How to make online products on satellite images and integrate them to agricultural applications.

How to make online products on satellite images and integrate them to agricultural applications.

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How to make online products on satellite images and integrate them to agricultural applications.

It is a scope of workouts that might be useful for precision agriculture applications. You can learn how to query satellite images to form scenes in RGB, NDVI with any color scheme with flexible customization of any parameters for any crops. Just integrate any query into the application to get instant result.

http://owm.io/cases/agri

Satellite imagery: Landsat 8 and its Band Combinations.

Satellite imagery: Landsat 8 and its Band Combinations.

In the current version of VANE Language, we use images from Landsat8 satellite which captures the Earth’s entire surface every 16 days. The satellite makes hundreds of images with a unique name for each one like LC81410552016219LGN00 and a pixel size of 30 meters, each image consists of 11 bands, the size of the uncompressed image is 2 GB.

Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands 1 to 7 and 9. New Band 1 (ultra-blue) is useful for coastal and aerosol studies, and also new Band 9 is applicable for cirrus cloud detection. The resolution of Band 8 (panchromatic) is 15 meters. Thermal Bands 10 and 11 provide more accurate surface temperatures and are collected at 100 meters. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).

By default, we get B2, B3, B4, B5, B7, but it is possible to download any other bands

Queries for satellite images with VANE SQL v 0.2: Layers

Queries for satellite images with VANE SQL v 0.2: Layers

Set up the number and type of the layers that will construct your image to get grayscale image, RGB composed of 3 layers, or from combination of any other available layers.
In the current version of VANE Language, we use images from Landsat8 satellite. By default, we get B2, B3, B4, B5, B7, but it is possible to download any other bands. In the forthcoming version of the language, you can configure formulas like (b4-b3)/(b4+b3), NDVI(b4,b3) and many others.

Beautiful maps. Snows and Glaciers: Greenland.

Beautiful maps. Snows and Glaciers: Greenland.

The face of our planet is changing constantly. There are several reasons of this process, they are of natural and technogenic (i.e. a result of human activity) origin.

One of such processes that we can observe during the past decades is ice melting. And in particular, there is melting of big glaciers. This factor is a crucial one that impacts changing of the Earth’s surface the most. The matter is that this type of ice melting has the most influence on the level of the World Ocean, in comparison with melting of sea ice which is already in the water. Thus, observation of glacier melting has a great significance not only as a signal point in global warming, but also due to its impact on the level of a global ocean.

Queries for satellite images with VANE SQL v 0.2: Tiles or polygons

Queries for satellite images with VANE SQL v 0.2: Tiles or polygons

Request an area of your interest by two ways that are tiles (XYZ) and polygons consisting of a number of geographical coordinates (lat/lon)

Parameters:

{z}/{x}/{y} query by a tile server. Z is the zoom level, and X and Y identify the tile.
{polygon} encoded GeoJSON string, contains pairs of coordinates describing all points of selected polygon.

Combination of satellite imagery and weather maps

Combination of satellite imagery and weather maps


Weather maps: Rain modelling areas rendered by the VANE openweathermap platform


Maps is a very visually intuitive way for analysing large-scale weather phenomena like cloudness, pressure, rains, observing it in dynamic etc.

Maybe you’ve already tried to play out with our “Map Editor” tool - custom styled maps renderer that provides tile layers based on every 3 hr constantly updatable weather data sets. 

We have just launched beta Jupyter notebooks on our VANE platform!

We have just launched beta Jupyter notebooks on our VANE platform!

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

Use of satellite technology for decreasing risks in agriculture. NDVI index

Use of satellite technology for decreasing risks in agriculture. NDVI index

The exploration of outer space and launching of satellites started in the 1960s. Having some romantic flair in the beginning, the satellite story went on and nowadays satellites are launched often and almost everywhere around the globe and their number is going to grow in the future. Together with that, a new question arose, what to do with a bunch of new information received? A huge lot of information has been gained in the satellite era, but its practical appliance can be seen only in certain spheres of economics.

 One of the spheres where data received from satellites are used effectively is agriculture. There is a range of agricultural problems which can be solved with the help of these data. Satellites are classified into ones of high resolution, of medium and of small. Basically for agriculture satellites of medium resolution of 5-250m per pixel are used. What can be seen in such satellite images and what tasks can be done with the help of satellite data?

Unity of satellite and weather data to battle fires

Unity of satellite and weather data to battle fires

Meanwhile space dimension technologies highly evolve and artificial space satellites explore other planets for decades already, satellite technologies for observing the surface of our planet also become more complicated. With analysis of data received from satellites one can if not solve entirely then as minimum contribute to solving of global issues in many aspects of human life such as agriculture, logistics on ground surface and in open water, environmental pollution, etc.

Big Data and analytical systems. Weather and satellite data on air pollution for exact forecasts

Big Data and analytical systems. Weather and satellite data on air pollution for exact forecasts

In recent years, the situation with air pollution is becoming more alarming and attracted the attention of leading media and politicians at the highest level. This is especially true of large cities such as Beijing, Los Angeles, Mexico City, Tehran, Johannesburg and other densely populated settlements, which are under the influence of various risk factors, such as dense traffic, the presence of air polluting enterprises located poorly in terms of ecology, where pollution levels sometimes exceed all standards many times.