An Approach to Use Earth Observation Data as Support to Water Management Issues in the Ethiopian Rift

Earth Observation data has become available and obtainable in continuously increasing quality as well as spatial and temporal coverage. To deal with the massive amounts of data, the WaCoDiS project aims at developing an architecture that allows its automated processing. The project focuses on the development of innovative water management analytics services based on Earth Observation data such as provided by the Copernicus Sentinel missions. The goal is to improve hydrological models including but not limited to: a) identification of the catchment areas responsible for pollutant and sediment inputs; b) detection of turbidity sources in water bodies and rivers. The central contribution is a system architecture design following the Microservice architecture pattern: small components fulfil different tasks and responsibilities (e.g. managing processing jobs, data discovery, process scheduling and execution).&#160; In addition, processing algorithms, that are encapsulated by Docker containers, can be easily integrated using the OGC Web Processing Service Interface. The orchestration of the different components builds a fully functional ecosystem that is ready for deployment on single machines as well as cloud infrastructures such as a Copernicus DIAS node or commercial cloud environments (e.g. Google Cloud Platform, Amazon Web Services). All components are encapsulated within Docker containers.The different components are loosely coupled and react to messages and events which are published on a central message broker component. This allows the flexible scaling and deployment of the system. For example, the management components can run on physical different locations than the processing algorithms. Thus, the system supports the reduction of manual work (e.g. identification of relevant input data, execution of algorithms) and minimizes the required interaction of domain users. Once a Processing Job is registered within the system, the user can track the status of it (e.g. when it was last executed, if an error occurred) and will eventually be informed when new processing results are available.In summary, this work targets to develop a system that allows the automated and event-driven creation of Earth Observation products. It is suitable to run on Copernicus DIAS nodes or on dedicated environments such as a Kubernetes Cluster.In our contribution, we will present the event-driven processing workflows within the WaCoDiS system that enables the automation of water management related analytics services. In addition, we will focus on architectural details of the Microservice oriented system design and discuss different deployment options.

Download Full-text

CROP DATA RETRIEVAL USING EARTH OBSERVATION DATA TO SUPPORT AGRICULTURAL WATER MANAGEMENT

Engenharia Agrícola ◽

10.1590/1809-4430-eng.agric.v39n3p380-390/2019 ◽

2019 ◽

Vol 39 (3) ◽

pp. 380-390 ◽

Cited By ~ 3

Author(s):

João Rolim ◽

Ana Navarro ◽

Pedro Vilar ◽

Cátia Saraiva ◽

Joao Catalao

Keyword(s):

Water Management ◽

Data Retrieval ◽

Earth Observation ◽

Observation Data ◽

Agricultural Water ◽

Agricultural Water Management ◽

Earth Observation Data

Download Full-text

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽

10.26643/gis.v12i3.5187 ◽

2019 ◽

Vol 12 (3) ◽

pp. 12-14

Author(s):

Eicher, A

Keyword(s):

Earth Observation ◽

Observation Data ◽

Business World ◽

The Earth ◽

Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

Download Full-text

Mapping slums and model population density using earth observation data and open source solutions

2019 Joint Urban Remote Sensing Event (JURSE) ◽

10.1109/jurse.2019.8808934 ◽

2019 ◽

Author(s):

Tais Grippa ◽

Stefanos Georganos ◽

Sabine Vanhuysse ◽

Moritz Lennert ◽

Nicholus Mboga ◽

...

Keyword(s):

Population Density ◽

Open Source ◽

Earth Observation ◽

Observation Data ◽

Model Population ◽

Earth Observation Data

Download Full-text

Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data

Remote Sensing ◽

10.3390/rs13010005 ◽

2020 ◽

Vol 13 (1) ◽

pp. 5

Author(s):

William Straka ◽

Shobha Kondragunta ◽

Zigang Wei ◽

Hai Zhang ◽

Steven D. Miller ◽

...

Keyword(s):

Air Quality ◽

Los Angeles ◽

Environmental Impacts ◽

Demographic Data ◽

The United States ◽

Earth Observation ◽

Observation Data ◽

Washington Dc ◽

Mobility Data ◽

Earth Observation Data

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

Download Full-text

2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques

Remote Sensing ◽

10.3390/rs13071310 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1310

Author(s):

Gabriele Bitelli ◽

Emanuele Mandanici

Keyword(s):

High Resolution ◽

Exponential Growth ◽

Smart City ◽

Earth Observation ◽

Urban Environments ◽

Observation Data ◽

High Resolution Imagery ◽

Observatory Data ◽

Methods And Techniques ◽

Earth Observation Data

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]

Download Full-text

Quantifying Tree Cover Loss in Urban Forests within Nairobi City Metropolitan Area from Earth Observation Data

Environmental Sciences Proceedings ◽

10.3390/iecf2020-07952 ◽

2020 ◽

Vol 3 (1) ◽

pp. 78

Author(s):

Francis Oloo ◽

Godwin Murithi ◽

Charlynne Jepkosgei

Keyword(s):

Land Cover ◽

Metropolitan Area ◽

Land Surface ◽

Urban Forest ◽

Urban Forests ◽

Earth Observation ◽

Tree Cover ◽

Observation Data ◽

Vegetation Health ◽

Earth Observation Data

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

Download Full-text