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.

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.

Renewable energy has grown rapidly nowadays. One of them is wind energy which also has been developed in Indonesia. One of the major technical challenges for wi Renewable energy has grown rapidly nowadays. One of them is wind energy which also has been developed in Indonesia. One of the major technical challenges for wi