Brazilian Forest Dataset: A new dataset to model local biodiversity

ABSTRACT

The Intergovernmental Panel on Climate Change and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services have emphasised unequivocal evidences about the impact of human actions on climate and biodiversity at alarming rates. In Brazilian terms, 2019 has been marked by controversial discussions among politicians and environmentalists, leading to misinformation and misinterpretations that clearly motivate the continuous collection and scientific analysis of data to support sustainable solutions. Aiming at dealing with this issue, this manuscript brings two contributions: (i) the creation of the Brazilian Forest Dataset, including Brazilian seed plants, Fraction of Absorbed Photosynthetically Active Radiation, meteorological and geographical data composing 8,482 attributes to model and predict 20 vegetation types; and (ii) the feasibility analysis on modelling this dataset in light of supervised machine learning algorithms, so we devise confident results on the Brazilian biodiversity. Experimental results confirm Random Forest and Support Vector Machines successfully adjust models, enabling researchers to predict the occurrence of specific types of vegetation in different regions of Brazil as well as analyse how the prediction accuracy changes along time after the collection of new data. Our contributions bring important tools to support the study on the evolution of the Brazilian biodiversity.

KEYWORDS:

• Brazilian biodiversity
• machine learning
• data modelling
• forest dataset

Acknowledgments

This work was supported by CAPES (Coordination for the Improvement of Higher Education Personnel – Brazilian federal government agency). We also would like to thank FAPESP for supporting the Center of Mathematical Sciences Applied to Industry (CEPID-CeMEAI) under grant 2013/07375-0, which provided all computational resources to perform our experimental analyses. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of CAPES and FAPESP.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The Brazilian Forest Dataset is made available at https://data.mendeley.com/datasets/9x62992sw6/2, in conjunction with all source codes used to perform the experiments presented and discussed along this manuscript.

2. To illustrate this transformation, the geospatial position $(-54.93\mathrm{x}-10.06)$ would present a value equals to $1$ for the attribute ‘Urena Lobata’ and the label ‘Restinga’ while zero for all remaining ones.

3. More details on FAPAR can be found at http://fapar.jrc.ec.europa.eu/Home.php.

4. More information is available at https://power.larc.nasa.gov/.

5. More information is available at http://datasus.saude.gov.br/.

Additional information

Funding

This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [88887.463387/2019-00]; Fundação de Amparo à Pesquisa do Estado de São Paulo [2013/07375-0].