Remote sensing technology has been widely used in various applications related to natural resources and environment monitoring. In this paper, we evaluated the capability of new Sentinel-2A image to map the distribution and percent cover of seagrass in optically shallow water of Jerowaru coastal area, East Lombok. Seagrass distribution map was produced from radiometrically and geometrically corrected Sentinel-2A image with overall accuracy of 61.9%. Using empirical model, seagrass percent cover was predicted with maximum coefficient of determination (R2)

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Increases in the numbers of residents in a given location have the consequence of increasing the need for living space. However, diverse environmental conditions make it impossible to develop housing in every location. Spatial analysis is therefore useful in determining land suitability for housing development so that environmental problems are avoided. The aims of this study were to determine the projected land needs for housing in Pesisir Selatan Regency, West Sumatra, Indonesia, as well as to perform suitable area mapping for housing through spatial analysis using five physical parameters (slope, disaster vulnerability, river and beach border, and protected area). The results showed that the land needed for housing in Pesisir Selatan increased every year. By 2020, it is predicted that the land allocation for housing will be 15.6-51.15 km 2 . Based on the spatial analysis, 21.657% of the area had high suitability (S1) for housing, 18.616% had moderate suitability (S2), 6.782% had low suitability (S3), and 52.944% was not suitable (N1). It is predicted that in 2020, the government will have to use the low suitability area despite its more significant risks. Therefore, it will be necessary to pay attention to mitigation aspects and housing technique manipulation in the steep slope area.

Minimum Noise Fraction (MNF) is known as one of the method to minimize noise on hyperspectral imagery. In addition, there are not many studies have tried to show the effect of MNF transform on multispectral data. This study purposes to determine the effect of MNF transform on the accuracy level of vegetation density modeling using 10 meters Sentinel-2A spatial resolution (multispectral data) and to know the cause. The study area is located in parts of Sapporo City, Hokkaido, Japan. Vegetation density is modelled through vegetation index approach, Normalized Difference Vegetation Index (NDVI). The results show that the coefficient correlation of vegetation density data and vegetation index regression after MNF transformation (0.801623) has higher value than the same regression without the MNF (0.794481). However, better correlation does not represent the better accuracy on vegetation density modeling. Accuracy calculation through standard error of estimate shows the use of MNF in multispectral data for vegetation density modeling causes the decrease of model accuracy value. The accuracy of vegetation density model without involving MNF transformation reached 91.402 %, while the model accuracy through MNF transformation before vegetation density modeling reached 90.889 %. The insignificant increased accuracy is occurred due to the limited number of multispectral image information compared to hyperspectral image data.