Remote Sensing and GIS applications have made it relatively easier to monitor the overall health of forests. Various components of forests like tree heights, diameter and volume of stems, basal area and above ground biomass can now be easily be studied using the advanced remote sensing and GIS technologies. Radar remote sensing is different from visual remote sensing as it uses the microwave (1 mm to 1 m) section of the electromagnetic spectrum to obtain these images of the Earth. Microwave remote sensing is especially useful in areas of high cloud cover as it is able to penetrate through clouds. It is also a vital technique in studying the biophysical characteristics of forests, as the waves are able to penetrate the canopy. Stem volume, tree height and biomass estimation are few of the characteristics that can be easily estimated using microwave remote sensing. The device that emits these wavelengths also records the back scatter that is reflected back after the signal hits the target. Microwaves can penetrate clouds, rain, smoke, forest canopies as well as features below the surface of the Earth. Their ability to penetrate through forest canopies makes microwaves especially useful in the study of biophysical characteristics of forests. The penetrative powers of microwave remote sensing help in gathering data about the forest canopy, the stems present in the forest, the surface and surface cover of forests.
Microwaves are longer in their wavelength than visible light and are therefore able to penetrate forest canopies which makes them very useful for studying forest bio-physical characteristics. SAR Polarimetry (PolSAR), SAR Interferometry (InSAR) and Polarimetric SAR Interferometry (PolInSAR) are techniques that arise from SAR and are vital in the geo sciences field today.
SAR Polarimetry is the method in which radar transmits and receives signals in various polarisations. It then records and measures the backscattered signals that vary according to the characteristics of the target. From these measurements a scattering matrix is created. This matrix is useful in understanding the way in which the image pixels respond to the various polarization arrangements.
SAR Interferometry is the study of analysing and combining the signals of the same target from differing angles at different times. The combination of the two coherent images is then used to generate a phase difference between the two images, known as an interferogram .
Coffee is a major producer in Latin America and we at Ceinsys propose to use SAR and optical imagery to map coffee plantations and further delve into the issue of coffee rust. 70.24 million bags of Arabica were exported in the twelve months ending January 2016, compared to 68.97 million bags last year. It has been seen that there has been a 2.5% average annual growth rate in global coffee consumption since 2011.
Hemileia vastatrix (coffee rust) is an issue that plagues almost all of the world’s coffee growing areas. It is an infection that hits the coffee leaves and turns them yellow and eventually kills the coffee plant. As coffee is a shade loving species, it is usually mixed with natural vegetation and therefore mapping coffee can be complicated. Identification of coffee rust in a plantation can be even more complicated, while many studies have tried to address this issue, but no method has yet been completely successful.The disease has caused $1 billion in damage to coffee plants across Latin America and the Caribbean since 2012. USAID estimates that production will fall by as much as 15-40 percent in the next few years, with job possible job losses as high as 500,000. While Coffee rust is not new, it’s been around for a while, but has failed to reach high altitudes where many of the higher quality coffee is grown because the cold temperatures have made it uninhabitable for the fungus, but with climate change increasing temperatures in the coffee growing regions, those high altitude coffee plants are now hospitable to the fungus, and it has been devastating to the crops.
Remote sensing and GIS has proved to be exceptionally useful in identifying and targeting weeds in most infested agricultural areas. It is necessary that decision makers and managers in the agricultural industry employ methods and technology that are able to clearly define areas infected by coffee rust. This will help to focus the management practices on the vulnerable areas thereby saving time and cost as well providing an efficient way of targeting and removing coffee rust disease from coffee plantations. The use of remote sensing and GIS data and technology can also help with control and management of those parameters that stimulate the growth of coffee rust.The traditional way to map coffee plantations and identify coffee rust infested areas has been through field work which has been largely unsuccessful.

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