Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping
MetadataShow full item record
de Bie, C. A. J. M.; Khan, M. R.; Smakhtin, Vladimir; Venus, V.; Weir, M. J. C.; Smaling, E. M. A. 2011. Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping. International Journal of Remote Sensing, 32(21):6673-6693. doi: http://dx.doi.org/10.1080/01431161.2010.512939
Permanent link to this item: http://hdl.handle.net/10568/40414
Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources. Satellite Pour l'Observation de la Terre (SPOT)-Vegetation 10-day composite normalized difference vegetation index (NDVI) images (1998-2002) at 1 km2 resolution for a part of the Nizamabad district, Andhra Pradesh, India, were linked with available crop calendars and information about cropping patterns. The NDVI images were used to stratify the study area into map units represented by 11 distinct NDVI classes. These were then related to an existing land-cover map compiled from high resolution Indian Remote Sensing (IRS)-images (Liss-III on IRS-1C), reported crop areas by sub-district and practised crop calendar information. This resulted in an improved map containing baseline information on both land cover and land use. It is concluded that each defined NDVI class represents a varying but distinct mix of land-cover classes and that the existing land-cover map consists of too many detailed 'year-specific' features. Four groups of the NDVI classes present in agricultural areas match well with four categories of practised crop calendars. Differences within a group of NDVI classes reveal area specific variations in cropping intensities. The remaining groups of NDVI classes represent other land-cover complexes. The method illustrated in this article has the potential to be incorporated into remote sensing and Geographical Information System (GIS)-based drought monitoring systems.