Michal Kepka

Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing

Intensive farming on land represents an increased burden on the environment due to, among other reasons, the usage of agrochemicals. Precision farming can reduce the environmental burden by employing site specific crop management practices which implement advanced geospatial technologies for respecting soil heterogeneity. The objectives of this paper are to present the frontier approaches of geospatial (Big) data processing based on satellite and sensor data which both aim at the prevention and mitigation phases of disaster risk reduction in agriculture. Three techniques are presented in order to demonstrate the possibilities of geospatial (Big) data collection in agriculture: (1) farm machinery telemetry for providing data about machinery operations on fields through the developed MapLogAgri application; (2) agrometeorological observation in the form of a wireless sensor network together with the SensLog solution for storing, analysing, and publishing sensor data; and (3) remote sensing for monitoring field spatial variability and crop status by means of freely-available high resolution satellite imagery. The benefits of re-using the techniques in disaster risk reduction processes are discussed. The conducted tests demonstrated the transferability of agricultural techniques to crisis/emergency management domains.

3D Cartography as a Platform for Remindering Important Historical Events: The Example of the Terezín Memorial

Creation of 3D web maps is rapidly developing field with increasing importance and huge impact on 3D cartography. It is dealing not only with perceiving of space and space-relations of objects in 3D environment (apart from traditional 2D cartography), but thanks to the approachability of data via Internet, also with accessibility of those 3D web maps for the general public.

Sensor-based Database with SensLog: A Case Study of SQL to NoSQL Migration.

Sensors gained a significant role in the Internet of Things (IoT) applications in various industry sectors. The information retrieved from the sensors are generally stored in the database for post-processing and analysis. This sensor database could grow rapidly when the data is frequently collected by several sensors altogether. It is thus often required to scale databases as the volume of data increases dramatically. Cloud computing and new database technologies has become key technologies to solve these problems. Traditionally relational SQL databases are widely used and have proved reliable over time. However, the scalability of SQL databases at large scale has always been an issue. With the ever-growing data volumes, various new database technologies have appeared which proposes performance and scalability gains under severe conditions. They have often named as NoSQL databases as opposed to SQL databases. One of the challenges that have arisen is knowing how and when to migrate existing relational databases to NoSQL databases for performance and scalability. In the current paper, we present a work in progress with the DataBio project for the SensLog application case study with some initial success. We will report on the ideas and the migration approach of SensLog platform and the performance benchmarking.

The SensLog Platform – A Solution for Sensors and Citizen Observatories

SensLog is an integrated server side Web based solution for sensor data management. SensLog consists of a data model and a server-side application which is capable of storing, analyzing and publishing sensor data in various ways. This paper describes the technical advancements of the SensLog platform. SensLog receives measured data from nodes and/or gateways, stores data in a database, pre-processes data for easier queries if desired and then publishes data through the system of web-services. SensLog is suitable for sensor networks with static sensors (e.g. meteorological stations) as well as for mobile sensors (e.g. tracking of vehicles, human-as-sensor). The database model is based on the standardized data model for observations from OGC Observations & Measurements. The model was extended to provide more functionalities, especially in the field of users’ hierarchy, alerts and tracking of mobile sensors. The latest SensLog improvements include a new version of the database model and an API supporting citizen observatories. Examples of pilot applications using SensLog services are described in the paper.

Open Farm Management Information System Supporting Ecological and Economical Tasks

A Farm Management Information System (FMIS) is a sophisticated tool managing geospatial data and functionalities as it provides answers to two basic questions: what has happened and where. The presented FOODIE (Farm-Oriented Open Data in Europe) and DataBio (Data-Driven Bioeconomy) approach may be recognized as an OpenFMIS, where environmental and reference geospatial data for precision agriculture are provided free of charge. On the other hand, added-value services like yield potential, sensor monitoring, and/or machinery fleet monitoring are provided on a paid basis through standardised Web services due to the costs of hardware and non-trivial computations. Results, i.e. reference, environmental and farm-oriented geospatial data, may be obtained from the FOODIE platform. All such results of whatever kind are used in the European DataBio project in order to minimise the environmental burden while maximising the economic benefits.

Challenges of agricultural monitoring: integration of the Open Farm Management Information System into GEOSS and Digital Earth

From a global perspective, agriculture is the single largest user of freshwater resources, each country using an average of 70% of all its surface water supplies. An essential proportion of agricultural water is recycled back to surface water and/or groundwater. Agriculture and water pollution is therefore the subject of (inter)national legislation, such as the Clean Water Act in the United States of America, the European Water Framework Directive, and the Law of the Peoples Republic of China on the Prevention and Control of Water Pollution. Regular monitoring by means of sensor networks is needed in order to provide evidence of water pollution in agriculture. This paper describes the benefits of, and open issues stemming from, regular sensor monitoring provided by an Open Farm Management Information System. Emphasis is placed on descriptions of the processes and functionalities available to users, the underlying open data model, and definitions of open and lightweight application programming interfaces for the efficient management of collected (spatial) data. The presented Open Farm Management Information System has already been successfully registered under Phase 8 of the Global Earth Observation System of Systems (GEOSS) Architecture Implementation Pilot in order to support the wide variety of demands that are primarily aimed at agriculture pollution monitoring. The final part of the paper deals with the integration of the Open Farm Management Information System into the Digital Earth framework.