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News Article

Distribution of Amazon tree species mapped

High resolution Landsat data reveals species distributions of economically important tree species

In many remote areas of dense forest complete coverage field data for species distributions is often absent or scarce. It is in the absence of this data that species distribution modelling really comes to the fore; by using coarse resolution bio/climatic variables a predictive map can be generated designed to reflect the likely distribution of the chosen species over a much larger area than it would be possible to collect ground level data from. Oftentimes these areas are also of great scientific or economic interest making any insights into the species composition of these areas valuable. The amazon rainforest is a good example of such an area; manual data collection would be unfeasible on a large scale however better understanding of its composition is key to improving how it is managed along with monitoring its ecological functioning.

Whilst remote sensing has previously been used to map areas of the Amazonian rainforest, the resulting species distributions often lack real world usability due to their coarse resolution. In a bid to create a finer scale species distribution model researchers based at the University of Turku used a mixture of remote sensing and field-level data concentrated in a small test area of the Peruvian Amazon to create a predictive model capable of application to areas beyond that of the ground-level data. The remote sensing data used by the researchers included Landsat data which has already been used to map differences in plant species composition suggesting that it could be also used to explore the wider distribution of distinct taxonomic groups as done here. The study area covered 2600km2 within the Iberia district of the Southern Peruvian Amazon, however, the researchers only used field-level data, forest censuses from managed concession areas, totalling 4000ha. This was used to plot individual trees with diameters at breast height >30cm creating absence/presence data for each species within each Landsat pixel. A total of 5 genera from this census data were used: Apuleia, Amburana, Crepidospermum, Manilkara, all chosen for their economic value and relative taxonomic certainty. This was then correlated with the climatic parameters given by the Landsat remote sensing data to create a model capable of predicting wider distribution of the species. Elevation data from the Aster satellite was then used to further improve this model as elevation often correlates with soil moisture and drainage.

An ability to generate higher-resolution data is desirable as it increases its usability among stakeholders such as conservation, land planning and forest management bodies. This study’s methods were able to generate a 30m resolution landscape scale distribution model demonstrating that currently available mapping data is sufficient to create fine-scale maps, opening the door for Landsat to be used in the generation of actionable species maps relevant for practical forest management.


To read the full paper follow the citation below

Pérez Chaves, P., Ruokolainen, K. and Tuomisto, H., 2018. Using remote sensing to model tree species distribution in Peruvian lowland Amazonia. Biotropica. DOI: 10.1111/btp.12597


To find over 350 similar papers use the search string below in the Forest Science Database:

("remote sensing" OR "satellite imagery" OR "landsat" OR "normalized difference vegetation index") AND ("tropical forest*") AND (species)