We have just launched beta Jupyter notebooks on our VANE platform! Now you can test your data science algorithms on Landsat8 satellite imagery. Weather and IoT are coming soon! http://owm.io/jupyter/start
In recent years, the problem of decreasing productivity of agriculture has arisen sharper due to climate change and long-term weather fluctuations; there is even the threat of famine in some countries, which are more dependable on crop productivity. This problem can be solved with traditional and known for centuries tools, i.e. by expansion of sowed land through deforestation.
Simultaneously, the intensification of manufacturing or the expansion of cultivated land into previously wild areas can lead to additional emission of greenhouse gases due to the removal of trees and also these processes can increase the amount of used fertilizers. These both actions are factors contributing to climate change and appearance of negative weather phenomena such as drought.
Our booth is A191
Let's meet at Web Summit in Lisbon https://websummit.net!
See you on OpenWeatherMap stand A 191 in the Big Data Exhibition Area of Pavilion 3 on Wednesday, November 9.
These are maps, which you can compose by a combination of any three bands. For instance, use SWIR (band 7) instead of the red spectrum (band 4) and use NIR (band 5) instead of the green one (band 3), then you will get a map of land victimized by fire (See Fire detection, above).
To get a map of a necessary color, just choose a combination of bands and you will find out what it looks like to see the world through eagle or wolf eyes.
The NDVI is a major vegetation index for the agricultural industry and farming, and with VANE Language you can customize a colour scheme to get a proper picture. Here in contrast to just the NDVI option a user can set a color scale and define the colors of zones.
The NDVI, i.e. the normalized difference vegetation index, is a simple graphical indicator of biomass active photosynthetically.The NDVI is one of the most common and widely used indexes for evaluation of vegetated areas, their quality and quantity. Using the NDVI it is possible to detect presence of plants, its dynamics of emergence and growth. For getting of this index there is Band 5 which measures the near infrared spectrum, or NIR (Near Infrared). This part of the spectrum is reflected by water in leaves of healthy plants. Maps of the NDVI with dynamics (in various seasons) let tracing peculiar features and deviations of seasonal vegetation.
Thank you for the article Francesco Azzola
This post describes how to use OpenWeatherMap UV index. This is an interesting API because we can use it to explore some important aspects about Android and location aware API. Openweathermap provides this API for free! As you may already know, OpenWeatherMap provides also a full set of API about weather information: you can get current weather conditions, forecast, historical information and so on. This information is free and we can use OpenWeatherMap API free of charge.
Focusing on this article, at its end, we will build an Android app that gets UV index and show it using Material design guidelines.
Before diving into the app details is useful to have an idea about UV index.
One of the simplest operations is to generate RGB map. Here an image consists of Bands 4-3-2 which correspond to the well-known RGB color model. Red, green and blue spectra combine together for creation of full color images.