Abstract. Along with remote sensing technology development, vegetation monitoring can be performed using satellite imagery or Unmanned Aerial Vehicle (UAV) data. UAV imagery with a high resolution, between 3–5 cm at an altitude <100 m, is able to present specific land conditions without being affected by the weather. Information related to vegetation density is one of the components in the Environmental Impact Analysis (EIA) study of a proposed project development due to vegetation removal. In this study, information from consumer-grade cameras of a low-cost UAV platform was explored to classify vegetation density using the potential of RGB imagery-based vegetation index (VI). The correlation coefficient (R2) between field observation data and the seven different values of VI demonstrated moderate to strong correlation. The highest linier correlation of 80.16% (R2 = 0.64) was performed by the Green Red Vegetation Index (GRVI). Classification of the vegetation density was established by applying the object-based image analysis method through the combination of supervised machine learning algorithm of Support Vector Machine (SVM) and the GRVI vegetation index. The vegetation density classification consists of very low, low, medium, high, and very high-density classes. The data can be utilized in determining vegetation management efforts from the presence of a proposed project in the EIA study. The use of UAV imagery is considered effective in identifying vegetation density.

Tsunami events have extensive implications for sustainable development, due to the social, economic, and environmental impacts they can cause. Take into account the competence of geospatial and local knowledge; these sources are integrated to assess tsunami risk assessment by utilizing satellite imageries. The study area of Talcahuano experienced severe damages due to the 2010 Chile earthquake and tsunami. The geospatial information of the damaged areas was identified by processing the surface reflectance values between pre- and post-disaster images of the satellite optical and SAR data. The DEM data was also employed to delineate areas according to the inundation height data. Local information of tsunami inundation was generated through a DIG participatory mapping. Both types of inundation maps showed good agreement with the results of field surveys. Furthermore, a map of the potential tsunamiinundation zone was produced to aid integrated risk assessments in the area. The map showed areas with high, medium, and low inundation level adopted from the satellite images and the DIG map. Keywords: Tsunami Inundati Satellite Imageries, Optical and SAR Sensor, DIG, Disaster Risk Reduction

This research proposes to find out the present state of the water's purity in Tukad Badung through an examination of the Pollution Index (PI) adopted from the methodology presented by the Water Quality Index (WQI), which was presented by the Indonesian Ministry of Environment. A series of water samples were taken at three river flow points in the Denpasar City area, representing the upstream, middle, and downstream areas. The water quality parameters were determined based on the characteristics of domestic wastewater pollutant sources that are identified in watershed areas. In accordance with Regulation 22 of the Government of 2021 concerning quality standards for river water used for drinking water, the average concentrations of Total Suspended Solid (TSS), Ammonia, Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD) parameters have exceeded quality standards. Meanwhile, the oil and grease parameter, as well as detergents, still meet quality standards. The PI at three monitoring points was obtained at 2.41 – 4.27, categorized as lightly polluted. In the future, efforts to deal with water pollution are critical to meet the downstream water quality standards used as drinking water.

Information about damage areas is important due to the large-scale disasters worldwide. In the last decade, both optical and SAR remote sensing were applied in many disaster researches, such as tsunami damage detection. In this study, the ALOS AVNIR-2 and PALSAR images are used to extract the damaged areas caused by the 2010 Chile earthquake. In the processing of ALOS/AVNIR-2, the inundation area was estimated based on the NDVI calculation and classification. Furthermore, damaged areas of the ALOS/PALSAR are extracted by integrating the AVNIR-2 image for water mask and the DEM image for elevation mask. The damaged area result of AVNIR-2 is 8.91 Km2 and for the PALSAR is 8.72 Km2 that is along the coastal areas. The image results showed a good agreement and corresponding area according to the institutional map of the inundation area. Future study in another area is needed in order to strengthen the processing method.