Information on rubber tree (Hevea brasiliensis) areas and stages of rubber tree growth is needed in making decisions to maximise land use and for efficient farm management. The use of conventional methods in collecting this information requires a long time, high costs, and constraints to access certain areas. Therefore, this study was conducted to evaluate Landsat-8 OLI and Sentinel-2 images in detecting and mapping the rubber tree area. This study presents a pixel-based supervised classification approach to obtain an accurate map of land cover and rubber tree growth stage distribution using resampled 10 m spatial resolution of Sentinel-2 and pansharpened 15 m Landsat-8 OLI. Seven land cover classes (bare soil, water, mature rubber, immature rubber, oil palm, forest, and built-up area) were classified using support vector machine (SVM), artificial neural network (ANN) and spectral angle mapper (SAM). The results showed that the highest classification accuracy was obtained using SVM, 87.22% for Sentinel-2 and 85.74% for Landsat-8. Next, the classification accuracies of ANN were almost similar with 86.17% and 82.39% for Sentinel-2 and Landsat-8, respectively. SAM has produced less than 60% of acceptable accuracy for both datasets. The performance of the aforementioned classifiers was statistically tested using a McNemar test. The test showed that the p-value between SVM and ANN was not significant and thus, ANN and SVM produced similar accuracies and outperformed SAM for both cases. In this study, the best output produced via SVM from Sentinel-2 was selected to produce the thematic map due to the spatial accuracy advantage of Sentinel-2 compared to Landsat-8. The calculated areas of immature and mature rubber from the thematic map were 7.79 km² and 10.93 km², respectively, which then used to estimate the number of tappers needed for the management of rubber. It is concluded that the Sentinel-2 Multispectral Instrument (MSI) data can be recommended to be used in rubber cultivation area assessment.

关于橡胶树（巴西橡胶树）的信息）橡胶树生长的区域和阶段是做出决策以最大化土地利用和有效农场管理的必要条件。在收集此信息中使用常规方法需要长时间，高成本以及访问某些区域的限制。因此，本研究旨在评估Landsat-8 OLI和Sentinel-2图像在橡胶树区域的检测和制图中的作用。这项研究提出了一种基于像素的监督分类方法，该方法使用Sentinel-2的10 m空间分辨率和15 m的Landsat-8 OLI进行锐化，从而获得准确的土地覆盖和橡胶树生长阶段分布图。使用支持向量机（SVM）对七个土地覆被类别（裸土，水，成熟橡胶，未成熟橡胶，油棕，森林和建筑面积）进行分类，人工神经网络（ANN）和光谱角度映射器（SAM）。结果表明，使用SVM可获得最高的分类精度，Sentinel-2为87.22％，Landsat-8为85.74％。接下来，ANN的分类精度几乎相似，Sentinel-2和Landsat-8分别为86.17％和82.39％。对于两个数据集，SAM产生的准确度均不到60％。使用McNemar检验对上述分类器的性能进行统计检验。该测试表明，SVM和ANN之间的p值不显着，因此，在两种情况下，ANN和SVM产生的精度都相似，并且优于SAM。在这项研究中，由于Sentinel-2与Landsat-8相比在空间准确性方面的优势，因此选择了Sentinel-2通过SVM产生的最佳输出来生成专题图。分别为²和10.93 km ²，然后用于估算橡胶管理所需的攻丝数量。结论是，可以建议将Sentinel-2多光谱仪（MSI）数据用于橡胶种植面积评估。