Oil palm plantations provide the highest amount of foreign exchange earnings for the Indonesian government. However, according to the Indonesian Ministry of Agriculture, the foreign exchange has dropped to 20%. Therefore, to increase oil palm productivity, the President Republic of Indonesia issued Instruction No. 6 of 2019 regarding National Action Plan for Sustainable Oil Palm Plantation 2019–2024, one of the actions to increase national oil palm productivity is strengthening data infrastructure. Considering that the oil palm plantations in Indonesia are highly extensive and spread across almost all parts of Indonesia, cloud computing remote sensing technology is a satisfactory solution for monitoring oil palm plantations. This study aims to develop a spatial database system for monitoring oil palm plantation in Indonesia based on cloud computing remote sensing data. The methodology includes user needs and spatial data identification as Indonesian regulations, combined with focus group discussions, normalized geographic data normalization, and spatial database development using conceptual, logical, and physical models and system design and data visualization. The resulting spatial database system for monitoring oil palm plantations in Indonesia based on cloud computing remote sensing has been constructed by leveraging data saved in the cloud on ArcGIS Living Atlas of The World. The data recorded in ArcGIS Online are then linked to a visualization system created using the ArcGIS operation dashboard. The dashboard has been developed based on user needs and displays information about oil palm plantation age, administration boundaries, rainfall, terrestrial ecosystems, hot spots, and weather conditions.

In 1997, the worst forest fire in Indonesia occurred and hit mangrove forest areas including in Sembilang National Park Banyuasin Regency, South Sumatra. Therefore, the Indonesian government keeps in trying to rehabilitate the mangrove forest in Sembilang National Park. This study aimed to identify the mangrove forest changing and to predict on the future year. The situations before and after forest fire were analyzed. This study applied an integrated Markov Chain and Cellular Automata model to identify mangrove forest change in the interval years of 1989–2015 and predict it in 2028. Remote sensing technology is used based on Landsat satellite imagery (1989, 1998, 2002, and 2015). The results showed mangrove forest has decreased around 9.6% from 1989 to 1998 due to forest fire, and has increased by 8.4% between 1998 and 2002, and 2.3% in 2002–2015. Other results show that mangroves area has continued to increase from 2015 to 2028 by 27.4% to 31% (7974.8 ha). It shows that the mangrove ecosystem is periodically changing due to good management by the Indonesian government.

Oil palm phenology has many advantages in managing the sustainability of oil palm plantations. The phenology of oil palms is a key issue in harvest estimation, fruit bunch production, estimating oil palm taxes, replanting, fertilization, and detecting oil palm disease. One of the recently developed methods of oil palm phenology involves the use of remote sensing technology. We evaluated and reviewed the current state of oil palm phenology based on remote sensing and conducted an optimized systematic review of recent scientific publications, specifically focusing on scientific peer-reviewed papers published between 1990 and 2021, comprising over 100 existing journal papers on remote sensing for oil palm phenology. The review includes a description of the state of the art and the mapping of oil palm phenology based on sensors, biophysical tree parameters, and classification techniques and also describes the state of the art in the development of regression models of oil palm phenology based on wavelength, biophysical tree parameters, and the type of regression model. Finally, the review provided an opportunity to develop suitable techniques for the identification, classification, and the construction of regression models of oil palm phenology. There is a lot of potential in combining multisensor approaches, suitable classification methods, and regression models for oil palm phenology. For future studies on oil palm phenology, we recommend integrating machine learning with oil palm biophysical parameters based on multisensor remote sensing technologies.

Estimating the biophysical parameters during the phenology cycle are very important and the key parameter for indicating the productivity of oil palm plantations. In many countries, the oil palm plantation has a very large area, therefore remote sensing technology is needed to estimate biophysical parameters in large areas. The special characteristics and potential of Synthetic Aperture Radar (SAR) data in acquiring geometric and dielectric properties of biophysical parameters have led to their identification in the context of vegetation monitoring. This study, we have investigated and developed models for estimating the oil palm phenology by applying multiple linear regression (MLR). The methodology includes the biophysical parameters estimated using Sentinel-1A for extracting the canopy height model (CHM), radar vegetation index (RVI), backscattering on VV and VH, aboveground biomass, texture entropy, and texture energy. Then applied multiple linear regression (MLR) analysis for developing model and assess  its ability. The result found the best model for estimating oil palm phenology using 4 parameters.

Information about oil palm phenology is required for oil palm plantation management, but using spaceborne polarimetric radar imagery remains challenging. However, spaceborne polarimetric radar on X-, C-, and L-band is promising on structure vegetation and cloud area. This study investigates the scattering model of oil palm phenology based on spaceborne X-, C-, and L-band polarimetric Synthetic Aperture Radar (SAR) imaging. The X-, C-, and L-band polarimetric SAR are derived from spaceborne of TerraSAR-X, Sentinel-1A, and ALOS PALSAR 2. Study area is located in oil palm plantations, Asahan District, North Sumatra, Indonesia. The methodology includes data collection, preprocessing, radiometric calibration, speckle filtering, terrain correction, extraction of scattering value, and development of scattering model of oil palm phenology. The results showed different scattering characteristics for the X-, C-, and L-band polarimetric SAR of oil palm for age and found the potential of the scattering model for oil palm phenology based on the X-band on HH polarization that showed a nonlinear model with R2 = 0.65. The C-band on VH and VV polarization showed a nonlinear model with R2 = 0.56 and R2 = 0.89. The L-band on HV and HH polarization showed a logarithmic model with R2 = 0.50 and R2 = 0.51. In this case, the most potential of the scattering model of oil palm phenology based onR2is using C-band on VV polarization. However, the scattering model based on X-, C-, and L-band is potentially to be used and applied to identify the phenology of oil palm in Indonesia, which is the main parameter in yield estimation. For the future phenology model needs to improve accuracy by integrating multisensors, including different wavelengths on optical and microwave sensors and more in situ data.