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.

Mangroves are a unique and complex major component of coastal zones in the tropics and subtropics. They represent the transitional ecosystem at the confluence of sea, land, and fresh water. The main components of their vegetation are generally evergreen trees or shrubs that grow along the coastlines, estuaries, brackish water, or deltas.¹ Mangroves provide important products and various ecosystem services.² They not only play an important role in the sustainability of coastal ecosystems but also provide important socioeconomic and cultural benefits for coastal communities.¹ They are also known as the most intense coastal carbon absorbers in the world and play a central and growing role in the global carbon cycle.³ According to Donato et al.,⁴ the amount of carbon storage per unit area of mangrove forests is five times larger than that estimated for temperate, boreal, and tropical terrestrial forests. The high growth rates of trees and plants, anaerobic soils, and stagnant water that can retard decomposition of biomass enable mangrove forests to store a large amount of carbon over the long term.⁵

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.

Bandung has the highest case of dengue fever in West Java, which is 3134 cases in 2014. The spread of dengue fever can be analyzed spatially by spatial autocorrelation to find out the correlation relationship pattern between locations. The method of spatial autocorrelation was using the Moran Index method. From these methods can determine the relationship of spatial autocorrelation in the spread of dengue fever and determine spatial distribution pattern of dengue fever in Bandung. The results of spatial autocorrelation analysis shows the spatial autocorrelation in dengue hemorrhagic cases in Bandung, the one-way significance test indicates a positive autocorrelation in Dengue hemorrhagic cases in Bandung and spatial distribution pattern is the spatial distribution pattern of the cluster.Kata kunci: spatial autocorrelation, spatial pattern, Moran Index, dengue fever.ABSTRAKKota Bandung merupakan kota yang mempunyai kasus penyakit demam berdarah tertinggi di Jawa Barat yang berjumlah 3134 kasus pada tahun 2014. Sebaran penyakit demam berdarah dapat dianalisis secara spasial yaitu dengan autokorelasi spasial untuk mengetahui pola hubungan korelasi antar lokasi. Metode autokorelasi spasial yang digunakan adalah metode Indeks Moran. Dari metode tersebut dapat menentukan hubungan autokorelasi spasial dalam penyebaran penyakit demam berdarah dan menentukan pola sebaran spasial penyakit demam berdarah di Kota Bandung. Dari hasil analisis autokorelasi spasial menunjukan terdapat autokorelasi spasial pada kasus demam berdarah dengue di Kota Bandung, uji signifikansi satu arah menunjukan adanya autokorelasi positif pada kasus demam berdarah Dengue di Kota Bandung dan pola sebaran spasial dihasilkan adalah pola spasial sebaran cluster.Keywords: Autokorelasi Spasial, Pola Spasial, Indeks Moran, demam berdarah.

Most of the previous geoscience studies in Jakarta found that the area is affected by land subsidence. However, it is difficult to understand the spatial distribution since continuous geodetic observation is not available. The development of SAR satellite-based remote sensing enables ground deformation observation over a long period and in regular time. This study presents the ground deformation time series analysis for the Jakarta Metropolitan area based on the InSAR technique. Using the small baseline subset approach, we construct time series of ground deformation constrained from Sentinel-1 interferograms provided by ASF DAAC Hyp3. Mintpy tool is utilized to obtain the time series analysis. It is found that for 7.6 years observation period since late 2014, land subsidence was only locally spotted in certain areas. The average velocity of ground deformation in the Jakarta Metropolitan area varies from −5.8 cm/year to 1.2 cm/year. It means uplift phenomena are also detected in Jakarta metropolitan area. It is also found that the time series of ground deformation fluctuates in seasonal patterns and may relate to ground water recharge during the rainy season.