Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

中文翻译：

---

使用随机森林算法从多分辨率，多时间和多传感器数据映射咖啡种植园的分布

印度尼西亚是世界第四大咖啡生产国。咖啡种植园占全国120万公顷，产量为500公斤/公顷。但是，有关印尼咖啡种植园分布的信息有限。这项研究旨在评估分类模型的准确性，并确定其用于绘制咖啡种植园的重要变量。该模型获得了29个变量，这些变量是从多分辨率，多时间和多传感器的遥感数据的整合中得出的，即全锐化GeoEye-1，多时间Sentinel 2和DEMNAS。应用随机森林算法（树= 1000，mtry =所有变量，最小节点大小：6），此模型的总体准确度，kappa统计信息，生产者准确度和用户准确度分别为79.333％，0.774、92.000％和90.790％，分别。此外，12个最重要的变量分别实现了总体准确性，kappa统计数据，生产者准确性和用户准确性79.333％，0.774、91.333％和84.570％。我们的结果表明，随机森林算法可以有效地绘制农林业系统中的咖啡种植园。