Applications of Terra MODIS data for Iraq marshland monitoring

• Published in: SPIE Proceedings, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX
• Main Authors: DESPINI, Francesca, TEGGI, Sergio, L. Bovio, F. Immordino
• Other Authors: U. Michel, D.L. Civico, Despini, Francesca, Teggi, Sergio, L., Bovio, F., Immordino
• Format: Conference Object
• Language: English
• Published: SPIE 2009
• Subjects: wetland; remote sensing; MODIS; classification; Aerosol Robotic Network
• Online Access: http://hdl.handle.net/11380/625356; https://doi.org/10.1117/12.830065

Since the 1970s, the Iraq Marshlands have been damaged significantly, but recently (May 2003-March 2004), more than 20% of the original marshland area has been re-flooded.The goal of the work is to observe the evolution of the marshes in terms of extension and to evaluate the success of wetland restoration on the base of multispectral and multitemporal MODIS images collected in 2007-2008.MODIS (MODerate resolution Imaging Spectroradiometer) has a viewing swath width of 2,330 km and views the entire surface of the Earth every one to two days. Its detectors measure 36 spectral bands between 0.405 and 14.385 μm, and it acquires data at three spatial resolutions -- 250m, 500m, and 1,000m.These data with their low spatial resolution but high time frequency are suitable for regional-scale time-series studies.The satellite data have been corrected for atmospheric effects using an IDL (Interactive Data Language) procedure based on MODTRAN and 6S radiative transfer codes. These radiative transfer codes require, in input, atmospheric vertical profiles, aerosol optical thickness(AOT) and columnar water vapour content (WV). Vertical profiles are obtained from the nearest meteorological station or by climatological data set. AOT and WV are retrieved either from the MODIS MODATML2 atmospheric product, or from the AERONET (Aerosol robotic network).Then different classifications (Pixel- and Object-Oriented) have been tested, compared and discussed to evaluate the best approach to apply on regional-scale time-series studies