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Abstract. Monitoring of mangrove damage in Java requires special attention because the mangrove vegetation has been under pressure from various other land uses which are considered more productive. This paper applied quick-mangrove-damage-detection technique using Landsat 8. The purpose of this study is to develop mangrove damage identification algorithm using Landsat 8. The findings from field survey in Segara Anakan-Cilacap show that major mangrove logging generates the growth of minor mangrove, specifically Derris and Acanthus type; the minor mangrove cover area is categorized as high density based on NDVI value. The index use does not meet the actual condition in the field. This study proposes a new index as mangrove quality indicator. The new proposed mangrove index is derived from 2 bands that could differentiate mangrove vegetation where different digital number of two bands is higher from mangrove forest than non-mangrove forest. That phenomenon is caused the low of SWIR spectral on mangrove forest due to absorption by wet soil below the mangrove forest where flooded in high tide. The new mangrove index is formulated as (NIR -SWIR / NIR x SWIR) x 10000. The new mangrove index has good correlation with density of major mangrove in the field, and also good correlation with mangrove degradation map. Mangrove index has been functioning properly and can be applied in Segara Anakan, Cilacap and potentially can be applied in other locations.

Cochlodinium  polykrikoides  red  tides  have  occurred  in  summer  every  year  at coastal  waters  of  the  South  Korea.  Chlorophyll-a  concentration  data  estimated  from  ocean color satellite SeaWiFS (Sea-viewing Wide Field-of-view  Sensor)  were  used to detect the red tide in this study. The high value of chlorophyll-a concentration used  to detect red tide was analyzed  and   compared  with  red  tide  map  produced  by  National  Fisheries  Research  and Development Institute of Korea (NFRDI). Based on SeaWiFS data and NFRDI red tide map, it was  found  that  high  chlorophyll-a concentration  of  ≥  5  mg/m3in  SeaWiFS  images corresponded to the red-tide occurrence with some limitations.

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.