World production of palm oil increased spectacularly in the last 20 years, especially in Indonesia and Malaysia. As the largest producer, good management in oil palm plantation is very important, the expansion of plantation also must be well planned, because its existence must not affect the surrounding environment. Therefore the information of oil palm age or condition of their growth is needed. Remote sensing has significant potential to aid oil palm monitoring and detection effort. It also provides a cost-effective method to these purpose and at same time provides side specific assessments of management areas, Synthetic Aperture Radar (SAR) is crucial for this task. The SAR is an active sensor that operates in all weather condition and daylight independent delivering information all year around at the time that is needed. SAR is sensitive to texture, size and orientation of structural objects, moisture content and ground conditions. This study has objectives to compare the methods that have been developed to monitor oil palm by using optic data and SAR data. The data that used are Landsat 8 and Sentinel-1.The study area is Asahan district North Sumatera. The regression analysis by using regression method indicates that oil palm age can be monitored by using NDVI or backscatter of SAR values with growth model. The R2 of model for Landsat 8 is 0.85 and 0.77 for Sentinel 1. Both models can be used for monitoring the condition and age of oil palm.

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Landsat-8 has various channels that function to identify an object. The vegetation index algorithm which is based on remote sensing involves several bands and can describe the percentage of canopy and density of vegetation. More than 100 vegetation index algorithms and each can be used in accordance with the research objectives. In this paper we will discuss the utilization of Landsat-8 metric data with the parameters of Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) and several parameters in metric data with various features to produce indications of rapid land change, especially to detect changes in tree cover area to lose tree cover and vice versa. For this purpose, the annual Landsat-8 metrics data is located in Riau Province. To compare both NDVI and NBR parameters, the trial and error method is used and the results are compared visually to the two different images of the year. The result is that the NBR parameters with a maximum-70 feature and the threshold for tree cover loss and tree cover gain respectively more than -0.1 provide tangible results in looking at the tree cover changes in Riau Province. In the analysis, other information is needed, for example, a map of the Forest Area to see further whether the changes that occur are in the forest area or not, which will certainly provide different treatment.

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