Scaling effect of area-averaged NDVI is known as a source of error induced by differences in spatial resolution of two independent measurements over a fixed area. It deteriorates accuracy in parameter retrieval via NDVI, thus its mechanism needs to be fully understood for a better rectification of NDVI. The objective of this study is to investigate error bounds of averaged NDVI within a fixed a...

Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass “yield”). We evaluated the NDVI–yield relationship for a southern Alberta pra...

Multispectral remote sensing has potential to provide quick and inexpensive information on sugarcane aphid, Melanaphis sacchari (Zehntner), pest status in sorghum fields. We describe a study conducted to determine if injury caused by sugarcane aphid to sorghum plants in fields of grain sorghum could be detected using multispectral remote sensing from a fixed wing aircraft. A study was conducted...

Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI timeseries data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions th...

Spectral vegetation indices obtained from satellite sensors have been used to measure and monitor the Earth’s vegetative cover from local, regional, to global scales. The normalized difference vegetation index (NDVI) has been the most widely-used index and shown to correlate with green leaf area index, green biomass, and net primary productivity. The NDVI from the two sensors, National Oceanic ...

Using the fuzzy rule-based classification method, seventeen phases of NDVI (Normalized Difference Vegetation Index) images acquired from 1982 to 1998 were classified respectively based on NDVI and climate images. And then, spatiotemporal dynamics of NDVI and land use are described. Finally, this paper analyzes NDVI trends and their relationships with climate and human activity under the land co...