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Global Nominee

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Software that predicts the deforestation of Amazon Rainforest based on socio-economical data


The Amazon Rainforest represents approximately half of the rainforest remained in the planet and it serves as a habitat for an extremely rich biodiversity[1]. It has, for example, 2.5 million insect species[2], over 40,000 plant species and 2,000 birds[3]. Moreover, approximately 60% of the forest is located in Brazil[1].

The Amazon rainforest has been severely deforested over the years.Between 1991 and 2000, the total area of forest lost in the Amazon rose from 415,000 to 587,000 square kilometers, what is equivalent to the size of Greece[4]. The main sources of deforestation if human settlement and the use of the soil for agriculture and cattle raising[5]

In this project, we created a software capable of predicting the level of deforestation of the Amazon Rainforest based on specific data on economy and politics. This software could help:
  • Citizens vote for parties who have been more successful in preventing deforestation over the years
  • Governments to analyze who they could introduce measures to decrease deforestation indirectly (by, for example, combating inflation or increasing the per capita income.
  • Companies to better use their resources to minimize their damage on local ecosystems

This project is divided into two parts:

Part I: Analyzing deforestation

We used EarthData to find pictures of Amazon Rainforest at the first day of each year since 1962. After that, we compared each pixel of the Amazon Rainforest area between two consecutive years in order to create a score of deforestation at each year. For that we used the Java Picture API.

Part II: Creating the Simulator

We wrote a machine learning algorithm to predict the level of deforestation based on:
  • The party most represented in Congress
  • The party most represented in Senate
  • The party in presidency
  • Per Capita income
  • Inflation rate
  • Unemployment rate
  • Population
  • Budget devoted to the ministry of environment

For that we used data from:

  • IBGE
  • Banco Mundial
  • Planalto Website
  • Ministries websites (ministerio da fazenda and ministerio do meio ambiente)
  • Senate Website
  • Congress Website
  • Data obtained from the part I of the project

How it works

Today, there are two ways for the user to interact with the software:

1. By answering the questions at the java compiler

2. By answering the questions at a google form

Moreover, the user can provide the values for all variables analyzed or chose to work with some default values - that is, the values correspondent to the current situation in Brazil.


With the software, you can simulate the socio-political environment in the country and see the predicted deforestation of the Amazon Rainforest based on that.

Business Model

The canvas of the project is available at: click here

Next Steps:

  • Introduce more variables to the machine learning code
  • Expand to a global Scale to include not only the Amazon rainforest, but any ecosystem in the world
    • It is also possible to modify the program to predict the impact of companies in their local environments
  • Work on the graphic interface and create an app


[1] "WNF: Places: Amazon".

[2]"Photos / Pictures of the Amazon Rainforest". Archived from the original on December 17, 2008.ved 4 June 2016.

[3] Da Silva; Jose Maria Cardoso; et al. (2005). "The Fate of the Amazonian Areas of Endemism". Conservation Biology. 19 (3): 689–694. doi:10.1111/j.1523-1739.2005.00705.x

[4] Centre for International Forestry Research (CIFOR)

[5] Various (2001). Bierregaard, Richard; Gascon, Claude; Lovejoy, Thomas E.; Mesquita, Rita, eds. Lessons from Amazonia: The Ecology and Conservation of a Fragmented Forest. Yale University Press. ISBN 0-300-08483-8.

Observation: I did't find a place to submit my code. Therefore, if you want access to it, please contact me and I will immediately provide it through the form of communication you judge the best.


SpaceApps is a NASA incubator innovation program.