Exploratory: generated automatically from the UmaTerra platforms' data; not independently validated or peer reviewed.
Eight INMET stations in three regions: the capital (Florianópolis), the Santa Catarina coast (Itajaí, Laguna, Itapoá and Urussanga) and the mountains (São Joaquim, Bom Jardim da Serra and Rancho Queimado). The metro region has a single station here, the capital’s. What they and the satellite record: land use from 1985 to 2025, temperature and rain from 2000 to 2025, the surface heat island, the risks read separately, the biodiversity in GBIF, and protected areas.
How to cite: Biondo, Leandro M. (2026). Florianópolis: the capital, the Santa Catarina coast and the mountains, read with one ruler. UmaTerra. https://umaterra.com.br/en/stories/florianopolis/
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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 capital, the Santa Catarina coast and the mountains each have a different set of INMET stations, with series of different lengths. Pick a region to frame the map. Curitibanos and Ituporanga lie outside the regions.
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. In the capital the city grows over farmland. On the coast, urbanisation advances over farmland. In the mountains, forest grows a little, and so does farming.
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.
Literature on Florianópolis points to more heatwaves in the 2010s and warmer nights in the conurbation. Our 2003–2025 data show a rise in the number of heatwave months in the metro region; maximum temperature has no clear trend, and the coast’s minimum falls, without significance.
In Florianópolis many people feel summer nights are warmer. The data show more heatwave months in the metro region (p = 0.006); the minimum shows no significant trend, which helps separate night heat from day heat.
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.