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3 November 2010 Application of Landsat data to map and monitor agricultural land cover
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Proceedings Volume 7841, Sixth International Symposium on Digital Earth: Data Processing and Applications; 784111 (2010) https://doi.org/10.1117/12.873241
Event: The Sixth International Symposium on Digital Earth, 2009, Beijing, China
Abstract
Agriculture is one of the major economic sectors of Mongolia and the country's economy is very much dependent on the development of agricultural production. Being the rural and poorest conditions of Mongolia, 60-90% of its labor force employed in agriculture and agricultural sector has a prominent economic role. Mongolian agriculture has been successful in increasing food grains production in the past, guided by the goals of self-sufficiency in the country. The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. Objective of this study to monitor in the agricultural land cover changes in the Tov aimag, as there is important agricultural producing area in Mongolia. We have developed approaches to map and monitor land cover and land use change across in the Tov aimag using multi-spectral image data. In this study, maximum likelihood supervised classification was applied to Landsat TM and ETM images acquired in 1989 and 2000, respectively, to map cropland area cover changes in the Tov aimag of Mongolia. A supervised classification was carried out on the six reflective bands (bands 1-5 and band 7) for the two images individually with the aid of ground based agricultural monitoring data. Results were then tested using ground check data.
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B. Erdenee, Gegen Tana, and Ryutaro Tateishi "Application of Landsat data to map and monitor agricultural land cover", Proc. SPIE 7841, Sixth International Symposium on Digital Earth: Data Processing and Applications, 784111 (3 November 2010); https://doi.org/10.1117/12.873241
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