pixelcooker

Vegetation cover data is one of the most important biophysical parameters that can be effectively and efficiently extracted from Remote Sensing Satellite Imagery. A wide range of vegetation cover extraction methods have been developed since years ago, ranging from indirect regression using NDVI data and field measurements, greenness index conversion, and many others. These methods can give vegetation cover information in raster format that can be used for many kinds of applications. For example, you can incorporate vegetation cover data as additional input for biomass estimation, soil erosion modeling, vegetation cover multitemporal analysis, et cetera.  Vegetation cover is best estimated using low to medium-resolution satellite imagery. On this one, these days we have so many options of satellite images, and most importantly, they are mostly free to use and free to download. LANDSAT, ALOS AVNIR, SPOT 5/6/7, SENTINEL-2, MODIS, ASTER just name a few of them. Specifically for LANDSAT...

All remote sensing users must be familiar and used to playing with multispectral bands combination to get certain information from imagery. If you are deeply understand about spectral response curve of surface earth objects, you will know that certain object (vegetation for example) gaves different spectral response compared to another objects. For satellite imagery consist of more than 3 bands (RGB in digital camera), different bands combination in RGB planes gaves different emphasizes to landcover colors in the imagery. Hence they are called multispectral imagery. Most of low to medium resolution imaging satellite currently operational in the earth orbit are multispectral, like landsat, modis, noaa avhrr, and many others.  Multispectral imagery number of bands can range from 4 bands to dozens bands, but not reaching hundreds. When the bands number reached more than one hundred bands it is called hyperspectral imagery and its analysis and image processing workflow would be pretty ...