Remote Sensing and Vegetation Indices

Hi everyone

The paper I read (Wójtowicz et al., 2015) summarize the various systems of Remote-Sensing used in agriculture and gives a clear view over the common methods of Vegetation-Indices. In agriculture, it is among other things of great importance to act in an economic way. Applied on production means this the knowledge of the actual state is worth the money and provides high yields as well as less deficit in producer goods.

The use of Remote-Sensing and the associated Vegetation-Indices, to generate information about the field, is since the 1950’s a customary method; and with the improvements in technology over the last 70 years, Remote-Sensing is nowadays in width application. (http://earthobservatory.nasa.gov/Features/RemoteSensing/)

The astonishing about Remote Sensing is that electromagnetic radiation gives us an idea of how healthy the plants are or if they for instance need nutrition. The source of electromagnetic waves is first the sun, and because every surface on earth reflects them on its own way, we can make assumptions whether it is for example a house or a plant. Due to the fact that a plant in water stress or nutrient undersupply has again another reflectance of the wavelength, we gain lots of information about the biophysical features on the field. (http://lms.seos-project.eu/learning_modules/agriculture/agriculture-c00-p01.de.html)

Collecting data in Remote-Sensing in the earlier days has been an expensive procedure, but nowadays there are several ways to do so, and with the UAV’s (Unmanned aerial vehicle) the prize became more reasonably. The three ways to gain remote-sensing data are: Satellites, Airborne (Aircraft, UAV, Drone) and Ground-Based Methods.

The Vegetation-Indices used in agriculture are ratios or differences of several wavelengths, such as VIS (visible light, 380-780nm), NIR (near infrared, 780-3000nm), TIR (thermal infrared, 7000-14000nm) and other more. The most frequently calculate Vegetation-Index is the NDVI (normalized difference vegetation index), which determines the density of green on a patch of land. More specialised statements can be made with other Vegetation-Indices. As a further example, scientists can estimate the nitrogen status of plants with the CI (chlorophyll index). The high correlation between the indices and the biophysical characteristics is of great use in agriculture and environmental-specific questions. Some application examples:

• ·         Forecasting of yield
• ·         Nutritional requirements of plants
• ·         Detection of diseases and pest-damages
• ·         Assessment of water demands of plants
• ·         Weed control

In combination with ground-truth samples and reducing fault factors, such as soil background, Vegetation-Indices has very little susceptibility to errors. (Wójtowicz et al., 2015)

For our project in Schinznach-Dorf I suspect it would be good to know what’s the amount of water running through the plants during a day. By using the UAV, we can produce aerial images of the tree nursery and estimate how much water is in the soil by using TIR-Data and the RRI (relative reflectance index). Probably the CWSI could be helpful as well. Further investigations on that topic are necessary. Find out which RS-Information are needed by the guys from the water-balance team and developing a measurement design will be the next steps in our project.

Thanks for reading

bye

Yield = Ertrag

Susceptibility = Anfälligkeit

Pest = Schädlinge
article: Wójtowicz et al., 2015; http://agrobiol.sggw.pl/~cbcs/articles/CBCS_11_1_3.pdf