Earth Observation Big Data Analysis Platform

Developed for the government of Kyrgyzstan, the most innovative platform for satellite imagery analysis

Customer

Kyrgyzstan is a mountainous, landlocked country in Central Asia with an estimated population of 6 million people (2016).
The major source of employment is the agriculture sector, which is largely made up of smallholder farmers. Poverty is high in Kyrgyzstan. Almost two thirds of the population live in rural areas, and rural poverty levels are about 60 per cent. Livestock plays a crucial role in food security for smallholder farmers, and as a safety net. Almost half of the country is pastureland – some 9 million hectares – and herding plays a key role in its economy, society and culture.



Challenge

The main threats identified by Central Asian pastoralists include the disruption of mobility routes, the widespread promotion of mining and associated land acquisitions, and the land degradation triggered by inappropriate land uses and climate changes.
Climate change trends and projections in Central Asia have important implications for pastures, crops, and agro-pastoral livelihoods. Annual average temperatures are steadily rising in Central Asia and around the world
Warming in Kyrgyzstan has been similar to or greater than average global temperature rise
Pasture productivity, hay fields, and fodder crops are strongly influenced by climate conditions. The 2007 IPCC report concludes with a high level of confidence that Central Asia is highly vulnerable (highest rating) to land degradation from climate change impacts.



Solution

The platform developed for Kyrgyzstan, is able to acquire data from the two NASA satellites, Aqua and Terra, and perform a deep learning analysis on the imagery to compare all the anomalies of the last 12 months compared to the latest 15 years trends.
In particular the platform is able to examine the vegetation cover of the region based on the surface reflectance. The whole data consisting of several TeraBytes of high resolution satellite images have been preprocessed to remove any interfernce from the atmosphere. In addition to the Normalized Difference Vegetation Index (NDVI), also rainfall historical data can be analyzed in different way.



INFORMATION

Region: Central Asia

Industry: GOvernment

Type: Geo-Spatial and Web Mapping

Engagement model: Dedicated Team

Duration: 4 Months

Staff: 1 Project manager, 3 Geo-Spatial Data Analyst, 2 Developers and UX/UI experts

Development: QGIS, PostgreSQL, PostGIS, GeoDjango, Python



TECHNOLOGIES

Result

The project has been a full success, and will be presented in the next month from the Governement and to the Climate Change data scientist communities to use it on different regions







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