The use of remote sensing data for urban studies has increased along with the availability of Very High-Resolution (VHR) satellite data such as IKONOS, Quickbird, Worldview, and the Pleiades. This study aimed to evaluate the use of Pleiades-1A imagery and object based image analysis (OBIA) method to extract the information of urban green spaces in some areas of Jakarta, Indonesia. Multiresolution segmentation and spectral difference segmentation were then applied to the imagery respectively. Support Vector Machine (SVM) was performed for the classification phase, followed by an expert-knowledge refinement. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified Soil Adjusted Vegetation Index (MSAVI) were derived from the imagery to help the classification process. The results showed two classes of landcover, that consists of ''urban green'' and ''non-urban green''. The accuracy assessment was then performed using the visual interpretation followed by field measurements as reference data. By using the area-based similarity measurement framework, this study scored 86 % for overall accuracy. The similarity measurement showed values above 87 % for all 20 samples. This study found that the proposed methods gave a more into ''similar'' results to the reference data, than the ''dissimilar''. The segmentation and classification rule set built in this study still need further study to see how effective the proposed method when applied to different cities with a different landuse/landcover characteristic.

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The increasing volcanic activity of Anak Krakatau volcano has raised concerns about a major disaster in the area around the Sunda Strait. The objective of the research is to fuse Landsat-8 OLI (Operational Land Imager) and Sentinel-1 TOPS (Terrain Observation with Progressive Scans), an integration of SAR and optic remote sensing data, in observing the lava flow deposits resulted from Anak Krakatau eruption during the middle 2018 eruption. RGBI and the Brovey transformation were conducted to merge (fuse) the optical and SAR data.  The results showed that optical and SAR data fusion sharpened the appearance of volcano morphology and lava flow deposits. The regions are often constrained by cloud cover and volcanic ash, which occurs at the time of the volcanic eruption.  The RGBI-VV and Brovey RGB-VV methods provide better display quality results in revealing the morphology of volcanic cone and lava deposits. The entire slopes of Anak Krakatau Volcano, with a radius of about 1 km from the crater is an area prone to incandescent lava and pyroclastic falls. The direction of the lava flow has the potential to spread in all directions. The fusion method of optical Landsat-8 and Sentinel-1 SAR data can be used continuously in monitoring the activity of Anak Krakatau volcano and other volcanoes in Indonesia both in cloudy and clear weather conditions.

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