Introduction
One of the most exciting developments in helping us understand the world around us is through earth observation or remote sensing using satellites, drones/Unmanned Aerial Vehicles (UAVs), and other aircraft. Remote Sensing is the acquisition and processing of information about an object or phenomenon without making contact with the object. Just as the eye sees objects from afar, these eyes in the sky provide a synoptic view of the earth, often at global scale in the case of earth observation satellites. These are becoming indispensable in providing consistent spatial data at regular intervals (ranging from a few days to a few hours revisit times). They are fitted with sensors that go beyond the visible spectrum we can see and can be invaluable to get insights for climate, hydrology, land use change, cryosphere, oceans, environmental, and other analysis.
Space-based satellites capture data about the planet using sensors that measure visible, infrared, near infrared to radar / radio part of the spectrum. Various objects (soil, water, vegetation) have different reflectance patterns that are captured by satellites (raw data), adjusted for atmospheric effects and cloud cover, and then converted into images using classification methods. For example, wheat, rice and corn all have different reflectance patterns and once the classifications are applied to the raw data, the image will show the location of these crops. Satellites usually monitor the smaller spatial areas of each for shorter time intervals or monitor most of the planet at longer intervals (a few days to a few weeks). Complex applications of remote sensing data include LiDAR and radar datasets, which are used for monitoring flows or lake levels.
There are a number of exciting Geographic Information System (GIS) software products (both proprietary and free/open-source) that are capable of analyzing data from remote sensing as well as those captured by digitization/digitalization. These include Environmental Systems Research Institute (ESRI), Quantum (QGIS) GIS, System for Automated Geoscientific Analyses (SAGA) GIS, Geographic Resources Analysis Support System (GRASS) GIS, R + spatial packages, IDRISI , Intergraph and Hexagon, Harris, and several others. Some of these include new online repositories where the data and analysis resides in the cloud and can be accessed with appropriate scripts (e.g. as in the case of Google Earth Engine).
There are great new ways in which the developing world can leapfrog traditional development pathways to access a world of geospatial data (both from curated “bottom-up” global data such as from UN agencies, as well as a range of “top-down” earth observation data from global sources such as NASA, ESA, Planet, partnerships such as GEO, and also including a range of increasingly important national space agencies). Countries are exploring ways to make their data accessible (see MASDAP from Malawi) and moving towards more systematics National [Geo]Spatial Data Infrastructure (NSDI/NGDI) platforms. Costs are now no longer a real constraint and knowledge is the new currency to access geospatial power. Spatial awareness is improving with the mainstreaming of maps and geotagged photos in our smartphones, GPS in cars, etc. The innovations in data services (e.g. using OGC standards), visualization (see a great example here for an interactive Earth Wind Map), cloud analytics, and knowledge packaging (e.g. e-books, Portals, Apps) using geospatial data are truly revolutionizing the concepts of sustainable development.
The key in operationalizing these technologies is to help merge the in-situ and earth observation data, other inputs and analytical services in order to create decision-support platforms that can be accessed in virtual or physical environments as indicated below.
