# Calculating a Regional NDVI
## Educational Objective

The fight against famine and diseases, as a consequence of poor agricultural practices, has been won rather successfully with the help of a method called precision agriculture (precision farming). As ecological and mostly economic reasons drove the development of this technology in industrial nations, low cost approaches in developing countries were an attempt to have an impact on health risks and to support disease control. Precision farming makes use of GPS and GNSS data, which enable the farmer to locate his position in the field and create maps of them in order to implement several spatial variables such as crop yield, organics matter content or the moisture level. Especially one important variable which needs to be taken into account is the Normalized Difference Vegetation Index which allows scientists to analyze changes in the health and density of Earth’s vegetation as viewed from space. With this knowledge farmers are able for example, to determine where on their field which amount of fertilizers needs to be spread, in order to be sufficient. Consequently, there will be no redundant fertilizer spread and farmers can manage their operations more economically and ecologically viable. In developing countries precision farming has the potential to solve critical problems such as crop failure or kidney insufficiency, which is a disease most likely related to the use of pesticides and herbicides. However, precision farming as practiced in industrial nations, has relatively high investment and operating costs, which are resources developing countries do not have. Therefore, the lack of knowledge and shortage of money to apply the knowledge on agricultural processes calls for a low cost approach. The NDVI, with only two channels of information coming from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA’s Terra and Aqua satellites, is a very practicable and easily usable measurement. Since all maps and NDVI measurements of the whole globe are made publicly available by the NASA, the idea is to spread the knowledge and functionalities of NDVI maps, and make them accessible for people all over the world who want to help with and work on projects which demand demand low cost approaches. The objective of this thesis is to develop a page in Wikiversity, offering a tutorial on how to calculate the NDVI for a specific region, and to give insights to subsequent steps in spatial analysis. It is intended to use only open-source software to make the work available to anybody and provide a tool that can also be used in developing countries, in order to, for example minimize health risks as a result of the application of fertilizer.

## Technical Procedure

The chlorophyll pigments in plant leaves absorb visible light in the wavelength range of 0.4 to 0.7 µm, which is then used in the process of photosynthesis, and they reflect near-infrared light in the range of 0.7 to 1.1 µm. Thus, the more leaves a plant has, that are actively doing photosynthesis, the more these wavelengths are affected. The NDVI is identified as the difference-sum ratio of red (visible) and infrared wavelengths.
    $NDVI={\frac {NIR-VIS}{NIR+VIS}}$
The NDVI-value ranges from minus one (-1) to plus one (+1); the higher the NDVI value, the more likely it is for the vegetation in that pixel to be dense and healthy. Conversely, the lower the NDVI value, the vegetation in that pixel does probably consist of tundra, desert or alike greenery.

## Application

The information of a plant’s health is of great importance for people and institutions related to agriculture, forestry and similar areas. Due to the steadily increasing world population and the demand for agriculture to ensure sufficient food production, technology is needed to make available land and plants more efficient. More than 6 billion people rely on agriculture as a source of food. Moreover, the global population is expected to increase by 50% within the next 40 years. Although the yield of many crops has increased in the last century, neither the future growth nor the environmental impacts such as, for example a sudden decrease in a crop’s resistance against diseases can be predicted. Since most of the world’s high quality land is already used for the aforementioned purpose, the need for technology to support farmers and industry to manage their land in a non-destructive /-intrusive way becomes more significant. Furthermore, innovative technology is required to assist farmers with the management of pollutants and the inevitable increase in pesticide usage.

### Precision Farming

Several studies (Ma et al. (1996), Shanahan et al. (2001), Shanahan et al. (2003), Solari et al. (2008) showed that the NDVI is a suitable index to evaluate the nitrogen status, the chlorophyll content, the green leaf biomass, as well as the grain yield of a plant. This predicts the NDVI to be an essential tool to support the management of a field. The challenge of meeting the global demand for food, while, at the same time, managing natural resources in a sustainable way, requires a focus on alleviating crop stress (Henik 2012). Therefore, Henik (2012, p. 7) notes, “[a]n understanding of the physiochemical impacts of plant stress and their spectral responses, through the use of remote sensing, can allow producers to make decisions that are agronomically sound[,] environmentally friendly, and economically feasible.” An early recognition of variability in plant growth on a given field makes it possible to identify factors such as nutrient availability, environmental limitations and/or other factors that could limit the yield (ibid.). The combination of global positioning systems and remote sensing techniques are the cornerstone of assessing spatial and temporal data on a scale that can be immediately processed when managing the field. Information about soil fertility, pest management/usage of pesticides and seasonal performance can be linked to spatial variation, and thus support decision-making related to crop production management. Both Verhulst et al. (2009) and Hatfield & Prueger (2010) have found out that the NDVI is a useful index to evaluate fertility management and practices such as tillage. Just like the other studies mentioned above, Henik (2012) worked with high resolution NDVI images using an active sensor, which emits irradiance and measures the reflectance. Those sensors cannot only be used for a pre-analysis of a field but also for instant action management. One way to do this is to mount sensors on agricultural machines, and to measure the wavelengths reflected from the crops in front of the vehicle. Wavelengths are analysed and set into spatial relation using GPS. The program then computes recommendations of amounts of fertilizer for a specific point on the field and subsequently spreads it automatically. Nevertheless, high resolution imagery for NDVI is a rather costly technology which is only economically feasible in countries, where there is already a high agricultural technology standard. Developing countries do not have the financial opportunities to afford high resolution NDVI imagery.

### Risk Management

As mentioned before, satellite imagery is another way to remotely gather data of a specific area, in order to calculate the NDVI. Even though, the information obtained from satellites are available to anybody at no cost, the resolution is significantly lower than imagery obtained from active sensors used in precision farming. Currently, the maximum resolution of freely available satellite imagery is 30 meters as compared to a few centimetres down to millimetres of an active precision farming sensor. These low resolution images, however, are suitable for the management of several agricultural practices in developing countries. The motivation behind writing this thesis was the development of a low cost approach for precision farming to specifically decrease environmental and health problems in developing countries. Several studies (Held et al., 2016; Lorenz, 1985; Pawar et al., 2016; Medical International, 2013) have found a significant relation between the exposure to pesticides and health problems such as chronical diseases like kidney insufficiency. Those who spread out the pesticides on the field are particularly endangered. Besides rather basic improvements, such as spraying techniques, sufficient protection clothing and health hazard awareness, an overall reduction in pesticide usage is an efficient method for both health and environmental risk minimization. At this point, information about an area’s NDVI is helpful to give answers to the questions Is there a potential for pesticide and fertilizer reduction? and Where on the field can less pesticides and fertilizers be spread?. In order to explain how NDVIs can help minimize health and risk hazards on site, the capacity building programme, as wells as the idea of a living lab is introduced in the following chapter.