The Sustainable Development Goals are the blueprint to achieve a better and more sustainable future for all. They address the global challenges we face, including those related to poverty, inequality, climate, environmental degradation, prosperity, and peace and justice. The Goals interconnect and in order to leave no one behind, it is important that we achieve each Goal and target by 2030

Starting with this quote from the UN’s official website about the Sustainable Sustainable Goals (SDGs), it is well understood that SDGs is all about how to better manage our resources for a more prosperous economic and societal future.

Governments, industry, and scientists have long recognized the critical importance of Earth observations as an information source in support of many sectors of society. Earth observations (from satellite, airborne, and in-situ sensors) provide accurate and reliable information on the state of the atmosphere, oceans, coasts, rivers, soil, crops, forests, ecosystems, natural resources, ice, snow and built infrastructure, and their change over time, are directly or indirectly necessary for all functions of government, all economic sectors and almost all day-to-day activities of society. Earth observation programmes represent the largest investment globally in relation to applications of satellites by national governments — typically through their national space agencies — recognizing their capacity to address such critical challenges as climate change, water availability, food security, natural disaster mitigation, safe and secure transport, energy and resources security, agriculture forestry and ecosystems, coasts and oceans, health issues, and national security.

Do you know that United Nations Sustainable Development goals are supported by the geospatial industry?

UN SDGs provide guidances of goals, targets and indicators that helps us grow economically and socially in a sustainable manner. Earth Observation and GIS technologies have a huge impact on both monitoring the implementation of the targets and calculating specific indicators.

SDG Indicator 15.3.1: Proportion of land that is degraded over total land area

To give you an example of how GIS and Earth Observation support the UN SDGs, we present you the SDG Indicator 15.3.1.

Proportion of land that is degraded over the total land area is a very important metric that is associated with how natural resources are used and how human activities compete over the same area.

The SDG 15.3.1 is divided into three sub-indicators:

  1. Land cover (ISO standard) – flexible classification system provides compatibility in terms of aggregation/disaggregation, between existing/future monitoring, and can accommodate national circumstances. Here high and medium resolution EO imagery provide the input data to perform the land cover classification.
  2. Land productivity – well-established methods for the use of vegetation indices (NDVI) to evaluate trends in net primary productivity (NPP) –variety of corrections techniques given national circumstances (rainfall, cloud cover, growing season, and other inter-annual variability, etc.)
  3. Carbon stocks – IPCC published methodology (IPCC, 2006) for carbon stocks can be employed to estimate the change in stocks based on land cover change or national calculations based on FAO SOC cookbook

About the Instructor

I'm a Remote Sensing and a Surveying Engineer. I received my degree from NTUA in 2010, where I also received my Ph.D. in hyperspectral remote sensing in 2016. From graduation in 2010, my career started as a Researcher Associate and Teaching Associate in the Laboratory of Remote Sensing of NTUA. From that time I also worked at several private companies as a Remote Sensing Expert and Geospatial Analyst. From the beginning of 2015 I was positioned as Senior Earth Observation Expert. During these years, I have participated in more than 20 funded European Commission and European Space Agency projects, have over 16 peer reviewed scientific publications in the field of Remote Sensing, and have an international patent in hyperspectral data compression.My main research and professional interests are in the optical remote sensing area, where I specialize in data (images, point measurements) processing and algorithm design and development. Some of the software tools that I operate to accomplish my research and business dreams are SNAP, ENVI, IDL, QGIS, ERDAS Imagine, ArcGIS, and Python. I have been working with these tools since 2008.

Dimitris Sykas

Remote Sensing Expert