2001) proposed Random Forests for classification and clustering. The Random Forest algorithm is ensemble learning using the decision tree principle. Input vectors are used to grow decision trees and build a forest. A classification decision is reached by sending an unknown input vector down each tree in the forest and taking the majority vote among all trees. The main focus of this research is to evaluate the effectiveness of Random Forest in classifying pixels in multispectral image data acquired using satellites. In this paper the effectiveness and accuracy of Random Forest, neural networks, support vector machines, and nearest neighbor classifiers are assessed by classifying multispectral images and comparing each classifier\u27s results. As unsupervised classifiers are also widely used, this research compares the accuracy of an unsupervised Random Forest classifier with the Mahalanobis distance classifier, maximum likelihood classifier, and minimum distance classifier with respect to multispectral satellite data

Spatio-temporal variability in dune plant communities using UAV and multispectral data

O mapeamento da vegetação, através da identificação do tipo e distribuição das comunidades e espécies vegetais, é crucial para analisar a cobertura vegetal e os padrões espaciais. A compreensão das variabilidades espaciais e temporais das plantas dunares em ligação com a morfodinâmica permite uma maior compreensão do dinamismo e evolução dos ambientes costeiros. Tal análise pode contribuir para o desenvolvimento de planos de gestão costeira que ajudam a implementar a biodiversidade costeira e estratégias de protecção. Esta dissertação apresenta uma abordagem para avaliar a utilização de imagens multiespectrais e explorar a variabilidade da vegetação dunar costeira com dados recolhidos à distância por um Veículo Aéreo Não Tripulado (UAV). Foram escolhidas quatro zonas de estudo diferentes na parte oriental da Península de Ancao, distribuídas alongshore, e cobrindo a backhore e a crista das dunas até à base do lee das dunas. Foram utilizados dados de campo e de UAV, em diferentes épocas, nomeadamente ao longo de um período de dois anos. Foi utilizada uma abordagem de classificação em duas etapas, baseada num índice de vegetação de diferença normalizada e num classificador de Floresta Aleatória. Os resultados mostram desempenhos de classificação de alta precisão ao condensar a cobertura do solo em menos classes e também em áreas menos densamente vegetativas. As classificações resultantes foram posteriormente processadas em termos de alterações transfronteiriças e alterações sazonais. Estas técnicas mostram um elevado potencial futuro para avaliar a vegetação das áreas de dunas costeiras e para apoiar a gestão costeira.The mapping of vegetation, by identifying the type and distribution of plant communities and species, is crucial for analysing vegetation coverage and spatial patterns. Understanding dune plant spatial and temporal variabilities in connection with morphodynamics gives further insight in dynamism and evolution of coastal environments. Such analysis can contribute to the development of coastal management plans that helps to implement coastal biodiversity and protection strategies. This dissertation presents an approach to assess the use of multispectral imagery and explore the variability of coastal dune vegetation with remotely sensed data collected by an Unmanned Aerial Vehicle (UAV). Four different study zones were chosen at the eastern part of the Ancao Peninsula, distributed alongshore, and covering the backshore and the dune crest until the base of the dune lee. Field and UAV data were used, in different seasons namely over an extend of two years. A two-step classification approach, based on a normalized difference vegetation index and Random Forest classifier, was used. The Results show high accuracy classification performances when condensing the groundcover into fewer classes and also in less densely vegetated areas. Resulting classifications were further processed in terms of cross-shore changes and seasonal changes. These technics show a high future potential to assess the vegetation of coastal dune areas and to support coastal management

Random forest effectiveness for Bragança region mapping: comparing indices, number of the decision trees, and generalization

Mestrado de dupla diplomação com o Institute Agronomic and Veterinary Hassan IIRemote sensing is a domain that tends to use satellite images for classification and Land Use/Cover (LULC) mapping. For this purpose, classification algorithms are used, which are numerous and diverse, and it is necessary to establish decision criteria when choosing the algorithm. Ultimately, the main decision criterion will be the accuracy obtained in classification because the accuracy of classification may differ from one algorithm to another, even within the same algorithm, according to its variables. But there are other equally important criteria: it depends on the nature of the task, the quantity and types of data available, the type of response expected, the time and computational resources available, the depth of our knowledge about the algorithms. The methodology of each part of the work was described and the criteria for comparison were established. In this research, with the same training data, the same validation data, the same application context (7 classes), and the same image data (Sentinel-2), we tested 15 iterations with the Random Forest classification algorithm, with different tree number decision values, and 3 iterations with vegetation and soil indexes, for the production of the LULC map of the Bragança region (northeast Portugal). Finally, we evaluate the accuracy of the classification, before and after the post-classification tasks (generalization, fragmentation and removal of isolated pixels). The results obtained show that a classification with an nb-trees = 1000, including vegetation and soil indices, and after post-classification tasks, provided excellent precision results (Coefficient Kappa = 0.93, Overall accuracy = 96%, and marginal errors of omission & commission below 4%).A teledetecção é um domínio que tende a utilizar imagens de satélite para classificação e mapeamento de Uso/Cobertura da Terra (LULC). Para este fim, são utilizados algoritmos de classificação, que são numerosos e diversos, sendo necessário estabelecer critérios de decisão ao escolher o algoritmo. Em última análise, o principal critério de decisão será a precisão obtida na classificação, porque a precisão da classificação pode diferir de um algoritmo para outro, mesmo dentro do mesmo algoritmo, de acordo com as suas variáveis. Mas existem outros critérios igualmente importantes: depende da natureza da tarefa, da quantidade e tipos de dados disponíveis, do tipo de resposta esperada, do tempo e dos recursos computacionais disponíveis, da profundidade dos nossos conhecimentos sobre os algoritmos. A metodologia de cada parte do trabalho foi descrita e os critérios de comparação foram estabelecidos. Nesta investigação, com os mesmos dados de formação, os mesmos dados de validação, o mesmo contexto de aplicação (7 classes), e os mesmos dados de imagem (Sentinel-2), testámos 15 iterações com o algoritmo de classificação Random Forest, com diferentes valores de decisão de número de árvores, e 3 iterações com índices de vegetação e solo, para a produção do mapa LULC da região de Bragança (nordeste de Portugal). Finalmente, avaliámos a exactidão da classificação, antes e depois das tarefas de pós-classificação (generalização, fragmentação e remoção de pixels isolados). Os resultados obtidos mostram que uma classificação com um nb-trees = 1000, incluindo índices de vegetação e solo, e após tarefas de pós-classificação, forneceu excelentes resultados de precisão (Coeficiente Kappa = 0.93, Precisão geral =96%, e erros marginais de omissão & comissão abaixo de 4%)