Changes in Croplands as a Result of Large Scale Mining and the Associated Impact on Food Security Studied Using Time-Series Landsat Images

Geographic information systems and satellite remote sensing information are emerging technologies in land-cover change assessment. They now provide an opportunity to gain insights into land-cover change properties through the spatio-temporal data capture over several decades. The time series of Landsat images covering the 1985–2009 period is used here to explore the impacts of surface mining and reclamation, which constitute a dominant force in land-cover changes in the northwestern regions of the Czech Republic. Advanced quantification of the extent of mining activities is important for assessing how these land-cover changes affect ecosystem services such as croplands. The images employed from 1985, 1988, 1990, 2000, 2002, 2003, 2004, 2005, 2006, 2007, 2008, and 2009 assist in mapping the extent of surface mines and mine reclamation for large surface mines in a few selected areas of interest. The image processing techniques are based on pixel-by-pixel calculation of the vegetation index, such as NDVI. The NDVI values are classified into the defined classes based on CORINE Land Cover 2000 data in a 3280 km strip of Landsat images. This distribution of NDVI values is used to estimate the land-cover classes in the local areas of interest (184 km, 368 km, 737 km, and 1,474 km). Thus, the approximate land-cover stability of the 3,280 km strip during the whole 1985–2009 period is used to explore land-cover disturbances in the local areas of surface mines. In the case of NDVI, it also includes variations, presumably caused by seasonal vegetation effects, and local meteorological conditions. However, the main trends related to mining activities during the long-term period can be clearly understood. As a OPEN ACCESS Remote Sensing 2010, 2 1464 result, other objectives can be explored in the 1985–2009 period, such as cropland changes to other land use classes, changes of cropland patterns, and their impacts on food security. The presented spatio-temporal modeling based on long time series from 12 satellite images provides considerable experience for processing NDVI in the framework of identification of land-cover classes and also, to a certain degree, cropland variability with its impact on food security.