The characterization and modeling process is illustrated with the vegetation phenology of the Ganges Brahmaputra delta using a MODIS vegetation index time series.
So we developed an estimation model using a new vegetation index VIPD that reflects all information of hyper-multispectral data, and validated the model using Landsat/TM data.
The 1990/1991 Alaska Vegetation/Land Cover map [37] used in this study was derived from the Advanced Very High Resolution Radiometer (AVHRR) data set, and the classification was developed using the phenology of a vegetation index (AVHRR/NDVI) collected during the 1991 growing season with 1 km spatial resolution.
The 1990/1991 Alaska Vegetation/Land Cover map [ 37] used in this study was derived from the Advanced Very High Resolution Radiometer (AVHRR) data set, and the classification was developed using the phenology of a vegetation index (AVHRR/NDVI) collected during the 1991 growing season with 1 km spatial resolution.
We investigated the spatiotemporal variations in vegetation cover using a Normal Difference Vegetation Index (NDVI) time-series data set obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) and climate data from 2000 to 2010.
Vegetation was extracted by using a Normalized Difference Vegetation Index (NDVI) image at first, and then a more accurate classification result of shrub and grassland is obtained by integrating the height information from LIDAR data.
The distribution of onshore vegetation was extracted by using Soil Adjusted Vegetation Index, one of the indices dealing with vegetative activity, and then was classified into two vegetation types by the supervised classification method using [Natural Environmental Resources and their Oil Spill Protection Priorities for the Coastline of Abu Dhabi Emirate, U.A.E., p. 120, 2000] as the supervisor.
For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices.
To address this question, the spatiotemporal changes of vegetation growth in upper Shiyang river basin together with their responses to climate changes were investigated using SPOT VEGETATION Normalized Difference Vegetation Index (NDVI) and climate datasets from 1999 to 2013.
Because we can compute both NDVI and EVI from the 2001 Landsat 7 bands, it is possible to compare NASA-CASA NPP results to the BEF NPP estimates using either vegetation index from the satellite imagery.
