I always use NDVI to extract data related to vegetation, which I further style appropriately and include in my layers stack, especially when I produce topographic or hiking maps. NDVI takes advantage of the contrast of the characteristics of two bands from a multispectral raster dataset: the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the NIR band. The Normalized Difference Vegetation Index (NDVI) is a standardized index commonly used to generate an image displaying greenness (relative biomass). Normalized Difference Vegetation Index (NDVI) In order to reference its bands correctly, I will always have to consult Table 2 in Part One, which shows the correspondence between original Sentinel-2 bands and the composite’s bands in ArcGIS Pro. Modified Normalized Difference Water Index (MNDWI).Īll calculations will happen in the multiband image I produced in Part One.Normalized Difference Vegetation Index (NDVI),.Through the Band Arithmetic function from the Raster Functions pane.Īt the following paragraphs I will demonstrate how I calculate three specific indices in ArcGIS Pro, using both ways.Through the Indices Gallery at the Tools group on the Imagery contextual tab on the ribbon and.
#GEOGRAPHER BAND STARS PRO#
In ArcGIS Pro there are two ways to calculate such indices: Picture 1: Natural Color band composite.Īnother way (my way) is to calculate certain spectral indices, to extract data related to the three aforementioned zones and then process and style this data according to my needs and taste. One way to produce a map is to just use the Natural Color as a basemap and superimpose vector data. I can easily distinguish three principal geomorphological zones: the coastline, which is quite complex in this example, the lowland occupied by cultivations and the mountainous area. Have a look at Picture 1, which shows the Natural Color band composite of my area of study. However, I often use (or misuse!) spectral indices to produce more realistic maps. It is common practice for scientists to use spectral indices to make measurements or detect changes on the dynamic surface of our Planet. Spectral Indices are extremely useful to monitor specific phenomena, such as floods, wildfires, deforestation etc. In order to extract measurable information from multiband imagery, we often conduct mathematic calculations among various bands to produce new, quantified, single-band raster files, the so-called Spectral Indices. But they are suitable only for qualitative interpretation. The different band combinations are key to point-out such phenomena and in many cases they can serve as excellent basemaps for thematic mapping.
#GEOGRAPHER BAND STARS HOW TO#
In Part One I briefly described what is Sentinel-2 data, where and how to get it and how to process it, to produce various band combinations, in order to understand specific phenomena.
#GEOGRAPHER BAND STARS SERIES#
This is the second part of the blog post series about making outstanding maps using Sentinel-2 imagery in ArcGIS Pro.