As climate change exposes wildlife to increasing heat stress, one way many species will cope is by taking advantage of microclimate variation within their habitats. In tropical forest environments however, understorey microclimate variation is difficult to measure. The goal of this project was to determine whether remote sensing data can be meaningfully correlated with ground temperature measurements in the Brazilian Atlantic Forest, in order to assess whether satellite data could be used to predict microclimate variation within tropical forest areas. Exploratory regression was used to select best-fit models to predict ground temperatures based on varying combinations of variables derived from Landsat 8 data. Average temperature deviation was found to be best explained by a combination of normalized moisture vegetation index (NMVI) and the near-infrared (NIR) Landsat band. These results suggest that NIR and NMVI measurements can be used as a meaningful proxy of microclimate variation.
7 thoughts on “Assessing the feasibility of using satellite data to predict understorey microclimate variation in the Brazilian Atlantic Forest”
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In summer 2019 I collected data on microclimate variation in the Atlantic Forest as part of a project examining microclimate selection by white-lipped peccaries. For this project I wanted to test whether my ground measurements of temperature correlated well with satellite data, because it would be very useful from a conservation ecology standpoint to be able to predict tropical forest microclimate variation from satellite data without needing to make measurements on the ground.
Here’s the link to the pdf for the project!
https://wernkelab.org/introgis2020/?attachment_id=445
How do the seasons interplay with microclimate? What season was the imagery you examined from, and is it possible you could attain different results if you were to use imagery from another season?
Good question! Yes, I definitely think the season I examined data from influenced the results. The data I looked at is from the dry season, which in semi-deciduous forest means that many tree species were experiencing some degree of water stress. NVMI, one of the variables that was significantly correlated with ground temperature, measures the moisture content of vegetation, so values for that would definitely be higher in satellite imagery from the wet season, and possibly less variable, so I don’t know if it would still correlate as well with ground temperature.
Really awesome project! I knew someone who also studied white-lipped peccaries and it was a great experience for her! I would love to know more about how species take advance of microclimate variations. Does this mean species migration to cooler areas within the Brazilian Atlantic Forest?
Thank you! They don’t exactly migrate, but they use different areas of their home-ranges more intensively during different seasons. Part of that is definitely due to seasonal differences in food distribution, but I’m interested in figuring out the role microclimate selection plays in their behavior. So far I only have data from the Brazilian winter, and I found that they select for warmer microclimates during winter.
Interesting correlations here–what do you think your next steps should be? What do you think the NVMI is measuring that accounts for the correlation? Is there something about the plant community composition? Is it plant health? Plant phenology (you mention semi-deciduous trees as dominant)). NVMI is a band ratio between NIR and SWIR, so it makes sense, but how might you then use the NVMI to predict hot/cold spots? I also wonder what is your N? How many weather stations, and how might you design a field project optimizing ground truth for this RS project? If you were able to generate a predictive raster of hot/cold microclimates, that would be a big contribution!