Mapping the distribution of tree species is often a slow and laborious process, especially in the biodiverse Amazonian forests. Reaching canopy species in these dense forests is physically challenging and expensive. The species distribution maps that are created typically have low spatial resolution, making them unsuitable for effective conservation and forestry management.
New Methods for High-Resolution Mapping
Researchers at the University of Turku have developed a method of modeling tree species distribution in Peruvian lowland Amazonia using satellite imagery and machine learning techniques in order to produce higher resolution habitat suitability maps.
Using Satellite Imagery and Machine Learning
The study, recently published in the journal Biotropica, used two freely available satellite imagery sources. Landsat imagery was used to identify variation in vegetation while ASTER data was used to collect elevation data. Both satellite data sources register sunlight that is reflected from the ground surface or forest canopy in different wavelength bands. The authors of the study note that past studies have shown that combining elevation data with remotely sensed data from satellite imagery can be successfully used to differentiate plant species composition in the Amazon forest.
Predicting Habitat Suitability
The researchers of this current study used remotely sensed data and a maximum entropy algorithm (MaxEnt) to predict habitat suitability at 30-meter resolution for five timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). By combining remotely sensed data with machine learning, researchers were able to produce a map modeling the potential distribution of tree species in lowland Amazonia.
The Study
Pérez Chaves, P., Ruokolainen, K. & Tuomisto, H. 2018. Using remote sensing to model tree species distribution in Peruvian lowland Amazonia. Biotropica.
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