Usage of small dimensions RPAS ( drones ) has a real and practical benefit on forest, environment and agriculture management. Drones can obtain Digital Surface Models and pseudo-NDVI orthomosaics that can be manipulated by Geographic Information Systems (hereinafter GIS) allowing professional and rigorous studies along with technically supported decisions.
We provide here an example of woody crop inventory done with our drones and GIS (QGIS) Software. It can be noted how powerful this information can be to properly manage and take decisions on precision agriculture and forest plantations.
In the following example droning used one of our drones (specifically the DE820) and a NDVI camera to survey the whole field. We did it in one flight at height of 100m above ground. Flight took 10 minutes.
The field was mainly populated by olive trees in the south of Spain.
Once on the ground, we were able to generate the Orthomosaic and both DTM (Digital Terrain Model) and DSM (Digital Surface Model). By using these three sources of information and the raster and vector calculator on the GIS, we could detect and classify every olive tree as an independent entity.
Having every olive tree entity located in the GIS database, a lot of valuable information can be extracted:
Woody crop was identified from surface and other vegetation by means of geographical calculations taking into account a series of parameters like shape, height, area and elevation gradient. At the same time NDVIb color disambiguation was applied to better refine the tree selection.
In this example we were able to differentiate every olive tree from the rest of the terrain and other vegetation present in the study zone.
GIS and drone technology is a game changer in Precision Agriculture and Environment management. This information provides an efficient, massive and rigorous way to manage field crops, forests, and any other geographic inventory.
Data obtained in this way can create massive lists/catalogs of fine geolocated entities allowing categorization, historical comparisons, etc.
The fact that every olive tree has been categorized, geolocated and found their characteristics is of a great importance now and even more in the future. Almost every new technology related to automatic crop harvest or automatic fertilizer application will have to be supported by these data.
Moreover, historical data across the years can give precious information about growth patterns and diseases that can me data crossed against production rates per tree etc.
Full post here.