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Title: Monitoring olive mills waste disposal areas in Crete using very high resolution satellite data
Authors: Agapiou, Athos 
Papadopoulos, Nikos G. 
Sarris, Apostolos 
Keywords: Remote sensing;Very high resolution images;Crete;Olive mill disposal areas
Category: Civil Engineering
Field: Engineering and Technology
Issue Date: 1-Dec-2016
Publisher: Elsevier B.V.
Source: Egyptian Journal of Remote Sensing and Space Science, 1 December 2016, Volume 19, Issue 2, Pages 285-295
Abstract: This paper evaluates the efficiency of different image analysis techniques applied to high resolution multispectral satellite data so as to identify olive oil waste disposal areas in the island of Crete where huge quantities of wastes are produced. For this purpose very high spatial resolution images including Pleiades, SPOT 6, QuickBird, WorldView-2 and GeoEye 1 have been exploited. The research included the application of the Normalised Difference Vegetation Index, Olive Oil Mill Waste Index as well as Principal Component Analysis. Moreover Intensity-Hue-Saturation transformation was carried out. Furthermore, unsupervised classification was performed for a variety of classes (5; 10 and 15) over the same area for two different periods. In addition, supervised linear constrained spectral un-mixing technique has been applied for the WorldView-2 image, to evaluate the potential use of sub-pixel analysis. Indeed, as it is demonstrated NDVI and OOMW indices may be used to enhance the exposure of disposal areas in high resolution satellite datasets, while the application of the PCA and HIS transformations seems to be able to further improve the results. Unsupervised classification techniques, with no ground truth data, can sufficiently work; however temporal changes of the disposal areas can affect the performance of the classifier. The use of spectral library was able to detect OOMW areas with a relatively high rate of success improving the results from the unsupervised classification. Finally, a COSMO-SkyMed radar image has been examined and fused with a hyperspectral EO-ALI image, indicating that such kind of datasets might be also explored for this purpose.
ISSN: 1110-9823
Rights: © 2016 National Authority for Remote Sensing and Space Sciences
Type: Article
Appears in Collections:Άρθρα/Articles

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