Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels |
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| Institution: | 1. University of KwaZulu-Natal, School of Agricultural, Earth & Environmental Sciences, Geography Department, P/Bag X01, Scottsville, Pietermaritzburg 3209, South Africa;2. University of Witwatersrand Johannesburg, School of Geography, Archaeology and Environmental Studies, Private Bag X3, Wits 2050, Johannesburg, South Africa;1. Leibniz Institut für Agrartechnik Potsdam-Bornim, Max-Eyth-Allee 100, D-14469 Potsdam, Germany;2. Julius Kühn-Institut (JKI), Bundesforschungsinstitut für Kulturpflanzen, Stahnsdorfer Damm 81, D-14532 Kleinmachnow, Germany;1. Geography, Environmental Management and Energy Studies, University of Johannesburg, P. O. Box 524, Johannesburg 2006, South Africa;2. Peace Parks Foundation, P.O. Box 12743, Stellenbosch 7613, South Africa;1. Aristotle University of Thessaloniki, Faculty of Agriculture, Laboratory of Remote Sensing, Spectroscopy and GIS, 54124 Thessaloniki, Greece;2. Interbalkan Environment Centre, Loutron 18, 57200 Lagadas, Greece;3. Aristotle University of Thessaloniki, Faculty of Agriculture, Laboratory of Phytopathology, 54124 Thessaloniki, Greece;4. Aristotle University of Thessaloniki, Faculty of Agriculture, Laboratory of Agricultural Engineering, 54124 Thessaloniki, Greece |
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| Abstract: | The prospect of regular assessments of insect defoliation using remote sensing technologies has increased in recent years through advances in the understanding of the spectral reflectance properties of vegetation. The aim of the present study was to evaluate the ability of the red edge channel of Rapideye imagery to discriminate different levels of insect defoliation in an African savanna by comparing the results of obtained from two classifiers. Random Forest and Support vector machine classification algorithms were applied using different sets of spectral analysis involving the red edge band. Results show that the integration of information from red edge increases classification accuracy of insect defoliation levels in all analysis performed in the study. For instance, when all the 5 bands of Rapideye imagery were used for classification, the overall accuracies increases about 19% and 21% for SVM and RF, respectively, as opposed to when the red edge channel was excluded. We also found out that the normalized difference red-edge index yielded a better accuracy result than normalized difference vegetation index. We conclude that the red-edge channel of relatively affordable and readily available high-resolution multispectral satellite data such as Rapideye has the potential to considerably improve insect defoliation classification especially in sub-Saharan Africa where data availability is limited. |
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| Keywords: | Random forest NDVI-RE NDVI Support vector machine |
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