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Cheema, MJM, Bastiaanssen, WGM (2010). Land use and land cover classification in the irrigated Indus Basin using growth phenology information from satellite data to support water management analysis. AGRICULTURAL WATER MANAGEMENT, 97(10), 1541-1552.

Abstract
Water resources planning and management is fundamental for food security, environmental conservation, economic development and livelihoods. In complex basins like the Indus Basin, water is utilized by different land cover and land uses. Up to date information about these Land Use and Land Cover (LULC) classes provide essential information on the water flow path. Traditionally, landscapes are described by cover type. For water management analysis, the information on land use is vital. To this end, a classification of LULC in the Indus Basin (covering 116.2 million hectares of Pakistan, India, China and Afghanistan) has been made. Vegetation index images freely available from SPOT-Vegetation satellite were used to describe the phenological cycle of all agro-ecosystems at a spatial resolution of 1 km x 1 km. An unsupervised clustering technique was adapted to classify 27 land use classes. Ground information and expert knowledge on the growing patterns of crops was used to label the resulting LULC classes. This helped to discern specific crops and crop rotations. An error matrix was prepared using ground truthing data to evaluate the classification accuracy. Existing global, regional and local studies were also considered for validation. The results show an overall accuracy of 77%, with the producer's accuracy being 78% and user's accuracy 83%. The Kappa coefficient (0.73) shows moderate agreement between on ground and satellite derived map. This is deemed sufficient for supporting water management analysis. The availability of major crop rotation statistics and types of forests and savanna is key information for the input data in hydrological models and water accounting frameworks. (C) 2010 Elsevier B.V. All rights reserved.

DOI:
10.1016/j.agwat.2010.05.009

ISSN:
0378-3774

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