Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016
<p>Increasing global demand of vegetable oils and biofuels results in significant oil palm expansion in southeastern Asia, predominately in Malaysia and Indonesia. The land conversion to oil palm plantations has posed risks to deforestation (50 % of the oil palm was taken from fores...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-04-01
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| Series: | Earth System Science Data |
| Online Access: | https://www.earth-syst-sci-data.net/12/847/2020/essd-12-847-2020.pdf |
| Summary: | <p>Increasing global demand of vegetable oils and biofuels
results in significant oil palm expansion in southeastern Asia, predominately
in Malaysia and Indonesia. The land conversion to oil palm plantations has posed
risks to deforestation (50 % of the oil palm was taken from forest during
1990–2005; Koh and Wilcove, 2008), loss of biodiversity and greenhouse
gas emission over the past decades. Quantifying the consequences of oil palm
expansion requires fine-scale and frequently updated datasets of land cover
dynamics. Previous studies focused on total changes for a multi-year
interval without identifying the exact time of conversion, causing
uncertainty in the timing of carbon emission estimates from land cover
change. Using Advanced Land Observing Satellite (ALOS) Phased Array type
L-band Synthetic Aperture Radar (PALSAR), ALOS-2 PALSAR-2 and Moderate
Resolution Imaging Spectroradiometer (MODIS) datasets, we produced an annual
oil palm area dataset (AOPD) at 100 m resolution in Malaysia and
Indonesia from 2001 to 2016. We first mapped the oil palm extent using
PALSAR and PALSAR-2
data for 2007–2010 and 2015–2016 and then applied a
disturbance and recovery algorithm (Breaks For Additive Season and Trend – BFAST) to detect land cover change
time points using MODIS data during the years without PALSAR data (2011–2014
and 2001–2006). The new oil palm land cover maps are assessed to have an
accuracy of 86.61 % in the mapping step (2007–2010 and 2015–2016). During
the intervening years when MODIS data are used, 75.74 % of the detected change
time matched the timing of actual conversion using Google Earth and
Landsat images. The AOPD revealed spatiotemporal oil palm dynamics
every year and shows that plantations expanded from 2.59 to 6.39<span class="inline-formula">×10<sup>6</sup></span> ha and
from 3.00 to 12.66<span class="inline-formula">×10<sup>6</sup></span> ha in Malaysia and Indonesia, respectively (i.e. a net
increase of 146.60 % and 322.46 %) between 2001 and 2016. The higher
trends from our dataset are consistent with those from the national
inventories, with limited annual average difference in Malaysia (0.2<span class="inline-formula">×10<sup>6</sup></span> ha)
and Indonesia (<span class="inline-formula">−0.17</span><span class="inline-formula">×10<sup>6</sup></span> ha). We highlight the capability of combining
multiple-resolution radar and optical satellite datasets in annual
plantation mapping to a large extent by using image classification and
statistical boundary-based change detection to achieve long time series. The
consistent characterization of oil palm dynamics can be further used in
downstream applications. The annual oil palm plantation maps from 2001 to
2016 at 100 m resolution are published in the Tagged Image File Format with
georeferencing information (GeoTIFF) at
<a href="https://doi.org/10.5281/zenodo.3467071">https://doi.org/10.5281/zenodo.3467071</a> (Xu et al., 2019).</p> |
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| ISSN: | 1866-3508 1866-3516 |