摘要：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 6.39×106 ha and from 3.00 to 12.66×106 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 (0.2×106 ha) and Indonesia (−0.17×106 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 https://doi.org/10.5281/zenodo.3467071 (Xu et al., 2019).
