World Space Week 2020 aims to hightlight the benefits satellite technology brings to humanity: improving our everyday lives via enormous contribution to various fields and industries.
When it comes to the monitoring of extended territories which are located near or on the edges of scenes, the user faces a time-consuming and annoying process of manual selection and combining numerous suitable images so that they reliably cover their area of interest (AOI).
For all players of the modern agricultural market (regardless of whether they are farmers, ag traders or insurers) not only is the assessment of land productivity genuinely urgent, but so is constant monitoring of its efficient use. Farmers can improve yields through tracking growth dynamics, thus developing healthy field trends based on abnormal cases. Insurers can conduct a more effective assessment of land conditions, not here and now, but over the course of a long period of time using historical data. Ag traders are able to schedule logistic spending plans more accurately. Lastly, scientists can monitor the biosphere to track phenological crop distribution, observe global warming, etc.
Landsat 8 is an Earth observation satellite built, launched and operated by a collaboration of NASA and USGS. Data survey is performed by two main sensors which are adjusted into prescribed bands. The satellite operates in visible light, near InfraRed; ShortWave InfraRed to Thermal (LongWave) infrared. The bands are pre-set to 11 bands in total differed by the wavelength of their vision.
High-resolution imagery from a trusted provider has always been of a great demand due to the highest detail and accuracy it offers as well as an unlimited scope of the fields it could be applied. One could hardly find a better source of data when it comes to an in-depth analysis of the area, as it provides an unrivaled frequency and enhanced characteristics.
Change Detection is designed to compare the spatial representation of two points in time and measure changes caused by differences in the variables of interest.