Comparative analysis of Worldview-2 and Landsat 8 for coastal saltmarsh mapping accuracy assessment Sikdar M.M. Rasel* a , Hsing-Chung Chang a , Israt Jahan Diti b , Tim Ralph a , Neil Saintilan a a Department of Environmental Sciences, Macquarie University, North Ryde, NSW-2109, Australia. b Department of Soil Science, Faculty of Agriculture, Rajshahi University, Rajshahi-6000, Bangladesh. ABSTRACT Coastal saltmarsh and their constituent components and processes are of an interest scientifically due to their ecological function and services. However, heterogeneity and seasonal dynamic of the coastal wetland system makes it challenging to map saltmarshes with remotely sensed data. This study selected four important saltmarsh species Pragmitis australis, Sporobolus virginicus, Ficiona nodosa and Schoeloplectus sp. as well as a Mangrove and Pine tree species, Avecinia and Casuarina sp respectively. High Spatial Resolution Worldview-2 data and Coarse Spatial resolution Landsat 8 imagery were selected in this study. Among the selected vegetation types some patches ware fragmented and close to the spatial resolution of Worldview-2 data while and some patch were larger than the 30 meter resolution of Landsat 8 data. This study aims to test the effectiveness of different classifier for the imagery with various spatial and spectral resolutions. Three different classification algorithm, Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were tested and compared with their mapping accuracy of the results derived from both satellite imagery. For Worldview-2 data SVM was giving the higher overall accuracy (92.12%, kappa =0.90) followed by ANN (90.82%, Kappa 0.89) and MLC (90.55%, kappa = 0.88). For Landsat 8 data, MLC (82.04%) showed the highest classification accuracy comparing to SVM (77.31%) and ANN (75.23%). The producer accuracy of the classification results were also presented in the paper. . Keywords: Saltmarsh, Worldview-2, Landsat 8 OLI, Classification, Assessment, MLC, SVM, ANN. 1. INTRODUCTION Saltmarsh is an intertidal community and treated as ‘Ecological Endangered Community’ (EEC) in Australia [1]. For these reasons, monitoring and dynamic change analysis of saltmarsh is a pressing issue and scientists are much more dependent on high quality remote sensing data for mapping and monitoring of saltmarsh and their proactive management [2-4]. However, this community usually lives with dominant mangroves. This causes a problem in selection of scale and radiometric properties of the remotely sensed data when performing classifications. The scale or spatial resolution is the projected area on the ground associated with the radiance measurement of the sensors. It is the smallest distinguishable spatial unit (ground resolution element) recorded in a remotely sensed data. For example, the spatial resolution is 2 m for Worldview-2 data and 30m for Landsat 8 OLI or EO-1 Hyperion data. Sometimes the spatial resolution denotes the ground sampling distance in an image after image re-sampling that can be varied from the actual spatial resolution recoded by the sensor. Spectral resolution means the range over the electromagnetic spectrum the energy is measured and recorded by the sensor. The use of higher spectral resolution images usually improve the capability of detecting spectral variability within a land cover by increasing the discrimination capacity [5]. But spatial resolution may be more important than spectral resolution when spatial extent of land cover types is an issue. However, both spatial and spectral resolution are two fundamental characteristics of a remotely sensed image for any application, like as classification or feature extraction. Remote sensing image classification accuracy is affected by two factors: firstly, influence of boundary pixels and secondly, finer spatial resolution that increases the spectral –radiometric variation of land cover types [6]. Optimum spectral and spatial resolution determination for vegetation mapping has been an ongoing area of research in remote sensing [7]. It becomes more challenging when dominant and fragmented plant species patches are distributed together in a same community, similarity to our study site. Our study area supports an extensive temperate saline coastal wetland system comprising mangrove, saltmarsh and mixed mangrove–saltmarsh habitats [8]. About 4,257 ha of these wetlands Please verify that (1) all pages are present, (2) all figures are correct, (3) all fonts and special characters are correct, and (4) all text and figures fit within the red margin lines shown on this review document. 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