The illusion of completeness
Global disaster monitoring platforms — including PlanetSentry — display events from authoritative feeds like NASA EONET, GDACS, USGS, and NOAA. These feeds are comprehensive for the event types they cover: earthquakes above a certain magnitude, tropical cyclones with active advisories, volcanic eruptions detected by satellite, large wildfires with measurable thermal signatures. The resulting display looks complete. It is not.
Significant categories of disaster events are systematically underrepresented or absent from standard monitoring feeds. Understanding these gaps is essential for anyone relying on monitoring platforms for situational awareness. What you see is important. What you don't see may be equally important.
Slow-onset events: droughts, famines, and sea level rise
Most global event feeds are designed for rapid-onset events — earthquakes, storms, eruptions. Slow-onset events like drought, famine, desertification, and chronic sea level rise develop over months to years and do not fit neatly into event-based data models. A drought that devastates agriculture across the Sahel over a six-month period may never appear as a discrete 'event' in EONET or GDACS, despite affecting millions of people.
The humanitarian impact of slow-onset events often exceeds that of rapid-onset disasters. The 2011 Horn of Africa famine killed an estimated 260,000 people — more than any single earthquake or hurricane in recent decades. Yet famine never appears as a point on a map in standard disaster monitoring feeds. Specialized systems like the Famine Early Warning Systems Network (FEWS NET) track these events, but they operate separately from the feeds that global monitoring platforms typically aggregate.
Small-scale and localized events
EONET and GDACS have implicit thresholds for event inclusion. Earthquakes below magnitude 4.5 are generally not included in global feeds. Localized flash floods, landslides, and urban fires that affect hundreds or thousands of people but don't register at a global scale may not appear. In aggregate, these small-scale events cause enormous cumulative harm — particularly in developing countries where informal settlements are vulnerable to landslides and flooding that never reaches international attention.
The International Disaster Database EM-DAT attempts to capture these smaller events but relies on media reports and government documentation that varies enormously in quality and completeness. Many countries lack systematic disaster loss reporting. The result is a significant undercount of events, particularly in sub-Saharan Africa, Central Asia, and parts of South America.
Urban heat and air quality crises
Heat waves are now recognized as the deadliest weather-related hazard in many countries, yet they are poorly represented in event-based monitoring feeds. A heat wave that kills hundreds of people through heat stroke, cardiovascular stress, and exacerbation of chronic conditions is a disaster by any definition — but it does not produce the dramatic imagery or discrete event signature that triggers inclusion in satellite-based monitoring systems.
Similarly, severe air quality events — wildfire smoke intrusions, industrial pollution episodes, dust storms — cause significant health impacts but are not always captured as discrete events in global disaster feeds. The health burden of degraded air quality is chronic and cumulative, making it invisible to systems designed to detect acute events.
Transparency about what PlanetSentry shows
PlanetSentry is transparent about its sources and their coverage. The platform shows events from EONET, GDACS, USGS, and NOAA — not 'all disasters.' Users should understand that the absence of events in a region does not mean the region is hazard-free. It may mean that the active hazards in that region do not match the event types tracked by the platform's source feeds.
This transparency is itself a form of value. By clearly identifying what sources contribute to the monitoring surface and what those sources cover, PlanetSentry helps users calibrate their expectations and seek complementary information sources for hazard types not covered. A monitoring tool that claims to show everything but actually shows a subset is more dangerous than one that honestly describes its scope.