Palm oil production has been identified as one of the major drivers of
deforestation for tropical countries. To meet supply chain objectives,
commodity producers and other stakeholders need timely information of land
cover dynamics in their supply shed. However, such data are difficult to obtain
from suppliers who may lack digital geographic representations of their supply
sheds and production locations. Here we present a "community model," a machine
learning model trained on pooled data sourced from many different stakeholders,
to develop a specific land cover probability map, in this case a semi-global
oil palm map. An advantage of this method is the inclusion of varied inputs,
the ability to easily update the model as new training data becomes available
and run the model on any year that input imagery is available. Inclusion of
diverse data sources into one probability map can help establish a shared
understanding across stakeholders on the presence and absence of a land cover
or commodity (in this case oil palm). The model predictors are annual
composites built from publicly available satellite imagery provided by
Sentinel-1, Sentinel-2, and ALOS DSM. We provide map outputs as the probability
of palm in a given pixel, to reflect the uncertainty of the underlying state
(palm or not palm). The initial version of this model provides global accuracy
estimated to be approximately 90% (at 0.5 probability threshold) from spatially
partitioned test data. This model, and resulting oil palm probability map
products are useful for accurately identifying the geographic footprint of palm
cultivation. Used in conjunction with timely deforestation information, this
palm model is useful for understanding the risk of continued oil palm
plantation expansion in sensitive forest areas.

棕榈油生产被确认为热带国家森林砍伐的主要原因之一，在供应链目标实现过程中，商品生产商和其他利益相关方需要及时了解供应区的土地覆盖动态数据。本研究提出了一种 “社区模型”，这是一个由许多不同利益相关方提供的汇总数据进行训练的机器学习模型，用于开发一种特定的土地覆盖概率地图，即半全球油棕地图。该方法的优势在于包括多样化的输入，可以根据新的训练数据轻松更新模型，并在提供输入影像的任何年份运行模型。将多样化的数据源整合成一个概率地图有助于利益相关方共同理解土地覆盖或商品（在本例中为棕榈油）的存在和缺失。模型的预测因子是由 Sentinel-1、Sentinel-2 和 ALOS DSM 提供的公开卫星影像建立的年度复合图像。我们提供的地图输出是给定像素中棕榈的概率，以反映底层状态（是否为棕榈树）。根据空间分区的测试数据，该模型的初始版本在全球范围内的准确性约为 90%（在 0.5 概率阈值下）。这个模型和生成的油棕概率地图产品对于准确确定棕榈种植的地理范围非常有用。结合及时的森林砍伐信息使用，这个棕榈模型对于了解在敏感森林区域持续扩张油棕种植的风险非常有用。