Besides to the effects from tidal, coastline position changed due to abrasion and accretion.Therefore, it is necessary to detect the position of coastline, one of them by utilizing Landsat data by using edge detection and NDWI filter. Edge detection is a mathematical method that aims to identify a point on a digital image based on the brightness level. Edge detection is used because it is very good to present the appearance of a very varied object on the image so it can be distinguished easily. NDWI is able to separate land and water clearly, making it easier for coastline analysis. This study aimed to

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

<strong>Detection of Vegetation Height in Mahakam Delta Using Remote Sensing. </strong>The vegetation height is a vertical distance between top of the vegetation to ground surface. Vegetation height is one of the parameters for vegetation growth. There are various methods to measure vegetation height; one of them is the use of remote sensing technology. This study aims to map vegetation height in Mahakam Delta by using height models derived from remote sensing data. Such models are Digital Surface Model (DSM) and Digital Terrain Model (DTM). DSM was generated using a combination of interferometric processing of ALOS PALSAR interferometry, X-SAR, Shuttle Radar Topography Mission (SRTM), and geodetic height of Icesat/GLAS satellite imagery. This integration technique incorporated the Digital Elevation Model (DEM) method. The geoid model used in this study was EGM 2008. The following step was the correction of height errors of DSM. Terrain correction was undertaken to convert DSM into DTM, while vegetation heights were obtained from subtraction of DSM and DTM. Vertical accuracy verification refers to a tolerance of 1.96σ (95%) or ~80 cm. In DSM, a vertical accuracy value of 60.4 cm was obtained so that the DSM is feasible for mapping with scale of 1: 10,000, while the DTM was 37 cm so it is also applicable for mapping with such scale. Based on the subtraction of DSM and DTM, the vegetation heights in Mahakam Delta varied between 0 and 64 m.