Calamity in Kalimantan: Palm Oil Growth at the Expense of Diversity

Category: Mapping Landscape Changes and Species Distribution
Project Team: Indonesia Agriculture
Team Location: NASA Goddard Space Flight Center – Greenbelt, Maryland

Using a variety of NASA Earth observations and ancillary data, a risk map of palm oil expansion in Central Kalimantan, Indonesia, was generated. Image Credit: Indonesia Agriculture Team

Using a variety of NASA Earth observations and ancillary data, a risk map of palm oil expansion in Central Kalimantan, Indonesia, was generated. Image Credit: Indonesia Agriculture Team

Authors:
Kyle T. Peterson
Michael Riedman
Abigail Childs

Mentors/Advisors:
Dr. Naikoa Aguilar – Amuchastegui (World Wildlife Fund)
Aakash Ahamed (Universities Space Research Association/NASA Goddard Space Flight Center)

Past/Other Contributors:
Sean McCartney (Center Lead)

Abstract:

Indonesia is the world’s leading producer of palm oil. To keep pace with the continued worldwide expansion of palm oil demand, the government of Indonesia formulated an agricultural policy with the express purpose of doubling palm oil production by 2020. Unfortunately, palm oil plantation expansion comes at the cost of natural rainforest and biodiversity loss in the Central Kalimantan region. Although the government imposed a moratorium on deforestation in 2011 and extended it to the present, there has been insufficient enforcement and deforestation continues to be a pressing issue in the region. The purpose of this project was to work with the World Wildlife Fund (WWF) to establish current natural forest areas and to identify current palm oil plantations, including those on protected lands. A second component of the project was to delineate future suitable locations for palm oil plantations that do not cause rainforest loss by creating a risk map of areas that are most vulnerable to deforestation. The suitability analysis of palm oil plantations relied on MaxEnt to model palm oil plantation locations. This model used known plantation locations, continuous data from remote sensing systems including Landsat 8, Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), Aqua, and Terra, along with ancillary data, to best predict other current and future locations of palm oil plantations. This analysis was overlaid by both a fuzzy weighted linear combination and a geographically weighted regression to compare different approaches. By mapping and predicting future locations, conservation groups can more effectively allocate their resources to prevent rainforest degradation.

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