Paving the Way towards an Armenian Data Cube

Abstract

Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices

“…Such data cubes and Figure 11. Urbanization results are compared for 2015 from [44] (left) and the ARDC (right) to gain confidence that the urban footprint results were reasonably accurate. The [44] results (left) are based on proprietary X-band radar and the ARDC results (right) are based on Landsat-8 optical data.…”

“…SOfS offers interesting potential for environmental monitoring and can serve as a pilot example, which can be of interest for other countries/regions. To test its robustness, SOfS is currently being applied in two protected areas in Italy and France (Gran Paradiso and La Vanoise National Parks) where in-situ measurements will help validate satellite observations [43], as well as in Armenia to provide insights for water management of Lake Sevan, the largest lake in the country that has significant economic, cultural, and recreational value [44].…”

“…To turn this algorithm into an integrated and effective mechanism to monitor snow cover and its variability, providing actionable information to decision makers, we are aiming to (1) consolidate the algorithm "Snow Observations from Space" (SOfS) to get the best results possible for monitoring snow cover conditions using both optical and radar imagery allowing the identification of dry and wet snow, and (2) automate the algorithm and transform it into a service that allows measurement of different periods and scales (e.g., a value every month, trimester, year at national, cantonal, and SOfS offers interesting potential for environmental monitoring and can serve as a pilot example, which can be of interest for other countries/regions. To test its robustness, SOfS is currently being applied in two protected areas in Italy and France (Gran Paradiso and La Vanoise National Parks) where in-situ measurements will help validate satellite observations [43], as well as in Armenia to provide insights for water management of Lake Sevan, the largest lake in the country that has significant economic, cultural, and recreational value [44].…”

“…Urbanization results are compared for 2015 from [44] (left) and the ARDC (right) to gain confidence that the urban footprint results were reasonably accurate. The [44] results (left) are based on proprietary X-band radar and the ARDC results (right) are based on Landsat-8 optical data. Overall, the primary urbanization results are quite similar and suggest the fractional cover threshold approach yields sufficiently accurate results to apply to other years in the time series.…”

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

“…It is worth mentioning that, as a relatively complete grid-based ecology, Open Data Cube is increasingly being used to systematically manage and process Earth observation data [63], such as the Australian Geoscience Data Cube [67], Colombian Data Cube [75], Swiss Data Cube [76], China Data Cube [77], and Armenian Data Cube [78]. Based on these data cubes, EO data management, analysis, Remote Sens.…”