Mapping, Agriculture, Multitemporal, land use, land cover, crop
Both agricultural area expansion and intensification are necessary to cope with the growing demand for food, and the growing threat of food insecurity which is rapidly engulfing poor and under-privileged sections of the global population. Therefore, it is of para-mount importance to have the ability to accurately estimate crop area and spatial distribution. Remote sensing has become a valuable tool for estimating and mapping cropland areas, useful in food security monitoring. This work contributes to addressing this broad issue, focusing on the comparative performance analysis of two mapping approaches (i) a hyper-temporal Normalized Difference Vegetation Index (NDVI) analysis approach and (ii) a Landscape-ecological approach. The hyper-temporal NDVI analysis approach utilized SPOT 10-day NDVI imagery from April 1998-December 2008, whilst the Landscape-ecological approach used multi-temporal Landsat-7 ETM+ imagery acquired intermittently between 1992 and 2002.
Pixels in the time-series NDVI dataset were clustered using an ISODATA clustering algorithm adapted to determine the optimal number of pixel clusters to successfully generalize hyper-temporal datasets. Clusters were then characterized with crop cycle infor-mation, and flooding information to produce an NDVI unit map of rice classes with flood regime and NDVI profile information. A Landscape–ecological map was generated using a combination of digitized homogenous map units in the Landsat-7 ETM+ imagery, a Land use map 2005 of the Mekong delta, and supplementary datasets on the regions terrain, geo-morphology and flooding depths. The output maps were validated using reported crop statistics, and regression analyses were used to ascertain the relationship be-tween land use area estimated from maps, and those reported in district crop statistics.
The regression analysis showed that the hyper-temporal NDVI analysis approach explained 74% and 76% of the variability in re-ported crop statistics in two rice crop and three rice crop land use systems respectively. In contrast, 64% and 63% of the variability was explained respectively by the Landscape-ecological map. Overall, the results indicate the hyper-temporal NDVI analysis ap-proach is more accurate and more useful in exploring when, why and how agricultural land use manifests itself in space and time. Furthermore, the NDVI analysis approach was found to be easier to implement, was more cost effective, and involved less subjective user intervention than the landscape-ecological approach.