Rubber (Hevea brasiliensis) is a tropical tree crop cultivated for the industrial production of latex. The trees are tall, perennial and long-lived, and are typically grown in plantations. In most rubber-producing countries, smallholders account for more than 85% of plantation area. Traditional practices mean that it can be difficult to monitor rubber plantations for management purposes. To overcome issues associated with monitoring traditional practices, remote sensing approaches have been successfully applied in this field. However, information on this is lacking. Therefore, this study aims to document the current status, history, development and prospects for remote sensing applications in rubber plantations by using the PRISMA framework. The review focuses on the application of optical remote sensing data in rubber. In this paper, we discuss the current role of remote sensing on specific subject areas, namely mapping, change detection, stand age estimation, carbon and biomass assessment, leaf area index (LAI) prediction and disease detection. In addition, we elaborate on the benefits gained and challenges faced while adapting this technology. These include the availability and free access to satellite imagery as the greatest benefit and the presence of clouds as one of the toughest challenges. Finally, we highlighted four potential areas where future work can be done: (1) Advancements in remote sensing data, (2) algorithm enhancements, (3) emerging processing platforms, and (4) application to less studied subject areas. This paper gives insight into strengthening the potential of remote sensing for delivering efficient and long-term services for rubber plantations.
Land surface phenology derived from satellite data provides insights into vegetation responses to climate change. This method has overcome laborious and time-consuming manual ground observation methods. In this study, we assessed the influence of climate on phenological metrics of rubber (Hevea brasiliensis) in South Sumatra, Indonesia, between 2010 and 2019. We modelled rubber growth through the normalised difference vegetation index (NDVI), using eight-day surface reflectance images at 250 m spatial resolution, sourced from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites. The asymmetric Gaussian (AG) smoothing function was applied on the model in TIMESAT to extract three phenological metrics for each growing season: start of season (SOS), end of season (EOS), and length of season (LOS). We then analysed the effect of rainfall and temperature, which revealed that fluctuations in SOS and EOS are highly related to disturbances such as extreme rainfall and elevated temperature. Additionally, we observed inter-annual variations of SOS and EOS associated with rubber tree age and clonal variability within plantations. The 10-year monthly climate data showed a significant downward and upward trend for rainfall and temperature data, respectively. Temperature was identified as a significant factor modulating rubber phenology, where an increase in temperature of 1 °C advanced SOS by ~25 days and EOS by ~14 days. These results demonstrate the capability of remote sensing observations to monitor the effects of climate change on rubber phenology. This information can be used to improve rubber management by helping to identify critical timing for implementation of agronomic interventions.
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