The current work aimed at developing and testing a methodology which can be applied to low spatial resolution satellite data to assess inter-annual crop area variations on a regional scale. The methodology is based on the assumption that within mixed pixels such variations are reflected by changes of the related multitemporal Normalised Difference Vegetation Index (NDVI) profiles. This implies that low resolution NDVI images with high temporal frequency can be used to update land cover estimates derived from higher resolution cartography. More particularly, changes in shape of NDVI profiles are quantified by Spectral Angle Mapping (SAM), which has the advantage of being nearly insensitive to inter-year NDVI differences caused by meteorological variability. A calibration phase is then necessary to convert the information derived from SAM into relevant area variations. This is carried out by a regression estimator tuned on the data of a training year for which both low resolution NDVI data and higher resolution land cover references are available. The methodology can also cope with inter-annual differences in the crop phenological cycles through temporal shifting of the reference NDVI profiles. The proposed methodology was applied in a study region in central Italy to estimate area changes of winter crops from low resolution NDVI profiles. The accuracy of such estimates was assessed by comparison to official agricultural statistics. The method showed promise for estimating crop area variation on a regional scale, with the accuracy of the final results dependent on the quality of the satellite data time series and of the reference high resolution land cover maps.
Estimation of inter-annual crop area variation by the application of spectral angle mapping to low resolution multitemporal NDVI images
F MASELLI
2006
Abstract
The current work aimed at developing and testing a methodology which can be applied to low spatial resolution satellite data to assess inter-annual crop area variations on a regional scale. The methodology is based on the assumption that within mixed pixels such variations are reflected by changes of the related multitemporal Normalised Difference Vegetation Index (NDVI) profiles. This implies that low resolution NDVI images with high temporal frequency can be used to update land cover estimates derived from higher resolution cartography. More particularly, changes in shape of NDVI profiles are quantified by Spectral Angle Mapping (SAM), which has the advantage of being nearly insensitive to inter-year NDVI differences caused by meteorological variability. A calibration phase is then necessary to convert the information derived from SAM into relevant area variations. This is carried out by a regression estimator tuned on the data of a training year for which both low resolution NDVI data and higher resolution land cover references are available. The methodology can also cope with inter-annual differences in the crop phenological cycles through temporal shifting of the reference NDVI profiles. The proposed methodology was applied in a study region in central Italy to estimate area changes of winter crops from low resolution NDVI profiles. The accuracy of such estimates was assessed by comparison to official agricultural statistics. The method showed promise for estimating crop area variation on a regional scale, with the accuracy of the final results dependent on the quality of the satellite data time series and of the reference high resolution land cover maps.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
