Exploratory: generated automatically from the UmaTerra platforms' data; not independently validated or peer reviewed.
Twelve INMET stations in four regions of the metropolitan area, from Angra dos Reis to the Lakes region, and what they and the satellite record: land use from 1985 to 2025, temperature and rain from 2000 to 2025, the surface heat island, the landslide and flooding risks read separately, the biodiversity recorded in GBIF, and protected areas. The numbers are not exact everywhere: the stations have series of different lengths, and the story shows where the reading is firm and where it is only a clue.
How to cite: Biondo, Leandro M. (2026). Rio de Janeiro: the lowland, the mountains, the coast and the lakes, read with one ruler. UmaTerra. https://umaterra.com.br/en/stories/rio-de-janeiro/
Loading the numbers…
measured by INMET, MapBiomas, MODIS, SRTM, GBIF or WDPA. estimated a reading that depends on a choice (a threshold, a buffer, a model). missing a part that did not respond this time.
The metropolitan region, the mountains, the Angra coast and the Lakes region each have a different set of INMET stations, with series of different lengths. Pick a region to frame the map. Silva Jardim (one station) was left out, because it does not fit the four regions of this story.
On the left, land use in 1985; on the right, in 2025, from MapBiomas’ annual mapping (Collection 11). The bar shows the same area at both dates: what changes shows along the line.
The buttons switch the layer on the map: land use in 2025, slope above 20°, valley floors, up to 10 m above the nearest river (HAND), and night surface temperature, mean 2016 to 2025. Each answers a different question, and none adds the others.
Same legend, same frame size. The lowland urbanises; the mountains and the Lakes region gained forest where there had been farm and pasture.
Pick a station. The map shows its 5 km disc at both dates, with the bar between them. Below, the composition in rings of 1, 2, 5 and 10 km shows whether the change stays near the station or reaches farther. The flow diagram (sankey) shows where each 1985 class went.
Each station has its own annual series of temperature, rain and apparent temperature, from months with at least 80% of data. The chart shows the chosen station’s series and its trend with the statistical test.
To compare regions whose stations have different years, each station is compared with its own mean, and the mean of those differences becomes the regional line. Choose the variable.
The difference between surface temperature in a 5 km disc and in a 20 to 30 km ring around each station, measured by MODIS. Drag the year and switch between day and night. Surface temperature is not air temperature, and the ring includes lowland and sea.
Landslide depends mostly on relief; flooding depends on valley floors and the city. Regional heavy rain weighs on both, but it does not enter as a class in either. Choose the risk to see its layer and the table for each station. The class thresholds are choices, not measurements.
Species records published in GBIF for each region and in 5 km discs around each station. Beyond volume, the table shows who sends them: how much of the output comes from the ten and the hundred most active names, and whether the observation is human or from collections.
The World Database on Protected Areas (WDPA, from UNEP-WCMC and IUCN), inside each region, and the forest of 1985 and 2025 inside and outside them. The comparison shows where the forest is; it does not measure the effect of protection.
Studies of Rio partly agree with what we measured and partly disagree. Heavy rain near the sea and in the Serra do Mar grows in reviews of daily station data; the rise in temperature and in warm nights appears in INMET studies for 1961–2012; the surface heat island effect in Rio is stronger by day than by night. The recent Lakes-region drought is documented by INEA; we found no long-term rainfall trend there.
Many people feel the year has got drier, that heat at home has got worse and that rain arrives concentrated. This story does not measure perception. The feeling may come from recent droughts, from the city’s night heat, or from a rain pattern that annual totals do not show. Each of these hypotheses needs its own source (interview, neighbourhood record, station) before it becomes a claim.
This reading uses three tools from the platform. If you research or write a report on an area, each can feed your work: biodiversity through GBIF, protected areas, and each place’s image through land use.
Written with Claude, by Anthropic, from INMET, MapBiomas, MODIS, SRTM, GBIF and WDPA data read through Earth Engine, the climate-shift database and UmaTerra’s own tools, and from the sources cited. No number was typed by hand: each comes from the builders in the story’s working folder.