Abstract
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.
Abstract (translated)
棕榈油生产被认为是热带国家森林砍伐的主要驱动因素之一。为了满足供应链目标，商品生产商和其他利益相关方需要及时了解他们在供应链中的土地覆盖动态。然而，从可能缺乏数字地理表示的供应商处获得这种数据是非常困难的。在这里，我们提出了一个“社区模型”，一种基于许多不同利益相关者共同提供的数据集的机器学习模型，以开发特定的土地覆盖概率图，例如半全球油棕地图。这种方法的优势包括纳入各种输入数据、能够轻松更新模型以获取新训练数据并运行模型以处理任何可用的输入图像的能力。将多样数据来源集成到一个概率图中可以帮助利益相关者之间建立对土地覆盖或商品（例如油棕）存在和缺失的共同理解。 模型预测器是由来自Sentinel-1、Sentinel-2和ALOS DSM等公共卫星影像的年度复合构建的。我们提供每个像素的概率棕榈油，以反映底层状态的不确定性（棕榈或不是棕榈）。这个模型的最初版本估计全球准确度约为90%（在0.5概率阈值下）。这个模型及其油棕概率图产品对于准确识别棕榈油种植的地理足迹非常有用。与及时的森林砍伐信息相结合，这个棕榈油模型对于理解敏感森林区域中持续油棕种植扩展的风险非常有用。
URL
https://arxiv.org/abs/2405.09530
