26 May 2016 Comparative analysis of Worldview-2 and Landsat 8 for coastal saltmarsh mapping accuracy assessment
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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.
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Sikdar M. M. Rasel, Sikdar M. M. Rasel, Hsing-Chung Chang, Hsing-Chung Chang, Israt Jahan Diti, Israt Jahan Diti, Tim Ralph, Tim Ralph, Neil Saintilan, Neil Saintilan, "Comparative analysis of Worldview-2 and Landsat 8 for coastal saltmarsh mapping accuracy assessment", Proc. SPIE 9864, Sensing for Agriculture and Food Quality and Safety VIII, 986409 (26 May 2016); doi: 10.1117/12.2222960; https://doi.org/10.1117/12.2222960
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